Ana Sanz

Yann Algan

Fabrice Collard

Catherine Dehon

Patrick Fève

Pietro Garibaldi

Marjorie Gassner

Jennifer Hunt

Franck Portier

Robert Putnam

Yves Zenou

Local Social Capital and Geographical Mobility: Some Empirics and a Conjecture on the Nature of European Unemployment *

Keywords: JEL Classification: J2, J61, Z1 European unemployment, geographical mobility, social capital

European labor markets are characterized by the low geographical mobility of workers. The absence of mobility is a factor behind high unemployment when jobless people prefer to remain in their home region rather than to go prospecting in more dynamic areas. In this paper, we attempt to understand the determinants of mobility by introducing the concept of local social capital. Using data from a European household panel (ECHP), we provide various measures of social capital, which appears to be a strong factor of immobility. It is also a fairly large factor of unemployment when social capital is clearly local, while other types of social capital are found to have a positive effect on employability. We also find evidence of the reciprocal causality, that is, individuals born in another region have accumulated less local social capital. Finally, observing that individuals in the South of Europe appear to accumulate more local social capital, while in Northern Europe they tend to invest in more general types of social capital, we argue that part of the European unemployment puzzle can be better understood thanks to the concept of local social capital.

In an in ‡uential work, [START_REF] Bertola | Wage Inequality and Unemployment: United States vs. Europe[END_REF] documented the inability of European workers to move to more dynamic regions. This arose according to the authors, because of wage and income compression, lowering the returns from mobility. Low mobility and wage compressing labor market institutions have indeed been central in many explanations of unemployment in Europe (see [START_REF] Layard | Unemployment. Macroeconomic Performance and the Labour Market[END_REF]Layard and[START_REF] Layard | Labor Market Institutions and Economic performance[END_REF].

In this work, we want to better understand the determinants and implications of low mobility and provide an alternative explanation for low mobility. We do not focus on the returns of mobility, as in [START_REF] Bertola | Wage Inequality and Unemployment: United States vs. Europe[END_REF], even though this dimension will be present, but, in a dual perspective, on the costs of mobility.

The novelty of our approach is …rst to give an explicit content to mobility costs, namely social capital. In a companion paper [START_REF] David | Local social capital and geographical mobility. A theory[END_REF], we have indeed developped the concept of local social capital and provided a theoretical model in which moving from one region to another had a cost to individuals in terms of social capital losses. We found two relevant concepts of socical capital: the part of social capital which is made of connections with friends, relatives and the closer family, and the part of social capital which is more linked to professional ties.

While both have a local component, the …rst part is presumably more local than the second part. We argued in a simple model that local social capital has a negative impact on mobility and, to a lesser extent, a positive impact on unemployment. We pointed out implications on the determinants of European unemployment and the possible inability of labor market reforms to make signi…cant progress in terms of employment, given the likely existence of multiple equilibria.

In this paper, we attempt to match some of the concepts with the data. We show that local social capital a¤ects the cost of moving. Based on various measures of local social capital, we …nd that it is a dominant factor of immobility. It is also a fairly large factor of unemployment when social capital is clearly local, while other types of social capital are found to have a positive e¤ect on employability. We also …nd evidence of the reciprocal causality, that is, individuals born in another region have accumulated less local social capital. This gives some credibility to a theory of multiple equilibria developped in [START_REF] David | Local social capital and geographical mobility. A theory[END_REF].

Finally, observing that individuals in the South of Europe appear to accumulate more local social capital, while in Northern Europe they tend to invest in more general types of social capital, we argue that part of the European unemployment puzzle can be better understood thanks to the concept of local social capital.

A few recent works are related to ours. We survey them more in detail in our companion paper. Let us only cite [START_REF] Bentolila | Social Contacts and Occupational Choice[END_REF] who emphasize the potential negative links between social capital and labor markets: jobs obtained through social networks tend to have a wage discount, distorting choices towards ine¢ ciency. [START_REF] Belot | Friendship ties and geographical mobility, evidence from the BHPS[END_REF] show that geographical proximity of friends matters for mobility decisions in the UK. In a di¤erent context, Spilimbergo and Ubeda (2004a and b) argue that US black workers are less mobile than white workers because of family ties (2004b) and successfully test this using the PSID survey. Kan (2007) also assesses the impact of local social capital on residential mobility. He also explores the role of friends and relatives using the PSID data. In Spilimbergo and Ubeda (2004a), they model how the double matching in the labor market and social environment can explain migration patterns. An implication is the existence of multiple equilibria.

In Section 2, we de…ne the concept of local social capital and then propose a match between theory and the data. We attempt to measure social capital relevant to the European context using panel data analysis from the European Community Household Panel: for instance, local social capital can be approximated by measures such as the intensity of relations with friends and neighborhood connections, while being a member of clubs, associations, etc. is a more global type of social capital.

In Section 3, we then explore the links between mobility and social capital in Europe. We carry out various empirical exercises to underline the mechanisms at work. The …rst causality, from social capital to mobility, is tested treating …rst social capital as given or predetermined.

Mobility is measured as year-to-year change of "area of residence". We indeed …nd, as expected, that our measures of local social capital lead to a strong reduction of residential mobility. We use a succession of di¤erent instruments for social capital, such as regional social capital, individual lags, family composition, regional turnout in elections and regional population density.

The second causality, from mobility to social capital accumulation, is also veri…ed in the data: individuals living in a region di¤erent from their region of birth indeed have accumulated less social capital. We …nally explore the role of local social capital on unemployment: here, interestingly, social capital of various sorts have opposite e¤ects on unemployment: neighborhood variables usually raise unemployment, while club membership and friends reduce it. We …nally attempt to correlate social capital with traditional labor market variables such as reservation wages -as de…ned by individuals and the duration of unemployment before re-employment.

Finally, in Section 4, we summarize our insights and then explore how social capital could help understand intra-European di¤erences in unemployment at the country level.

Local social capital 1.De…nition

In this Section, we clarify our meaning of the expression social capital, for which there are many de…nitions. The key concept is the localness of social capital.

Assume an individual endowed with S units of social capital, leaving in a region (say, the South) to study or work in another region (say, the North). Once the individual leaves the South, her social capital is partly depreciated: she only retains a fraction of it, because she looses friends or will see them less often. Let us denote by the depreciation rate, which describes the degree of localness of social capital. We may think for instance that by leaving the native region, she looses of her friends, or meets her relative less frequently. Therefore, a higher level of local social capital may be associated with a reluctance to move to another region.

At the same time, a higher level of social capital can provide information about job o¤ers [START_REF] Granovetter | Getting a job : a study of contacts and careers[END_REF], this both in the South and in the North. Social capital may thus have an ambiguous e¤ect on unemployment. Social capital may also be more or less local. Then, for a given level of social capital, a more local type of social capital reduces the likelihood an individual moves toward job opportunities, and therefore to be employed.

Therefore, and along the lines of [START_REF] David | Local social capital and geographical mobility. A theory[END_REF], we expect to …nd a negative link between measures of local social capital and mobility, and a link between social capital and unemployment that may be either positive or negative, depending on whether social capital is local or more general.

Matching the concepts to the data

We will attempt to …nd an empirical counterpart to the concept of local social capital. We will base the analysis on a widely used dataset, the European Community Household Panel Survey (ECHP). In our context, it is particularly useful because it surveys various dimensions of the social life and social capital of individuals. In the literature surveyed in [START_REF] Glaeser | An Economic Approach to Social Capital[END_REF], many works rely on trust questions to approximate social capital. However, [START_REF] Glaeser | An Economic Approach to Social Capital[END_REF] argue that, along with [START_REF] Putnam | Bowling Alone: The Collapse and Revival of American Community[END_REF], one should focus instead on association membership.

There is indeed implicitly a revealed preference argument here: in a survey interview, talking about con…dence and trust is relatively cheap. Further, in European countries of di¤erent languages, responses may be subject to translation bias. Here, we will indeed follow the logic of [START_REF] Glaeser | An Economic Approach to Social Capital[END_REF] and [START_REF] Putnam | Bowling Alone: The Collapse and Revival of American Community[END_REF] and use information of three types: club membership as in [START_REF] Glaeser | An Economic Approach to Social Capital[END_REF], and the frequency of social contacts with friends and neighbors.

We tend to prefer the latter two, because they are presumably more closely associated with the concept of localness we have in mind. Soccer club is local, but chess or Scrabble associations bring usually country-wide ties.

Data description

More precisely, the social capital measures are derived from the three following questions in the ECHP survey:

1. Are you a member of any club, such as a sport or entertainment club, a local or neighborhood group, a party etc...?

2. How often do you talk to any of your neighbors?

3. How often do you meet friends or relatives not living with you, whether here at home or elsewhere?

Questions 2 and 3 precisely correspond to a type of social capital that is clearly local. The "friend"question may in part refer to professional social capital (that is, capital that depreciates when the individual is unemployed). The "club" question may refer to less local social capital.

Even though it is phrased as suggesting non-professional social capital, it may be professional if associations are professional ones. We have however no direct evidence in one sense or the other. 1The answer to the …rst question is yes/no (and is attributed the value 1 or 0). The answer to the last two questions de…nes a frequency on a discrete support, as follows: 1. On most days;

2. Once or twice a week; 3. Once or twice a month; 4. Less often than once a month; 5. Never.

In order to simplify the exposition of the results, we build an index measure as follows: The top of Table 1 presents relevant summary statistics.

