Looking for the Causes of the Recession:Italy in the Early 1990’s*

Charles Grant, University College LondonRaffaele Miniaci University of PaduaGuglielmo Weber University of Padua, Northwestern University, IFS and CEPR

This draft: December 29th, 1997

Abstract:This paper investigates the causes of the Italian recession of the early 1990’s by estimating

deviations from ‘normal’ consumption using household level data for 1985-94. The data set used is aparticularly rich but as yet unexplored source recently released by ISTAT. It contains detaileddemographic and expenditure information for over 30,000 Italian households each year. The mainfindings are that the decline in consumption was larger for the working age households. The fall inconsumption was also stronger in the south, among the self-employed, and among public sectoremployees. The decline can be dated from the third quarter of 1992. These results can be reconciledwith the life-cycle model of consumption in which there is a permanent and unexpected shock to lifetimeincome induced by the pension and other reforms introduced by the Amato and succeedinggovernments.

* An earlier version of this paper was circulated under the title: “Changes in Consumption Behaviour: The ItalianRecession of the Early 1990’s”. We gratefully acknowledge the financial support from the TMR project ‘Savings,Pensions, and Portfolio Choice’ (contract grant no. ERBFMRXCT960016). This paper was written while the first authorwas at Prometeia, Bologna, as a TMR research fellow. We would like to thank ISTAT for providing the data on theirpublic use tapes and particularly Giuliana Coccia and everybody in the consumption sector at ISTAT. We would also liketo express special thanks for the many helpful comments of Orazio Attanasio, Gadi Barlevy, Agar Brugiavini, ElsaFornero, Daniela Mantovani, Carlo Mazzaferro, and all who attended the July 1997 conference in Tilburg, Holland, wherean earlier draft of this paper was presented. The views expressed and any remaining errors are, of course, our own.

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1. Introduction

This paper investigates the behaviour of consumption during the early 1990’s for Italian households.This is the first period in recent Italian history when there was a drop in annual real consumerexpenditure (-2.6% in 1993), and it marks the start of a low growth phase for the Italian economy.Immediately prior to this period, a confidence crisis in the Italian currency and government debt had ledto major political reforms, aimed at balancing the state-provided pension system by cutting benefits andat reducing the government debt by increasing taxation and curbing public sector pay.

In this paper we investigate a major business cycle episode by comparing its effects onconsumption for different groups of the population. This method was applied by Attanasio and Weber(1994) to assess the likely causes of the British consumption boom of the late 1980’s; it is applied hereto evaluate which of the key policy changes of 1992 had the largest impact on Italian consumerbehaviour in the following years. In both papers, the identifying assumption is made that householdschoose a consumption path over their entire lifetime subject to a budget constraint and that thisconsumption path can be estimated. Therefore any changes to consumption behaviour are interpreted asadjustment to changes in the present value of lifetime income. In this paper in particular a crucial role isplayed by the assumption that consumers are both forward-looking and selfish, in the sense that theyderive utility from their own consumption at all ages but don’t derive utility from consumption of theiroff-spring, once their children have left home.

We seek to understand the recession, a macroeconomic phenomenon, by using microeconomicevidence based on household consumption data contained in the consumer expenditure survey releasedby the Italian statistical office (ISTAT) and covering both a six and half year period prior to therecession and (two and half years of) the recession itself. This approach allows an investigation of thediffering behaviour of different subgroups, to gain an understanding of the possible causes of therecession, and to differentiate between them. In so far as there is no full insurance in consumption, thenature of the recession is best understood by investigating the response to the shock of various groupsof the population. In particular this paper relates the recession to changes in lifetime wealth of differentgroups in the population induced by important policy changes that took place from the Summer of 1992onwards, something that aggregate time series data would find difficult to do. Various hypothesesabout the recession can have quite clear-cut implications on household level data, although they wouldbe observationally the same at the aggregate level. In this paper we report estimates of a flexibleconsumption equation and then attempt to explain the failure of the developed model to predict therecession: any explanation must be due to omitted factors, but for an explanation to be credible it mustbe consistent with the observed deviations from predicted consumption during the recession.

A similar approach has been taken in two related papers: Attanasio and Brugiavini (1996),henceforth AB, who investigate saving behaviour, and Miniaci and Weber (1996), henceforth MW, whoinstead analyse consumption responses. Both of these papers use the Bank of Italy survey, whereconsumption is computed on the basis of recall questions, but emphasise the need to use other datasources to investigate consumer behaviour in greater detail.

In this paper we use a new and relatively unexplored data set (the ISTAT family budget survey)to study the changes in consumption that took place during the early 1990’s. This survey is much largerthan the Bank of Italy survey, is run more frequently (annually rather than biannually) and has muchmore detailed, diary-based responses for a wide number of consumption categories. This enables a moreinformative analysis than either AB or MW were able to attempt in several directions. On the one hand,we are able to look at alternative definitions of consumption, including and excluding spending on

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durable goods, on health and on housing. On the other hand, we can explore key educational andregional differences thanks to annual sample sizes of over 30,000 households. However, the ISTATsurvey has relatively little information on income or savings: in this paper we therefore concentrate onthe behaviour of consumption.

The paper is organised as follows: first, in Section 2 some pertinent details regarding Italy duringthe early 1990’s are related; Section 3 includes a brief description of the data, and a comparison of it tothe national account data; Section 4 describes the econometric model; the results are presented inSection 5. In Section 6 the estimates are discussed in the light of key policy reforms of the period, andcompared to some simulation results of the pension reform. We present our concluding remarks inSection 7.

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2. Italy in the early 1990’s

The National Accounts show that the period from 1992 has seen sluggish growth inconsumption, and the growth rate of consumption has remained weak into 1997. This is in sharpcontrast to the late 1980’s when Italy, in common with most Western European economies, saw a rapidexpansion in consumption levels. Household consumption (defined here as total, deflated consumerexpenditure) begins to fall in the third quarter of 1992, and it did not begin to recover until the secondhalf of 1993. The national account data saw consumption decline 2.6% in 1993 (the first year since thewar where household consumption has fallen), although excluding car purchases the figure is only -1.2%. This contrasts to an average annualized rate of growth of 3.9% in 1986-1989. The contrastwhen durable consumption is examined is even more striking, showing a decline of over 12% in 1993,compared to a growth rate of over 8% each year in the late 1980’s. Consumption growth has remainedsluggish in the period since 1993, averaging little more than 1% per year. In fact, consumption grewmore rapidly than GDP from 1986 until the second quarter of 1992, and then fell more rapidlythroughout the rest of 1992 and the first half on 1993. Later, consumption growth has been positive butsmaller than GDP growth. This is prima facie evidence that there was a fundamental change inbehaviour during 1992.

Table 2.1: Annualized growth rates of macroeconomic variables.year quarter GDP consumption durables

1985 2.6 2.4 9.61986 2.9 3.7 8.11987 3.1 4.2 8.81988 4.1 4.2 11.81989 2.9 3.5 9.41990 2.1 2.5 0.81991 1.3 2.8 3.11992 1 0.6 3.2

2 0.2 2.4 1.13 -0.5 -1.24 -0.5 -3.6

1993 1 -1.3 -4.72 0.9 -1.4 -12.23 -2.7 0.84 4.9 3.2

1994 1 1.2 2.72 3.4 2.2 1.93 6.2 1.34 1.6 1.5

Source: Economic Bulletin, and Relazione del governatore, Bank of Italy.Several papers have sought to explain the remarkable increase in aggregate consumption levels in

the late 1980’s as due to the increased generosity of the public pension system caused by reforms thattook place in the 1970’s.1 As will be seen, the reform of pension provision during the early part of the1990’s can also explain much of the recession.

1 See, for instance, Rossi and Visco (1995).

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The early 1990’s were characterised by a number of important macro-events (see table 2.2below), that prompted major reforms affecting the public pension system, taxation, wage indexation,public sector workers pay and Government-owned enterprises. Perhaps the two most important eventswere the General elections of April 1992, that saw a large protest vote against the long-standing policyof high government spending (this was perceived as leading to massive public debt and wide-spreadcorruption), and the end of the Lira participation in the Exchange Rate Mechanism of the EuropeanMonetary System of September 1992 (that was forced by speculation in the foreign exchange marketsagainst weaker currencies). This second event had important short term consequences on budgetarypolicies, because it increased substantially the default risk premium on Italian government bills, making itimperative for the Government to cut its budget deficit both in the short and in the long run.2

The pension reforms have been emphasised as a potential cause of the recession, since, as arguedin AB, any unexpected and permanent shock to pension wealth will cause (using the life-cycle model) acorresponding adjustment in saving and current consumption. The Amato pension reforms involved asubstantial reduction in pension wealth for younger households, hence these people may have seen areduction in consumption, provided that public pensions are a good substitute for private income.3 Theyalso changed the basis of pension indexation from nominal wage growth to price inflation, thus reducingexpected payments for all. The pension reforms also affected those working in the public sector, andthose whose income rose more steeply over their working lives (typically the better educated). ABshowed, that the changes in the saving rate, and the propensity of taking up a private pension policywere consistent with their analysis of the Amato reforms, but emphasised the need for a larger survey, anissue that this paper can address.

The reforms during this period also encompassed introducing stricter tax compliance4 measuresfor the self-employed, discussed by MW. MW also suggest some alternative possible causes of therecession: the large increase in tax rates (tax revenue rose by 5% in real terms in 1993) and the wideranging loss of jobs made possible by changes in employment legislation5. Automatic wage indexationalso ended (and was replaced by an agreement to make inflation adjustments part of industry-wide wagenegotiations) and a privatisation program was announced. By 1994 a new government, underBerlusconi, had taken office promising further economic reforms. Although this government collapsedbefore it could introduce the pension reforms and privatisation programme it envisaged, some further,albeit weaker, reforms were passed in 1995 by the Dini government. The Dini government also provedshort-lived, and early general elections were necessary in April 1996, that produced a centre-leftgovernment led by Prodi. Into 1997 Italy is still grappling with bringing its pension system into balance,and the tax increases, designed as a temporary measure to meet the Maastricht criteria, look to havebecome permanent. While it is difficult to construct testable hypotheses about the marked politicalinstability of 1992-96, this increased uncertainty is likely to have had important effects on consumption.

2 The end of 1992 and 1993 also introduced considerable political uncertainty: the Danish referendum of June 1992 andthe Tangentopoli trials both caused the political and economic direction of the country to become less sure, and soonafterwards saw the ‘collapse of the 1st republic’.3 Several papers discuss this, including Brugiavini (1991) and Jappelli (1995).4 The income tax receipts from the self-employed increased by 37%.5 See, for instance, Bertola and Ichino (1995).

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Table 2.2: Political and economic events during 1992-1995.

1992 January Bank Independence Central Bank given monetary independence, start of aprivatisation programme over next few years.

April General Election The traditional parties (Christian Democrats, Socialists,and Communists) all do badly. By the next election nonewill remain in their 1992 form.

June 2 Danish Referendum Denmark fails to ratify the Maastricht treaty.

