The use of demographic trends and long-term population projections in public policy planning at EU, national, regional

and local level

Chapter 2: The use of national population projections in public policy planning – past attempts

Draft Background Report to the Project

Harri Cruijsen1

DEMOCAST

1. Introduction

Over the past 50 years the vast majority of European countries has regularly produced

national population forecasts by sex and age. Almost all of them were compiled by the

national statistical offices probably for reasons of efficiency (these agencies are usually

mandated to collect, store and analyse demographic time series) and objectivity (the products

made by national statistical agencies agiencies ought to be neutral in all aspects).

Apart from these so called official national population forecasts, also other sets of population

projections have being made. Since the early 1970s the United Nations bienially publishes

population projections by sex and age for all countries of the world. The European

Commission started the compilation of its series of national and regional long-term population

scenarios by sex and age in the early 1980s, and since then four revisions have been produced

and disseminated. Recently an international research team, financially supported by the

European Commission, produced and published the first set of stochastic national population

forecasts by sex and age for EU-15. And finally, academics, mostly at the request of other

international organisations (e.g. Council of Europe), incidentally prepare alternative sets of

national population projections.

1 Contact: DEMOCAST, Vluchtheuvelstraat 2, NL-6621 BK Dreumel, Netherlands ; phone: +31-487-570687; Email: hvckroon@kabelfoon.nl

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All these projections provide fairly general but basic information on the future size and

changes of the population by sex and age at national level. The outcomes and sometimes also

the underlying fertility, mortality and international migration assumptions can be applied for

all kind of related demographic or social projections, such as household, educational, and

labour supply and labour demand projections. Naturally, also regional population projections

may be derived.

Secondly, all kind of sex and age specific ceteris paribus projections or demographic impact

analysis can be made by multiplying the results of national population forecasts with for

example recently observed health costs by sex and age, or the ’consumption profiles’ of care

facilities by sex and age. Or, like it is regularly done for estimating the impact of changes of

pension policies, the outcomes of national population forecasts are first used to make more

detailed projections on subpopulations such as the working age or the elderly population, and

thereafter the latter projections are applied on adjusted pension schemes.

Thirdly, national population forecasts may be used for estimating the future demand of public

or recently privatised goods and services such as gas, water, and electricity. Other examples

are the projection of the production of new schools, houses, hospitals, and elderly homes.

Finally, demographic forecasts are sometimes useful to predict changes in private consumer

good markets. Especially, future changes in the consumption of strongly sex and age related

items such as cigarettes and parfumes can be, to some extent, predicted by applying

population forecasts by sex and age on expected sex and age specific consumption patterns.

So the starting hypothesis can be that in most industrialised countries national population

forecasts are probably widely used for all kind of different, and more specific projections,

simulations or impact analysis. However, very few countries seem to be interested in the

(actual) use of official population forecasts, and therefore very few attempts have been made

to look into who uses these forecasts and for what purpose. A survey by e-mails sent out in

Spring 2005 among national population forecasters within EU-25, revealed that within Europe

only the Netherlands and the United Kingdom have ever tried to systematically examine the

use of demographic projections2. Outside Europe, Australia and Canada have reported results

of users surveys.

2 A few other European countries responded that a limited group of users of population projections working in the public sector may express their needs and wishes during the process of revising national population forecasts.

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In this report the principal findings of these attempts will be reviewed. More detailed

outcomes can be found in the respective background articles and papers.

2. The Netherlands

Statistics Netherlands seem to be the first statistical agency that has approached (a group of

well over 50) principal users by means of a survey questionnaire to get informed about their

actual use, their wishes and their suggestions with respect of the population variables

projected, the reliability of the forecasts, the content and use of variants, the update frequency,

the ways of dissemination, etc. (Statistics Netherlands, 1983). The users were working in

various sectors: government (national, regional, local), research institutes, universities,

multinationals.

Outcomes of this study were presented and discussed on a seminar held on 19 May 1983 at

the premises of Statistics Netherlands in Voorburg. In addition, the following four invited

papers were presented and discussed:

1. the role of national population forecasts in determining future housing needs;

2. national population forecasts as an instrument of planning, marketing and exploring

the labour market at the Dutch national post and telecom company (PTT);

3. the use of national population forecasts by Philips for exploring the future needs of its

products;

4. the use of national population forecasts for the compilation of various scenarios

developed by Shell NL.

It appeared that the vast majority of users was only in need for short and medium term sex

and age population structures according to the Medium variant. The long-term results, the

outcomes of the other two variants (Low and High), the set of underlying demographic

assumptions, and the future annual numbers of live births, deaths and international migration

were hardly applied.

Some important recommendations and suggestions were made. Firstly, there was a general

wish for the production of national household forecasts, consistent with the national

population forecasts. Secondly, most users were in need for a revised set of projections every

3 years. Thirdly, some users were in favour of getting variants with larger margins and/or

scenarios. Or as it was formulated in the summary of the above mentioned article: “No

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objections were made against a possible enlargement of the area between the extreme variants

to express the uncertainty about future developments. This uncertainty should, according to

some speakers, be part of some general socio-economic hypothesizing in the form of

scenarios rather than the ’narrow minded’ thinking of possible demographic developments

only“.

After this seminar Statistics Netherlands has modified and extended its set of national

population forecasts. Subsequently, national household forecasts, forecasts of the population

by major groups of foreign origin, and stochastic population forecasts have been added to the

system. Furthermore, the update frequency was increased to annual revisions, historical

projection errors were analysed, and extrapolation models were expanded with variables such

as birth order, causes of death and migration motive. Principal users within the government,

and experienced scientists were invited to annually discuss the draft assumptions. Finally,

publications and data bases have been made as users friendly as possible.

1. The United Kingdom (and Australia)

The uses to which variuos types of demographic projections are put was the subject of parallel

postal surveys organised in Spring 1990 in the United Kingdom and Australia (Joshi et al.

1990). 556 questionnaires were sent out in the UK, whereas in Australia 345 forms were

mailed. Around half of the organisations approached sent in replies, from private and non-

profit sectors as well as public. The UK survey revealed widespread and growing interest in

demographic forecasts, particularly strong in the Health Service and Local Government. Two

thirds of the respondents answered that the relevance to their work was central or major.

However, no more than one third expressed an interest in results with a time horizon of 20

years or more.

In both countries, national population forecasts by sex and age were cited most frequently, in

the UK by 81 per cent of those who used any. Regional population projections were the next

most frequently applied (70 per cent), whilst both labour force and household projections

were cited by just over half of all respondents.

On the question ’why do you use them?’ many different answers, often supplemented with

additional comments were given. The overall response given most frequently was ’research’

(76 per cent), followed by ’planning’ (42 per cent). Local governments also frequently

mentioned ’housing’, ‚’education’ and ’welfare’, indicating that they are the main responsible

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authorities in planning and producing these public goods. Many private firms, as expected,

were mentioning ’marketing’, ’recruitment and personnel’, ’location’, and ’investment’.

Respondents were also asked about a number of actual and potential features in projections.

Age and sex composition was of interest of use to virtually all users. Future numbers on the

population by social class/economic group or by ethnic group were requested by 74 and 64

percent respectively. More than half of the respondents were also looking for marital status,

family and/or household projections.

Almost two thirds of those who answered said that they need to update their projections

annually. Among those with planning horizons of 20 years or more, annual updating was

much less common (26 per cent). As a matter of fact, the official population forecasts in the

UK are published on a biennial cycle. They normally appear in the second year following the

year on which they are based. On the whole, the majority of users were not complaining about

this practice.

However, one third of all respondents declared that they were making thier own demographic

projections. Particularly outside central government, official population projections did not

meet all demands. Especially those planning for local and/or small areas prefer to make their

own projections based on local knowledge and needs.

The question about whether various variants on projections were consulted yielded overall

percentage scores of 46 per cent for fertility, 36 per cent for mortality, and 20 per cent for

international migration. One third of the respondents were not consulting variants at all.

Especially academics expressed a strong interest in applying alternative demographic futures,

whereas both education authorities and private firms showed relatively low scores of demand

or interest. About one third of respondents wanted to have more variants than provided by the

official forecasts.

Other wishes expressed by the respondents were:

• a finer geographical breakdown of projections, involving improved methodology for

smaller areas;

• better information on the present;

• variants on internal migration assumptions;

• more frequent household projections;

• data supplied on disks.

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Unfortunately, there is no documentation available whether all wishes that were expressed in

this survey have been fulfilled over the past 15 years.

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4. Canada

Statistics Canada has tried to examine the use of demographic projections by gathering

information from indirect sources such as the demand for future-oriented reports and analytical

studies (Statistics Canada 1999). Therefore the information provided was considered to be only

partial, since there was no direct mechanism to know how and by whom the projection reports and

data contained therein were used.

The major conclusion was that Statistics Canada’s set of population, household and family

projections at both national and regional level provide the basic data for social and economic

planning at regular intervals, and serve a wide clientele of federal departments, provincial

governments, business, academic institutions and the general public. The principal users are the

federal departments. They generally attach great importance to the quality of the population

projections since they are used for formulating national/provincial socio-economic programs and

policies involving millions of dollars.

In addition, it was stated that the official set of demographic projections form the base for

developing other specialized projections. Finally it was notified that requests for costumized

projections executed by Statistics Canada , both for policy decisions and program development,

have increased considerably. Especially the demand for costum-made, cost-recoverable

projections for smaller sub-provincial geographic areas as well as the demand for more specialised

projections covering elderly population, ethnic and language groups have increased.

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References

Joshi, H., and I. Diamond, 1990, Demographic Projections: Who Needs to Know?, In: Population

Projections: Trends, Methods and Uses, OPCS Occasional Paper 38.

Statistics Canada, 1999, On the Use and Users of Demographic Projections in Canada, Paper

prepared by M.V. George and submitted to the Joint ECE-Eurostat Work Session on Demographic

Projections (Perugia, 3-7 May 1999).

Statistics Netherlands, 1983, Seminar on the Use of the National Population Forecasts. In:

Maandstatistiek van de Bevolking, 83/9.

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The use of demographic trends and long-term population projections in public policy planning at EU, national, regional

and local level

Chapter 3. Population projections in pension policy and public finance planning

1. Population projections in pension policy and public finance planning in Finland (p. 2) by Jukka Lassila and Tarmo Valkonen 2. Population projections in pension policy and public finance planning – the Lithuanian case (p. 19) by Audronė Morkūnienė

3. Demographic projections and the regulation of annuity markets (p. 29)

by Niku Määttänen

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Case report for Lot 1

Population projections in pension policy and public finance planning in Finland

by Jukka Lassila and Tarmo Valkonen

18.8.2005

Contents 1. Introduction 2. Population projections in pension policy planning 2.1 The Finnish private sector pension reform and the role of population projections 2.2 Demographic uncertainty and pension expenditure: results from the DEMWEL project 3. Population projections in public finance planning 3.1 The Committee for the evaluation of the development of social expenditure (The SOMERA Committee) 3.2 Government’s Report on the Future 4. Conclusions and recommendations

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1. Introduction The sensitivity of the long-term scenarios of public sector finances to population projections is well-documented (see e.g. Oxley, 2001). An obvious reason for this is that current governments are committed, by legislation, to react to changes in demographic development (Heller, 2003). Pension reforms are on the agenda of practically every EU country. The need for reforms has been observed and accepted when population forecasts have been used as inputs in pension expenditure projections. That is perhaps the single most important fact that shows the significance of population projections in policy planning, although on the global level the population explosion discussion may be comparable. Similar things can be said about public finances. Thus pension policies merit special treatment in this study. This case study aims to analyze the actual impact of population projections on pension policy and public finance planning in EU countries. The two cases studied, Finland and Lithuania have different prospects of economic, demographic and social policy variables. The quality of the initial demographic data is also different. A further reason for choosing these cases is that these countries have been the first in EU in which there have been explicit analyses of the implications of demographic uncertainty on public sector variables using both the conventional scenario approach and the stochastic population forecasts (SOMERA, 2003 and Alho, Lassila and Valkonen, 2005). A third country, which will be referred to often, is United States, where scenarios based on stochastic population forecasts are more familiar to planners (see e.g. Lee, 2004). The case study also aims to provide a set of more general research results describing the way how uncertainty in demographic forecasts affects the outcomes from current policy rules and how alternative policies may be designed to assess the uncertainty. An example is the use of longevity adjustment in pension policy. We utilize here the previous research of the group as well as the future results of the UPE and DEMWEL1 projects. 2. Population projections in pension policy planning 2.1 The Finnish private sector pension reform and the role of population projections Population projections have long been used in Finnish pension planning. The main Finnish pension arrangement, the private sector earnings-related pension system, was implemented in the early 1960s. The necessity to scale down the system became evident during early 1990s, partly because of population projections. Before that the system had grown through various political decisions for thirty years, under favourable demographic conditions. Apparently, one significant incident during 1990s was when an assumption of 1 UPE (Uncertain population of Europe) and DEMWEL (Demographic uncertainty and the sustainability of social welfare systems) are EU’s 5th Framework research projects.

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continuous decline in mortalities was included as an alternative scenario in a routine exercise of pension projections. The resulting very high pension contribution rate projections draw the attention of the decision makers. Among other factors, this led to a number of reforms in mid-1990s (see e.g. Lassila and Valkonen, 2002). We consider a recent major example of pension policy in Finland and study the role of demographic projections in and behind it. The Finnish pension system consists of two main parts. The earnings-related pension system aims to provide retirement income sufficient for consumption comparable both to that of working years and to current workers’ consumption. It covers risks related to old age, disability, long-term unemployment of ageing workers, and death of family earners. The national pension guarantees a minimum income in cases where the earnings-related pension is absent or insufficient. Both these first pillar systems are mandatory. Although the earnings-related system is statutory by law, it is largely privately run. Voluntary pensions, whether employer-based or industry-wide supplementary pensions (second pillar) or personal pension arrangements (third pillar), are becoming more common but still of minor importance in Finland. A more detailed presentation of the system can be found from Hietaniemi and Vidlund (2003). The latest major private-sector earnings-related pension reform, agreed in 2001-2002, mainly took effect from the beginning of 2005. The agreement was justified by similar reasons as many other recent reforms in Europe, namely to mitigate the rising pension costs due to population ageing. The large reform package consists of a combination of measures that were expected to improve both the economic and social sustainability of the pension system. The main aims of the reform were to postpone average retirement age, curb the expected increases in the contribution rates and to support ageing workers’ ability to cope with their work. A further initial and widely-held target was to simplify the pension rules and make them both more transparent and more actuarial. The first aim was promoted by rewarding continued participation in working life and by restricting access to early retirement schemes. Financial sustainability is further strengthened by starting to apply life expectancy adjustment to the accrued pension rights. The agreement was a political compromise, negotiated between central organisations of employers and trade unions and representatives of the central government. Interestingly, the main disagreements were between various trade unions, see Lassila (2004). Besides the vested interests of the unions, some details of the agreement reflect the aim of protecting the generations near retirement from abrupt changes in pension rules. Earlier every employment contract and self-employment period added to the pension, after age 23. The pensionable wage was aggregated over the last 10 years of each contract. The accrued pension right was vested, even if changing the employer or stopping work. After the reform accrual starts at the age of 18. Instead of using

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employment contracts and the pensionable wage concept, every year’s earnings and accrual rates affect directly the future pension (the career model). The “target” level of benefits used to be 60 % of wages. This accrued in about 40 years: 1.5 % per year between ages 23 – 59 and 2.5 % per year between 60 – 65. There was no upper absolute limit to benefits, but an upper percentage limit is 60 % of the highest pensionable earnings. After the reform there is no ceiling at all. Accrual rate is 1.5 % per year between ages 18 – 53, 1.9 % per year between ages 53 – 62 and 4.5 % per year between 63 – 68. Pension rights and benefits were index-linked, with 50-50 weights on wages and consumer prices during working years and 20-80 weights after age 65. After the reform the change in indexation takes place at actual retirement, irrespective of age. The weights before retirement are 80 – 20 on wages and consumer prices and 20 – 80 after retirement. Contributions are collected from both employers (on average 16.8 % of wages in 2002) and employees (4.6 %). Future changes have been agreed to be shared 50-50 between employers and employees. After the reform employees aged 53 and over pay contributions that are about 1,27 times higher than younger employees’ rate, reflecting (but far from fully paying) their higher accrual. The additional part is prefunded. The reform brought the Swedish-type longevity adjustment to the pension system. It will start cutting monthly pensions for all cohorts reaching the age of 62 in 2010 or later. The reform introduced major changes to early-retirement pensions. The unemployment pension is gradually being abolished and the lower age limit for entitlement to continued unemployment allowance will be increased. Also, age limits for the part-time pension and the early old-age pension will be increased and the individual early retirement pension will be abolished. Retirement on an old-age pension is flexible between ages 62 and 68. The private-sector earnings-related system is partially funded. Funding is collective but based on individual pension rights. Individual pension benefits do not depend on the existence or yield of funds. Funds only affect contributions. When a person receives pensions after the age of 65, his/her funds are used to pay that part of the pension benefit that was prefunded. The rest comes from the PAYG part, the so-called pooled component in the contribution rate. In the reform some additional funding between 2003 – 2013 was decided, amounting to 7.5 % of the insured wage sum in 2013. In addition, the increase employee’s contribution rate for persons aged 53 or more also adds to funding. What was the role of population projections in the latest pension reform in Finland? The reform was planned in secrecy, so no direct knowledge is available. Still, it is clear that demographic worries were behind the reform, as they have been in all pension reforms since at least 1993 (to be confirmed). The direct demographic ingredient, the longevity adjustment, had been analysed in two research-type reports (Lindell, 1999, and Lassila and Valkonen, 2000) before reform was

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decided. The main viewpoint in the reform process and in the studies was the expected effect. This picture emerges from several sources, e.g. from the justification arguments of the law proposal. Stochastic analysis (Alho 2003) was done after the reform was decided. It appears that the risk aspect perhaps occurred to no-one before or during the reform, although there is a fine line here. In Lindell (1999) alternative mortality developments were considered to the extent that alternatives existed, the group did not make its own predictions. No explicit risk statements were made. In Lassila and Valkonen (2000) two demographic projections that were based on the stochastic simulations by Alho (1998) were used, and the authors made explicit risk statements, although their text also mainly considered the expected outcomes. In the demographic scenarios used by the pension system, the estimates of the future decline in mortality in Finland used to be constrained by the developments in Sweden. It was thought that life expectancies could certainly increase in Finland but not to higher levels than in the western neighbour. This practice was apparently abandoned about ten years ago. Public pension systems usually work on the pay-as-you-go (PAYG) principle, or are partially funded and partially PAYG. Thus, with given pension rules, their future benefit expenditures and contribution revenues depend crucially on future demographics. Whether the future seems problematic or not, or seems requiring policy changes or not, can in principle be decided by choosing a population projection such that the desired future outlook emerges. In a broader setting, all long-run outlooks of public finance can be manipulated in the same way. There are of course factors limiting the possibilities for manipulation. An honest and uncorrupted political and social system and an open decision-making culture with critical watch-dogs should prevent significant malpractices. Still there probably is room for some manipulation in every system, if there is a large will for that among the decision-makers. In Finnish pension policies, the fact that social partners in practice decide upon the private-sector earnings-related pension system, and are used to do that behind closed doors as much as possible, inevitably raises doubts about the neutrality of demographic projections behind the pension projections. Official demographic projections are not the only ones used. More favourable projected outcomes make the system look more solid and reduces the need for policy changes, compared with what unfavourable projections would do. On the other hand, completely unrealistic pension policies are not in the interests of social partners. Opinions on how to interpret the Finnish pension history in this respect probably vary among experts outside the pension system. One may say that after any policy change the systems tries to paint as bright a picture on the future as possible by bending the various assumptions, demographics included, to some degree. Also, the future picture should justify the decisions that were made. But this does necessarily mean that the projections used in actual decision-making were optimistic or manipulated in some other way.

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To summarise, population projections have had and do have an important effect on pension policies in Finland. The effects comes from a continuous process that includes population projections, studies using the projections by researchers, and pension projections made by experts in the Finnish Centre for Pensions and in the Social Insurance Institution2. While the process described above seems satisfactory in many respects, the obvious candidate for a weak link is that policy measures need be studied before the reform process. During the process there is not enough time to carry out serious research. How, then, can the relevant “right” measures be identified to be studied at the right time before the reform? Who should choose them? Pension policy measures are not the easiest to study. Time horizons are long and the effects must be looked at from several angles. In the Finnish system an added feature is that the social partners, who in practice decide upon pension policies, disagree on many issues, which may make it difficult for any institution within the pension realm to try to foresee future reform ingredients. In practice the representatives of social partners have asked a few trusted experts, working in pension companies, to consider what policies and policy measures could be useful. This process has not been carried out publicly and thus has been almost completely unknown also to many people within the system, let alone those outside it, including most members of parliament and other politicians. Even though the system may have repressed innovative policy design within, it is open to influences from outside. Longevity adjustment, for example, was first studied and implemented in Sweden. It was natural for the Finnish system to study it also, as there inevitably was the sustainability concern caused by the continuously declining mortality and increasing life expectancy. This also emphasises the role of international and academic pension research. 2.2 Demographic uncertainty and pension expenditure: results from the DEMWEL project DEMWEL (Demographic uncertainty and the sustainability of social welfare systems) is a 5th framework research project (QLK6-CT-2002-02500) financed partly by the European Commission. The project combines economic analysis of population ageing with statistical analysis of demographic uncertainty. Stochastic population simulations, which quantify demographic uncertainty, are used as inputs in economic models. DEMWEL deals with three groups of questions. The first group concerns expenditure issues. The basic question is, given the estimates of demographic uncertainty, how uncertain are the ageing cost projections? The second group relates to policy issues. How should the recognition of demographic uncertainty and its consequences for expenditure projections affect the policy targets that are set? How does it affect the use of policy 2 The Finnish Centre for Pensions is the central body of the Finnish statutory earnings-related pension scheme and the Social Insurance Institution the central body of the national pension scheme.

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instruments? Could new instruments be designed to deal with these uncertainty features? The third group of questions involves methodological issues. How can economic methods used in ageing research, such as generational accounts and overlapping-generations models, deal with demographic uncertainty? How should stochastic population simulations be developed to better serve economic analysis? Expenditure issues On expenditure issues, the project provides estimates for each participating country on how demographic uncertainty transforms into uncertainty of the economic consequences of ageing. Demographic uncertainty is large. DEMWEL shows that, as a consequence, economic uncertainty is also large. Table 1 summarises the uncertainty in pension expenditures per GDP ratio in 2030 and 2050. The countries considered are Denmark, Finland, Germany, the Netherlands, Belgium, Spain and the United Kingdom. Source: Lassila, J. and T. Valkonen (2005). Table 1. Pension expenditure, % of GDP (for the Netherlands, % of total wage bill) Country and year d1 Q1 Md Q3 d9

50%range per Md

80%range per Md

Denmark 2004 9,8 2030 12,7 13,6 14,5 0,13 2050 11,8 13,7 15,6 0,28

Finland After reform 2030 14,1 14,6 15,2 15,6 16,2 0,07 0,14 2050 12,8 13,6 14,5 15,6 16,6 0,14 0,26

Germany 2001 11,5 2030 13,1 13,5 13,8 14,4 14,7 0,07 0,12 2050 12,5 13,2 13,9 14,9 15,8 0,12 0,24

Netherlands 2004 12 2030 24,7 25,9 27,2 28,2 29,3 0,08 0,17 2050 28,3 30,3 32,7 35 37,5 0,14 0,28

Belgium 2003 9,2 2030 11,2 11,5 11,9 12,2 12,4 0,06 0,11 2050 11,1 11,8 12,6 13,4 14,1 0,13 0,24

United Kingdom 2003 6,4 2030 6,4 6,7 7,1 7,4 7,6 0,09 0,17 2050 6,1 6,7 7,4 8,0 8,6 0,18 0,34

Spain 2003 9,7

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2030 12,2 12,7 13,4 14,1 14,9 0,11 0,20 2050 16,9 18,5 20,6 22,9 25,5 0,22 0,42

Based on several hundred simulations, the table presents the first and ninth deciles (d1 and d9), first and third quartiles (Q1 and Q3) and the median (Md) of the predictive distributions. In the two rightmost column of Table 1 the width of 50% and 80% predictive interval is compared to the median. The figures show some similarity across countries. In 2030 the 80% ratios vary between 0,12 and 0,22 and in 2050 between 0,24 and 0,42. The second column from the right displays similar ratios for the 50 % intervals. They are also strikingly similar across countries (except for Spain and the UK). To some degree one could generalize that this means that the uncertainty in pension expenditures is roughly proportional to the size of the system. Without any generalising it is clear that the uncertainty is large. The width of the 50 % interval, an interval which as likely includes the expenditure figure as not, is in 2050 several percentage points, and the 80 % predictive interval is in some cases wider than the expected increase in expenditures from the current year to 2050. The above are straightforward measurement results, although calculated in complicated way, and not surprising as such for any who has looked at stochastic population simulations. Their relevance must follow from the effect they have to future decision making. Europe has practically no experience in this yet. We are in a phase where a still rather small number of researchers are trying to grasp the meaning of the results and communicate them in their respective countries. Thus it is worth looking at the U.S. discussion. A common argument against stochastic population simulations is that the magnitudes of uncertainty in future demographic developments are “too large”. James P. Smith states this clearly in his comments on Lee and Tuljapurkar (2001): “One of the reasons I find the variances in their fertility projections unreasonable is that I am implicitly using some structure, while they seem to be unwilling to introduce any. I do not think fertility rates will rise to anywhere near 3, because I believe the fundamental changes in women’s labor market role over the past few decades will continue. These changes – higher labor force participation rates and higher wages – should keep fertility down. While their scepticism about structure is understandable, some ideas about the structural determinants of these demographic outcomes should be a companion, not a substitute, for these demographic projections.” He also states that “The most useful aspect of demographic forecasting involves obtaining the best estimate of the baseline projection.” Whether adding structures described by Smith would improve demographic forecasts is a controversial issue. Keyfitz (1982, p. 747) concludes that “What emerges from all this is the realization that forecasting is more problematic than demographic research, which tends to center on understanding the past – no mean task in itself – and on inferring conditional causal relations that are useful for policy advice. We have found few instances in which understanding the past and the conditional relations can be brought to bear on forecasting.”

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Despite doubts like Smith’s, the uncertainty calculations have been taken seriously. McFadden comments on Lee and Tuljapurkar (2001): “A major contribution of Lee and Tuljapurkar is to provide machinery that quantifies this uncertainty and makes it clear that public policy on Social Security and Medicare must do more than just achieve positive expected balances. Their redesign must be sufficiently flexible and adaptive to ensure feasibility under a broad range of demographic outcomes.” But how broad a range should be considered? A practice should be created. This can be viewed as something that is decided within the political process. But are there alternatives, and what is the role of experts here? We could offer some candidates, and sketch the way to apply them. Policy issues On policy issues, DEMWEL has produced pension policy evaluations for e.g. Germany and Finland. These have not yet been published, and thus we do not know how they are received. Some other policies have been published, however. Lassila and Valkonen (2004) studied prefunding of health and long-term care expenditure. Public health and long-term care services are predominantly used by old people and financed by taxes paid by working-age people. Fluctuating sizes of generations create variations in tax rates, similar to what occurs in pension contribution rates. Prefunding is a commonly suggested cure for this variation in pension systems – could and should expenditure on health and long-term care also be prefunded, and if so to what degree? They studied several prefunding rules using Finland as an example country. The results show that if the focus is on tax smoothing during the next few decades, an effective rule is a buffer fund whose construction is based on the current population forecast. But if the time horizon is lengthened, the benefits of using rules conditional on new demographic information become evident, even though they may result in higher tax-rate variation during the first few decades. The study showed that tax smoothing difficult, once the uncertainty in demographic projections is explicitly recognized. This probably has some relevance for policy planning, although it is not immediately clear what. The study was originally published in Finnish in 2003, and stirred some discussion among politicians and experts whether such funding would be desirable, but the demographic uncertainty aspects did not get much attention. Alho, Jensen, Lassila and Valkonen (2005) showed that large amounts of simulations provides a way for finding proper magnitudes of policy measures. Their example is the indexing of pension benefits to reflect the size of the working population: how big should the weight of the number of workers be relative to the weights of average wages and consumer prices? Simulations are likely to provide better quantitative answers than analytical attempts do. Strategy approach

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The use of stochastic population simulations leads naturally to thinking about policy strategies instead of single policies. Alho, Lassila and Valkonen (2005) state this as follows: “In the case of pensions, a sustainable policy strategy is a set of rules such that both the contributors and the pensioners know, beforehand, what will be done in any reasonable future circumstance, and they accept the future actions, or at least cannot force a change of the system. The difference between a policy and a policy strategy is a very practical one. Considerations about sustainable pension policies are usually based on one set of base assumptions about future demographics, productivity, interest rates etc. In contrast, considerations about sustainable pension strategies must be based on a large number of possible states of the world that cover a realistic range of economic and demographic developments. We may think of the whole Swedish NDC system as a step towards implementing a strategy rather than merely being a set of policy instruments. It is “designed to be financially stable, i.e. regardless of demographic or economic development it will be able to finance its obligations with a fixed contribution rate and fixed rules for calculating benefits” (Settergren 2001, p.1). Adjustment mechanisms have been defined, even a “brake” if things go badly (see Könberg, Palmer and Sundén, 2003).” Diamond had earlier pondered the strategy approach (Comments on Auerbach and Hassett, 2001): “At one extreme, we could consider legislating a complete strategy as a function of complete description of the states of nature. ... To move toward reality, ..., we would think about limits on the complexity allowed in the strategy and, perhaps more important, our inability to list all states of nature. Recognizing this inability is where the rules/discretion debate needs to start.” Diamond commented also the longevity adjustment: “It seems to me unlikely that an optimal system would adapt completely on the benefit side and not at all on the tax side.” “So choosing an automatic adjustment makes it less likely that other factors will be adjusted. But the indexing selected will be a function of a very incomplete list of the considerations that would go into a congressional decision to spread the cost of adjusting to financial difficulty around the system.” We give the last word here to Alho, Lassila and Valkonen (2005): “We view the combination of stochastic population simulations and a numerical overlapping-generations model as a first step towards a more comprehensive model. Stochastic population simulations for all EU countries and some other European countries are being produced in the EU’s 5th framework research project UPE. In another EU project DEMWEL several European research institutes are working together to create models where future uncertainty is handled in a manner more advanced. But even with current

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models many aspects of uncertainty in the economic consequences of population ageing can be explored, and that is also the aim in DEMWEL. In future work, it might be useful to extend the notions of sustainability and sustainable strategies into a probabilistic direction, complemented with a “viability” concept with soft or unknown limits of acceptability. A theoretical challenge is to achieve a better concordance of the demographic and economic models. Alternative descriptions of how uncertainty is taken into account in actual practice, when it is not clear what the relevant decision horizon might be, is one aspect of such work. Creating pension strategies more sustainable than the current ones is important. As Disney (1999) puts it: If the future paths turn out to be unsustainable, there are stark choices left: to adjust other public finances or to change the rules ex post. Stochastic simulations with models combining the economic and demographic ingredients of the pension system can be used to crash-test the current systems and reveal the circumstances where their potential weaknesses become crucial. Similarly, the simulations will help to design alternative, and possibly very complicated, pension policy strategies, and test their consequences both at the system level and the individual level.” 3. Population projections in public finance planning 3.1 The Committee for the evaluation of the development of social expenditure (The SOMERA Committee) On April 19, 2000 the Ministry of Social Affairs and Health appointed a committee to study the development of social protection expenditure and the securing of financing for social security in the long run as the operational environment of social security changes. The Committee’s term was to end on December 31, 2001, but it was continued until March 31, 2002. The main report (SOMERA 2002) gives a general view of social security expenditure and the current situation concerning financing, future challenges and necessary basic definitions of policy. In addition to this report an extensive background report was drawn up in which viewpoints and alternative estimations concerning social expenditure and the development of financing are examined more thoroughly. According to the Committee it is possible, also in the future, to secure an adequate standard of comprehensive social security. The changes in operating environment provide challenges for social security and its financing, however. Important changes are the operating environment, which has become more international, and the changing age structure of the population. According to the Committee, society’s preparedness to meet the changes in the operating environment can be improved by a systematic improvement in work ability and functional capacity, prolonging the number of years in working life, evening out the