Z i;t = I [X i;t = 1] + I [X i;t =
2 Mobility, unemployment and local social capital

Short-Run

The mechanisms we want to highlight can be uncovered estimating the following equations: The sample is restricted to the active population. A full description of the variables can be found in Appendix. See Table 1 for summary statistics. Note that we also estimated an employment equation (instead of unemployment) of the larger sample of 26-55 year old individualsthus including non-participants-but found no qualitative di¤erence. We will thus only display the mobility and the unemployment results. To estimate these equations, we will proceed as follows. In the …rst set of regressions, we consider social capital measures as exogenous and run simple probit regressions. We introduce the various measures of social capital separately or together, because there could be some positive correlation across individuals in these three social capital variables. 3 We will also check whether the inclusion of 92 regional e¤ects a¤ects the results.

P i;t+1 m = m x i;t + m f
In a second step, we attempt to control for potential endogeneity and instrument social capital with various instruments. We tried several of them, which generally yield the same kind of results. A …rst instrument is the lag of individual social capital. It is highly correlated with contemporaneous social capital, but can be criticized for not removing all endogeneity. A second type of instrument is the average social capital in the region 4 . It is more likely to be exogenous, but could be weaker, that is, less correlated with contemporaneous social capital of the individual. In that case, we also use the lagged value of social capital at the regional level. We will report these two types of IV estimations, since each of them has pros and cons.

Alternative instruments we also used are described in Appendix 5 .

Table 2 o¤ers a summary of the mobility regressions with all speci…cations discussed above.

Full Tables A-1 to A-3 in Appendix present the other coe¢ cients. Generally speaking, all three variables measuring social capital have negative and signi…cant e¤ects on mobility. In IV regressions, as the full tables in Appendix (Tables A-2 and A-3) indicate, the number of observations is also reduced by 25% approximately, due to the presence of lags and, when instruments are the regional level of social capital, due to the fact that the region of residence is sometimes missing. There is thus some e¢ ciency loss in the estimates, but generally speaking, the coe¢ cients remain negative and usually signi…cant. In particular, the "neighbor" variable 3 The correlation structure is actually 0.32 between "friend" and "neighbor" but close to zero between "club" and the …rst two variables. 4 The richness of the dataset used by [START_REF] Belot | Friendship ties and geographical mobility, evidence from the BHPS[END_REF] allows them to explore other instruments describing the environment in which the individual grew up as a child. They consider the number of biological siblings in the household when the individual was fourteen years old, his birth-order, the level of education of his parents and whether he grew up in a rural or urban area. 5 Technically, in all IV regressions, we follow a two-stage procedure: we …rst regress the social capital measures on the instruments, and use the projection as regressors in probit regressions. The IV regressions we present therefore have biased standard errors. The reason for using this procedure is that the standard ivprobit procedure in Stata 9 provides the s.e. correction but however does not support clustering and the panel dimension. We are thus left with two alternatives: either we choose to correct for the bias in s.e. due to IV, or to correct for the bias due to individual clustering due to the panel dimension. We chose here to correct for clustering, the main reason being that the implementation of the correction procedure proposed by [START_REF] Wooldridge | Econometric Analysis of Cross-Section and Panel Data[END_REF] for the IV correction would actually lead the s.e. to be lower than with the uncorrected s.e. as displayed in the IV tables. The s.e. displayed here are thus an upper bound for the "true" s.e. has a signi…cant and negative impact on mobility, while the "club" variable is typically no longer signi…cant. Note also from the full Tables A-2 and A-3 that the other variables included present reasonable coe¢ cients: women are less mobile, as well as house owners, older people, large families and married individuals. Unemployed are not signi…cantly more mobile, but the educated are clearly much more mobile. To be exhaustive, Table A-4 in Appendix produces …xed e¤ect regressions for the mobility equation. Without surprise, the coe¢ cients of social capital turn out to be much less signi…cant, even though the "neighbor" variable remains quite signi…cant. This indicates that even the -necessarily moderate-variations in time in social capital "neighbor" for a given individual generate a decline in mobility. Other coe¢ cients displayed are those for which time variation was observed.

Table 3 shows a summary of individual unemployment regressions. Results are relatively stable across speci…cations, but with interesting changes in sign and signi…cance across social capital measures. The main …ndings are that the "neighbor" has a positive impact on the unemployment probability, while "club" has a negative impact on unemployment, this with or without regional e¤ects. The "friend"variable is generally not signi…cant, or marginally positive, except in the last set of IV regressions when instruments are social capital at the regional level. 6Full Tables A-5 to A-7 present the other coe¢ cients.

Finally, we attempted to estimate a multinomial logit model where the dependent variable, denote it by y i;t+1 , takes value 1 if the individual stays in the region and works, 2 if she stays and is unemployed, 3 if she moves and works, 4 if she moves and is unemployed. However, at this stage, the multinomial one fails at explaining individual unemployment, where the coe¢ cient are never signi…cant, but the same results as in Tables 2 and3 are obtained for inter-area mobility.

We do not reproduce those results, available upon request.

We can summarize the e¤ect of social capital on mobility and unemployment in o¤ering a summary, as displayed in Table 4. This table calculates the conditional mobility rate and the conditional unemployment rate for two groups of individuals in the sample (say, an Italian male, owner, 36-45 years old, etc... and a dutch woman, tenant with rent, 26-35 years old, etc...). The mobility rate is respectively, in the absence of social capital (all social capital variables …xed to zero) 0.20% and 6.31%, while unemployment rates are 4.18% and 7.79%. We next consider the impact of the maximum amount of social capital (1 for "club"and the highest possible frequency of visits of friends and neighbors). The largest impact on mobility is the "friend"variable, which can bring back the mobility rate almost to zero for the Italian male and reduces it by two thirds for the Dutch woman. The impact of social capital on unemployment is more ambiguous. As far as "club"is concerned, the unemployment risk is reduced by a third, while instead, the other two variables contribute to an increase of that risk by a third approximately.

These are extremely large numbers. Note however they are obtained in the most favorable case in which coe¢ cients are the largest. The impact of social capital is divided by two if one considers alternative speci…cations, for instance the regular probit model without instruments.

Nevertheless, they remain large enough to deserve more attention than what has usually been the case. A …nal remark concerns the facts that high social capital individuals may be individuals endowed with better skills to make new connections. In this case, to the extent that our IV regressions have not su¢ ciently taken care of such an endogeneity problem, we can argue that this leads to an underestimation of the magnitude of the negative e¤ects of social capital on mobility: individuals with better skills at making friends, if anything, should be more, not less mobile. The e¤ect on unemployment might also be larger than what we measure, since such individuals are presumably also more able to obtain jobs. Our claim, based on these regressions, is not to measure any structural coe¢ cient: it is actually much more modest, to argue that social capital and notably its local component deserves the full consideration of labor economists. 7

Long-Run

As already argued in introduction, the link between social capital and mobility potentially goes both ways. In the short-run, social capital is predetermined to a large extent, hence the regressions in the previous sub-Section are a good …rst investigation. However, in the long run, it might be that individuals forecast future mobility episodes and so, endogenously determine their level of social capital. Thus we need to dissociate short-run mobility from long-run mobility and we now explore how social capital may depend on long-run mobility. Now, the mobility variable P i;t m takes value 0 if "the individual was born in the country of current residence and has lived in the same region since birth" and 1 in the opposite case. We regress our measures of local social capital on this long-run mobility variable, which is now predetermined. Table 5 shows that indeed, having experienced a geographical mobility episode in the past leads to a huge discount in social capital, giving some sense to the concept of localness of social capital.

The full table is in Table A-8 in Appendix.

Additional regressions

The ECHP contains a few additional variables which can be used to complement the previous analysis. In particular, we explore the impact of social capital on unemployment and its duration.

In order to simplify the exposition of results, we limit tables to a few coe¢ cients (that of social capital) and of very few additional variables when relevant, such as whether individuals receive unemployment bene…ts. The sample and the speci…cation are kept as close as possible from the previous set of regressions: sex, house tenure, age categories, education measured in years, household size, marital status and time dummies. For each dependent variable, we present in general three di¤erent speci…cations: the …rst is without geographical control, the second adds country dummies and the third one has regional dummies. Table A-9 in Appendix presents these regressions.

In the …rst part of Table A-9, we analyze the e¤ect of social capital on a variable re ‡ecting whether individuals have experienced long term unemployment. The idea is to test whether social capital can be powerful enough to signi…cantly reduce the risk of long spells of unemployment: connected people may be able to obtain a new job immediately after a layo¤. For that, we restrict the sample to people looking for a job, not working or working less than 15 hours a week. We …nd that only the "club" variable has a negative impact on having been unemployed at a time, while the other social capital variables have no e¤ect.

A second test is to determine, for currently employed individuals, whether social capital reduced the probability of having experienced a spell of unemployment before the current job.

The idea is similar to the previous one: connected people may move easily from job-to-job. The e¤ect of "club"is still strong and negative, while now, we …nd marginally positive impact of the "neighbor" variable.

In the second part of Table A-9, we regress the number of months of unemployment before the current job on our social capital variables and the usual controls. 8 . To these usual controls, we add a set of time controls, not at the time of the survey, but at the time of entrance in the previous job. That is, if an individual is interviewed in 1996 and has been employed in the current job for six years, that individual is attributed a time dummy (y90=1), in order to control for the economic cycle at the time of job search. 9 Columns (1) to (3) only consider the length of unemployment of those having experienced some unemployment (log speci…cation) while Columns ( 4) to ( 6) also include those having faced no unemployment. We …nd that belonging to a club is generally associated with shorter unemployment spells, and that the neighboor variable is marginally positively signi…cant.