June 18 Amato Government New government which proposes modest political andeconomic reforms and corrective budget.

July 31st Accord Government announces proposals to end wage indexation,6

and introduces wages policy. Reform of public sectoremployment.

September ERM crisis Italy is forced to leave the Exchange Rate Mechanism, andthe Lira experiences a substantial devaluation. 7

September Budget Sweeping (‘Amato’) reform of the pension system and largetax increases (introduction of a minimum tax). Cuts inpublic expenditure and freeze in public sector wages.

1993 April Ciampi Gov. Amato government collapses under corruption scandals andnew, non-party government of technicians appointed underCiampi.

May Mini budget Modest increases in indirect taxes and a small reduction inexpenditure.

July Accord Tripartite agreement on previous year’s accord betweenunions, employers and government. Freeze on wages, two-tier wage negotiations and measures to address youthunemployment.

August New Electoral Law New electoral law approved for both houses. Change fromproportional representation to (modified) first-past-the-post.

September/October

Budget Further cuts in public expenditure. Further minor changesto pensions (public pensions) and the minimum tax.

1994 March General Election First election under new electoral system. Victory for theright over the left.

May BerlusconiGovernment

New government appointed with Berlusconi as the prime-minister.

September Pensions Further comprehensive pension reforms proposed, but theywill not be enacted.

December 21st Governmentcollapses

Berlusconi government collapses when the NorthernLeague leaves the governing coalition.

1995 January Dini Government New government under Dini is appointed, promisingpension reform, further privatisation and to address thebudget deficit.

February Mini Budget Further tax increases and spending cuts. Lira continues tofall.

May Pensions The ‘Dini’ pension reforms which aim to balance paymentsand contributions by 2035.8

6 The system of indexation, scala mobile, acted in a way such that it increased the wages of low earners by relatively morethan the wages of higher earners.7 By the end of 1993 it had fallen nearly 20% in real terms.8 However payments and contributions are actually unlikely to balance, motivating the Prodi government to again reviewpension legislation.

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3. Data Description

We use a new and as yet relatively unexplored data set, the Survey of Family Budgets (FBS) publishedby the Italian statistics office, known as ISTAT.9 Few papers have used this data set so a discussion ofthis source is included below. The public use version of the survey includes relatively disaggregatedresponses about consumption made by a Italian households, together with a number of householdcharacteristics and the families overall income. The survey is structured as a repeated cross-section.Altogether some 32,000 households are sampled (by a single interview) throughout the year. This paperuses approximately 300,000 observations from 1985-94, the last year in which this survey is currentlyavailable. Unfortunately data is not available for the period before 1985. The unit of reference is the‘famiglia anagrafica’ or the household as officially registered10, excluding the very small number ofpeople who live in military or religious or other institutions. Sampling is in two stages; firstmunicipalities are chosen and then households are chosen from the municipality. Some smaller regionsare relatively over sampled (Valle d’Aosta, Trentino Alto Adige, Umbria, Basilicata, and Sardinia). Thefamilies are interviewed by the local municipality, who rarely employ professional staff. Weightsinversely proportional to the probability of being surveyed are reported. Altogether around 84% ofsampled households have responses reported in the survey.

Data is collected on current spending (over a 10-day period), non-basic goods and services (overthe month), and durable goods (over the quarter). Unfortunately the responses that are publiclyavailable refer to some 64 expenditure categories (31 basic goods and 33 non-basic goods) in which it isnot easy to distinguish between durable and non-durable spending. This paper makes a somewhatarbitrary division between durable and non-durable expenditure based on these definitions but recognisesthat this separation is far from perfect. Income data, we believe, is likely to be measured imprecisely asresponses are based on a single question about normal monthly income, and (see Brandolini, 1993) insome 40% of cases ISTAT have corrected the income figure upwards by, on average, nearly 30%.Saving11 is also measured poorly, being based on a single question in which respondents are invited toclassify themselves into one of 16 categories. Neither income nor saving responses are analysed in thispaper.

Below, the national account statistics and averages drawn from the ISTAT survey are compared.The definition of consumption made by the household is slightly different between the two statistics.Some of these differences relate to the treatment of financial services and housing, although theproportion of spending made on these goods is fairly stable. A second issue is that the basis by whichhouseholds were sampled was updated in 1986 and also at the end of 1991 and beginning of 1992.These changes were phased in and may have affected the time-series behaviour in these years. Aconsequence of the first change was that the average age of the household head in the survey rose quitesharply between 1985 and 1987 and that average family size fell. The second change meant that thesampling was updated in line with the 1991 census returns, and again this caused an adjustment to theaverage family size.12 It may also have changed the proportion of people sampled in each area, althoughno evidence of this was found upon examination of the data available for the relatively large regions thatare defined in the released data (see table 3.2). During the last years of the survey improvements were 9 For a more thorough description of the survey, see “Indagine sui Consumi delle Famiglie”, (various years) or Brandolini(1993).10 The survey also reports the effective number of people living in the household. Excluded are the small number ofhouseholds where these two figures do not match.11 Negative saving is not allowed and the question is ambiguous as to whether it refers to the level of saving or to thechange in the level of saving over the past year.12 As a result, comparison between the national account data and the ISTAT data is made for per capita consumption.

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made in the response rate, particularly in the rural south; this may have reduced the average per-capitaincome reported in the survey. Hence, results were also obtained using the data from the north only.

Figure 1: Growth Rates of Consumption

The diagram shows that the behaviour of the per capita growth rate of consumption in theISTAT survey and the national accounts are broadly similar (the correlation is 0.866), albeit with somesmall differences. Both show that consumption grew rapidly in the 1980’s, but the growth rate wasslightly larger in the ISTAT survey. Both also show that consumption declined in the early 1990’s, butthe decline began in 1992 in the ISTAT survey, and the decline was also larger than for the nationalaccount data. The recovery in 1994 was stronger for the ISTAT survey. Despite these caveats, thematch is reasonably good between the two surveys, allowing some confidence in the results.

3.1 Definitions

This paper will divide the population into various subgroups defined on year-of-birth, region ofresidence, and educational category. This paper uses the data set after excluding certain observations: allhouseholds of 8 or more members; all households with incomplete information; all households where theeffective number of members did not match the registered number of members (a very small number); allhouseholds recording no consumption. Altogether only a small number of observations were excluded,less than 5%. The regions are defined as in the national accounts and they are displayed in table 3.1.

Table 3.1: The definitions of the regions used in this paper.

North (N) North-west (NW) Piedmont, Valle d’Aosta, Lombardy, Liguria

North-east (NE) Veneto, Trentino-Alto Adige, Friuli-Venezia Giulia, Emilia-Romagna

Centre (C) Tuscany, Umbria, Marche, Latium

South (S) South (S) Abruzzo, Molise, Campania, Apulia, Calabria, Basilicata

Islands (I) Sicily, Sardinia

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Cohorts are constructed on the basis of year-of-birth. All observations where the ‘household head’ (orthe ‘reference person’, as is officially known) was born in the same five year period were assigned to thesame cohort (as defined below). Those observations where the household head was born before 1911 orafter 1965 were excluded from the survey because the cell-sizes would have been too small.Educational categories and employment categories were also defined, as is expounded later.

Table3.2: Cohort definition and cell sizeCohort (5-year) Year-of-birth Age in 1993 Ave. cell size Min Cell Size

(quarterly)

1 1911-1915 80 363 2662 1916-1920 75 376 2883 1921-1925 70 649 5604 1926-1930 65 714 6515 1031-1935 60 743 7026 1936-1940 55 812 7467 1941-1945 50 772 7428 1946-1950 45 878 8219 1951-1955 40 788 71710 1956-1960 35 668 43911 1961-1965 30 366 100

The table reveals that average cell size is substantially lower for the youngest and the two oldestcohorts. This calls for extra caution in interpreting results on two accounts:a) quarterly or biannual cells are relatively small;b) if household formation is the reason why the average cell size is small for the youngest cohort,

projected growth for this cohort may reflect systematic composition effects;c) if mortality is the cause of the relatively small cell size for the oldest cohorts (as opposed to a smaller

fraction of the population being born in the corresponding periods), and mortality rates depend on thestandard of living (the rich live longer), composition effects may also come into play.

There is prima facie evidence of attrition for these cohorts: cell size decreases over time for cohorts 1and 2 and instead markedly increases for cohort 11.13 The likely effect of attrition on our econometricanalysis will be discussed when interpreting the estimates in Section 5.

13 For cohort 1, cell size falls from 444 in 1985 to 243 in 1994; for cohort 2 from 450 to 280. Cohort 11, on the otherhand, starts with 100 in 1985, followed by 157 in 1986, and ends with 813 in 1994.

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4. Theoretical Framework

This paper uses a simple regression method to analyse a macroeconomic episode: it splits the sample in acontrol period and a treatment period, and estimates average group differences for consumptionbetween predictions based on the control period and actual realisations over the treatment period.14 Itshall concentrate on understanding the path of consumption for two reasons: consumption depends uponthe level of permanent/life-cycle income, which is the topic of particular interest in this paper (ratherthan current income which will reflect both permanent and transitory effects15), and also due to the lowquality of the income data in the ISTAT survey.

The behaviour of consumption is modelled using a number of explanatory variables. The aim isto explain the path of consumption for various subgroups of the population. The underlying theoreticalmodel being used is the life-cycle model of consumption originally propounded by Modigliani andBrumberg (1954), but recently enriched to capture heterogeneity and uncertainty.

The sample is split between a ‘control’ period, in which it is believed that consumption is stableand experiences no exceptional macroeconomic shocks, and a ‘treatment’ period, the period ofparticular interest (in this case the recession that followed the 1992 reforms). Log-consumption for theith household H in time t belonging to cohort (or subgroup) c is defined as:

H zitc

tc

itc

itc= + ′ +δ γ ε (4.1)

where δtc are the group or cohort (conditional) means at time t whileεit

c represents individualheterogeneity. A cohort is normally defined as a group of households whose head was born in the sameperiod (five-year interval), but in this paper we shall also consider a finer classification, wherehouseholds are also required to live in the same (broad) region or heads of household to have the sameeducational attainment.

The z are a set of auxiliary explanatory variables, which can be of two types: variables thatexplain the variation in consumption over the life-cycle (for instance family size, number of children),and those variables that measure life-cycle income (for instance region, education) and do not changeover the life-cycle. The first type of variable helps to predict changes in consumption behaviour over thelifetime, and in particular will help us to test various hypotheses about the recession. The δ’s representmeans over cohort-cells. Of fundamental interest is the behaviour of these means during the treatmentperiod, and to investigate whether they change vis-à-vis the control period. To do this someassumptions must be made about the dynamics of this variable during the control period, but norestrictions are made during the treatment period so as to capture any changes in behaviour that takeplace during the recession. These changes are interpreted as shocks to lifetime income. For the controlperiod the time-cohort means are decomposed into age and cohort effects, assuming that there are nopure time effects.16

14 The technique was first implemented by MaCurdy and Mroz (1990). Attanasio and Weber (1994) used it to examine theUK consumption boom of the late 1980’s.15 Indeed, if, for instance, it is maintained that the recession was due to changes in pension wealth, then although theindividual’s life-cycle wealth, and hence consumption, will be affected, there will be no change in current income due tosuch reforms (ignoring any possible labour market effects).16 Unfortunately age, cohort, and time effects are not separately identifiable due to the linear relationship between them.In this sense this interpretation of the parameters is somewhat arbitrary. It implicitly interprets growth in the economy asdue to cohort effects.

