Abstract
This paper is the first systematic attempt to investigate the factors affecting time persistence in individual remittance behaviour. By using micro-level longitudinal data from the German Socio-Economic Panel (SOEP), we apply a wide variety of discrete choice static and dynamic panel models to analyse the decision to remit. Our results provide evidence in favour of an intertemporal strategy. The persistence in remittance decisions is significantly influenced by “true state dependence”: migrants that remitted in the previous year have a significantly higher propensity to remit this year as well. We also show that remittance time patterns depend on both observable and unobservable individual socioeconomic characteristics, and in particular, that the household’s transnational composition plays an important role in determining remittance behaviour.
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Notes
In general, remittances could also be present in the budget constraint in so far as they are meant to finance some kind of asset accumulation. However, considering this additional channel would add nothing to the discussion that follows and we omit this point for the sake of conciseness.
The data used in this paper was extracted using the Add-On package PanelWhiz for Stata. PanelWhiz (http://www.PanelWhiz.eu) was written by Dr. John P. Haisken-DeNew. See Haisken-DeNew and Hahn (2010) for details. The PanelWhiz generated Stata script to retrieve the data used here is available from us upon request. Any data or computational errors in this paper are our own.
Formal guest workers programmes were implemented in West Germany during the 1950s and 1960s. Foreign workers were recruited from Southern Europe first (bilateral agreements with Italy and Greece were signed in 1955 and 1960, respectively), but soon from Turkey and former Yugoslavia as well.
Immigrants who entered the SOEP in the 1980s indicated Yugoslavia as their home country. Aggregate data have been calculated as mean values for the group of current countries that were once enclosed in the Federal Republic.
A discussion on the way we deal with this drawback in our analysis is presented in Section 4.
Results are available from the authors upon request.
Such figures are in line with statistics provided on SOEP data by other recent studies as Holst et al. (2012) but lower compared to studies related to other host countries. Unheim and Rowlands (2012) for example find that around 21 % of migrants surveyed in the second wave of the Longitudinal Survey of Immigrants to Canada send money abroad, while Schans (2009) show different shares of remitters for different ethnic groups in the Netherlands, ranging from 40 % among Turks, Moroccans and Surinamese to less than 10 % among Antilleans. A higher share of remitters might be explained by a cohort of very recent immigrants in the case of Canada, interviewed between 6 and 24 months after arrival, in line with other findings for recent immigrants to Australia (Bettin et al. 2012). Table 1 shows huge differences across nationalities, beside a very unstable pattern over time. The most notable discrepancy with respect to Schans (2009) is related to Turkish migrants and might be due to a mix of sample-specific characteristics such as years since migration, citizenship status, education etc.
In order to verify the robustness of our findings, we estimated models comparable to those presented later in Section 5 also at the household level, by making specific assumptions on individual-level characteristics. Results are very similar, in terms of size, signs and significance level and are available upon request.
Descriptive statistics for all explanatory variables are reported in Appendix B .
The method put forward in Heckman (1981) was based on Gauss-Hermite quadrature methods, which was deemed too complex to implement to be in widespread use for a long time, so the empirical literature has mostly relied on an alternative approach devised by Wooldridge (2005) that is somewhat simpler to implement with standard software. Wooldridge’s idea, however, is quite difficult to generalise to autocorrelated disturbances, and we prefer not to use it here. Besides, Miranda (2007) finds Heckman’s estimator to have better finite-sample properties by Monte Carlo simulation.
It should be noted that, even with a very efficient C implementation of the GHK algorithm, the estimation procedure is extremely CPU-intensive, so that computing standard errors via a bootstrap-based procedure was not a viable option. All estimates were computed by using the DPB gretl package: see Lucchetti and Pigini (2015).
This was found to be empirically preferable to the common choice of including age squared both in terms of model fit and numerical stability.
We adopt the convention of indicating with j(i) the country from which individual i comes from.
