sarvimaki, mattin etal (2007) long-term effects of forced migration

8/7/2019 Sarvimaki, Mattin etal (2007) Long-term effects of forced migration

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Long-Term Effects of Forced Migration

Matti Sarvimki, Roope Uusitalo, and Markus Jntti

May 26, 2007

Abstract

After the World War II Finland ceded a tenth of its land area to the Soviet Union. As

a consequence 11% of the population, 430,000 persons, were evacuated and settled intothe remaining parts of Finland according to a plan which determined a specific destination

area to refugees from each village in the ceded area. In this paper, we use individual-level

panel data to study the long-term impacts of the evacuation on the refugees. We also use

the exogeneous variation in the characteristics of the assigned destination areas to analyze

the effect of host community characteristics to the economic success of the refugees.

We find that forced migration increased long-term income. The effect is strongest

among men, the young and those at lower quantiles of the income distribution. We at-

tribute a large part of these gains to increased regional mobility. We also find that having

been placed to a wealthier destination area had a positive effect on long-term earnings.

JEL classification:

Keywords:

PRELIMINARY, PLEASE DO NOT QUOTE WITHOUT PERMISSION

1 Introduction

In the Paris peace treaty after the World War II, Finland ceded the Karelian peninsula, EasternKarelia and the easternmost parts of Lapland to the Soviet Union. In total, the ceded area

was roughly ten percent of the total pre-war land area. The entire population from 60 rural

Department of Economics, Helsinki School of Economics and Government Institute for Economic ResearchDepartment of Economics, Helsinki School of Economics and Labor Institute for Economic ResearchDepartment of Economics and Statistics, bo Akademi University

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municipalities and three cities including Viipuri, the second largest city of Finland at the time,

was evacuated within a few weeks. By 1950, these migrants were settled to the remaining parts

of the country.

The Karelian evacuation caused a massive migration flow. Altogether 430,000 people, 11%

percent of the 1940 population, migrated from Karelia to the remaining parts of Finland. Theserefugees were settled according to a Migrant Placement Plan drafted by the Ministry of Agri-

culture in June 1945. This plan assigned refugees from each Karelian village to a specific

municipality in the remaining parts of the country. The number of refugees allocated to each

receiving municipality was mainly based on the availability of land that could be seized from

the local landowners. The distribution of refugees across the receiving municipalities was based

on mechanical application of geographical rules aiming to re-settle the refugees to areas whose

farming conditions resembled those in the ceded area.

In this paper, we evaluate the effect of forced migration on the long-term labor market out-

comes of the migrants. In addition, we study the impact of the characteristics of the assigned

placement area on the economic success of the migrants. The key to our analysis is the exoge-

nous nature of the migration decision caused by the lost war, and the exogenous initial allocation

of the refugees across the different placement areas. To perform the analysis, we have access to

unusual individual-level data on the economic status of the refugee and non-refugee population

in the target area from strictly comparable sources. Our data record both the situation before

the war and post-war outcomes up to fifty years after the initial placement.

The fundamental difficulty in the analysis of the impact of migration is caused by selectivity

problems. Generally, both the migration decision and the location choice of the migrants de-pend on the observed and unobserved characteristics of the migrants and the destination areas.

A branch of recent literature on the effects of migration has attempted to solve these problems

by concentrating on historical episodes leading to massive migration flows. After the seminal

analysis of Cuban migrants to Miami (Card, 1990), researchers have evaluated the effects of

Algerian repatriates returning to France (Hunt, 1992), Portuguese returning from Africa (Car-

rington and de Lima, 1996), Jews moving to Israel from the Soviet Union (Friedberg, 2001),

and ethnic Germans moving from East Europe to Germany after the fall of the Berlin Wall

(Glitz, 2006). The focus of these studies has been on the impact of immigration flows on the

host country employment and wages. We will return to this issue and examine how the lo-cal economy adjusted to the inflow of Karelian refugees in our companion paper (Sarvimki,

Uusitalo and Jntti, 2007). In this paper, we focus on an equally important issue: the impact

of migrating on the migrants themselves. This question is not quite as often evaluated based

on quasi-experimental analysis, largely due to the lack of comparable data on migrants before

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migrating. The closely related question of how immigrants assimilate has been more exten-

sively researched. The impact of local labor market characteristics on immigrant outcomes

has been studied in a quasi-experimental setting by, for example Edin, Fredriksson and slund

(2003, 2004) and Piil Damm (2006). The setup also resembles the analysis of neighborhood

characteristics in a setting where welfare recipients were encouraged to move into wealthierneighborhoods (Katz, Kling and Liebman, 2001, 2007) or were placed to neighborhoods in a

quasi-random fashion (Oreopoulos 2003).

The rest of this paper is organized as following. Section 2 provides the details of the settle-

ment policy implemented after the war. Section 3 presents a simple theoretical model highlight-

ing the potential difficulties in assesing the impact of forced migration. The empirical methods

are described in Section 4 and the data in Section 5. Section 6 presents the empirical results,

and section 7 concludes with some final comments.

2 The Settlement Policy

World War II entailed three distinct wars for Finland. A secret addendum to the treaty between

Germany and the Soviet Union defined Finland to belong to the Soviet Unions spehere of

interest. Following unsuccessful negotiations on the Soviet Unions territorial demands, the

Red Army attacked Finland in November 1939. In the peace treaty ending the Winter War

in March 1940, Finland ceded roughly 10 percent of its territory to the Soviet Union. The

population of these areas had been evacuated during the war. The Emergency Settlement Act

(pika-asutuslaki) was enacted in July 1940 to settle the refugees in the rest of the country. Theexecution of the Act was suspended in June 1941 as Finland joined Germanys attack to the

Soviet Union. By the end of August, the Finnish troops had reoccupied the ceded areas and on

December 6th the Finnish parliament declared that the ceded areas were re-united to Finland.

