Factores sociocognitivos en el desarrollo de carrera

The estimation results are reported in Table 2.3. To allow for a heterogeneous im-pact of migration determinants, separate results are reported for migration from

advanced and from developing countries. Our preferred methodology is BCFE estimation of equation (2.21). The standard errors used to calculate the t-statistics are simulated using the bootstrap algorithm as outlined in Everaert and Pozzi (2007). They are robust to both sectional heteroscedasticity and cross-sectional error correlation. To link our results to those in the literature, we also report results from (i) FE estimation of restricted versions of equation (2.21) in-cluding either lagged migration or the migrant stock and (ii) FE estimation of equation (2.21) not correcting for the dynamic panel data bias. For these estima-tors, standard errors are simulated in a similar way as for the BCFE estimator.⁹ We also experimented with GMMd and GMMs estimations but these were unsat-isfactory as the results were highly sensitive to the choice of instruments. Conse-quently, we do not discuss the GMM results but some of the results can be found in Table 2.9 in the Appendix. One interesting point to note though is that, in line with the results from the Monte Carlo simulation, the Sargan-Hansen test rejects the validity of the moment conditions underlying the GMMs estimator. Further-more, we tested if the model specification in equation (2.21) is appropriate by adding the second lag of ln M_dot to the estimation equation. The coefficient for the second lag of ln M_dot turned out insignificant for both advanced and devel-oping origins, yet the first lag remained significant indicating that our results are robust for this alternative specification¹⁰.

Table 2.4 reports long-run elasticities of migration determinants calculated from the BCFE estimation results. The first three columns report semi long-run effects, while the last three columns report full long-run effects. With respect to the latter, it should be noted that they are calculated assuming the strong link between flows and stocks as given in equation (2.18). In our dataset this link is less strong, though, as stock data are not constructed from the flow data such that the evolution

9The matlab code for the BCFE estimator is available upon request.

10The estimation results for this model are available upon request.

Table 2.3: Estimation results

Dependent variable: ln M_dot Sample period: 1998-2007

Advanced origins Developing origins

FE(1) FE(2) FE(3) BCFE FE(1) FE(2) FE(3) BCFE

ln MST_dot 0.73^∗∗∗ 0.44^∗∗∗ 0.46^∗∗∗ 0.82^∗∗∗ 0.24^∗∗∗ 0.23^∗∗∗

(6.19) (4.82) (6.10) (7.75) (4.54) (4.55)

ln M_dot−1 0.48^∗∗∗ 0.44^∗∗∗ 0.61^∗∗∗ 0.65^∗∗∗ 0.61^∗∗∗ 0.75^∗∗∗

(9.38) (7.58) (8.51) (27.67) (24.02) (13.50)

ln w_dt−1 0.98^∗∗∗ 0.93^∗∗ 0.70^∗∗∗ 0.59^∗∗ 1.92^∗∗∗ 2.78^∗∗∗ 1.82^∗∗∗ 1.58^∗∗∗

(3.45) (2.00) (2.59) (2.32) (5.82) (4.08) (5.43) (4.89) ln w_ot−1 −0.30 −0.29 −0.34^∗ −0.37^∗∗ 0.08 0.15 0.05 0.00

(−1.53) (−1.00) (−1.75) (−2.20) (0.81) (0.82) (0.53) (0.02) ln ps_dt−1 −0.24 −0.39 −0.41^∗∗ −0.49^∗∗∗ 0.66^∗∗ 0.86^∗ 0.58^∗ 0.37

(−1.39) (−1.58) (−2.40) (−2.95) (2.22) (1.68) (1.86) (1.19) ln e_dt−1 1.06^∗ 2.56^∗∗∗ 0.96 0.26 −0.29 1.86^∗∗ −0.69 −1.56^∗∗

(1.81) (3.11) (1.63) (0.46) (−0.58) (2.12) (−1.45) (−2.30)

ln e_ot−1 0.10 0.31 0.30 0.29 −0.18 −0.30 −0.17 −0.17

(0.27) (0.54) (0.79) (0.84) (−0.75) (−0.69) (−0.72) (−0.78)

ln ∆wdt 1.34^∗∗∗ 1.56^∗∗ 1.40^∗∗∗ 1.51^∗∗∗ 2.07^∗∗∗ 2.47^∗∗∗ 2.21^∗∗∗ 2.45^∗∗∗

(2.83) (2.37) (2.75) (2.85) (4.35) (3.22) (4.82) (5.00)

ln ∆wot −0.02 0.16 −0.06 −0.22 0.12 0.09 0.10 0.08

(−0.08) (0.46) (−0.24) (−0.85) (0.99) (0.56) (0.82) (0.59) ln ∆ps_dt 0.52^∗∗ 0.60^∗∗ 0.55^∗∗ 0.55^∗∗ 1.08^∗∗∗ 0.82^∗ 1.15^∗∗∗ 1.33^∗∗∗

