Formal and Informal Remittance Flows: Estimation and Determinants

Marin ROSCOVANErasmus School of Economics

332013mr@student.eur.nl

Abstract I study the determinants of officially recorded remittance flows for a sample of 20 developing economies in Central and Eastern Europe for a period from 1990 to 2011. For a subsample of my data, I am able to estimate the share of unofficial (informal) remittance flows over the entire period. To estimate the unofficial share of remittances I append a set of regression models with a dummy variable to capture surprisingly low remittance flows in the larger migrant countries, following the methodology proposed by Page and Plaza (2005). My results show that remittance flows are positively related to the country’s GDP, the dollar exchange rate, and negatively related to the risk premium rate and the labor force participation rate. Finally, my analysis provides an estimate of the unofficial remittance flows for 14 out of 20 countries in my sample. I find that on average a significant 48% share of the total remittances are sent to 14 out of 20 countries through informal channels.

Supervisor: Prof. Laura HeringErasmus School of Economics

Table of Contents

1. Introduction..........................................................................................................................................3

2. Literature Review.................................................................................................................................4

Theoretical Determinants of Remittances....................................................................................................4

Formal and Informal Remittance flows........................................................................................................7

Determinants of Informal Remittance Flows................................................................................................9

3. Hypothesis Development...................................................................................................................11

4. Methodology......................................................................................................................................13

5. Data....................................................................................................................................................15

6. Results................................................................................................................................................20

Regression Analysis....................................................................................................................................20

Estimation of Informal Remittances...........................................................................................................23

7. Conclusions........................................................................................................................................24

8. References..........................................................................................................................................25

1. Introduction

A study by the World Bank reports that despite the consequences of the global financial crisis

cash remittances have grown at a fast rate over the last years. The report suggests that in 2011

the recorded transfers increased by 12.1% and that they are expected to grow even more by

2014 ( Ratha and Silwal, 2011). Even these estimates, however, are very likely to underreport

the true value of total remittances, since a large share of remittance flows are believed to be

sent through informal channels. Some studies suggest that the unrecorded remittances (sent

through unofficial channels) could take up from 35% to 250% of the recorded values (Celent,

2005; AITE, 2005) In this paper I test the determinants of recorded remittance flows and

estimate the unofficial remittance values for a sample of 20 developing countries in Central and

Eastern Europe.

The current literature suggests a number of theoretical determinants of remittances, where the

main question posed is why people send remittances. Two main reasons stand behind this

decision: (i) individual motives as in Stark (1995) and in Agarwal and Horowitz (2002) who study

altruistic incentives and strategic motives to remit, or in Cox (1987) who studies remittances as

means of exchange, and in Rosenzweig, (1988) who qualifies remittances as insurance, and (ii)

family arrangements discussed in Bernheim, Shleifer, and Summers (1985). In a second stage,

migrants decide to choose the sending channel for their remittance flows. These can be formal

or informal channels. The main drivers of an individual’s decisions on channels to remit are: the

costs associated with the transaction, the security of the funds and the geographic accessibility

(Freund and Spatafora, 2004).

In this study I estimate the determinants of remittance flows for 20 developing countries in

Central and Eastern Europe. I rely on the econometric methodologies proposed by Page and

Plaza (2005) and by Freund and Spatafora (2004) to estimate the total remittance flows,

controlling for migration levels, GDP, the risk premium, the dollar exchange rate and the labor

participation rate. I find that migration, GDP, and exchange rate determine remittance

positively, while the risk premium and the labor participation rate affect remittances negatively.

These results are in line with previous studies that have focused on different samples and

periods.

Finally I use the above mentioned econometric models to estimate the informal remittance

flows for the 20 countries in my sample. I find that on average, an economically sizeable share

of 60% of total remittances is sent through informal channels.

The rest of this paper is organized as follows. Section 2 reviews the relevant literature. Based on

the findings in the literature, Section 3 elaborates a set of testable hypothesis and implications.

Section 4 and 5 discuss the methodology and data respectively. I present my results in section 6.

The last section concludes.

2. Literature Review

Theoretical Determinants of Remittances

In today’s globalized world economy, economic integration goes beyond international trade and

capital movements, and increasingly involves international labor mobility and the associated

financial flows. The time trend in these particular series has been very pronounced throughout

the last decade. Clearly, the decision to remit is tightly linked to the causes of migration. To

better understand the current state of remittance flows in developing countries, I first start by

laying down the main driving forces of these important phenomena. Later I look at the factors

that determine an individual’s decision when choosing the provider for remitting money.

Towards this end, I intend to better understand the characteristics of remittance flows as well as

identify a suitable model to estimate the number of both formal and informal remittance flows

for the developing countries in Eastern Europe

While there is clear evidence that migration and remittances are tightly linked, the primary

determinants of these phenomena seem to differ substantially (Funkhouser, 1995). Particularly,

it seems that remittance behavior is not simply predicted by migrant’s characteristics and its

analysis requires specific attention. According to Rappoport and Docquir (2005), the main driver

of international migration is international wage differentials, implying that people are ready to

incur substantial moving costs in order to access to international migration. Such migration

costs are typically financed by remittances. As such, economists recognize migration as an

informal family arrangement, with benefits in the realms of risk-diversification, consumption

smoothing, and intergenerational financing of investments. In that respect, remittances play a

central role in such implicit contracts.

Remittances are primarily driven by two types of motives. These are: (i) individual motives and

(ii) family arrangements which are similar to those that also drive migration. I describe each of

these classes of motives below. Before that, however, I note that my definition of remittances is

consistent with Culiuc (2006) and includes funds brought back by the migrants themselves.

Individual motives The most common motive to remit is the fact that migrants care for their

families left behind, or the so called altruistic incentives described in detail by Stark (1995). This

altruistic motive towards spouses, children, parents, and members of larger kinship and social

circles has been more often assumed than tested, and only recently Agarwal and Horowitz

(2002) provide comprehensive evidence of its important economic and statistical significance

Another important individual motive considered by the literature is to view remittances as

means of exchange. As such, remittances may serve as a way to “buy” various goods or services

that facilitate taking care of migrant’s assets or relatives at home. These motivations are most

consistent with temporary migration, and signal, according to Cox (1987), the migrant’s

intention to return. Both, the altruistic motive and the means of exchange motives are driving

remittances in a world with perfect information.