About two thirds of the refugees returned to their homes. In the summer of 1944, the Red Army

pushed the Finnish troops back to roughly the same line of defense they had held at the end of

the Winter War. The Paris peace treaty with the allied forces in 1947 restored the borders of the

1940 peace treaty with some additional areas ceded to the Soviet Union (See map in Figure 1).1

Virtually the entire population of the ceded areas was again evacuated. In addition, Finland also

agreed to pay USD 300 million in war reparations to Soviet Union and to expel German troops

from its territory.

1In addition to the area ceded in 1940, the Petsamo area was ceded to the Soviet Union. Furthermore, thePorkkala Peninsula was leased for a Soviet naval base for fifty years. Following an improvement in internationalrelations and changes in military technology that made land-based artillery less important for protecting Leningradfrom the sea, Porkkala was returned to Finland already in 1956.

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The war left Finland with approximately 92,000 dead and 228,000 injured (out of total

population of about 4 million). Much of the countrys production capacity was destroyed in the

war and further cuts in capacity were caused by war reparations. For example, a quarter of the

Finnish commercial fleet was handed over to the Soviet Union. Altogether war reparations took

about 15 percent of the government budget between 1945 and 1949 (Tarkka 1990).Settling the 420,000 refugees was a major burden to the Finnish economy after the war.

Finland was still a predominantly agrarian society, with roughly one half of the working age

population employed in agriculture. Similarly, almost a half of refugees were farmers. The

only feasible option at the time was to resettle the refugee population to areas were they could

get their main income from farming.

In May 1945, the Parliament approved the Land Acquisition Act (maanhankintalaki) that

guided the settlement policy. The refugees who had owned or rented land in the ceded areas

and had received their principal income from agriculture were entailed to receive land from

remaining parts of Finland. Land was primarily taken from the state, the local governments

(municipalities) and the church, but the required amount far exceeded the capacity of the public

sector. Thus roughly two thirds of the cultivated fields, a half of land that could be cultivated and

a third of forest land was seized from private owners. The committee drafting the law proposed

an explicit progressive expropriation schedule for seizing private land.2 (Pihkala, 1952)

The implementation of the Land Acquisition Act was entrusted to the Department of Land

Settlement in the Ministry of Agriculture. Altogether 147 local land redemption boards were

responsible for the expropriation measures and the same number of local settlement boards

had a duty to locate applicants for land. Refugees from each Karelian village were settledinto a designated target municipality. The number of refugees placed to each municipality

was mainly affected by the availability of suitable land, which again depended on the pre-war

farm size distribution and on the quantity of state-owned land in the municipality. The most

important factor in allocating refugees across receiving municipalities was the location of their

municipality in the ceded area. Refugees from the western parts of the Karelian peninsula were

settled along the southern coast, refugees from the eastern part of the Karelian peninsula north

of the first group and the refugees from Northern Karelia even further north. No migrants were

placed in the very North of Finland, where conditions for agriculture are extremely unfavorable.

Refugees from the municipalities surrounding Viipuri, the largest city in Karelia, were settledclose to the capital, Helsinki, and the refugees from Sortavala, the second largest Karelian city,

2The proposed schedule required private land owners to cede up to 20-80 % of their land holdings dependingon the size of their farms. No land was expropriated from farms smaller than 25 hectares. The landowners werecompensated with government bonds yielding 4 % nominal interest. Inflation eventually wiped out about fourfifths of their value. Pihkala (1952) discusses the acquisition policy and its consequences in detail.

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moved elsewhere after the initial settlement. Still, the settlement plan had a strong impact on

the location choices and therefore the characteristics of the assigned municipality are poten-

tially good instruments for the characteristics of municipality where the refugees finally settled.

The remaining question is the extent to which the characteristics of the municipality of origin

are correlated with the characteristics of the target municipality, which might lead to better thanaverage refugees being settled to better than average municipalities. We will be able to examine

this issue in more detail using data on the pre-war characteristics of the municipalities of origin

and municipalities of destination.

3 Theory

[TBA]

4 Empirical Approach

Suppose for now that the true data generating process is

yijt = Ri +Xi0+ Qjt + uit (1)

where y is the outcome of interest for individual i living in location j at time t, Ri is a dummy

indicating refugee status,Xi0 is a vector of observable characteristics measured before the war,

Qjt is an unobservable measure of labor market quality and u summarizes individual specificunobservable factors. We continue under the following assumptions (a) E [Riuit|Xi0] = 0, (b)

E [RiQjt|Xi0] > 0 and (c) E [Qjtuit|Xi0] > 0.

The first assumption states that refugee status is independent of unobserved characteristics

once we condition for the observed characteristics. While the assumption is not directly testable,

the essentially random location of the new border gives it plausibility. Further, in Section 6.1

we provide indirect evidence of its validity using the available data from the pre-war period.

The second assumption states that we expect refugees to migrate into better post-war labor

markets. This assumption is motivated by the theoretical model [to be added]. It follows from

the fact that the resettlement first moved individuals to new labor markets. While refugees had

limited capability to choose their initial labor market, more refugees tended to be assigned to

more prosperous municipalities. More importantly, we expect that also later regional mobility

among the refugees is high, as they had few location-specific references pulling them to stay

in their initial allocation areas. This may have, for example, increased their willingness to

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accept job offers requiring migration. Both reasons suggest that there is a positive correlation

between refugee status (Ri) and post-war labor market quality (Qjt).

The third assumption states that we expect individuals with above average unobserved char-

acteristics to be located in above average labor markets. This assumptions is often called en-

dogenous sorting in the social interactions literature and self-selection in the migration lit-erature. Again our simple theoretical model provides a formal argument. To gain intuition,

suppose that u simply represents unobserved ability and that returns to ability are higher in the

high quality labor markets. We should then expect high ability individuals to migrate into high

quality labor markets.