GDP per capita is expressed in constant 2005 international dollars. Data are drawn from World Development Indicators database. We also tried the inclusion of other macro time-varying variables, such as growth rates and GDP volatility (Amuedo-Dorantes and Pozo 2013) and indicators of institutional quality, but they were never significant in any specification and were eventually dropped.
During the interview, the home country was not chosen from a predefined list, but rather declared freely. For this reason, a non negligible share of individuals list as their home country a territorial entity that is not recognised as a sovereign state per se or no longer exists as such. As a consequence, data for Benelux are calculated as means between those for Belgium and the Netherlands. For Kurdistan and Ex-Yugoslavia, we make use of data for Iraq and Serbia, respectively.
It should be stressed, however, that the size of the coefficients is not directly comparable across estimation methods (columns).
We split the sample according to countries’ income level and we distinguish between rich countries (all OECD and EU countries plus non OECD high income countries) and middle and low income countries. Alternatively, we distinguish between traditional immigration countries for Germany (Greece, Italy, Spain, Turkey and Ex-Yugoslavia) and more recent immigration countries. The last subsampling criterion we employ refers to the individual citizenship status: we restrict the sample to either migrants who always had German citizenship in our time period or to those who never had it.
By employing SOEP data for 1984–1995, Dustmann and Mestres (2010) and Sinning (2011) also find a positive effect of the age of the migrant on the probability to remit, but they do not control for a nonlinear impact. Menjivar et al. (1998), in contrast, find an inverted U-shape relationship between the age of the immigrant and the amount remitted in the main equation and a U-shape relationship in the selection equation.
It should be noted, however, that possible endogeneity of income is likely to be a major issue in a remittance equation (amount) but probably represents a minor issue in the decision whether to remit or not (Bettin et al. 2012). In addition, endogeneity would only prevent us from reading the estimated coefficients as behavioural parameters, but would not hinder our main purpose here, which is the study of persistence in remittance decisions and the factors which affect it. We, therefore, leave this issue for future work.
Such a result would not be in contrast with inheritance-related motives to remit either. As de la Briere et al. (2002) point out, the effect of the number of potential heirs (siblings, in our case) is a priori ambiguous. On the one hand, sharing parent’s assets with siblings decreases the return to investment in remittances; on the other hand, competition among heirs can increase the parent’s response to their child’s transfers and thus stimulate more remittances. Our estimation results seem to support the prevalence of the first effect (sharing effect) on the second one (competition effect).
The correction term can be interpreted as the possible influence on \(y^{*}_{i,t}\) of the expected utility from future choices; see Bartolucci and Nigro (2010), Section 3.2 for further details.
This also applies to the “time since migration” variable, which becomes collinear with age.
References
Aggarwal R, Horowitz AW (2002) Are international remittances altruism or insurance? Evidence from Guyana using multiple-migrant households. World Dev 30 (11):2033–2044
Ambler K, Aycinena D, Yang D (2014) Remittance responses to temporary discounts: a field experiment among Central American migrants. NBER Working Papers 20522, National Bureau of Economic Research, Inc
Amuedo-Dorantes C, Pozo S (2006a) Remittances as insurance: evidence from Mexican immigrants. J Popul Econ 19(2):227–254
Amuedo-Dorantes C, Pozo S (2006b) The time pattern of remittances: evidence from Mexican migrants. Well-being Soc Policy 2(2):49–66