In reality, it is often the case that various informational asymmetries influence economic

decision making, including the decision to remit. One such informational imperfection may

relate to the way employer at destination evaluation the migrants’ productivity. These

individual skills are generally unobservable, or imperfectly observable, hence, employers might

decide to provide fix salaries for the migrant during certain evaluation periods. If migrants are

able to influence these beliefs, remittances may be used strategically and aim at positive

selection among migrants. As such, remittances gain a strategic motive specific to the context of

migration where it has first been developed (Stark, 1995). Stark (1995) argues that remittances

may be part of a strategic interaction to positively select migrants. The idea behind such

selections is simple. With heterogeneous migrant skills and unobserved productivity on the host

country labor market, employer will discriminate migrants by paying migrant workers exactly for

the average productivity of the minority group to which they belong. If that is the case, then

skilled and unskilled migrants may decide to co-operate where the later may bribe the first in

order to maintain them home. Additionally, those left behind must also control potential free

riders, since in such a world unskilled migrants may have high incentives to migrate.

Since income is very volatile in most developing countries where agriculture plays an important

role of the economy, migrants would insure their remaining family members subject to such

volatility risk against drops in their income. In this case, remittances have an insurance motive

and should be inversely correlated with the incomes of families at home (Rosenzweig, 1988). At

the national level, this implies counter-cyclicality of remittance flows (Culiuc, 2006).

TABLE 1: Theoretical motivations to remitThe table below presents the theoretical determinants to remit as predicted by the literature. These are: migrant’s income, the (recipient) household’s income, the value of (recipient) household’s assets, the severity of adverse short run shocks to (recipient) household’s income, the migrant’s risk tolerance level, the number of migrants per in a given household, the time since migration, and finally the migrant’s location distance from her family. ‘+’ denotes an economically significant positive effect, ‘-‘ denotes an economically significant negative effect, while ‘x’ denotes economically insignificant effects. Nonlinear effects are denoted using ‘-/+’, suggesting a first negative and then positive relationship, while ‘?’ reflect undetermined economic effects.

Explanatory effects Individual Motives Familial arrangementsAltruism Exchange Inheritance Strategic

MotiveInsurance Investment

Migrant Income + + + + x +Household Income - ? x - x ?Household assets x X + x x xShock to household + ? x + + +Migrant risk level ? + ? + + +Migrant education x - x + x +No. of migrants in household

- X -/+ x x x

Time since arrival - X x - x xDistance from family - X - x x +Source: Rapoport and Docquier (2005)

Familial arrangements Similar to the insurance and moral hazard motive described above,

families might use remittances as repayments of loans on investments in education and/or

migration. This way, remittances might serve an Investment motive that familial implicit

contracts aim at increasing family income rather than at reducing uncertainty. Implementing

such loans may require complex decision procedures as to the amount to be financed, the

various sources to be solicited for fund-raising, and the recipients of the loans.

Except when strong mutual altruism is in place, remittances would only take place when there is

a welfare gain for all the parties concerned. Clearly, remittances imply a specific arrangement

between senders and recipients that might have a number of motives. However, such temporal

interaction may result in opportunistic behavior. That is the sender might deviate from any form

of commitment taken. To enforce such family arrangements, the literature proposed two

different motives. One, relates to a threat of denied access to family inheritance and/or return.

Economically, this refers to the theory of strategic bequest initiated by Bernheim, Shleifer, and

Summers (1985). The second enforcement devise is closely related and defined as social norms

in the literature.

Obviously, it is never the case that remittances are driven by one of the motives described

above. In fact, most often, remittance flows are a result of a combination of different motives,

this mixture varying from region to region. I summarize below the most popular variables that

might influence remittances according to the current theoretical literature. Table 1 also presents

the directions, whenever possible, of these economic relationships.

In the next section, I look at the channels migrants chose to send their remittances through. I

explain why migrants may choose formal and informal channels to remit, and argue which of

these channels are more suitable for both remittance senders and recipients.

Formal and Informal Remittance flows Recently аn increаsing аmount of аttention hаs been given to the size аnd impаct of informаl

remittаnces. While formаl remittаnces refer to those remittаnces which enter а country through

officiаl bаnking chаnnels, informаl remittаnces include those money trаnsfers which occur

through privаte, unrecorded chаnnels. Such privаte trаnsfers include remittаnces brought home

by friends, relаtives аnd even the migrаnt himself/herself. While formаl remittаnces to

developing countries now totаl over $483 billion (2011) a year, the level of informаl remittаnces

is unknown becаuse they tend to flow through unrecorded chаnnels. Estimаtes of the size of

informаl remittаnces vаry widely, rаnging from 35 to 250 percent of formаl remittаnces (Celent,

2005; AITE, 2005).

In one of the few empiricаl аttempts to estimаte the size of informаl remittаnces Freund аnd

Spаtаforа (2005) use insights from the literаture on shаdow economies to estimаte informаl

remittаnces for more thаn 100 developing countries. Results suggest thаt informаl remittаnces

аmount to 35 to 75 percent of formаl remittаnces to developing countries. Findings аlso suggest

thаt the size of informаl remittаnces vаries by region: informаl remittаnces to Eаstern Europe

аnd Sub-Sаhаrаn Аfricа аre high, while those to Eаst Аsiа аnd the Pаcific аre relаtively low.

Other work suggests thаt the level of informаl remittаnces аlso vаries by type of migrаnt, thаt is,

internаl or internаtionаl migrаnt. For exаmple, а recent household survey in Ghаnа (Аdаms,

2007) found thаt while only 1 percent of internаl migrаnts remit through formаl chаnnels

(bаnks, Western Union, post offices), 43 percent of internаtionаl migrаnts remit through formаl

chаnnels. These figures аre interesting becаuse they reveаl thаt fully one-hаlf of аll internаtionаl

migrаnts in Ghаnа prefer to remit through informаl chаnnels, nаmely, through friends аnd

relаtives.