Note that assumption (a) is our key identifying assumption, while the other two merely

complicate identification. That is, if assumption (a) holds, but (b) and (c) do not, estimating

in equation (1) is straightforward. To see what assumption (b) implies, consider first estimating

equation (1) using a a specification excluding (unobserved) Q. This yields an OLS estimand of

with the probability limit

plim R = + R (2)

where R captures the correlation between refugee status and labor market quality.4 This is an

estimate of the total impact of forced migration and as such the key parameter of interest. The

effects consists of two parts. The first part () contains the direct effect due to loss of, say,

location specific human capital. The second part is the effect due to increased mobility.

Having estimated the total effect, we next decompose it into the two parts discussed above.

To do this, we need a consistent estimate of either or and R. A straightforward approach

for attempting to directly estimate is to control for Q in (1) by adding a set of labor marketspecific dummies. However, this does not yield consistent estimates. To see why, suppose that

we would observe Q and include it in the regression. Then least squares estimate of would

have the probability limit

plim RQ = + (3)

In the Appendix we show that in general = 0, and at least when Xi0 is excluded, < 0. The

bias follows from the assumption that Qjt is correlated with uit. Since we also assume that Riand Qjt are correlated, this endogeneity bias is transmitted to the estimate of . Note, however,

that QR contains useful descriptive information: it is the average difference in expected incomebetween a refugee and non-refugee, who were similar in their pre-war characteristics and who

lived in similar labor markets in 1970. Further, it provides a lower bound for (at least in the

4More precisely, R is the probability limit of the OLS estimator of refugee status in regression ofQ on Ri andXi0.

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case whereXi0 is excluded).

Since we cannot directly estimate , we next consider the prospects of identifying and

R. The latter is trivially identified ifQ is observed [discussion about using a proxy]. However,

estimating is troublesome. The key problem is endogenous sorting [Assumption (c)] due to

which it is unlikely that a regression model estimated from cross-sectional (or panel) data with-out an experimental setting provides convincing evidence on the effect of labor market quality.

That is, if the labor market characteristics (Qjt) are positively correlated with the unobserved

determinants of the outcome (uit), least squares estimates of are biased upwards. The second

potential problem is that labor market quality is difficult to measure. Hence OLS estimates of

potentially suffer from significant attenuation bias towards zero.

To overcome these problems, we use the characteristics of the assigned placement area as

instruments for the characteristics of the municipality of residence. As long as at least some of

the refugees stay in the areas where they were assigned, the instrument has predictive power and

is identified. In the treatment effects literature this is analogous to using intention to treat as an

instrument for receiving treatment, thus solving the problem due to selective non-compliance.

The remaining concern is that the characteristics of the assigned placement area might be

correlated with the characteristics of the municipality of origin. Since the pre-war location

choice was a result of endogenous sorting, this sorting may be partly transferred into the place-

ment plan. However, we can control for this bias by including the characteristics of the munic-

ipality of origin in the regression model. The equation system that we estimate using only data

for the refugees, is therefore

yijt = Wjt +Xi0+ uit

Wjt = Zj01 +Xi02+vit (4)

where W is a proxy ofQ and Zj0 is a vector of characteristics of the assigned placement area

measured before the war. The effects of municipality characteristics Wjt are identified provided

that the settlement plan creates variation in the municipality characteristics after conditioning

on the pre-war municipality characteristics.

We can now combine the information of equation (2) and estimands R, R and to obtain

a consistent estimate of...[other points to be discussed]

1. Minimum distance estimator (see appendix)

2. Equation (4) can also be used to assess the impact of the characteristics of the initial

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destination area.

3. Resettlement is likely to have had general equilibrium effects. Thus, the estimates may

not be informative about the counter-factual world, where Finland had not ceded any

areas. In other words, the experiment is likely to have affected both the controls and

the treated. Hence, the counter-factual state for the refugees is the one, where they had

lived on the other side of the post-war border not the one, where they had stayed in the

ceded areas.

4. Quantile regressions

5 Data

Our primary source of data is the 1950 population Census, the first full census ever implementedin Finland. Data was collected by personal interviews and the interview data then stored on the

Census forms. Information on each dwelling unit filled one form. These cards were then filed in

boxes sorted by municipality and within municipalities in alphabetical order. In 1997, Statistics

Finland took a sample of the data by picking one in ten boxes. Nearly all information on the

Census forms was keyed into a database The resulting sample contained about 114,000 dwelling

units with 411,629 persons from 392 of Finlands 547 municipalities. Based on each persons

first and last names along with their date and place of birth, Statistics Finland matched these

data to the Population Register in order to find the social security number which had been

introduced in the late 1960s to each person. Thus, in order to end up in the final sample, theperson had to be alive and live in Finland in late 1960s. Social security numbers were then used

to merge the 1950 census file to a longitudinal census file containing results from population

censuses performed every five years since 1970. This allows following the 1950 census sample

through each subsequent Population Census up to the year 2000.5

The 1950 census contains information on the various household characteristics, place of

birth and residence, education, occupation and sector of employment. Unfortunately, earnings

or wages were not included in the original census forms. Interestingly, the 1950 census also con-

tains retrospective information concerning municipality of residence in 1939. Other informationfrom 1939 includes the occupational status and industry codes referring to September 1st, 1939

two months before the war began. The same codes are available from 1950. This creates an

unusual situation where we have longitudinal micro data on refugees and non-refugees from

5Ideally we would also have data from the 1960 census. However the original punch-cards were destroyed inthe 1970s and the magnetic tapes (where the data had been stored) were damaged in storage.

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pre- and post-migration periods. Same survey instruments were administered to both groups

and hence all information is fully comparable.

Linking the 1950 Census to the longitudinal Census data collected in 1970 substantially

increases the amount of available information. Most importantly, also annual earnings are

recorded in the 1970 Census. This provides an opportunity to evaluate the long-term effectsof migration 25 years after the war ended. Even longer-term outcomes can be observed by

examining the earnings around 19801990 when most of the (surviving) refugees are already

retired from the labor force. Since pension income is based on the accrued pension rights from

each employment spell, it should be a good proxy for lifetime income.

