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.
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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
Keywords
- Migration
- Remittances
- Persistence
- State dependence
- Discrete panel data models
JEL Classification
- F22
- F24
- C23
- C25