One importаnt fаctor cаusing migrаnts to remit through informаl chаnnels is the high cost of

trаnsferring funds through bаnks аnd trаnsfer аgencies. In 2000 the аverаge cost of remitting

money to 8 Lаtin Аmericаn countries wаs higher than 10 percent of the аmount being sent

(Orozco, 2006). By 2006 the trаnsаction cost of remitting money to these Lаtin Аmericаn

countries hаd declined to 5.6 percent, but still this figure is much higher thаn what is chаrged by

informаl chаnnels.

From а policy perspective, it is importаnt to reduce money trаnsfer costs in order to increаse

the аmount of remittаnces returning through formаl chаnnels. Remittаnces sent through officiаl

bаnking chаnnels cаn fаcilitаte finаnciаl sector development in developing countries in а

number of wаys: (1) аs bаnk deposits from remittаnces increаse, bаnks аre аble to mаke more

loаns; (2) remittаnce receivers who use bаnks cаn gаin аccess to other finаnciаl products аnd

services; аnd (3) bаnks thаt provide remittаnce trаnsfer services аre аble to “reаch out” to

unbаnked recipients аnd those with limited finаnciаl intermediаtion (Аggаrwаl et аl, 2006). Аlso,

in economies where the finаnciаl system is underdeveloped, remittаnces mаde through officiаl

chаnnels cаn help аlleviаte credit constrаints аnd promote growth (Giuliаno аnd Ruiz-Аrrаnz,

2006).

Determinants of Informal Remittance FlowsWhile the remittance flows have been growing intensively for the past two decades, most of the

studies have managed to capture only its formal aspect. As such, it is consistently argued that

the true value of remittances is severely underestimated. Previous studies estimate that the

informal channel represents from 35% to 250% of the recorded remittance flows varying from

country to country (Celeste (2005), AITE (2005)). Below I look at some of the main factors that

affect the choice of the distribution channels.

There are numerous factors migrants take into consideration before choosing the distribution

channel. According to Freund and Spatafora (2004), the main factors determining the choice of

provider of remittances are: cost of transaction, speed of transaction, security of funds,

geographic accessibility, and convenience in terms of familiarity and language. I elaborate on

each of these in what follows.

The cost of transaction Typically, the transfers made through informal channels are considered

to be cheaper than formal ones. Various studies estimated the costs of remitting to vary

between 1 – 5 % of the total value of remittance (Sander, 2003, Swanson and Kubas, 2005). On

the other hand, formal remittance flows (banks, money transfer operations, etc.) are as high as

13% of total remittances. High transactions costs associated with the formal channel are due to

the high minimum fee charged by the financial institutions. Consequently the average cost

declines as the amount of transaction goes up.

Speed The informal remittance channel is usually faster and less bureaucratic since it does not

require ownership of a bank account or going through the paperwork process associated with

the money transfer operators (Freud and Spatafora, 2004). The door-to-door service is one of

the easiest and most commonly used methods in the informal sector. One might argue that

remittance transfers through informal channels are slower. Sending money through a friend, for

example, might take indeed longer than making use of modern banking services. This argument,

however, cannot be extended to developing economies as it requires a well-functioning banking

system and infrastructure which in most of the developing world are underdeveloped.

Security of funds Sending money through informal sector is less secure, there is always risk

associated with the fact that the recipient will not get the money (Freud and Spatafora, 2004).

The formal channel provides this security through legal contracts.

Geographic accessibility One other important factor that people take in consideration when

choosing how to send remittances is the ease of access to the formal and informal channel. Both

the sender and the receiver of the remittance may incur additional costs depending on how far

it is for them to get to the informal transfer service provider. Usually the additional costs

represent the transportation cost and the opportunity cost of sending money (Yujuico, 2009).

Convenience in terms of familiarity and language The attractiveness of either of the two;

formal and informal channels could also depend on: the level of financial awareness, the

familiarity with the company providing the service and the regulations involved when sending a

remittance. Many people do not know that banks provide these services or that money transfer

operators (such as Western Union and Vigo) exist. Additionally many migrants are not familiar

with financial transactions or don’t even possess a bank account.

These are few of the main drivers associated with the choice of the distribution channel, in what

follows I will present the hypothesis of my research and provide an econometric model to test

them as well as try to estimate the true remittance flows.

The consequences of remittances are even more complex. Since remittances are the most

visible outcome of migration, I treat the two phenomena independently. The figure below

briefly describes the full range of economic consequences of migration and remittances. The

figure above maps various channels through which migration and remittances have important

economic effects. These are: (i) poverty reduction and income distribution, (ii) macroeconomic

stability and the balance of payments, (iii) public sector, and (iv) economic growth. This is

depicted in the figure below. While these effects are interesting, studying them is beyond the

scope of this thesis. For a detailed discussion on this issues, see Culiuc (2006).

FIGURE 1: The economic effects of migration and remittances on the sending countryThe figure below depicts the short and long term effect of migration and remittances on the economy and the economic growth. Green cells indicate positive effects on growth, and orange indicate negative effects on growth.

Source: Culiuc (2006)

3. Hypothesis Development

Previous studies include a number of variables that determine the level of remittance flows in a

country. In what follows, I model remittances as a function of the total number of migrants, the

receiving countries’ GDP, a countries risk profile, and its overall level of education. Below, I

explain how, according to the literature, each of these might affect the total remittance flows to

a country.

According to previous research (Page and Plaza, 2005; Freund and Spatafara 2004), the number

of migrants a country produces affects considerably the amount of remittance flows towards

that country. As one of the main reasons for migrants to remit is altruism (Agarwal and

Horowitz, 2002), it is only reasonable to say that the more people migrate the larger the total

amount of remittance flows. These studies estimate positive relationship between the two

variables, as such, I test the following hypothesis:

Hypothesis 1 Ceteris paribus, there is a positive relationship between the number of immigrants

in one country and its level of incoming remittance flows.

Freund and Spatafora (2004) report that as the size of the countries’ economy increases the

amount of remittance flows will also increase, this is due to the fact that as the GDP grows,

prices, and therefore the cost of living, go up as well. Consequently, migrants will send more

money home in countries with relatively larger GDP.