We have access to a smaller random sample of the data originally stored by Statistics Fin-

land, which we complemented with municipality-level tax and population data.6 We limit the

analysis to individuals aged 14 to 54 in 1939 and exclude those, whose municipality of resi-

dence in 1939 is unknown (438 observations); who lived in the partly ceded municipalities (991

observations) or in the land Islands (41 observations); and who lived in 1939 in a municipality

for which data on taxable income per capita is missing (796 observations). This leaves us with

information on 36,107 individuals, of whom 4,075 are refugees.

Table 1 reports sample means for selected variables for refugees and non-refugees in 1939,

1950 and 1970. The pre-war means are relatively similar. The main difference is the higher

share of entrepreneurs and lower share of blue-collar workers among future refugees. Further,

the share of population working in manufacturing and living in urban areas is a bit smaller in

the ceded area, while the share working in the service sector is larger. Future refugees are also

more likely to be included in the socioeconomic category unemployed / out of labor force /unknown and to the industry category unknown. However, while statistically significant, the

magnitudes of these differences are relatively small. Importantly, the average taxable income

per capita in the municipality of residence is not significantly different between refugees and

non-refugees. The only large difference is that the refugees were far less likely to speak Swedish

as their mother tongue.

The next two columns report means in 1950. Compared to 1939, the population share

employed in agriculture has declined by more than ten percentage points and the decline has

been substantially larger among the refugees. Note that the shares were equal before the war,

but in 1950 refugees were eight percentage points less likely to work in agriculture than non-refugees. On the other hand, the difference in the fraction of blue-collar workers between

6Statistics Finland has published detailed annual tables listing taxable earnings based on reports from localtax boards. Usually each municipality had one tax board, large cities had sometimes several. Before the warthis information is very detailed listing the number of taxpayers by income bracket. After the war less detail isavailable, but municipality-level averages can still be calculated with reasonable precision.

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refugees and natives has vanished. Refugees are still more likely to work in the service sector,

which is also reflected in a higher share working in the public sector.7 They are now also more

likely to live in urban areas, though the difference remains small in magnitude. Not surprisingly

given that the refugees had lost their homes only six years earlier there is a clear difference

in the fraction living in owner-occupied housing.The last two columns report means in 1970. Note that the refugees are still less likely

to work in agriculture and more likely to work in service and construction sectors and now

also in manufacturing. Further, the difference in living in urban areas has intensified. This

reflects the fact that refugees were substantially more likely to migrate between 1950 and 1970

than non-refugees.8 As a consequence, refugees now live in municipalities with higher taxable

income per capita. The refugees are still less likely to live in owner-occupied housing. This

might indicate long-term effects on wealth or simply be a consequence of high mobility. Most

importantly, refugees have higher annual earnings than non-refugees, suggesting that forced

migration might have positive long-term effects on income.

Table 3 reports education of refugees and non-refugees in 1950, when education is first

observed. The sample has now been split into three age cohorts and means for men and women

are reported separately. The young cohort consists of those aged 1419; the middle cohort of

those aged 2034; and the old cohort of those aged 3554 in 1939. The motivation for this

split is that the young cohort had just (or not yet) entered the labor market at the beginning of

the war. This should give them more flexibility to change their sector of employment or to alter

their decisions to acquire education as a response to becoming a refugee. Note also that in 1970,

this cohort was in the prime of their working careers. The middle cohort, on the other hand,had already made their educational decisions at the beginning of the war. Hence the means in

Table 3 should be a good proxy for their education in the pre-war period. The logic of further

splitting the sample is that the middle cohort had already retired from the labor force.

Table 3 suggests that the population in the ceded areas was somewhat better educated than

the rest of the population. The main difference is that refugees are more likely to have completed

the six year primary education, in particular among the old cohort. Note, however, that the

differences in young and middle cohorts are quite small in magnitude. Further, non-refugee

men in the middle cohort and women in the old cohort are a bit more likely to have completed

the matriculation exam, which was a prerequisite for attending university.7See Tables 2 and 3 for a closer look of these transitions8The binary migration variable is defined to be one if the municipality of residence in 1970 differs from 1950

and zero otherwise.

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6 Results

This section reports the results of the regressions discussed in Section 4. We begin by asking

whether we can find indirect evidence of the refugees differing from the rest of the population

prior to the war. We then present our main results on the total effect of forced migration on

economic outcomes. We also attempt to decompose this effect into a direct effect and the

effect due to higher mobility. Finally, we consider the impact the characteristics of the initial

destination area on long-term income.

6.1 Pre-War Differences

In Section 5 we saw that the mean characteristics of refugees were relatively similar to those

of the rest of the population. We next assess whether available data suggest that there was

significant differences in unobserved characteristics affecting labor market outcomes.The only available variable in the micro-data indicating labor market success in 1939 is

socioeconomic status. To study the possible differences, we first regress a binary employment

status on future refugee status. Next we include age, age squared, speaking Swedish as ones

mother tongue, living in urban area and taxable income per capita (and its square) in the mu-

nicipality of residence as controls. If future refugees differed on average from the rest of the

population in their unobserved characteristics, these differences should show up in the esti-

mate for future refugees status after observable characteristics have been controlled for. Table

4 reports the results. For the most part, we find no differences in the pre-war employment. Cu-

riously, however, 1419 year old girls living in the ceded area were less likely to be employedthan those in the rest of the country. We also find a negative, but small, association between

future refugee status and pre-war employment for the old cohorts of both men and women.

Unfortunately, we do not observe individual level pre-war income. Hence we need to rely

on an indirect measure, namely the municipality level taxable income per capita. Figure 3

presents the geographical distribution of this measure. Two patterns emerge. First, taxable

income is highest in the cities and market towns. Second, Southern Finland and Southwest

in particular is more prosperous than the rest of the country.9 However, the figure does not

suggest that the ceded areas had formed an exception to these patterns. Figure 4 providesanother look at the distributions. It plots the empirical cumulative distributions of log taxable

income per capita for the ceded areas and the remaining municipalities. The distributions are

9The four municipalities in the very Northeast form a visible exception to this rule. While these municipalitieshave a dramatic effect to the map, one should note that they were extremely sparsely populated. In 1939, only0.4% of the total population lived in these municipalities.