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development of pension expenditure, improving the efficiency of social security, emphasising the characteristics that promote activity and independent initiative in social security and revising the way of financing and mode of action in social welfare and health care. Appropriate social security contributes to securing a functional society in different economic phases and preserve social cohesion, which supports competitiveness. If these basic definitions of policy are implemented it will be possible to better meet the changes in the operating environment. In order for the definitions to be implemented, people will enter working life earlier and stay on longer, the level of employment will need to be raised and the use of care services for the elderly postponed by an increase in functional capacity. SOMERA made several alternative scenarios with respect to demographic and economic factors. For demographics, low and high future fertility were considered. “Low” meant a total fertility rate of 1,5, and “high” a cohort fertility rate of 1,9, which means a total fertility rate of 1,8. The base value was comprised by slightly lowering the one-year averages in 1996 – 2000, producing a TFR of around 1,72. Low and high net migration alternatives consisted of 3000 and 10000 people immigrating (net) each year, the base alternative being 5000. A low mortality scenario was also produced, assuming that the decline in mortality continues, initially even accentuates, especially in ages 45+, until 2050 when the rate of decline slows down but less than in the base case. Finally, a “demographic crisis” scenario was produced by combining the low fertility, low net immigration and low mortality assumptions. These scenarios were reported in a background report that was published later in 2002 (in Finnish only). Some of the results of the low mortality, low fertility and high immigration scenarios were presented in graph form in Appendix 3 of the main report. They were not discussed or referred to in the main text. In the background report there was a lengthy summary on the alternatives and discussion on assessing the uncertainties. The results are presented in support ratios, which include labour force participation and unemployment effects. Thus purely demographic considerations are difficult to evaluate. Still, one gets an impression that the variability the committee considered was not particularly large. Consider for example the demographic crisis scenario. Although low fertility, low net immigration and low mortality were assumed to be permanent features, the numerical low fertility and low migration assumptions were not particularly low, compared to the picture of the size of the uncertainty that stochastic population simulations paint. (What about the mortality assumption?) 3.2 Government’s Report on the Future This report whose full name is “Government report on demographic trends, population policy and preparation for changes in the age structure - Good society for all ages”, given to the Parliament, where the Parliament’s Committee for the Future organized the discussions, is not just a public finance planning paper but has a wider scope. Key themes are demographic trends, population policy and preparation for changes in the age

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structure. We study it here because of its in-depth discussions on demographic developments and what is known about factors affecting them. The Government’s report considered population projections in its Annex report 1 as follows. “One should be careful if one wants to base decisions on population projections. Projections from different sources can be different, and it is not easy to argue one scenario to be more credible than others. Alternative scenarios are often made for this reason. Their useful aspect is that force one to think about the consequences of the risks involved in the decisions. Risk-averse decision makers should also consider weak alternatives and their consequences. Decisions are usually made on the basis of one given knowledge base and assumptions concerning future developments. The problem is that the assumptions may well be affected by wishful thinking and over-confidence. Often the actual developments will, however, not turn out as assumed. In decision making, less attention has been paid to what to do in case the expectations do not come true and the risks connected to bad scenarios materialize.” (translated by JL) The report’s Annex report 3 considered population policies. Chapters written by different experts considered population policies in historical and European perspective, the factors affecting fertility, the role of immigration, and the consequences of changes in fertility and immigration, among other. As recommendations, the first issue the report emphasizes is that modern population policy is needed. This means securing fertility and increasing immigration. The main report also discussed the uncertainty in demographic projections, and gave examples by comparing the projections made in 2001 and 2004 by Statistics Finland. The comparison consisted the year when mortality exceeds fertility (2019 v. 2023), the year total population starts to decline (2023 v. 2028), highest population (5 300 000 v. 5 446 000), how many more people in ages 65+ in 2030 than in 2001 or 2004 (600 000 v. 610 000), how many people less in ages 15 - 64 in 2030 than in 2001 or 2004 (370 000 v. 320 000), how many people less in ages below 15 in 2030 than in 2001 or 2004 (120 000 v. 65 000). Annex 1 presented also some results of stochastic population simulations. The government also decided one should regularly and utilizing the updating of demographic projections a broader assessment of population ageing, its consequences and how to prepare for it. In this assessment, a focused view of population development, incl. immigration, functionality of ageing people, labour market participation, health and care expenditure and the effect of demographics to economic growth and sustainability of public finances will be created. Main deficiencies and targets for improvements in preparedness will be identified. New issues can be raised. The assessment should utilize the government’s network of forecasting and envisioning the future and the foreseeing and analytical work that is done in different administrative branches and research institutes. The first assessment should be done when the next demographic projection is available, and the government must be informed of the assessment during 2008. The Committee for the Future, which prepares Parliament’s response to the Government’s Report on the Future during each electoral period, itself claims to be

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unique in the world. It is one of the Parliament of Finland’s 15 standing committees. The Committee has 17 members who are all members of Parliament and represent different political parties. What makes the Committee different is the nature of its functions and its new fields of tasks. Its task is to conduct an active and initiative-generating dialogue with the Government on major future problems and means of solving them. Since the problems of the future and above all its opportunities cannot be studied through traditional parliamentary procedures and work methods alone, the Committee has been given the specific task of also following and using the results of futures research. Indeed, the Committee can be said to be making policy on the future, because its goal is not research, but rather policy. The current Committee, which was formed after the elections in March 2003, has started with five special issues: 1. The Future of the Finnish Information Society, 2. The Future of Public Health Care, 3. Human Security as an Extensive Long-term Phenomenon, 4. Regional Innovation Systems, and 5. Social Capital in View of Future Risks for Children and Young People. In the official statement the Committee for the Future says that without active measures Finnish demographics are a cause for concern. Without active population policies, increasing employment rates, sustainable economic growth, active immigration policies and developing services and benefits for the ageing people Finland will not manage the future challenges. The officials of the Committee for the Future made an incoherent background memo about the Government’s report, presenting e.g. calculations with 100-year expected outcomes, but also discussing Swedish-type pension policies and “making the rules known in advance”. It remains to be seen what if any impact the Government’s report will have. The discussion on how to deal with demographic uncertainties in sustainability evaluations is continuing among some of the experts who took part in preparing the report.

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4. Conclusions and recommendations Relating the previous to the decision-making framework, we notice the following. From the citizen’s point of view a central question is: Could population ageing have been noted earlier? If the need to change pension systems had been realised earlier, the necessary changes would have been smaller (in the ideal case). They would have taken effect further in the future, and people would have had more time to adjust. Another important issue is the role of research in the decision-making process. In Finland some research was done on the consequences of the pension measures considered before the reform was agreed. This research was done in secrecy. No public debate on the possible measures was possible, and no peer evaluation of the research was possible. Only after the reform was decided (decided in practice; it took over a year to pass it through the legislation process but no essential changes were made in the process) was it possible to start more comprehensive studies on its effects. From the citizen’s point of view, this may be evaluated in different ways. One could think that citizens should benefit from an effective and comprehensive research behind the decisions. On the other hand, in international comparison the Finnish pension reform outcomes do not fair badly. So, if secrecy is the price that must necessarily be paid for good results, then let’s pay it. But is it necessarily so? The Finnish pension system has gone through several reform processes during the last ten years. In this it differs from the practice in Sweden, where one major reform took several years but then produced conditional rules concerning pension benefits and their indexation. It seems that the need for further reforms is essentially reduced in Sweden by these conditional policies. This was a very long process, but that may be compensated by the possible future feature of not needing to make new reforms. This topic of conditional pension policies has been raised here because a) it has been applied, b) it may be more credible than non-conditional policies, c) and thus more informative to citizens, at least in principle, d) it includes a dynamic trade-off in policy-making process, e) it has implications for supporting activities such as demographic projections and the research utilising them. We recommend that this topic be researched and included in the EU’s relevant research programmes. This may offer important possibilities for improved policy practices. What is the role or scope of conditional policies in other fields? The first impression is that they are not so directly applicable in, e.g., immigration policy. Technically they could be, the number of accepted immigrants could be indexed to domestic labour force, but would this be useful to a degree that justifies it?

References: Alho, J.M. (2003): Predictive Distribution of adjustment for Life Expectancy Change. Working Papers 3, Finnish Centre for Pensions, Helsinki. Alho, J.M., S. E. H. Jensen, J. Lassila and T. Valkonen (2005): Controlling the Effects of Demographic Risks: The Role of Pension Indexation Schemes. Journal of Pension Economics and Finance, Vol. 4, No. 2 (July 2005), 139 - 153.

Alho, J. M., J. Lassila and T. Valkonen (2005): Demographic Uncertainty and the Evaluation of Sustainability of Pension Systems. In R. Holzmann and E. Palmer (eds.): Pension Reform: Issues and Prospect for Non-Financial Defined Contribution (NDC) Schemes. The World Bank, 2005. Auerbach, A. J. and K. Hassett (2001): Uncertainty and the Design of Long-Run Fiscal Policy. In Auerbach, A. J. and R. D. Lee (eds.): Demographic Change and Fiscal Policy, Cambridge University Press. Bongaarts, J. and R.A. Bulatao (eds.) (2000): Beyond Six Billion: Forecasting the World's Population. Panel on Population Projections, Committee on Population, National Research Council. Diamond, Peter (2001): Comments on Auerbach and Hassett. Disney, R. (1999): Notional Accounts as a Pension reform strategy: An Evaluation, Pension Reform Primer Nr. 1. World Bank Group, Washington D.C.

George M.V. (1999): On the Use of Demographic Projections in Canada, ECE/Eurostat Joint Work Session on Demographic Projections (Perugia, 3-7 May 1999). Heller, P.S. (2003): Who Will Pay? Coping with Aging Societies, Climate Change, and Other Long-Term Fiscal Challenges. IMF. Hietaniemi, M. and M.Vidlund (eds.), (2003): The Finnish Pension System, The Finnish Centre for Pensions, http://www.etk.fi.

Joshi Heather and Ian Diamond (1994): Demographic Projections: Who Needs to Know?, ECE/Eurostat Joint Work Session on Demographic Projections (Mondorf-les-Bains, 1-4 June 1994). Keyfitz, N. (1982): Can Knowledge Improve Forecasts? Population and Development Review 8, No. 4, p. 729-751. Kotlikoff, L.J. and S. Burns (2004): The Coming Generational Storm. MIT Press.

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Könberg, B., E. Palmer and A. Sundén (2003): The Swedish NDC Reform – The Road to 1994 Legislation, the Long Implementation Phase and Remaining Loose Ends. A paper prepared for the World Bank and RFV Conference on NDC Pension Schemes, Sandhamn, Sweden, September 2003.

Lassila, J. and T. Valkonen (2002): Prefunding in a Defined Benefit System - the Finnish Case. In Martin Feldstein and Horst Siebert (editors): Social Security Pension Reform in Europe. University of Chicago Press. Lassila, J. and T. Valkonen (2005): Demographic Uncertainty and Pensions: A Summary. Unpublished DEMWEL paper. Lee, Ronald (2004): Quantifying Our Ignorance: Stochastic Forecasts of Population and Public Budgets, Center for the Economics and Demography of Aging. CEDA Papers: Paper 2004-0001CL Lee, R. and S. Tuljapurkar (2001): Population Forecasting for Fiscal Planning: Issues and Innovations. In Auerbach, A. J. and R. D. Lee (eds.): Demographic Change and Fiscal Policy, Cambridge University Press. Li Nan, R. Lee and S. Tuljapurkar (2004): Using the Lee-Carter Method to Forecast Mortality for Populations with Limited Data, International Statistical Review 72:1:19-36. McFadden (2001): Comments on Lee and Tuljapurkar. In Auerbach, A. J. and R. D. Lee (eds.): Demographic Change and Fiscal Policy, Cambridge University Press. Oxley, H. (2001): OECD experience with projecting age-related expenditure. In T. Boeri, A. Börsch-Supan, A. Brugiavini, R. Disney, A. Kapteyn and F. Peracchi (eds.): Pensions: More Information, Less Ideology, Assessing the Long-Term Sustainability of European Pension Systems: Data Requirements, Analysis and Evaluations. Settergren, O. (2001): The Automatic Balance Mechanism of the Swedish Pension System. Riksförsäkringsverket. Downloadable at http://www.rfv.se/english/publi/index.htm. Smith, James P.(2001): Comments on Lee and Tuljapurkar. In Auerbach, A. J. and R. D. Lee (eds.): Demographic Change and Fiscal Policy, Cambridge University Press. SOMERA (2002): Report by the SOMERA Committee. Ministry of Social Affairs and Health, Committee Report 2002:4eng. SOMERA (2002b): The Development of Social Expenditure in the Long Term (in Finnish). Background Report of the SOMERA Commission. Helsinki 2002. 436 p. Publications of the Ministry of Social Affairs and Health, 2002:21.

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Audronė Morkūnienė 2005.07.03 POPULATION PROJECTIONS IN PENSION POLICY AND PUBLIC FINANCE PLANNING LITHUANIAN CASE In Lithuania population projections are used mainly in planning pension policy and public finance at the central government level. Official demographic forecasts are provided by the Department of Statistics of Lithuania. They take into account uncertainty in manner that usually three scenarios (the optimistic, pessimistic and base line) are presented. As regards public finance, demographic projections are used not only in the sense that pension expenses affect heavily public finance. The projections are used in planning state budget income and expenses as well. Ministry of Finance does short up to medium term forecasts of the balance of the state budget using demographic data. There are several other examples then demographic data is used in planning and decision making. However, in these cases like state budget assignments to municipal budgets or Patient Fund distributions to local offices demographic projections are not used widely and have very little impact on long term decisions. Therefore further in this paper the focus will be placed on the planning of pension policy where demographic projections played and still play a substantial role. Pension policy Historical overview First time when demographic projections were used in planning pension policy in long term was preparation of pension reform of 2004 which started in 2000. Before that forecasts were produced by different instruments but they had no serious impact on decision making. Lithuanian pension system the first time was reformed in 1995 putting it on the basis of social insurance. This was so-called parametric reform which tried to abolish distortions in the system inherited from the soviet law of 1956. Since 1995 the retirement age which used to be 55 for women and 60 for men started to be increased by 4 months per year up to 62.5 for men and by 6 months per year up to 60 for women. Pension formula was changed to include working record of 25 years instead of one last year. All early retirement provisions so abundant in the soviet law were abolished and compensated with

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temporal benefits payable up to retirement for those who were already eligible. Only periods which were covered either with contributions’ payment or benefits were considered as insurable years. There was provision for increments in case of deferred pension. Pension formula consisted of two parts: basic pension and supplemental pension which supposed to play different roles in indexing pensions. Basic pension being almost flat was to be indexed to prices; supplemental part included the average wage in economy which gave indexation to wages. Pension insurance itself was arranged according these two parts: self-employed were obliged to insure themselves to at least basic part, while hired workers and civil servants were insured to full pension. The system included old age, disability and survivors’ pensions. The reform of 1995 was really a major one. All pensions in payment were recalculated according to new formula. Despite that, no mathematical evaluation of the reform outcomes was carried out. At that time neither the need to forecast the long term sequences was perceived nor the tools to make such prognosis were available. Social insurance pensions after the 1995 reform were financed through the separate social insurance fund which administration was called Sodra. It used only 3-5 years term budget forecast which was based on simple calculation adding some growth to the last year’s figures. Such assumptions as GDP growth, inflation, and wage growth were provided by the ministries of Finance and Social Security; others were constructed by Sodra’s staff. Such short term calculation is still used in the same manner today and it serves as good comparison base for the long term forecast’s figures in first 5 years. The Ministry of Social Security and Labour used to have a model created by International Labour Organisation which forecasted the whole social sector income and expenses using the demographic prognosis among other indicators. It covered pensions, family benefits, labour sector measures, housing, partly education and health. Being so wide and complicated in inputs it was very imprecise therefore the ministry was unable to base the policy planning on its outcomes and it was never used in decision making. However, one may say that the pension reform of 2004 was erected by the demographic forecasts. Approximately since 1994 the World Bank was very active in Central and Eastern European countries promoting a three tier pension reform which reduces the size of state provided pension system and introduces mandatory savings in privately managed individual accounts. The proposal with some ideological reasoning was mainly based on the demographic forecast which showed a catastrophic shape of the state run pension systems in next 50 years. Not surprisingly the first tool to forecast Lithuanian pension system balance in long term was model PROST (Pension Reform Option Simulation Toolkit) developed by the World Bank. It used Lithuanian data of social insurance pensions but the model itself was very

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universal. It did not take into account any specifics of Lithuanian pension system. Lack of Lithuanian account was especially felt in the indexation of pensions because the model was rather sensitive to it. It should be noted that at the time then the first forecasts were produced the pension system just like other public sectors in Lithuania was in a very difficult situation due to so-called Russian crises or oil crises of 1999. Sodra experienced deep deficit, the indebtness rose up to 15% of its budget, payments delayed. The background for very pessimistic forecast was favorable. Media was full of pensioners’ complains and workers’ fears about their own retirement. Bad prognosis for the future accelerated very much the public debate on the liabilities of pension system and ability of state to fulfill them. This became one the main arguments for the partial privatisation of the system. However, these prognoses were done by the World Bank itself rather than Lithuanian authorities. The forecast seconded the overall fears expressed to the pension system, while in short term it promised some relief. However, at that time due to the atmosphere in Lithuanian society this short term improvement was not taken into consideration at all. First more serious attempts to forecast public expenses in pension system were forced by above mentioned public debate on the future of Sodra. Both broader audience and policy makers wished to see some “material” evidences if this bad shape of social insurance pension’s budget was not just temporal. It was very broadly accepted that reform towards privatisation was needed. However, the concrete scenario how to design the reform and how much it would cost were necessary. In the beginning of 2000 the Government created working group from experts of both public and private sectors. Its task was to prepare White Paper for the pension reform which had to include a long term forecast of the outcomes of the proposed reforms. After six months the White Paper was presented to the Cabinet of Ministers. The forecasts were based on multiple runs; however they were presented in usual manner as base line, optimistic and pessimistic scenarios. The presumptions for the scenarios were based partly on official forecasts (like demography or GDP growth) or were defined by the expert estimations. As it was mentioned, the forecasts were a bit doubtful due to too much universal model used. Nevertheless, the main part of the White Paper focused on the design of the future reform and possible its implementation, forecast being only supplemental. The next phase for the calibration of the reform and using the forecasts was presenting the proposal to the Parliament in spring of 2001. The proposal inspired hot ideological debates. In addition, the Social Affairs Committee asked to produce more precise and reliable financial estimation of the reform and gave the proposal back to the Government. At that time the Ministry of Social Security and Labour received technical assistance from the World Bank in form of short term projects. It was expected that Lithuanian government would take a loan later to cover the reform expenses. One of the project

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experts prof. J.P.Wiese proposed to use other model, namely PRISM (Pension Reform Illustration and Simulation Model), which also was not developed particularly for Lithuania. One has to acknowledge that all future prognoses were run by this model and pension policy decisions were based on its outcomes. The forecast done in 2001 showed the temporally deficit in coming 3-5 years, after that the substantial surplus had to arrive. This was based very much on the demographic structure of population, on previous rather high fertility rates and small generation of pensioners born during the Second World War or post War time. Since 1995 increased retirement age also helped to balance the pension system. Graph.1. Balance of social insurance pension system in % of GDP, keeping it as it is (no increases in retirement age, pension replacement rate constant 42%)

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

However, the long term forecast was still not so optimistic. It showed that after 50 years Sodra would experience a deficit up to 2% of GDP, even if pension replacement rate was kept at such low level as in nowadays (42% of net average wage). One should note that presumption that replacement rate would be at that level for long period was unrealistic, having in mind that this pension was practically the only income source in old age for the most of retirees and the social insurance fund would experience surpluses. Therefore forecasts with increments in replacement rate were made and they showed the following. Graph 2. Balance of social insurance pension system in % of GDP, when pension replacement rate increases

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-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

Red line: retirement age is not increased (60 for women, 62.5 for men), pension replacement rate increased up to 60% in 2010. Blue line: retirement age increased up to 65 for both genders, pension replacement rate up to 60% in 2010. As one might expect, in this case the surpluses in the pension system were to be lower and shorter, and deficits much deeper, up to 3.5% of GDP (or almost half of current pension expenses). The increase in retirement age seemed to be helpful instrument; however, its application was not as much likely as increases in pensions. Argument for the pension reform acquired the new aspect. While talking about tremendous difficulties which the future governments would face in keeping the pensioners’ level of living, reform promoters argued that only now there was favourable moment for the reform and it would be not wise not to use it. The coming surplus had to be somehow reserved for the future generations to fulfill liabilities to those who were now paying social insurance contributions. It seemed that the best way to do it was to channel part of contributions to the individual accounts where they could be untouched until the one’s retirement. It was very obvious that leaving all the extra money in Sodra would lead to unsound increases in current benefits which in the future would make the liabilities of the system even more insupportable. The decision was to handle the sums over private investment companies leaving Sodra with administration of the register of participants and their contributions. The first reform proposal included mandatory participation of younger generations in private pension funds with contribution rate of 5% deductible from social insurance. As the debates over the partial privatization of the pension system focused on the mandatory nature of switching and high contribution rate which would require substantial resources to implement the reform, the proposal eventually was adjusted.

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It was accommodated in the sense of terms of participation in the new system – no one group of population was mandated, and in the sense of the reform pace – part of contributions going out of Sodra had to be increased step by step starting from 2.5% in 2004 and adding one percentage point each year till it reached the initially proposed rate in 2007 (5.5%). The reform design was well supported by working part of population and business society. However, the proposal would not have future without promises to spend some part of surplus for current pensioners and increase their benefits. The forecasts took into account both actions – part of contributions going to private pension funds, increases in replacement rate of average pension up to 60% in 2010; and increases in retirement age after 2006 when the current rise had to be ended. The forecast showed much smoother balance line with long term deficit as well, however, not at such deep level. Graph 3. Balance of social insurance pension system in % of GDP with partial its privatization (pension replacement rate increases up 60 % in 2010, retirement age increases up to 65 for both genders, deductions to pension funds from 2.5% to 5.5%, full participation of those under 30)

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

The forecasts included all reform features while it was difficult to reflect the voluntary character of the system – model could not take into account if only part of particular cohort would participate in the system. Therefore the assumption was made that younger generation (under 30) switched to the new system entirely which reflected almost 23% of all insured. In long run it meant that after 35 years the participation in the new system would be full.

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Unfortunately, the reality was a bit different. One of reasons for voluntary enrolment was common believes that only small part of insured would switch in first years of the new system operation. Therefore for the purposes of planning the reform costs and the need for budget allocations, calculations were made separately, using actual Sodra data and applying some simple estimation. Calculation was made for coming 6 years (2004-2009) estimating three scenarios (min, max and the real one) based on participation rate by age and income group. The pension replacement rate 60% had to be reached in 2009, therefore part of surplus in calculations was assigned to these increases. State budget allocations were planed to fill the lacking funds. The assumptions of macroeconomic indicators such GDP or wage growth, number of workers, inflation were official ones and the same for each run. The calculations were presented to the Cabinet of Ministers and later to the committees of the Parliament. In the last spring session of 2003 the reform package was adopted leaving a bit more than two months for real preparation to implement it as from the 1st of January 2004. The reform was well argued out and much welcomed therefore it became possible to take that. Now comparing the actual expenses with forecasted ones, one can say that the major differences are in the number of participants of the new system. Nobody expected that these figures would be so high and actually fulfill the max scenarios. Min scenario involved 12% participation of all insured, max – 56%, and the real one (or most likely) – 22% in 2009. Practice showed that in 2005 we already got 53% rate of participation. The real surpluses occurred to be 2-3 times higher than forecasted as well. This was due to unexpected GDP growth of 6-9% per year. However, the growth of replacement rate was not achieved, despite the sums channeled to pensions’ increases were twice higher than planned. One may argue that plans were too ambitious indeed. But not only the GDP grew faster than expected, so did the wages. As a result pension replacement rate stayed almost constant during these years. Another part of plan – to receive state budget allocations to fully fill the gap after pension increases also was adjusted. After long negotiations Ministers of Finance and Social Security agreed to share reform costs between social insurance and state budgets on 50 to 50 basis. This meant substantially larger amounts of reform costs to be financed from Sodra itself. One should acknowledge that in the forecast migration was not reflected. At the time when the first forecasts were produced the flows of migration were not significant and there were no estimations how could they change in the future. Therefore the saldo of migration flows was assumed to be zero. After joining the EU, it became obvious that emigration figures can not be neglected any more. In 2004 migration saldo was -10 thousand based on the data of Population Register. The newest demographic forecast prepared by the Department of Statistics already includes this figure and assumes that migration saldo will go to zero until 2030. However, it may again be that these

25

expectations are too optimistic, as the real amounts of current emigration may occur to be much higher. Notably that in 2001 stochastic forecast for Lithuanian population was done under research project Economic Effects of Ageing and Demographic Uncertainty in Lithuania sponsored by PHARE ACE Programme. It was the first time when uncertainty was really dealt with while forecasting the Lithuanian demographics. The approach was very innovative both for academicians and decision makers. Unfortunately, the research results were not properly disseminated despite they were presented at the workshop in Vilnius. Partly this was due to the fact that there were no funds under project’s budget available for publication therefore project was left little known even to academic society. On the other hand, government decision making procedures were and still are not adopted to handle with stochastic proposals, therefore it was perceived more than scientific experiment rather than a helpful tool. Today’s state While implementing pension reform of 2004 Ministry of Social Security and Labour received technical assistance from Swedish National Board of Social Security. After experience with PROST and PRISM it was understood that Lithuania would benefit if it had model better adjusted to its system. From this project another project with Swedes elaborated which was devoted solely to the creation of mathematical model forecasting social expenses. It plans in 2007 to have a model covering pensions, other social benefits, labour market figures, some health indicators. The model will be very detailed and based on the actual features of Lithuanian social security system. However, the model will not take a stochastic approach but will be based on scenario formation for every run. The creation of Swedish model was included in Action Plan of implementation of Government program. This shows that the need for better forecasting is well understood at political level as well. There are plans to adapt PRISM which covers pensions and is the main forecasting instrument at the moment. Ambitions are to include more Lithuanian aspects in it, namely to better reflect voluntary participation in the new partially funded system. The pressure to have reliable forecasts of public expenses is especially felt after joining the EU. Lithuania participates in various working groups created or encouraged by the European Commission, which base their investigations on middle or long term forecasts. The National Strategy Report on Pensions which presents development of the system up to 2050 is almost ready. The latter was one of the tasks assigned by Brussels. In National Strategy Report forecasts were based on Eurostat data and prognosis. They are a bit different from the previously used, especially the rate of fertility. In most runs performed before the fertility rate was kept at the today’s level 1.3 as there was no indication that it could be higher in the future. While Eurostat demographic prognosis is based on the growth of fertility rate up to 1.65. Of course, this has an impact on the shape of the balance curve – the future deep deficits are later.

26

Graph 4. Balance of the social insurance pension system in % of GDP, using Eurostat data

Balance of pension system bugdet % GDP

-6,0%

-5,0%

-4,0%

-3,0%

-2,0%

-1,0%

0,0%

1,0%

2,0%

3,0%

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

2055

2060

2065

2070

pension reform without reform Ret.age 60/62.5

Pension reform: partial privatization of pension system, increases in pension replacement rate up to 60% in 2045, increase in retirement age up to 65 for both genders starting in 2007 Without reform: no privatization, increases in pension replacement rate up to 60% in 2045, increase in retirement age up to 65 for both genders starting in 2007. Ret.age: no privatization, increases in pension replacement rate up to 60% in 2045, no increase in retirement age. Conclusions Lithuanian case of pension policy planning clearly indicates that demographic considerations laid behind the major reform decisions. Pension reform towards partial privatization itself was inspired by threatening forecast on the future of the system due to worsening demographic indicators. Even more, the decision to channel part of current contributions to individual funded accounts was much backed by the existing and future age structure of Lithuanian population. This case is good example how public policy debate was influenced by demographic forecast. Not only the current bad shape of social insurance budget was of public concern but its long term state and possibility to fulfill liabilities to future pensioners were well perceived as well. Practice shows that not only policy decisions are heavily influenced by demographic projections but demographic projections are dependent on policy targets as well. The

27

main indicators such as fertility, mortality rates and flows of migration usually are affected at least by expectations of the officials. Having in mind the significant impact of the forecasts, one should take into account the accuracy of the prognosis and uncertainty which is inherent in it. However, still too little attention is paid to this aspect of the forecasts. Usually three scenarios are presented and decisions are based on so-called base-line. The elaboration of the scenarios is not well backed by truly scientific investigations and approaches. This could be explained both by not readiness of the bureaucracy to handle stochastic forecasts and weak availability of the proper instruments. In Lithuania even in academic world stochastic forecasts are not used widely therefore it is unrealistic to expect their practical application. Link between scientific investigations and government decisions could be strengthened if academicians showed more initiative on the topics where government plans to take action. On the other hand, it is obvious that unless the scientific evaluation gets to the Government bureaucratic routine, the investigations are left to purely academic society. Due to the uncertainty differences between projections and actual trends are inevitable, not to mention discrepancies which occur due to later political decisions. The best solution would be full awareness of the decision makers and broad public as well of the probability nature of the forecast on which decisions are made. So far this aspect is not fully recognized by decision makers therefore there is no evaluation and adjustment procedures set. Still governments feel more comfortable making firm decisions rather than probable. Could be this partly due to the fact that societies itself do not accommodate uncertainty in their expectations and like more definite promises rather than probable? The efficiency in the use of demographic tools could be improved by developing more user friendly, easily adoptive forecasting instruments which deal with uncertainty as well. The political will to use more precise instruments in backing policy decision is more and more obvious. Forecasting became essential tool in deciding on pension policy options. This could be explained both by availability of models and the need perceived by politicians. Anyway, the feedback between elaborated forecasting instruments and willingness to use prognosis in decision making obviously exists. The success in applying in one sphere could strike interest in other spheres as well. This depends much if there are ongoing debates on the topics which are of great concern to broader public.

28

Demographic projections and the regulation of annuity markets

Niku Määttänen, ETLA

August 2005 Introduction The market for annuities is growing in many countries, in part because of the increasing popularity of defined contribution pension schemes3. There is also a clear public interest in trying to ensure that private annuity markets work properly. Consequently, insurance companies and special pension annuity companies are usually subject to extensive regulation by public authorities. In this section, we discuss the use of demographic projections in the context of the regulation of private annuity markets. We argue that long-term stochastic population forecasts could be very useful in the regulation of private annuity markets. Mortality risk and the regulation of annuity providers The main purpose of the regulation of the private annuity market is to make sure that annuity providers are able to cover their future commitments. The main instruments of regulation are reserve requirements and investment constraints which are set as a function of estimated liabilities and risks formed by existing contracts of an annuity provider.4

The main risks for annuity providers are usually held to relate to errors in mortality and interest rate assumptions (Blake and Burrows 2000, Davis 2002). While life expectancy has improved dramatically over the last two centuries, it has proven very difficult to forecast improvements in it accurately. This is what is meant be mortality

3 By annuity, we mean a contract that allows an individual to exchange a stock of wealth for a stream of income that continues until death. These contracts are also sometimes referred to as pension annuities or life annuities. 4 Davis (2002) presents a good general discussion about the regulation of annuities markets with references to a number of academic papers about the issue.

29

risk in this context. Together with the interest rate, mortality is the key variable determining the net present value of the liabilities of an annuity provider. Consequently, regulators of annuity providers (as well as the annuity providers themselves) need to consider carefully how mortality may develop in the future. We do not have a comprehensive knowledge about the way the mortality risk is taken into account by the public authorities in charge of the regulation of the annuity markets across different EU-countries. Apparently, however, a common way to account for uncertainty is to take a point estimate for average future mortality from a deterministic demographic projection and adjust that mortality with a fixed “precautionary” margin. The adjusted forecast for future mortality is then taken as an input when computing the present value of a particular set of annuity contracts.5

Taking a fixed a precautionary margin has the important advantage of being a simple way to take the mortality risk into account. However, in many cases, it may also be a very problematic way. For an example, consider a case where two insurance companies are specialized in selling quite different annuity products. The first company sells deferred annuities with a guaranteed future annuity rate to relatively young individuals and the second one provides single premium immediate annuities to relatively old individuals. It seems likely that the mortality risk is a more serious problem for the first company because the relevant mortality rates are those in the more distant future and they should therefore be seen as more uncertain. Comparing the risks between these types of contracts requires an estimate about how rapidly the uncertainty around mortality increases towards the future. Current regulatory practices may compare these risks in a very misleading way. As a result, regulation may distort the relative price of these two types of annuity contracts in a way that is not justifiable based on the true mortality risks involved. As another example of the difficulty in taking mortality risk appropriately into account, consider life insurance as a hedge against the mortality risk of annuities. Insurance companies may (and often do) try to hedge mortality risk by selling life

5 The precautionary margin may be related to two things: uncertainty about average mortality in the total population and adverse selection. Adverse selection means in this context that annuitants are likely to live longer than the average population of the same age.

30

insurance contracts so as to balance the mortality risk associated with annuity contracts. This is because while decreasing mortality decreases the average return to annuities (from the company’s point of view), it increases the return to life insurance contracts. However, because mortality improvements are not spread evenly across ages, and because usually only relatively young individuals are interested in life insurance, it is not easy to evaluate the efficiency of such a hedging policy. Analyzing life insurance as a hedge against mortality risk in annuities requires comparing the uncertainty of future mortality rates across different ages.

How should the mortality risk be taken into account? Recent research in demographics has quantified the uncertainty of population projections and produced stochastic population forecasts which include uncertainty by construction. For instance, the EU funded project “Uncertain Population in Europe” (UPE) has produced long-term stochastic population forecasts for 18 EU countries [add a reference]. In principle, at least, stochastic population forecasts would be ideal for assessing the mortality risks in the annuity market because they take the uncertainty associated with future mortality explicitly into account. The elements that are needed from the stochastic population forecasts are point-estimates and uncertainty parameters for future mortality rates. Given the details of an annuity contract (or a portfolio of different annuities and life insurance contracts) and the relevant assumptions about the interest rate, one can then evaluate its net present value in fully stochastic terms. What should be done next? Stochastic population forecasts provide arguably the best available way of quantifying mortality risk in the annuity market and comparing the risk characteristics of different annuity contracts. Of course, this doesn’t imply that public authorities will ever use them or even that they should use them. It may be that as they presented now, stochastic population forecasts are simply too complicated to be used in practice when assessing the risks in the annuity market.