Finally, we also tried similar regressions with a measure of the reservation wage but found them disappointing, probably due to measurement error and do not report them here.

However, our conclusions from these additional regressions is that social capital has positive impact on employability when it is not local, and has a negative or nul impact when it is local, which is in line with the previous results in this paper and the theoretical results in David et al.

(2008).

Summary

We …rst summarize the few stable relations in the data. Our regressions suggest unambiguous e¤ects of social capital on individual mobility and adverse e¤ects on individual employment probabilities too. The magnitude of the e¤ects was even quite striking. It notably appears that:

1. Individuals endowed with more local social capital as described by the variables "friend", "neighbor" or "club" are less likely to move to another region in the short-run.

2. Individuals endowed with more local social capital such as described by the variables friends or neighbors are more likely to become unemployed in the short-run.

3. In contrast, individuals who are members of a club are less likely to become unemployed in the short-run.

4. Workers in a region di¤erent from their birth region have a lower stock of social capital in all three dimensions measured ("friend", "neighbor" and "club"). and suggesting interpretations and extensions of our results.

A conjecture on European unemployment

We now try to document a conjecture developped in the introduction: can we really explain country-di¤erences in aggregate unemployment? Table 6 indicates that there is indeed a North-South divide in the nature of social capital: in the South of Europe (and in Ireland too), social capital seems to be more local, explaining the lower geographical mobility rates and di¢ culty to reduce unemployment. In the North of Europe, being part of clubs is instead much more frequent. To the extent that being a member of a club (such as a Scrabble or a chess league) helps to cope with mobility-this is indeed a very good way to re-establish a social network when moving to a new city-, we may have an interesting characterization of the way labor markets work in Europe.

We can go one step further here. Without claiming much causal evidence, but just as a way to summarize the data, we now regress national unemployment on these average measures of social capital, in a cross-country analysis, in an approach inspired by Layard-Nickell (1999). Table 7 regresses the log of unemployment on country-averages of local social capital. Table 8 adds conventional covariates capturing labor market institutions, Table 9 uses lagged employment protection measures for the period before 1990 as instruments, as well as owner occupation rates. The main …nding is that the "friend" variable is signi…cant and raises unemployment.

The "neighbor" variable also raises unemployment but only when it is alone. Multicollinearity with "friend" leads however to instability when both are added. These regressions are only to be thought as preliminary steps. They indicate that the social organization of populations may matter much more than what is commonly believed.

Beyond these last three summary regressions based on macro data, the empirical analysis based on microdata suggests that, to understand better European unemployment, more is to be learnt from non-standard factors. Notably, social capital may be an interesting avenue to explore, con…rming the fruitfulness of the concept. "na" refers to non-available data. See Section 1 for more details on the methodology used to construct these indexes. Marital status: in the survey, this variable refers to the following question: Present Marital Status. The possible answers are (1) Married, (2) Separated, (3) Divorced, (4) Widowed, (5) Never married.

Regional dummies: in the survey, this variable refers to the following question: Region in which the Household is Presently Situated. The classi…cation followed for this question is the NUTS2 AGGREGATES from the European Commission, which considers comparable regions with a population comprised between 800 000 and 3 millions inhabitants. For more information, please refer to the following web site: http : ==ec:europa:eu=comm=eurostat=ramon=nuts. Note also that some countries like the Netherlands have not …lled this question.

A.2 Description of the instruments

As explained in the text, we tried several intruments. The one we found most convincing is the average level of social capital in the region where the individual lives: it is clearly exogenous to the individuals and fairly correlated to individual's social capital. We have however made several other attempts.

A …rst set of additional instrument relates to the regional vote and turnout in elections. In particular, we considered regional turnout at parliamentary elections. The intuition is that higher turnout is the sign of higher social cohesion, hence more social capital. Exogeneity in the unemployment/mobility equation is insured by the fact that we choose lagged turnout, that is, the last election before year 1990 in each available country. The data are missing for Austria, Greece and France. Unfortunately, correlation with our measures of social capital was poor, resulting in important loss of e¢ ciency.

A second set of additional instruments can be found in the anthropological analysis of family structures. [START_REF] Todd | L'Invention de l'Europe[END_REF], a well known demographer and anthropologist, has argued that such structures are extremely stable over the pace of centuries, and can be categorized in four or …ve groups, based on the balance of authority (nuclear vs. "souche", that is, patriarcal) and of the type transmission of land and wealth (equalitarian, each o¤spring getting an equal share, or inequalitarian, the elder getting the largest share). Combining these two criteria leads, according to Todd, to a map of regions in Europe where in each region, one type of the four possible family structure is dominant, with sometimes several types coexisting. Note that we discovered after doing this that Spilimbergo and Ubeda (2004b) had used similar instruments for their US study. In attributing a number for each category of structure, we build an instrument for social capital which is used in individual regressions. It appears to be very correlated with our measures of social capital. With this set of instruments, the results are robust for the mobility equation. However, for the unemployment equation results were not consistent across speci…cations. ECHP data (some data was not available for Germany and Sweden). The reference for the interpretation of the dummy variables is an employed male with rent-free accommodation, married, aged 16-24, living alone. Robust z statistics in parentheses. * significant at 5%; ** significant at 1%

A.3 Additional tables

  riendf req i;t + m neibf req i;t + m club i;t + i;t+1

					m	,
	P i;t+1 u	=	u x i;t + u f riendf req i;t + u neibf req i;t + u club i;t + i;t+1 u	,
	where P i;t+1 m	and P i;t+1 u	are respectively the probabilities of moving to another area and of
	being unemployed for individual i in period t + 1, the time period being a year. x i;t is a vector
	of exogenous controls: Sex (1 if female, 0 if male), house tenure (categorical variable stating
	whether the individual is owner of his house, whether he rents it or has it for free), age category
	(16 to 25, 26 to 35, 36 to 45, 46 to 55 and 56+), number of years of education, a dummy variable
	for unemployment, household size (1, 2, 3, 4 persons or 5 and more); marital status (married,
	separated, divorced, widowed or never married), time e¤ects. The variables f riendf req i;t and
	neibf req i;t will correspond to our measures of social capital. Hence, the predictions to be tested
	are whether m and m (and possibly m ) are negative and whether u , u or u are uniformally
	positive or possibly negative.

Table 1 :

 1 Summary statistics

	Variable	Observations Mean Std Dev Min Max
	Friends	566281	0.488	0.41	1
	Neighbors	566281	0.438	0.43	1
	Club	500053	37.4%	0.48	1
	Geographic Mobility	441024	0.7%	0.08	1
	Male	566281	57.1%	0.49	1
	Female	566281	42.9%	0.49	1
	Rent-free accommodation	560878	3.4%	0.18	1
	Owner	560878	71.6%	0.45	1
	Tenant with rent	560878	25.1%	0.43	1
	Age category 16-25	566281	15.4%	0.36	1
	Age category 26-35	566281	28.4%	0.45	1
	Age category 36-45	566281	26.0%	0.44	1
	Age category 46-55	566281	21.0%	0.41	1
	Age category 56-65	566281	9.3%	0.29	1
	Years of education	566281	10.5	5.53	25
	Employed	566281	90.1%	0.30	1
	Unemployed	566281	9.9%	0.30	1
	Living alone	566281	8.9%	0.29	1
	Two members in household	566281	19.9%	0.40	1
	Three members in household	566281	22.3%	0.42	1
	Four members in household	566281	28.2%	0.45	1
	Five members in household	566281	20.7%	0.41	1
	Separated	552771	1.4%	0.12	1
	Divorced	552771	4.7%	0.21	1
	Widowed	552771	1.4%	0.12	1
	Never married	552771	33.8%	0.47	1

Notes: The summary statistics are calculated from the ECHP data over the period 1994-2001. 14 EU countries are considered: Denmark, Netherlands, Belgium, Luxembourg, Ireland, Italy, Greece, Spain, Portugal, Austria, Finland, Sweden, Germany, UK. The sample is restricted to the active population.

Table 2 :

 2 Summary of the Results for Inter-Area Mobility

		probit	probit	probit	probit	probit	probit	probit	probit
	Friends	-0.139			-0.104	-0.115			-0.079
		(5.06)**			(3.54)** (3.94)**			(2.51)*
	Neighbors		-0.149		-0.125		-0.159		-0.141
			(5.48)**		(4.29)**		(5.44)**		(4.46)**
	Club			-0.063	-0.059			-0.059	-0.055
				(3.08)** (2.85)**			(2.59)**	(2.40)*
	Time Dummies	Yes Yes No Yes Yes Yes Yes Yes Yes No No No Yes Yes Yes Yes
		IVprobit IVprobit IVprobit IVprobit IVprobit IVprobit IVprobit IVprobit
	Friends	-0.454			-0.378	-0.413			-0.330
		(5.08)**			(3.65)** (4.39)**			(2.99)**
	Neighbors		-0.146		-0.001		-0.145		-0.007
			(2.27)*		(0.01)		(2.07)*		(0.08)
	Club			-0.040	-0.010			-0.021	0.006
				(0.86)	(0.21)			(0.41)	(0.12)
	Time Dummies	Yes Yes No Yes Yes Yes Yes Yes Yes No No No Yes Yes Yes Yes
		IVprobit IVprobit IVprobit IVprobit
	Friends	-0.253			0.260
		(0.99)			(0.69)
	Neighbors		-0.876		-0.866
			(2.99)*		(2.01)*
	Club			-0.605	-0.415
				(1.40)	(0.91)
	Time Dummies	Yes	Yes	Yes	Yes
	Country Dummies	Yes	Yes	Yes	Yes
	Region Dummies	No	No	No	No
	Notes: As additional explicative variables, the above regressions include sex, house tenure, age categories, years of education, a dummy for unemployment, household size, marital status. For the first
	set of IV estimations, social capital measures are instrumented by their lag and the lag of the other explicative variables. For the second set of IV estimations, social capital measures are instrumented
	by the regional average of social capital measures and their lag. See Tables 10, 11 and 12 in the Appendix for more detailed results. Robust z statistics in parentheses. * significant at 5%; ** significant
	at 1%