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quite similar, though the distribution in the ceded areas is a bit more compressed. Finally, Table

5 reports results from municipality level regressions, where the dependent variable is log taxable

income per capita. The reported coefficients are for a dummy indicating that the municipality

was to be ceded after the war. In columns (2) and (4) we include controls for the share of

labor force working in agriculture and dummies for market towns and rural area (cities beingthe comparison group). Columns (1) and (2) report results from unweighted regressions. In

columns (3) and (4) regressions are weighted by municipalitys population in 1939. To check

robustness, we also estimate the regressions using an alternative definition of taxable income.

None of the estimates are statistically significantly different from zero.

Hence we conclude that we find no evidence of large pre-war differences between future

refugees and the rest of the population. If anything, there is a negative association between

living in the ceded areas and employment rates.

6.2 Total Effect

Lets now turn to post-war outcomes. Table 6 presents coefficients for being a refugee in re-

gressions controlling for age, age squared, Swedish-speaking, taxable income per capita (and

its square) in the municipality of residence and fixed-effects for interactions between sector of

employment, socio-economic status and living in urban area. All controls are from 1939, ex-

cept the tax data, which is from 1938. These results correspond to the estimand R discussed

in Section 4. Recall that it also provides an upper bound for parameter in equation (1). More

importantly, it captures the sum of direct and indirect effects of forced migration.

Rows I and II report results from regressions where the dependent variable is a binary em-

ployment measure in 1950 and 1970, respectively. The point estimates are close to zero and

they are estimated quite precisely. The only statistically significant (at 10% level) estimand is

for the young cohort of women in 1950, but even this estimate is small in magnitude, suggest-

ing one percentage point higher employment probability for refugees. Further, the likelihood

that one estimand out of 16 is significant by chance is considerable. Hence we conclude that

becoming a refugee had no effect on neither the short-term or long-term employment rates.

We next study the impact on long-term earnings. Row III reports the effect on log annual

taxable income in 1970.10 For the young and middle cohorts, we find large positive effects onboth men and women. The impact is particularly strong among young cohorts suggesting that

becoming a refugee at age 1419 increased income at age 4550 on average by about 19% and

16% for men and women, respectively. The effect for the middle cohort who were 2035

10We include zeros by using log(1+taxable annual income) as the dependent variable.

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years old at the beginning of the war and 5165 years old in 1970 is around 11% and 9%.

For the old cohorts most of whom were already on pension in 1970 the point estimates are

negative, but we cannot reject the null of no effects. Figure 4 presents further results from 95

quantile regressions on the log of taxable income in 1970. The effect is larger in the lower end of

the income distribution, in particular for men. In summary, the results suggest that, on average,forced migration considerably improved income more than 30 years after the first evacuation.

We next ask, whether this effect was particular to the late years of the working careers or

whether it reflects change in lifetime income. To do this, we use taxable income for pensioners

as a proxy for lifetime income. For the young cohort we use data from 1985 and for the middle

and old cohort from 1980. The results (row IV) are similar, though a bit smaller in magnitude, as

the estimates on 1970 earnings. Hence, forced migration seems to have increased also lifetime

income.

These results are in line with our simple theoretical model. Recall that in the model, income

increases solely due to assuming that forced migration leads to higher regional/sectoral mobility.

Rows V to VII support this assumption. In particular, the effect on regional mobility is large.

According to the point estimates, refugees were 715 percentage points more likely to change

their municipality of residence between 1950 and 1970 than comparable non-refugees (compare

to 22% of non-refugees migrating during this period). The point estimates also suggest that

refugee males were 9 percentage points and women 3 percentage points more likely to change

the sector of employment between 1939 and 1950 (compare to 50% of non-refugees changing

sector during this period). Refugee males were also somewhat more likely to change sector

between 1950 and 1970, but the effect is small in magnitude.

6.3 Decomposition

In this section we attempt to decompose the income effect discussed above into the part ex-

plained by increased mobility and other factors trough which forced migration affects long-term

income. We concentrate on log annual taxable income in 1970.

Table 6 reports estimates for refugee status on five specifications. The first column gives

results from regressions without any controls and the second column repeats the estimates in

Table 5. Controlling for the 1939 characteristics has some impact on the point estimates, butthe qualitative results are not affected. We next control for the sector of employment in 1970

(column 3). This has only minor impact on the estimates. In contrast, including a set of mu-

nicipality of residence fixed-effects (columns 4 and 5) changes the estimates considerably. The

association between income and refugee status vanishes for young and middle cohorts and be-

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comes negative for the old cohorts. These estimates correspond to the estimand RQ discussed

in Section 4. Note that it provides a lower bound estimate for the parameter in equation (1).

Further, it has clear descriptive interpretation. That is, young and middle cohort refugees and

non-refugees who had similar characteristics in 1939 and who worked in the same labor market

in 1970 also had similar earnings in 1970.We next turn to estimating the parameter in equation (1)... [TBA]

6.4 Impact of the Initial Destination Area

TBA

7 Conclusions

Evaluating the effects of migration on the migrants poses a difficult task. The fundamentalproblem is that migrants are typically a highly selected group of the population. In this paper,

we have analyzed the effects of migration on a group that has been forced to migrate due to the

post-World War II evacuation of the eastern parts of Finland. Our results indicate that, in the

long-run, forced migration may have had large positive effects. Several measures of economic

status over 30 years after the first evacuation indicate that the refugees performed better than

non-refugees, whose economic situation was similar before the war. We attribute a large part of

these gains to increased occupational and regional mobility. Forcing the refugees to move away

from low-income farming occupations and to find jobs from the industrial and service sectorsseem to have improved their long-term economic outcomes.