Amuedo-Dorantes C, Pozo S (2013) Remittances and portfolio values: an inquiry using immigrants from Africa, Europe, and the Americas. World Dev 41 (C):83–95
Bartolucci F, Nigro V (2010) A dynamic model for binary panel data with unobserved heterogeneity admitting a \(\sqrt {n}\)-consistent conditional estimator. Econometrica 78(2):719–733
Bauer T, Sinning M (2011) The savings behavior of temporary and permanent migrants in Germany. J Popul Econ 24(2):421–449
Becker GS (1974) A theory of social interactions. J Polit Econ 82(6):1063–93
Bernheim BD, Shleifer A, Summers LH (1985) The strategic bequest motive. J Polit Econ 93(6):1045–76
Bettin G, Lucchetti R, Zazzaro A (2012) Endogeneity and sample selection in a model for remittances. J Dev Econ 99:370–384
Bollard A, McKenzie D, Morten M, Rapoport H (2011) Remittances and the brain drain revisited: the microdata show that more educated migrants remit more. World Bank Econ Rev 25(1):132–156
Bouyiour J, Miftah A (2015) Why do migrants remit? testing hypotheses for the case of Morocco. Journal of Migr 4(2)
de la Briere B, Sadoulet E, de Janvry A, Lambert S (2002) The roles of destination, gender, and household composition in explaining remittances: an analysis for the Dominican Sierra. J Dev Econ 68:309–328
Brown R, Carling J, Fransen S, Siegel M (2014a) Measuring remittances through surveys. Demogr Res 31(41):1243–1274
Brown RP (1997) Do migrants’ remittances decline over time? Evidence from Tongans and Western Samoans in Australia. Contemp Pac 10:107–151
Brown RP, Leeves G, Prayaga P (2014b) Sharing norm pressures and community remittances: Evidence from a natural disaster in the pacific islands. J Dev Stud 50(3):383–398
Brown RPC, Poirine B (2005) A model of migrants’ remittances with human capital investment and intrafimilial transfers. Int Migr Rev 39(2):407–438
Carling J (2008) The determinants of migrant remittances. Oxf Rev Econ Policy 24(3):581–598
Chamberlain G (1984) Panel data. In: Griliches Z, Intriligator MD (eds) Handbook of Econometrics, vol 2. Elsevier, chap 22, pp 1247–1318
Chort I, Gubert F, Senne JN (2012) Migrant networks as a basis for social control: Remittance incentives among Senegalese in France and Italy. Reg Sci Urban Econ 42(5):858–874
Constant A, Massey DS (2003) Self-selection, earnings, and out-migration: a longitudinal study of immigrants to Germany. J Popul Econ 16(4):631–653
Constant A, Zimmermann K (2011) Circular and repeat migration: counts of exits and years away from the host country. Popul Res Policy Rev 30(4):495–515
Constant AF, Zimmermann KF (2012) The dynamics of repeat migration: a Markov chain analysis. Int Migr Rev 46(2):362–388
Cox D (1987) Motives for private income transfers. J Polit Econ 95(3):508–46
Cox D, Eser Z, Jimenez E (1998) Motives for private transfers over the life cycle: an analytical framework and evidence for Peru. J Dev Econ 55(1):57–80
Czaika M, Spray J (2013) Drivers and dynamics of internal and international remittances. J Dev Stud 49(10):1299–1315
Dimova R, Wolff FC (2009) Remittances and chain migration: Longitudinal evidence from Bosnia and Herzegovina. IZA Discussion Papers 4083, Institute for the Study of Labor (IZA)
Docquier F, Rapoport H, Salomone S (2012) Remittances, migrants’ education and immigration policy: Theory and evidence from bilateral data. Reg Sci Urban Econ 42(5):817–828
Dustmann C, Mestres J (2010) Remittances and temporary migration. J Dev Econ 92(1):62–70
Dustmann C, Soest AV (2002) Language and the earnings of immigrants. Ind Labor Relat Rev 55(3):473–492
Duval L, Wolff FC (2010) Remittances matter: longitudinal evidence from Albania. Post-Communist Econ 22(1):73–97
Duval L, Wolff FC (2012) Longitudinal evidence on financial expectations in Albania: Do remittances matter? Econ Transit 20(1):137–161
Echazarra A (2011) Accounting for the time pattern of remittances in the Spanish context. Working Paper 5-2010, Centre for Census and Survey Research, University of Manchester
Facchini G, Patacchini E, Steinhardt M (2015) Migration, friendship ties and cultural assimilation. Scand J Econ 117(2):619–649