31

In order to alleviate this problem, the research community should provide tools that make it feasible to use stochastic population forecasts when assessing the mortality risks involved in the annuity market. One way to proceed would be to develop a computer programme that takes as inputs the details of a portfolio of annuity and life insurance contracts and produces as an output various statistics about the distribution of possible returns to the portfolio based on current estimates about the stochastic properties of future mortality. Given the increasing importance of private annuity markets and the relevance of the mortality risk for annuity providers, that might turn out to be an important application of the recent scientific research in demographics. References: Blake, David and Burrows, William (2001), “Survivor Bonds: Helping to Hedge Mortality Risk”, Journal of Risk and Insurance, 68(2), 339-348. Davis, Philip E (2002), “Issues in the regulation of annuities markets”, Center for Research on Pensions and Welfare Policies, working paper 26/02. James, Estelle and Dimitri Vittas (2000), “Annuity Markets in Comparative Perspective: Do Consumers Get Their Money’s Worth?”, World Bank, Policy Research Working Paper 2493.

32

The use of demographic trends and long-term population projections in public policy planning at EU, national, regional

and local level

Chapter 4. Population projections in immigration policy

1. Do Demographic Projections Affect Immigration Policies in Germany? (p. 2) Case Study on Germany by Herbert Brücker

2. The use of demographics when designing and implementing migration policies – the Netherlands (p. 27) by Harri Cruijsen

3. Population Projections and Immigration Policy: The Spanish Case (p. 53) by Namkee Ahn

2

Do Demographic Projections Affect Immigration Policies in

Germany?

Case Study on Germany

LOT 1: The use of demographic projections in policy planning (VC/2004/440)

Herbert Brücker1

DIW Berlin and IZA Bonn

1 Introduction

The objective of this case study is to analyse how demographic projections are used in the

area of immigration policies in Germany. As in other Member States of the European Union

(EU), the immigration of nationals from non-Member States of the EU and the European

Economic Area (EEA) is regulated at the national level. Since less than one-third of the

migrant population in Germany stems from other Member States of the EU and the EEA, the

regulation of the main part of immigration remains in the domain of national policies. The

role of non-EU and non-EEA countries for immigration to Germany is likely to increase

further in the future, given the large differences in the economic, social and demographic

conditions between Germany and non-EU and non-EEA countries in its neighbourhood.

Within Germany, immigration is regulated at the national level, although the Chamber of the

Federal States (‘Bundesrat’) has to approve the immigration legislation. The Federal States

(‘Bundesländer’) and municipalities however are largely in charge of policies which are

relevant for the integration of the migrant population, i.e. education and training policies,

child care, housing and other infrastructure policies.

The available demographic projections provide important information which is relevant for all

areas of immigration policies. It is undisputed, that the German economy and society is

affected by a rapid ageing process. According to all population projections, the share of the

population in an age above 65 will -- without a change in fertility and immigration rates --

increase from 15 per cent of the population in the year 2000 to about 30-40 per cent in the

1 Contact: German Institute for Economic Research (DIW Berlin), Königin-Luise-Str. 5, D-14191 Berlin, phone: +49-30-89789-442; fax: +49-30-89789-108; Email: hbruecker@diw.de.

3

year 2050. This process of ageing has started in the mid 1970s, but it will gain momentum

from the year 2010 onwards. Birth rates have been, at 1.3-1.4 children per woman,

remarkably constant during the last decades. Although there remains some uncertainty with

regard to future fertility and mortality rates, it is rather unlikely that domestic developments

will mitigate the ageing process in Germany. The demographic change has far-reaching

consequences. Social security systems are ill-prepared. Particularly, the pay-as-you-go

pension scheme will come under increasing pressure from the year 2010 onwards, and may

run in a severe crisis after 2030. Similar problems may arise for the health insurance system.

Several reforms of the pension and health-care system have been carried out during the last

decade, but all these reforms do not address the ageing problem sufficiently. Under

consideration of demographic change, the public debt burden has to be reduced by 6 per cent

of GDP per annum in order to meet the inter-temporal budget constraint of the public sector.

The impact of immigration on the demographic structure of the population has been analysed

by many demographic projections in form of different scenarios. As a bottom line, these

projections demonstrate that immigration policies can not reverse the ageing process in

Germany, but they can contribute to mitigate it. Although the impact of migration on

demographic change and the possible implications on public finances and social security

systems are well studied in Germany, immigration policies largely ignore the findings of these

studies. The main force which drives immigration policy in Germany is the fear that

immigration will increase unemployment in Germany, and, hence, will create further

problems for social cohesion. Consequently, the number of migrants has been reduced in

Germany at the turn of the millenium after the migration surge in the 1990s. A move from

immigration policies which pursue the objective to protect the labour market to a policy

which considers the demands of demographic change is not in sight.

Thus, demographic projections play not a major role in German immigration policies. The

question is whether this can be traced back to shortcomings of the demographic projections or

to other causes. The main demographic trends are well studied in Germany, although further

research (e.g. better assessment of future migration trends, of the size and structure of the

foreign population, of the regional distribution of the migrant population) could substantially

improve the knowledge on future developments and reduce uncertainty. However, since

immigration policies is largely driven by labour market considerations in Germany, even a

better knowledge on demographic trends would not necessarily increase the influence of

4

demographic projections. A better understanding of the labour market impact of migration is

also needed. There exists already a considerable research on the effects of migration on

employment and wages in Germany. However, there is a lack of knowledge about the links

between the effects of migration on the demographic structure and its direct and indirect

implications on labour markets. As an example, migration may on the one hand reduce

unemployment by lowering social security contributions and indirect labour costs, while it

may on the other hand increase unemployment in case of wage rigidities through a higher

labour supply. A better understanding of the links between migration, demographic change

and labour markets could contribute to resolve the seeming conflict between policies which

are driven by the objective to reduce unemployment and those which try to mitigate the

effects of ageing.

The remainder of this case study is structured as follows. Section 2 provides an overview on

international migration to Germany and on German immigration policy. Section 3 analyses

the potential impact of migration on the demographic structure of the population and

discusses whether and to what extend the existing projections address the requirements of

policy planning in the field of immigration policies. Section 4 discusses the consequences of

migration on public finances and social security systems under consideration of the

demographic change. Section 5 discusses the policy response to these developments. The final

section summarises the findings and concludes.

2 A brief chronic of immigration and immigration policies in Germany

In absolute terms, Germany was in the post-WWII period the largest destination of migrants

in Europe. Even in relative terms it is the largest destination after Luxembourg and

Switzerland. The annual net migration saldo amounts to 2.5 per 1,000 in Germany during the

period 1950-2000, compared to 0.8 per 1,000 in the EU-15.2 The number of foreign citizens

residing in Germany is reported at 6.717 millions by the end of 2004, which corresponds to a

share of 8.1 per cent of the population (Statistisches Bundesamt, 2005).3 Note that cumulative

net immigration is, at 9.5 million people (11.3 per cent of the population) in the period 1950-

2 Calculation by the author based on statistics of the UN Population Division (2002). 3 The German migration statistics has been revised in 2004. This has reduced the number of foreign residents by 8.4 per cent relative to 2003.

5

2000, much larger than the number of foreign residents. This can be traced back to the large

immigration of ethnic Germans and an increasing number of naturalisations.

We can distinguish four phases of migration into Germany:4 The first phase is characterised

by forced migration after the end of WW II, which involved the immigration of 8 million

people from Central and Eastern Europe to Germany and another 2.6 million East Germans to

Western Germany in the years 1945-49. Note that the overwhelming share of these migrants

are not reported as international migration by the German migration statistics, if it is

registered at all.

The second phase of migration begins after full-employment has been achieved in the late

1950s. At that time, labour shortages evolved in many sectors of the German economy,

namely in the manufacturing sectors. Germany responded to these shortages by opening its

labour market for temporary migrants and installed the so-called guest-workers programme.

On the basis of bilateral agreements with the origin countries, workers were directly recruited

in the source countries by German firms in co-operation with the Federal Employment

Services. Work contracts and residence have been temporary. However, the temporary

arrangements were not enforced, and, as a consequence, the guest-worker programme resulted

in permanent migration. Germany recruited mainly manual workers from Southern European

(Italy, to a lesser extent Spain and Portugal), and South-eastern European countries (Turkey,

former Yugoslavia, Greece). Since labour migration during this phase had, intended or not,

largely a permanent character, it left its imprint in the structure of the foreign population in

Germany until today.

The recruitment of foreign workers stopped abruptly in the wake of the recession that

followed the first oil-price shock in 1973. In the face of increasing and persistent

unemployment, Germany never readopted the policies of active labour recruitment. The third

phase of migration, which begins with abolishment of active labour recruitment in November

1973, is therefore characterised by restrained migration. The main channels of entry became

family reunification and humanitarian migration. The free movement of labour and other

persons in the EU affected less than one-third of the migrant population in Germany. The

introduction of the free movement for Greece, Spain and Portugal in the late 1980s and early

1990s did not result in a migration surge. This can be traced back to the fact that the number 4 For an overview on German migration episodes see Brücker et al. (2002), Fertig/Schmidt (2001), Straubhaar/Zimmermann (1993) and Zimmermann (1994).

6

of migrants from these countries in Germany has been already at or close to their equilibrium

levels, such that the removal of legal and administrative migration barriers has had no further

effect. Non-EU countries like Turkey and the former Yugoslavia became more and more the

main source for migration to Germany. As a consequence of tighter migration restrictions and

increasing unemployment the annual net migration saldo dropped from 4 per 1,000 in the

1960s and early 1970s to 1.5 per 1,000 in the late 1970s and 1980s.

The fourth phase of migration is heavily affected by the break-down of the Berlin wall and the

civil wars in the former Yugoslavia. The annual migration saldo increased to 4.4 per 1,000 in

Germany in the 1990s. The main sources of migrants were ethnic Germany immigrating from

Central and Eastern Europe and refugees from the former Yugoslavia. Moreover, 1.5 million

people moved from Eastern to Western Germany. Migration from Turkey, the CIS and other

Central and Eastern accelerated also in the 1990s. The number of legal migrants from the

Central and Eastern European accession countries increased to 600,000 persons in Germany,

and another 350,000 citizens from other Eastern European countries are officially reported.

Germany is therefore in absolute terms the main destination of migrants from Central and

Eastern Europe, and in relative terms the second destination after Austria. In addition, an

unknown number of illegal migrants from Central and Eastern Europe reside and work in

Germany.

Since the end of the 1990s, the net migration rate declined to below 100,000 persons p.a. in

Germany. This can be traced back to the decline in economic growth and increasing

unemployment in Germany, the repatriation of refugees to the former Yugoslavia and the

tightening of migration barriers vis-à-vis non-EU countries, e.g. in the field of humanitarian

migration. Although the German government started some initiatives to attract high-skilled

migrants (e.g. the Green-Card initiative, the new immigration law), these measures have been

quantitatively negligible if they had an impact at all. It is to early to assess whether after the

migration surge in the 1990s a new phase of restrained migration has begun, but at least the

figures from the last five years indicate a significant slow-down of migration to Germany.

Altogether, German immigration and immigration policies have been heavily affected by (i)

the conditions in the labour market, and (ii) external policy events such as the breakdown of

the Berlin wall and forced migration of ethnic Germans. Demographic considerations did not

affect migration policies in the past. Nevertheless, at an annual migration saldo of 2.5 per

7

1,000 in the post-WW II period, migration has had an important impact on the demographic

structure of the population.

3 The impact of migration on the demographic structure of the population

3.1 Overview on main findings

Low fertility rates and an increasing life expectancy will yield an increasing age of the

population in Germany. Without immigration, the population will decline Germany from 82

at the beginning of this century to 59 millions in 2050. The ratio of the population in an age

above 60 to the population in an age between 20-60 will increase from 0.4 to 1.05 at the same

time. Ageing is not a continuous process, it will accelerate from 2015 onwards and achieve its

peak in the decade between 2030 and 2040. The share of the young cohorts in the population

will start to decline already before 2015.

Table 3.1 presents the main demographic projections for Germany.5 The most important

projection is the coordinated projection of the Federal Statistical Office (“Koordinierte

Bevölkerungsvorausberechnung”), which is based on the findings of main institutes and

individual researchers at universities.6 The assumptions on fertility are, at between 1.3 and 1.5

children per woman, relatively similar, although it is important to note that the uncertainty on

fertility rates of those cohorts which are not yet born is substantial. Moreover, some

differences exist with regard to the assumptions on life expectancy. The most important

variable which is hard to predict is the size of future migration. The main demographic

projections for Germany are not based on forecasts on future migration flows, instead they

apply scenarios which are based on different assumptions on the migration rate. Many

projections calculate several variants with a net migration saldo between 100,000 and 300,000

persons p.a., and most studies assume that a net migration saldo of 190,000 to 250,000

persons p.a. is realistic. Note that a net migration saldo of almost 200,000 persons p.a. is the

average for the last 50 years.

5 See Birg (1998a, 1998b), Eurostat (2001), Prognos (1999), Statistisches Bundesamt (2003), Ulrich (2001), UN (2001a, 2001b). 6

8

According to the medium projections in these studies, the population will decline from 82

millions in 2002 to between 67 and 76 millions in 2050. The share of cohorts in an age above

60 will increase from 15-16 per cent to between 29 and 38 per cent at the same time (Table

3.1).

Table 3.1 A comparison of demographic projections for Germany

order to analyse the impact of immigration on the demographic structure of the population

and on the fertility and mortality rates of the foreign population.

Statistisches Bundesamt (2003) DIW Eurostat (1999) Birg et al . (1998a) Ulrich (2001) UN (2001a) Prognos (2001)

net migration p.a. 200 000 270 000 250 000 225 000 190 000 207 000 270 000

population

2002 82.5 82.5 82.1 82.3 82.2 82.4 82.22010 83.1 81.9 83.4 82.7 n.a. n.a. n.a.2050 75.1 67.1 76.0 73.1 68.3 73.3 72.8

2000 n.a. 0.77 n.a. n.a. n.a. 0.75 n.a.2010 n.a. 0.79 n.a. n.a. n.a. 0.81 n.a.2050 n.a. 1.04 n.a. n.a. n.a. 1.08 n.a.

2000 0.61 0.55 0.6 n.a. 0.25 n.a. n.a.2010 0.63 0.57 0.65 n.a. n.a. n.a. n.a.2050 0.84 0.79 0.86 n.a. n.a. n.a. n.a.

0-20 16.1 15.47 n.a. 15.7 n.a. 23,55) n.a.20-60 47.2 46.24 n.a. 46 n.a. 41.2 n.a.60 + 36.7 38.29 n.a. 38.4 n.a. 35.3 n.a.

fertility 1.4 1.4 1.45 1.4 1.357) 1.5 1.4

males 81.1 75.5 80.0 80.0 78.1 78.9 79.7females 86.6 81.7 85.0 86.4 84.5 84.5 83.5

Sources : Schulz (1999), Birg (1998a, 1998b), Eurostat (2001), Prognos (1999), Ulrich (2001), UN (2001a, 2001b), Statisches Bundesamt (2003);calculations by the author.

net migration saldo p.a. (persons)

ratio of 0-20 and 60+ age groups relative to 20-60 age group

ratio of 0-20 and 65+ age groups relative to 20-65 age group

millions

share of age group in % of population

In

we look at the population projection of the DIW Berlin in some more detail. Migration can

affect the age structure of the population by four channels: First, it can increase the share of

young cohorts in the population, since the average age of newly arrived immigrants is below

that of the native population. Second, the return migration of older cohorts implies that the

foreign population does not age at the same speed as the native population. Third, fertility

rates of the foreign woman are (slightly) higher than those of native woman. Fourth, a shorter

life expectancy of the foreign population reduce again the average age of the population.

Thus, the impact of immigration does not depend on the assumptions on net migration rates

alone, but also on the assumptions with respect to the age of immigrants and return migrants

9

Inter alia, the population projection of DIW assumes that

• the number of children per foreign woman is 1.5, compared to 1.3 per native

ver time;

For a simu f migration, we present here three scenarios. The first scenario

assume ns p.a.,

hare of the cohorts in an age above 60 would increase from

and

eless, even in

woman;

• the ratio between the immigration rate and the return migration rate remains

constant o

• the average age of immigrants and return migrants is increasing over time.

lation of the effects o

s zero net immigration, the second scenario a net immigration of 135,000 perso

and the third scenario a net migration of 270,000 persons p.a. Note that a net migration of

135,000 persons p.a. would be a substantial decline relative to the 1990s, but slightly higher

than in the first four years of this decade. A net migration saldo of 270,000 persons p.a. is

slightly below the net migration saldo in the 1990s, but well above the average migration

saldo in the period 1950-2000.

In case of zero net migration the German population would decline from 82 millions to 59

millions in the year 2050. The s

22.5 to 44.3 per cent in the same time period. The ratio of the cohorts in an age above 60 and

to the cohorts in an age between 20 and 60 would increase from 0.4 to 1.05. Finally, the ratio

of the non-working age population, i.e. the population in an age between 0-20 and in an age of

above 60 to the population in an age between 20 and 60 would increase from 0.8 to 1.38.

In the scenarios with a positive net migration saldo the share of the cohorts above an age of 60

increases from 22.5 per cent in 2002 to 40 per cent (net immigration of 135,000 persons)

38 per cent (net immigration of 270,000 persons). The ratio of the population in an age above

60 to the cohorts in an age between 20 and 60 would increase from 0.4 in 2002 to 0.88 (net

immigration of 135,000 persons) and to 0.83 (net immigration of 270,000 persons) in 2050.

Finally, the ratio of the population in an age above 60 and below 20 to the population in an

age between 20 and 40 would increase from 0.8 in 2002 to 1.22 (net immigration of 135,000

persons) and to 1.16 (net immigration of 270,000) persons in 2050 (Table 3.2).

According to this projection, a net immigration of 710,000 persons p.a. would be necessary in

order to hold the number of the population in the working age constant. Neverth

this case the ratio of the non-working age population to the working age population would

achieve a level of around 1 in 2050.

10

Table 3.2 Evolution of age dependency ratios in migration scenarios

case of zero net migration, the population of foreign origin would increase by around

cannot stop the ageing process, but can mitigate it. A net

2002 2010 2015 2020 2030 2040 2050

zero net migration 0.40 0.49 0.54 0.62 0.89 0.97 1.05net migration of 135 000 persons p.a. 0.40 0.47 0.53 0.59 0.87 0.96 0.88net migration of 270 000 persons p.a. 0.40 0.46 0.51 0.57 0.76 0.79 0.83

zero net migration 0.80 0.81 0.85 0.93 1.23 1.30 1.38net migration of 135 000 persons p.a. 0.80 0.80 0.85 0.93 1.21 1.29 1.22net migration of 270 000 persons p.a. 0.80 0.80 0.83 0.89 1.11 1.12 1.16

Sources: Calculations of DIW. See text for assumptions of projections.

ratio of age cohorts 60+ and 0-20 to 20-60 age cohorts

ratio of age cohorts 60+ to 20-60 age cohorts

The different projections have implications for the size of the foreign population in Germany.

In

500,000 persons until the year 2050. In case of a net immigration of 135,000 persons p.a., it

would increase by 7.3 millions, and in case of a net immigration of 270,000 persons p.a. by

13.2 millions until 2050. Thus, a substantially increase in the share of the population with

foreign origin is necessary if immigration should affect the demographic structure of the

population. This has of course important consequences for integration policies, since the main

source for immigration in Germany are countries with relatively low human capital

endowments. Immigrants from Central and Eastern Europe are an exception here, but under

reasonable assumptions immigrants from this area will form only a minority of the future

migrant population in Germany. Nevertheless, since at least a part of the foreign population

will be naturalised and assimilation as well as marriages with the native population will

increase over time, the high share of the population of foreign origin will be less visible than

these figures indicate (Table 3.3).

Thus, altogether the results of these projections confirm the finding in the literature that under

reasonable assumptions migration

immigration similar to that in the 1990s can reduce the ratio of the cohorts with an age above

60 to the working age population (20-40 year cohorts) from 1.05 (zero net migration) to 0.83.

This can have important consequences for public finances and social security systems, as we

will see in the next section.

11

Table 3.3 Population of foreign origin in the different projections

ulate the net

igration rate, since return migration is voluntary. The key decision variable is the gross

the existing demographic projections sufficient to guide immigration policies?

the po ion on the demographic structure of the population.

t net migration scenarios.

2002 2010 2015 2020 2030 2040 2050

zero net migration 7.34 8.27 8.45 8.58 8.64 8.36 7.79net migration of 135 000 persons p.a. 7.34 9.83 10.75 11.58 12.98 14.02 14.62net migration of 270 000 persons p.a. 7.34 10.69 12.29 13.81 16.52 18.71 20.45

Source: Calculations by the author.

population of foreign origin (millions)

It is important to note in this context that immigration policies cannot directly reg

m

migration rate, which is much larger than the net migration rate. As an example, at the given

relations in Germany, a gross immigration of 930,000 persons p.a. is necessary to achieve a

net immigraton of 270,000 persons p.a. in Germany. Thus, immigration policies has to accept

relatively high gross immigration rates if it wants to affect the demographic structure of the

population.

3.2 Are

The existing projections in Germany provide information on the main demographic trends and

tential impact of migrat

Nevertheless, in many studies important information is missing which may be relevant for the

assessment of policy options in the area of immigration policies:

• Most studies do not provide information on the stock of the foreign population which

is accumulated over time as a result of the differen

Exceptions are the above quoted results from the DIW projections and of the

population projections of the UN (2001a, 2001b). Since the economic, social and

demographic behaviour of the foreign population differs from the native population

this information is however highly relevant for all policy areas which have deal with

the integration of migrants (education and training policies, labour market policies,

social policies, housing, infrastructure policies, etc.). Moreover, for integration

policies it would be relevant to know how marriages between the native and the

12

foreign population affect the stocks of the native and the foreign population in the long

run.

An important weakness of all studies is that the net migration scenarios are based on

simp

•

le extrapolations of long-term averages from the past or rely on ad hoc

assumptions. Although migration potentials are difficult to estimate and affected by a

number of economic, social and political variables, estimates of macro migration

models could contribute to reduce uncertainty on future migration flows and the

consequences of policy changes. Moreover, for integration policies it would be

important to know how the source country composition will change in the future. It is

worth noting in this context that the assumption of most migration scenarios that the

net migration saldo will remain constant over time or even increase is highly

problematic. Although high income differences between Germany and other countries

of the EU-15 on the one hand and the neighbouring countries in the East, the South-

East and Northern-Africa on the other hand suggest that the migration potential is far

from exhausted, the global ageing process may reduce the migration potential in the

future. As can be seen in Figure 3.1, the share of the age cohorts with a high

propensity to migrate is declining in all main source regions of migration into

Germany and the EU-15. Particularly affected are the Central and Eastern European

countries, where the share of the cohorts in an age from 20 to 29 declines from 16.5

per cent in 2000 to below 11 per cent in 2025. However, in all potential source

countries the share of the age cohorts with a high propensity to move will decline by

around one-third during the next 30 years. This has important implications for

immigration policies: An immigration policy which waits until the ageing process can

be felt severely in the home country may not achieve the target to influence the

demographic structure of the population, since the propensity to migrate may decline

in the source countries even if borders are opened. Moreover, migration will have

negative spillovers for the public finances and social security systems in the source

countries, since they are affected by the ageing process as well.

13

Figure 3.1 Share of age cohorts with a high propensity to move (20-29 years)

Sources: Worldbank (2003), calculations by the author.

9

10

11

12

13

14

15

16

17

18

19

20

21

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

shar

e of

20-

29 a

ge c

ohor

t in

tota

l pop

ulat

ion

(%)

Northern Africa

Turkey

Central and EasternEurope

former Yugoslavia

EU-15

• Finally, the impact of migration on the demographic structure of the population at the

regional level is not well researched in Germany -- if any research exists at all. The

migrant population in Germany – as in all other Member States of the EU – is highly

concentrated on urban regions. Moreover, immigration is concentrated on prosperous

regions with low unemployment and high wage levels in Germany (i.e. the

industrialised regions in southern Germany, the Rhein-Main area and Hamburg).

These regions have to bear the main costs of integration and adjust their infrastructure

to the requirements of migration e.g. in the areas of housing, public transport,

education and child care facilities. Further research on the implications of different

immigration policies for the demographic structure of the population at the regional

levels could therefore substantially improve the knowledge of policy makers at the

regional and municipal level.

14

4 Impact of migration on public finances and social security systems

There is a long-running debate on whether migration can be used as a tool to alleviate the

economic consequences of the rapidly ageing population in Germany. Although a large

number of authors argue that migration cannot hold up the ageing process and should

therefore not be considered as a policy instrument (e.g. Coleman, 1992; OECD 1991), some

authors argue that immigration can at least mitigate the adjustment processes (Börsch-Supan,

1993; Straubhaar/Zimmermann 1993). A number of studies also exist which try to assess the

fiscal impact of migration on the source countries. Several cross-sectional studies have tried

to estimate the fiscal contribution of migrants relative to natives (Simon 1984, Blau 1984 for

the USA, Riphahn 1998, Steinmann/Ulrich 1994, Simon 1994 for Germany), but these studies

fail to capture the impact of migration on the demographic structure of the population. Others

focus on the impact of migration on pay-as-you-go pension systems by considering its impact

on the demographic structure of the population, but do not consider the overall impact of

migration on the revenues and expenditures of public finances and social security systems

(e.g. Felderer 1994, Börsch-Supan 1994). This Section refers to a more comprehensive

approach, which calculates the net present value of the fiscal contribution of migrants in the

framework of the generational accounting approach for Germany (Bonin 2001,

Bonin/Raffelhüschen/Walliser 2000).

Generational accounting is based on the intertemporal budget constraint faced by the public

sector. If chain-letter (Ponzi) games are ruled out, the present value of future taxes must equal

the present value of future government consumption and debt servicing. Generational

accounting assesses the intertemporal sustainability of public finances by calculating both the

expected taxes and expenditures of public finances, based on long-term projections of the

underlying fiscal and demographic variables. The difference between the expected

government consumption and debt servicing on the one hand, and the expected tax revenue on

the other, is referred to as the sustainability gap. In the case of Germany with no migration,

the sustainability gap has been calculated to be 6.1 per cent of GDP p.a.

Migration can affect the sustainability gap of public finances through two ways: firstly, the

net tax contributions of migrants directly affect the governmental budget balance. If the net

contribution of migrants is positive, the sustainability gap declines. Secondly, immigration

increases the number of potential tax payers, on whom future tax increases can be levied.

15

Thus, migration can reduce the sustainability gap by increasing the tax base, even if the

migrants’ contributions to the present budget are negative.

Consider first the net effect of migrants to the balance of the public finances and social

security systems. According to Bonin/Raffelhüschen/Walliser (1999) and Bonin (2001), the

net contributions of migrants to the public finances vary with the age of the respective

cohorts: while net tax payments – all social security system transfers and governmental

budget expenditure– are positive over the remaining life-cycle of migrants who immigrate at

an age between 11 and 48 years old; those of the younger and older cohorts are negative. As

an example, average net tax payments over the life cycle of a migrant who immigrates at the

age of 30 amount to 110,000 EURO, while a migrant who immigrates before his first birthday

creates a net burden to the public finances of 60,000 EURO. At present, around 78 per cent of

the immigrants belong to cohorts which contribute to a budget surplus over their remaining

life-cycle. The net contribution of the representative migrant to the public finances amounts to

some 50,000 EURO.

Consider now the overall impact of migration on the sustainability gap in the government

budget in Germany. As mentioned above, the sustainability gap in public finances can be

estimated at 6.1 per cent of the German GDP under the assumption of zero migration. A

lump-sum tax of around 1,300 EURO p.a. is needed to close this gap. An immigration of

200,000 persons p.a. reduces this gap through the migrants’ net contribution to the budget and

through the increased number of tax payers by 1.1 per cent of German GDP, which implies

that the lump-sum tax falls to 1,060 EURO or by 18 per cent per capita. The net gain for the

native population is even higher; the sustainability gap declines by 24 per cent (Bonin 2001).

These estimates are rather conservative, since they are based on the present tax payments and

transfer income patterns for the foreign population in Germany. As a consequence of relative

low human capital endowments, the net present value of the income earned by foreign citizens

is around 20 per cent below that of natives, and transfers for unemployment exceed those of

natives by 65 per cent. However, higher transfers from the unemployment insurance and

social insurance do not cancel out the lower transfers made to foreign citizens relative to

natives by German pension schemes. If future migrant cohorts possess better human capital

endowments, which is a reasonable assumption in the case of an immigration policy which

tempts to attract high skilled migrants, the net contribution of migrants to the public budget

16

may even exceed the 50,000 EURO mentioned in the Bonin/Raffelhüschen/Walliser (1999)

and Bonin (2001) studies.

Altogether, this generational accounting exercise demonstrates that, against the background of

the rapidly ageing population, the fiscal gains for natives from future immigration in Germany

are substantial. It is reasonable to assume that the fiscal gains from migration in the other host

countries in western Europe are similar to those in Germany, since they are exposed to the

same demographic trend. With reasonable assumptions for potential migration, it is rather

unlikely that migration can stop the ageing process, but it can mitigate the fiscal consequences

of ageing significantly. On the other hand, migration will aggravate the fiscal burden in the

source countries of German migration, in particular in Central and Eastern European

countries. The age of the population will increase even more rapidly there than in Germany

and the EU-15, since fertility rates have declined sharply since the start of transition. This

does not imply that migration is inefficient: the economic consequences of the ageing process

can be alleviated, if production factors – physical capital as well as human resources – move

to those countries and regions where their productivity is highest. However, migration may

lead to externalities. For example, if the education of a skilled migrant who is hired in

Germany or other western European countries is financed by governmental expenditures in

Central and Eastern Europe, then migration creates a negative externality for the budget in the

source country and a positive one for that in the host country. To compensate the source

country for these expenditures would not only be fair, it would also help to improve the

efficiency of the allocation of human resources.

5 Do German immigration policies respond to the demographic challenge?

The analysis above suggests that migration is not the solution to the demographic challenge

which the German society and economy certainly faces, but that it can contribute to mitigate

the problem. Immigration policies should therefore be a part of a policy package which

addresses the problems which the demographic change creates for public finances and social

security systems. The demographic challenge is very well perceived in the political discussion

of Germany. The German government has appointed several commissions of experts, which

should develop policy recommendations for immigration policies and other policy areas under

consideration of demographic developments.

17

5.1 Recommendations of expert commissions

The Independent Commission ‘Immigration’ (‘Süssmuth’-Commission)

With regard to migration, the most important commission was the so-called ‘Süssmuth

Commission’, which had the objective to develop proposals for immigration policies.7 This

commission published its report in 2001. The report includes a chapter on the consequences of

demographic change based on population projections and analyses the adverse impact of

ageing on economic growth, the public debt burden, and social security systems (Kommission

Zuwanderung, 2001, pp. 26-36). Interestingly enough, it makes explicit recommendations for

family policies in this context (p. 36), but draws no explicit conclusions for immigration

policies. However, it acknowledges that an immigration policy, which attract a permanent

immigration of skilled migrants, can mitigate the ageing problem (p. 17). Moreover, the report

states that a coherent immigration policy should consider the requirements of ‘demographic

and social developments, the state of the economy and the labour market’ at the same time (p.

271). More specifically, it recommends to decide on annual contingents for migrants in

certain areas. It does not make any quantitative recommendations.

Altogether, the report is well informed regarding the demographic projections which exist in

Germany, but not very specific with respect the quantitative consequences which follow from

demographic projections for immigration policies.

7 The official name of this commission is ‚Unabhängige Kommission Zuwanderung’ (independent commission for immigration), but is labelled in the public ‘Süssmuth’-commission after the name of its chair Rita Süssmuth.

18

The report of the Council for Immigration and Integration (2004)

The German Minister for Internal Affairs established in 2003 an independent ‘Council for

Immigration and Integration’ (‘Sachverständigenrat für Zuwanderung und Integration’),

which consists of six experts from sciences, employers federations, trade unions, and politics.

The objective of the council is to analyse the effects of immigration for the economy and the

labour market, to assess the integration of the migrant population, and to evaluate the German

capabilities to absorb and integrate migrants.

The first (and last) report of the Council was presented in October 2004.8 It again includes a

Chapter on demographic change, which analyses the main trends in the demographic

projections (Sachverständigenrat für Zuwanderung und Integration, 2004, pp. 115-124). The

report states that the attraction of young immigrants with high skill levels can mitigate the

demographic problem and requests a “qualitative and quantitative regulation of immigration,

which targets young people with high skill levels” (p. 121). However, it does not state that the

number of migrants has to be increased significantly.

In his final statement the Council recommends to grant 25,000 work and residence permits for

high-skilled workers from non-EU countries. This figure is certainly too small to have any

impact on the demographic structure of the German population. Against the background of

high and increasing unemployment, this recommendation was nevertheless perceived by the

German Minister of Internal Affairs (‘Bundesminister des Inneren’) as well as by many other

political voices as much too high. The Council was later dissolved by the Minister of Internal

Affairs.