Naturally, our results are specific to an exceptional historical period. After the war, Finland

went through a rapid structural change and the economic success of the refugees was linked to

their faster change from farming to industrial and service occupations. It was also affected by

an exceptionally generous immigration policy offering compensation for lost property in the

ceded areas and substantial help in resettling to the other parts of the country.

Clearly, resettling individuals by force is hardly a relevant policy option. Nevertheless, we

argue that there are lessons to be learned from this forced mass migration. For example, the

results regarding the importance of regional and occupational mobility are relevant for regionalpolicy. Our results suggest that regional policy encouraging mobility might be a preferred

alternative to a policy that attempts to support people at declining regions at least in the long-

run.

15


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Appendix

[TBA]

16


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References

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Economics 118 (1): 329-357.

Edin, Per-Anders, Peter Fredriksson & Olof slund (2004): Settlement policies and the economicsuccess of immigrants.Journal of Population Economics 17(1): 133-155.

Glitz, Albrecht (2006): The Labour Market Impact of Immigration: Quasi-Experimental Evidence.CreAM Discussion Paper12/06. University College London.

Friedberg, R. (2001): The impact of migration on the Israeli labor market. Quarterly Journal ofEconomics 18(2):221-251.

Hunt, Jennifer. (1992): The Impact of the 1962 Repatriates from Algeria on the French LaborMarket.Industrial and Labor Relations Review 45(3):556-72.

Katz, Lawrence F., Jeffrey R. Kling & Jeffrey B. Liebman (2001): Moving to Opportunity inBoston: Early Results of a Randomized Mobility Experiment. Quarterly Journal of Economics116: 607-54.

Jeffrey R. Kling & Jeffrey B. Liebman & Lawrence F. Katz (2007): "Experimental Analysis ofNeighborhood Effects,"Econometrica 75(1): 83-119.

Oreopoulos, P. (2003): The Long-Run Consequences of Growing up in a Poor Neighborhood.Quarterly Journal of Economics, 118, 1533- 1575.

Pihkala, Kaarlo U. (1952): The Land Settlement Program of Finland.Land Economics 28(2):147-

159.

Piil Damm, Anna (2006): Ethnic enclaves and immigrant labour market outcomes: Quasi-Experimental evidence, CReAM Discussion Paper07/06. University College London.

Sarvimki, Matti, Roope Uusitalo & Markus Jntti: Labor market adjustment to mass migration,work in process.


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TABLE 1 SAMPLE MEANS

1939 1950 1970

non-refugees refugees



Age 29.4 29.3 40.4 40.3 58.9 58.8Female 0.55 0.55 0.55 0.55 0.56 0.56

Lives in urban area 0.25 0.24 * 0.29 0.30 * 0.35 0.43 * Native tongue Swedish 0.10 0.01 * 0.10 0.01 * 0.09 0.00 *

Works in the public sector na. na. 0.14 0.18 * 0.09 0.11 *Single na. na. 0.15 0.14 0.27 0.27

Owner occupied housing na. na. 0.57 0.48 * 0.75 0.72 *Migrated since previous period na. na. 0.23 1.00 * 0.22 0.33 *Taxable income per capita in the

municipality of residence 51.7 46.6 638 675 13.0 14.1 *

Annual taxable income na. na. na. na. 10.7 11.5 *

Socioeconomic status

entrepreneur/ass. family member 0.24 0.27 * 0.38 0.35 * 0.22 0.17 *

white collar worker 0.10 0.10 0.18 0.19 * 0.13 0.14 blue collar worker 0.28 0.23 * 0.42 0.43 0.23 0.26*unemployed/out of labor force 0.27 0.29 * 0.03 0.03 0.42 0.43

16 years old or younger 0.11 0.12 0.00 0.00 0.00 0.00

Employed 0.67 0.64 * 0.97 0.97 0.43 0.43

Sector (conditional on not unknown/missing)

Agriculture 0.48 0.48 0.38 0.30 * 0.33 0.24 *Manufacturing etc. 0.20 0.16 * 0.26 0.25 0.22 0.25 *

Construction 0.06 0.07 0.09 0.11 * 0.10 0.12 *

Service etc. 0.25 0.29 * 0.27 0.33 * 0.35 0.40 *

Sector unknown 0.35 0.38 * 0.10 0.10 0.42 0.43

Observations 32 032 4 075 32 032 4 075 26 688 3 396* indicates statistically significant difference at 95% level. Taxable income per capita in the municipality of residence is fromthe municipality level data, except for 1970 where it is the sample mean of individual taxable income in the microdata. For

this measure, statistical significance of differences is assessed with least-squares regression on refugee status, wheremunicipality level clustering has been accounted for. For other variables statistical significance in differences is assessedwith group mean comparison t-test.


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TABLE 2 TRANSITIONS BETWEEN SECTORS 19391950 AND 19501970

Non-refugees

Sector 1950 Sector 1970Agr. Man. Con. Serv. Out Agr. Man. Con. Serv. Out

Agr. 0.70 0.06 0.03 0.05 0.16 Agr. 0.42 0.05 0.04 0.06 0.44Man. 0.05 0.76 0.05 0.12 0.03 Man. 0.03 0.28 0.04 0.15 0.50

Con. 0.09 0.13 0.66 0.09 0.03 Con. 0.04 0.09 0.21 0.12 0.54Serv. 0.07 0.14 0.04 0.71 0.04 Serv. 0.02 0.06 0.02 0.41 0.48

Sector1939

Out 0.29 0.24 0.09 0.27 0.12Sector1950

Out 0.02 0.03 0.01 0.06 0.88

Refugees

Sector 1950 Sector 1970Agr. Man. Con. Serv. Oth. Agr. Man. Con. Serv. Out

Agr. 0.60 0.10 0.06 0.09 0.16 Agr. 0.36 0.06 0.05 0.06 0.47Man. 0.06 0.63 0.10 0.17 0.04 Man. 0.02 0.30 0.04 0.14 0.51Con. 0.08 0.20 0.61 0.06 0.04 Con. 0.04 0.10 0.22 0.13 0.50