Table 3 :

 3 Summary of the Results for Individual Unemployment

		probit	probit	probit	probit	probit	probit	probit	probit
	Friends	0.005			0.005	-0.004			-0.003
		(0.39)			(0.38)	(0.27)			(0.18)
	Neighbors		0.052		0.061		0.045		0.058
			(4.04)**		(4.53)**		(3.23)**		(3.90)**
	Club			-0.131	-0.133			-0.135	-0.137
				(11.74)** (11.87)**			(10.57)** (10.67)**
	Time Dummies	Yes Yes No Yes Yes Yes Yes Yes Yes No No No Yes Yes Yes Yes
		IVprobit IVprobit IVprobit IVprobit IVprobit IVprobit IVprobit IVprobit
	Friends	0.116			0.096	0.090			0.068
		(2.86)**			(2.00)*	(2.02)*			(1.29)
	Neighbors		0.163		0.165		0.145		0.160
			(5.36)**		(4.59)**		(4.27)**		(3.96)**
	Club			-0.254	-0.271			-0.244	-0.260
				(10.72)** (11.24)**			(9.06)**	(9.47)**
	Time Dummies	Yes Yes No Yes Yes Yes Yes Yes Yes No No No Yes Yes Yes Yes
		IVprobit IVprobit IVprobit IVprobit
	Friends	0.572			0.328
		(5.88)**			(2.11)*
	Neighbors		0.231		0.308
			(2.23)*		(1.84)
	Club			-2.153	-2.245
				(14.16)** (13.45)**
	Time Dummies	Yes	Yes	Yes	Yes
	Country Dummies	Yes	Yes	Yes	Yes
	Region Dummies	No	No	No	No
	Notes: As additional explicative variables, the above regressions include sex, house tenure, age categories, years of education, a dummy for unemployment, household size, marital status. For the first
	set of IV estimations, social capital measures are instrumented by their lag and the lag of the other explicative variables. For the second set of IV estimations, social capital measures are instrumented
	by the regional average of social capital measures and their lag. See Tables 14, 15 and 16 in the Appendix for more detailed results. Robust z statistics in parentheses. * significant at 5%; ** significant
	at 1%

Table 4 :

 4 Probabilities of moving and being unemployed: examples

	Probability of →	Moving Unemployment

Note: the above probabilities are computed from the fitted values of the instrumental variable probit regression from Tables

11 and 15

with country dummies and all social capital measures introduced as regressors.

Table 5 :

 5 Summary of the Results for Long-Run Social Capital Investment

	Social Capital:	Friendfreq	Neibfreq	Club
	Estimation Method:	OLS		OLS		Probit
	Born in another region	-0.04	-0.04	-0.039	-0.034	-0.116	-0.09
		(13.43)** (13.25)** (10.93)** (9.40)** (9.25)** (6.89)**
	Time Dummies	Yes	Yes	Yes	Yes	Yes	Yes
	Country Dummies	Yes	No	Yes	No	Yes	No
	Region Dummies	No	Yes	No	Yes	No	Yes

Notes: As additional explicative variables, the above regressions include sex, house tenure, age categories, years of education, a dummy for unemployment, household size, marital status. See Table

17

in the Appendix for more detailed results. Robust z statistics in parentheses. * significant at 5%; ** significant at 1%

Table 6 :

 6 Aggregate social capitalNotes: the Table displays the average value of the social capital measures by country over the period 1994-2001 for the active population.

	Country	f riendf req neibf req club
	Nordic countries and the UK
	Denmark	43	41.8	62.1
	Finland	45.9	52.3	52.5
	Sweden	43.6	na	69.4
	UK	57.6	28	62.7
	Western Europe
	Austria	35.8	46.3	49.5
	Belgium	41	39	39.4
	Germany	14.7	na	32.8
	Ireland	78.9	62	48.7
	Luxembourg	44.8	47.3	41
	Netherlands	42	36.7	48
	Southern Europe
	Greece	69.5	80.8	12.1
	Italy	57.6	54.7	23.8
	Portugal	47.8	66.6	21.5
	Spain	74	68.1	28.5
	Correlation with:
	f riendf req	1	0.61	-0.22
	neibf req	0.61	1	-0.79
	club	-0.22	-0.79	1

Table 7 :

 7 Log Unemployment Rate Regressed on our Measures of Social Capital.

	Friends	1.176		1.746
		(4.10)***		(3.32)***
	Neighbors		1.729	0.388
			(4.52)***	(0.72)
	Constant	1.434	1.085	0.879
		(8.87)*** (4.84)*** (4.00)***
	Observations	95	80	80
	Adjusted R 2	0.14	0.2	0.29

Note: The estimation is by OLS using 8 time periods

(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)

and 12 European economies: Austria, Belgium, Denmark, Germany, Greece, Ireland, Italy, Portugal, The Netherlands, Spain, Sweden, UK. Our measures of social capital is based on ECHP data and the unemployment rates are the standardized OECD rates. Absolute value of t statistics in parentheses. ** significant at 5%; *** significant at 1%

Table 8 :

 8 Log Unemployment Rate Regressed on Measures of Social Capital and Labor Market Institutions.Note: The estimation is by Random Effect GLS using 8 time periods(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001) and 12 European economies: Austria, Belgium, Denmark, Germany, Greece, Ireland, Italy, Portugal, The Netherlands, Spain, Sweden, UK. Our measures of social capital is based on ECHP data, data on labor market institutions is taken from Nickell and Layard (1999) and the unemployment rates are the standardized OECD rates. Some ECHP data was not available for Germany and Sweden. Constant and time dummies not reported. Absolute value of z-statistics in parentheses. * significant at 5%; ** significant at 1%

	Friends		0.92		7.239
			(2.44)**		(6.69)**
	Neighbors			2.518	-2.672
				(6.83)** (3.25)**
	Total Tax Wedge	0.03	0.026	0.041	0.03
		(6.71)** (5.74)** (10.41)** (8.80)**
	Union Density	0.003	0.000	0.004	-0.021
		(1.38)	(0.06)	(1.46)	(4.88)**
	Union Coverage Index	0.527	0.39	0.042	-0.765
		(3.49)** (2.49)*	(0.27)	(4.58)**
	Co-ordination (Union+Employer)	-0.274	-0.124	-0.145	-0.495
		(5.52)**	(1.59)	(2.70)** (4.77)**
	Replacement Rate	0.002	0.000	0.004	-0.004
		(0.96)	(0.16)	(1.78)	(1.94)
	Benefit Duration	0.095	0.126	0.197	0.236
		(2.59)** (3.35)** (4.33)** (6.82)**
	Active Labor Market Policies	-0.000	-0.000	0.024	0.127
		(0.1)	(0.02)	(2.33)*	(7.37)**
	Observations	87	87	72	72
	Number of countries	12	12	10	10
	R 2	0.6219	0.6516	0.8142	0.8976

Table 9 :

 9 Log Unemployment Rate Regressed on Measures of Social Capital and Labor Market Institutions, Where Social Capital Measures are Instrumented.Note: The estimation is by Instrumental Variable Random Effect GLS using 8 time periods(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001) and 12 European economies: Austria, Belgium, Denmark, Germany, Greece, Ireland, Italy, Portugal, The Netherlands, Spain, Sweden, UK. Social capital is instrumented by the employment protection index and owner occupation rate before 1994 from Nickell and Layard (1999). Our measures of social capital is based on ECHP data, data on labor market institutions is taken from Nickell and Layard (1999) and the unemployment rates are the standardized OECD rates. Some ECHP data was not available for Germany and Sweden. Constant and time dummies not reported. : in the survey, this variable refers to the following question: Age when the Highest Level of General or Higher Education was Completed. The possible answers are numbers between 9 and 75. To correct for potential bias we followed the procedure proposed in Wasmer et al.(2005).Household size: in the survey, this variable refers to the following question: Household Size (Total Number of Household Members at Present). The possible answers are numbers between 1 and 96. We grouped the answers into …ve categories: 1, 2, 3, 4 and 5+.