Hypothesis 2 Ceteris paribus, the higher the GDP of a country, the higher its incoming

remittance flows.

Related to Hypothesis 2 one should consider the level of risk in the receiving economy. Incoming

remittances can be higher in riskier economies as these countries might face larger migration

flows. On the other hand, remittances can be as well lower in riskier economies as migrants

might find better use for their money due to higher opportunity costs. Hence, ex-ante the

relationship between the level of risk and a country’s remittance flows is unclear and remains an

empirical question. As this has not been quantified in the past, I take the opportunity cost

approach and formulate my hypothesis as follows.

Hypothesis 3 Ceteris paribus, higher risk premium in the receiving country decreases its level of

remittance flows.

Higher educated people are expected to work and earn more. Moreover, some studies report

that more educated people are more likely to migrate to another country (Adams and Page,

2004). Other studies conclude that people with higher income tend to remit more and

consequently higher educated people remit more often or simply remit higher amounts.

Therefore I formulate my next hypothesis as follows:

Hypothesis 4 Ceteris paribus, the larger the amount of higher educated people in the country of

origin, the larger its remittance flows.

An appreciation of the local currency versus the US dollar, leads to immigrants sending more in

dollar terms so that the recipients in the country of origin maintain a constant purchasing

power. As we observe such a trend for a number of countries in my sample, I formulate the

hypothesis as follows:

Hypothesis 5 Ceteris paribus, the higher the local currency exchange rate versus the US dollar -

the greater the remittance flows sent by immigrants to their country of origin.

I present the methodology below.

4. Methodology

As mentioned before informal remittance flows represent an important share of total

remittance flows. The absence of an estimate of informal remittance flows clearly creates

severe biases in estimating the impact of the number of total official recorded remittances on

the economy. To test the hypotheses above, as well as to estimate the informal remittance

flows, I use a model based on Page and Plaza (2005) and Freund and Spatafora (2004). To

predict informal remittances, I first assume that the official recorded levels of remittances are

smaller than the total remittance flows that comprise informal and formal remittances. Page

and Plaza (2005) identify two situations where total recorded remittance flows are

underreported. First, countries where there is data on migration but no data on remittances

mostly likely have remittance flows which are not reported. Second, total remittance flows are

considered underreported in cases when the number of migrants as a share of country

population is much larger than the official recorded remittances as a share of country GDP. As in

Page and Plaza (2005), I assume that whenever I observe each of the two cases above, the total

registered remittance flows are underreported.

I first assume that the official level of remittances is explained by a set of controls as described

in my hypothesis section. Consequently, I estimate the following model.

LOGREMIT ¿=α+β1MI G¿+β2 LABFORC E¿+β3RISKPREM ¿+β4 LOGGDP¿

+β5EXCHRAT E ¿+ϵ ¿ (1)

where LOGRE MIT ¿ is the logarithm of remittance levels in a country i at time t , LOGGDP¿ is

the logarithm of GDP and MI G¿ is migrants as a share of population in country i at time t , and

EXCHRAT E¿ is the local currency exchange rate versus the US dollar. LABFORC E¿ , is the

labor force as a share of population, and RISKPREM ¿ is the risk premium associated with the

corresponding market i at time t . Page and Plaza (2005) estimate a similar model except that

they control for the share of country’s population over 25 years that has completed secondary

education. As this data is unavailable, I use labor force as a proxy. In this model, as noted by

Page and Plaza (2005), given that total remittance flows are underreported in at least some

situations, e¿ is and error term that is no longer normally distrusted but is rather asymmetric,

that constrains most observations to lie below the regression plane. I estimate the model as

presented above but also with country and time dummies to capture the relevant time and

cross-sectional unobservable variation.

To support the hypothesis presented in the previous section, the model must predict:

β1>0 , β2>0 , β3<0 , β4>0∧β5>0 .

If the error term in model (1) is assumed to be normally distributed, with mean zero, then the

predicted number of total formal and informal remittance flows derived from estimating the

model using official data will most likely under predict the actual number of total recorded

remittance flows due to reporting bias. To overcome this technical issue, Page and Plaza (2005)

suggest to estimate the equation using an asymmetric error term or to split the composite error

term into an asymmetric and a symmetric error term. In this way I conform to the previously

mentioned assumption that the reported remittance flows are less than or equal to the total

remittance flows. Consequently I am able to predict the total remittance flows for countries

where severe underreporting is registered. Unfortunately we do not have evidence on the

distribution of the asymmetric part in the error term. In order to deal with this problem, I follow

Page and Plaza (2005) and let the regression plane to lie above the ”average“ estimate in the

migration – remittance space by adding a dummy variableMIGI to my model, which is 0 when

the ratio of migrants as a share of population to remittances as a percentage of GDP is greater

than 2 or there are migrants but no reported remittance flows, if either of the two criteria is not

met the recorded official remittances flows are estimated correctly. Therefore I estimate the

following model:

LOGREMIT ¿=α+β1MI G¿+β2 LABFORC E¿+β3RISKPREM ¿+β4 LOGGDP¿

+β5EXCHRAT E ¿+β6MIGI+ϵ ¿

As argued in Page and Plaza (2005) this variable represents a simple way to deal with

asymmetry of the error term implied. With this estimate in hand, predicting informal remittance

levels is simple. I take all observations for which MIGI is equal to 0 and set it equal to 1. I then

predict the total remittance for this subsample and subtract the recorded remittance levels to

get the informal flows. I discuss in greater detail the data and my estimation results in the

following sections.

5. Data

In order to estimate the models presented above, I collect incoming remittance flows and other macroeconomic variables for 20 low-income countries from Eastern-Europe, from the IMF Balance of Payments statistics for a period of 20 years, from 1990 to 2011.The Balance of Payments defines the workers’ remittances as the transfers made by the migrants from the host country to the country of origin independently on the duration of their stay in the host country. I present the descriptive statistics for the main variables in my dataset in the table below:

TABLE 2: Descriptive Statistics

The table presents the overall descriptive statistics for the variables employed in this study. The sample comprises an unbalanced panel with 424 observations for 20 developing economies in Central and Eastern Europe from 1990 to 2011.