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In particular, we model the δ’s in the following way:

δ α β θtc c

ctt

ct tcp age u= + + +

∈∑( )

Ω (4.2)

The αc represent fixed cohort effects, there is a polynomial in age, and also included are set of time-cohort dummies θct that reflect that the δ’s are unconstrained during the treatment period (when t ∈Ω ).When we define cohorts by year-of-birth and education we implicitly allow the shape of the agepolynomial to be education specific, as implied by recent work on the dynamic solution of a life-cyclemodel with time-varying demographic discount rates and uncertainty (Attanasio et al., 1997).

Substituting (4.2) into (4.1) gives:

H p age z uitc c

itc

ctt

ct tc

itc= + + ′ + + +

∈∑α γ β θ ε( )

Ω (4.3)

The βct represent the shift from estimated (or normal) consumption for cohort c at time t in comparisonto the level of consumption that is predicted using the control period.17 In this sense this term isincluded as part of the ‘error’ term, although it is a different concept to an error that arises from aregression.18 Then economic theory, and any understanding of the institutional changes that occurred atthe time, are used to explain these ‘shifts’ in consumption.

This model is quite restrictive in that it imposes a tight specification on the way the δtc behaves:

the specification shown in equation (4.2) assumes that there exists a common age-profile to log-consumption, which is shifted according to the cohort the household belongs to. For our analysis tomake sense, we need to assume that the ut

c are i.i.d.. The estimated utc can be studied to confirm this

(this issue is discussed in the Appendix 2). We also impose restrictions on the way other socio-demographic characteristics affect desired log-consumption. In contrast, the specification during thetreatment period is extremely flexible, allowing a very thorough analysis of the behaviour ofconsumption, and any changes to it, during this period.

This theoretical framework should be accompanied by two remarks:

• We implicitly define a recession as a period of time in which consumption is below predicted (ortrend) consumption, rather than the more standard definition of a recession being ‘two-consecutivefalls in quarterly GDP’.19 Equivalently, there is a recession if the βct are negative (and conversely aboom if they are positive. This difference will be important when it comes to interpreting this size ofthe ‘falls’ in the level of consumption.

17 On the assumption that the expectation of u t

c is zero. To ensure this the cohort mid-age is used, and the serial

correlation properties of the u’s are investigated. This is discussed in the Appendix 2.18 Perhaps the most intuitive way of thinking about this is to consider a standard OLS regression, in which the sum of thesquares of the errors (denoted X) are minimized. In this model again OLS is used but excluding a certain portion of theerrors from X which is being minimized. Specifically excluded are the average time-cohort deviation from ‘normal’consumption that occur during the treatment period.19 Or perhaps falls in consumption, rather than GDP, since the topic of interest is consumption.

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• We impose that the age-profile of consumption is invariant between cohorts and that cohorts onlydiffer by a fixed effect that captures the degree to which one generation is wealthier than another. Butwe observe spending rather than consumption and this contains goods of differing durability. Ifwealthier cohorts consume proportionally more of durable goods (i.e.: if durable goods are a luxury),and if durable goods depreciate at a low rate, we would expect their age profile of spending to beshifted to the left. When we take total spending instead of consumption we implicitly rule this out.When we take non-durable spending we rely instead on different assumptions, such as separability inpreferences between durable and non-durable goods.

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5. Results

Some (log)-consumption equations, as developed above, have been estimated. Consumption iscomputed as the ratio of total expenditure net of housing and health expenditure to a monthly priceindex.20 Housing is deducted because it is a largely imputed measure and does not reflect actual moneypayments. Health expenditure is heavily affected by statutory payments on publicly provided healthservices. The regression contained cohort-quarter dummies for the treatment period [1992:Q3,1994:Q4]and deviations from ‘normal’ consumption are tabulated in table 5.1. The results are also presentedgraphically in Figure 2 (for an explanation of the construction of these graphs see Appendix 4).

Table 5.1: Average period percentage deviations from ‘normal’ consumption by cohort and quarter (standard errors inparenthesis).

Time C o h o r t (age in 1993) all1 (80) 2 3 (70) 4 5 (60) 6 7 (50) 8 9 (40) 10 11 (30)

92:Q3 -5.05(3.39)

-10.95(3.15)

-13.75(2.30)

-9.17(2.23)

-9.09(2.09)

-13.28(2.12)

-7.12(2.15)

-7.42(1.99)

-11.61(2.09)

-6.28(2.27)

-12.39(2.74)

-9.72(0.67)

Q4 -9.45(3.20)

-7.57(3.14)

-8.74(2.27)

-10.71(2.13)

-10.41(2.29)

-5.79(2.14)

-6.44(2.17)

-7.84(1.97)

-12.94(2.09)

-13.07(2.20)

-12.38(2.58)

-9.59(0.67)

93:Q1 -9.51(3.56)

-21.36(3.37)

-16.34(2.35)

-5.17(2.24)

-11.25(2.20)

-15.94(2.14)

-16.16(2.26)

-16.25(1.96)

-16.38(2.10)

-20.09(2.18)

-18.37(2.51)

-15.17(0.67)

Q2 -13.91(3.58)

-11.70(3.36)

-18.58(2.38)

-10.81(2.23)

-12.64(2.14)

-16.25(2.19)

-18.52(2.14)

-14.87(2.05)

-19.73(2.10)

-15.95(2.16)

-16.13(2.55)

-15.68(0.67)

Q3 -7.60(3.51)

-9.64(3.20)

-22.59(2.44)

-20.12(2.25)

-17.20(2.24)

-14.52(2.16)

-15.72(2.13)

-16.22(2.13)

-18.45(2.08)

-17.32(2.17)

-24.64(2.48)

-17.40(0.68)

Q4 -12.40(3.74)

-12.06(3.25)

-13.22(2.35)

-18.66(2.38)

-10.93(2.33)

-17.70(2.15)

-15.58(2.24)

-16.07(2.03)

-14.40(2.08)

-20.84(2.10)

-16.94(2.42)

-15.82(0.68)

94:Q1 -13.24(3.90)

-12.39(3.32)

-12.36(2.41)

-13.20(2.43)

-10.79(2.19)

-16.65(2.17)

-19.15(2.15)

-18.79(2.10)

-19.99(2.11)

-18.28(2.21)

-22.81(2.58)

-16.63(0.68)

Q2 -20.53(3.75)

-10.69(3.65)

-8.82(2.51)

-12.02(2.34)

-10.10(2.27)

-12.35(2.23)

-13.80(2.26)

-17.96(2.07)

-16.78(2.09)

-14.24(2.18)

-25.00(2.54)

-14.70(0.69)

Q3 -13.73(3.88)

-22.30(3.61)

-19.09(2.56)

-19.12(2.41)

-17.49(2.34)

-18.88(2.29)

-15.66(2.33)

-15.36(2.05)

-12.75(2.11)

-20.47(2.16)

-16.63(2.40)

-17.21(0.69)

Q4 -24.46(3.99)

-19.20(3.59)

-17.78(2.59)

-13.91(2.31)

-23.92(2.30)

-16.10(2.22)

-14.59(2.28)

-23.00(2.03)

-20.53(2.16)

-21.66(2.19)

-23.15(2.51)

-19.69(0.69)

92 -3.77(1.56)

-5.50(1.56)

-8.43(1.10)

-6.55(1.07)

-5.58(1.03)

-6.60(1.01)

-2.43(1.03)

-5.26(0.98)

-6.81(1.03)

-5.13(1.06)

-8.46(1.26)

-5.88(0.34)

93 -10.77(1.70)

-13.53(1.59)

-17.58(1.14)

-13.54(1.10)

-13.02(1.07)

-16.10(1.04)

-16.53(1.05)

-15.86(0.98)

-17.23(1.00)

-18.59(1.02)

-19.04(1.02)

-16.02(0.34)

94 -17.96(1.82)

-16.02(1.69)

-14.36(1.20)

-14.52(1.14)

-15.42(1.09)

-15.97(1.06)

-15.90(1.07)

-18.81(0.98)

-17.46(1.01)

-18.68(1.03)

-21.70(1.16)

-17.05(0.34)

92:Q3-94:Q4

-12.54(1.09)

-13.48(1.02)

-14.99(0.73)

-13.12(0.70)

-13.26(0.68)

-14.71(0.66)

-14.28(0.67)

-15.30(0.62)

-16.34(0.64)

-16.99(0.65)

-18.99(0.75)

-15.11(0.22)

F(cohorts 1-5 equal)=1.14 Prob=0.38F(cohorts 6-10 equal)=0.93 Prob=0.45 F(cohorts 1-10 equal)=2.22, Prob=0.02

20 The regression included a constant, 12 month dummies, log(family-size), dummies for married couple, femalehousehold head, the presence of working age children, children 0-2, 3-5, 6-14, 15-23, and old persons, a cubic polynomialin age, cohort, region, education, and, of course, the treatment period dummies. Some results were also obtained for theperiod 1987-1994, and/or including health expenditure, although they did not differ substantially from the results reportedin the tables. Excluding the treatment period also little affected the coefficients on the parameters estimated in theregression. The full results of the regressions discussed in the text are reported in the Appendix.

13

When the deviations from ‘normal’ consumption are investigated for the period before 1992:Q3it was found that the errors were small, and there is a clear starting date to the recession of the thirdquarter of 1992. The recession seems to get significantly worse at the beginning of 1993, and again atthe end of 1994. Young people have reduced consumption more, although there is a significantreduction in consumption by older cohorts too. Formal F-tests (reported at the bottom of the table)reject the null hypothesis that all cohorts experienced the same downfall, but fail to reject the hypothesesof equal downfalls across younger cohorts (6-10: aged 35-55 in 1993) and across older cohorts (1-5:aged 60-80 in 1993).21

The key findings therefore are:a) the recession had a larger impact on all working age households;b) there was a smaller but roughly uniform shortfall of consumption for households past retirement age

(60 years).

Figure 2: Full Sample Estimates for Total Consumption

One aspect of our results (in table 5.1 and Figure 2) needs commenting upon. The size of the1992-3 recession in the table is about 15%, but the national accounts show that consumption fell 2.7%between the third quarter 1992 and the second quarter of 1993. The table also shows no improvementbetween 1993 and 1994, when the national accounts suggest the recession had ended.