Frankel J (2011) Are bilateral remittances countercyclical? Open Econ Rev 22 (1):1–16
Freund C, Spatafora N (2008) Remittances, transaction costs, and informality. J Dev Econ 86(2):356–366
Funkhouser E (1995) Remittances from international migration: a comparison of El Salvador and Nicaragua. Rev Econ Stat 77(1):137–146
Funkhouser E (2006) The effect of emigration on the labor market outcomes of the sender household: a longitudinal approach using data from Nicaragua. Well-Being Soc Policy 2(2):5–25
Funkhouser E (2012) Using longitudinal data to study migration and remittances. In: Vargas Silva C (ed) Handbook of Research Methods in Migration. Edward Elgar Publishing, Inc., pp 186–206
Gathmann C, Keller N (2014) Returns to Citizenship? Evidence from Germany’s Recent Immigration Reforms. IZA Discussion Papers 8064, Institute for the Study of Labor (IZA)
Gayle GL, Viauroux C (2007) Root-n consistent semiparametric estimators of a dynamic panel-sample-selection model. J Econ 141(1):179–212
Geweke J (1989) Bayesian inference in econometric models using Monte Carlo integration. Econometrica 57(6):1317–39
Ghosh B (2006) Migrants’ remittances and development: Myths, rhetoric and realities. Tech. rep., International Organization on Migration
Gibson J, McKenzie DJ, Rohorua H (2006) How cost-elastic are remittances? Estimates from Tongan migrants in New Zealand. Pac Econ Bull 21(1):112–128
Grieco E (2004) Will migrant remittances continue through time? a new answer to an old question. Int J Multicul Soc 6(2):243–252
Haisken-DeNew JP, Hahn MH (2010) Panelwhiz: Efficient data extraction of complex panel data sets—an example using the German SOEP. J Appl Soc Sci Stud 130(4):643–654
Hajivassiliou VA, McFadden DL (1998) The method of simulated scores for the estimation of LDV Models. Econometrica 66(4):863–896
Hall RE (1978) Stochastic implications of the life cycle-permanent income hypothesis: theory and evidence. J Polit Econ 86(6):971–87
Heckman JJ (1981) Heterogeneity and state dependence. In: Studies in Labor Markets, NBER Chapters. University of Chicago Press, pp 91–140
Heiss F (2011) Dynamics of self-rated health and selective mortality. Empir Econ 40(1):119–140
Hoddinott J (1994) A model of migration and remittances applied to Western Kenya. Oxf Econ Pap 46:459–476
Holst E, Schäfer A, Schrooten M (2008) Gender, migration, remittances: Evidence from Germany. Discussion Papers of DIW Berlin 800, DIW Berlin, German Institute for Economic Research
Holst E, Schäfer A, Schrooten M (2010) Gender, transnational networks and remittances: Evidence from Germany. Discussion Papers of DIW Berlin 1005, DIW Berlin, German Institute for Economic Research
Holst E, Schäfer A, Schrooten M (2011) Remittances and gender: Theoretical considerations and empirical evidence. IZA Discussion Papers 5472, Institute for the Study of Labor (IZA)
Holst E, Schäfer A, Schrooten M (2012) Gender and remittances: Evidence from Germany. Fem Econ 18(2):201–229
Honoré BE, Kyriazidou E (2000) Panel data discrete choice models with lagged dependent variables. Econometrica 68(4):839–74
Hyslop DR (1999) State dependence, serial correlation and heterogeneity in intertemporal labor force participation of married women. Econometrica 67 (6):1255–1294
Keane MP (1994) A computationally practical simulation estimator for panel data. Econometrica 62(1):95–116
Keane MP, Sauer RM (2009) Classification error in dynamic discrete choice models: Implications for female labor supply behavior. Econometrica 77(3):975–991
Kyriazidou E (2001) Estimation of dynamic panel data sample selection models. Rev Econ Stud 68(3):543–72
Liu Q, Reilly B (2004) Income transfers of Chinese rural migrants: some empirical evidence from Jinan. Appl Econ 36(12):1295–1313
Lucas RE, Stark O (1985) Motivations to remit: evidence from Botswana. J Polit Econ 93(5):901–918
Lucchetti R, Pigini C (2015) DPB: Dynamic Panel Binary data models in Gretl. Gretl working papers 1, Università Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali. http://ideas.repec.org/p/anc/wgretl/1.html