REM LOGREMIT

LOGGDP

MIGPOP

LABFORCE

RISKPREM

EXRATEUSD

ALL COUNTRIESMean 0.049 8.588 10.312 0.085 58.960 17.609 6.612

Std. Dev.

0.073 0.872 0.712 0.069 6.249 32.690 41.835

Min 0.000 4.538 8.851 0.000 41.000 -16.248 0.000

Max 0.497 10.021 12.269 0.243 71.300 176.747 456.621Obs 333.000 334.000 424.000 391.000 405.000 175.000 380.000

The mean of total remittance flows in the 20 countries is 1250 million USD with a standard

deviation of 182 million. The smallest value of remittances is Lithuania in 1993, whereas the

largest remittance flows in my dataset were recorded in Poland in 2007.

The independent variable ℜM ¿ is the share of total recoded remittance flows as a share of the

respective country’s GDP. The average remittance flow represent 5% of the country’s GDP in

my sample. The maximum value of ℜM ¿is recorded in Bosnia and Herzegovina in 1998,

whereas the smallest value of remittances as a share of GDP is recorded in Lithuania in 1993.

As mentioned before migration and remittances flows are closely related, therefore I look at the

effect that the share of outgoing migrants in one countries’ population has on REM. I download

5-year estimates of migration. To complete the missing values I use linear interpolation to find

the estimates for rest of the years.1 On average migrants represent about 8.5 % of the total

population for all countries in my dataset. The lowest migration is reported in Moldova in 1995.

On the other hand, Estonia had an impressive 25% of total population migrating abroad in 1990.

To proxy for the overall risk level in an economy, I download risk premium statistics on lending

for the 20 countries in my sample. The International Financial Statistics database provides the

yearly risk premiums for over 120 countries. The mean risk premium reported in my database is

17.6%. I find the highest risk premium in Bulgaria in 1996 to be 176.75% and the smallest is

negative 16.247% in 1999 in Armenia. Negative risk premium values represent severe violations

from the Capital Asset Pricing Model but are not unlikely, especially in developing economies.

1 Linear interpolation is a method of curve fitting using linear polynomials (Raymond, 2003)

Another variable that I consider in model (1) above is the labor force participation rate which is

the percentage of all the people above 15 years that are involved in the supply of goods and

services in a country. The data is provided by the International Labor organization on a time

horizon of 60 years. The mean labor participation rate in the database used in this paper is

almost 59%. The smallest labor participation rate is registered in Moldova in 2010, with only

41% of the people able to work being economically active. Whereas in the same year

Kazakhstan reported a labor participation rate of 71%, which is the maximum value in my panel

dataset. Finally, the data on the local currency exchange rate versus the dollar comes from

World Bank database.

6. Results

This section presents regression estimates for model (1) and predicts the total (official and

unofficial) remittance flows for a sample of 20 countries and, ultimately, provides estimates for

the informal remittance flows for my sample of Eastern European countries from 1990-2011. In

the next subsection I look at the coefficient estimates of my model and test the corresponding

hypothesis as presented in section 3 above. Next, I provide, where possible, the estimates of

total remittance flows and compute the informal (unofficial) share of remittance inflows for the

countries in my sample.

Regression Analysis

Table 3 presents the results for my model where the dependent variable is the logarithm of the total number of remittance flows in country i at time t . Columns 1 – 8 provide important insights on how the logarithm of the GDP, the number of migrants as a share of the total population, the risk premium, the labor participation rate and the local currency exchange rate versus the dollar in each of the studied Central and Eastern European economies affect the logarithm of the total number of remittance flows. I estimate the model without and with country and time dummies to control for potential unobservable that are country or time specific.

TABLE 3: Determinants of log of Total Remittances

The table presents robust regression estimates for logarithm of remittance flows. Models 1, 3, 6 and 7 are multivariate regressions with no country and time dummies, whereas Models 2, 4, 5, 8, 9 and 10 are multivariate regressions with country and time dummies. Also models 1-5 are estimated with no interpolation for the number of migrants from a country, whereas Models 6-10 are estimated by means of data interpolation for the number of migrants from one country. Standard errors are in parenthesis. *, **, and *** indicate significance at 10%, 5%, and 1% levels, respectively.

Dependent Variable LOGREMIT

1 2 3 4 5 6 7 8 9 10

Independent Variables

LOGGDP 0.557*** 0.495 0.371*

0.5950.0111

0.496***

0.493***

0.916***

1.100*** 0.600

(0.112)(0.66

9) (0.189)(0.781)

(0.678)

(0.0479)

(0.0576) (0.342) (0.314)

(0.404)

MIGPOP

-2.443-

4.714 -1.989

-4.665

-4.025

-2.255*

** -0.320 -3.249* 0.814

-8.224*

*

(1.503)(7.51

6)(2.456

)(5.858)

(6.087) (0.672)

(0.608)

(1.893)

(1.918)

(3.859)

LABFORCE -0.0387

***

-0.041

7*

-0.0436

*

-0.046

4

-0.0607

*

-0.0398

***

-0.0405

***

-0.0413

***

-0.0226

**-

0.0107(0.012

5)(0.0240)

(0.0238)

(0.0373)

(0.0328)

(0.00606)

(0.00819)

(0.0114)

(0.0108)

(0.0106)

RISKPREM

-0.0051

4

0.00626

0.0119 0.0005

85

-0.0032

6*

-0.0039

1** -

-(0.006

08)(0.008

28)(0.008

78)(0.001

51)(0.001

87)(0.001

69) -EXRATEUSD

0.000799**

0.0275

-0.0566

2.426

3.290*0.00113***

-0.221*

** 0.0121 0.02060.0058

4(0.000333)

(0.0198)

(0.224)

(2.005)

(1.811)

(0.000252)

(0.0440)

(0.0788)

(0.0697)

(0.00415)

MIGI

0.759***

0.596**

0.513***

0.721***

(0.195)

(0.248) (0.116)

(0.0843)

Page | 18

Country Fixed Effects No Yes No

Yes YesNo No Yes Yes Yes

Time Fixed Effects No Yes No

Yes YesNo No Yes Yes Yes

Constant 5.254***

-4.327

7.508**

5.794ноя.23

5.918***

6.077*** 2.261 -1.164 1.760

(1.112)(10.7

7) (3.018)(8.531)

(7.510) (0.528) (0.853) (3.270) (3.011)

(3.246)

Observations67 67 34

3434 292 150 150 150 292

R-squared0.455 0.837 0.427

0.8850.911 0.394 0.435 0.849 0.874 0.806

Results in table 3 shows that LOGGDP¿ affects LOGREMIT ¿ positively, statistically and

economically significant, at 1% confidence. A one percent increase in LOGGDP¿ increases

LOGREMIT ¿ by 1.1%, which is economically sizeable. These results are consistent with

Hypothesis 2.