This discrepancy is due to the definition of a recession used in this paper: a recession is a periodof time in which consumption falls below predicted or ‘normal’ consumption. The prediction will reflectthe fact that the economy grows over time, thus in the prediction the economy will grow roughly 4% peryear as it did over the control period (attributed to age and cohort effects). Thus if during the treatmentperiod the level of consumption in a particular year was equal to the level of consumption in thepreceding year, the results would record that consumption had ‘fallen’ 4% below predictedconsumption. By the end of the treatment period the results show that consumption is approximately

21 Cohort 11 is not considered in these tests for reasons discussed later.

14

15% below predicted consumption, which is a reflection of the 3 years of growth that has been forgone:consumption is roughly equal to its 1991 value.22

Another aspect of our results concerns extreme cohorts. If we look at cumulated effect by thefourth quarter of 1994, we realise that two of the largest downfalls are found for cohorts 11 (theyoungest) and 1 (the oldest).

The very large impact for cohort 11 could reflect attrition: our predictions are based on thecontrol period, when cohort 11 was steadily increasing in size. If late household formation is morecommon among better educated and wealthier individuals, the predicted growth rate for this cohortmight be inflated because of composition effects. However, the process of household formation is muchless marked over the treatment period (when the cohort mid-age is 30-31), so the discrepancy betweenpredicted and actual consumption could be over-estimated for this cohort.23

Inspection of Figure 2 reveals that the large downfall for cohort 1 is attributable to one specialfeature of our estimates: the predicted level of consumption continues to increase with age even forthose cohorts that are past retirement age. This surge in predicted consumption late in life is particularlymarked for cohort 1. Part of the decline below ‘normal’ consumption could be due to overestimating thepredicted level of consumption for older cohorts.

The estimated average age profile for consumption may be rising in old age, even though theactual profile is falling for each household alive in two adjacent periods, if the poor die younger than therich.24 In this case the cohort composition fails to remain constant with age. The composition effect islikely to be most important for cohorts of individuals in their sixties and seventies: for younger ages, theprobability of death is small; for older ages, the surviving cohort has become more homogenous inwealth. For cohort 1, for example, the composition effect would be strong over the control period, butnegligible over the treatment period, and this could explain why we get a rising predicted profile and afalling average profile over the treatment period. Given also that low average cell size for cohorts 1 and2 shown in Table 3.2, we shall take results for these cohorts (aged 75 or more in 1993) with somecaution.

A general problem with our analysis (that could affect estimated age profiles at the extremes ofthe age distribution) lies with the definition of ‘head of household’25: in the common case of extendedfamilies (working adult children often live with their parents in Italy, as documented in MW), the age ofthe head is an ill-defined concept. Extended families are a serious problem, but some partial solution mayexist. In those cases where the elder male is reported as head, even though he is retired and lives with hisson or daughter’s family, one can implement a procedure to change the head (this is done by MW forthose 3-5% of households where such situation is occurring).

After experimenting with changing the household head it was concluded that the rise in predictedconsumption after retirement age could not be attributed to mis-specifying the ‘true’ household head.Indeed, households with working age children did not suffer a greater fall in consumption compared toother types of families. While it is true that they are different to those households without working agechildren, in that they had higher levels of consumption, they are also dissimilar to households headed bysomebody of the child’s age with their parents living with them. However, extended families are

22 This discrepancy also reflects the differences between ISTAT survey and National Accounts growth rates highlighted inFigure 1. In Appendix 3 we show that the size of the recession is reduced to 13% when these differences are taken intoaccount.23 We allow for a cohort-specific intercept, but the coefficients on all other variables are restricted to be the same acrosscohorts. However, the shape of the age polynomial at its lower end is heavily influenced by the average age profile for theyoungest cohort.24 The effect of differential mortality on wealth-age profiles is discussed in Attanasio and Hoynes (1995).25 i.e. the member of the household who is really making the purchasing decisions.

15

intrinsically difficult to model, and the issue may be more complex than simply reassigning the householdhead.

The differential mortality and household formation issues discussed above are likely to affect lessthe cohort composition if we take the more homogenous group of better educated households living inthe North. This way we also have a higher proportion of nuclear families in the sample (less educatedhouseholds and households living in the South are also more likely to live in extended families).

For these reasons it is worth displaying results when a regression was run on the restricted subsetof all Northern households where the head had at least attended some secondary education; this is donebelow (see Table 5.2 and Figure 3).26 Here it can be seen that there are two peaks to the falls: the fall islargest for those around retirement and also for the youngest cohort.

Table 5.2: Average period percentage deviations from ‘normal’ consumption by cohort and time period for bettereducated in the North only (standard errors in parenthesis).Time cohort (age in 1993) All

1 (80) 2 3 (70) 4 5 (60) 6 7 (50) 8 9 (40) 10 11(30)

92:H2 -2.5(6.1)

-0.8(5.1)

-8.2(3.5)

-8.4(3.1)

-7.0(2.9)

-9.4(2.5)

-7.0(2.4)

-7.0(2.0)

-12.3(2.1)

-8.0(2.1)

-8.5(2.5)

-8.3(0.7)

93:H1 -10.1(6.6)

-23.7(5.4)

-8.3(3.7)

-0.5(3.2)

-19.6(2.9)

-16.3(2.7)

-19.0(2.5)

-11.6(2.1)

-16.2(2.0)

-17.7(2.1)

-15.9(2.4)

-15.0(0.8)

93:H2 -24.9(7.6)

-11.6(5.5)

-14.1(3.5)

-18.9(3.3)

-16.9(3.1)

-18.1(2.5)

-13.2(2.4)

-13.2(2.1)

-14.8(2.1)

-20.9(2.0)

-21.5(2.3)

-17.0(0.8)

94:H1 -8.9(7.4)

-0.1(6.0)

-4.3(3.7)

-16.6(3.5)

-14.5(3.1)

-14.1(2.7)

-13.4(2.4)

-18.2(2.2)

-16.9(2.1)

-17.0(2.1)

-24.1(2.5)

-16.1(0.7)

94:H2 -7.2(7.6)

-6.7(6.3)

-13.2(4.1)

-17.0(3.4)

-14.1(3.2)

-16.3(2.8)

-12.0(2.4)

-16.8(2.1)

-13.2(2.1)

-18.6(2.1)

-19.9(2.4)

-15.8(0.7)

all -9.9(2.9)

-8.8(2.4)

-9.5(1.5)

-12.0(1.4)

-14.4(1.3)

-14.8(1.1)

-12.8(1.0)

-13.3(0.9)

-14.7(0.9)

-16.6(0.8)

-18.3(0.9)

-14.4(0.3)

When we compare Table 5.2 to Table 5.1 we notice much reduced deviations for cohorts 1-3, aged 70or more in 1993. This can be attributed to the fact that the model now predicts flat consumption profilesafter retirement (as shown in Figure 3). We can also see that the average consumption ‘fall’ is 14.4% -only slightly smaller than the average fall for the full sample (15.2%), and that even in this sub-samplethe model fails to predict that consumption may fall (by large amounts) at any age.

26 The sub-sample includes all households where the head graduated at least the first tier of secondary education , ormiddle school (‘scuola media inferiore’)

16

Figure 3: Middle School graduates living in the North - Total Consumption

Whichever set of results we use, we find a 15% fall in consumption, and thus, by implication, alarge shock to lifetime income. The issue of whether such large shock can be reconciled with the reformsthat took place in 1992 and 1993 is addressed in Section 6.

Here we want to stress that the large figure may partly reflect the fact that in the ISTAT surveyconsumption both grows slightly more quickly in the control period and falls further in the treatmentperiod than in National Accounts data (see Figure 1 in Section 2): both would exaggerate the fall inconsumption that results from the definition of the recession employed in this paper. There were many(minor) modifications to the sampling and weighting in the ISTAT survey during throughout 1985-1994, which may have contributed to the mismatch between the national accounts and the ISTATsurvey.

There are three possibilities regarding the quality of our survey data:(a) the ISTAT survey is fully reliable;(b) the changes in sampling affected all subgroups equally;(c) the changes affected different groups by different amounts.

Implicitly this paper maintains that the first statement holds true. If the second statement is true then thecomparisons between subgroups will still be reliable. It is still possible to argue that, for instance, youngpeople have reduced consumption by more than old people. Only the third proposition isinsurmountable. If the second statement is true then the size of the drop in consumption will not be wellestimated. To address this issue some regressions using a difference term have been run, i.e. a termbased on the difference between the national account and the survey per capita consumption. Theseresults, and an explanation, are contained in Appendix 3.

17

5.1 Non-durable expenditure.

The early nineties saw a marked fall in durable purchases, as documented in Section 2. This ishardly surprising, given the remarkable growth in durable spending over the previous boom period (thelate eighties): by the early nineties consumers had accumulated remarkable stocks in durable goods.Given enough relative price variability, one could expect consumers to reduce spending on durablegoods in favour of non-durable goods and services even in the absence of a recession. Overall spendingcould be lower, and yet consumers could be enjoying the same utility level by running down theirdurable stock. But if non-durable spending was also reduced we should infer that consumers areadjusting their consumption path to decreased life-time resources. It is this link between non-durableconsumption and overall utility that motivates our interest in distinguishing between durable and non-durable components of consumer expenditure (even though this split can only be achieved by makingsome strong assumptions on the nature of various miscellaneous expenditure items in the ISTAT publicuse tapes).

Some results are recorded for non-durable consumption in Table 5.3. The fall in non-durableconsumption is smaller than the fall using total consumption, being some 10%. This is a reflection of theslower growth rate during the control period and smaller absolute fall during the treatment period.Again the recession can be dated from the third quarter of 1992; the first period in which consumptionclearly falls below predicted consumption.

Table 5.3: Average period percentage deviation from predicted non-durable expenditure (standard errors in parenthesis).Time Cohort (age in 1993) All

1 (80) 2 3 (70) 4 5 (60) 6 7 (50) 8 9 (40) 10 11 (30)

92:H2 -6.01(2.04)

-7.88(1.93)

-8.63(1.40)

-7.81(1.34)

-7.07(1.33)

-5.95(1.31)

-5.50(1.32)

-4.69(1.22)

-10.08(1.29)

-5.75(1.38)

-9.24(1.65)

-7.06(0.40)

93:H1 -9.29(2.24)

-13.11(2.08)

-14.93(1.46)

-4.70(1.38)

-7.94(1.34)

-10.05(1.33)

-11.11(1.35)

-11.53(1.24)

-12.95(1.29)

-11.67(1.35)

-12.44(1.60)

-10.81(0.40)

93:H2 -8.18(2.26)

-7.55(2.00)

-12.40(1.48)

-13.89(1.42)

-8.84(1.40)

-10.24(1.33)

-9.07(1.35)

-9.29(1.28)

-10.07(1.28)

-11.99(1.33)

-12.44(1.55)

-10.56(0.40)

94:H1 -13.76(2.42)

-9.00(2.16)

-5.87(1.53)

-8.01(1.47)

-6.04(1.38)

-9.20(1.37)

-9.64(1.37)

-10.95(1.29)

-10.98(1.30)

-8.86(1.38)

-17.13(1.63)

-9.72(0.41)

94:H2 -16.41(2.49)

-15.71(2.23)

-12.35(1.60)

-10.07(1.46)

-14.26(1.43)

-10.75(1.40)

-9.56(1.43)

-11.50(1.27)

-9.31(1.33)

-13.26(1.37)

-14.10(1.57)

-11.97(0.41)

all -10.35(0.92)

-10.42(0.86)

-10.81(0.62)

-8.82(0.59)

-8.73(0.58)

-9.21(0.56)

-8.95(0.57)

-9.55(0.52)

-10.68(0.54)

-10.41(0.55)

-13.17(0.63)

-10.00(0.18)

A comparison of Tables 5.1 (total consumption) and Table 5.3 (non-durable component) highlights thefollowing features:a) the second half of 1992 saw comparable declines in the two aggregate (9.6% versus 7.1%)b) by the end of 1993 the average downfall in total consumption was a massive 15.8%; a much smaller

shortfall can be found for non-durable spending (10.6% )c) 1994 saw further declines for total spending (19.7% by the end of the year), whilst its non-durable

component remained stable (12%).d) differences across cohorts are smaller for non-durable expenditure. If we look at the average

treatment period effect (bottom line) we see that the youngest cohort still shows the largest downfall,but for the remaining working age cohorts the shortfall is only one or two percentage points larger

18

than for recently retired cohorts (and of comparable magnitude to the shortfall for the three oldestcohorts).