	Friends	3.712		13.436
		(4.09)**		(6.12)*
	Neighbors		2.632	-7.112
			(7.00)** (4.36)**
	Total Tax Wedge	0.015	0.042	0.02
		(2.21)* (10.46)** (4.00)**
	Union Density	-0.011	0.004	-0.043
		(2.31)*	(1.51)	(5.25)**
	Union Coverage Index	-0.027	0.025	-1.456
		(0.11)	(0.16)	(5.15)**
	Co-ordination (Union+Employer)	0.332	-0.14	1.044
		(2.06)*	(2.60)** (5.22)**
	Replacement Rate	-0.006	0.004	-0.011
		(1.5)	(1.82)	(3.35)**
	Benefit Duration	0.219	0.199	0.269
		(3.90)** (4.37)** (6.03)**
	Active Labor Market Policies	0.001	0.024	0.215
		(0.16)	(2.29)*	(6.57)**
	Observations	87	72	72
	Number of Countries	12	10	10
	R 2	0.4976	0.8140	0.8525
	Absolute value of z-statistics in parentheses. * significant at 5%; ** significant at 1%

Education

Table A - 1 :

 A1 Probit Regression Explaining Inter-Area Mobility at Time t+1Note: The estimation is by Probit using ECHP data on 8 time periods(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001) and 12 European economies: Austria, Belgium, Denmark, Germany, Greece, Ireland, Italy, Portugal, The Netherlands, Spain, Sweden, UK. Our measures of social capital is based on ECHP data. (Some data was not available for Germany and Sweden.) The reference for the interpretation of the dummy variables is an employed male with rent-free accommodation, married, aged 16-24, living alone. Robust z statistics in parentheses. * significant at 5%;

	Friends at t	-0.139			-0.104	-0.115			-0.079
		(5.06)**			(3.54)**	(3.94)**			(2.51)*
	Neighbors at t		-0.149		-0.125		-0.159		-0.141
			(5.48)**		(4.29)**		(5.44)**		(4.46)**
	Club at t			-0.063	-0.059			-0.059	-0.055
				(3.08)**	(2.85)**			(2.59)**	(2.40)*
	Male	ref	ref	ref	ref	ref	ref	ref	ref
	Female	-0.014	-0.012	-0.02	-0.015	-0.015	-0.013	-0.022	-0.018
		(0.66)	(0.58)	(0.9)	(0.69)	(0.66)	(0.57)	(0.94)	(0.75)
	Rent-free accommodation at t	ref	ref	ref	ref	ref	ref	ref	ref
	Owner at t	-0.516	-0.52	-0.499	-0.504	-0.5	-0.506	-0.491	-0.497
		(10.21)** (10.29)**	(9.48)**	(9.54)**	(9.47)**	(9.58)**	(8.93)** (9.01)**
	Tenant with rent at t	-0.095	-0.099	-0.087	-0.09	-0.088	-0.093	-0.085	-0.089
		(1.83)	(1.9)	(1.6)	(1.66)	(1.6)	(1.69)	(1.47)	(1.55)
	Age category 16-25 at t	ref	ref	ref	ref	ref	ref	ref	ref
	Age category 26-35 at t	-0.039	-0.023	-0.042	-0.053	-0.028	-0.012	-0.04	-0.046
		(1.29)	(0.76)	(1.31)	(1.67)	(0.85)	(0.38)	(1.15)	(1.32)
	Age category 36-45 at t	-0.309	-0.283	-0.305	-0.322	-0.298	-0.273	-0.304	-0.313
		(8.69)**	(8.00)**	(8.27)**	(8.63)**	(7.67)**	(7.08)**	(7.47)** (7.64)**
	Age category 46-55 at t	-0.434	-0.405	-0.437	-0.453	-0.435	-0.406	-0.445	-0.453
		(10.62)** (10.02)** (10.41)** (10.64)**	(9.75)**	(9.25)**	(9.69)** (9.73)**
	Age category 56-65 at t	-0.536	-0.502	-0.525	-0.539	-0.568	-0.536	-0.571	-0.576
		(9.48)**	(8.90)**	(8.92)**	(9.11)**	(9.46)**	(8.97)**	(9.13)** (9.17)**
	Years of education at t	0.014	0.014	0.014	0.014	0.012	0.012	0.013	0.012
		(7.07)**	(7.15)**	(7.04)**	(6.71)**	(5.66)**	(5.65)**	(5.56)** (5.28)**
	Employed at t	ref	ref	ref	ref	ref	ref	ref	ref
	Unemployed at t	-0.009	-0.002	-0.018	-0.006	0	0.007	-0.008	0.003
		(0.26)	(0.05)	(0.54)	(0.17)	(0.01)	(0.2)	(0.22)	(0.09)
	Living alone at t	ref	ref	ref	ref	ref	ref	ref	ref
	Two members	-0.16	-0.152	-0.149	-0.156	-0.133	-0.126	-0.119	-0.123
	in household at t	(4.53)**	(4.31)**	(4.01)**	(4.21)**	(3.40)**	(3.24)**	(2.89)** (2.97)**
	Three members	-0.31	-0.3	-0.298	-0.299	-0.281	-0.271	-0.266	-0.264
	in household at t	(8.56)**	(8.29)**	(7.86)**	(7.87)**	(7.15)**	(6.92)**	(6.43)** (6.37)**
	Four members	-0.442	-0.43	-0.435	-0.433	-0.406	-0.395	-0.391	-0.387
	in household at t	(11.86)** (11.55)** (11.05)** (10.99)**	(9.98)**	(9.71)**	(9.01)** (8.91)**
	Five members	-0.497	-0.485	-0.482	-0.48	-0.477	-0.465	-0.459	-0.454
	in household at t	(11.47)** (11.20)** (10.61)** (10.57)** (10.33)** (10.10)** (9.45)** (9.35)**
	Married at t	ref	ref	ref	ref	ref	ref	ref	ref
	Separated at t	0.141	0.132	0.12	0.121	0.131	0.123	0.106	0.106
		(1.56)	(1.46)	(1.2)	(1.2)	(1.53)	(1.43)	(1.11)	(1.11)
	Divorced at t	0.011	0.006	0.023	0.023	0.018	0.014	0.035	0.037
		(0.27)	(0.16)	(0.54)	(0.54)	(0.41)	(0.32)	(0.77)	(0.8)
	Widowed at t	0.004	0.006	0.025	0.035	-0.002	0.001	0.014	0.022
		(0.05)	(0.07)	(0.27)	(0.37)	(0.02)	(0.01)	(0.14)	(0.22)
	Never married at t	-0.14	-0.15	-0.153	-0.151	-0.186	-0.195	-0.206	-0.205
		(5.11)**	(5.45)**	(5.32)**	(5.26)**	(6.16)**	(6.42)**	(6.43)** (6.38)**
	Time dummies	Yes Yes No Yes Yes Yes Yes Yes Yes No No No Yes Yes Yes Yes
	Observations	403568	403568	385403	385403	357122	357122	339086	339086
	Pseudo R 2	0.1610	0.1584	0.1587	0.1592	0.1818	0.1774	0.1782	0.1800

** significant at 1%

Table A - 2 :

 A2 Instrumental Variable Probit Regression Explaining Inter-Area Mobility at Time t+1

	Friends at t	-0.454			-0.378	-0.413			-0.330
		(5.08)**			(3.65)** (4.39)**			(2.99)**
	Neighbors at t		-0.146		-0.001		-0.145		-0.007
			(2.27)*		(0.01)		(2.07)*		(0.08)
	Club at t			-0.040	-0.010			-0.021	0.006
				(0.86)	(0.21)			(0.41)	(0.12)
	Male	ref	ref	ref	ref	ref	ref	ref	ref
	Female	-0.006	-0.003	-0.009	-0.005	-0.008	-0.005	-0.011	-0.008
		(0.24)	(0.11)	(0.33)	(0.20)	(0.32)	(0.21)	(0.41)	(0.28)
	Rent-free accommodation at t	ref	ref	ref	ref	ref	ref	ref	ref
	Owner at t	-0.475	-0.474	-0.455	-0.459	-0.454	-0.454	-0.442	-0.445
		(8.64)** (8.63)** (7.91)** (7.95)** (8.05)** (8.04)** (7.45)** (7.48)**
	Tenant with rent at t	-0.053	-0.055	-0.038	-0.038	-0.045	-0.048	-0.036	-0.036
		(0.94)	(0.97)	(0.64)	(0.63)	(0.78)	(0.82)	(0.58)	(0.58)
	Age category 16-25 at t	ref	ref	ref	ref	ref	ref	ref	ref
	Age category 26-35 at t	-0.048	-0.005	-0.029	-0.063	-0.024	0.016	-0.017	-0.046
		(1.25)	(0.13)	(0.72)	(1.55)	(0.56)	(0.39)	(0.38)	(1.01)
	Age category 36-45 at t	-0.348	-0.278	-0.310	-0.368	-0.324	-0.260	-0.302	-0.351
		(7.81)** (6.63)** (6.98)** (7.63)** (6.65)** (5.69)** (6.16)** (6.62)**
	Age category 46-55 at t	-0.450	-0.371	-0.409	-0.472	-0.434	-0.361	-0.407	-0.461
		(8.90)** (7.80)** (8.12)** (8.69)** (7.89)** (7.01)** (7.40)** (7.76)**
	Age category 56-65 at t	-0.557	-0.476	-0.508	-0.570	-0.582	-0.508	-0.554	-0.607
		(8.51)** (7.44)** (7.43)** (8.05)** (8.37)** (7.49)** (7.65)** (8.06)**
	Years of education at t	0.011	0.012	0.013	0.011	0.009	0.010	0.010	0.009
		(4.97)** (5.42)** (5.13)** (4.41)** (3.67)** (3.99)** (3.71)** (3.13)**
	Employed at t	ref	ref	ref	ref	ref	ref	ref	ref
	Unemployed at t	0.019	0.015	-0.004	0.010	0.045	0.042	0.028	0.040
		(0.48)	(0.36)	(0.10)	(0.25)	(1.07)	(0.98)	(0.62)	(0.90)
	Living alone at t	ref	ref	ref	ref	ref	ref	ref	ref
	Two members	-0.198	-0.174	-0.167	-0.188	-0.166	-0.146	-0.134	-0.150
	in household at t	(4.83)** (4.26)** (3.82)** (4.29)** (3.70)** (3.27)** (2.78)** (3.10)**
	Three members	-0.325	-0.303	-0.298	-0.313	-0.294	-0.274	-0.266	-0.278
	in household at t	(8.06)** (7.54)** (6.99)** (7.30)** (6.81)** (6.40)** (5.77)** (5.99)**
	Four members	-0.421	-0.394	-0.392	-0.410	-0.380	-0.355	-0.344	-0.358
	in household at t	(9.97)** (9.37)** (8.68)** (8.95)** (8.32)** (7.84)** (6.96)** (7.16)**
	Five members	-0.491	-0.464	-0.456	-0.474	-0.474	-0.450	-0.438	-0.452
	in household at t	(9.86)** (9.36)** (8.66)** (8.89)** (9.00)** (8.60)** (7.81)** (7.97)**
	Married at t	ref	ref	ref	ref	ref	ref	ref	ref
	Separated at t	0.065	0.051	0.036	0.043	0.053	0.040	0.022	0.029
		(0.64)	(0.51)	(0.32)	(0.38)	(0.57)	(0.43)	(0.21)	(0.28)
	Divorced at t	-0.014	-0.022	-0.005	-0.001	-0.007	-0.015	0.011	0.016
		(0.30)	(0.46)	(0.09)	(0.01)	(0.13)	(0.29)	(0.20)	(0.29)
	Widowed at t	0.023	0.013	0.042	0.053	0.024	0.016	0.037	0.046
		(0.22)	(0.12)	(0.39)	(0.49)	(0.22)	(0.14)	(0.31)	(0.39)
	Never married at t	-0.120	-0.145	-0.148	-0.130	-0.169	-0.193	-0.204	-0.187
		(3.76)** (4.54)** (4.32)** (3.78)** (4.88)** (5.55)** (5.40)** (4.96)**
	Time Dummies	Yes Yes No Yes Yes Yes Yes Yes Yes No No No Yes Yes Yes Yes
	Observations	316463	316463	299625	299625	279365	279365	262824	262824
	Pseudo R 2	0.16	0.16	0.16	0.16	0.18	0.18	0.18	0.18