Serv. 0.06 0.16 0.05 0.70 0.03 Serv. 0.02 0.07 0.03 0.40 0.49Sector1939

Out 0.21 0.26 0.10 0.33 0.10 Sector1950

Out 0.01 0.02 0.03 0.07 0.87

TABLE 3 TRANSITIONS BETWEEN SOCIOECONOMIC STATUS 19391950 AND 19501970

Non-refugees

Status 1950 Status 1970Entr. Whitec. Bluec. Out Entr. Whitec. Bluec. Out

Entr. 0.88 0.02 0.08 0.02 Entr. 0.44 0.03 0.10 0.42

White 0.09 0.81 0.08 0.02 White 0.07 0.49 0.09 0.36Blue 0.13 0.07 0.79 0.02 Blue 0.09 0.06 0.43 0.42Out 0.32 0.20 0.42 0.05 S

tatus

1950

Out 0.07 0.20 0.10 0.63Status

1939


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TABLE 4 EDUCATION,1950

Cohorts

Young Middle Old

non-

refugees refugees

non-

refugees refugees

non-

refugees refugeesMen

Primary 0.80 0.83 0.72 0.76 * 0.54 0.63 *Secondary 0.04 0.05 0.04 0.05 0.03 0.03Tertiary 0.05 0.05 0.04 0.03 * 0.03 0.02

None / unknown 0.11 0.07 * 0.20 0.16 * 0.41 0.32 *

Observations 3 373 423 6 932 898 4 078 504

Women

Primary 0.79 0.82 0.71 0.76 * 0.53 0.57 *Secondary 0.07 0.09 0.07 0.07 0.05 0.05

Tertiary 0.04 0.03 0.03 0.02 0.02 0.01 * None / unknown 0.09 0.06 * 0.19 0.15 * 0.41 0.37 *

Observations 3 629 475 8 110 1 073 5 910 702* indicates statistically significant difference at 95% level (group mean comparison t-test). YoungCohort: 25-30; Middle Cohort: 31-45; and Old Cohort: 46-65 years old in 1950.

TABLE 5 DIFFERENCES IN PRE-WAR EMPLOYMENT

Men Women

Cohorts Cohorts

All Young Middle Old All Young Middle Old

(1) (2) (3) (4) (5) (6) (7) (8)

No controls -0.01 -0.02 0.01 -0.01 -0.04 -0.09 -0.01 -0.05

(0.01) (0.02) (0.01) (0.01) (0.01) (0.02) (0.02) (0.02)

With Controls 0.00 0.01 0.01 -0.02 -0.03 -0.08 0.01 -0.02

(0.01) (0.02) (0.01) (0.01) (0.01) (0.02) (0.02) (0.02)OLS estimates; standard errors in parenthesis. Young Sample: 15-20; Middle Sample: 21-35; and OldSample 36-55 years old in 1940. Excluding Ahvenanmaa and partly ceded areas. Bold, underlined anditalics indicate statistical significance at 1%, 5%, 10% level. Controls: age, age squared, speaking

Swedish as one's mother tongue, living in urban area and taxable income per capita (and its square) in themunicipality of residence.


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TABLE 6 DIFFERENCES IN PRE-WAR TAXABLE INCOME PER CAPITA

(1) (2) (3) (4)0.07 0.02 0.03 0.02log taxable income per

capita, 1939 (definition I) (0.08) (0.04) (0.10) (0.04)0.07 0.00 0.02 0.00log taxable income per

capita, 1939 (definition II) (0.10) (0.06) (0.11) (0.06)

Controls no yes no yes

Weighted no no yes yesMunicipality level least squares estimates on an indicator for becoming ceded area;standard errors in parenthesis. Controls: share of labor force in agriculture, dummies formarket town and rural municipality. Weights: 1938 population. Sample sizes are 583 (defI) and 555 (def II).

TABLE 7 TOTAL EFFECTS

Men Women

Cohorts Cohorts

All Young Middle Old All Young Middle Old

(1) (2) (3) (4) (5) (6) (7) (8)Employed, 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00(I)

1950 (0.00) (0.01) (0.00) (0.01) (0.00) (0.01) (0.00) (0.01)Employed, 0.00 0.03 -0.02 0.01 0.00 0.01(II)

1970 (0.01) (0.02) (0.02) (0.01) (0.03) (0.01)Annual Taxable 0.09 0.19 0.11 -0.09 0.08 0.16 0.09 -0.02(III)

Income, 1970 (0.03) (0.05) (0.04) (0.07) (0.03) (0.08) (0.04) (0.03)Pension Income, 0.11 0.11 0.10 0.15 0.14 0.09 0.15 0.12(IV)

1980/85 (0.03) (0.04) (0.05) (0.13) (0.04) (0.03) (0.04) (0.14)Migrates, 0.11 0.12 0.08 0.15 0.11 0.07 0.11 0.15(V)

1950-1970 (0.02) (0.03) (0.02) (0.03) (0.02) (0.02) (0.02) (0.03)Changes Sector, 0.09 0.12 0.09 0.07 0.03 0.03 0.03 0.03(VI)

1939-1950 (0.01) (0.02) (0.02) (0.02) (0.01) (0.02) (0.01) (0.01)Changes Sector,

0.03 0.03 0.04 0.00 0.01 0.01 0.02 0.01(VII)

1950-1970 (0.01) (0.03) (0.02) (0.02) (0.01) (0.03) (0.02) (0.02)OLS estimates. Standard errors (in parenthesis) are robust to arbitrary heteroscedasticity and municipality levelclustering.Young Sample: 45-50; Middle Sample: 51-65; and Old Sample 66-85 years old in 1970. Excluding theland Islands and partly ceded areas. Bold, underlined and italics indicate statistical significance at 1%, 5%, 10%level, respectively. Controlling for age, age squared, Swedish-speaking, log taxable income per capita in the 1939municipality of residence (and its square) and interactions between sector, socio-economic status and living in urbanarea in 1939. Pension Income is measured in 1980 for middle and old cohorts and in 1985 for the young cohort.