Figure 4: Full Sample Analysis for Non-durable Goods and Services

agelog(consumption)

Finally, we present a set of estimates for non-durable consumption for better educated households wholive in the North. This compares directly to Table 5.2 above.

Table 5.4: Average period percentage deviations from non-durable ‘normal’ consumption by cohort and time period forbetter educated in the North only (standard errors in parenthesis).Time Cohort (age in 1993) All

1 (80) 2 3 (70) 4 5 (60) 6 7 (50) 8 9 (40) 10 11(30)

92:H2 -3.8(5.1)

-1.5(4.2)

-6.2(2.9)

-6.5(2.6)

-5.8(2.5)

-3.6(2.1)

-6.3(2.0)

-4.0(1.7)

-10.2(1.7)

-4.1(1.8)

-6.6(2.1)

-5.8(0.6)

93:H1 -10.5(5.6)

-18.5(4.6)

-5.8(3.1)

-1.7(2.7)

-12.1(2.5)

-8.3(2.2)

-12.7(2.1)

-9.9(1.8)

-11.6(1.7)

-12.1(1.7)

-12.9(2.0)

-10.5(0.6)

93:H2 -21.3(6.3)

-7.7(4.7)

-8.6(3.0)

-10.8(2.7)

-9.3(2.6)

-10.9(2.1)

-6.9(2.0)

-7.4(1.8)

-9.8(1.7)

-13.0(1.7)

-13.8(1.9)

-10.4(0.6)

94:H1 -7.9(6.2)

-3.5(5.1)

-1.2(3.1)

-9.3(2.9)

-9.9(2.6)

-8.9(2.2)

-6.5(2.0)

-9.0(1.8)

-9.5(1.7)

-8.8(1.8)

-18.6(2.1)

-9.2(0.6)

94:H2 -6.0(6.4)

-2.3(5.3)

-7.8(3.4)

-10.2(2.9)

-8.7(2.7)

-10.6(2.3)

-6.8(2.1)

-8.3(1.8)

-6.6(1.8)

-11.8(1.7)

-13.2(2.0)

-9.3(0.6)

all -9.3(2.4)

-5.7(2.0)

-5.4(1.3)

-6.9(1.1)

-9.2(1.1)

-8.4(0.9)

-7.8(0.8)

-7.7(0.7)

-9.6(0.9)

-10.1(0.7)

-13.2(0.8)

-9.0(0.3)

The average deviation is now 9.0%. If we disregard extreme cohorts (1 and 11), we see that youngerworking age households (cohorts 9 and 10) experience the largest shortfalls (9.6% and 10.1%) togetherwith the cohort that is exactly at retirement age in 1993 (cohort 5, -9.2%). Other working age cohortsare more severely affected than the cohorts past retirement age in 1993.

19

5.2 RegionsIn Italy regional differences have been historically of major relevance, at least between the

industrialised North and the more rural South. Much effort has gone into bridging this distance, withregional policies aimed at building infra-structure in the South and starting up industry in selected areas.Also, the Italian welfare system has concentrated benefit payments to some depressed areas of theSouth. Overall, there is evidence that in the 1980s the South started catching up with the North. Anotherreason why a comparison between North and South may be interesting is the more traditional nature offamily structure in the South: nuclear families are still numerous, and extended families are common.

Yet another regional comparison may be revealing: the North Eastern part of Italy is highlyexport orientated, and its manufacturing sector is largely made of medium to small sized firms located inclusters (the so-called ‘industrial districts’). The North West is instead characterised by more traditionalfirms, medium to large, surrounded by smaller firms that carry out jobs for the larger firms.

We analyse regional differences by running two separate regressions for the North and the South.Within each, we construct time-cohort-region dummies for the treatment period for the various sub-regions. This approach allows for any differences in the effect of the explanatory variables between thetwo broad regions: perhaps the age-profile of consumption is different in the North compared to theSouth, or it may be believed that any possible improvements in the response rate during the treatmentperiod in the South were biasing the results.27 It restricts coefficients to be the same within these broadregions for the control period, but allows for different responses during the treatment period.

We show in Table 5.5 results obtained by implementing this approach, . We see that thecontinental South cut consumption the most in 1992 and 1993, and the South overall suffered anaverage shortfall of 18.1% (that compares to a more modest 13.7% by the North). Within the North,the NE did much better early on (when the effects of the lira devaluation were more important), but bythe end of 1994 its shortfall was not noticeably smaller than the NW. By then, the Central region faredalmost as poorly as the continental South. But average downfalls confirm the wide-spread belief that theNorth East did relatively well in the recession, and the South relatively poorly.

Table 5.5: Percentage deviations from ‘normal’ consumption by region (standard deviations in parenthesis).

N o r t h S o u t hNW NE C N S I S&I

92:H2 -7.4(1.0)

-8.0(1.2)

-8.7(1.2)

-7.9(0.6)

-16.1(1.1)

-7.4(1.5)

-13.3(0.8)

93:H1 -14.4(1.0)

-7.6(1.2)

-19.0(1.2)

-13.8(0.6)

-21.5(1.1)

-13.5(1.6)

-18.9(0.8)

H2 -17.5(1.0)

-12.8(1.2)

-16.3(1.2)

-15.9(0.6)

-19.6(1.1)

-14.7(1.6)

-18.0(0.8)

94:H1 -14.9(1.0)

-12.1(1.2)

-17.6(1.2)

-14.9(0.6)

-18.3(1.2)

-15.5(1.7)

-17.4(0.8)

H2 -15.6(1.0)

-13.9(1.2)

-19.1(1.2)

-16.2(0.6)

-22.8(1.2)

-24.0(1.7)

-23.2(0.8)

92(H2)-94 -14.0(0.4)

-10.9(0.5)

-16.1(0.5)

-13.7(0.3)

-19.6(0.5)

-14.8(0.7)

-18.1(0.4)

27 Brugiavini (1996) explicitly compared the ISTAT and Bank of Italy surveys and reached the conclusion that the qualityof the ISTAT survey in the South was weaker than in the North.

20

In Figure 6 we graphically present the age-profiles for the two macro-regions.

Figure 6: Regional Analysis for Total Consumption

(a)

(b)

21

5.3 Education

The recession can also be analysed by looking at different educational groups. This is of interestfor a number of reasons:a) returns of education may have changed in a permanent way in the early nineties as a result of changes

of the pay structure between state and private employees;b) there may have been specific effects of policy changes on different educational groups.

On the former point, it is worth recalling that the 1992 budget froze public sector salaries andwages, by eliminating automatic seniority-related pay rises and by postponing public sector paynegotiations. In 1993 the trade unions agreed to end automatic inflation adjustments. Further to that,the strong need to cut government debt and the gradual privatisation programme meant that publicsector employees probably perceived the downward pressure on their pay to be permanent.

On the latter point, one of the reforms undertaken by the Amato government was to change thebasis on which the pension was received. The reform meant that the size of the pension would be basedon earnings over the entire working life, rather than a pension being received in proportion to the lastfive years salary. AB suggested that this should affect those with a steeper earnings profilecomparatively more, and that better educated people have a steeper earnings profile.

This provides a motivation to investigate the changes to behaviour of different educationalclasses. In this paper we are able to more thoroughly make this analysis, because of the much largersample size. We therefore assign the household to an educational category on the basis of the highestlevel of education attained by the household head, allow the shape of the age polynomial to varyaccording to education, and investigate treatment responses by these five educational groups.

Our evidence (shown in Table 5.6) is mixed. It appears that more educated people reducedconsumption in the South but not in the North. This may reflect the different type of jobs educatedworkers have in different regions: in Southern Italy the key employer of high-school and collegegraduates is the Government, whereas in Northern Italy educated workers are mostly employed in the(private) industry and service sectors.

When all regions are analysed together, it seems that people with high school diplomas anduniversity degrees did suffer a little more than others, but the pattern is not very strong. However, thesetables ignore the fact that on average younger people are better educated than older people. When ageis also controlled for it was found that there were no important differences in the changes inconsumption behaviour of the different defined educational subgroups: any differences seen below canbe attributed to the different age profiles of the groups. This is fairly simple to explain. The Amato andsubsequent reforms made pensions a function of contributions over the entire working life rather thanjust the last five years. This affects those with a steeper earnings profile, typically those who are bettereducated. However, wage indexation was abolished, and wage indexation had worked in a way thatartificially depressed wage differences. The other liberalising reforms of the labour market may alsohave increased wage inequality. The reduction in wealth caused by the new formula calculating pensionwealth is likely to be offset by the increase in the level of wealth resulting from the labour marketreforms. Overall, the results show that there is no net effect.