The report of the Enquête-Commission ‘Demographic Change’ (2002)

The Enquête-Commission “Demographic Change – Challenges of Our Ageing Society for the

Individual and Politics” of the German Federal Parliament (Deutscher Bundestag) presented a

comprehensive report on the consequences of demographic change and ageing in March

2002.9 Again, this report analyses the different facets of the ageing process based on the main

8 Sachverständigenrat für Zuwanderung und Integration, „Migration und Integration – Erfahrungen nutzen, Neues wagen“, Jahresgutachten, Berlin, October 15, 2004. 9 Deutscher Bundestag, 14. Wahlperiode, Schlussbericht der Enquête-Kommission „Demographischer Wandel – Herausforderungen unserer älter werdenden Gesellschaft an den Einzelnen und die Gesellschaft“, Drucksache 14/8800, 28.03.2002.

19

demographic projections and discusses inter alia the impact of migration on the demographic

structure of the population. The implications of migration on the labour market, problems of

integrating foreigners in the economy and society and implications for the welfare state are

analysed as well. The report acknowledges in its policy recommendations the need for a

regulation of the immigration to Germany, but it suggests however a rather restrictive

approach towards immigration policies: The immigration of foreign workers should be limited

to cases where no native workers can fill the position. The education and training of natives is

preferred to further immigration (Deutscher Bundestag, 2002, p. 132).

Altogether, the expert commissions in Germany have been reluctant to draw any quantitative

policy recommendations from the demographic projections which exist in Germany. Note that

these commissions represent compromises between influential groups in the German society

such as trade unions, employers federations and the main political parties, which are in turn

heavily affected by the general political mood in the population. Moreover, although the key

demographic trends are acknowledged in the reports, their policy recommendations are highly

affected by the fear that migration may further increase unemployment in Germany.

5.2 The response of immigration policies

Immigration policies in Germany have been neither under the Social-Liberal government

(1969-1982) nor under the Christian Democrat-Liberal government (1982-1998) influenced

by demographic considerations. The Social-Liberal government stopped the recruitment of

guest-workers in the wake of the first oil-price shock in November 1973, and restrained

migration after that. The Kohl-government did not undertake major changes in the field of

labour migration. However, it agreed in the beginning of the 1990s with the oppositional

Social-Democrats a change in the constitution, which allows to restrict asylum seeking in

Germany. This reduced the number of asylum seekers and other humanitarian migrants in

Germany significantly.

After 1998, the coalition government of the Social-Democrats and the Greens started several

initiatives to liberalise immigration policies. First, conditions for naturalisations have been

improved. Second, the Government started a so-called ‘Green-Card’ initiative, which should

20

attract high-skilled workers especially in the IT-Sector. Third, a new immigration law has

been adopted, which improves immigration conditions particularly for high-skilled workers.

The ‘Green-Card’-initiative was adopted at May 31, 2000, for the period 2000-2004. The

objective of the ‘Green-Card’-initiative is to attract high-skilled professionals especially in the

IT-sector. It grants persons with a university degree and other professionals with an annual

salary above 50,000 Euros a temporary work permit for 5 years. Individuals which obtain a

degree from a German university and reside already in Germany can also apply. At an annual

number of much less than 10,000 successful applicants p.a. the contingent of Green-Cards is

not utilised. Most applicants are students which already reside in Germany. The low

utilisation rate can be traced back to the temporary character of the work permits, limited

opportunities for family members to receive residence and work permits, and other

administrative and legal barriers. The instrument of the Green Card was replaced by the new

immigration law in 2004, which allows the immigration of high-skilled professionals under

certain circumstances.

Although the ‘Green-Card’-initiative had only negligible quantitative implications, it had

important effects on the immigration debate in Germany. As a result of these debate, the

coalition government adopted a new immigration law, which however failed to pass the

second chamber of the German parliament (Bundesrat), which is controlled by the opposition

parties. Finally, after a compromise between the ruling Social-Democrats and Greens and the

opposition of Christian-Democrats and Liberals the immigration law was adopted in July

2004. The main changes are that (i) highly qualified can, if they are admitted to Germany,

receive a permanent residence permit from the beginning and that a work permit is granted to

their family members, (ii) that self-employed receive a residence permit if they invest 1

million Euros and create 10 workplaces, and (iii) that the integration is supported by the

government (language courses, etc.). However, the new immigration law maintains the old

rules which exclude labour immigration. Exceptions are only possible in individual cases for

high-skilled workers if a public-interest in their employment exists.

21

6 Conclusion

Altogether, German immigration policies did not respond to the demographic challenge in any

quantitatively meaningful way. This is however not or not mainly the consequence of a lack

of information. There exist in Germany a large number of demographic projections, which

simulate the possible implications of migration on the demographic structure of the

population. However, there remains a lack of information in certain areas. In particular, the

information on future migration and the structure of the population of foreign origin could be

improved by providing forecasts on the migration potential and the assimilation of the migrant

population. Moreover, scenarios of migration and their impact on the demographic structure

at the regional level are needed at the regional level in order to improve the knowledge of

decision makers at the level of the municipalities and the ‘Bundesländer’. Nevertheless, the

existing projections allow an assessment of the main demographic trends, the effects of

migration on the demographic structure of the population and its consequences for the

economy and the welfare state.

The demographic projections are well-known in the German public and their results have

been considered by several commissions appointed by the German government and

parliament. Nevertheless, neither these commissions nor the legal and administrative

measures by the German government or parliaments have drawn any conclusions from these

projections. Inter alia, this can be traced back to the fact that the German economy is affected

by high and increasing unemployment rates in the 1990s and 2000s. In this context,

immigration is perceived as an additional burden rather than a solution to the problems which

the German society and economy faces.

It is beyond the scope of this paper to address the implications of migration on unemployment

and wages in detail. However, it is worth noting that a number of econometric studies exist in

Germany, which find no or only very small effects of immigration on unemployment.10

However, the findings of these studies may be affected by endogeneity and measurement

problems. Computable general equilibrium (CGE) models, which simulate the impact of

migration under realistic assumptions on wage rigidities and unemployment find small effects 10 See Hatizius (1994); Mühleisen/Zimmermann (1994); Pischke/Velling (1997); Trabold/Trübswetter (2003); Winkelmann/Zimmermann (1993); Winter-Ebmer/Zimmermann (2000).

22

(see Boeri/Brücker, 2005; Brücker 2002). As a punch-line, an additional migration of 1% of

the population can increase unemployment by 0.2%-percentage points at the maximum.

Moreover, these are short-run effects. In the long-run, when capital accumulation, trade and

production structures adjust, the effects of migration on unemployment disappear. Thus, the

employment effects of migration are much smaller than widely assumed.

Another aspect which hinders a change in German immigration policies are higher

unemployment and welfare dependency risks of migrants. Indeed, unemployment and welfare

dependency rates are higher among migrants than among natives on average. However,

econometric studies show, that migrants are not more affected by unemployment and receive

no more social assistance than natives do if we control for their human capital characteristics

(Riphahn, 1999). Thus, a proper regulation of immigration can reduce the costs of

unemployment. It is nevertheless not realistic to attract only high-skilled migrants.

Demographic developments and the convergence of per capita income levels in the EU has

changed the source country composition of German migrants substantially in the direction of

low- and middle income countries with relatively low human capital endowments. The

Central and Eastern European countries are a notable exception in this context. As a

consequence, an immigration policy which targets the high-skilled may increase the average

skill levels of the migrant population, but there will remain an important skill gap to the native

population under any reasonable assumption. Hence, unemployment and social assistance

rates will remain higher in the migrant population compared to the native population. This

does however not imply that immigration is a burden to the German welfare state. In contrast,

the generational accounting exercise in Section 4 has shown that migrants are net contributors

to the welfare state, which can inter alia be traced back to the pay-as-you-go pension scheme

in Germany.

While the impact of migration on demographic change as well as its impact on the labour

market is well researched, the links between these two areas are not. Immigration can

substantially reduce social security contributions, since it increases the ratio of the population

in working age to the population in non-working age. Since the German social security system

is largely financed by contributions on labour, a higher immigration rate would reduce labour

costs, and, ceteris paribus, increase employment. An analysis of the ambiguous impact of

migration on employment under consideration of wage rigidities and social security systems

is missing in the literature. New insights about these links can however contribute to resolve

23

the seeming conflict between the objectives of reducing unemployment and mitigating the

ageing process in immigration policies.

24

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26

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27

The use of demographics when designing and implementing

migration policies – the Netherlands

Draft Background Report to the Project

LOT 1: The use of demographic trends and long-term population projections in public policy planning at EU, national, regional and local level (VC/2004/440)

Harri Cruijsen11

DEMOCAST

1. Introduction During the second half of the 1940s the government of the Netherlands developed and

implemented parallel to the process of postwar reconstruction of the economy and society

various new tools of public policy making. Partly due to the general wish of many nationals to

leave the country, partly motivated by macro-economic reasons (i.e. the demand for

agricultural labour drastically decreased), and indirectly fuelled by fast demographic change

(i.e. the number of families with young children rose drastically immediately after World War

II), the Dutch government introduced around 1950 a series of measures to actively assist

emigration to traditional and/or popular immigration countries: Australia, Canada, New

Zealand, South Africa, and the USA (Vermeulen en Böcker, 1992). Bilateral agreements with

the countries concerned were established, subsidies were given for transport, and

comprehensive counselling and guidance programmes were prepared and provided (Zorlu and

Hartog, 2001). The overall aim was to realise an annual outmigration of 20-25 thousands

workers; including family members the total outflow was estimated to be around 50 thousands

people a year . The first, official migration policy was born, and many would follow,

especially during the 1980s and 1990s.

During the same era the first set of official population forecasts by sex and age was prepared

and produced by Statistics Netherlands. The very first one, published in 1950, with a time

horizon of 30 years, did surprisingly not contain any explicit assumption on international

migration. Implicitly one expected that inflows and outflows would counterbalance in the

11 Contact: DEMOCAST, Vluchtheuvelstraat 2, NL-6621 BK Dreumel, Netherlands ; phone: +31-487-570687; Email: hvckroon@kabelfoon.nl

28

short, medium and long run. However, the second one, disseminated in 1951, assumed a

constant, annual net outmigration level of 50 thousands people for the period 1951-1981,

exactly following the official target of Dutch emigration policy.

In all official forecasts made until 1975 this obvious but to some extent also controversial link

between producers and users of demographic forecasts was silently kept. In 1975 Statistics

Netherlands compiled for the first time two alternative demographic futures based upon short

and medium term migration assumptions that somewhat deviated from the overall official

target not to be or to become an immigration country. However, the long-term assumption

assuming zero net migration was not abandoned, so one could state that this set of forecasts

still reflected a strong influence of the migration policy makers.

Actually it was not before 1980 that Statistics Netherlands was able to produce a

comprehensive set of national population forecasts by sex and age, independent from official

migration policies. As from 1980, international migration has been fully treated as one of the

three components of future demographic change, with its own features including a relatively

high volatility compared to fertility and mortality due to its proven strong links with often

unpredictable political events in countries of origin, fairly unstable economic developments in

both countries of origin and destination, and unforeseeable changes in migration policies in

other developed countries. As from 1990 the impact of past, actual and possible future

migration policies on various groups of migrants has been studied more or less continously,

and the respective results have been basically judgementally incorporated in subsequent

revisions of the forecasts.

In this paper we will examine the other side of the coin: did Dutch migration policy makers

use offical long-term national population forecasts when designing and implementing their

policies, and if so, what and how?

Due to time constraints and for reasons of relevance and efficiency, only a restricted number

of important migration policy documents produced during the period 1979-2005 has been

examined.

Before to answer the above mentioned research questions, first brief reviews will be given of

principal long-term migration trends for the Netherlands (chapter 2), the latest official long-

term migration projections (chapter 3), and the history of migration policies (chapter 4).

29

2. Principal long-term migration trends

During the 20th century the Netherlands experienced several waves of immigration flows (see

figure 1). The highest peaks of immigration with levels of well over 100,000 persons were

recorded:

• in 1946 due to considerable inflows of repatriating nationals;

• in 1975 due to a large inflow of nationals returning or emigrating from Surinam and

the regularisation of well over 10 thousands of illegal ‘guest workers’;

• in 1979 and 1980 as a result of a strong increase of immigration of people with a

Surinam nationality and migration due to family reunification among other non-

nationals (mainly persons with a Turkish or Moroccan citizenship);

• during the period 1990-1993 due to a dramatic increase of asylum seekers;

• during the period 1996-1999 due to an ongoing high level of asylum seekers,

increased labour migration and migration due to family formation.

Emigration was generally much lower and also more stable. Peaks with levels of well over

70,000 persons have only been recorded in 1943 (outflow of nationals forced to work in

Germany), and in 1952 (assisted emigration of nationals to Australia, Canada, New-Zealand,

South Africa and the USA). However, if one also takes into account corrections which could

not be classified as reported migration, out-migration peaked also in calendar years in which a

population census was held (1909, 1920, 1930 and 1947).

1. Immigration and emigration, the Netherlands, 1900-2004

(x1,000)

0

50

100

150

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

immigration

emigration

emigration incl.corrections

30

In total during the period 1900-1999 almost 6 million people officially registered themselves

as immigrants in the Netherlands, whilst almost 5 million legal emigrants were measured.

Hence, according to the official figures, the direct, net impact of international migration to

population growth was 1 million persons. If one includes the corrections originating from

population censuses and (since 1971) due to regular cleaning of municipal population

registers, migration contributed no more than 700 thousands people. As a matter fact, natural

growth (the difference between live births and deaths) contributed well over 10 million

people!

Nevertheless, during the last 3-4 decades international migration has become a principal

component in population growth (see figure 2). Especially during the 1990s net migration was

almost as equally important as natural population growth was.

Both figures illustrate that at the beginning of the 21th century international migration flows

remained important but also showed once again its volatile character: after the historical high

of 133 thousands persons in 2001, immigration dropped to a level of less than 90 thousands

persons in 2004. The annual number of emigrants (incl. corrections), on the contrary, rose

from 77 thousands to 112 thousands. Therefore, in the Netherlands annual net migration

changed over a period of no more than 3 years from a level of +57 thousands to -22 thousands

persons! Both the economic recession and the introduction of new, more restrictive admission

policies for asylum and family formation have been frequently mentioned as major causes for

this dramatic, recent change.

2. Natural growth and net migration, the Netherlands, 1900-2004

(x 1,000)

-100

-500

50

100

150200

250

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

natural growth

net migration

net migration incl.corrections

31

Figures 4-6 tell us more about the compositional changes in immigration flows since 1950. It

is obvious that over the past 4-5 decades the inflows to the Netherlands have become larger,

and more heterogeneous.

During the early 1950s immigration was almost completely determined by return migration of

nationals and newcomers born in Indonesia. Also in the beginning of the 1960s, when the

Netherlands “lost” its last colony in the east, New Guinea, a significant group of nationals

repatriated.

Meanwhile, during the second half of the 1950s a first window of entry was opened for labour

migrants coming from and/or born in the EU. Over the years this group of immigrants has

increased gradually, partly due to the enlargement of the EU.

During the first half of the 1960s the admission possibilities for basically low-skilled, male

adults originating from Turkey and Morocco were introduced. This so-called programme for

guest workers stopped abruptly after the oil crisis of 1973. As the term guest worker implies,

the respective migrants were supposed to stay temporarily, but the vast majority did not

return, and virtually all who stayed used their right to get a residence permit for their families.

Especially in 1979 and 1980 migration due to family unification peaked due to the pre

announcement and implementation of a set of more restrictive rules as from 1981. Since

around mid 1980s the children of the first generation of Turkish and Moroccan migrants have

used their rights to marry a partner abroad, and thereafter to get a residence permit for their

spouse. This so-called migration due to family formation has rapidly become the dominant

part of Turkish and Moroccan inflows.

As mentioned before, in 1975 and also in 1979 and 1980, immigration from Surinam peaked.

However, also before, between and after these calendar years, this ‘colonial’ inflow of people,

in the figures combined with the immigration from the Dutch Antilles, was considerable.

Finally, the Netherlands has increasingly become an attractive destination country for

students, labour migrants, and asylum seekers from ‘rest of the world’ (i.e. outside the

European Union, former colonies, Turkey and Morocco). In 2001 this rest category of

immigrants reached a (for the time being) historical high of 70 thousands people. Expressed

as percentage in total migration, its share amounted well over 52 per cent. However, mainly

due to the introduction of more restrictive rules for asylum in 2001 and a rapidly increasing

unemployment rate, the inflow of this rest category dropped to a level of 41 thousands in

2004, being almost 44 per cent of the total immigration to the Netherlands.

32

4. Immigration by major groups of citizenship, the Netherlands, 1950-2003

(x1,000)

020406080

100120140160

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

OtherMoroccanTurkish

other EUNationals

5. Immigration by major groups of country of birth, the Netherlands, 1950-2003

(x1,000)

020406080

100120140160

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

OtherSurinam +DAIndonesiaMoroccoTurkeyOther EUNetherlands

6. Immigration by major groups of country of origin, the Netherlands, 1950-2003

(x1,000)

020406080

100120140160

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

Other

Surinam +DA

Indonesia

Morocco

Turkey

Other EU

33

3. Latest official long-term migration projections

Statistics Netherlands is producing a new, revised set of long-term national population

projections every 2 years. At the end of 2004 the latest set covering the period 2004-2050 was

published, comprising:

• stochastic population forecasts by sex, age, marital status and household position;

• stochastic population forecasts by sex, age and major groups of country of origin;

• stochastic household forecasts by type.

Our first and principal interest here is the (underlying) set of long-term migration

assumptions. The quantitative assumptions are prepared for the following 12 categories of

migrants by country of origin12:

1. Netherlands (including second generation)

2. EU-24

3. Turkey

4. Morocco

5. Indonesia

6. Surinam

7. Dutch Antilles (including Aruba)

8. Other Europe (excluding Turkey)

9. Africa (excluding Morocco)

10. Asia (excluding Indonesia and Japan)

11. Latin America (excluding Surinam, and Dutch Antilles)

12. Other (North-America, Japan and Oceania).

Figure 7 presents the size of the sub-populations 2-12 at 1 January 2005. All together these

groups comprised well over 1.6 million people, or almost 10 per cent of the total population.

However, in terms of immigration, these groups accounted for almost 75 per cent of the total

inflow in 2004.

12 Country of origin is compiled by combining two basic concepts: (first or second) generation, and country of birth. As an example: country of origin Turkey means that the first generation of people is considered, i.e born in Turkey with at least one of the parents born abroad. The second generation of people, allmost all of them born in the Netherlands, is covered by the category country of origin the Netherlands.

34

7. Foreign-born population by major groups of origin, the Netherlands, 1 January 2005

(x1,000)

0

50

100

150

200

250

300

350 EU-24TurkeyMoroccoIndonesiaSurinamDutch Antilles + ArubaAfricaAsiaLatin AmericaOther EuropeOther

Past and future international immigration trends are supposed to be determined by the

following factors (Alders, 2005):

• The Dutch admission policies concerning migration and asylum;

• The attractiveness of the Netherlands for migrants in general (climate, political

stability, wealth and welfare, culture, etc.), and macro-economic and labour market

conditions in particular;

• The size and composition of the first and second generation of migrants, already

officially residing in the Netherlands;

• Trends and policies of migration in neighbouring countries;

• Economic and political developments in countries of origin.

Surprisingly, past and future demographic changes in the size and composition of the

Dutch born population and in the populations of the countries of origin are not mentioned

as well as possible ecological crises.

Analysis based on annual information of immigration by motive, available since 1995,

indicates that for each of the groups distinguished the mix and influence of each of these

factors significantly differs. For example, the vast majority of the inflows originating from

the EU-24 consist of labour migrants, whilst most of the migrants from Turkey, Morocco

and Surinam are reporting family reasons as principal motive to migrate.

35

The currently largest group of immigrants - those born in the Netherlands - is expected to

decline somewhat due cohort effects: the less numerous future generations are supposed to

emigrate and re-immigrate with similar propensities as recent generations do. One has

assumed that 60 per cent of the group that leaves the country will eventually return.

The second but one largest group of immigrants - those born in one of the other 24 EU

countries – is expected to almost double in the medium and long run due to (assumed)

decreasing unemployment levels (partly is a result of ageing), and generally higher

mobility intensities within the EU especially for people of the new Member States. A

similar future development is expected for the group Other, i.e. those born in North

America, Japan or Oceania. However, the immigration from the rest of Europe and from

Indonesia will only return to average levels observed during the last 10-15 years. The total

annual inflow from so called Western countries of origin (excluding the Netherlands, and

including Indonesia), will increase from 36 thousands in 2004 to 57 thousands people in

2015 and remain constant thereafter (see figure 8).

8. Immigration by major groups of origin, the Netherlands, 2004-2049

(x1,000)

020406080

100120140

2004 2009 2014 2019 2024 2029 2034 2039 2044 2049

Dutch

Non-Western

Western

Total

Concerning the total annual inflow from non-Western countries of origin a slight increase

is foreseen, from 32 thousands in 2004 to 37 thousands persons from 2020 onwards.

Immigration from Surinam and Dutch Antilles is expected to return in the long

respectively in the short run to somewhat higher average levels observed during the last

10-15 years. Also the inflows from Asia and Latin America are expected to increase

somewhat. Study and labour are mentioned as principal motives for the increasing

numbers of Asians to migrate to the Netherlands, whilst immigrants from Latin America

origin are mainly leaving their continent for family reasons.

36

The immigration from Turkey, on the contrary, is expected to decline somewhat after

2020. The annual inflow from Morocco and that of the rest of Africa will remain more or

less constant. Behind the latter assumptions there is an implicit confirmation that the

recently implemented set of more restrictive policies for both migration due to family

formation and migration due to asylum will continue to be effective. This implies for

example that one predicts that the ultimate proportion of those marrying abroad among

new (or third) generations of Turkish and Moroccan origin will drop to levels of about 50

per cent of recently estimated levels. Concerning migration due to asylum, one expects a

constant annual number of 15 thousands asylum seekers. In addition, one expects that only

half of them will be legally registered, and that half of the latter group will cause one

average 1 additional migrant due to family reasons. Hence, one foresees a constant annual

inflow of 7.5 thousands asylum migrants and 2.5 thousands follow-up migrants.

Nothing is explicitly mentioned about the possible impact of common, future European

admission and asylum migration policies, currently in preparation. Obviously one

implicitly believes that the Netherlands has recently succeeded to already implement

future EU wide legislation and/or has achieved to reach future EU average levels of

migration due to family formation or asylum. Only concerning the possible consequences

of the enlargement of the EU Turkey one explicitly states that due to the uncertainties

around the occurrence, timing, restrictions and implications of such a future, political

event, no additional migration to and from Turkey has been forecast.

With respect of past and future outflows, no comprehensive explanatory framework seems

to be developed. Recently observed strong increases of out-migration levels are related to

a mixture of possible factors: a decreasing popularity for staying in the Netherlands

among migrants due to a worsening of the social and political climate (especially for those

living in the bigger cities), the bad economy, and recent changes in the housing market

(houses in the neighbouring countries have become relatively cheaper) (De Jong, 2005).

For the near future, an improvement of the economic conditions is expected. By means of

a return migration model by sex, age and duration of stay, the future annual numbers of

six groups of migrants are predicted (De Jong and Nicolaas, 2005). The key indicator is

the ultimate proportion that returns. With respect of the generation that entered the

country in 1995, the assumed return proportions vary from 15 per cent for groups of

immigrants that basically came to the Netherlands for family reasons (Turkish and

37

Moroccan women) to 70 per cent for those who migrated for economic reasons (men from

Western countries).

For the group of Dutch born the future emigration numbers are obtained by using constant

sex and age specific emigration rates.

Figure 9 summarizes the results of these projections. In the long run the annual number of

emigrants of all groups considered is expected to decrease due to either population decline

(born in the Netherlands) or lower future numbers of immigrants compared with the recent

past and/or increasing average duration of stay (non-Western and Western).

9. Emigration by major groups of origin, the Netherlands, 2004-2049

(x1,000)

0204060

80100120

2004 2009 2014 2019 2024 2029 2034 2039 2044 2049

Dutch

Non-Western

Western

Total

Finally, these long-term migration forecasts have been supplemented with 67 per cent and

95 per cent projection intervals. Figure 10 illustrates that Statistics Netherlands is fairly

uncertain about the future course of net migration: there is a chance of one third that the

Netherlands either will become an emigration country until around 2040 or will

structurally experience historically high net immigration levels as from around 2010.

10. Net migration, oberved and projection intervals, the Netherlands, 1900-2050

(x1,000)

-100

-50

0

50

100

150

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

2020

2030

2040

2050

ObervedForecast95% Lower bound67% Lower bound67% Upper bound95% Upper bound

38

Figure 11 shows that the size of almost all subpopulations distinguished will increase in

the long run13. Especially the number of people from Asia, Latin America, and Other

Europe are expected to rise drastically. All together, the population with a foreign origin

living in the Netherlands will increase from 3.1 million in 2005 to well over 5.0 million in

2050. The proportion foreign in the total population is predicted to rise from 19 to almost

30 per cent.

Naturally there is a lot of uncertainty around these “best guesses”. For example, it is

expected that there is a two third chance that the total population with a foreign origin in

2050 will be between 3.7 and 6.2 million people. The 67 per cent projection interval for

the proportion foreign in the total population in 2050 is also fairly wide: it is assumed to

vary from 25 to 34 per cent.

11. Long-term change of population by origin, the Netherlands, 2050

(2004 = 100)

0

50

100

150

200

250

300

350 EU-24TurkeyMoroccoIndonesiaSurinamDutch AntillesAfricaAsiaLatin AmericaOther EuropeOther

4. Migration policies – a brief history

Most contemporary reviews divide Dutch admission policies since 1945 into three periods

(see for example WRR 2001 and Ching Lin Pang 2002):

13 The data series behind this graph cover both those born abroad (first generation) and those born out of at least one parent born abroad (second generation).

39

1. 1945-1973: the period of decolonisation of the Dutch East Indies/Indonesia and the

active recruitment of unskilled or low skilled migrant or ‘guest’ workers.

The national government applied a fairly generous admission policy for those

repatriating from the Dutch East Indies: in principle everybody with Dutch

citizenship or born in Indonesia with at least one Dutch parent got more or less

automatically a permanent residence permit. A large group of Moluccans, mostly

former soldiers in the Dutch-Indies Army and their families got after arrival a

temporary permit of stay, but a few years these were replaced by permanent

residence permit.

The first groups of guest workers, basically originating from Southern Europe, got

their contracts directly from individual employers. At the beginning of 1960s,

when increasingly people from Turkey and Morocco were recruited, the Dutch

government stepped in and tried to regulate this labour migration by bilateral

agreements with the countries involved. The national recruitment policy stopped

during the first oil crisis in 1973.

2. 1973-1985: the period of developing and implementation of a restrictive

immigration policy.

The first oil crisis, immediately followed by an economic crisis in 1973 caused a

strong decrease in the demand for labour and rapidly increasing levels of

unemployment in all Western European countries. Therefore, virtually all national

governments in Western Europe changed their admission policies from a system of

inclusion to one of exclusion, especially in relation to migrants from non-western

countries arriving unsolicited (Broeders 2001). The Dutch government prepared

and adopted during the period 1973-1985 a series of measures and regulations to

ban unskilled or semiskilled labour migration, to prevent illegal migration, and to

diminish migration due to family reunification and family formation. In addition,

one tried to avoid new inflows of ‘post colonial’ migration.

In practice however, most of these policies failed or at least did not really diminish

immigration levels. A few actions had even an opposite effect. For example, the

official Dutch rhetoric about a strict policy towards the application of a transitional

regulation of five years (1975-1980) under which the free movement of people

between the Netherlands and Surinam continued to apply, created in the latter

country at the end of 1970s an atmosphere of ‘now or never’.

40

A similar situation occurred with migration due to family reunification. In and

around 1980 when the Dutch government and parliament were for the first time

discussing a fairly restrictive admission policy for the spouses and children of

migrant workers, many of the people and families concerned decided not to wait

for these rules and to migrate to the Netherlands before it was too late.

Even the recruitment freeze of guest workers in 1973 created an additional,

unwanted inflow of immigrants, because the abrupt stop created a large group of

illegals in the Netherlands for the first time. During a one-off regularisation in

1975, approximately 15 thousands undocumented migrants got the possibility to

legalise their stay.

All these examples show that in this first phase of restrictive admission policy-

making the Dutch government underestimated the psychological impact of its

debates and decisions on (potential) groups of immigrants. Furthermore, one

realised fairly late that immigration was no longer a temporary phenomenon.

Actually, it was not before 1998 that the Dutch government officially stated the

‘unmistakable fact that the Netherlands has become an immigration country’.

Finally, it was obvious that to achieve a proper balance or to find a good comprise

between international obligations and humanitarian rights on the one hand and the

implementation of a highly selective and restrictive immigration policy on the

other hand was a far from easy task.

In 1983 the Dutch government produced a first comprehensive immigration policy

document, the so-called Memorandum on Minorities. Two basic ideas were

applied in this document: those immigrants who prefer to stay and settle in the

territory should be allowed to do so and their integration should be supported and

encouraged, and those who prefer to return should be assisted in their endeavour.

A restricted number of immigrant target groups were identified, including former

guest workers. Various more specific measures and policies were pre announced to

promote emancipation and equal opportunities, to fight against discrimination, and

to alleviate the backward position of immigrants in the areas of education, labour

market, housing, social welfare and health.

3. 1985-present: the period of expansion and refining of migration policies.

41

After a few years of decreasing immigration levels, the inflows started to rise

again around mid 1980s. Migration due to family formation of the children of the

guest workers became a new and emerging phenomenon, but in particular

‘irregular’ or ‘spontaneous’ asylum caused unexpected high waves of immigration.

In order to diminish (or postpone) inflows due to family formation, a new set of

more restrictive rules was implemented in 1991, and in 2003 further expanded and

refined. According to the latest measures:

• The minimum age for family formation should be 21 years (was 18 years);

• The standard amount to be earned by the wage earner should be 120 per

cent of the minimum wage under the Dutch Minimum Wage and Minimum

Holiday Allowance Act (was 100 per cent); this implies EUR1382.18

including allowance or EUR1318.60 excluding holiday allowance per

month;

• The partner already living in the Netherlands has to have adequate housing;

• Marital migrants need to pass a test of knowledge of a body of 500

common Dutch words before coming to the Netherlands.

The first large influx of asylum seekers in the beginning of the 1990s was

responded by the Dutch government with a series of more restrictive rules adopted

in 1994 and 1995. Partly these measures were copied from those somewhat earlier

implemented in the neighbouring countries. Amongst others the following two

basic principles were introduced (WRR 2001):

• The principle of safe countries of origin, according to which an asylum

request is declared unfounded if the asylum seeker comes from a country

considered safe by the country handling the request. ‘Safe’ means that the

political, civil and human rights in the country are sufficiently guaranteed;

• The principle of third countries of reception, designed to stop ‘asylum

shopping’, refers to a situation where an asylum seeker has entered a

country via another EU or a non-EU country that is considered safe.

The 2000 Aliens Act further streamlined and tightened the Dutch asylum

procedure. In particular the often lengthy asylum procedure was drastically

42

shortened. In order to achieve that the following three measures were introduced

(SOPEMI 2003):

• A decision by the immigration authorities (IND) on an asylum request

should be issued within six months;

• The option of lodging an objection to a decision by the immigration

authorities was withdrawn;

• Every person whose asylum request is approved receives the same status,

regardless of the grounds for the asylum.

Other admission policies that became more restrictive during the past 20 years

were those concerning nationals marrying with a partner abroad (same

requirements as from non-nationals), and with respect of persons originating from

the Dutch Antilles (introduction of an obligation to find a paid job within 3

months). On the other hand, as a result of different treaties adopted in the 1990s,

the possibilities and rights of EU-14 citizens to migrate to the Netherlands have

increased greatly. And very recently, a new regulation was introduced to facilitate

the migration of so called ‘knowledge workers’.

The 2000 Aliens Act also aimed to improve return policies. For example, the

period of removal of rejected asylum seekers was shortened to maximum 28 days.

Finally, in 2003 a series of new measures was introduced to combat undocumented

or illegal migration, and to implement a more effective implementation of return

migration policy.

Meanwhile migration integration policies were almost continuously evaluated and

modified. Concerning legal aspects, the possibilities for acquiring Dutch

nationality were widened in 1985, and naturalisation without renouncing one’s

original nationality has become possible since 1990. The adoption of the Equal

Treatment Act in 1994 excluded distinctions in the regulations linked to

nationality, country of birth, culture and language.

In the socio-economic field proportional participation of migrant groups in the

public sector was targeted at the end of the 1980s, and the in the private sector it

was encouraged by agreements at central level between trade unions and

employers’ organisations. Various incentives and measures were introduced to

reach these goals, and also to combat discrimination.

43

In the field of education, various new instruments were developed and

implemented to strengthen human capital in general, and self-reliance and

independence in particular. For example, a system of compulsory introduction

programmes for new-comers with an emphasis to shortly learning the Dutch

language was implemented in 1998. In 2003 these programmes also became

accessible for old-comers.

In the field of housing, several (indirect) efforts were put forward to open the

market for rented accommodation, to eliminate forms of practical discrimination

and to combat the concentration of non-indigenous persons in old urban wards

with relatively poor housing.