The results reported in table 3, column 1, show that MI G¿ affects LOGREMIT ¿ negatively. This

is contradictory to previous results in the literature and my Hypothesis 1 which states that there

is a positive relationship between the number of immigrants in one country and its level of

incoming remittance flows. However, as I introduce more variables as well as country and time

fixed effects, the effect turns positive. In column 9, for example, I find that a 10% increase in

MI G¿ increases LOGREMIT ¿ by 8.5%. In fact, the estimated sign to MI G¿ varies widely for

some specifications. For robustness, I estimate alternative columns of model (1) with either the

raw migration data (no interpolation) or excluding MI G¿completely from the model. In the first

robustness exercise, I find that the effect of MI G¿has a negative impact onLOGREMIT ¿,

however, this effect is mostly insignificant statistically. In the second robustness exercise, I find

that the coefficients to other variables in the model remain unchanged. These results are

available upon request.

Table 3 also presents the estimates for LABFORC E¿ and RISKPREM ¿ which are both negative

and statistically significant in all models. A one percent increase in the risk premium decreases

the total number of remittance flows by 0.04%. Though statistically significant at 5%

confidence, the economic significance of the effect seems weak. Still, these results cannot

reject Hypothesis 3.The negative coefficient for LABFORC E¿ is inconsistent with Hypothesis 4.

A 1% increase in LABFORC E¿ decreases LOGREMIT ¿ by 0.02%, an effect which is statistically

significant at 1% confidence level. The economic significance seems marginal. It turns out that

Page | 19

LABFORC E¿ is either not a good proxy for the educated people in a developing economy, or

captures a multitude of effects. In particular, the larger the labor force in a country, the less

likely it is that citizens will migrate to other economies in search for a job, and hence remittance

flows are likely to be smaller. The correlation labor force has with education deserves a more

thorough attention outside of the focus of this study.

The reported estimates for EXCHRAT E¿ are positive. Interestingly, EXCHRAT E¿ changes sign

when I introduce country and time dummy variables and losses significance. There is no

theoretical prediction on this variable and I abstract from this concern. Still the fact that the

relationship between EXCHRAT E¿ and LOGREMIT ¿ is positive does not reject Hypothesis 5. I

now turn to the estimation of informal remittance flows as a share of total remittance flows.

Estimation of Informal Remittances

Based on previous research it is fair to say that the official recorded remittance flows are

severely underreported. Therefore estimating the level of informal remittance flows using

modern econometrics techniques is a relevant and valuable exercise.

To estimate the informal remittance flows as a share of total remittance flows, I follow the

methodology proposed by Page and Plaza (2005). As discussed in the methodology section,

there are two situations where total recorded remittance flows are likely to be underreported.

First, countries where there is data on migration but no data on remittances mostly likely have

remittance flows which are not reported. Second, total remittance flows are considered

underreported in cases when the number of migrants as a share of country population is much

larger than the official recorded remittances as a share of country GDP. As in Page and Plaza

(2005), I assume that whenever I observe each of the two cases above, the total registered

remittance flows are underreported. To single out the underreported cases in my sample, I add

to my model a dummy variable called MIGI , which is equal to 0 when the ratio of migrants as a

share of population to remittances as a percentage of GDP is greater than 2 or there are

migrants but no reported remittance flows, if either of the two criteria is not met the recorded

official remittance flows are considered to be reported correctly and MIGI equals 1. I estimate

model (1) for all observations where I observe remittance flows. For observations where I do

not observe remittance flows, I can predict these values using the estimated coefficients in the

previous step and the corresponding available observations for independent variables. The

observations where MIGI is 0 lie below the regression plane forcing the asymmetric error term

Page | 20

in model (1) to be positive.2 Its interpretation now can be attributed to the reporting bias in

total remittance flows. To get informal remittance flows, I first predict the total remittance

flows for the subsample where by my assumption these are too small (with MIGI equal to 0)

and subtract the recorded remittance levels to get the informal flows. I present the results of

the estimated informal remittance flows as a share of total remittances in table 4 below.

The results confirm the idea that the unrecorded remittance flows represent a large share of

the total remittances in the less developed countries. On average a sizeable 48% of total

remittances come through informal channels in 14 out of 20 countries in my analysis. For the

remaining countries I accept that the recorded official remittance flows have been reported

correctly, and in such countries informal remittance flows are very low.

It is reasonable to assume that none of the estimates in Table 4 is completely accurate but

these can certainly serve as guidance to economic policy. For example, for Armenia my results

show the informal share of remittances to be around 30% of the total remittance flows.

Robberts and Banaian (2004), on the other hand, report that 38% of the total remittances sent

to Armenia are sent through informal channels, a result which is close in magnitude to my

estimate.

2 In a univariate regression, the regression plane is a line called the regression line. Its coordinates are given by the expected value of the dependent variable and the estimated coefficient times the values of the corresponding independent variable. With multiple explanatory variables, the regression line is now a multidimensional plane, where the dimensionality of the plane is given by the number of explanatory variables plus one (the dependent variable) in the model. By setting the dummy equal to 1, I force the regression points to be below that plane on the dimension where the remittance levels are lower than expected,

Page | 21

TABLE 4: Share of Unofficial Remittance Flows in Total Remittances by Country

The table below presents the estimated share of unofficial remittance flows as a share of total remittances in each of the countries used in the dataset. The table includes the predicted estimates for model 1 – specification 9.