22

Table 5.6: Percentage deviations from ‘normal’ consumption by educational category (standard errors in parenthesis).Time region education

none(1)

primary(2)

middle school(3)

high school(4)

university(5)

92 N -5.0(2.0)

-7.0(0.8)

-3.4(0.9)

-4.3(1.0)

-4.7(1.8)

93 -19.2(2.1)

-14.4(0.8)

-16.2(0.9)

-15.9(1.0)

-13.4(1.8)

94 -20.4(2.2)

-16.3(0.9)

-15.9(1.0)

-16.6(1.1)

-13.2(1.8)

92-94 -14.6(1.1)

-12.4(0.4)

-11.8(0.4)

-12.3(0.5)

-10.5(0.9)

92 S -7.1(1.8)

-7.9(1.2)

-8.9(1.2)

-13.8(1.5)

-18.3(2.3)

93 -11.8(1.9)

-18.2(1.3)

-21.8(1.3)

-23.9(1.5)

-25.4(2.4)

94 -18.3(2.1)

-22.2(1.3)

-21.6(1-4)

-25.2(1.5)

-24.5(2.5)

92-94 -11.9(0.9)

-15.8(0.6)

-17.3(0.6)

-21.0(0.7)

-22.7(1.2)

92 All -6.0(1.4)

-7.3(0.7)

-5.1(0.7)

-7.1(0.8)

-9.3(1.4)

93 -15.0(1.4)

-15.5(0.7)

-17.9(0.7)

-18.3(0.8)

-17.4(1.4)

94 -19.2(1.5)

-18.1(0.7)

-17.6(0.8)

-19.3(0.9)

-16.8(1.5)

92-94 -13.0(0.7)

-13.5(0.3)

-13.5(0.3)

-14.9(0.4)

-14.5(0.7)

23

5.4 Employment sector

Ideally the survey design would allow the population to be distinguished between those currentlyworking in the public sector, employees in the private sector, those who are already retired, those whoare self-employed and are now subject to the minimum tax, and others.28 Then it could be seen whetherpublic sector workers had been adversely affected during the treatment period, and if the implementationof a minimum tax had reduced the consumption of the self-employed. Unfortunately, it is not possible toproperly classify people by economic sector in this way. The separation, at best, is imperfect.Nevertheless, an attempt has been made to investigate the various sectors that have just been suggested.

Table 5.7: Percentage deviations from ‘normal’ consumption by employment sector (standard errors in parenthesis).

self-employed public admin. Other services old people other(A) (B) (C) (D) (E)

decline -17.1(0.8)

-16.3(0.9)

-17.8(0.5)

-12.5(0.6)

-14.2(0.5)

Test 1: A=E, F=12.55, prob.>F=0.00 Test 4: D=E, F= 4.54, prob.>F=0.03Test 2: B=E, F= 5.25, prob.>F=0.02 Test 5: B=C, F= 1.54, prob.>F=0.21Test 3: C=E, F=13.33, prob.>F=0.00

These results show that old people (those over sixty) did significantly better than younger members ofthe population, but that they nevertheless suffered a large and significant decline in consumption. Theself-employed, those employed in public administration, and ‘other services’ (largely comprising teachersand hospital staff) reduced consumption by significantly more than other people of working age. It isnot possible to say that those in ‘public administration’ and those in ‘other services’ are different.

These results suggest that pension reform and/or the significant rise in taxes must have played animportant part in the reduction in consumption that was experienced from the third quarter of 1992.Those over 60 suffered a decline of over 12%, and because of their age they can not be concerned abouttheir future job prospects. These declines could not be attributed to mis-specifying the household head.The results also show that people of working age have suffered an additional decline of nearly 2%. Thereforms in the public sector seem to have caused consumption to decline by a further 3%, and similarresults are obtained for consumption made by the self-employed. These last declines suggest that thepublic sector reforms (either the process of privatisation, the reduction in employment levels, or thereform of public sector pension) have reduced the wealth of public sector employees and that the movesto ensure that the self-employed pay taxes has also reduced their lifetime wealth.

An attempt was made to see if the deeper recession in the South could be explained by the higherlevel of dependency on the public sector and on self-employment: but while the same general pattern ofreductions was shown as in the North, and in the whole of Italy, the decline for all categories wassignificantly larger in the South than in the North. It may be that the categorisation of household headsis not good enough to properly test this hypothesis; that the decline in the South can be attributed to theincreased prevalence of public sector employment (either directly, or indirectly through state subsidies ofprivate sector employment or through state holding companies) and self-employment in the South. Thesurvey design means this issue remains unresolved.

28 The ‘Others’ category includes both employees in employment and the unemployed. In the survey few heads areclassified as unemployed.

24

6. Discussion

The results highlight that the drop in consumption below the expected level was substantial,amounting to 9% or more. All cohorts suffered a decline and the fall was particularly pronounced forthe youngest cohorts but also large for those over the age of retirement. The fall was also larger in theSouth (especially for better educated people in this region). Can the reforms undertaken from 1992explain this reduction in consumption, and by implication the reduction in lifetime income?

Before proceeding it should be noted that two types of analysis are possible: one that attempts toexplain the average fall in consumption, the other that focuses on how the downfall for a particulargroup compares to the downfall of others. The former analysis implicitly assumes that the projectedgrowth rate is the correct benchmark, the latter applies the ‘difference of difference’ technique. Theresponse of different subgroups is compared to the predictions of alternative explanations of therecession. In neither case is the shock to lifetime income explicitly calculated, nor is any attempt made tomodel the way in which a shock to a household’s income affects current consumption. This would bedifficult to do without strong identifying assumptions.

In this paper we have mostly applied the difference of difference approach to evaluate ourresults. This implicitly recognises that the average level of the discrepancy is heavily influenced by thechosen control period (if we had been able to take the whole of the 1980s as our control period,projected average growth would have been smaller). But given that Italy experienced high growththroughout the post-war period, one could argue that a projected 3-4% growth rate is not completelyunreasonable.

In this section, we shall therefore ask the following questions: Why did consumption fall by anestimated minimum of 9% to some 15% below ‘normal’ consumption during the recession? Which, ifany, of the reforms could have induced such a large decline in lifetime income?

Recall that the main economic reforms of this period were:(a) pension reform(b) labour market reforms(c) public sector pay freeze and privatisation programme(d) large tax increases in order to try to balance the budget.

The pension reform comprised of a gradual raising of the retirement age (though this measure has beenwidely circumvented) and a cut in the level of provision (for those with less than 15 years ofcontributions) by changing the basis on which entitlement is calculated29 (the statutory level ofcontributions was largely unaffected even though the system would continue to be unbalanced).Henceforth, a pension will be received in proportion to the level of earnings over the entire working liferather than just the last few years, and the number of years in which contributions must have been madewas gradually raised.30 Lastly, the reforms meant that in future pensions would rise at the rate ofinflation rather than with wage increases: this change would be felt both by those currently retired andthose who will receive a pension in the future.

29 In Italy, on retirement the pension to be received is calculated as a function of earnings prior to retirement. This amountis then updated every year after retirement in line with wages (before the Amato reforms) and prices (afterwards). Boththe Amato reforms and the Dini reforms involved an adjustment in the formula by which the initial value of the pensionupon retirement was calculated: both also tried to encourage many ‘marginal’ workers to make contributions (especiallythe self-employed) and to curb early retirement, an issue of particular relevance in the public sector where it had beenpossible to retire on a full pension at the age of 35.30 This was true of the Amato reform in 1992. Hurd (1996) shows that eligibility for benefits has substantial predictivepower for the timing of retirement. This suggests that this reform would have caused those affected to defer retirement,rather than retire with a reduced pension.

25

In Table 6.1 below we present some naive calculations of the effect on the present value offuture lifetime income (assuming income to be the sum of future wage and pension income and also theliquidazione31 or severance pay) of such a reform for various rates at which the future is discounted.(Further simulations were run including current assets, current financial assets and current non-housingassets - all obtained from the Bank of Italy Survey - but results did not substantially change). It isassumed that the aggregate rate of growth is 1.5% and that the employee experiences a rate of growth inhis income of 2.5% each year between the age of 25 (when it is assumed he becomes a householder) andthe age of 50, then wages grow at 1.5% until the stylized individual retires at age 60 with a pensionworth 75% of his final salary, which then grows in line with the aggregate economy.32 The individual isassumed to live until 80. The change in pension income that the chart highlights corresponds to thechange in the starting value of pension and the fact that the pension no longer grows at 1.5% per yearafter retirement; instead it increases in line with inflation. The starting value is unchanged for somebodywho is 55 or more; the average over the last 10 years rather than the last 5 years for those with over 15years contribution (those between 40 and 55 in this case); and the average annual salary between theirage now (with the salary assumed to earn a return of 1% per year, as in the reforms) and at the time ofretirement for all those who are younger than 40. Also calculated is how such reforms affect theaggregate economy; this figure is a weighted average of the previous columns, with the weights obtainedfrom the distribution in the ISTAT survey. The reductions in pension wealth, shown in the first tworows, are remarkably similar to those obtained by, for instance, Mazzaferro (1995).

Table 6.1: Percentage decline in the current value of future lifetime income at discount rate δ (first two rows showschange in pension wealth only, the last 3 show the effect if contributions also increase 5%).

δ A g e all75 70 65 60 55 50 45 40 35 30 25

0 -3.0 -6.6 -10.1 -13.5 -13.5 -16.6 -16.6 -16.6 -18.8 -21.3 -23.8 -10 -2.7 -5.5 -7.7 -9.5 -9.5 -12.8 -12.8 -12.8 -15.1 -17.7 -20.3 -0 -2.6 -5.8 -8.9 -12.0 -9.2 -9.6 -8.4 -7.6 -7.9 -8.4 -8.7 -8.41 -2.5 -5.6 -8.6 -11.4 -8.6 -8.8 -7.6 -6.7 -6.9 -7.2 -7.4 -7.7

2.5 -2.5 -5.4 -8.2 -10.7 -7.7 -7.7 -6.4 -5.4 -5.4 -5.5 -5.5 -6.75 -2.4 -5.1 -7.5 -9.5 -6.4 -6.0 -4.7 -3.7 -3.5 -3.3 -3.1 -5.3

7.5 -2.4 -4.8 -6.8 -8.4 -5.2 -4.7 -3.3 -2.5 -2.1 -1.9 -1.6 -4.310 -2.3 -4.5 -6.2 -7.4 -4.2 -3.6 -2.4 -1.6 -1.3 -1.0 -0.8 -3.41 -2.5 -5.6 -8.6 -11.4 -9.7 -10.6 -9.8 -9.2 -9.6 -10.1 -10.5 -9.2

2.5 -2.5 -5.4 -8.2 -10.7 -8.9 -9.7 -8.9 -8.3 -8.6 -8.8 -9.1 -8.45 -2.4 -5.1 -7.5 -9.5 -7.8 -8.4 -7.6 -7.1 -7.2 -7.2 -7.2 -7.3

The table highlights that the average reduction in total ‘wealth’ (discounted future life-timeincome) due to the pension reform lies between 3.4% and 8.4%, depending on the chosen discountfactor. For example, if we take a 2.5% discount rate, we see that the average fall in wealth was 6.7% -

31 In Italy there is a compulsory savings scheme: the employer keeps a share of wage income which is then paid to theemployee when he leaves his job. It is assumed that this amounts to the equivalent of 3 times the final, annual salary andthat it is spent continuously over the retirement period.32 The aggregate growth rate chosen corresponds to the average rate of growth during the 1980’s. If a higher rate ischosen, 2.5% for instance, then the decline in total wealth is larger. Over the long term wages must approximately makeup a constant proportion of GDP. However, to ensure that the model has the property that older workers are paid morethan younger workers at any point in time, it is necessary that the individuals wages grow by strictly more than GDPgrowth (wages increase due to GDP growth and seniority effects). The differential chosen means that somebody who is 55is paid approximately 30% more than somebody who is 25.

26

this compares to our average estimated shortfall of non-durable consumption of 10% (see Table 5.3). Ifwe make the assumptions that the reform was unanticipated and that non-durable consumption isproportional to total consumption (i.e. to the sum of non-durable consumption and services from thedurable goods stock), we can tentatively conclude that the pension reform can account for much of therecession.