Note: The estimation is by IV Probit using ECHP data on 8 time periods

(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)

and 12 European economies: Austria, Belgium, Denmark, Germany, Greece, Ireland, Italy, Portugal, The Netherlands, Spain, Sweden, UK. Our measures of social capital is based on ECHP data. (Some data was not available for Germany and Sweden.) Here, social capital is instrumented with its lag and the lag of the other explicative variables. Robust z statistics in parentheses. * significant at 5%; ** significant at 1%

Table A - 3 :

 A3 Instrumental Variable Probit Regression Explaining Inter-Area Mobility at Time t+1

	Friends at t	-0.253			0.260
		(0.99)			(0.69)
	Neighbors at t		-0.876		-0.866
			(2.99)**		(2.01)*
	Club at t			-0.605	-0.415
				(1.40)	(0.91)
	Male	ref	ref	ref	ref
	Female	-0.019	0.007	-0.084	-0.037
		(0.78)	(0.28)	(1.55)	(0.61)
	Rent-free accommodation at t	ref	ref	ref	ref
	Owner at t	-0.489	-0.510	-0.452	-0.481
		(8.76)** (9.03)** (7.36)** (7.59)**
	Tenant with rent at t	-0.090	-0.121	-0.099	-0.124
		(1.57)	(2.07)*	(1.61)	(1.99)*
	Age category 16-25 at t	ref	ref	ref	ref
	Age category 26-35 at t	0.022	0.046	0.015	0.039
		(0.53)	(1.15)	(0.36)	(0.83)
	Age category 36-45 at t	-0.263	-0.219	-0.245	-0.206
		(5.05)** (4.93)** (5.06)** (3.26)**
	Age category 46-55 at t	-0.369	-0.315	-0.349	-0.301
		(6.29)** (6.24)** (6.20)** (4.10)**
	Age category 56-65 at t	-0.503	-0.422	-0.483	-0.406
		(6.81)** (6.02)** (6.44)** (4.39)**
	Years of education at t	0.010	0.006	0.018	0.011
		(3.91)**	(1.95)	(3.07)**	(1.63)
	Employed at t	ref	ref	ref	ref
	Unemployed at t	0.047	0.095	-0.007	0.054
		(1.09)	(2.05)*	(0.15)	(0.91)
	Living alone at t	ref	ref	ref	ref
	Two members	-0.160	-0.158	-0.151	-0.146
	in household at t	(3.60)** (3.52)** (3.10)** (3.06)**
	Three members	-0.306	-0.287	-0.301	-0.279
	in household at t	(7.13)** (6.71)** (6.44)** (5.93)**
	Four members	-0.396	-0.369	-0.382	-0.354
	in household at t	(8.71)** (8.24)** (7.80)** (7.16)**
	Five members	-0.465	-0.433	-0.445	-0.412
	in household at t	(8.89)** (8.40)** (7.92)** (7.47)**
	Married at t	ref	ref	ref	ref
	Separated at t	0.057	0.010	-0.002	-0.038
		(0.60)	(0.11)	(0.02)	(0.35)
	Divorced at t	-0.005	-0.029	-0.014	-0.030
		(0.10)	(0.57)	(0.25)	(0.52)
	Widowed at t	-0.018	-0.010	-0.002	0.009
		(0.17)	(0.09)	(0.02)	(0.08)
	Never married at t	-0.160	-0.196	-0.196	-0.228
		(4.31)** (5.60)** (5.40)** (4.68)**
	Time Dummies	Yes	Yes	Yes	Yes
	Country Dummies	Yes	Yes	Yes	Yes
	Region Dummies	No	No	No	No
	Observations	286362	286362	269339	269339
	Pseudo R 2	0.16	0.16	0.17	0.17

Note: The estimation is by IV Probit using ECHP data on 8 time periods

(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)

and 12 European economies: Austria, Belgium, Denmark, Germany, Greece, Ireland, Italy, Portugal, The Netherlands, Spain, Sweden, UK. Our measures of social capital is based on ECHP data. (Some data was not available for Germany and Sweden.) Here, social capital is instrumented by the regional average of social capital measures and their lag. Robust z statistics in parentheses. * significant at 5%; ** significant at 1%

Table A - 4 :

 A4 Fixed-Effect Logit Regression Explaining Inter-Area Mobility at Time t+1

	Friends at t	0.048			0.106
		(0.63)			(1.23)
	Neighbors at t		-0.291		-0.345
			(3.57)**		(3.84)**
	Club at t			-0.082	-0.077
				(1.12)	(1.05)
	Rent-free accommodation at t	ref	ref	ref	ref
	Owner at t	-0.407	-0.416	-0.271	-0.281
		(2.42)*	(2.47)*	(1.46)	(1.52)
	Tenant with rent at t	0.464	0.458	0.561	0.56
		(2.80)** (2.77)** (3.09)** (3.08)**
	Age category 16-25 at t	ref	ref	ref	ref
	Age category 26-35 at t	-0.351	-0.351	-0.335	-0.33
		(2.87)** (2.87)** (2.52)*	(2.47)*
	Age category 36-45 at t	-0.426	-0.424	-0.36	-0.361
		(2.25)*	(2.24)*	(1.73)	(1.73)
	Age category 46-55 at t	-0.307	-0.31	-0.208	-0.215
		(1.15)	(1.17)	(0.71)	(0.73)
	Age category 56-65 at t	0.006	0.012	0.067	0.076
		(0.02)	(0.03)	(0.16)	(0.18)
	Years of education at t	0.019	0.02	0.018	0.018
		(2.22)*	(2.27)*	(1.91)	(1.9)
	Employed at t	ref	ref	ref	ref
	Unemployed at t	0.314	0.325	0.28	0.294
		(2.96)** (3.05)** (2.48)* (2.59)**
	Living alone at t	ref	ref	ref	ref
	Two members	0.018	0.015	-0.08	-0.069
	in household at t	(0.17)	(0.15)	(0.72)	(0.63)
	Three members	0.043	0.047	-0.023	0.001
	in household at t	(0.35)	(0.39)	(0.17)	(0.01)
	Four members	0.228	0.24	0.146	0.173
	in household at t	(1.67)	(1.77)	(1.00)	(1.18)
	Five members	0.406	0.413	0.361	0.386
	in household at t	(2.26)*	(2.29)*	(1.85)	(1.97)*
	Married at t	ref	ref	ref	ref
	Separated at t	0.003	-0.012	-0.094	-0.11
		(0.02)	(0.06)	(0.4)	(0.47)
	Divorced at t	0.031	0.035	0.011	0.009
		(0.19)	(0.21)	(0.06)	(0.05)
	Widowed at t	0.835	0.846	0.707	0.709
		(1.64)	(1.66)	(1.3)	(1.32)
	Never married at t	0.108	0.104	-0.05	-0.052
		(0.87)	(0.84)	(0.37)	(0.39)
	Time dummies	Yes	Yes	Yes	Yes
	Observations	12013	12013	9717	9717
	Number of individuals	2306	2306	2062	2062

Note: The estimation is by Fixed-Effect Logit using ECHP data on 8 time periods

(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)

and 12 European economies: Austria, Belgium, Denmark, Germany, Greece, Ireland, Italy, Portugal, The Netherlands, Spain, Sweden, UK (some data was missing for Germany and Sweden). Our measures of social capital is based on ECHP data. Robust z statistics in parentheses. * significant at 5%; ** significant at 1%

Table A - 5 :