However, many of these new actions appeared to be insufficiently successful. For

example, the immigrant introduction programmes suffered a relatively high

premature dropout, showed disappointing achievements among the participants,

and had too limited differentiation in the course content (Brink et al, 2002). In

response to these shortcomings, the Dutch Minister of Aliens Affairs and

Integration recently announced a new, much stricter system to be implemented in

2006 (Ministry of Justice 2005).

More in general the current Dutch government is opting for a new approach on

immigration and immigration integration, first described in the letter Integration

Policy New Style (Ministry of Justice 2003). It can be broadly characterised as a

farewell to multiculturalism as the cornerstone of Dutch integration policy: “In this

integration policy, a great deal of emphasis has been traditionally put on accepting

differences between minorities and the native Dutch population. In itself, there is

nothing wrong with that, but it is often interpreted to mean the presence of new

ethnic groups is a good thing and automatically enriches our society. One loses

sight of the fact that not everything that is different is consequently also good.

Having newcomers cultivate their own cultural identities does not necessarily

bridge any gaps. The unity of society should be sought in what people who take

part in it have in common with each other, in what they share”. According to the

new approach, immigrants should speak Dutch, respect the laws and regulations

and abide by “basic Dutch norms”, such as earning a living, taking care of one’s

surroundings, respecting other people’s physical integrity and sexual preferences,

and accepting the notion of equality between men and women.

44

5. Have Dutch migration policy makers been using long-term population projections?

Officially the Minister of Aliens Affairs and Integration is the ultimate responsible person of

designing and implementing migration policies in the Netherlands. The day-to-day execution

of policies is done by the Immigration and Naturalisation Services (IND), in close co-

operation with the Aliens Police and the Royal Marechaussee.

New admission and integration policies or revisions of existing migration policies are

generally prepared and decided in three steps. First a special Parliamentary Committee, an

Interdepartmental Commission, the Netherlands Scientific Council for Government Policy

(WRR) or another group of experts is requested to analyse past trends, the actual situation and

future developments, and to provide promising policy recommendations. Thereafter, and

sometimes simultaneously an internal group of migration policy makers starts to draft

proposals of new legal acts or regulations, or amendments thereof. Finally, there are

discussions and agreements first within the Council of Ministers, and thereafter between the

Council and the Parliament.

The first and the last part of this chain are generally fairly well documented. However, the

reasons behind certain choices and preferences of the work done within the Ministry as well

as the content of the discussion within the Council of Ministers are not public. Therefore, a

complete answer to the questions whether and how long-term population projections are used

when designing and implementing migration policies in the Netherlands cannot be fully

addressed.

Secondly, due to time and budgetary constraints it is impossible to provide a comprehensive

picture of what has been applied during the last 2-3 decades. Such an inquiry would exceed

many times the resources available. In order to provide a feasible and meaningful review of

the Dutch case, a selected set of 15 important policy documents and principal legal acts was

examined, produced during the period 1979-2004 by the Scientific Council for Government

Policy (WRR), the Committee of Socio-Economic Affairs (SER), the Ministry of the Interior

and Kingdom Relations, the Ministry of Justice and the Parliamentary Inquiry Committee on

Integration Policy.

First for each of these documents the principal policy recommendation or decision will be

mentioned, as well as the principal demographic observations. The use of long-term

population projections will be summarised. Thereafter, some conclusions will be presented.

45

Ethnic Minorities (WRR 1979)

Principal policy recommendation: active policies designed to promote the participation of

minorities on equal terms will necessitate a tightening of restrictive immigration policy.

Principal demographic observations: migrants will most probably stay permanently, and

therefore migrant stocks will continue to grow.

Major reasons: return migration rates are low, and family reunification will continue.

Use of long-term population projections: reference to Final Report of National Commission

on Population Matters (published in 1976): natural population growth should end as quickly

as possible Alternative B of 1975-based national population forecasts is ‘virtually optimal’

(14.3 million inhabitants in 2000). In order to achieve this, net migration need to be

negligible, therefore restrictive immigration policy needed. However, immigration due to

family reunification must be accepted. Furthermore, the government should prepare itself for

future immigration flows from the Antilles, new EU Member States (Greece, Portugal and

Spain), Turkey (due to a new association), and South Africa.

Memorandum on Minorities (Ministry of Interior 1983)

Principal policy decisions: continue restrictive admission policy towards non-nationals,

encourage return migration among ethnic minorities, and design and implement an integration

policy aiming to establish a society in which members of ethnic minorities have got both

individually and as a group equal opportunities and full chances to develop.

Principal demographic observations:??

Use of long-term population projections:??

Immigrant Policy (WRR 1989)

Principal policy recommendation: integration of newcomers should be more effective.

Principal demographic conclusion: not only migrants will remain permanently, also

immigration flows will persist, in spite of restrictive immigration policy. Major reasons: large

differences in wealth and political freedom between the Netherlands (and the rest of the

Western World) and less developed countries, increasing population pressure in less

developed countries, better and cheaper ways of communication, more knowledge about the

West in the Third World, and the presence of significant numbers of migrants in many

Western countries. Therefore, migration due to family reunification and family formation will

continue.

46

Use of long-term population projections: 1987-based population forecasts assume

permanently high net immigration levels; first medium-term forecasts for Turkish and

Moroccan population show increasing stocks; experimental long-term projections indicate

that the share of foreign population in the total population will increase to minimum 9 per

cent to maximum 17 per cent in 2032; other long-run simulations demonstrate that

continuation of the actual levels of net migration cannot prevent population ageing, but will

lower its speed.

General Memorandum on Policies for the Integration of Ethnic Minorities (Ministry of

Interior 1994)

Principal policy decisions: tighten restrictive admission policy, re-orientation of integration

policy based upon the concept of equality of all citizens, with more emphasis on effectiveness

in general, and solving problems at regional level in particular.

Principal demographic observation: instead of decline, net immigration has strongly increased

during the period 1983-1993, mainly due an increase of asylum and family reunification

migration.

Use of long-term population projections: latest forecasts assume a net immigration level of 50

thousand people per year. Size and composition by sex, age, motive and occupational status

may considerably fluctuate.

Newcomers Integration Act (Ministry of the Interior and Kingdom Relations 1998)

Principal goal: increase self-reliance and participation of newcomers.

Principal demographic observation: every year ten thousands of people enter the country.

Use of long-term population projections: in spite of recent decline, it is realistically to account

for substantial inflows in the next decade (no reference to any forecasts).

Getting Chances, Taking Chances (Ministry of the Interior and Kingdom Relations 1999)

Principal policy recommendation: empowerment of integration policy.

Principal demographic observation: the Netherlands has become an immigration country, and

immigration and integration is of all times.

Use of long-term population projections: none.

1999 Report on the Integration Policy for Ethnic Minorities (Ministry of the Interior and

Kingdom Relations, 1999)

47

Principal policy recommendation: empowerment of integration policy.

Principal demographic observations: 17 per cent of the population of the Netherlands is of

foreign origin, and well over 9 per cent belongs to the group of ethnic minorities. In the three

major cities (Amsterdam, Rotterdam, the Hague) the majority of the young belongs to the

category foreign origin.

Use of long-term population projections: the number of persons with a non-Western origin

will increase from 1.3 million in 1998 to 2.3 million in 2015. This implies that in 15 years

well over 20 per cent of the total population will be of foreign origin.

2000 Aliens Act (Ministry of Justice 2000)

Principal policy decision: a shortening of admission procedures and more clarity about rights

and duties of people of foreign origin.

Principal demographic observation: over the past years the number of people that applied for

a residence permit has increased.

Use of long-term population projections: none.

Creating and Grasping Opportunities – Promoting Labour Participation among Ethnic

Minorities (SER 2000)

Principal policy recommendation: measures intended to integrate ethnic minorities into the

work process should be implemented consistently and more effectively.

Principal demographic observations: the share of ethnic minorities in the total population

amounts almost 9 per cent, and since 1992 net migration of ethnic minorities is mainly

determined by the inflow of asylum seekers.

Use of long-term population projections: the proportion of the group of people with a non-

Western origin in the total population will increase to 14 per cent in 2015. The share of those

with a foreign origin is expected to amount 22 per cent in 2015. The share of those that belong

to the second generation will rise. In 2015 for Turks and Moroccans the second generation

will outnumber the first generation. For the other groups of foreign origin (Surinam, Dutch

Antilles, Africa, Asia, Latin America) the first generation will be still larger. The annual

number of asylum requests is assumed to remain around 40 thousands. It is expected that

about 50 per cent will be (provisionally) approved, that creates in the longer run an inflow of

30 thousands people (including follow-up migrants).

During the next 15 years annual net immigration of persons with a non-Western origin is

expected to continue at a level of around 30 thousands. The annual numbers of immigrants of

48

non-Western origin are assumed to vary from 52 to 57 thousands, whilst the annual numbers

of emigrants may fluctuate between 20 and 28 thousands.

Finally, the group of asylum migrants in the total population of ethnic minorities will increase

from 25 to 40 per cent.

The Netherlands as Immigration Society (WRR 2001)

Principal policy recommendation: the perspective of an immigration country calls for the

effective co-ordination of admissions and integration policy.

Principal demographic observations: the Netherlands is characterised by a relatively high

level of immigration. The migrants come from highly diverse countries of origin, each with

their own separate culture, customs, norms and values. However, a clear majority of the

inflows consists of people in the ‘non-problematic’ category: Dutch nationals, EU citizens and

labour migrants together account for 60 per cent of the total.

Use of long-term population projections: recent forecasts suggest a stabilisation of the

population at 18 million during the period up to 2050, based upon the assumption that net

immigration will gradually decline from a level of 50 thousands to 30 thousands persons a

year. These estimates are however hedged about by considerable uncertainty. If the

underlying assumptions are not borne out in reality the figures could be considerably higher.

It may however safely be assumed that the pattern of growing cultural diversity in the

Netherlands will be sustained.

Integration from the Perspective of Immigration (Ministry of the Interior and Kingdom

Relations 2001)

Principal policy decision: admissions and integration policies will be connected.

Principal demographic observation: in spite of more restrictive immigration policy, the annual

numbers of immigrants remain high and diverse.

Use of long-term population projections: during the next decades intra European mobility will

increase, as a result of the introduction of free movement of people and the planned

enlargement. According to the latest UN projections the population of for example Germany,

Italy and Spain will decline considerably during the next 50 years. The total population of

EU-15 will decrease with 10 per cent. The population in the 10 EU Candidate Countries

situated in Central and Eastern Europe will shrink with 20 per cent. Especially countries such

Germany, Italy and Spain will have a need for immigration, whereas the CEE countries will

profit from a maintenance of the domestic labour force. It is difficult to predict what the

49

consequences will be of the strong population increase in Turkey (from 66 million to 100

million in 2050).

According the latest projections the population of non-Western origin will increase

substantially during the next 10 years. The size of the first generation will increase from 0.9

million in 2000 to 1.2 million people in 2010. The size of the second generation will rise from

0.5 million to 0.8 million persons. Therefore, the total population of non-Western origin will

amount 2 million people in 2010. For 2050 a total number of 3.9 million people has been is

predicted. The share of the non-Western population in the total population of the Netherlands

is expected to increase from 9 per cent to 22 per cent in 2050. The fastest growing group of

foreign origin will be those coming from Asia, especially from Iraq, Iran and Afghanistan.

2003 Report on the Integration Policy for Ethnic Minorities (Ministry of the Interior and

Kingdom Relations, 2003)

Principal policy recommendation: various elements of the integration policies should be

modified and a few new instruments should be added.

Principal demographic observations: since 2001 the annual number of immigrants of non-

Western origin is declining. However, there is an increase of immigration from Turkey and

Africa. Due to the introduction of the Aliens Act 2000 the number of asylum requests has

dropped drastically.

Use of long-term population projections: official forecasts assume for both Turkey and

Morocco a follow-up migration of 4 thousands people a year, mainly due to family formation.

Integration Policy New Style (Letter from the Minister of Alien Affairs and Integration to the

Lower Chamber of the Dutch States General dated 16 September 2003)

Principal policy decision: various elements of the integration policies will be modified and a

few new instruments will be added.

Principal demographic observation: none.

Use of long-term population projections: none.

Building Bridges (Final Report of the Parliamentary Inquiry Committee on Integration Policy

2004)

Principal policy conclusion: the integration of a large number of immigrants has succeeded

entirely or partly.

Principal demographic observation: ??

50

Use of long-term population projections: ??

General Memorandum on the Revision of the Introduction System (Ministry of Justice 2005)

Principal policy decision: various components of the introduction systems will be improved or

improved.

Principal demographic conclusion: none.

Use of long-term population projections: none.

The overall picture that emerges from these 15 selected historical policy documents is that

information on past and future demographic developments has been frequently and

increasingly used for making migration policy recommendations. Especially information on

past and future changes of the population of non-Western origin has been applied. It often

supported and sometimes even justified proposals to tighten admission procedures (i.e. for

asylum seekers and migration due to family formation) or to intensify and/or modify

integration policies.

However, most policy documents produced by the government and all legal acts considered

do not or hardly contain any reference to demographic trends in general and long-term

population projections in particular. Obviously, one does not need quantitative information to

present or declare official, legal statements on rights, obligations and sanctions concerning

new or revised migration policies.

Furthermore, those who have been using official population forecasts, hardly apply figures for

the long run. Most have been examining trends with a time horizon of no more than 10 years.

Moreover, long-term future developments on population growth, population ageing or

medium-term changes in the size and composition of the working age population have not

been considered as important input for preparing migration policy recommendations. Also

projected demographic trends in neighbouring or other European countries are virtually

absent.

Finally, very few migration policy (recommendation) makers have been attempting to use

results of projection variants. Outcomes from official stochastic population forecasts,

available since 1998, seem not to be applied so far.

51

References Alders, M., 2005, Bevolkingsprognose 2004-2050: veronderstellingen over immigratie

(‘Population Forecasts 2004-2050: Assumptions on Immigration’). In: Bevolkingstrends 53(2), pp. 33-38, Statistics Netherlands, Voorburg/Heerlen.

Brink. M. et al., 2002, Verscheidenheid in integratie. Evaluatie van de effectiviteit van de

WIN (‘Diversity in Integration. Evaluation of the Effectiveness of the Introduction Act’).

Broeders, D., 2001, Immigratie- en integratieregimes in vier Europese landen (‘Immigration

and Integration Regimes in four European Countries’), WRR Working Documents W125, the Hague

Ching Lin Pang, 2002, Migration Policies in the Netherlands. Committee of Socio-Economic Affairs (SER), 2000, Creating and Grasping Opportunities –

Promoting Labour Participation among Ethnic Minorities De Jong, A, 2005, Bevolkingsprognose 2004-2050: veronderstellingen (‘Population Forecasts

2004-2050: Assumptions’). In: Bevolkingstrends 53(1), pp. 19-23, Statistics Netherlands, Voorburg/Heerlen.

De Jong, A, and H. Nicolaas, 2005, Prognose van emigratie op basis van een

retourmigratiemodel (‘Forecast of Emigration based upon a Return Migration Model’). In: Bevolkingstrends 53(1), pp. 24-31, Statistics Netherlands, Voorburg/Heerlen.

Parliamentary Inquiry Committee on Integration Policy, 2004, Bruggen bouwen – eindrapport

(‘Building Bridges - Final Report’). Ministry of Interior, 1983, Nota Minderheden (‘Memorandum on Minorities’). Ministry of Interior, 1994, Contourennota Integratiebeleid Etnische Minderheden (‘General

Memorandum on Policies for the Integration of Ethnic Minorities’). Ministry of the Interior and Kingdom Relations, 1998, Wet Inburgering Nieuwkomers

(‘Newcomers Integration Act’). Ministry of the Interior and Kingdom Relations, 1999, Kansen krijgen, kansen pakken

(‘Getting chances, taking chances’).

Ministry of the Interior and Kingdom Relations, 1999, Rapportage Integratiebeleid Etnische

Minderheden 1999 (‘Report on the Integration Policy for Ethnic Minorities 1999). Ministry of the Interior and Kingdom Relations, 2002, (Integratie in het perspectief van

immigratie (‘Integration from the Perspective of Immigration’). Ministry of the Interior and Kingdom Relations, 2003, (Rapportage Integratiebeleid Etnische

Minderheden 2003 (‘Report on the Integration Policy for Ethnic Minorities 2003’).

52

Ministry of Justice, 2000, Vreemdelingenwet 2000 (‘Aliens Act 2000’). Ministry of Justice, 2004, Integratiebeleid Nieuwe Stijl (‘Integration Policy New Style’).

(Letter from the Minister of Alien Affairs and Integration to the Lower Chamber of the Dutch States General dated 16 September 2004).

Ministry of Justice, 2005, Contourennota “Herziening van het inburgeringsstelsel” (‘General

Memorandum on the Revision of the Introduction System’). Scientific Council for Government Policy (WRR), 1979, Ethnic Minorities - part A: Report to

the Government, SDU, the Hague. Scientific Council for Government Policy (WRR), 1989, Allochtonenbeleid (‘Immigrant

Policy’), SDU, the Hague. Scientific Council for Government Policy (WRR), 2001, The Netherlands as Immigration

Society, SDU, the Hague. SOPEMI, 2003, Migration and Migration Policies in the Netherlands 2003 (drafted by Snel,

E, J.de Boom and G. Engbersen). RISBO, Rotterdam. Vermeulen, H. and A. Böcker, 1992, De studie van migratie in Nederland. Een bibliografisch

overzicht (‘The study of migration in the Netherlands. A bibiographic review’). In: Migrantenstudies 4, pp. 21-29.

Zorlu, A. and J Hartog, 2001, Migration and Immigrants: The Case of the Netherlands. Tinbergen Institute Discussion Paper, TI 2001-042/3.

53

Lot 1: The use of demographic projections in policy planning (VC/2004/440)

Population Projections and Immigration Policy: The Spanish Case

Namkee Ahn

FEDEA (Madrid)

1. Introduction Spain has experienced an unprecedented immigration boom during the last 5 years. According to the Municipality Register Records, Spanish population has increased from 40.5 millions in 2000 to 44 millions in 2005 (both on the first of January) mainly due to immigration. The number of foreigners residing in Spain has increased from 923,879 in 2000 to 3,691,500 in 2005. This means an annual net increase of 554,000 immigrants during this 5 year period. Whether this trend will continue or not, and in what intensity, are extremely important questions but difficult to predict. On the other hand, the recent massive immigration can have a lasting impact on population structure in the future. In this paper, we examine demographic projections, the immigration policy during the last two decades and the implications of this recent immigration boom on the demographic and economic situation in Spain.

Table 1: Total Population and Immigrants in Spain Year (January 1st) Total population Immigrants % immigrants1998 39,852,650 637,085 1.61999 40,202,158 748,954 1.92000 40,499,790 923,879 2.32001 41,116,842 1,370,657 3.32002 41,837,894 1,977,944 4.72003 42,717,064 2,664,168 6.22004 43,197,684 3,034,326 7.02005 43,975,400 3,691,500 8.4Source: Municipality Register Records It is well known that the recent immigration boom is mainly due to a massive entry of illegal immigrants. Using various sources of information, we can measure an approximate number of illegal immigrants. The number of illegal immigrants is computed as the difference between the number of immigrants according to the Municipality Register Records14 (provided by the Spanish Statistical Institute) and the number of legal residents (provided by the Ministry of Interior).We can also distinguish the numbers including and excluding those from EU 14 We assume that most illegal immigrants register in their residence of municipality. First, the registration does not require any legal status. Second, the registration provides some important benefits such as public health care and education. Third, one of the requisites for all previous regularization campaigns of illegal immigrants was the proof of residence for certain duration.

54

countries. As of January 1st of 2005, there were 1,714,256 immigrants without residence permits. Excluding those from the EU countries who do not need a residence permit, the number of illegal immigrants amounts to 1,405,204. Among those, 1,159,673 are in ages 16-64. This would be a reference number when we evaluate the magnitude of the last regularization campaign.

Table 2: Legal and Illegal Immigrants (January 1, 2005) All immigrants Legal Difference (Illegal)Total 3,691,547 1,977,291 1,714,256 Age <16 552,288 268,880 283,408 Age 16-64 2,961,552 1,601,615 1,356,937 Age 65+ 177,707 106,606 71,101 Excluding EU Total 2,861,430 1,456,226 1,405,204 Age <16 460,656 235,495 225,161 Age 16-64 2,357,056 1,197,383 1,159,673 Age 65+ 43,718 23,162 20,556Source: The number of all immigrants is from the Municipality Register Records, and the number of legal immigrants is from the Ministry of Interior. The impact of immigration will depend substantially on the characteristics of the immigrant population. Usually, labor immigrants from less developed countries (Africa, Latin America and Eastern Europe) are relatively younger than the native population, while those from Northern Europe (in particular Great Britain and Germany) are older due to a large proportion of retired people. From the economic perspectives, the impact of immigration will depend on the labor market situation, productivity and the costs and benefits in the social security system of immigrants. Usually, labor immigrants contribute to the production and the social security system of the host country as they enter at young working ages. In many cases they have children in schooling ages who will benefit from free educational system in Spain. On the other hand, many of those who come from Northern Europe are retired. They receive their pensions from their origin countries and spend a part of them in Spain. They usually receive free health care benefits in Spain. In this paper, we examine the demographic and economic implications of the recent immigration boom in Spain. To do so, we first examine the demographic and labor market characteristics of immigrants in comparison to the native Spanish population. Then, we summarize Spanish immigration policies during the recent decades. We compare the projection of the Spanish pension system to explore the effect of recent immigration boom. Finally, we explore the effects of immigration of a different timing on the population structure in the future. In specific, we examine the effects of recent immigration boom if it happened in 10, 20 and 30 years later. 2. Characteristics of Immigrants Gender and Age Distribution of Immigrants There is a large difference in gender and age distribution of population between natives and immigrants. As can be seen in Figure 1a, the immigrant population is much younger than the

55

native population. The difference between the two is largest at young working ages: the proportion of the population aged 20-39 is 52% among immigrants compared to 31% among natives. On the other hand, the proportion of those aged 50 and over is only 14% among immigrants while it is 34% among natives. By gender, we observe a similar pattern between the two groups when we look at all immigrants together. However, there are some differences in gender distribution according to the country of origin as we will see later. The younger average age of immigrants relative to natives contributes to the rejuvenation of the general population. However, there is a concern regarding the timing. As we can see in Figures 1a and 1b, the most numerous age groups among the immigrant population coincide with those of natives as the baby-boom generations are now in ages 25-44. Thus, recent immigrants contribute to make even larger the bulge of baby-boom generation in the population age structure. This means that to maintain similar age structure (or similar aged dependency ratio) the number of immigrants in the future will have to be increasingly larger than now to compensate much smaller baby-bust generations.

Figure 1a: Gender and Age Distribution (%) of Natives and Immigrants

20 15 10 5 0 5 10 15 20

0-45-9

10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-84

85+

Natives Immigrants

FemaleMale

Source: Municipality Register Records January 1, 2005.

Figure 1b: Gender and Age Distribution (in numbers) of Natives and Immigrants

56

2000000 1500000 1000000 500000 0 500000 1000000 1500000 2000000

0-45-9

10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-84

85+

Natives Total

FemaleMale

Region of Origin By the regions of origin of immigrants, we can see that almost 40% of immigrants originate from America (most of them from Latin America) and about 20% from both EU15 and Africa, 16% from East Europe and 5% from Asia or Oceania. The gender distribution varies somewhat according to the region of origin. While male immigrants are substantially greater in Africa and Asia, the opposite is true for America.

Table 3: Distribution of Immigrants by Region of Origin Total Male Female EU 15 0,198 0,194 0,204 Rest Europe 0,164 0,162 0,166 Africa 0,191 0,244 0,130 America 0,396 0,341 0,457 Asia 0,050 0,058 0,042 Oceania 0,001 0,001 0,001

Source: Municipality Register Records January 1, 2005 We also observe substantial differences in age distribution by nationality. The difference is largest between immigrants from developed European countries (EU15) and those from the rest of the world. Up to age 14 there is a small difference by nationality. Above age 15 there is a clear difference. While those from the EU15 are relatively evenly distributed, those from the rest of the world are concentrated in ages 20 to 49 with the peak at ages 25-29. Another interesting phenomenon is a similarity between those from the rest of Europe (mostly from Eastern Europe) and those from the rest of the world (mostly from Africa and Latin America). This similarity indicates that the immigrants from both regions have a similar motive, labor migration.

Figure 2: Age Distribution of Natives and Immigrants

57

Age Distribution (%) of Natives and Immigrants

0

5

10

15

20

25

0-4 5-9 10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84

85+

Natives EU15 Rest Europe Rest of World

Source: Municipality Register Records January 1, 2005.

Labor Market Situation of Immigrants Labor market participation rate also varies substantially between natives and immigrants. Among those aged 16 and more, the participation rate is 70% among immigrants while it is only 56% among natives. This difference is mainly due to the age differences between the two population as shown earlier. When the age is controlled, the participation rate varies much less between immigrants and natives. At ages 16-24, the participation rate is higher among immigrants than among natives while at ages 55-64 the opposite is true. At the rest of ages the participation rate is similar.

Figure 3: Labor Force Participation Rates

Labor Force Participation Rates

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

16+ 16-19 20-24 25-29 30-34 35-44 45-54 55-59 60-64 65+

All immigrants Natives

Source: Spanish Census 2001.

The participation rate varies substantially among immigrants by country of origin. Those from Eastern Europe, Africa, America and Asia have a participation rate much higher than those from Europe.

Table 4: Labor Market Participation Rates of Immigrants and Natives Immigrants from

58

Natives All EU15 Rest Europe

Africa America Asia

All 16+ 55,58 70,01 50,02 76,24 76,99 76,64 74,4416-19 30,25 49,32 37,92 52,40 54,19 50,26 40,3020-24 63,55 75,33 64,36 78,24 75,79 76,85 73,9425-29 84,85 81,90 82,08 84,04 81,73 81,47 81,8930-34 84,96 82,98 81,82 85,08 83,19 83,01 81,0635-44 80,37 81,31 75,70 84,86 81,48 83,29 81,1245-54 69,80 71,21 58,97 78,04 77,09 78,27 77,0555-59 52,87 44,57 35,81 42,66 59,37 61,52 61,3960-64 31,81 25,61 20,21 22,32 42,60 42,17 45,0065+ 3,00 6,50 4,34 4,95 15,94 12,83 13,98

Source: Spanish Census 2001 There are also large differences in the type of work (occupation and industry) that occupy immigrants compared to natives. Most immigrants from Africa, America and Eastern Europe occupy low-skill jobs in which few natives are employed. For example, according to the Spanish Labor Force Survey in 2003, more than 64% of the immigrants from Eastern Europe and Latin America dedicate in the areas of domestic service, hotels and restaurants, agriculture or construction while only 24% of natives do so (Garrido and Toharia, 2004). It is clear that the majority of immigrants occupy the jobs that many natives are not willing to take. Fertility Rate among Immigrants Another factor through which immigration can affect future population structure is childbearing behavior. Usually, the fertility rate is higher among the immigrant population than among the native population. In fact, the main reason of the recent upshot in the Spanish fertility rate (from 1.2 to 1.3) can be attributed completely to a higher fertility rate among immigrants. The proportion of births by immigrants has increased from 5% in 1999 to 12% of total in 2003. The total fertility rate among immigrants is estimated to be twice that of natives (Delgado and Zamora, 2004). The higher fertility rate among immigrants, although it tends to converge over time to that of the natives, could help to increase the number of young population in Spain. In summary, the differences between the immigrant and native populations are large in both demographic and socio-economic aspects. The immigrant population is younger, participates more in the labor market, and occupy lower-skill jobs than the native population. This difference could help alleviate some problems caused by population ageing, such as imbalance in the social security system and manpower shortage in the labor market. 3. Immigration Policy in Spain The first important immigration law was established in 1985 (Ley Orgánica 7/1985). This law was very restrictive and its main objective was to restrict the establishment of immigrants in Spain. Given the political situation at the time of elaboration of the law, just before the entry of Spain into the European Union, it appears that one of the main ideas behind the law was to please the main countries of then EU, such as Germany, France and Belgium where the immigrant population was already an important share of their population and a source of political concern.

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According to this law, legal immigration was extremely difficult as it was difficult to obtain work or residence permits, and in case of obtaining one, most of them were of a short duration. The law also lacked in warranting fundamental rights regarding social protection of immigrants which in some occasions was corrected by the rulings of Spanish Supreme or Constitutional Courts and the European Tribunal of Human Rights. In spite of this restrictive law and the constant risk of police persecution and expulsion for illegal immigrants, the number of those in Spain has increased rapidly during the 1990s. Recognizing the inadequacy of the law within the EU framework in addition to the apparent failure in achieving intended objectives (one of which was to restrict illegal immigration), the Spanish Government decided to carry out a reform of the immigration law. The new law was elaborated in 2000 (Ley Orgánica 4/2000 and 8/2000) and came into effect in 2001. The main objectives of the reform were to guarantee equal rights to legal immigrants as natives and to integrate them in the labor markets. On the other hand, there were still many obstacles to enter legally and the rights of illegal immigrants were further limited. The reform focused mostly on the control of immigration flows and the establishment of the method of expulsion of illegal immigrants, without considering legalization of their situation. The result appears to be a massive entrance of illegal immigrants and the creation of abusive mafias in trafficking of illegal immigrants. Next reform was carried out in 2003 (Ley 14/2003). The main changes introduced were strengthening the control of illegal immigration and trafficking of persons, allowing the access to the information of Municipality Register Records by the police, and relaxing the conditions for the reunion of family members. It is quite obvious that all the immigration laws have focused on the repression and control of illegal immigrants instead of their social integration. The immigration policy based on these laws has resulted in a resounding failure as it is apparent in the ever-increasing number of illegal immigrants during the last decade. Faced with this reality, the Spanish government has employed the method of periodic (although it is officially called ‘extraordinary’) regularization (legalization) of irregular immigrants. There have been regularization campaigns several times during the last two decades (1986, 1991, 1996, 2000, 2001 and 2005). The main requirement for regularization was a simple proof of residence in Spain for certain duration (usually 6 months). Obviously, the almost periodic regularization campaigns requiring only the proof of residence for certain duration could have triggered the recent boom of illegal immigration, and serve as clear evidence of the failure of the Spanish immigration policy during the last decade. The restrictive immigration laws which dominated during the last two decades in Spain may be understood better if we considered the labor market situation during this period. Since the mid 1980s, Spain has suffered the worst experience of unemployment. The unemployment rate was higher than 15% during the most of the last two decades and reached the maximum of 25% in 1994. The last two decades were also the period of the entrance into the labor markets of the Spanish baby boom generations born in the 1960s and 1970s. Consequently, economic recessions in the mid-1980s and early1990s hit hardest the young population as witnessed by the extremely high (around 50%) youth unemployment rate during the period. Under these circumstances most politicians seemed to be worried that immigrants may worsen the labor market situation of natives. Now, with the unemployment rate down to 10% and much smaller baby-bust generations entering the labor market, there is a serious concern of manpower shortage in many sectors of the economy. This change in the labor market environment may be affecting the political decisions on immigration.

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One of the major political decisions taken by the new Spanish government (in power since 2004) was the regularization campaign of irregular immigrants, which was carried out during the period between the 7th of February and the 7th of May 2005. One main difference of this campaign from the previous ones is that one of the requisites for application is a work contract in addition to the proof of residence. Therefore, the primary target has been illegal underground employment of irregular immigrants. This seems to be a clear indication that the Government is aware of the future demographic situation and is taking some steps in immigration policy to increase the proportion of the working age population at least in the short and medium run. Close to 700,000 applications have been filed, which indicates that the number of approved applications is likely to be larger than in any previous regularization campaigns. Although the final outcome of the last campaign is not yet known as the applications are still under the process of evaluation15, the number of legal immigrants (including family members who will join the legal immigrants) is likely to increase substantially during the coming years. Characteristics of 2005 Regularization Applicants The characteristics of the applicants are similar to those seen earlier. The share of those from Latin America is about 50%, those from Eastern Europe take 25%, and those from Africa a little less than 20%. It shows an increasing entry of Eastern Europeans. By the sector of work, the four major sectors, domestic service, agriculture, construction and hotel & restaurant, take 77%, a share somewhat greater than shown in 2001 Census.

Table 5: By Nationality (%) Nationality Total % Women Total 687,138 41.24 Ecuador 20.33 52.06 Romania 17.22 44.33 Morocco 12.51 14.89 Colombia 8.24 52.88 Bolivia 6.87 56.07 Bulgaria 3.72 44.04 Argentina 3.47 43.56 Ukraine 3.23 50.11 Pakistan 2.19 1.59 China 1.91 31.91 Uruguay 1.55 43.99 Brazil 1.52 59.59 Senegal 1.44 8.06 Venezuela 1.17 54.31 Russia 1.16 70.44 Algiers 1.15 8.01 Paraguay 1.09 64.44 Nigeria 1.04 39.72 Mali 1.02 1.01 Chile 0.72 47.34 Others 8.44 33.54

15 According to the Ministry of Labour and Social Affaire, as of the 15th of July 2005, among the 504,786 applications reviewed so far, the approval rate was 89%.