Makina D, Masenge A (2015) The time pattern of remittances and the decay hypothesis: Evidence from migrants in south africa. Migr Lett 12(1)
Mazzucato V (2009). In: DeWind J, Holdaway J (eds) Simultaneity and networks in transnational migration: lessons learned from a simultaneous matched sample methodology, pp 69–100
Menjivar C, Da Vanzo J, Greenwell L, Valdez RB (1998) Remittance behaviour among Salvadoran and Filippino immigrants in Los Angeles. Int Migr Rev 32(1):97–126
Merkle L, Zimmermann KF (1992) Savings, remittances, and return migration. Econ Lett 38(1):77–81
Miranda A (2007) Dynamic probit models for panel data: a comparison of three methods of estimation. United Kingdom Stata Users’ Group Meetings 2007 11, Stata Users Group. http://ideas.repec.org/p/boc/usug07/11.html
Osili UO (2007) Remittances and savings from international migration: Theory and evidence using a matched sample. J Dev Econ 83(2):446–465
Piracha M, Zhu Y (2007) Precautionary savings by natives and immigrants in Germany. SOEPpapers 33, DIW Berlin, The German Socio-Economic Panel (SOEP). http://ideas.repec.org/p/diw/diwsop/diw_sp33.html
Poirine B (1997) A theory of remittances as an implicit family loan arrangement. World Dev 25(4):589–611
Rapoport H, Docquier F (2006) The economics of migrants’ remittances. In: Kolm S, Mercier Ythier J (eds) Handbook on the Economics of Giving, Altruism and Reciprocity, vol 2. Elsevier, pp 1135–1198
Rasch G (1960) Probabilistic models for some intelligence and attainment tests. Denmark Paedogiska
Ratha D, Sirkeci I (2010) Remittances and the global financial crisis. Migr Lett 7(2):125–131
Rosenzweig MR (1988) Risk, implicit contracts and the family in rural areas of low-income countries. Econ J 98(393):1148–70
Schans D (2009) Transnational family ties of immigrants in the Netherlands. Ethnic and Racial Studies 32(7):1164–1182
Schmidt CM (1997) Immigrant performance in Germany: Labor earnings of ethnic german migrants and foreign guest-workers. Q Rev Econ Finance 37 (Supplement):379–397
Semykina A, Wooldridge JM (2013) Estimation of dynamic panel data models with sample selection. J Appl Econ 28(1):47–61
Simati MA, Gibson J (2001) Do remittances decay? Evidence from Tuvaluan migrants in New Zealand. Pac Econ Bull 16(1):55–63
Sinning M (2011) Determinants of savings and remittances: empirical evidence from immigrants to Germany. Rev Econ Househ 9(1):45–67
Sirkeci I, Cohen JH, Ratha D (eds) (2012) Migration and remittances during the Global Financial Crisis and Beyond. The World Bank
Stark O (1978) Economic-demographic interaction in the course of agricultural development: the case of rural-to-urban migration (Research Report No. 2/78). David Horowitz Institute for Research of Developing Countries, Tel Aviv
Stewart MB (2007) The interrelated dynamics of unemployment and low-wage employment. J Appl Econ 22(3):511–531
Unheim P, Rowlands D (2012) Micro-level determinants of remittances from recent migrants to Canada. Int Migr 50(4):124–139
Wooldridge JM (2005) Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity. J Appl Econ 20(1):39–54
Yang D (2008) Coping with disaster: the impact of hurricanes on international financial flows, 1970–2002. The BE Journal of Economic Analysis & Policy 8(1 (Advances)):Article 13
Yang D, Choi H (2007) Are remittances insurance? Evidence from rainfall shocks in the Philippines. World Bank Econ Rev 21(2):219–248
Zibrowius M (2011) Convergence or divergence? Immigrant wage assimilation patterns in Germany. IWQW Discussion Paper Series 03/2011, Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung (IWQW)
Acknowledgments
We wish to thank three anonymous referees, Prof. Klaus F. Zimmermann, Tineke Fokkema, Alessia Lo Turco, Claudia Pigini and Alberto Zazzaro, and the participants at a seminar at the University of Hamburg for useful comments and suggestions. We also thank the German Institute for Economic Research (DIW Berlin) for making the GSOEP dataset available. All errors are ours.