Country Share of unrecorded Remittances

Romania 0.57Russia 0.499Ukraine 0.387Bulgaria 0.561Latvia 0.514Lithuania 0.467Estonia 0.43Belarus 0.494Kazakhstan 0.503Armenia 0.284Azerbaijan 0.438Croatia 0.497Turkey 0.605Poland 0.404

My analysis shows that the highest amount of unrecorded remittances is registered in Turkey,

where 60.5% of the total remittances are sent through informal channels. On average, informal

remittance flows are around 50% of total remittance flows. These findings confirm the

suspicion in the literature that informal remittance flows are a significant component of total

remittance flows and require serious policy focus.

1. Conclusions

Previous studies suggest that recorded remittance flows are considerably understated and that

the unofficial remittances could take a value up to 250 % of the recorded ones. In this paper I

estimate informal remittances in 13 developing countries from Central and Easter Europe by

using a hybrid version of the models suggested by Page and Plaza (2005) and by Freund and

Spatafora (2004). My results show that in line with previous literature, migration, GDP, and

exchange rate determine remittance positively, while the risk premium and the labor

participation rate affect remittances negatively. For 13 of the 20 countries in my sample, I

estimate the share of unofficial remittances and find that these represent between 28% and

61% of the officially recorded flows.

Page | 22

Future research should include other important determinants like: the black market risk

premium and the transaction costs associated with sending remittances. Unfortunately there is

not much publicly available information concerning these variables and detailed micro datasets

are virtually inexistent. Alternative ways to estimate informal remittance flows are through use

of survey data. These however turn to be noise and imprecise due to various reporting biases.

Econometric techniques as those in this paper remain to be more effective.

2. References

Adams R.H., and Page J., 2005, “Do international migration and remittances reduce poverty in

developing countries?,” World Development volume 33, Issue 10, 1645-1669

Adelman I., and J. Edward Taylor, 1992, “Is Structural Adjustment with a Human Face possible?

The Case of Mexico,” Journal of Development Studies 26, 387-407

Agarwal R., and Horowitz A., 2002, “Are international remittances altruism or insurance?

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of Political Economy 93 , 1045-1076

Celent, 2002, “Global Money Transfers”

Cox D., 1987, “Motives for private transfers,” Journal of Political Economy 95, 508-46,

Culiuc A., 2006, “Integrating Migration and Remittances into a Development Strategy: The Case

of Moldova,” Kennedy School of Government Harvard University

Freund C., and Spatafora N., 2005, “Remittances: transaction costs, determinants, and informal

flows,” World Bank Policy Research Working Paper No. 3704, World Bank, Washington, DC

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EL SALVADOR AND NICARAGUA,” The Review of Economics and Statistics , vol. 77, no.1, 137-146

Giuliano P., and Ruiz-Arranz M. 2005, “Remittances, financial development and growth,” IMF

Working Paper, International Monetary Fund, Washington DC

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Lucas R. E. B., and Stark O., 1985, “Motivations to remit: Evidence from Botswana,” Journal of

Political Economy 93, 901-18

Page J., and Plaza S., 2005, “Migration Remittances and Development: A Review of

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336

Pieke F. N., Van Hear N., and Lindley A., 2007, “Beyond control? The mechanics and dynamics of

‘informal’ remittances between Europe and Africa,” Global Networks volume 7, issue 3, 348-366

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paper series, No. 1531

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finance”, Worldbank

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countries,” Economic Journal 393, 1148-70

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of microfinance in Africa,” Paper prepared for the Conference on Current Issues in Microfinance,

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3. Appendix

TABLE 5: Descriptive Statistics

The table presents descriptive statistics for the variables employed in this study. The sample comprises an unbalanced panel with 424 observations for each of the 20 developing economies in Central and Eastern Europe from 1990 to 2011 individually.

REM LOGREMIT

LOGGDP

MIGPOP

LABFORCE

RISKPREM

EXRATEUSD

ALBANIA Mean 0.155 8.835 9.616 0.025 62.129 7.055 0.009

Std. Dev.

0.041 0.286 0.374 0.002 1.508 2.132 0.002

Min 0.094 8.181 8.851 0.020 60.400 4.078 0.007

Max 0.270 9.175 10.113 0.028 64.400 11.933 0.013Obs 20.000 20.000 22.000 22.000 21.000 15.000 20.000

ARMENIAMean 0.077 8.421 9.479 0.171 63.305 11.346 0.008

Std. Dev.

0.028 0.473 0.334 0.035 3.657 18.094 0.025

Min 0.045 7.815 9.080 0.094 57.100 -16.248 0.002

Max 0.122 9.098 10.067 0.211 68.500 74.048 0.110Obs 17.000 17.000 22.000 22.000 21.000 17.000 19.000

AZERBAIJANMean 0.155 8.482 9.977 0.025 62.129 7.055 8.254

Std. Dev.

0.013 0.731 0.439 0.014 0.566 5.701 22.574

Min 0.001 6.799 9.485 0.029 61.900 1.178 1.018

Max 0.052 9.275 10.802 0.068 64.300 18.871 92.251Obs 14.000 14.000 22.000 22.000 21.000 15.000 20.000

BELARUSMean 0.011 8.128 10.340 0.115 59.581 - 0.014

Std. Dev.

0.006 0.945 0.243 0.003 3.848 - 0.027

Min 0.000 5.602 10.084 0.112 55.100 - 0.000

Max 0.024 8.859 10.784 0.123 67.200 - 0.087Obs 19.000 19.000 22.000 22.000 21.000 0.000 17.000

BOSNIA AND HERZEGOVINAMean 0.224 9.296 9.845 0.016 44.952 - 0.609

Std. Dev.

0.111 0.078 0.353 0.007 0.749 - 0.093

Min 0.112 9.183 9.099 0.007 43.800 - 0.458

Max 0.497 9.437 10.268 0.026 46.300 - 0.749Obs 14.000 14.000 18.000 22.000 21.000 0.000 15.000

BULGARIAMean 0.036 8.710 10.286 0.010 54.129 27.086 29.093

Std. Dev.