 A5 Probit Regression Explaining Individual Unemployment at Time t+1Note: The estimation is by Probit using ECHP data on 8 time periods(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001) and 12 European economies: Austria, Belgium, Denmark, Germany, Greece, Ireland, Italy, Portugal, The Netherlands, Spain, Sweden, UK. Our measures of social capital is based on

	Friends at t	0.005			0.005	-0.004			-0.003
		(0.39)			(0.38)	(0.27)			(0.18)
	Neighbors at t		0.052		0.061		0.045		0.058
			(4.04)**		(4.53)**		(3.23)**		(3.90)**
	Club at t			-0.131	-0.133			-0.135	-0.137
				(11.74)**	(11.87)**			(10.57)**	(10.67)**
	Male	ref	ref	ref	ref	ref	ref	ref	ref
	Female	0.12	0.118	0.115	0.113	0.113	0.112	0.109	0.107
		(12.42)**	(12.24)**	(11.63)**	(11.42)**	(10.70)**	(10.57)**	(9.92)**	(9.74)**
	Rent-free accommodation at t	ref	ref	ref	ref	ref	ref	ref	ref
	Owner at t	-0.123	-0.122	-0.122	-0.121	-0.115	-0.115	-0.116	-0.115
		(5.00)**	(4.97)**	(4.87)**	(4.83)**	(4.56)**	(4.54)**	(4.48)**	(4.45)**
	Tenant with rent at t	0.078	0.08	0.065	0.067	0.108	0.109	0.096	0.097
		(3.01)**	(3.07)**	(2.44)*	(2.51)*	(4.04)**	(4.08)**	(3.50)**	(3.55)**
	Age category 16-25 at t	ref	ref	ref	ref	ref	ref	ref	ref
	Age category 26-35 at t	-0.052	-0.052	-0.052	-0.051	-0.079	-0.079	-0.08	-0.08
		(3.50)**	(3.52)**	(3.42)**	(3.39)**	(4.97)**	(4.94)**	(4.87)**	(4.88)**
	Age category 36-45 at t	-0.101	-0.102	-0.092	-0.091	-0.149	-0.149	-0.14	-0.14
		(5.79)**	(5.85)**	(5.17)**	(5.09)**	(7.81)**	(7.81)**	(7.14)**	(7.15)**
	Age category 46-55 at t	0.005	0.003	0.014	0.015	-0.05	-0.05	-0.041	-0.042
		(0.24)	(0.18)	(0.74)	(0.76)	(2.41)*	(2.43)*	(1.94)	(2.00)*
	Age category 56-65 at t	0.18	0.177	0.176	0.174	0.15	0.148	0.145	0.142
		(7.49)**	(7.39)**	(7.48)**	(7.39)**	(5.78)**	(5.71)**	(5.73)**	(5.58)**
	Years of education at t	-0.018	-0.018	-0.017	-0.017	-0.021	-0.021	-0.019	-0.019
		(19.52)**	(19.28)**	(17.32)**	(16.92)**	(19.55)**	(19.33)**	(17.22)**	(16.94)**
	Employed at t	ref	ref	ref	ref	ref	ref	ref	ref
	Unemployed at t	1.931	1.928	1.926	1.922	1.904	1.902	1.901	1.897
		(149.89)**	(149.47)**	(148.08)**	(147.44)**	(133.96)**	(133.49)**	(132.39)**	(131.71)**
	Living alone at t	ref	ref	ref	ref	ref	ref	ref	ref
	Two members	0.098	0.099	0.093	0.095	0.068	0.069	0.064	0.065
	in household at t	(4.43)**	(4.46)**	(4.09)**	(4.15)**	(2.69)**	(2.72)**	(2.41)*	(2.44)*
	Three members	0.128	0.127	0.118	0.118	0.073	0.073	0.061	0.06
	in household at t	(5.75)**	(5.72)**	(5.14)**	(5.12)**	(2.91)**	(2.90)**	(2.33)*	(2.30)*
	Four members	0.15	0.149	0.139	0.138	0.081	0.08	0.066	0.065
	in household at t	(6.60)**	(6.56)**	(5.97)**	(5.94)**	(3.13)**	(3.11)**	(2.47)*	(2.44)*
	Five members	0.204	0.203	0.192	0.191	0.123	0.122	0.107	0.106
	in household at t	(8.64)**	(8.60)**	(7.89)**	(7.85)**	(4.60)**	(4.58)**	(3.84)**	(3.81)**
	Married at t	ref	ref	ref	ref	ref	ref	ref	ref
	Separated at t	0.202	0.205	0.171	0.175	0.224	0.227	0.191	0.194
		(5.72)**	(5.80)**	(4.64)**	(4.73)**	(6.08)**	(6.16)**	(4.94)**	(5.03)**
	Divorced at t	0.211	0.212	0.196	0.197	0.264	0.265	0.251	0.252
		(8.85)**	(8.92)**	(7.91)**	(7.98)**	(9.72)**	(9.75)**	(8.79)**	(8.84)**
	Widowed at t	0.016	0.015	0.012	0.011	-0.004	-0.005	-0.007	-0.008
		(0.36)	(0.36)	(0.27)	(0.26)	(0.08)	(0.1)	(0.15)	(0.17)
	Never married at t	0.205	0.207	0.202	0.204	0.224	0.225	0.222	0.224
		(14.53)**	(14.67)**	(14.03)**	(14.12)**	(14.64)**	(14.71)**	(14.13)**	(14.20)**
	Time dummies	Yes Yes No Yes Yes Yes Yes Yes Yes No No No Yes Yes Yes Yes
	Observations	413599	413599	381493	381493	360291	360291	328309	328309
	Pseudo R 2	0.3439	0.3438	0.3439	0.3437	0.3646	0.3642	0.3643	0.3644

Note that[START_REF] Belot | Friendship ties and geographical mobility, evidence from the BHPS[END_REF], who address a similar question to ours, have more precise data than us (although for a single country the UK): their data allows them to explore two dimensions of the strength of social ties: location of the closest friends and frequency of contacts. Their results actually emphasize the importance of the …rst factor.

We have tried a few other speci…cations, one including the log of this variable (but we need to replace the zero with, arbitrarily, either 1/365 or half of this number) which improves the signi…cance of coe¢ cients at the cost of introducing some arbitrariness. We also tried dummy variables for the …ve possible answers, to detect non-monotonicity. We did not …nd any non-monotonicity for the impact of social capital on mobility, and decided to keep a simple, linear speci…cation throughout.

The lack of signi…cance of "Friends" can also be explained by a light non monotonicity of its e¤ect on unemployment. Indeed, individuals with both very low or very high frequency of visits to friends are those with highest unemployment rates, while individuals with intermediate frequency have lower unemployment. To make sense of the non-monotonicity, we would need to introduce additional ingredients in the theory exposed next Section, notably search and network e¤ects à la[START_REF] Granovetter | Getting a job : a study of contacts and careers[END_REF]. See e.g. the series of papers by Calvó-Armengol (2004), Calvó-Armengol and Jackson (2004) or Calvó-Armengol and Zenou (2005). Here, we do not explore further this interesting question and leave it for future research.

...as many of them have recently started to recognize (e.g. Calvo and Jackson 2006, Calvo and Zenou 2005, Cahuc and Fontaine 2002).

Note that here, we do not have any right-censoring problem since we know the exact number of months of unemployment prior to re-employment, contrary to a classical duration analysis where we would observe transitions between di¤erent stats instead. In addition, the sample is restricted to persons that are "normally working" at the time of the survey, i.e. working more than 15 hours per week, and that started their current job at the earliest 2 years before they joined the survey.

Note an obvious limitation of this speci…cation, as well as the next speci…cations: we investigate the relationship between past unemployment experience of an individual and current social capital. We see the results presented in those tables as correlations between variables rather than deterministic relationship.

ECHP data (some data was not available for Germany and Sweden). Here, social capital is instrumented by the regional average of social capital measures and their lag. Robust z statistics in parentheses. * significant at 5%; ** significant at 1% Notes: As additional explicative variables, the above regressions include sex, house tenure, age categories, years of education, household size, marital status. For the set of IV estimations, social capital measures are instrumented by the regional average of social capital measures and their lag. Robust z statistics in parentheses. * significant at 5%; ** significant at 1%