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Appendices
Appendix A: Immigrants’ countries of origin
Afghanistan | Costa Rica | Ireland | Portugal |
Albania | Croatia | Israel | Romania |
Algeria | Czech Republic | Italy | Russia |
Argentina | Denmark | Japan | Singapore |
Armenia | Egypt | Jordan | Slovakia |
Australia | El Salvador | Kazakhstan | Slovenia |
Austria | Eritrea | Korea | South Africa |
Azerbaijan | Estonia | Kurdistan | Spain |
Bangladesh | Ethiopia | Kyrgyzstan | Sri Lanka |
Belarus | Ex-Yugoslavia | Latvia | Sweden |
Belgium | Finland | Lebanon | Switzerland |
Benelux | France | Liberia | Tajikistan |
Bolivia | Georgia | Lithuania | Thailand |
Bosnia-Herzegovina | Ghana | Luxembourg | Trinidad and Tobago |
Brazil | Great Britain | Macedonia | Tunisia |
Bulgaria | Greece | Mexico | Turkey |
Canada | Holland | Moldavia | Ukraine |
Chad | Hungary | Namibia | USA |
Chile | Indonesia | Paraguay | Uzbekistan |
China | Iran | Philippines | Venezuela |
Columbia | Iraq | Poland | Vietnam |
Appendix B: Descriptive statistics
Variable | Mean | SD | Min | 5 % | 95 % | Max |
|---|---|---|---|---|---|---|
Remitted | 0.127 | 0.332 | 0 | 0 | 1 | 1 |
Male | 0.471 | 0.499 | 0 | 0 | 1 | 1 |
Age | 40.425 | 11.774 | 17 | 21 | 60 | 65 |
Young | 0.085 | 0.279 | 0 | 0 | 1 | 1 |
Decades since mig | 2.121 | 1.078 | 0.100 | 0.600 | 4 | 6.300 |
Stay in Germany | 0.716 | 0.451 | 0 | 0 | 1 | 1 |
German nationality | 0.451 | 0.498 | 0 | 0 | 1 | 1 |
Education years | 10.896 | 2.500 | 7 | 7 | 15 | 18 |
Education years 2 | 124.972 | 60.300 | 49 | 49 | 225 | 324 |
Employed | 0.692 | 0.462 | 0 | 0 | 1 | 1 |
Individual income (ln) | 9.654 | 1.064 | 2.996 | 7.560 | 10.920 | 13.305 |
Household income (ln) | 10.264 | 0.563 | 3.689 | 9.301 | 11.093 | 15.270 |
No. adults | 2.444 | 0.939 | 1 | 1 | 4 | 8 |
No. children | 1.027 | 1.199 | 0 | 0 | 3 | 10 |
Partner home | 0.008 | 0.088 | 0 | 0 | 0 | 1 |
Children home | 0.040 | 0.195 | 0 | 0 | 0 | 1 |
Parents home | 0.211 | 0.408 | 0 | 0 | 1 | 1 |
Siblings home | 0.051 | 0.221 | 0 | 0 | 1 | 1 |
Per capita gdp differential (ln) | −0.973 | 0.629 | −4.584 | −1.810 | 0.008 | 0.796 |
Appendix C: Stability of the state dependence parameters across subsamples
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Bettin, G., Lucchetti, R. Steady streams and sudden bursts: persistence patterns in remittance decisions. J Popul Econ 29, 263–292 (2016). https://doi.org/10.1007/s00148-015-0565-9
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DOI: https://doi.org/10.1007/s00148-015-0565-9