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Source: Ministry of Labor and Social Affairs

Table 6: By Sector Sector Total % Women Domestic service 31.67 83.40 Construction 20.76 5.08 Agriculture 14.61 16.87 Hotel & Restaurant 10.36 50.26 Small commerce 4.77 28.61 Textile manufacturing 2.58 23.79 Others 15.24

Source: Ministry of Labor and Social Affairs Almost 700,000 applications filed for regularization are clear evidence that the most Spanish employers who are employing illegal immigrants without a formal contract are willing to employ formally those immigrants. As we have seen earlier, the estimated number of working-age illegal immigrants as of January 1st of 2005 was 1,159,673. The ratio between the applicants and the stock of illegal immigrants is 0.59. Considering that some illegal immigrants were not eligible for the regularization due to the requirement of minimum duration of residence of 6 months, the effective ratio is likely to be substantially higher than 0.59, maybe one close to the participation rate among the immigrants according to 2001 Census, about 0.7. This means that a great majority of illegal immigrants who are working in the informal sector and their employers have applied for regularization. The popular perception that many employers do not employ natives to take advantage of illegal immigrants to avoid social security tax might be wrong. It seems more likely that the employers who employ illegal immigrants do so simply because they could not find other sources of manpower. 4. Population Projections by the Spanish National Statistical Institute The changes in the fertility rate during the last 30 years, from the TFR near 3 up to the mid-1970s to close to one since the mid-1990s, plays a dominant role in the current and future Spanish demographic situation. Accompanied by an ever-increasing life expectancy and low immigration (until very recently), this change has lead to a rapid population ageing in Spain. The extremely low fertility rate during the last 15 years which followed relatively high fertility rates for decades since the Second World War leads to a much severe population ageing starting around 2030. Population projections showed clearly this phenomenon. Traditional population projections apply three different scenarios on future vital statistics. In case of the Spanish National Statistical Institute (INE), in their projection done in 1995 which was based on 1991 Census data, they assumed 34,000 annual net immigrants for the whole projection period in all scenarios. Obviously, this assumption was taken from the experience just before the time of projection, the average in the period 1987-1994 in this case. The increasing immigration experienced during the second half of the 1990s led to the increasing deficit in the number of immigrants in this projection. Recognizing this deficit, the Spanish Statistical Institute carried out a revision in 2000 on the population projection. They adopted three different migration scenarios. Scenario one assumed that net immigration in 2001 is the average of the numbers in the previous two years, and thereafter decreasing linearly to reach 160,000 in 2005 and constant at that level thereafter. Scenario two assumed that net immigration decreases linearly to reach zero in 2020 and stay at zero thereafter. Scenario 3

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assumed that the net immigration stays constant at 250,000 for all years. Projected total population and aged dependency ratios are in the following graphs. Total population reaches 43 millions in the two low immigration hypotheses and 44 millions in the high immigration case in 2013. The difference increases rapidly thereafter across scenarios. In 2050, total population ranges from 35 to 46 millions. The aged dependency ratio increases slowly and stays at similar levels across scenarios until 2020. Between the years 2000 and 2020, it increases from 0.25 to 0.30 for all scenarios. Thereafter, the increase rate is greater and the variation across scenarios also increases. In 2050, it ranges from 0.51 to 0.65.

Figure 4: Spanish Population Projection (INE 2000)

Spanish Population Projection

34

36

38

40

42

44

46

48

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

mill

ions

scenario 1 scenario 2 scenario 3

Figure 5: Aged Dependency Ratio (INE 2000)

Aged Dependency Ratio

0,2

0,3

0,4

0,5

0,6

0,7

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

scenario 1 scenario 2 scenario 3

In 2005, based on the 2001 Census, the Spanish National Statistical Institute has produced another population projection. As the projection was carried out in the early 2005, this projection tries to incorporate recent development in immigration. Two different projections are carried out. The main difference between the two scenarios is the number of immigrants. The number of immigrants during the first 5 years 2002-2006 is the same in both scenarios which are taken from the Municipality Register Records, real for the years 2002 and 2003 and estimated for 2004-2006. The average annual net immigrants during these 5 years are 528,181. Thereafter, the number of net immigrants decreases slowly reaching around 270,000 in the high immigration scenario and around 110,000 in the low immigration scenario in the year 2020 and staying constant thereafter.

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Total population in Spain increases to 53 millions in 2050 under the high migration hypothesis and 44 millions under the low migration hypothesis. The aged dependency ratio stays constant until 2010 and then starts to increase first slowly until 2030 and more rapidly thereafter. It reaches 0.62 in 2050 under the low migration and 0.55 under the high migration hypothesis. With respect to population aging, one thing that stands out in both projections (2000 and 2005) is that in the long run (in 2050) the aged dependency ratio will be more than twice that now under all hypotheses. However, the difference in the aged dependency ratio between different migration hypotheses is not small. Higher immigration will have some effects on delaying population aging.

Figure 6: Spanish Population Projection (INE 2005)

Spanish Population Projection (INE)

40000000

42000000

44000000

46000000

48000000

50000000

52000000

54000000

2002

2005

2008

2011

2014

2017

2020

2023

2026

2029

2032

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High immigration Low immigration

Figure 7: Aged Dependency Ratio (INE 2005)

Aged Dependency Ratio Projection (INE)

0,20,250,3

0,350,4

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0,550,6

0,65

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5. Effect of Immigration on the Pension System One of the most studied issues regarding population aging is the pension system. As one of the determinants of the financial situation of the pension system is the ratio between

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pensioners and workers, immigration has immediate effects on the pension system. Our earlier projection of the Spanish pension system (Ahn et al, 2005) used population data which did not account for the recent immigration boom. The population observed in 2005 is about 2 million more than that in the projection and this difference is mostly due to immigrants. Here, we carry out a simple exercise to measure the effect of incorporating the new immigrants into the pension system. We assume that the population size in the base year has increased by 2 millions but maintain the same future vital statistics used in the earlier projection. For simplicity, we compare the median projections. Ratio Contributors/Pensioners The increase of immigrants by 2 millions raises the number of contributors by about one million as the participation and unemployment rates for immigrants are assumed to be the same as natives. The difference between the old and the new projection decreases slightly over time as the new immigrants start to retire and the number of their children in working ages is smaller as we assumed the same fertility rate among immigrants and natives. The number of pensioners does not change in the beginning as most new immigrants are under the retirement age. However, over time the number of pensioners increases, reaching one million more in 2050 as most of them are retired. Consequently, the contributor-pensioner ratio is higher in the beginning in the new projection and slowly converges to the old projection.

Figure 8: Number of Contributors

13.000

14.000

15.000

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17.000

18.000

19.000

20.000

21.000

2004

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MillionsMedian Forecast Median Forecast (old)

Figure 9: Number of Pensioners

65

7.0008.000

9.00010.00011.00012.000

13.00014.00015.000

16.00017.000

2004

2006

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MillionsMedian Forecast Median Forecast (old)

Figure 10: Ratio of Contributors/Pensioner

1,0

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Pension Expenditure With new immigrants at working ages the GDP increases. Since the number of pensioners does not change much, the pension expenditure as a proportion of GDP decreases. In the first year of projection, the reduction is 0.43% of GDP and the difference decreases slowly reaching a zero difference around 2040.

Figure 11: Pension Expenditure (% GDP)

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8,0

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(%)Median Forecast Median Forecast (old)

Balance and Accumulated Debt of the Pension System As the contributor/pensioner ratio is higher in the beginning, the balance of the pension system is accordingly improved by 0.4% of GDP. However, the balance becomes worse in the new projection than in the old one around 2040. Accumulated surplus of the pension system reaches a maximum of 26% of GDP in 2022 in the new projection relative to 19% in the old one. The year that the surplus gets exhausted and enters deficit is delayed by two years from 2033 to 2035.

Figure 12: Balance of Pension System (% GDP)

-12,0

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-6,0

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3,0

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Figure 13: Debt of Pension System (% GDP)

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-180

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(%)Median Forecast Median Forecast (old)

6. Effect of Different Timing of Immigration: An Experiment In this section, we carry out an experiment to explore the effect of different timing of immigration on the future population structure. We take the point forecast in the Ahn et al (2005) as the baseline population. In this forecast, the recent immigration is not included. If we include recent immigration, the baseline population increases by 2 millions. We carry out several population projections with different timing of entrance of these 2 million immigrants holding all other things the same. In specific, we consider four different timing, 2004, 2014, 2024 and 203416. As we can see in Figure 14, total population jumps up by 2 millions in the year that we include these 2 million immigrants. For the rest of years, it changes slowly and smoothly. In 2050, the total population is very similar independent of the timing of the entrance of immigrants.

Figure 14: Total Population

Spanish Population

41000000

42000000

43000000

44000000

45000000

46000000

47000000

w/o immigrants 2 millions in 2004 2 millions in 2014 2millions in 2024 2 millions in 2034

16 The baseline forecast includes a smoothly moving net annual immigration between 120,000 to 180,000 during the projection period. In our experiment we increase the number of net immigrants by 2 million (in addition to the baseline) immigrants for each specific year of experiment.

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On the other hand, the aged dependency ratio varies much more at the end of the projection period by the timing of immigration. In the year that those 2 million immigrants enter, the aged dependency ratio decreases by about one percentage point independently of the year of entry. However, the effect in the following years differs by the year of entry. In 2050, the aged dependency ratio varies by 6 percentage points, between 0.62 if the entry were 2034 and 0.68 if the entry were 2004. This difference should not be taken as small since we have considered only one shot of 2 million immigrants, only 5% of the start-off native population. This suggests that the timing of immigration could make some differences in the population structure in the future.

Figure 15: Aged Dependency Ratio

Ratio 65+/15-64 (Spanish Population)

0,2

0,25

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0,35

0,4

0,45

0,5

0,55

0,6

0,65

0,7

w/o immigrants 2 millions in 2004 2 millions in 2014 2millions in 2024 2 millions in 2034

7. Final Remarks All past projections of the Spanish population predicted a serious population aging. Aging appears to be more acute from 2030 onwards due to the entrance of the baby-boom generation into retirement ages. During the last 5 years a dramatic change has occurred in one of the vital statistics which affect total population and its age structure, namely a massive increase in the number of immigrants. As this is something unexpected, most previous population projections were carried out under the hypothesis of much lower net immigration. Consequently, the projected population and its age structure appear far from realistic. The last official population projection (2005) appears to adjust to this reality first by incorporating most immigrants in the current population figure and second by using hypothesis of substantially greater net migration in the future. On the other hand, past immigration policies of the Spanish government can be characterized as a complete ignorance or disconnection to the population projections and future population situation. They were mostly driven by short-term electoral purposes. This is apparent in the main objective of the principal immigration laws approved during the last two decades, which focused on the repression of illegal immigrants. There is a hint of change in the immigration policy of the new government. The recent regularization of irregular immigrants suggests that the new government may be taking some measures to improve future population situation. In particular, the requirement of a work contract for regularization suggests that the government may be aware of the current Spanish labor market situation and the economic implications of immigration.

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The recent immigration boom (2000-2005) coincided with the baby-boom generation in their young working ages, therefore, fattening even more the bulge of the baby-boom generation in the current population age structure. Therefore, the recent immigration boom will be beneficial to the social security system in the medium term until they start to retire. However, as they will start to retire sometime in 30 or 40 years, we would need more immigrants or higher fertility rates to maintain population age structure. Our analysis of the recent population projections and the effect of immigration on the pension system suggest that recent immigration boom and future immigration can lessen somewhat the burden of population aging in Spain in the medium term but will worsen in the long term. An experiment of population projection with different timing of immigration shows that different timing of immigration could make some differences in the age structure of the future population.

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References Ahn, N., García, JR. and J. Alonso (2005) “Demographic uncertainty and pension system: Spanish case”, mimeo, FEDEA, Madrid Delgado, M. and F. Zamora (2004) “Españolas y extranjeras: su aportación a la fecundidad en España”, Economistas, 99. Garrido, L. and L. Toharia (2004) “La situación laboral de los españoles y los extranjeros según la Encuesta de Populación Activa”, Economistas, 99.

The use of demographic trends and long-term population projections in public policy planning at EU, national, regional

and local level

Chapter 5. Population projections in urban and regional policy

1. The use of regional and local population projections in public policy planning - A case study of the Helsinki Region (p.1)

by Seppo Laakso 2. The use of demographics when designing and implementing spatial and housing policies by the Dutch government (p.25)

by Harri Cruijsen

Seppo Laakso∗:

The use of regional and local population

projections in public policy planning

A case study of the Helsinki Region

October, 2005

∗ Researcher, Dr.Soc.Sc (econ) Urban Research TA Ltd Mannerheimintie 44a FIN-00260 Helsinki, Finland Tel. +358 9 561 2493 seppo.laakso@kaupunkitutkimusta.fi

2

Background The study is a part of the research project "The use of demographic trends and long-term population projections in public policy planning at EU, national, regional and local level". This preliminary report includes initial results of a case study on the use of regional and local population projections, mainly in the Helsinki region and the city of Helsinki. The final report of the study will contain also comparisons with respect to the use and development needs between Helsinki and selected other cities/regions. The study presents a summary of the organisation, data sources, methodology and use of regional and local population projections. It presents also an initial analysis of the uncertainty problems and development needs of regional and local projections. Finally, the study includes conclusions and recommendations concerning the use and methods of regional and local population projections. The study is based on the reports and other documents of the projection makers and interviews and a questionnaire query to the representatives of the Uusimaa Regional Council and the City of Helsinki and selected other cities/regions.

1 Introduction Information about future population development in the form of forecasts, projections or scenarios are needed at regional and local level especially for land use and infrastructure planning and the planning of regional and local public services, especially heath, social and education. They are policy domains in which the need and demand are highly dependent on the number, structure and location of the population. The public sector has a dominant role in the organisation of the service and there is a need for long or middle run perspective in planning of investments and supply networks. Population structure and population development have several special features at regional and local level compared with national level. This affects essentially the approaches and possible uses of regional and local population projections and other information concerning future population. First, there are significant differences with respect to population structure – especially age and socio-economic – between regions of the country, between municipalities within regions and even between districts within municipalities. Figure 1.1 shows the variation of the age structure within the city of Helsinki. The proportion of primary school aged children (7-15 years) is lowest in the inner city and “old” high-density suburbs while it is rather high in “new” suburbs and “old” low-density suburbs. The proportion of aged population (over 65 years) is highest in old suburbs constructed mainly in 1950s and 1960s and quite high also in the inner city while it is rather low in all new (constructed in 1980s or later) residential districts.

3

Children 7-15 years old Ageing inhabitants over 65 years old

Figure 1.1: The proportion (%) of children in primary school age (7-15 years) and aged (65+ years) inhabitants of total population by residential district in the city of Helsinki 2004 (Source: The city of Helsinki Urban Facts, Statistics 2004/6) Second, migration has a significant role in the population change at regional and local level while at national level (in most EU-countries) its role is smaller compared with natural demographic factors, births and deaths. Regional and local migration it is closely connected with regional economy and local housing markets and tends to be rather volatile with strong cyclical and random variations. This is demonstrated in figure 1.2 presenting the annual net migration and natural population changes in the city of Helsinki.

Net migration BirthsDeaths Population change

% o

f pop

ulat

ion

-2

-1

0

1

2

3

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Figure 1.2: Net migration, births, deaths and total population change relative to population in the City of Helsinki 1960-2004 Another feature is the strong selectivity of migration with respect to age. Figure 1.3 presents net migration between the city of Helsinki and the rest of the country (outside the region) with respect to age. The figure shows that Helsinki as the centre city of the region gets a major migration surplus of young adults while Helsinki looses families with children. The special role of migration in regional and local population developments is a major challenge for population projections.

4

In-migration Out-migrationNet-migration

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ons,

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Figure 1.3: Migration between the city of Helsinki and Finland (outside the region) by age, 2001-2003 in average

2 Regional and local population projections in Finland and in the Helsinki Region Regional and municipal population projections are made in Finland by several public organisations, Statistics Finland, regional councils and some large and middle sized cities, as well as some research institutes. According to the information from other EU-countries national Statistical offices make regional population projections among others in Belgium, Netherlands, Denmark and Sweden. In the following the focus is limited in the Helsinki region and the city of Helsinki. Statistics Finland calculates and publishes local population projections at municipal level every 3 years. The projection is made by a demographic component model for each municipality of the country (444 municipalities in 2004). The latest version was published in 2004 for the years 2005-2040. Municipal projections can be aggregated to various regional levels, like NUTS 4 (sub-regional units) and NUTS 3 (regions). Municipal projections are balanced with Statistics Finland’s national projection. Regional councils are responsible for regional land use planning, other regional strategic planning and promoting of several kinds of regional interests. There are 20 regions and regional councils in Finland. Regional councils are active users of population projections. Most of them use population projections of Statistics Finland for their planning purposes but some of the regional councils make also their own projections. The Helsinki region belongs to the Uusimaa Regional Council which makes its own population and labour force projections for planning purposes. Projections are updated every 3 or 4 years when a new round of the planning process is started. The council allocates approximately 3-4 manpower months during each round to the population projection project. The method of the projection is based on the demographic component model. However, in spite of the similarities in the method and data sources, the projections are independent of the ones published by Statistics Finland. Population projections are made for the entire Uusimaa region and for each municipality of the region (NUTS 3, 26 municipalities). Labour force projections are made for the whole region and within the region for 3 sub-regional units

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and in addition separately for the cities of Helsinki, Espoo and Vantaa. The time horizon of the projections is the same as in the regional plan, 25-30 years. The latest version of the projections was made in autumn 2004 for the years 2004-2030. The Uusimaa Regional Council negotiates with individual municipalities about population projections and consequently the results are to some extent reflected by the views of municipality leaders. The Helsinki region consists of 12 municipalities and covers 40 % of the land area and 90 % of the population of the Uusimaa region (1,3 million). The big municipalities of the Helsinki region – cities of Helsinki, Espoo and Vantaa – each make their own population projections at city and district level. Helsinki has also a regional projection included. In Helsinki and Espoo the projection is updated every year and in Vantaa every 2 years. Each city allocates about 3 manpower months for the projection project each round. All three cities use the same district level demographic component model, designed by the city of Helsinki, but the projections are made independently in each city. The time horizon of the district level projections is about 10 years. In addition, the cities make longer run (20-25 years) projections at city level. The Helsinki Metropolitan Area Council (YTV) is a joint organisation of the cities Helsinki, Espoo, Kauniainen and Vantaa. It is responsible for transport system planning, regional public transport provision, waste management and air quality management for its four member municipalities. It also maintains regional databases and conducts studies on different issues affecting the region. YTV uses both the population projections made by the member cities and by the Uusimaa Regional council. The other municipalities of the Uusimaa region do not have capacity to make systematically their own population projections. Instead they use the projections made by Statistics Finland and the Uusimaa regional council for their planning and policy purposes. In other Nordic countries the capital cities (Stockholm, Copenhagen, Oslo) and many other large cities have quite similar population projection systems as in the city of Helsinki. There has been cooperation and change of experience between the Nordic cities to develop the methods and the use of projections.

3 The approaches, methods and data sources of regional population projections Technically, the regional and local population projections of Statistics Finland, Uusimaa Regional council and the cities of the Helsinki Metropolitan area are based on the same kind of demographic component model. Still, the approach and the way how assumptions are made differ significantly between the organisations.

3.1 Population projection model In the population projection model of the city of Helsinki for the regional and municipal level the projection is calculated using the following algorithm1. The population of the city of Helsinki (H) in year t+1 in age group i+1 (i=0,…,99+) and sex s (s = 1(male), 2(female)) is: (3.1) Hi+1,s,t+1 = Hi,s ,t – ki,s Hi,s,t – (gi,s + mi,s + ei,s) Hi,s,t + Ri,s + Oi,s + Ii,s where: 1 This is an rough outline omitting error terms in equations and several other details

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k i,s = age and sex specific mortality rate gi,s = age and sex specific out-migration rate from the city of Helsinki to the rest of the region mi,s = age and sex specific out-migration rate from the city of Helsinki to the rest of Finland (outside the region) ei,s = age and sex specific out-migration rate from the city of Helsinki to other countries Ri,s = number of in-migrants of age i and sex s to the city of Helsinki from the rest of the region Oi,s = number of in-migrants of age i and sex s to the city of Helsinki from the rest of Finland (outside the region) Ii,s = number of in-migrants of age i and sex s to the city of Helsinki from other countries.

Births during the year t+1 are calculated as follows:

(3.2) H0,s,t+1 = Σi H i,2,t fi cs (i=15,…,49) where: fi = age specific fertility rate of 15-49 years old women cs = proportion of sexes (boys/girls) of born children. The base data Hi,s ,t for the starting year t is based on population statistics while the parameters of the projection model – g, m R, O f and c – are based on studies of earlier years population statistics and on various assumptions. The projection is calculated recursively from year to year up to the end of the projection period. Depending on the approach the parameters change according to certain rules or are kept constant over the projection period.

3.2 Regional population projections of Statistics Finland The municipal level population projection model of Statistics Finland differs in details (eg. the migration parameters2) from the above model of the city of Helsinki but the basic idea is the same. Statistics Finland produces two projection versions: (1) a projection with migration and (2) a projection without migration. For the migration, fertility and mortality parameters municipalities are classified to groups according to location (region), size and characteristics. The parameters are calculated from historical data for all groups and applied for each municipality belonging to the group. In the projection with migration it is assumed that within each municipality group the age specific in and out migration parameters remain constant during the projection period and the migration patterns are similar as in the 4 previous years (years 2000 - 2003 in the 2004 version). In the projection without migration it is assumed that there is no migration in any municipality in any age group. Age specific fertility parameters are calculated for municipality groups from the last 3 years (2001 - 2003 in the 2004 version). In the projections it is assumed that fertility rates remain constant at the previous years’ level during the projection period. Respectively the age and sex specific mortality rates are based on the last 3 years data but in the projection it is assumed that life expectancy is increasing in a stabile manner for both sexes during the projection period.

2 The division of migration to regional and rest of the country is not used in Statistics Finland’s model.

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On the whole, the municipal and, respectively, the aggregated regional population projections of Statistics Finland demonstrate the population development if the recent trends of the main factors of population development continue as such. Projections do not include assumptions or speculation about changes in regional economy, labour markets or housing markets.

3.3 Regional population projection of Uusimaa Regional Council The Uusimaa Regional Council uses basically the same population projection model as the city of Helsinki. The main difference is that the division of in and out migration with respect to migration area (the region, rest of Finland, other countries) is not made. Assumptions concerning fertility rates and mortality rates are made according to same principles as in Statistics Finland’s projections. However, there is a major difference with respect to Statistics Finland concerning the approach and assumptions of migration parameters. First, net migration is considered as an economic process with close connection to regional output, labour markets and housing markets. Consequently, the assumptions concerning in-migration to the region as a whole and to each municipality are made on the basis of alternative scenarios concerning future regional output, labour demand and restrictions from housing markets, not just on the basis of the average of previous years. Second, population forecasts are a part of regional strategic planning. One of the main objectives of a regional plan is to create preconditions for stable economic growth, including population growth. Earlier regional population projections were called “population plans” instead of projections or forecasts, indicating that there were also politically set targets in population development. In the present approach Uusimaa Regional Council makes two projections:

(1) basic projection based on the assumption of stable output and employment growth - and consequently positive net-migration – in the Uusimaa Region

(2) maximal projection based on the assumption of fast output and employment growth – and consequently significant migration surplus; the basic idea in the maximal projection is that it represents the maximal growth for which the region should be prepared in the land use planning and other regional policy.

Not surprisingly, the population projections made by regions have in the past given significantly higher population growth figures in average for the regions than the projections made by Statistics Finland. The difference in the objectives and approaches explains partly the result. In general, the population (and labour force) projections of Uusimaa Regional Council, like several other regional councils, can be characterized as “if – then” type projections based on alternative scenarios of regional economy and linked to population via migration.

3.4 Regional and district level population projections of the city of Helsinki The population projection model of the city of Helsinki consists in fact of two models:

(1) Regional and city level long run population projection (2) District level middle run population projection.

The regional and city level projection is made for about 25 years. The model is a two-area (city of Helsinki and the rest of the region) combining the demographic component approach and regional economic approach. The basic ideas of the model algorithm are presented in

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equations 3.1 and 3.2 above. The link of population development to regional economy is outlined in figure 3.1. Following the idea of the close link between migration and regional labour markets the assumptions concerning future migration are based on alternative scenarios of regional economic development, like in the projections of the Uusimaa Regional Council. In addition, because of the fact that the bottle-necks in the availability of land restricts significantly the possibilities of housing construction in the city of Helsinki, migration is considered carefully in relation with housing supply. These links are outlined in figure 3.2. The last version of the projection made for years 2005-2030 (published in 2004) contained three alternative projections for the Helsinki region and the city of Helsinki based on differing scenarios of the regional economy:

(1) slow growth: based on the scenario of the region and its core industries loosing their competitiveness causing declining employment

(2) basic growth: based on the scenario that the regional economy grows in a stabile way

(3) fast growth: based on the scenario that the region and its core industries maintain their competitiveness and keep their positions in the growing markets, causing the regional economy to grow fast.

In earlier versions there have also studied other types of alternatives, eg. scenarios based on alternative land use master plans in Helsinki, or scenarios based on sudden immigration flows. The assumptions concerning fertility rates and mortality rates are in general made according to same principles as in Statistics Finland’s projections. Like the projections of Uusimaa Regional Council, the City of Helsinki’s regional and city level projections can be characterized as “if – then” type projections based on alternative scenarios of regional economy and city level land use and linked to population via migration. There are however certain differences in approaches of the Regional Council and the City of Helsinki. Especially the possibilities of negative economic development are considered very seriously by the City of Helsinki because of the severe consequences to the city’s economy while in regional planning by the Regional Council slow growth is considered rather an un-interesting marginal case.

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Economic development - international - domestic

Helsinki region’s competitiveness and attractivity - infrastructure - cost level, taxation - administration - service level, environment, safety - housing markets - image

Location of firms Investments Production

Demand for labour

Migration - domestic - international

Commuting from outside the region

Fertility Mortality

Natural population changes

Population of the region - number - age structure

Supply of labour - quantity - age structure - education

Figure 3.1: The basic relations of population developments of the Helsinki Region

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City of Helsinki’s attractivity - residential areas

and housing stock - local services - taxation - population structure - image

City of Helsinki’s share of the migration to and from the region

Migration between city of Helsinki and the region

Migration between the Helsinki region and outside areas - rest of the country - foreign countries

Supply of housing - existing housing

stock - vacant land for

construction - housing construction

Figure 3 The disprojectioprojectiocity levean officiinvestme The dist

(1) e

Fertility Mortality

Population of the city of Helsinki - number - age structure

.2: T

trictn wn is l proal stnts

rict lxisti

-

- -

Natural populationchanges

he basic relations of population developments of the city of Helsinki

level projection is made for 10 years and it is based on one selected city level hich was “the basic growth” alternative in the 2004 version. The district level made for about 100 districts (residential areas) of the city and balanced with the jection in each age group. The district level projection is updated annually. It has atus in the city administration and all departments must base their service and plans and decisions on it.

evel projection is made in two parts: ng population projection

based on the existing population by age and sex in each district in beginning of the starting year the basic idea of the projection algorithm is like in equations 3.1 and 3.2 except the effect of migration is projected using age specific net change coefficients which have been defined for district groups (see examples in figure 3.3)

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- variation with respect to fertility and mortality between districts is taken into account in the parameters of natural population changes (see examples of fertility parameters in figure 3.4).

(2) projection for the population in new housing constructed during the projection period - based on district level housing construction forecast - the first year (after the buildings are completed) population is forecasted using

district and housing type specific floor space per capita estimates - the following years are projected like in the existing population case.

After both parts are projected the separate projections are merged and balanced with the city level projection using age specific correction coefficiens.

0,6

0,8

1,0

1,2

1,4

1,6

1,8

2,0

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2,4

s 4 9 14 19 24 29 34 39 44 49Ikä

Net

tom

uutt

oker

roin

Pientaloalue

Kallio

Lähiö

Kanta-kaupunki

One family housingdominated suburbs

Inner city

Suburbs

Age

Net

chan

ge

00,6

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s 4 9 14 19 24 29 34 39 44 49Ikä

Net

tom

uutt

oker

roin

Pientaloalue

Kallio

Lähiö

Kanta-kaupunki

One family housingdominated suburbs

Inner city

Suburbs

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Net

chan

ge

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s 4 9 14 19 24 29 34 39 44 49Ikä

Net

tom

uutt

oker

roin

Pientaloalue

Kallio

Lähiö

Kanta-kaupunki

One family housingdominated suburbs

Inner city

Suburbs

Age

Net

chan

ge

0

Figure 3.3: Net change coefficients3 by age in selected residential area types (Kallio = Inner city high density area; Inner city = Inner city in average; Suburbs dominated by one family housing; Suburbs = suburbs dominated by multi-storey apartment housing.)

3 net change coefficient ni,t = Pi,t / Pi-1,t-1 where Pi,t population of age i in yea t

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0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,14

0,16

0,18

15 20 25 30 35 40 45Äidin ikä

Hed

.luku

Kanta-kaupunki

Kallio

Lähiö

PientaloalueOne family housingdominated suburbs

Age of the mother

Inner city

SuburbsFert

ility

coef

ficie

nts

One family housingdominated suburbs

Age of the mother

Inner city

SuburbsFert

ility

coef

ficie

nts

Figure 3.4: Fertility coefficients by female age in selected residential area types (Kallio = Inner city high density area; Inner city = Inner city in average; Suburbs dominated by one family housing; Suburbs = suburbs dominated by multi-storey apartment housing.) The parameters of the model (net change, fertility and mortality coefficients and floor space estimates are based on studies about the previous 3 years population changes at district level. The district level population projections of the City of Helsinki can be characterised as conditional forecasts based on the results of city level population projection and past years district level population changes.

3.5 Data sources In all regional and local population projections made either by Statistics Finland, regional councils of cities the data sources are basically the same:

- annual population statistics by Statistics Finland at regional, municipal and district level

- annual vital statistics by Statistics Finland at regional, municipal and district level, including among others migration, birth and death statistics.

The population statistics by age, sex and region/municipality/district is used as the starting data of population projections. Population and vital statistics are used to analyse the past development and to estimate the parameters used in projection models. In addition, in the district level model of the city of Helsinki, detailed data about residential construction plans are used to forecast the quantity and type of new housing in each district.

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4 The use of population projections The following review of the use of regional, city and district level population projections are based on interviews and questionnaire study to representatives of the Regional Council of Uusimaa and the City of Helsinki. In addition, information from other countries and cities/regions are used.

4.1 The use of population projections in regional planning Regional population projections are used for a wide range of policy and planning domains by the Uusimaa Regional Council. Typically in the tasks of regional planning population in one factor among a wide class of factors affecting decision making. Even when the capacity needs and investments of regional infrastructure of services are related to population the plans and decisions can not be made purely as a direct function of population projections. Land use and infrastructure planning

- planning of regional structure - evaluating capacity requirements of regional public infrastructure investments - evaluating land use requirements of residential, industrial etc. land uses - transport demand forecasts and transport planning

Planning of regional and local public services

- planning of regional service networks - education supply planning

Planning of regional and local market services

- planning of market service network as part of regional land use planning - planning the location of major retail trade concentrations

Labour market policy

- evaluating and projecting regional labour supply (population and labour projections are linked)

Immigration policy

- immigration policy as part of labour supply policy Housing policy

- evaluation of housing demand for regional land use planning - special housing programmes

Promotion of the region

- marketing of the region for investors

4.2 The use of city and district level population projections At the city and district level the use of population projections is concentrated in the planning of services. The use of district level population statistics and projections is most active in age-dependent services with dense local service network, typically children’s day-care, primary schools and various services for the elderly. The planning of local services is the main use of the city and district level population projections also in other Nordic capital cities.

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Land use and infrastructure planning - master planning of land use - public transport planning - local infrastructure (energy, water, sewage networks etc.)

Planning of local public services

- the main use of district level population projections - service network planning, including investment and disinvestment planning - budget planning: for example In Stockholm the projections are also used as a tool to

allocate the city’s service budget to districts - education (pre-school, primary school, high school, vocational schools etc.) - children’s day care - health service - services for elderly - youth services

Planning of regional and local market services

- major retail trade corporations use actively district level population forecasts published by the city for network planning

Local community associations and other grass root organisations have also become active users of population projections. The quality, availability and costs of local public services have an important effect on the welfare of inhabitants, and it is a topical issue in the public discussion at local level. Inhabitants take actively part in the discussion of the local service supply. In most districts the population and consequently the potential users of the services are declining causing pressure to cut public services. In general, there is over-capacity in several services – especially schools and children’s day care – due to declining number of children. At the same time there is lack of services in several new residential areas. There is a continuing conflict between city management wanting to cut service capacity and to make disinvestments, and residents wanting to save existing services. Population projections are used intensively as a tool of this discussion. The results of projection calculations are often questioned of criticized by service users. This sets additional requirements to the reliability, availability and assumptions of projections. Population projections can also be considered as an important part of democratic local decision making.

5 Examples of recent projections The following review is based mainly on the City of Helsinki’s population projections published in 2005. The three alternative projections for the Helsinki region and the city of Helsinki are presented in figures 5.1 and 5.2. The projections differ with respect to the economic scenario in the background (slow growth, basic growth, fast growth).