(2.16) (1.99) (2.32) (2.06) (3.30) (1.64) (3.72) (3.96) ln ∆edt 2.31^∗∗∗ 3.14^∗∗∗ 2.53^∗∗∗ 2.18^∗∗∗ 2.82^∗∗∗ 1.90^∗ 2.35^∗∗∗ 1.89^∗ (3.35) (3.79) (3.84) (3.12) (3.13) (1.82) (2.62) (1.79) ln ∆eot 1.26^∗ 2.11^∗∗∗ 1.10^∗ 0.76 0.13 0.34 0.14 0.05

(1.87) (2.55) (1.65) (1.13) (0.37) (0.69) (0.40) (0.15)

Notes: Each regression includes time dummies (not reported). t-statistics - between brackets - are robust to cross-sectional heteroskedasticity and cross-sectional error correlation. *, ** and *** indicate significance at the 10%, 5% and 1% level respectively. Advanced: 2223 observations and 247 cross sections. Developing: 3492 observations and 388 cross sections.

in flows and stocks is not fully compatible. The exact numbers of the full long-run effects reported in Table 2 should therefore be interpreted with care. Standard errors for the long-run effects are also simulated using the bootstrap algorithm. In line with Everaert and Pozzi (2007), we report the median and the 5^th and 95^th

percentiles of the simulated distribution of the long-run effects rather than the mean and the t-statistic. The reason for this is that the distribution of the long-run effects does not necessarily have finite moments, especially when the root of the dynamic process is close to unity. It should be noted that these percentiles are not necessarily finite either but they should be less vulnerable to large outliers in the distribution.

Table 2.4: Long-run estimation results

Dependent variable: ln M_do Sample period: 1998-2007

Semi LR (BCFE) Full LR (BCFE)

percentiles percentiles

median 5th 95th median 5th 95th

Advanced origins

Note: *, ** and *** indicate significance at the 10%, 5% and 1% level respectively.

Dynamic features of migration

Consistent with the findings in for instance Fertig (2001), Clark et al. (2002) and Pedersen et al. (2008), lagged migrant flows and migrant stocks appear to have the most pervasive impact on subsequent migration from both advanced and

develop-ing countries. The results from our preferred BCFE estimator suggest an elastic-ity of 0.61 (0.75) for lagged migrant flows from advanced (developing) countries and 0.46 (0.23) for the stock of migrants from advanced (developing) countries.

The fact that both are significant indicates that multicollinearity between these two variables is fairly small. In correspondence to earlier findings in (Dunlevy and Gemery, 1977) it seems that these variables do not measure the same phe-nomenon, supporting their simultaneous inclusion in the estimation equation. The significant coefficient on lagged migration flows suggests dynamic effects stem-ming from the process by which expectations about future earnings are formed and updated while the significant coefficient on migrant stock indicates network effects. Moreover, it is interesting to note that both levels and first-differences of the explanatory variables turn out significant. This suggests that even though mi-gration is essentially a forward-looking decision, it also strongly fluctuates with short-run cyclical conditions rather than being a steady flow.

With respect to the dynamic specification of the model and the estimation proce-dure, two points are worth mentioning. First, misspecifying the model especially by omitting lagged migration has a strong impact, most notably on the coeffi-cients of the migrant stock which (looking at the FE estimates) increase from 0.44 (0.24) to 0.73 (0.82) for migration from advanced (developing) countries. Mis-specifying the model by omitting the migrant stock results in a less pronounced increase in the coefficient on the lagged migrant flow. Second, correcting for the dynamic panel bias is very important for the coefficient on the lagged migrant flow, which rises from 0.44 (0.61) to 0.61 (0.75) for migration from advanced (de-veloping) countries. Also the coefficients on the other determinants are affected by misspecifying dynamics and/or ignoring the dynamic panel data bias. Espe-cially employment rates in the host country are only then found to be significantly positive for migration from both advanced and developing countries.

All these findings indicate that dynamics play a prominent role in the migration model and should definitely not be ignored, both when specifying the model and selecting the estimation method. Below, we discuss the estimation results for the determinants income and employment separately, focusing on the BCFE estima-tor.

Income

First, consistent with the findings in the empirical literature (see also Karemera et al., 2000; Mayda, 2010), per capita income in the destination country turns out to be one of the key incentives for migration to OECD countries. For both changes and levels, the coefficient is positive and highly significant across sources of mi-gration. This finding is also robust over the different specifications and estimation methods. Looking at the coefficients on the first-differences, a 1 percent rise in per capita income in the destination country results in a 1.51 (2.45) percent im-mediate temporary rise in the migrant flow from advanced (developing) countries.