0.027 0.720 0.270 0.004 2.481 45.898 96.804

Page | 25

Min 0.003 7.618 9.949 0.002 50.300 4.918 0.458

Max 0.083 9.283 10.728 0.014 58.800 176.747 456.621Obs 16.000 16.000 22.000 22.000 21.000 20.000 22.000

CROATIAMean 0.022 8.840 10.470 0.144 54.300 - 0.352

Std. Dev.

0.005 0.201 0.248 0.014 1.622 - 0.812

Min 0.015 8.362 10.012 0.099 52.300 - 0.120

Max 0.032 9.111 10.845 0.161 57.400 - 3.798Obs 19.000 19.000 22.000 22.000 21.000 0.000 20.000

ESTONIAMean 0.009 7.470 9.996 0.182 61.510 - 0.076

Std. Dev.

0.009 1.024 0.277 0.036 3.173 - 0.011

Min 0.000 6.109 9.577 0.133 57.200 - 0.057

Max 0.024 8.614 10.378 0.243 69.100 - 0.094Obs 17.000 18.000 17.000 22.000 21.000 0.000 18.000

GEORGIAMean 0.072 8.611 9.703 0.050 64.900 5.776 0.584

Std. Dev.

0.020 0.232 0.252 0.009 1.221 12.667 0.105

Min 0.054 8.321 9.400 0.036 63.400 -11.996 0.455

Max 0.129 9.007 10.157 0.070 66.500 19.540 0.792Obs 15.000 15.000 22.000 22.000 21.000 8.000 16.000

KAZAKHSTANMean 0.004 8.173 10.587 0.201 69.900 - 0.010

Std. Dev.

0.002 0.217 0.342 0.009 0.529 - 0.005

Min 0.001 7.775 10.227 0.187 69.100 - 0.007

Max 0.008 8.463 11.270 0.221 71.300 - 0.028Obs 17.000 17.000 22.000 22.000 21.000 0.000 18.000

LATVIAMean 0.016 8.280 10.017 0.187 60.586 6.059 1.768

Std. Dev.

0.007 0.470 0.290 0.030 3.670 2.317 0.177

Min 0.007 7.610 9.650 0.147 55.800 2.141 1.358

Max 0.026 8.842 10.527 0.243 69.000 10.514 2.080Obs 16.000 16.000 22.000 22.000 21.000 17.000 20.000

LITHUANIAMean 0.015 7.698 10.202 0.062 60.529 2.492 0.324

Std. Dev.

0.018 1.436 0.280 0.017 3.324 1.518 0.088

Min 0.000 4.538 9.843 0.038 55.900 -0.145 0.230

Max 0.050 9.330 10.674 0.094 66.400 5.757 0.564Obs 19.000 19.000 22.000 22.000 21.000 15.000 20.000

MACEDONIAMean 0.032 8.219 9.688 54.110 - 0.020

Std. Dev.

0.010 0.324 0.188 1.003 - 0.003

Min 0.015 7.801 9.365 51.500 - 0.015

Page | 26

Max 0.043 8.639 10.007 55.700 - 0.026Obs 16.000 16.000 22.000 0.000 21.000 0.000 18.000

MOLDOVAMean 0.194 8.514 9.411 0.066 56.690 6.665 0.120

Std. Dev.

0.108 0.797 0.231 0.046 8.699 6.632 0.064

Min 0.001 6.009 9.068 0.000 41.000 -8.014 0.072

Max 0.347 9.278 9.845 0.124 67.100 17.539 0.246Obs 17.000 17.000 22.000 17.000 21.000 16.000 18.000

POLANDMean 0.013 9.415 11.302 0.023 57.052 5.031 0.409

Std. Dev.

0.007 0.465 0.271 0.003 2.870 3.743 0.223

Min 0.005 8.764 10.810 0.021 53.900 0.963 0.230

Max 0.025 10.021 11.724 0.030 63.300 13.441 1.053Obs 18.000 18.000 22.000 22.000 21.000 15.000 22.000

ROMANIAMean 0.016 8.490 10.770 0.006 59.967 1.867 29.268

Std. Dev.

0.021 1.104 0.312 0.000 3.802 7.651 97.180

Min 0.000 6.954 10.400 0.006 55.000 -13.193 0.301

Max 0.055 9.972 11.301 0.006 66.200 10.368 445.790Obs 18.000 18.000 22.000 22.000 21.000 12.000 22.000

RUSSIAMean 0.005 9.436 11.747 0.082 61.629 95.097 0.138

Std. Dev.

0.002 0.242 0.281 0.003 2.623 21.647 0.138

Min 0.003 0.242 0.281 0.078 57.100 60.381 0.237

Max 0.012 9.106 11.292 0.087 67.700 131.965 0.032Obs 18.000 18.000 22.000 22.000 21.000 18.000 19.000

SERBIAMean 0.081 9.522 10.363 62.404 2.428 0.036

Std. Dev.

0.015 0.065 0.248 2.249 4.688 0.046

Min 0.057 9.433 9.784 58.962 -5.644 0.013

Max 0.098 9.595 10.679 65.232 6.710 0.169Obs 5.000 5.000 15.000 0.000 6.000 9.000 15.000

TURKEYMean 9.315 11.469 0.011 0.020 51.324 - 43.164

Std. Dev.

0.278 0.261 0.009 0.001 3.646 - 96.293

Min 8.863 11.116 0.001 0.019 47.000 - 0.597

Max 9.729 11.888 0.022 0.021 57.900 - 383.341Obs 22.000 22.000 22.000 22.000 21.000 0.000

UKRAINEMean 0.014 8.481 10.835 0.118 59.100 - 1.559

Std. Dev.

0.017 1.083 0.231 0.006 1.742 - 5.009

Min 0.000 6.778 10.495 0.112 57.000 - 0.126

Max 0.043 9.821 11.255 0.133 62.500 - 22.063

Page | 27

Obs 16.000 16.000 22.000 22.000 21.000 0.000 19.000

Page | 28