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Population 1980-2005Basic projectionFast growthSlow growth

1000

450

475

500

525

550

575

600

625

1980 1990 2000 2010 2020 2030

Figure 5.1: The population of the Helsinki Region in 1980-2004 and alternative population projections to 2030 (slow, basic and fast economic growth)

Population 1980-2005Basic projectionFast growthSlow growth

1000

800

1000

1200

1400

1600

1980 1990 2000 2010 2020 2030

Figure 5.2: The population of the City of Helsinki in 1980-2004 and alternative population projections to 2030 (slow, basic and fast economic growth) According to the projections the population increase of the region will continue quite fast even within the slow economic growth. This is mainly because of the significant natural growth (births-deaths) thanks to still young population. On the contrary, in the city of Helsinki population growth is much more dependent on the economic performance of the region and its effects on migration. Consequently, the range of the alternatives with respect to population growth is rather wide.

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The regional structure of population is changing rapidly according to the projections. The weight of the growth has shifted from the central city to the fringe of the region and this trend is expected to continue as figures 5.3 and 5.4 show.

City of HelsinkiRest of the region

1000

300

400

500

600

700

800

900

1980 1990 2000 2010 2020 2030

Figure 5.3: The population in the city of Helsinki and the in the rest of the region (“Muu seutu”) in 1980-2004 and the basic growth projections to 2030

5 - 9 %

10 - 14 %

15 - 19 %

20 - 24 %

25 - 29 %

30 - 34 %

35 - 39 %

Figure 5.4: Projected population change by municipality in the Helsinki regions 2003-2030 (source: Statistics Finland, 2004)

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An important feature of the population development is the changing age structure. This is demonstrated in figure 5.5 showing the projections for 15-year age groups in the city of Helsinki. According to the basic growth projection the number of children under 15 years will be declining up to 2015 while the young adults (15-29 years) and middle-aged (45-59 years) start to decline around 2015. On the contrary, the age group 60-74 years is growing fast during the next 15 years and the oldest group (over 75 years) starts to grow fast around 2015 causing remarkable pressure to the municipal services targeted for elderly.

0-14 15-29 30-4445-59 60-74 75+

1000

0

25

50

75

100

125

150

2005 2010 2015 2020 2025 2030

Figure 5.5: Population by age in the city of Helsinki according to the basic growth projection to 2030 According to figure 5.6 the growth of elderly population will be even faster in other municipalities of the region than Helsinki and the present cap between the city of Helsinki and the rest of the region with respect to the proportion of elderly will gradually shrink.

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Vantaa

05

10152025

2005

2010

2015

2020

2025

2030

%

The Fringe of the Helsinki Region

0

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%

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2005

2010

2015

2020

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2030

%

The Fringe of the Helsinki Region

Vantaa

Espoo and Kauniainen

elsinki

Figure 5.6: The proportion (% of population) of eldHelsinki, Espoo and Vantaa and the fringe of theStatistics Finland 2004)

H

Helsinki

0

5

10

15

20

25

2005

2010

2015

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%

erly people (over 65 years) in the cities of Helsinki Region 2005 – 2030 (Source:

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6 Problems and development needs of population projections

6.1 Uncertainty The major problem of the regional and local population projections is their uncertainty. Basically the uncertainty increases (1) when the time span of the projections grows (2) the degree of the regional detail increases. In the age and sex specific population projections the uncertainty caused by the factors connected with mortality and fertility variation are basically similar at regional level as at national level. The special feature of the uncertainty of regional and local projections is the dominance of migration on population development. As figure 1.2 shows there are strong changes in the trend but also significant cyclical and random variation in annual net migration of the City of Helsinki but these features are typical for most regions and municipalities. In summary, the main source of uncertainty of regional and local projections is connected with migration. Especially the future changes in migration trends caused by structural changes in regional economy are extremely difficult to anticipate in population projections. In the stochastic approach to population forecasts uncertainty is described by calculating confidence limits for projections in addition to mean or median projections. This is a reasonable approach at national level where the role of migration is smaller. In the case of regional projections - in cases like the city of Helsinki - confidence limits would “explode” within 25-30 years period due to strong historical variation with respect to in and out migration. In addition, one can argue that the stochastic approach is not relevant to describe the uncertainty caused by structural changes of regional economy, like unexpected booms and busts of core industrials, or changes in immigration flows from other countries. One way to demonstrate the uncertainty of population projections is to compare the results of past projections with realised development, provided the past projections are made with a comparable projection model and data. The projections of the city of Helsinki have been made with the same approach and model since 1994. Results for the total population are presented in figure 6.1 and table 6.1. In total population, the recent migration trends have reflected the projection to one direction or another. Still, except the undervaluation of the future growth in the 1994 projection the projections have hit reasonably well in spite of the strong variation in migration. The speed of decline (due to out-migration of families with children) in the number of children have been undervalued systematically up to the 2000 projection. The number of aged population (over 75 years) were undervalued in 1994 and 1996 projections because the decline in mortality rate of aged people proceeded faster than expected. Table 6.1: The population of the city of Helsinki in 2004 and the projections made in 1994-2002 for the year 2004 in selected age groups Age True

population 2004

Projection made in 2002 for 2004

Projection made in 2000 for 2004

Projection made in 1998 for 2004

Projection made in 1996 for 2004

Projection made in 1994 for 2004

Total 559 330 563 800 566 100 563 400 557 600 544 200 0-15 87 683 86 700 89 000 90 400 91 800 90 800 75+ 35 921 36 000 36 100 35 500 34 400 33 518

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Popul. 1980-2004 Basic growthFast growth Slow growthProj.1994 Proj.1996Proj.1998 Proj.2000Proj.2002

1000

450

475

500

525

550

575

600

625

1960 1970 1980 1990 2000 2010 2020 2030

Figure 6.1: City of Helsinki population 1960-2004, the projections made in 2004 for years 2005-2030, and the past projections since year 1994

6.2 Problems and development ideas by the users The researchers and planners responsible for regional and local population projections are aware of the uncertainty of projections. Their problem is that they do not have sufficient tools to analyse, describe and quantify the uncertainty. In most cases also the users of the projections (typically regional and municipal land use, infrastructure and service planners) understand the uncertainty problem. However in practice they must base their plans and proposals to some reasonable quantitative data concerning the future. From their point of view population projections are one piece of uncertain information among a wide range of uncertain information. Instead, for the decision makers (regional and municipal managers and politicians), at least in the Helsinki Region, it seems difficult to understand the uncertainty of population projections (as well as projections concerning regional economy, labour markets, housing markets etc.) and the potential risks it causes to decision making. On the other hand, the information needs concerning future population at regional and local level are quite robust for most purposes. Even when the capacity needs and investments of regional infrastructure of services are related to population the plans and decisions can not be made purely as a direct function of population projections. For example, in regional or local transport planning the capacity need depend on several other factors in addition to population. In service planning the capacity and organisation of the service network depends, besides on the number of population, on the use intensity, pricing, alternative supply etc. The representatives of the Uusimaa Regional Council and the Cities of Helsinki and Stockholm expressed several views concerning the problems and development needs of population projections:

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Data

- the quality of statistical data (regional and local population and vital statistics) is good - there are problems in the reliability and availability of data concerning existing

housing stock and plans of future housing constructions (essential information for the prediction of new areas population)

Methodology

- the assumptions and their consequences made in the projection process should be expressed clearly and with good reasoning

- better tools for analysing the sources of uncertainty in projections are needed - more research concerning the trends and characteristics of migration is needed - the methodology to define the parameters and set the assumptions in the district

model should be improved - in the process of the Uusimaa Regional Council there are political pressures from

municipality leaders influencing the projections causing problems with objectivity Uncertainty

- methods dealing with uncertainty caused from several different sources and parameters should be developed

- the volatility of housing construction and the difficulty to forecast the realisation of local housing projects is a major uncertainty problem

- tools for describing and reporting the uncertainty factors to planners, decision makers and the inhabitants should be improved and open discussion about the problem should be increased

Use of projections

- users of population projections (planners, decision makers, inhabitants) need education to understand the nature, possibilities and risks of projections.

7 Conclusions and recommendations

7.1 Motivation for the use of regional and local population projections In regions and municipalities where regional and local population projections are systematically produced, like in the Helsinki Region, they are first of all used for various planning and policy purposes, strategic management, land use planning and service planning. Strategic management of regions and municipalities: Population is an important part of regional economy because of the inhabitants’ role as labour suppliers, consumers, users of local public services and taxpayers. Consequently, the size, structure and location of population are key factors in the middle and long run planning of the land use, service networks and the finance of the municipalities and regional organisations. At the same time the strategies often include objectives for population development, typically stable growth.

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Reliable and well reasoned population projections serve as a base for reasonable and democratic planning and decision making. However, there are sometimes unrealistic illusions among regional and local decision makers about the possibilities to control population developments. In the long run there is a close link between land use planning – especially land allocation for housing construction – and migration. This is especially true in an urban area where there is growing demand for labour and consequently growing demand for housing. For example, in the city of Helsinki the population decreased in 1970s (see figure 6.1) and population projections were made showing the consequences for the age structure if this trend would continue. This caused severe discussion in the city and a new master planning process with the aim to increase significantly land for housing construction. This new policy helped to turn the population trend to growth again in 1980s. Still, in a stagnating region just allocating land for housing is not normally a sufficient instrument to affect population developments if the regional economy is in depression. Planning of service networks: The major part of public expenditure in Finland goes to the supply of regional and local public services, health, social, educational, cultural and leisure services. The demand for services depends on the size, age and socioeconomic structure, and the location of population. Consequently, the supply networks are local in most services. The quality, availability and costs of local public services have an important effect on the welfare of inhabitants and it is a topical issue in the public discussion at local level. Reliable and well reasoned population projections are needed for the planning and maintaining of optimal service networks (i. e. right capacity in best locations). In the city of Helsinki planning of service networks, including investment and disinvestment planning, is the most important use of the district level population projections. Service planning is an area where citizens and various local grass root organisations – in addition to politicians - take actively part in the discussion of the supply of services, especially when there are plans to close down some local services to reduce costs or cut over-capacity. This is easy to understand taking into account the crucial role of the services as a welfare factor. The results of projection calculations are often questioned or criticized by service users. This sets additional requirements to the reliability, availability and assumptions of projections. Population projections can also be considered as an important part of democratic local decision making. Planning of land use: Population projections are actively used in connection with other scenarios and forecasts for land use planning at regional and municipal level. They are used for the evaluation of land use needs for housing and other land use types and to plan infrastructure investments. In land use planning the time horizon is rather long but the data requirements are quite robust. Land use and infrastructure are more loosely connected with the size, structure and location of population than many local public services. Consequently, population projections are typically used as one data source among several others. However, reliable and well reasoned population projections are an essential requirement also for land use planning. Risks of unreliable projections: There are also risks of using population projections in regional and local planning and decision making. Political pressures may influence the projections, particularly if they are made by the planning or decision making organisations themselves. Especially, if population growth is a strong political objective for a region or a municipality there may be a risk to overestimate future growth in population projections. On the other hand, the political fears of the consequences of immigration may lead to the pressure to underestimate the number of immigrants in population projections. However, there is no evidence of these kinds of pressures in the case of the city of Helsinki which is the main focus in this study.

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The projections may fail for several reasons. The future developments in fertility and mortality rates are difficult to anticipate both at national and regional level. In the case of regional and local projections the structural changes in factors affecting migration are the major challenge. Overestimating or underestimating of future population change may lead to wrong decisions with respect to service networks, land use plans or infrastructure. However, in the case of the Helsinki Region or the city of Helsinki there is no evidence that biased population projections had caused poor decisions during the last 20 years. On the contrary, there is a lot of evidence of non-optimal decision making in the planning of service network due to ignoring the information of population projections. In some cases this has lead to local overcapacity of some services. However, these results cannot be generalized to other cities or regions or earlier time periods in the case of Helsinki.

7.2 Data and methodology Data sources: The availability and high quality of detailed population data is a necessary precondition for reliable regional and local population projections. Data is needed for two purposes. First, the cross-section population by age, sex and location of the starting point is necessary for the projections. Second, data of the various components of vital changes (births, deaths, migration) together with cross-section population from past years are needed to estimate the several parameters used in projection models. Population registers which cover all the population and are continuously updated by legally controlled administrative procedures provide the best basis for population data. Well functioning population registers in Nordic and several Central European countries make it possible to produce reliable and detailed enough population statistics needed for projections. However, there are major problems in the quality and availability of population statistics particularly in new EU-countries. The quality of population data sources should be improved in several countries as a precondition for the development of population forecasting. Especially the un-complete registration of out-migration is a major problem leading to biased statistics of cross section population and migration. The main problem in most population data systems in EU countries is the registration of out-migration. There are incentives for migrating people to in-register in the new country/region/municipality. On the contrary, in most cases there are no incentives to out-register in the old country/region/municipality. The most realistic solution to this problem would be organising the exchange of migration registration records within the EU. This would mean that the information of all in-migration records from the population registers of destination countries/regions/municipalities are delivered to the registers of source countries/regions/municipalities. Of course, there would be several legal and technical problems connected with this kind of system. However, this system has been in use within and between the Nordic countries for a long time and it functions well. Methodology: Better tools for analysing the sources of uncertainty in projections are needed. This requires systematic research and development of projection methodology. The use of stochastic approach should be studied also in the case of regional and local projections. Additional research concerning the trends and characteristics of migration at regional and local level is also needed The assumptions and their consequences made in the projection process should be expressed clearly and with good reasoning. This is important for the widening of the use of projections in decision making.

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7.3 Use of projections Spreading good practices: The good practices developed in countries, regions and municipalities where regional and local population projections are systematically produced and actively used should be spread and delivered widely. This need is not limited only to methodological and technical issues, like data sources and projection models, but also to the various potential uses of projections, communication between the producers, planners, politicians and citizen and the role of projections as part of democratic decision making. Communication between producers and users: The production of the projections by governmental organisations (like the national Statistical office) or independent research organisations normally guarantees the objectivity of population projections. On the other hand, if the producers remain totally outsiders with respect to the use of projections, the risk increases that projections are not considered reliable or they are totally ignored in decision making. There should be close communication between the producers, first hand users (typically planners), decision makers (managers and politicians) and the citizens. The planners and other officials who are the main users of the projections must know on what facts and assumptions the projections are based. They must also understand the risks of error in the projections. Also the politicians and the active inhabitants should be informed about the basic approaches, assumptions and risks of the projections. Producers should also accept the fact that the approaches and results of projections are criticized and be prepare to participate discussion. This is necessary to make the population projections an essential part of democratic decision making of local public services and land use.

References City of Helsinki Urban Facts (2004) Helsingin väestöennuste 2005-2030 (Population projection of the city of Helsinki 2005-2030). City of Helsinki Urban Facts Statistics 2004:18. Hanuchek, E. and J. Quigley (1978) An explicit model of intra-metropolitan mobility. Land Economis 54:411-429. Isserman, A. (1993) The right people, the right rates. Making population estimates and forecasts with an interregional cohort-component model. Journal of the American Planning Association 59: 45-64. Jones, H. (1990) Population Geography. Paul Chapman Publishing, London. Laakso, S. (1994) Helsingin väestökehityksen vaihtoehdot (The alternatives of population development in Helsinki 1994-2020). City of Helsinki Urban Facts discussion papers 1994:3. Statistics Finland (2004) Väestöennuste kunnittain 2004-2040 (Population projection by municipality in 2004-2040). WWW-publication. Uusimaa Regional Council (2004) Uudenmaan tulevaisuus 2035, utua vai totta? (The future of Uusimaa region, mist or true).

25

The use of demographics when designing and implementing spatial

and housing policies by the Dutch government

Draft Background Report to the Project

LOT 1: The use of demographic trends and long-term population projections in public policy planning at EU, national, regional and local level (VC/2004/440)

Harri Cruijsen4

DEMOCAST

1. Introduction The Netherlands has a long and rich history in spatial planning and forecasting. As one of the

densest and lowest countries of the world, urban and regional development has now already

for more than a century been one of the key policy areas for national, provincial and local

authorities.

Indeed the Housing Act of 1901 can be considered as the formal beginning of regional and

local planning and forecasting in the Netherlands (De Gans, 1997). It obliged all

municipalities of the Netherlands to assess and verify building regulations. This implied,

amongst others, the projection of future numbers of households and future housing demand.

Other areas in which regional demographic forecasts rapidly played a dominant role were:

town extension planning, regional planning, and the provision of schools (Bakker Schut,

1933).

In particular during the interwar period and in the three biggest cities (Amsterdam, Rotterdam

and the Hague), innovative and therefore fairly advanced and comprehensive sets of

demographic projections were compiled. In these days town planners were already convinced

that a sound plan had to serve the needs of several future generations (Angenot, 1935).

However, regional population forecasting in the Netherlands took on real public momentum

when the Committee for the Regional Population Forecasts (re)started its activities in

September 1946. The National Physical Planning Authority (NPPA), recently converted into

4 Contact: DEMOCAST, Vluchtheuvelstraat 2, NL-6621 BK Dreumel, Netherlands ; phone: +31-487-570687; Email: hvckroon@kabelfoon.nl

26

the Netherlands Institute for Spatial Research (RPB), became the authority responsible for

designing spatial plans. The production of a widely used and accpted set of regional

population forecasts by sex and age, consistent with the national population forecasts

produced by Statistics Netherlands, became a regular task of the NPPA since the end of the

1950s (Ter Heide, 1992).

During the 1960s this task was an essential part of the activities that were carried out on

behalf of the so called First and Second Memorandum on Spatial Planning, published in 1960

and 1966 respectively. Thereafter, the frequency of producing regional population forecasts

gradually increased, and since the early 1990s annual revisions have been made.

Also the spatial dimension changed over time. During the 1960s and 1970s NPPA was

basically producing a population projection at NUTS 2 level (i.e. for 12 provinces). Since the

early 1980s a comprehensive monitoring and projection system, called PRIMOS, has being

developed and applied to compiling population and households projections at NUTS-5 level

(i.e. for wel over 400 municipalities).

Finally, the time horizon and the handling of uncertainty has been modified. Instead of 15-20

years ahead and purely deterministically, one now ’dares’to look 25-30 years into the future

with a stochastic model.

However, at least one element remained constant over time: the component interregional

migration has always been predicted by using national population redistribution policies

and/or short and medium term programmes for new houses. Indeed these official regional

population projections have been used for a long time as a starting point and/or bench mark

for negotiations and decisions between national and regional authorities on the number and

composition of new dwellings needed. And additionally, on the need for new infrastructural

works, regional health and social care facilities, educational services, etc.

This paper aims to examine the contemporary use of these PRIMOS projections for spatial

policies in general, and housing policies in particular. Due to time constraints and for reasons

of relevance and efficiency, only a restricted number of important policy documents produced

by the national government during the period 2000-2005 has been investigated. Neither the

numerous applications of PRIMOS by regional and local authorities nor the use of any other

(often competitive) set of regional demographic projections have been studied. In other

words, we have only explored the ’top of the iceberg’.

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Before to answer the basic research questions – if, and how these projections have been used

– a summary will be given of the latest set of PRIMOS projections , including a brief

presentation of the projection methodology used.

2. Latest PRIMOS projections The latest set of regional demographic projections using the PRIMOS system has been

determined in October 2004 (ABF, 2005). These so called PRIMOS 2004 projections cover

the period 2004-2030, and comprise the following demographic elements at NUTS 5 or

municipal level5 (RPB, 2003):

Population by sex, single years of age and household position (alone, living together

with partner but without children, living together with partner and child(ren)), lone

parent, child living at parental home, other, and living in collective household);

Households by type (one-person household, household with one couple without

children, household with one couple and at least one child, lone parent family,

collective household).

Two additional non-demographic subjects have been projected:

Dwellings by type (rented/private, and one-family vs. multiple-family);

Qualitative demand for houses by type (rented/private, and price level).

A dynamic, cohort component projection model has been used, i.e. all relevant transitions are

annually predicted for every single sex and age population category. Apart from future

fertility, mortality, and migration (both interregional and international), also sex and age

specific changes due to household formation and dissolution have been projected.

In order to achieve consistency with national official forecasts, the municipal assumptions on

all demographic components but interregional migration were basically ‘borrowed’ from the

2003-based national population and household forecasts produced by Statistics Netherlands:

by applying, recently observed patterns of differences between municipal and national rates

and numbers, PRIMOS’ input on fertility, mortality, international migration, family formation

5 At the beginning of 2004 the total number of municipalities in the Netherlands amounted 489.

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and family dissolution was generated. Apart from fertility, no convergence is assumed, i.e.

municipal differences are expected not to change in the future.

With respect of future migration flows between municipalities by sex, age and household

position, a separate, fairly complex set of projection models has been applied.

Firstly, interregional migration flows between regions at NUTS 3 level are projected. Three

major groups of interregional migrants are distinguished: those who primarily move due to

work, education, or any other reason (e.g. due to pension or elderly care). The annual,

municipal departures and arrivals of the first two categories of migrants have been projected

by using forecasts of relevant non-demographic determinants. Interregional migration flows

due to work are supposed to be predominantly determined by regional changes in labour

supply and labour demand. Migration due to education is modelled by using expected

numbers of first year entrants for higher and university education, and the municipal

availability of these types of educational facilities. Migration due to ‘ageing’ is estimated by

using entry rates for elderly collective households.

During the second step, additional, basically short distance migration flows are projected.

These flows are primarily caused by household formation/dissolution, and the search for

better houses. The respective annual numbers are derived from the outcomes of the municipal

household forecasts and a sub model that simulates the housing demand of existing

households.

In the third step, the supply side of the regional and local housing markets is projected. The

expected numbers of new houses are brought in, as well as the numbers of houses that are

expected to become available due to already predicted departures or dissolutions of

households.

During the final step, the demand and supply side of regional and local housing markets are

combined. By means of in iterative process, consistency is achieved, and final annual

numbers of municipal migrants are determined.

The uncertainty of future developments is handled in a semi stochastic way. Two third

confidence intervals are prepared for the following components:

Fertility, mortality and international migration: these were taken over from the

official national population forecasts;

29

Household formation and dissolution: partly derived from the official national

household forecasts;

Headship rates of elderly for those who continue to live in a private household and

those who (need to) live in a collective household;

Regional distribution of growth of labour demand.

3. Have Dutch governmental spatial and housing policy

makers been using PRIMOS projections? Officially the Minister of Housing, Spatial Planning and the Environment is the ultimate

responsible person of designing and implementing spatial policies in the Netherlands.

However, both in the phase of preparation and in the phase of execution of these policies

many other authorities and institutions play an important role: the Netherlands Institute for

Spatial Research (RPB), the Central Planning Agency (CPB), the National Institute for Public

Health and the Environment (RIVM), the Scientific Council for Government Policy (WRR),

the national committee of experts on spatial planning and housing (VROMRaad), the

provinces, the municipalities, the universities, the private investment or development

companies, and finally the construction companies.

New or revised national long-term spatial planning strategies are generally prepared and

decided in three steps. First the advisory bodies mentioned are consulted and requested to

produce policy recommendations, Thereafter, and often simultaneously the Ministry of

Housing, Spatial Planning and the Environment, together with other relevant Ministries, start

to draft a document comprising proposals of improved future directions of spatial planning,

including different sets of future strategies and scenarios. Finally, there are discussions first

with citizens, market parties and other experts, followed by draft final proposals of the

Council of Ministers, and eventually final political agreements and decisions between the

Council and the Parliament.

The short and medium term spatial policies, especially those concerning housing, are

basically designed and implemented by municipalities in close co-operation with the

provinces. However, in spite of the ongoing process of decentralisation, the Dutch national

government has still significant powers to steer and control regional plans and decisions.

Amongst others this latter influence is articulated by a comprehensive, fairly detailed spatial

policy document, regularly produced by the Council of Ministers and to be discussed and

30

adopted in the Dutch Parliament. The last one, simply called General Memorandum on Space

(‘Nota Ruimte’) has been very recently prepared, agreed and published (Tweede Kamer der

Staten-Generaal 2005). It can be regarded as the latest official master plan of the government

on how the Netherlands should continue to optimise the use of its spatial possibilities for the

sake of a strong economy, a safe society with good living conditions, and to remain an

attractive country.

The time horizon basically covers the period 2005-2020, but there are some windows opened

until 2030. Apart from a more general chapter on the principal goals, the overall philosophy

of steering chosen, and the instruments of planning to be used, the final document comprises

numerous proposals to improve existing networks and cities, water management systems and

so-called Green Space. Furthermore, future plans for increasing the quality of more specific

areas and themes are presented (i.e. the Western part of the country (‘Randstad’), the coast,

energy supply, spatial policy for the ‘underground’).

Searching in this highly important but rather bulky spatial policy document (well over 200

pages!) for the use of the (latest) PRIMOS projections yields just one reference: in the chapter

on ‘Networks and Cities’, the expected national need for new houses is quantitatively

expressed for the periods 2000-2009, 2010-2019, and 2020-2029 based upon figures of

PRIMOS 2003. However, in the explanations of the use of these forecasts, one explicitly

states that the government has eventually chosen for a higher future housing demand scenario

to avoiding bad surprises. One continues to say: ‘in applying too low expectations one would

be confronted with risks such as shortage of space for vital functions, insufficient quality of

the supply side, flexibility and competition of the labour and housing markets’. Finally, one

adds that these estimates will be updated periodically.

More in general this document hardly uses demographics. Just a few, fairly general remarks

on the consequences of some key demographic trends have been made. For example in the

general, first chapter one notices that the spatial, migratory behaviour of the population will

probably change due the expected diminishing population growth and ongoing increase of

diversity within the Dutch society. Surprisingly, throughout the whole document nothing is

said about the possible impact of future population ageing on future spatial needs. Also the

existence and persistence of regional demographic differences is not explicitly mentioned.

The only relevant policy statement on deviant regional demographic circumstances one can

find is that in national protected areas with significant population decline, the leading zero

migration planning principle could be abandoned so that under certain conditions more houses

could be built.

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In order to get a more detailed and therefore more representative sample survey, two

somewhat older principal spatial and housing policy documents produced by the Dutch

government have been examined. Both of them are closely related to the General

Memorandum on Space, and therefore can be seen as preparatory to what the Dutch

Government and Parliament recently have discussed and decided.

Actually the procedure for the preparation of the latter document has started with the

Cabinet’s ratification of the draft Fifth National Policy Document on Spatial Planning on

December 15th 2000. This document, entitled ‘Making Space, Sharing Space’, contains the

national government’s principal spatial planning policy proposals for the period 2000-2030

(Ministry of Housing, Spatial Planning and the Environment 2000).

It uses in a qualitative sense and therefore as general background information several

demographics ranging from the differences in the age composition of the population of the

Netherlands with that of other European countries, and the changes in the (average) size of

households. At national level, latest official point forecasts on the total number of inhabitants

and households for the year 2030 are presented.

Long-term space requirements up to 2030 were estimated using a high demand forecast

mainly based upon the ‘Global Competition’ scenario drawn up by the National Central

Economic Planning Agency (CPB), but supplemented by a sharp population increase as a

result of immigration, and extra space for water, nature and recreation. However, one has also

examined the long-term consequences for space needed for homes, workplaces and

infrastructure if the population and economy would not grow as much as foreseen in the high

demand forecast. The results show considerable margins, but no specific policy conclusions

have been drawn thereof.

The future demands for space have also been translated into regional needs. Three different

projection models have been applied to generate future distributions at NUTS 1 level. None of

them refers to the latest PRIMOS projections.

The principal housing policy document selected carries the meaningful title ‘What people

want, where people live? – Housing in the 21st Century’ (Ministry of Housing, Spatial

Planning and the Environment 2001). This document can be regarded as the successor to the

policy document on Housing in the Nineties (Ministry of Housing, Spatial Planning and the

Environment, 1989), and as the first and probably also the most influential contribution of the

32

housing specialists of the Ministry of Housing to the General Memorandum on Space.

However, the time horizon is basically 2000-2010.

It comprises numerous references to demographics. The ongoing but diminishing population

growth, the acceleration of population ageing around 2010, the ongoing increase of the

number of households, the ongoing decrease of the average household size, the increasing

share of single-persons households, the growing proportion of people of non-Western

ethnicity are key demographic trends mentioned under the heading ‘macro developments’.

By using 2000-based national population and household forecasts, and extrapolating the

results of Housing Needs Survey 1998 the need for new houses and space for the so called

residential environment is predicted for the period 2000-2010. Principal conclusion: 35%

more space will have to be reserved through 2010 than provided for in the existing plans.

Another important policy conclusion basically driven by the use of demographics is that

number of people qualifying for assisted housing will burgeon over the coming decades.

No unique use has been made of the national and regional outcomes of the PRIMOS

projections. At both national and sub-national level (e.g. NUTS 1), one has quantified the

impact on the housing market of five different sets of long-term population and household

scenarios. However, no explicit policy conclusions on the significant differences presented

and therefore on the considerable risks and uncertainties shown have been drawn. The only

response that one may observe is that (again) the long-term outcomes of the most extreme,

space demanding scenario got most attention in the report.

4. Conclusions and recommendations

The Dutch Government has been intensively using demographic trends and projections for the

preparation of its latest set of long-term spatial and housing policies and strategies. Especially

available information on the future size and composition of households is applied; the

changes of the population in age and ethnicity are less often mentioned. The time dimension

of future analysis often stops in 2020, for some issues 2030 is the end year. The regional

dimension halts at NUTS 1 level, and therefore detailed (i.e. municipal ) regional

demographics seem not to be used.

The uncertainties of future developments have been made explicitly by examining the

consequences of various different sets of deterministic demographic scenarios. Until now the

the available two thirds confidence intervals produced by PRIMOS seem not to be applied.

33

Also no explicit proposals or conclusions have been formulated concerning the way to

incorporate uncertainty in spatial policy decisions

Three recommendations can be made:

1. Spatial consequences of a rapidly ageing and increasingly multicultural society should

be more intensively and quantitatively examined;

2. Due to the generally long time reaching impact of decisions on spatial use and

requirements, as well as due to the availability of national stochastic population and

household forecasts, the time horizon of a number of underlying national issues can be

expanded to 2050;

3. Serious efforts should be made to translate stochastically produced demographic

projections into spatial policy conclusions of future risks.

34

References ABF, 2004, PRIMOS 2004 – De toekomstige ontwikkeling van bevolking, huishoudens en

woningbehoefte (‘PRIMOS 2004 - The Future Development of the Population, Households and Housing Need’).

Angenot, L.H.J., 1935, Over de toekomstige omvang van de woningvoorraad in Nederland

(‘On the Future Size of the Housing Stock in the Netherlands’). In: Tijdschrift voor Volkshuisvesting en Stedebouw, Vol.16, nr.11, pp.196-203.

Bakker Schut, P., 1933, De bevolkingsbeweging in Nederland en in het bijzonder te ’s-

Gravenhage (‘Population Dynamics in the Netherlands and in The Hague in particular’). Samson NV, Alphen a/d Rijn.

Gans de. H.A., 1997, Demographic Forecasting in the Netherlands 1895-1945; The Analysis

and Implications of a Paradigm Shift. Amsterdam. Ministry of Housing, Spatial Planning and the Environment, 2000, Fifth National Policy

Document on Spatial Planning - Making Space, Sharing Space. Ministry of Housing, Spatial Planning and the Environment, 2001, What People Want, Where

People Live – Housing in the 21st Century. RPB, 2003, PRIMOS Prognose 2003 – Prognosemodel voor bevolking, huishoudens en

woningbehoefte (‘PRIMOS Forecast 2003 – Forecasting Model concerning Population, Households and Housing Need’).

Ter Heide, H, 1992, Bevolking en ruimte: verschuivend perspectief in onderzoek en beleid

(‘Population and Space: Shifting Perspectives in Research and Policy’). In: Bevolking en Ruimte, Nederlandse Vereniging voor Demografie, ‘s-Gravenhage.

Tweede Kamer der Staten-Generaal, 2005, Nota Ruimte (‘General Memorandum on Space’).

SDU, ’s-Gravenhage.

The use of demographic trends and long-term population projections in public policy planning at EU, national, regional

and local level

Chapter 6. Population Prospects of Turkey, 2005-2050

A Critical Assessment from an EU Perspective

by Juha M. Alho and Harri Cruijsen

Turkey2/26.7.05

Population Prospects of Turkey, 2005-2050.

A Critical Assessment from an EU Perspective

Juha M. Alho and Harri Cruijsen

1

Contents

Summary

1. Background

2. Components of Growth in Turkey and the EU in 1950-2000

2.1. Comparison of Growth Rates

2.2. Effect of Fertility and Mortality

3. Geographic Differences in Fertility in Turkey in 2003

4. Forecasts of Turkey and EU Until 2050

4.1. Changes in Past U.N. Forecasts

4.2. Plausible Alternatives

5. Assessment of Uncertainty

6. Comparison of Predictive Distributions

6.1. Revised Point Forecasts for Turkey

6.2. Turkey as Compared to the Largest EU Countries

7. Turkey’s Uncertain Demographic Future and Its Implications for Policy Planning in EU

Countries

References

Appendix. A Revised Point Forecast of Turkey in 2030

2

1. Background

When Poland and nine other countries joined the European Union (EU) in May 2004,

the total population of the EU increased from 383.4 million by 74.1 million, or by 19.3 %

(Eurostat 2004, NIDI 2005; own estimates). For comparison, the population of Turkey was

estimated to be 71.6 million, and therefore it also represents almost one fifth of the “old EU”.