Perhaps the most interesting feature of the reform revealed in Table 6.1 is that, even though thefall in pension wealth was much larger for the youngest members of the population, the change in theoverall level of wealth (or discounted future lifetime income) was largest for those around the age ofretirement. Younger people anticipate substantial wage income: pension wealth makes up a muchsmaller proportion of total lifetime income, hence the fall in their total wealth is much lower. This isaccentuated as the future is discounted by increasingly larger amounts; the fall in pension wealth is stillaround 20% for 25 year olds when the discount rate is as high as 10%, but the corresponding fall inwealth is only .8% for this group.

In Table 6.1 we also present some calculations that allow for 5% contributions increase. TheAmato reform did not include any provision to include contributions, but left the system unbalancedover a very long transition period. Workers may have then expected such increase in contributions as aresult. We see that (at a discount rate of 2.5%) the main effect is to make the wealth reduction roughlythe same for all working-age households. When further (not reported) simulations were run with risingcontributions over time then the size of the decline and the comparison between subgroups can changedramatically, with some simulations showing that the largest decline was for the youngest cohorts(providing they were sufficiently patient and contributions increased sufficiently quickly) but the resultswere highly sensitive to starting assumptions.

In Table 6.2 we compare one particular set of simulated results (for δ= 2.5% with no increase incontributions) to the estimated percentage deviations of non-durable consumption from ‘normal’consumption for cohorts 2-10. We take non-durable consumption on the assumption that this is set tosome proportion of life-time wealth, but we note that if non-durable consumption is a necessity, anddurable consumption a luxury, the fall in non-durable consumption is an underestimate of the fall in life-time wealth.

Table 6.2: Comparison of simulated and estimated percentage deviations from ‘normal’ consumption by cohort (standard errors in parenthesis).

Cohort (age in 1993) All2 3 (70) 4 5 (60) 6 7 (50) 8 9 (40) 10 1-11

Table 6.1 - δ= 2.5% -2.5 -5.4 -8.2 -10.7 -7.7 -7.7 -6.4 -5.4 -5.4 -6.7Table 5.3

whole sample-10.4(0.9)

-10.8(0.6)

-8.8(0.6)

-8.7(0.6)

-9.2(0.6)

-8.9(0.6)

-9.6(0.5)

-10.7(0.5)

-10.4(0.6)

-10.0(0.2)

Table 5.4better educated North

-5.7(2.0)

-5.4(1.3)

-6.9(1.1)

-9.2(1.1)

-8.4(0.9)

-7.8(0.8)

-7.7(0.7)

-9.6(0.9)

-10.1(0.7)

-9.0(0.3)

If we compare the simulated falls in life-time wealth corresponding to a 2.5% discount rate to theaverage deviations in non-durable consumption for the whole sample we see that our simulations canexplain most of the reduction in consumption by households close to retirement age (cohorts 4-7), butleave much of the fall for the very young and the very old unexplained. A much closer match for oldercohorts can be found if we look at the estimates for the sub-sample of better educated households who

27

live in the North. In so far as non-durable consumption is a good proxy for total consumption, thiscomparison leads to the conclusion that the pension reform alone provides a very good explanation forwhat caused middle-aged (and retired) consumers to cut their standard of living in the early 1990s.33

If we believe that the pension reform can explain all the estimated consumption downfall of1992-94 (at least for better educated households living in the North), we must argue that youngerhouseholds anticipated rising contribution increases to meet the growing transitional imbalance of thereformed pension system. This is possible, particularly because the Amato reform made a cleardistinction in benefits between workers with more than 15 years of contributions and younger workers,who were more heavily penalised. This may have been taken as a signal by younger workers that theirgeneration had been chosen to pay for the transition to a balanced pension system. (See MW for someevidence on the importance of the 15 years contributions cut-off point).

Another reason why working age households reduced their non-durable consumption more thanrecently retired ones (and on a larger scale the same is true for durable expenditure too) is a mix oflabour market reforms and increased taxation.

The labour market reforms involved the final abolition of the scala mobile which meant thatwages would no longer automatically increase in line with prices.. Earlier reforms (1991) had also madeit much easier to dismiss private sector employees than had previously been the case. The medium andlong term effects of these reforms are not well understood: especially the effect on wage levels. It doesseem likely that wage inequality will increase and, in the short run, there will be a sharp increase in theunemployment level.34 Longer term wage income becomes much more uncertain: several authors arguethis causes a higher level of saving and an increased correlation between income and expenditure.

The last two reforms were the public sector pay freeze (and privatisation programme that wasundertaken throughout this period) and the increase in the level of taxation. Public sector employeespay freeze covered not only wage settlements but also automatic seniority pay rises. All these measureswere designed to address the large budget deficit in an attempt to meet the Maastricht criteria. Theattack on public spending, and privatisation are likely to have had stronger effects on the South, bothbecause a large part of the southern economy is dependent on employment in the public administration,and because many private sector jobs rely, to a substantial degree, on state subsidies. By contrast, theincreased taxation fell disproportionately on the self-employed as the introduction of a minimum taxmade it increasingly difficult for this sector to avoid taxation.35 Income tax returns increasedsubstantially in 1993 following the introduction of this tax. Overall, tax receipts increased by 5%.

33 The effect on consumption depends upon the extent to which the reforms were unanticipated. It seemed clear to manyobservers that the pension system was unsustainable: if this view had been taken by the general public (although this isagainst the gist of the argument contained in Jappelli (1995) among others) then this would suggest that the innovation inconsumption would be much smaller. On the other hand, many commentators have shown that the system after thereforms was still unsustainable (even after the Dini reform) and any imbalance would largely be met by younger workers.This may cause younger cohorts to reduce consumption by more than is suggested in Table 6.1 above.34 See Bentolila and Bertola (1990) for a discussion of this point. Note that if the increased unemployment as a result ofthese reforms was only a short run effect then, while affecting current income, it will not affect permanent income andhence (in the theoretical scheme adopted in this paper) current consumption. However, the increased employmentvolatility may have implications for current consumption.35 The very low effective tax rates in this sector prior to 1992 meant that there were substantial advantages in being self-employed, and helps to explain (together with the fact that small firms were largely exempt from the employmentlegislation) why, in Italy, this sector accounted for such a large proportion of employment.

28

Without a full-scale simulation model of household behaviour under uncertainty that takes intoaccount incentive effects on labour supply and savings we cannot really say if our estimation results canbe interpreted as the optimal response of life-time consumers to the particular mix of policy changes thattook place in early 1990s. But the calculations shown above are useful in assessing the role of thepension reform, and shed some light on what other factors may have played a significant role in the1993-4 recession.

7. Conclusion

This paper develops a model of consumption by Italian households in normal years (a control periodcovering 1985Q1-1992Q2) and then calculates the deviations from the ‘normal’ level of consumption inthe early 1990’s (1992Q3-1994Q4). It shows that the fall in the level of consumption was substantialfor all age-groups, but stronger for households headed by working age people. The reduction in the levelof consumption was also stronger in the South, amongst those working in the public sector, and forthose who were self-employed. In contrast to other studies (Attanasio Brugiavini, 1996) we do not finda significantly different change in behaviour among the better educated compared to those less welleducated. Finally an attempt was made to reconcile these observed changes in consumption behaviourwith the change in life-cycle wealth induced by the various reforms of the early 1990’s.

We find that the Amato pension reform of 1992 can explain most of the fall in (non-durable)consumption for better educated households living in the North, and can particularly explain thereduction made by households nearing or past retirement age. For younger households there is asignificant unexplained component that could be due to the expectation of high pension contributions topay for the transition period.

The pattern of consumption shortfalls by region and education suggests that other reforms arealso likely to have an important impact: the pension reform explains about half of the non-durableconsumption shortfall for the young and the old when the full sample is considered. While no formalmodel has been developed, we suggest that the likely effect of the changes in employment regulationswould have been felt more by less educated workers. Also, these reforms have probably increasedincome uncertainty most for younger workers, thus reducing their level of consumption.36 The generalrise in taxes is likely to have caused some reduction in consumption particularly for the worst affectedgroup: the self-employed sector. The larger than average declines in consumption by public sectoremployees can be fully reconciled with the reforms that this group experienced. The deterioration ofpublic sector pay and the privatisation programme can also explain why consumption fell lower in themore state-dependent South.

While it is possible that there are other causes of the observed changes in behaviour, we believethat the evidence here strongly supports the hypothesis that there was a shock to lifetime incomeinduced by the reforms of the Italian economy during the early 1990’s and that in turn these induced thefalls in current consumption.

36 See, for instance, Kimball (1989) or Zeldes (1989).

29

Appendix 1: Estimation ResultsWe report here estimation results for the coefficients on explanatory variables over the control period. Inall regressions we also included an intercept and 11 monthly dummies. The columns are numberedsimilarly to the text. Standard errors in italics.

5.1 5.2 5.3 5.5A 5.5Bnumber of observations 285762 109359 285761 192044 93718R-squared 0.3576 0.2262 0.3891 0.3499 0.3329Adjusted R-squared 0.3572 0.2251 0.3888 0.3494 0.3318Regions

North West .3190 .0915 .2933 .0824.0039 .0041 .0033 .0032

North East .3175 .0769 .2671 .0804.0042 .0046 .0035 .0035

Centre .2362 .2254.0042 .0035

South -.0099 -.0012 -.0100.0041 .0034 .0039

Log(family size) .5639 .4510 .5273 .5589 .5686.0068 .0112 .0058 .0083 .0121

Sex (male) .1043 .1044 .1254 .1015 .1074.0045 .0071 .0037 .0052 .0086

age .0503 .0301 .0575 .0417 .0643.0078 .0126 .0066 .0098 .0129

age2/1000 -.3299 .1963 -.6428 -.1037 -.671.1606 .2731 .1359 .2002 .2701

age3/100,000 .0716 -.327 .308 -.0669 .324.104 .186 .0880 .129 .178

No working age children .5134 .5122 .3414 .5310 .4851.0219 .0313 .0185 .0276 .0362

EducationNone -.5765 -.5196 -.5439 -.6308

.0061 .0051 .0082 .0095Primary -.4435 -.3895 -.4225 -.4832

.0048 .0040 .0060 .0081Middle School -.3417 -.3295 -.2907 -.3319 -.3582

.0048 .0058 .0041 .0060 .0082High School -.1758 -.1666 -.1552 -.1684 -.1884

.0050 .0061 .0042 .0062 .0085Baby (0 - 2 years) -.1353 -.1214 -.1286 -.1578 -.1049

.0047 .0073 .0040 .0064 .0072Young Kids (3-5years) -.1172 -.1036 -.1079 -.1346 -.0950

.0041 .0065 .0034 .0055 .0062Kids (6 - 13 years) -.0827 -.0585 -.0696 -.0890 -.0719

.0029 .0049 .0025 .0039 .0046Other Kids (14-23 years) -.0010 .0180 .0094 .0072 -.0077

