amount remitted are presented. In terms of the overall purposefulness of the Heckman selection model, the correlation coefficient rho between the error terms of the participation and the outcome equation is significant as indicated by the chi-squared –statistic for rho at the bottom of the table. Thus, the null hypothesis of independent equations can be rejected, and the participation equation should be taken into account when estimating the outcome equation. However, this conclusion should not be regarded as definite, as the model is based on a bivariate normality assumption whose validity may be questioned, as discussed in section 5.2.1. Moreover, it should be noted that the identification variable indicating whether the migration has lasted for more than a year is statistically insignificant, suggesting that it may not be the best choice for an identification variable.
Comparing the Heckman selection model estimates to those of the tobit model, the same variable coefficients that were statistically significant in the tobit model are also significant in the participation equation of the Heckman selection model, and have the same signs as those in the tobit model. The outcome equation of the Heckman selection model, on the other hand, displays a rather different pattern in terms of the statistical significance of the variable coefficients than the participation equation, or the tobit model: the coefficients of variables indicating whether the migrant is the son or daughter of the household head at home, his age, whether he is residing in an OECD or an African country, whether he lives alone, and what the recipient household’s long-run expenditure is prove to be significant.
Thus, there are only two independent variables whose coefficients are statistically significant in both the participation and the outcome equation, indicating whether the migrant is residing in an OECD country, and whether he is living alone. Interestingly, the latter affects the probability of remittances positively, but the amount remitted negatively. The variables
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indicating the migrant’s education and his current residence are jointly statistically significant in both equations.
Table 4. Heckman selection model for probability and amount of remittances in Uganda
Likelihood of remitting Amount remitted Independent Variable Coefficient Robust std. err. Partial effect on the probability of remitting Coefficient Robust std. err.
Partial effect on the conditional expected
log amount remitted Sex 0.0978 0.1269 0.0235 -0.2555 0.2011 -0.1883 Married 0.2831* 0.1615 0.0687 0.0172 0.2163 0.2116 Son 0.0567 0.1628 0.0136 -0.6765*** 0.2328 -0.6375 Age 0.0094 0.0077 0.0023 0.0312** 0.0124 0.0376 Primary 0.1776 0.1547 0.0429 -0.0630 0.2854 0.0576 Secondary 0.4721*** 0.1534 0.1168 -0.0393 0.2955 0.2753 Tertiary 0.7595*** 0.1679 0.1982 0.5584 0.4095 1.0536 Duration -0.0078 0.0108 -0.0019 -0.0322 0.0273 -0.0375 OECD 0.8332*** 0.3131 0.2145 1.5156** 0.6307 2.0373 Africa 0.2181 0.2398 0.0536 1.0149* 0.6084 1.1615 Urban Uganda 0.5343*** 0.1792 0.1270 0.0773 0.3576 0.4494 Professional 0.1232 0.2869 0.0301 0.0338 0.4049 0.1176 Service 0.5079* 0.2794 0.1287 -0.5825 0.4480 -0.2420 Agriculture & Crafts 0.3312 0.2753 0.0806 -0.3216 0.3784 -0.1004 Reason 0.5903*** 0.1745 0.1395 0.3734 0.4973 0.7928 Employed 0.8375*** 0.2663 0.2028 -0.4185 0.3706 0.1771 Alone 0.3969*** 0.1304 0.0975 -0.4744** 0.2318 -0.2064 Size of HH -0.0112 0.0279 -0.0027 -0.0317 0.0450 -0.0394 Assets of HH 0.3780 0.2657 0.0855 -0.1657 0.3852 0.1023 Expenditure of HH -0.0349 0.0271 -0.0084 0.1187** 0.0499 0.0947 Duration > 1 year 0.2814 0.1721 0.0661 Constant -3.4901*** 0.5629 11.8932*** 1.2579 Joint significance Joint significance
Chi2 Prob > Chi2 Chi2 Prob > Chi2 Education 23.58 0.0000 Education 3.07 0.3803 Residence 12.78 0.0051 Residence 14.22 0.0026 Occupation 4.72 0.1933 Occupation 2.73 0.4359 Origin region 20.13 0.0005 Origin region 31.67 0.0000
Model statistics N 1092 Uncensored 339 Log pseudolikelihood -3111285.0 Chi2 159.82 Prob > Chi2 0.0000 Rho -0.6365 Rho = 0: Chi2 5.18 Rho = 0: Prob > Chi2 0.0228
Comparing the partial effects of the independent variables on the probability of remitting in the Heckman selection model to those in the tobit model, the magnitudes and signs of the effects prove to be rather similar to one another. For instance, according to the Heckman
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selection model, employed migrants are 20.3 percent more likely to remit than unemployed ones, and migrants having completed tertiary education are about 19.8 percent more likely to remit than migrants with no education. The corresponding effects estimated by the tobit model are 20.7 percent and 17.0 percent, respectively.
The partial effects of the independent variables on the conditional expected logarithmic amount remitted differ somewhat in the two models. In the Heckman selection model, the effects of whether the migrant is residing in an OECD country or in an African country, and whether he is the son of the household head are the largest ones of the statistically significant variables. Migrants residing in an OECD country remit about 204 percent more than migrants residing in rural areas of Uganda, which is a significantly smaller effect than the 394 percent suggested by the tobit model. In turn, migrants residing in African countries remit about 116 percent more than migrants residing in rural areas of Uganda, while the sons and daughters of household heads remit about 64.6 percent less than migrants with some other relationship to the household head.
In terms of the motives for remitting, the statistically significant variable coefficients in the participation equation have the same implications as the results obtained with the tobit model, and suggest that a variety of motives may be driving remittance behaviour of Ugandan migrants through the effects that the migrant’s income and education have on remittances. An interesting finding in the outcome equation, however, is the positive effect that the recipient household’s long-run expenditure has on the amount remitted. This variable proxies the recipient household’s long-run income and its partial effect on remittances suggests that a one percent increase in the recipient household’s income is associated with about a 9.5 percent increase in remittances.
This result implies that Ugandan migrants may be motivated by exchange or investment considerations. Under the exchange motive, the likelihood of remitting may be affected negatively and the amount remitted positively by the recipient household’s income: here, however, the former effect is statistically insignificant (Cox 1987:519). Under the investment motive, a positive relationship between remittances and the recipient’s long-run income appears when migration is constrained, but should become negative when migration is unconstrained (Rapoport and Docquier 2006:1159). This inverse U-shaped relationship between the variables, however, does not receive support from additional estimations. Yet, the investment motive cannot be disregarded altogether because of the positive effect that the
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migrant’s secondary and tertiary education have on the probability of remitting – under the exchange motive, education should affect remittances negatively, as educated migrants are less inclined to return home than uneducated ones (Ibid:1164).