Together Turkey and the ten new member states of 2004 have a population that is

approximately 38% of the “old EU”. The relative size of Turkey is expected to increase,

because its population is growing faster than that of the EU countries. It is of interest to

assess, how much faster.

Moreover, Germany with 82.5 million inhabitants is currently the only EU country

with a larger population than Turkey. The apportionment of political power within the EU is

roughly proportional to population size. Therefore, if Turkey eventually joins the Union, it

may be entitled to a larger share of political power than any of the other member states. By

itself, this fact may be a factor in the membership negotiations. It is of interest to assess

whether Turkey is likely catch up with Germany. Moreover, should Turkey eventually become

a member, it would be entitled to agricultural and other structural subsidies of the Union.

Whenever the allocations depend on population size directly or indirectly, the accuracy of

Turkey’s regional population estimates becomes an issue of considerable EU relevance.

Whether Turkey eventually does or does not join the Union, it will be a major factor

in the future demographic and economic development of Europe. In particular, Turkey will

have a younger age-distribution, so it can serve as a source of labor for the aging Europe. On

the other hand, losing working age population to other countries may have undesirable effects

on Turkey itself, for Turkey is also expected to age rapidly. It is of interest to assess the speed

3

of aging in Turkey relative to that of the EU countries.

Many uncertainties affect any assessment of future population trends in Europe, let

alone in Turkey. Past errors in population forecasts (e.g., Keilman 1997) demonstrate

conclusively that no one can say with certainty what will happen. Policy makers need to be

advised about the level of error one should expect (Alho and Spencer 2005, p. 227 and

Chapter 11). In this paper we will provide a probabilistic “back-of-the-envelope” assessment

of uncertainty that relies on direct information of the accuracy of past Turkish forecasts (as

discussed in Bongaarts and Bulatao 2000), and on recent work of the UPE project (UPE

2004).

2. Components of Growth in Turkey and the EU in 1950-2005

2.1. Comparison of Growth rates

Population growth is a function of three factors. First, intrinsic growth is determined

primarily by fertility and secondarily by mortality. The so-called intrinsic growth rate gives us

the annual rate of population growth that would follow if the fertility and mortality conditions

of a given calendar year would persist indefinitely, and the population would be closed to

migration. Second, the actual growth rate of even a closed population would be influenced by

the age-distribution. If there are many women in childbearing ages (say, 15-49, and most

importantly in ages 20-35) then even a low level of fertility can produce a large number of

children and positive growth. Third, net-migration obviously influences population. In

particular, it influences the size of the working age population, because migration intensities

typically are the highest in ages 20-30.

Table 1 presents the changes of the net reproduction rate (NRR), and annual

4

population growth rate (AGR) for Turkey and the largest EU countries over the period 1950-

2000. NRR is defined as the expected number of baby girls a new born baby girl is expected

to have under the fertility and mortality conditions of a given period. When NRR = 1, the

population reproduces itself exactly, when NRR > 1, the population more than reproduces

itself, i.e., it grows, and when NRR < 1, the population does not reproduce itself, i.e., it

declines. If the mean age at childbearing were known, the intrinsic growth rate could be

calculated. Taking the mean age at childbearing to be 28, a very rough approximation is that

the intrinsic growth rate is (in percentage terms) . 100×(NRR - 1)/28.

Table 1. Net Reproduction Rates (NRR) and Actual Growth Rates (as Percent, AGR) for

Turkey and Selected EU Countries, in 1950, 1975, and 2000.

NRR AGRPeriod 1950 1975 2000 1950 1975 2000

Turkey 2.29 2.53 1.16 2.72 2.42 1.66France 1.26 0.92 0.91 0.75 0.38 0.50Germany 0.85 0.70 0.67 0.56 -0.53 0.12Italy 1.09 1.05 0.58 0.64 0.53 0.28Poland 1.52 1.06 0.75 1.89 1.00 -0.02Spain 1.17 1.32 0.60 0.83 1.08 0.97United Kingdom 1.02 0.86 0.79 0.23 -0.03 0.45

Sources: 1950 - United Nations 2004 (averages for the period 1950-1955)1975, 2000 – Council of Europe 2003

The findings are unequivocal. All large EU countries have continued to grow during

the latter part of the 20th century but in all of them intrinsic growth has been, or has turned into

negative by the end of the century. The implied rate of decline varies between 0.3 per cent

5

(France) and 1.5 per cent per year (Italy). For the EU it is known that all countries have

become net receiving countries during the period 1950-2000. Thus the growth shown in the

recent past is due both to net migration and to the (still) favorable age-distribution. Turkey has

also grown, but at a rate that is about three times as large as that of Europe. This has been

driven by the strong intrinsic growth that is still almost 0.6 percent at the turn of the century,

despite the steep decline in fertility. Past high fertility has produced an age-distribution that

will be favorable for growth for the next 50 years, at least.

Reliable information about the effect of migration on population growth of Turkey

seems not to be available. Official figures stored in the demographic database of the Council

of Europe show that, apart from the year 1974, Turkey has been an out-migration country

during the period 1961-1980, with an accumulated population loss of close to 1 million

(Council of Europe, 2003). According to these data Turkey has been an in-migration country

since 1981. During the period 1981-2000 a total net inflow is estimated as 2.7 million people.

Peaks of annual net migration levels of well over 200,000 persons are reported for the years

1984, 1989, and 2000. In 1995-2000 the average annual net inflow is estimated at 120,000.

In contrast, the United Nations reports much lower net immigration estimates for

Turkey. According to the database used for the UN World Population Prospects 2004, Turkey

would have experienced during the period 1995-2000 on average a net inflow of no more than

27,000 per year. It is not entirely clear where the discrepancy originates.

In a supplementary technical note and in the Council of Europe’s Demographic

Yearbook 2003 one can find some information on the problems in measuring Turkey’s

migration flows. Population censuses (every ten years), border statistics, and residence and

work permit registrations are used as principal data sources on immigration. Estimates of

emigration are based on the data of Turkish citizens living abroad as collected by the Ministry

6

of Labour and Social Security. Hence, it is most likely that both immigration and emigration

flows are underestimated, but emigration flows more heavily so. In such a context, census

survival methods are probably the most reliable source of information on net migration.

Unfortunately, the resulting residual estimates of net migration are known to be often

unreliable due to possible errors in the estimates of vital rates, changes in the accuracy of the

censuses, or both. In particular, a decrease in census undercount leads to an artificial increase

in the estimates of net migration. This may well be the case in Turkey.

We tentatively conclude that it is likely that in the pre-1980 period negative net

migration probably attenuated the actual growth rate of Turkey, but since then net migration

may have had a neutral or a slightly positive effect.

Although the time series shown in Table 1 are too short for a reliable assessment, we

see that among the countries considered Turkey resembles most closely Poland albeit with a

lag of some 25-50 years.

2.2. Effect of Fertility and Mortality

The most commonly used summary measure of the level of fertility is the total

fertility rate. It is defined as the expected number of children a woman will have in her

lifetime provided that she survives to the end of childbearing ages and that the age-specific

fertility rates of a given period persist. The most commonly used measure of survival is life

expectancy at birth. It is defined as the expected length of life of a new born provided that the

mortality rates of a given period persist.

Table 2 describes changes of the total fertility rate and female life expectancy at birth

in Turkey and the largest EU countries for the period 1950-2000. We see the rapid decline in

7

fertility to below replacement value, which is currently slightly below 2.1 in the EU countries.

This confirms that the decline in intrinsic growth is primarily due to fertility. Life expectancy

has increased by roughly 10 years, or by about 0.2 years per calender year. In Poland both

changes have been faster than elsewhere. In Turkey, however, fertility remains well over

replacement level, despite the nearly halving of the TFR. The increase in life expectancy has

been phenomenal, approximately 0.5 years by calender year.

Table 2 supports the findings from Table 1. Turkey has followed Poland, with a lag

of several decades, and the other EU countries with a lag of half a century. Although not

detailed in Table 2, this holds for infant mortality, as well.

Table 2. Total Fertility Rate (TFR) and Female Life Expectancy at Birth (FLE) for Turkey and

Selected EU Countries, in 1950, 1975, and 2000.

TFR FLEPeriod 1950 1975 2000 1950 1975 2000Turkey 6.90 5.09 2.57 45.2 58.3 70.4France 2.73 1.93 1.88 69.5 76.8 82.7Germany 2.16 1.48 1.38 69.6 74.4 81.0Italy 2.32 2.21 1.24 67.8 75.7 82.5Poland 3.62 2.26 1.34 64.2 74.3 77.9Spain 2.57 2.80 1.24 66.3 76.1 82.5United Kingdom 2.18 1.81 1.64 71.8 75.2 80.2

Sources: 1950 - United Nations 2004 (averages for the period 1950-1955)1975, 2000 – Council of Europe 2003

8

3. Geographic Differences in Fertility in Turkey in 2003

Turkey Demographic and Health Surveys 2003 (TDHS-2003) has established

estimates of both age-specific and total fertility in Turkey by region based on a statistical

sample survey. Based on the report, the survey appears to have been carried out in a

technically sophisticated manner, so we regard its results as more reliable than direct estimates

obtained from the vital registration system.

The national estimate of the TFR is 2.23 children per woman (TDHS-2003, Table

4.1, p. 46; standard error of the estimate is SE = 0.05 children, p. 191). A comparison to Table

2 indicates a decline in 3 years of well over 1/3 child, or 13%. In rural areas total fertility is

still 2.65, but in urban areas it already below replacement at 2.06 (TDHS-2003, Table 4.1, p.

46; SE’s are 0.13 and 0.6, respectively, pp. 192-193). In Istanbul it is even lower, 1.83 (SE =

0.09, TDHS-2003, p. 199), but in Southeast Anatolia it remains at 4.19 (SE = 0.24, TDHS-

2003, p. 210).

The survey also inquired about the ideal number of children from women in ages 15-

49. In the country as a whole the number was 2.51 (SE = 0.020, TDHS-2003, p. 191), in urban

areas it was 2.45 (SE = 0.02, TDHS-2003, p. 192), and in rural areas it was 2.65 (SE = 0.042,

TDHS-2003, p. 193). In the leading Istanbul it was 2.34 (SE = 0.043, TDHS-2003, p. 199)

and in Southeast Anatolia it was 3.43 (SE = 0.091, TDHS-2003, p. 210).

Both the national trends and regional comparisons of actual and desired family size

suggest that a rapid decline in fertility is taking place.

9

4. Forecasts of Turkey and EU Until 2050

4.1. Changes in Past U.N. Forecasts

The U.N. has the unenviable task of producing population forecasts for all countries

of the world. The forecasts are known to be produced by highly qualified experts and the

methods used are generally considered to be sound (e.g., Bongaarts and Bulatao 2000).

However, together with all other forecasts, the U.N. forecasts are confined to the common

database of demographic information that is available at the time the forecast is made. Both

the rapid increase in life expectancy and fast decrease in fertility have caused important

revisions in both European forecasts and forecasts of Turkey. In addition, the U.N.

occasionally revises its basic outlook on some aspects, such as decline in fertility, or level of

net migration.

Table 3 compares the assumptions and results of two recent forecasts of the U.N., the

1998 revision and the 2004 revision (U.N. 1999, U.N. 2005). From column TFR we note first

that there has been relatively little change in the U.N. views regarding fertility in 2040-2050.

From column FLE we see that as regards mortality, the U.N. has become more optimistic over

time, although the long-term assumptions on life expectancy at birth for Turkey has been

lowered somewhat in the most recent revision as compared to the 2002 revision (details not

shown). However, the most significant revisions have been made for international migration.

From column NMR we see that earlier it was assumed for most countries that net migration

would level off to zero, but now this assumption has been abandoned. (For Germany the

change occurred by 1998 already.) In the EU countries, except in Poland, positive net

migration is expected. To appreciate the order of magnitude of the change, note that an

increase in the annual net immigration level of 1/1,000 accumulates in 50 years roughly to a 5

10

percent increase in the forecast. For Italy and Spain the impact of persisting net inflow on the

population size at 2050 is particularly visible in the column POP.

Table 3. U.N. Forecast (Medium Variant) of Total Population in 2050 (POP; in millions), and

Total Fertility Rate (TFR), Female Life Expectancy at Birth (FLE), and Net Migration Rate

(NMR, per 1,000) in 2045-2050, for Turkey and Selected EU Countries, in the 1998 Revision

and 2004 Revision.

Revision POP TFR FLE NMR

Turkey 1998 100.7 2.10 81.0 0.02004 101.2 1.85 80.1 -0.3

France 1998 59.9 1.96 86.0 0.02004 63.1 1.85 88.0 0.9

Germany 1998 73.3 1.64 84.5 2.72004 78.8 1.85 86.5 2.5

Italy 1998 41.2 1.66 85.3 0.02004 50.9 1.85 88.1 2.3

Poland 1998 36.3 1.90 83.0 0.02004 31.9 1.76 83.8 -0.5

Spain 1998 30.2 1.68 85.5 0.02004 42.5 1.85 88.3 1.4

United Kingdom 1998 56.7 1.90 84.4 0.02002 67.1 1.85 85.4 1.9

An additional conclusion one can draw from Table 3 is that the expert view of events

50 years into the future can change dramatically within a five year period. In the case of Spain,

the best estimate of population increased by over 40 %, for example!

11

4.2. Plausible Alternatives

Column POP of Table 3 indicates that the U.N. has revised its forecast of the EU

countries (except Poland) up, primarily by anticipating positive net migration. Life expectancy

is also expected to improve slightly more than earlier. The expected growth of Turkey, in

contrast, is more or less the same.

While the updated U.N. forecast appears to be a step in the right direction for the EU

countries, one can question several assumptions. For example, should the below replacement

value 1.85 that has been used for all countries be considered as the most plausible long-term

assumption? On average this is clearly higher than the current levels in the large EU countries.

There is no empirical evidence behind the assumption, so it may be too high. Also, one may

criticize the mortality assumptions. Over the past 4-5 decades increases in life expectancy

have repeatedly taken population forecasters by surprise (in fact, this was the case in the

forecasts made before World War II, as well). These factors have tended to underestimate the

expected process of aging. Finally, while the U.N. now does allow for positive net migration

to persist, the levels are clearly lower than those observed in the recent past. This increases the

expected size of the total population as compared to the zero assumption, but not as much as

one would expect based on the recent past level of net migration, which is higher still. The

implication is that while the positive net migration assumption slows down the expected

process of aging of the population age-distribution as compared to the zero assumption, it

does not do it to an extent would expect based on the most recent past level of net migration.

In the UPE (2004) forecast alternative assumptions were made that correct for the three

factors.

In the case of Turkey the same three factors come into play. Although Turkey’s

12

fertility is still below replacement, those same factors that have caused fertility in Poland, and

other countries of the former Soviet Union, to fall to very low levels may be in operation in

Turkey. Therefore, we expect total fertility not to stop at 1.85 but to continue to decline to a

lower level. Similarly, we expect that life expectancy increases faster than anticipated by the

U.N. In the case of net migration we have to somehow accommodate the conflicting views of

the national statistical agency and the U.N. The Turkish State Institute of Statistics has

assumed in its latest set of national population forecasts an average annual net migration of

100,000 in 2000-2005, and declining to zero by 2020. Our estimates, to be presented in

Section 6, that take the U.N. forecast as a starting point will incorporate such adjustments.

5. Assessment of Uncertainty

Appendix F of Bongaarts and Bulatao (2000) presents a statistical (random effects)

model for the errors of U.N. forecasts of all countries of the world, with jump-off times during

1970-1995. The world was divided into ten regions. With the exception of Poland, the EU

countries considered here belonged to “Southern, Western and Northern Europe”. Poland and

Turkey were included into a region comprising the former Socialist countries around Soviet

Union. The grouping was used to stabilize statistical estimation by “borrowing strength” from

other countries within the same region. Yet, all countries did have their own uncertainty

parameter that reflected the specific conditions of the country. Based on the error analysis, we

can estimate how large errors should one expect in future forecasts, if uncertainty would

remain as large as (but not larger than) in the past.

A preferred way to express forecast uncertainty is to use the language of probability

theory. The mean or median of a predictive distribution of future population describes the

13

most likely value, and spread around the center gives an indication of how uncertain the

forecast can be expected to be. Without going into details, we note that in practice, uncertainty

will be estimated from past data to match the level of error in the past forecasts (cf., Alho and

Spencer 2005, pp. 251-264).

In this illustration uncertainty is expressed as proportion to the median so, e.g., the

value 0.10 would correspond to a ten percent error, or the chances would be about 2/3 that the

future value would be within ± 10 % from the most likely value.

Table 4. Approximate Standard Deviation of the Predictive Distribution of the Total

Population for Lead Times 10, 30, and 50 Years into the Future Based on Errors of U.N.

Forecasts, and as Estimated by the UPE Project for the Year 2030.

U.N. Forecast Based UPELead Time/Year 10 30 50 2030

Turkey 0.017 0.086 0.183 N.A.France 0.014 0.060 0.120 0.063Germany 0.022 0.095 0.194 0.067Italy 0.012 0.050 0.103 0.085Poland 0.016 0.079 0.167 N.A.Spain 0.031 0.129 0.264 0.080United Kingdom 0.015 0.065 0.132 0.060

N.A. = Not Available.

Table 4 contains unpublished findings from the study. We note that as regards the

accuracy of the past U.N. forecasts, forecasting in Turkey has been, in relative terms,

approximately as difficult as in Germany and Poland, easier than in Spain, but more difficult

than in the other countries considered. As with any study of forecast accuracy one has to take

into account that the findings are specific to the time period considered. Countries do go

14

through periods of relative turbulence and calm. (e.g., Alho and Spencer 2005, pp. 221-223,

259-261).

For comparison we present the detailed analysis in UPE (2004) for the year 2030.

This can be roughly compared to lead time 30, but one should note that the forecast has jump-

off time 2003 instead of 2000, so it actually represents a lead time of 27 years. We find a

fairly close agreement on average, but note that the UPE values are closer together. This is

what one would expect: in some countries errors should be larger than in expected, in other

smaller than expected, when assessed after the fact.

As far as we know the accuracy of Turkish forecasts has not been studied earlier, so

the evidence presented here suggests that intuition gained from more detailed studies in the

EU countries (e.g., UPE 2004) should carry over to Turkey. The largest uncertainty indicated

by UPE (2004) for the EU countries considered here are for Spain and Italy. These are in large

part caused by the uncertainty of net migration. Since this is a major source of uncertainty for

Turkey, as well, we conclude that the standard deviation of 0.086 for the year 2030, is

appropriate.

Table 4 demonstrates once again the fact that demographic uncertainty is higher than

is generally believed by experts in the field. In a population of 50 million a standard deviation

of 0.1, or 10%, corresponds to 5 million people, and the chance is not higher than 2/3 that the

population is within such an interval. Based on Table 4 such uncertainty is typical over a 30

year forecast horizon. To be sure, this estimate is based on a statistical model that has been

fitted to past errors. Yet, in light of Table 3 the level of uncertainty may not be so

unexpectedly high. Expert forecasters (such as those at the U.N.) change their views of future

prospects in ways that have dramatic effects as they accumulate over time.

15

6. Comparison of Predictive Distributions

6.1. Revised Point Forecasts for Turkey

We will now describe an adjustment to the latest U.N. forecasts that incorporates the

aspects mentioned in Section 4.2. We will not recompute the whole cohort-component

forecast, but provide adjustments to the U.N. forecast for year 2030.

U.N. (2005) assumes that total fertility in Turkey will be approximately at 2.46, 2.31,

2.21, 2.11, 2.03, 1.96, 1.89 during 5-year periods starting at 2000, 2005, 2010, 2015, 2020,

2025, 2030 and that it will remain constant at 1.85 thereafter. We noted in Section 3 that

survey estimates put the national TFR at 2.23 at 2003 already, and Istanbul is lower still, at

1.83. Taking Poland as a leading indicator we think it is more likely that the TFR of Turkey

will decline to the low European level. In UPE (2004) the countries were divided into two

groups with the lower having an ultimate TFR of 1.40 and the higher a TFR of 1.80. Portugal

could not easily be included in either group, so a middle value of 1.60 was assumed for it. We

will similarly assume that the TFR of Turkey will reach 1.60 at 2030. This will be

implemented by multiplying the medium variant of the forecast of U.N. by the ratio of the

TFR we assume and the TFR assumed by the U.N., for surviving cohorts. The result of this

calculation is given in column TFR of Table A that is in the Appendix. We find that the

largest relative change is caused by the different jump-off value obtained from the survey as

compared to the assumptions of the U.N.

U.N. (2005) assumes that net migration will be approximately -50,000 a year during

the period 2000-2005 (roughly -0.71 per 1,000), followed by -10,000 a year for the period

2005-2015 (-0.12 per 1,000), and -30,000 thereafter (-0.34 per 1,000). In contrast, the latest

national population forecasts of Turkey (Turkish State Institute of Statistics) starts with

16

100,000 a year for the period 2000-2005, and a linear decline is expected until a zero net

migration is obtained in 2020. Both assumptions are empirically and conceptually on a weak

ground, so we will simply assume that net migration is zero for all years. When we take the

U.N. forecast as a starting point, this means that we have to add the corresponding amounts to

obtain the adjusted point forecast. We will assume that the negative net-migration is primarily

due to working age emigration. As a very crude approximation, suppose that net migration

involves ages 20-35 only. That age-group represents very nearly 25% of the total population

of Turkey during the time period of interest, 2000-2030. Thus, the rate would be

approximately -2.84 per 1,000 in that age-group in 2000-2005, -0.48 per 1,000 in 2005-2015,

and -1.36 per 1,000 thereafter. We will adjust the population counts by “taking out” the net

outmigration from the cohorts who will be in ages 20-60 in 2030, according to how long they

spent in the age bracket 20-35 during period 2005-2030. For example, the cohort who are in

age 40-45 at 2030, will spend 15 years in the age interval [20, 35) during years 2000-2030. On

average they are 7.5 years in the interval during 2005-2015 when their number is

unnecessarily depleted at the rate of 0.48 per 1,000 per year, and on average they are 7.5 years

in the interval in 2015-2030, when their number is depleted at the rate of 2.36 per 1,000 per

year. Thus, the adjustment to their number is a multiplication by exp(7.5×0.48/1000 +

7.5×1.36/1000) = 1.01390, or the effect is about 1.4 percent. Other cohorts are similarly

adjusted. The results are given in the column NET of Table A. We find that the largest

adjustment is for the cohort who are in age 35-40 in 2030. Their number is adjusted up by

approximately 1.8 percent.

U.N. (2005) assumes that life expectancy will more or less linearly increase by

almost 2.4 years per decade during the period 2000-2030. This implies a total gain of around 7

years, and that is a significant deceleration in improvement: during the period 1970-2000 the

17

total gain in life expectancy for Turkey amounted almost 14 years. Some deceleration may

well occur (during the periods 1970-1985 and 1985-2000 life expectancies increased by 8 and

6 years respectively), but let us assume that the total gain will be 10 instead of 7 years for the

period 2000-2030. Thus, instead of having a combined life expectancy of 74.5 years in 2025-

2030, we would expect a combined life expectancy of 77.5 years. There is no simple way in

which the life expectancy can be translated into changes in survival probabilities that we need

for adjustment. However, suppose the added 3 years come from ages 50+. From historical life

tables for Finland (Kannisto and Nieminen 1996) we find that in 1966-1970 female life

expectancy at birth was 73.66 and in 1971-1975 it was 75.32. In age 50.0 the remaining life

expectancies were 26.69 and 27.97, respectively. We are interested in determining a

proportional adjustment to mortality rates that would increase this value by 3 years. We find

that mortality would have to decrease by 30% over a 30 year period, in ages 50+. Assuming a

uniform change that totals 30%, we obtain (using interpolations from Table 10 of Kannisto

and Nieminen 1996, p. 67) the correction factors LE in Table A.

In summary, we expect the total population to be 94.0 million, which is almost

exactly the same as the U.N. forecast of 93.9 million. However, the ratio of the 65+

population to the population in ages 15-64, or old-age dependency ratio, increases to 17.8%

from 14.8%, and the young-age dependency ratio is decreased to 30.5% from 31.5%. Thus the

total age-dependency ratio increases to 47.8% from 46.3%.

6.2. Turkey as Compared to the Largest EU Countries

Table 5 presents both the point forecast (i.e., the median of the predictive

distribution) and its 80% prediction intervals for Turkey and the largest EU countries for the

18

year 2030. The latter are from UPE (2004). For Poland we do not have an assessment because

UPE (2004) did not include Poland. We find that the point forecast of Turkey is 14.7 % higher

than that of Germany, and clearly above the other large EU countries. However, both forecasts

are highly uncertain, so Germany still could be larger. An unequivocal statement can be made

in terms of probabilities. The UPE project produced a full predictive distribution of Germany

for the year 2030. This was accomplished by a numerical simulation consisting of 3,000

rounds. Using a lognormal approximation we can produce a similar set of 3,000 simulated

values for Turkey. Although Turkish immigration to Germany creates a negative correlation to

the predictive distributions of Turkey and Germany, the correlation is expected to be small,

since uncertainties in both intrinsic growth of the two countries are essentially uncorrelated,

and migration with other countries is much larger. Therefore, we may consider the two

distributions as practically independent. The principal result is that probability is

approximately 89% that Turkey is bigger than Germany in 2030.

Table 5. Predictive Distributions for the Population of Turkey and that of Large EU Countries

in 2030 (in Millions): the Median (Md), and First and Ninth Deciles (d1, d9).

Fractile Md d1 d9

Turkey 94.09 84.46 105.15France 65.23 60.48 70.19Germany 82.00 75.10 90.50Italy 57.97 51.90 64.67Spain 44.81 39.99 48.73United Kingdom 65.03 60.19 70.21

In addition, one might be interested in how much bigger Turkey is expected to be in

2030. As already noted, from the medians we find that Turkey is expected to be roughly 15 %

19

larger than Germany. Much more detail is available, however, since we have a full joint

predictive distribution of Germany and Turkey available. Let T be Turkey’s population size

and G be Germany’s population size, in 2030. We can derive the marginal distribution of the

variable (T - G)/G. From the marginal distribution we can determine that the probability that

Turkey will be 30 % larger than Germany is 10 %.

7. Turkey’s Uncertain Demographic Future and Its Implications for Policy Planning

in EU Countries

Although demography is rarely the only, or even the dominant, factor influencing

future economic, social, or political developments, it can certainly play a role. The future

relations between the EU and Turkey are a poignant example of this. Given that Turkey has

expressed strong interest in joining the Union as fast as possible, the political choices

available to the Union can be viewed as falling into three categories: (I) Turkey will be

accepted as a full member in a rapid schedule; (II) Turkey will be accepted as an affiliate

member with restricted rights and only slowly increasing participation into the various

programs of the Union; (III) Turkey will not be accepted to the Union and it will be treated on

a par with other non-Union countries.

There are three broad interest groups. (a) For Turkish people joining the Union holds

a promise of faster economic growth and prosperity. It would also strengthen the current

secular state against fundamentalist Islamic tendencies. (b) For the aging EU citizenship

Turkey’s joining may provide some relief in the form of labor migration and as a market for

goods produced in the EU, but it could also lead to changes in the political power in the EU as

a whole, but also within countries expected to gain new Turkish immigrants, notably in

20

Germany. (c) Whether or not Turkey joins could also have global consequences, because it

may have an effect on the stability of the oil-producing countries in the Middle East. In all

cases religion is an important consideration, but this is compounded by the fact that Turkey

has a large growing population.

Thus, from a decision theoretic point of view the decision maker is the political

structure of the EU (Parliament and Council of Ministers with the Commission and civil

servants in advisory roles), the decision alternatives are the choices (I) - (III) given above, and

the consequences of the erroneous decisions are borne by the citizens mentioned under (a) -

(c). The loss function is some aggregate measure of individual utilities under each of the

decision alternatives in the three interest groups. The actual losses will depend on how the

world (demographic development, in particular) turns out to be in the coming decades. The

role of forecasting is to provide a predictive distribution against which the expected loss of

utility due to any decision taken can be evaluated. For more details of the structure of such

decision problems, see Alho and Spencer (2005, pp. 351-363).

In view of the far-reaching consequences of the different policy choices, it would

seem that the study of Turkey’s population development would have a high priority in the EU

and the world. This is currently not the case, however. We have shown that

(1) the Turkish national statistical agency and the U.N. hold quite different views of

even recent past Turkish migration let alone the future migration. It would be

important to invest into better statistical estimation of the migration flows;

(2) sample survey based estimates of Turkish fertility are lower than those coming

from the vital registration system, and those used by the U.N. Fertility will probably

decrease faster and to a lower level that is assumed by the U.N. This will increase the

purchasing power of the Turks in the short term, but lead to faster aging in the longer

21

term;

(3) Turkish mortality is declining very fast, and it is likely that it will continue to

decline faster than is assumed by the U.N. or the national statistical agency. This

further accelerates the aging of Turkey, although the effect will take several decades

to fully manifest itself.

As far as we can tell, Turkey’s population development has not been adequately

taken into account in different levels of the Union’s administration, nor has it had a sufficient

impact on public policy debate. Human rights issues and the role of Islam are frequently

mentioned, but while these are of undeniable importance, it may well be that global political

pressures (including the interests of the United States) will become so strong that

independently of future demographics the choice (I) or some form of choice (II) will be

implemented. However, should this happen, the next question is how political power will be

apportioned in the EU and how Union resources will be allocated after Turkey has joined.

These discussions will be much more difficult than those that had lead to the rejection of the

EU constitution proposal in France and the Netherlands recently. In this discussion Turkey’s

size will be a major factor because its joining may have an effect on the whole mission of the

EU, but this has had little effect on the public policy debate so far.

Turkey provides also a concrete example of how the presence of demographic

uncertainty is readily recognized on the one hand, but little or nothing is done about it, on the

other. Yet, we have shown that the chances are 9 out of 10 that Turkey will be bigger than

Germany in 2030. This means that it will have to have a larger share of the political power

than any other EU country according to the existing principle of approximate proportional

representation. However, we have also shown that while we expect Turkey to be 15 % bigger

in 2030, Turkey could also be much bigger. For example, the chances are 1 out of 10 that

22

Turkey is 30 % bigger than Germany in 2030. Should the future produce a demographic

outcome in this neighborhood, it is clear that according to principles of equity Turkey would

have to be given a clearly larger share of the political representation than any other EU

country. Although 1 out of 10 is a small probability, it is not so small as to be discarded

altogether by prudent decision makers. As far as we know there has been no discussion

whatsoever about what the EU would do if this possibility were to materialize.

We anticipate that the above estimate would be disbelieved by many, but note that it

is a consequence of simple statistical models and existing empirical estimates. In general, it is

difficult to come up with estimates of this type without resorting to calculations, and it is

known that uncertainty is typically underestimated by experts (e.g., Alho and Spencer 2005, p.

250). To see that this conclusion is not specific to Turkey, note that we have also shown that

the U.N. forecasts of many EU countries have changed dramatically in the matter of a few

years. Thus, to the extent that forecasts are used to inform decision making, it is crucial that

decisions are robust to alternative population outcomes that are almost as likely as the median

of the predictive distribution. Coming back to Turkey, we point out that our analysis is fairly

crude is it is based on the study of the total population only. It seems that it would be well

worth the effort to produce a full stochastic forecast of Turkey, so that many of the sources of

uncertainty mentioned above could be explicitly addressed. It is known that increased data

accuracy is not always preferred in actual decision making (e.g., Alho and Spencer 2005, p.

354). Yet, in the case of Turkey it is unthinkable that the country be member of the Union and

not have population statistics and forecasts of comparable quality as those of the member

countries.

There are many additional aspects relating to the age-distribution and geographic

distribution of the Turkish population that we cannot adequately discuss in this limited study.

23

References

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24

Appendix. A Revised Point Forecast of Turkey in 2030

Table A. The U.N. (2003b) Forecast of Turkey for year 2030, its Proportional Adjustments

Due to Total Fertility (TFR), Net Migration (NET), and Life Expectancy (LE), and the

Resulting Adjusted Population.

Age U.N. TFR NET LE ADJ0-4 6602 0.95408 1.00000 1.00000 62995-9 6750 0.95567 1.00000 1.00000 645010-14 6857 0.95735 1.00000 1.00000 656515-19 6958 0.94570 1.00000 1.00000 658020-24 7039 0.93507 1.00341 1.00000 660425-29 7039 0.90650 1.01025 1.00000 644630-34 7042 1.00000 1.01715 1.00000 716335-39 6700 1.00000 1.01837 1.00000 682340-44 6416 1.00000 1.01390 1.00000 650545-49 6552 1.00000 1.00823 1.00000 660450-54 6221 1.00000 1.00361 1.00324 626455-59 5569 1.00000 1.00120 1.01301 564860-64 4612 1.00000 1.00000 1.02852 474465-69 3824 1.00000 1.00000 1.05519 403570-74 2741 1.00000 1.00000 1.10556 303075-79 1726 1.00000 1.00000 1.37955 238180-84 843 1.00000 1.00000 1.38019 116485-89 299 1.00000 1.00000 1.71338 51290-94 74 1.00000 1.00000 2.31853 17295-99 10 1.00000 1.00000 3.45174 35100+ 1 1.00000 1.00000 3.45174 3Total 93875 - - - 94029