.0026 .0046 .0022 .0034 .0042Not Married Couple -.0070 -.0317 .0209 -.0190 .0175

.0053 .0086 .0044 .0063 .0097

30

5.1 5.2 5.3 5.5A 5.5BOld (61 + years) -.0346 -.0266 -.0216 -.0376 -.0276

.0030 .0055 .0026 .0036 .0056No working age children

cohort 1 -1.5155 -1.5094 -1.1626 -1.5645 -1.4366.0300 .0549 .0254 .0370 .0518

cohort 2 -1.3383 -1.3551 -1.0059 -1.3872 -1.2562.0276 .0460 .0233 .0341 .0471

cohort 3 -1.1648 -1.2467 -.8621 -1.2191 -1.0663.0257 .0404 .0217 ..0320 .0434

cohort 4 -1.0140 -1.0531 -.7326 -1.0590 -.9361.0239 .0361 .0202 .0299 .0398

cohort 5 -.8524 -.8932 -.6059 -.8936 -.7826.0220 .0321 .0186 .0277 .03629

cohort 6 -.6865 -.7472 -.4848 -.7251 -.6216.0200 .0280 .0169 .0253 .0324

cohort 7 -.5573 -.5982 -.3898 -.5903 -.5016.0181 .0247 .0153 .02320 .0290

cohort 8 -.4143 -.4467 -.2941 -.4403 -.3688.0159 .0213 .0135 .0204 .0252

cohort 9 -.2864 -.2979 -.2112 -.2985 -.2646.0132 .0176 .0111 .0169 .0208

cohort 10 -.1548 -.1543 -.1201 -.1619 -.1415.0097 .0130 .0082 .0126 .0153

With working age childrencohort 1 -.6464 -.7120 -.5015 -.6454 -.6781

.0272 .0580 .0230 .0328 .0491cohort 2 -.5413 -.6364 -.3987 -.5503 -.5390

.0238 .0454 .0202 .0291 .0418cohort 3 -.4438 -.5372 -.3265 -.4564 -.4288

.0202 .0351 .0171 .0247 .0355cohort 4 -.3328 -.3949 -.2505 -.3535 -.2955

.0177 .0293 .0150 .02169 .0310cohort 5 -.2288 -.2664 -.1660 -.2448 -.2001

.0158 .0258 .0134 .0193 .0277cohort 6 -.1183 -.1327 -.0808 -.1223 -.1119

.0149 .0240 .0126 .0182 .0260

31

Appendix 2: The structure of the utc during the control period

The method described in Section 3 attributes any trend in the dependent variable to a combination ofcohort and age effects. If cohorts are defined over a year-of-birth band larger than just one year, eachhousehold age must consistently be taken to be the mid-age for the cohort.

In our work, we used 5-year wide cohorts. In table A1 we show what happens to the regressionerror if age is instead defined as the actual household age: we see that there is a significant time trend inthe error. This is true even for smaller year-of-birth bands (3 or 4). Failing to use the mid-age of thecohort also makes a substantial difference to the degree to which one generation is richer than another; itcauses the differences in the cohort dummies (and hence the implied rate of growth) to fall substantially.

Table A1: Table highlighting the trend in the error when the mid-age is not used.Cohort width coefficient on year standard error prob. of t-statistic significant(no. of years) (% growth rate) (at 5% level)

1 0.00 0.17 0.997 No2 0.37 0.17 0.091 No3 0.82 0.17 0.005 Yes4 1.23 0.17 0.001 Yes5 1.76 0.17 0.000 Yes10 3.07 0.17 0.000 Yes

To see how this can happen, let us consider the above diagram where the true consumption ageprofile is assumed to be flat. Within the cohort there are several sub-generations (denoted g1,...,g5)which differ in their average level of wealth. These sub-generations are observed at different ages (a tickdenotes a time period in which the cohort is observed): later born sub-generations are richer, and areobserved at younger ages. If all these sub-generations are assigned to the same cohort then the slope ofthe age-profile of the consumption function is under-estimated (as shown in the diagram, in which the

32

downward sloping profile is estimated using actual age). The consumption of each cohort will be over-predicted at the beginning of the control period and under-predicted at the end of the control period (aswill be the aggregate economy). By taking mid-age this within-cohort variability is not used inestimation.

The under-prediction of the aggregate trend can also affect comparisons between subgroups inthe treatment period. The under-prediction of the cohort specific trend depends ceteris paribus on thetrue shape of the part of age-profile of consumption that the cohort inhabits.37 The under-estimation isreduced as the slope of the age profile is reduced, and finally disappears when the slope approachesminus infinity. In our case the slope of the age-profile is almost constant, and hence the under-estimationis similar for all cohorts. However, if the true slope had been hump-shaped then not only would thetrend in consumption be under-estimated for all cohorts, but the under-estimation would be worse foryounger cohorts than for older cohorts. If there had been no deviation from trend in the treatmentperiod then all the dummies would be positive and the dummies would be larger for younger cohortsthan older cohorts.

Even when the errors are without trend, there is an issue about their serial correlation properties. Toaddress this issue, let’s rewrite the regression equation:

H p age z uitc c

itc

ctt

ct tc

itc= + + ′ + + +

∈∑α γ β θ ε( )

Ω (A1)

We can investigate whether the utc are either contemporaneously (as in equation A2) or are inter-

temporally (as in equations A3a and A3b) correlated.

( )E u utc

td ≠ 0 c d≠ (A2)

( )E u usc

tc ≠ 0 s t≠ (A3a)

( )E u usc

td ≠ 0 c d≠ s t≠ (A3b)

Obviously, it makes no sense to include the treatment period in the analysis of these errors, hence let’srestrict ourselves to the control period. To make things clear, the following process is undertaken: firstequation A1 is estimated on the whole data set; then the errors ut

c are taken for the control period only.

If we write [ ]u u ut t tM= 1 , , ’K then we can test A2 by testing whether u ut t′ is a diagonal matrix. A

Breusch and Pagan (1980) chi-squared test on the estimated off-diagonal elements of the variance-covariance matrix reveals that the cohort specific errors are contemporaneously correlated.

In order to test for serial correlation we consider the following vector auto-regressiverepresentation:

u R u et jj

t j t= +∑ −

37 The degree of under-estimation also depends on the growth rate of per capita income, and the number of years in thecontrol period; as the number of years becomes larger the under-estimation decreases. However, if the length of thecontrol period is increased it becomes increasingly unlikely that consumption will be stable throughout the period.

33

where et is a vector of (contemporaneously correlated) white noise terms. Here testing for auto-correlation is equivalent to testing whether the R matrices are null. If we have scalar auto-regression (asin A3a) then this would imply that the R’s are diagonal.

The errors ut were constructed as monthly errors in the manner stated above. Due to thesampling strategy used by ISTAT (whereby districts are not randomly assigned within a quarter), itmakes no sense to use lags of one month, two months, etc.. We also need to keep the number ofestimated parameters relatively small, and hence use quarterly lags up to one year (i.e. j = 3 6 9 12, , , ).The matrices R j were estimated by OLS, and found not to be significantly different from zero. Using

the control period only in the construction of the ut did not change the results, nor did taking only athree month lag. Testing whether the R j are null when they were imposed to be diagonal also left this

conclusion unaffected.

Appendix 3: Including a difference term.

As discussed in the main text, the sampling in the survey was changed slightly during the period 1985-1994. In the regressions presented in the text and in Appendix 1 this issue has been ignored. Here wepresent some evidence from a specification that ‘corrects’ for any mis-match between the ISTAT surveyand the national accounts. A diff term is constructed as being the difference between per capitaconsumption in the national account data and the ISTAT survey.

diff y yt tNA

tI= − (A9)

This difference term was then included as an explanatory variable in the regression. Underpinning theuse of this term is the belief that the national account data gives a reliable account of aggregateconsumption and that any changes in sampling in the ISTAT survey affected all groups equally: if groupswere differently affected, then any inter-temporal analysis using this data set would be invalid. Theresults are shown in table A2 below: the key finding is that the average downfall is now 13.2% asopposed to 15.1% (see Table 5.1).

Table A2: Percentage deviations from ‘normal’ consumption by cohort when the ‘diff’ variable is included (standarderrors in parenthesis).

time c o h o r t all1 2 3 4 5 6 7 8 9 10 11

92: H2 -6.6(2.4)

-8.1(2.3)

-9.8(1.7)

-8.2(1.6)

-8.3(1.6)

-9.5(1.6)

-5.8(1.6)

-6.0(1.5)

-10.8(1.6)

-8.5(1.7)

-11.3(2.0)

-8.4(0.5)

93: H1 -9.9(2.7)

-14.2(2.5)

-14.9(1.8)

-5.1(1.7)

-9.3(1.7)

-15.3(1.7)

-16.0(1.7)

-12.8(1.6)

-15.5(1.6)

-15.7(1.7)

-15.3(1.7)

-13.2(0.5)

H2 -7.8(2.8)

-8.5(2.4)

-15.1(1.9)

-16.4(1.8)

-11.4(1.8)

-15.0(1.7)

-14.2(1.7)

-13.2(1.7)

-13.7(1.7)

-16.6(1.7)

-18.5(1.9)

-14.3(0.5)

94: H1 -15.4(2.9)

-9.8(2.6)

-8.5(1.9)

-9.9(1.8)

-7.9(1.7)

-14.2(1.7)

-16.2(1.7)

-15.8(1.6)

-16.1(1.6)

-14.2(1.7)

-22.2(2.0)

-13.8(0.5)

H2 -17.5(3.0)

-18.4(2.7)

-16.1(2.0)

-13.5(1.8)

-18.3(1.8)

-17.1(1.7)

-14.4(1.8)

-16.5(1.6)

-14.0(1.7)

-18.8(1.7)

-17.8(1.9)

-16.4(0.5)

92:H2-94:H2

-11.0(1.1)

-11.5(1.0)

-12.8(0.7)

-10.5(0.7)

-10.9(0.7)

-14.2(0.6)

-13.3(0.7)

-12.8(0.6)

-14.0(0.6)

-14.9(0.6)

-17.2(0.7)

-13.2(0.2)

34

Appendix 4: An explanation of the diagrams

Some explanation of the construction of the graphs is necessary. For each cohort a mid-age wasassigned (for cohort 1, age 72 in 1985, age 73 in 1986 etc.; for cohort 2, age 67 in 1985, 68 in 1986etc.; and so on). The regression is then run and a predicted level of consumption for every combinationof cohort and year is calculated. In each year, each cohort has a predicted level of consumption which isplotted against the mid-age of that cohort. In the next year the cohort is one year older and this newyear’s predicted level of consumption is also plotted against the cohorts mid-age for that year. This isdone for every year; a line is plotted joining adjoining years of the same cohort. During the treatmentperiod the average actual level of consumption of the cohort was calculated and again a line connectingconsecutive years was drawn. For simplicity the diagrams do not label the actual year of theobservation. The difference between the predicted and the average level of log-consumption is therecorded in the tables.

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