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TABLE 8 REFUGEE STATUS AND ANNUAL EARNINGS,1970

Men (1) (2) (3) (4) (5)(I) All 0.15 0.09 0.08 0.00 0.01

(0.04) (0.03) (0.03) (0.03) (0.03)

(II) Young 0.25 0.19 0.10 0.07 0.04(0.05) (0.05) (0.04) (0.06) (0.05)

(III) Middle 0.14 0.11 0.11 0.01 0.04

(0.05) (0.04) (0.04) (0.04) (0.04)(IV) Old 0.00 -0.09 -0.08 -0.17 -0.14

(0.09) (0.07) (0.07) (0.08) (0.09)

Women

(V) All 0.05 0.08 0.03 -0.01 -0.01

(0.06) (0.03) (0.02) (0.03) (0.03)(VI) Young 0.20 0.16 0.07 0.06 0.06

(0.10) (0.08) (0.06) (0.10) (0.07)(VII) Middle 0.04 0.09 0.03 -0.01 -0.01

(0.05) (0.04) (0.03) (0.04) (0.03)(VIII) Old -0.12 -0.02 -0.02 -0.09 -0.09

(0.08) (0.03) (0.03) (0.04) (0.04)

Controlling for:

1939 characteristics no yes yes yes yes

1970 Sector no no yes no yes

1970 Municipality no no no yes yes

y = log(1+taxable income, 1970). OLS estimates. Standard errors (in parenthesis) arerobust to arbitrary heteroscedasticity and municipality level clustering. Young Sample: 45-50; Middle Sample: 51-65; and Old Sample 66-85 years old in 1970. Excluding the landIslands and partly ceded areas. Bold, underlined and italics indicate statistical significanceat 1%, 5%, 10% level, respectively. Base controls: age, age squared, Swedish-speaking,log taxable income per capita in the 1939 municipality of residence (and its square) and 30(sector) x (socio-economic status) x (urban) interactions in 1939.


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FIGURE 1 AREAS CEDED TO THE SOVIET UNION IN 1940 AND 1944

Source: Wikipedia, the free encyclopedia

FIGURE 2 THE MIGRANT PLACEMENT PLAN

Source: Laitinen (1995)


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FIGURE 3 TAXABLE INCOME PER CAPITA,1938

Dark colors indicate higher taxable income per capita levels. Dashed areas indicate missing data.

Bold lines indicate the new Eastern border.


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Figure 4 Empirical Cumulative Distribution of Taxable Income per capita, 1938

0

.2

.4

.6

.8

1

2 3 4 5

Remaining municipalities Ceded areas


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FIGURE 5 REFUGEE STATUS AND TAXABLE ANNUAL INCOME (1970),QUANTILE REGRESSIONS

0.2 0.4 0.6 0.8

0.0

0.1

0.2

0.3

0

.4

0.5

refugee

o

o

o

o

oo

o

o

oo

o

oo

oo

o

o o oo

oo

oo

o

o oo

o o o

oo

oo

o oo

oo

oo

o ooo

o oo

oo o

oo o

ooo

o o

o oo o o o

o

o oo

o oo

oo

oo

o

oo

oo

o

o

o

oo

o

o

o

o

0.2 0.4 0.6 0.8

0.0

0.1

0.2

0.

3

0.4

0.

5

refugee

o

o

o

o

oo

o

o

oo

o

oo

oo

o

o o oo

oo

oo

o

o oo

o o o

oo

oo

o oo

oo

oo

o ooo

o o

o

o

o o oo o

ooo

o oo o

o o o oo

o oo

o oo

oo

o oo

oo

oo

o

o

ooo o

o

o

o

Men

Women

Coefficients for refugee status from quantile regressions on log(annual taxable income,

1970). Sample: Individuals aged 4564 in 1970. Controlling for age, age squared,

Swedish-speaking, sector, socio-economic status, living in urban area and two measures

of taxable income per capita in the municipality of residence in 1939. Horizontal solid

line is the OLS estimate and dashed horizontal lines indicate 90% confidence intervals.

Shaded area indicates bootsrapped 90% confidence intervals (calculated with 1000

repetitions) for quantile regression estimates.

Quantile

Quantile

0.2 0.4 0.6 0.8

0.0

0.1

0.2

0.3

0

.4

0.5

refugee

o

o

o

o

oo

o

o

oo

o

oo

oo

o

o o oo

oo

oo

o

o oo

o o o

oo

oo

o oo

oo

oo

o ooo

o oo

oo o

oo o

ooo

o o

o oo o o o

o

o oo

o oo

oo

oo

o

oo

oo

o

o

o

oo

o

o

o

o

0.2 0.4 0.6 0.8

0.0

0.1

0.2

0.

3

0.4

0.

5

refugee

o

o

o

o

oo

o

o

oo

o

oo

oo

o

o o oo

oo

oo

o

o oo

o o o

oo

oo

o oo

oo

oo

o ooo

o o

o

o

o o oo o

ooo

o oo o

o o o oo

o oo

o oo

oo

o oo

oo

oo

o

o

ooo o

o

o

o

Men

Women

Coefficients for refugee status from quantile regressions on log(annual taxable income,

1970). Sample: Individuals aged 4564 in 1970. Controlling for age, age squared,

Swedish-speaking, sector, socio-economic status, living in urban area and two measures

of taxable income per capita in the municipality of residence in 1939. Horizontal solid

line is the OLS estimate and dashed horizontal lines indicate 90% confidence intervals.

Shaded area indicates bootsrapped 90% confidence intervals (calculated with 1000

repetitions) for quantile regression estimates.

Quantile

Quantile

sarvimaki, mattin etal (2007) long-term effects of forced migration

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