The coefficients on the one year lagged per capita income show that this 1 percent increase attracts an additional 0.59 (1.58) percent migrants from advanced (de-veloping) source countries in the next year. In the long run (see Table 2.4), this amounts to a 1.64 (7.00) percent increase in the migrant flow when only taking into account dynamics through the lagged migrant flow (semi long-run effects) and even to a 5.32 (14.82) percent increase when also taking into account the link between flows and stocks (full long-run effects). This suggest that taking into account network effects when calculating long-run effects is very important.

However, as noted above the exact numbers for the full long-run effects should be considered with care due to the somewhat loose connection between flows and stocks in our dataset.

Second, evidence for the impact of per capita income in the source country is less

evident. Both in the short and in the long run, the estimates indicate a statistically significant negative impact on migration for lagged per capita income in advanced origins, but an insignificant impact for per capita income in developing origins (see also Mayda, 2010). First-differenced per capita income at home does not influence the size of migrant flows.

Third, the impact of public services in the destination country is more ambiguous.

Immigrants from advanced origin countries prefer destinations with lower levels of public services: the level of public services has a statistically significant elas-ticity of -0.49 which results in a semi long-run elaselas-ticity of -1.19 percent and a full long-run elasticity of -2.31 percent. This finding might be explained by the fact that immigrants from advanced countries consider more public services to go to-gether with more social expenditures which can only be financed by higher taxes.

In the short run, the level of public services does not appear to have an impact on migration from developing countries, but the immediate response to an increase in public services, as captured by its first-difference, is found significantly posi-tive. In the long run, however, the level of public services does appear significant with the expected positive sign. Immigrants from developing countries may look upon public services as a safety net and move to countries where public services become more generous, in correspondence with the welfare state hypothesis (see also Borjas, 1999).

Employment

Migration from advanced countries seems independent of the actual level of em-ployment rates at home and abroad, and responds only in the short term to changes in the employment opportunities in the host country. In fact, for immigrants from advanced countries the coefficient of changes in the host country’s employment rate is the largest of all coefficients. Furthermore, also immigrants from

develop-ing countries respond positively to higher employment growth in the destination, though with a smaller and less significant coefficient. On average, a 1 percent higher growth in the host country’s employment rate results in a temporary in-crease in the bilateral migrant flow from advanced (developing) countries by 2.18 (1.89) percent. Against expectations, however, our estimates suggest that migrants from developing countries generally move to countries where employment oppor-tunities are lower. The same result is obtained in the long run, but the coefficient decreases when the link between stocks and flows is accounted for.

2.4 Conclusions

In this chapter we analyze the determinants of international migration to 19 OECD countries from both advanced and developing origin countries between 1998 and 2007 using the OECD’s International Migration Database. The contribution of this chapter is twofold. First, we estimate a dynamic model of migration based on Hatton’s (1995) model using a three-way panel data model. This framework allows to control for observed and unobserved time invariant bilateral effects like geographical, historical, political and cultural influences as well as for time effects like cyclical influences, policy changes, decreases in transportation and commu-nication costs, ..., which are common for all country pairs and reduce the risk of biased results. Including both lagged migration and migrant stocks allows us to separately identify network effects and dynamics stemming from partial adjust-ment. Second, we estimate this dynamic panel data model using an extended ver-sion of the iterative bootstrap algorithm suggested by Everaert and Pozzi (2007).

This estimator allows us to correct for the dynamic panel data bias of the FE esti-mator, which in our model is induced by the lagged migrant flow as well as by the migrant stock, and explicitly takes into account the dynamic relationship between

immigrant flows and stocks.

Our results strongly confirm the hypotheses of the human capital theory as well as the network theory of migration, though with a few exceptions. We find that recent immigration to the OECD is primarily driven by better income opportunities in the member states. The influence of income at home and employment rates both at home and abroad is much less pronounced. More specifically, our estimates suggest that immigrants from developing countries are primarily driven by per capita GDP in the host country, whereas variations in migration from advanced countries are determined largely by short-run fluctuations in employment rates abroad. Moreover, as expected, higher native wages in advanced countries seem to discourage immigration, but we find no evidence for an impact of home wages in developing countries.

Furthermore, migrants from advanced countries are unlikely to move to countries with high public services due to the link between social expenditures and tax rates.

This is not the case for migrants from developing countries, who consider public expenditures a safety net and prefer countries with rising social expenditures, pro-viding some indication for the welfare magnet hypothesis.

Finally, we find evidence of strong dynamic effects. Both the lagged migration flow and the migrant stock have a strong positive and significant impact on current migration, the former indicating dynamic effects stemming from the process by which expectations about future earnings are formed and updated while the latter indicates network effects. Further evidence that dynamics play a prominent role in the migration model arises from the observation that misspecifying the model by omitting the lagged migration flow or the migrant stock and/or not correcting for the dynamic panel bias has a strong impact on the estimation results. Therefore, care should be taken when specifying the dynamic structure of the model and selecting the estimation method.

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