Exil/Migration politique

Réfugié/exilé

Il est difficile de départager les motivations à l'origine des déplacements des personnes. Usuellement, on aime à distinguer une émigration économique qui a vu le jour dans l'Europe industrielle de la fin du XIXè et n'a cessé de grandir et

  s'universaliser depuis, et une migration politique qualifiée d'exil : le départ dramatique, l'horizon d'attente centré sur la patrie perdue, la sociabilité intense et réactive des exilés signent une expérience singulière. Pour autant, l'exilé du monde contemporain n'est pas non plus un réfugié ordinaire. Contrairement aux Huguenotsà qui le terme de « Réfugiés » est historiquement réservé jusqu'au XIXè sièclechassés du royaume de France par la politique religieuse de Louis XIV après 1685 ou les Proscrits quittant la France du Second Empire après le coup d'État de 1851, les réfugiés du XXè siècle ont dû quitter leur pays sans même professer une opinion politique ou religieuse particulière. Il entre donc dans l'exil un choix, la volonté de ne pas pactiser et de continuer le combat, alors que les réfugiés subissent leur départ du fait de leur race, de leur religion ou de leur appartenance sociale ou politique, comme le précisera la définition de la Convention de Genève de 1951. L'exilé est donc un réfugié particulier et l'exil ainsi compris, un phénomène plus sélectif, limité à tous ceux qui, militants, syndicalistes, journalistes, artistes, hommes politiques professionnels ou autres, conçoivent leur exil dans le cadre d'une pratique politique tournée vers le pays de départ. Néanmoins, ces exilés politiques ne sont pas forcément ceux qui se consacraient à des fonctions politiques avant leur départ et leur sociologie est plus diverse que la simple caste de l'élite politique traditionnelle. C'est dire aussi que l'exil peut politiser une vie. Même si l'exil existait dans les sociétés antiques où le bannissement était pratiqué, l'exil moderne n'est pas un simple éloignement spatial mais un « estrangement » bien plus stigmatisant. À ce titre, il apparaît comme un élément symbolique de la politique contemporaine, celle qui à partir du XIXè siècle se construit sur la rationalisation de l'État-moderne, la construction d'États-nations et la nationalisation poussée des sociétés européennes. Au XXè siècle, la poussée des nationalismes exclusifs, les conflits mondiaux ainsi que ceux liés et hérités de la décolonisation, l'arrivée au pouvoir des totalitarismes scandent des séquences temporelles dont chacune produit son lot de réfugiés et d'exilés politiques, depuis les Polonais qui arrivèrent à Paris après l'insurrection de Varsovie en 1831 jusqu'à certaines élites du monde arabe dispersées entre l'Europe et l'Amérique dans la deuxième moitié du XXè siècle. Stéréotypes, représentations de l'exil L'exil n'a pas bonne réputation et vit sur des stéréotypes toujours très actifs : une « école de scandale et de médiocrité » d'après Marx, pourtant exilé légendaire ; un apprentissage de l'échec, comme en témoignent certains grands exils du siècle, de celui des Russes blancs d'après 1917 ou des réfugiés anti-franquistes, sans parler des exilés d'Europe centrale à la fin des années 1940 et dans les années 1950. Pour les Français -mais la métaphore s'est internationalisée -le « syndrome de Coblence » plaque, depuis deux siècles, sur l'exil politique ses images négatives : celles du discrédit, de la gesticulation dérisoire et de l'intrigue en vase clos. C'est pourquoi les exilés répugnent à être comparés à des « Émigrés » -ceux de l'Émigration contrerévolutionnaire de l'après 1789. De fait, ils ne sont parfois ni illégitimes ni impuissants : à l'exception des Polonais sacrifiés sur l'autel de la Realpolitik alliée, les exilés norvégiens, hollandais, belges et français de Londres entre 1940 et 1944 retournèrent dans leurs pays comme les gardiens de l'honneur national et les représentants d'une opposition indomptable à l'Allemagne nazie. Ils réussirent à constituer leur exil en espace de luttes politiques contre leur régime en place dans leur pays d'origine. Exopolitie, diaspora, espace et temporalité de l'exil C'est ce sens d' « exopolitie », c'est-à-dire d'espace politique de l'émigration, qui est désormais mis en avant par certains politologues comme Stéphane Dufoix. Tout en assumant la rupture avec le territoire d'origine, les exilés veulent ancrer la politique dans la continuité et s'appliquent à construire des arguments justifiant la formation de mouvements politiques à l'étranger. Pour prétendre être les vrais dépositaires de l'autorité étatique et les vrais représentants du peuple asservi, les exilés opèrent la distinction capitale entre l'État et la nation, qui est en définitive le référent absolu qui guide leur conduite. Les exilés parlent toujours au nom d'une essence nationale qu'ils maintiennent dans sa vérité, à l'extérieur des frontières, contre le régime honni et parfois aussi contre leurs diasporas nationales qui ne se reconnaissent pas forcément dans leur combat. Le rapport de chaque exil politique envers son espace diasporique détermine d'ailleurs une typologie subtile, selon l'homologie plus ou moins grande entre les deux. Les diasporas parfois installées depuis longtemps dans un pays n'acceptent pas nécessairement les « leaders » potentiels que l'exil leur amène. Par exemple, les Italiens installés aux Etats-Unis depuis le début du XXè siècle n'épousaient pas pour l'essentiel les opinions des exilés antifascistes qui arrivèrent dans les années 1930. Contrairement aux accusations de fuite et de traîtrise qui les assaillent rapidement de la part des représentants officiels de leur pays d'origine, les exilés prétendent garder intact le trésor de la nation. Ceci explique pourquoi ils semblent souvent suspendus à un espace et une temporalité bloqués : l'espace est nié par la communion avec la patrie souffrante et le temps arrêté à la date du départ, rendant les évolutions du pays d'autrefois difficilement lisibles pour ceux qui l'ont quitté. D'où des décalages structurels lors du retour d'acteurs de longs exils qui redécouvrent un pays qui n'était plus que l'image rétinienne d'une mémoire douloureuse, la leur. La construction de la légitimité politique en exil passe aussi par l'explication du départ. À chaque exil, son événement fondateur : l'incendie du Reichstag pour les Allemands, l'armistice ou le Statut des Juifs pour les exilés français de 1940, le « coup de Prague » pour les exilés tchécoslovaques. Les pratiques internes de commémoration font partie de ces constructions légitimantes destinées au peuple et au pays d'origine mais permettant aussi de se positionner par rapport aux autres exilés. La lutte pour la reconnaissance internationale est alors la seule épreuve qui puisse départager les différents pôles de structuration de l'exil. Ce schéma s'applique au cas des Français exilés aux États-Unis pendant la Seconde guerre mondiale. Divisés en groupes concurrents et fluctuants, tous, gaullistes comme anti-gaullistes, essayèrent de se faire reconnaître comme interlocuteurs privilégiés du gouvernement américain. De même, en remontant dans le temps, on connaît désormais le rôle des grands exilés tchèques, hongrois ou « yougoslaves » sur les peacemakers de Versailles : reconnus par les Alliés comme les véritables porte-parole du sentiment populaire enfin émancipé du bâillonnement autocratique de l'Empire austro-hongrois, ils exercèrent une action de lobbying politique d'autant plus fructueuse qu'elle allait apparemment dans le sens du principe d'auto-détermination nationale des peuples, nouvel Évangile de l'après-1918. Cet espace politique d'opposition fonctionne donc sur le plan national, en engageant un sens de la nation, mais aussi transétatique, puisque chaque groupe d'exilés se comprend en fonction des autres groupes en exil dans d'autres pays : il existe un pôle floridien des exilés cubains anti-castristes mais aussi un pôle français et un pôle espagnol, qui interréagissent entre-eux de façon parfois hiérarchisée, parfois conflictuelle. Exil et transfert de cultures politiques Malgré l'identification fusionnelle ou sublimée avec la patrie perdue, le temps de l'exil permet des métamorphoses politiques et culturelles. Le retour ménage des transferts culturels, des exportations de cultures politiques dont les exilés se font les médiateurs. Au XIXe siècle, les cercles d'exilés qui se rencontraient dans le Paris de la Monarchie de Juillet furent un milieu propice à la maturation de l'idée révolutionnaire qui éclata en 1848. Le séjour parisien de Marx lui-même (de 1843 à 1845), très inséré dans la bohème révolutionnaire des années 1848, consacra son passage au communisme sous l'influence des cercles allemands émigrés et des organisations politiques françaises d'origine babouviste . Si la bohème parisienne exilée de la période orléaniste prépara le personnel politique des révolutions de 1848, l'exil des années 1930 et1940 prépara l'occidentalisation des élites de l'Europe d'après-guerre. D'une manière générale, le recrutement des émigrés politiques et intellectuels dans le War effort américain entre 1940 et 1945 a fait émerger une concordance d'opinions entre les Américains interventionnistes des nouvelles agences de guerre et les exilés. Ainsi, l'occidentalisation de l'Europe après 1945 doit-elle se comprendre, au rebours d'une simple « américanisation », comme un transfert culturel transatlantique dans le cadre d'une communauté de valeurs partagées. La trajectoire d'un certain nombre d'exilés est là pour garantir, dans les aléas de leurs existences, le lien entre les grands récits de l'après-guerre où s'enchevêtraient les réseaux euro-atlantiques forgés pendant l'exil des élites européennes aux États-Unis : l'influence américaine en Europe, l'anticommunisme, la modernisation du socialisme, l'édification de l'Europe. De manière profondément paradoxale, l'exil est ainsi partie prenante d'un processus d'internationalisation politique et culturelle, en dépit de la loyauté nationale passionnée professée par les exilés. Depuis 1945, les Etats-Unis sont un des lieux de cette internationalisation par la diversité des vagues d'exilés que le pays a accueilli, pas toujours de manière désintéressée. En effet, les processus de transfert n'excluent nullement une politique d'instrumentalisation des élites exilées par le pays d'accueil. La politique active de soutien et de financement par les Américaines de groupes d'exilés de l'Europe de l'Est relève d'une histoire cachée de la Guerre froide. De même, les choix gouvernementaux des Etats-Unis concernant les exilés d'Amérique latine sont largement dictés par des considérations de politique étrangère. Emmanuelle Loyer. Bibliography Conway M, Gotovitch J. (eds), Europe in exile. European communities in Britain, 1940-1945. New York, Oxford : Berghan Books. Dufoix, S. 2002. Politiques d'exil : Hongrois, Polonais et Tchécoslovaques en France après 1945. Paris : PUF. Traverso. E. 2004. La pensée dispersée. Figures de l'exil judéo-allemand. Paris : Léo Scheer. « Les Etats-Unis et les réfugiés politiques européens, des années 1930 aux années 1950 », Matériaux pour l'histoire de notre temps, 60, octobre-décembre 2000.