Empirical Results of the Study
8.3. Empirical Results on the Model of Macroeconomic Determinants of Remittances
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Table 8.4: Bivariate Correlations among the Variables used in the Remittances-Growth Model
RGDP RREM INF OEXR EAPOP RGFCF RGFCE DCPSB TRAD RFDI RODA PR RGDP
RREM INF OEXR EAPOP RGFCF RGFCE DCPSB TRAD RFDI RODA PR
1.0000
-0.0846 1.0000
-0.1055 0.0255 1.0000
-0.3364 -0.0547 -0.0453 1.0000
0.1226 -0.1744 -0.0087 0.6164 1.0000
0.9701 -0.1353 -0.1035 -0.2530 0.2281 1.0000
0.9935 -0.1232 -0.0917 -0.3218 0.1494 0.9675 1.0000
0.2554 0.1525 -0.1188 0.5084 0.4719 0.3309 0.2450 1.0000
-0.4156 0.0379 0.1999 0.4740 0.6001 -0.3049 -0.4035 0.2530 1.0000
0.5500 0.0159 -0.0758 0.0327 0.3031 0.6959 0.5261 0.4488 0.0444 1.0000
0.5566 0.1787 0.1569 -0.6213 -0.2456 0.4040 0.5450 -0.1996 -0.3284 -0.0760 1.0000 -0.4286 0.4691 0.0080 0.1510 -0.2788 -0.4597 -0.4520 -0.1237 -0.0434 -0.2577 -0.1458 1.0000
8.3. Empirical Results on the Model of Macroeconomic Determinants of
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determination model in South Asian countries jointly and significantly explain the macroeconomic determinants of remittances in the region.
Table 8.5: Results of Pooled OLS Estimation of Remittances Determinant Model
Variable Coefficient Probability Value
Lag Remittances to GDP Ratio 0.8632959*** 0.000
Inflation rate -0.0351227* 0.075
Official exchange rate -0.003113 0.609
Home-country income -0.001546*** 0.000
Host-country income 0.0000271 0.156
Broad money to GDP 0.014828* 0.091
Number of migrants to Population in Home Country
218.5936*** 0.000
Political rights 0.2220031*** 0.001
Dummy for September 11, 2001 0.6931617** 0.050
Constant term -0.5909857 0.229
Number of observations F-Statistic
Adjusted R2
153 370.49***
0.9563
0.000
Note: *, ** and *** indicate significance at 10 percent, 5 percent and 1 percent respectively.
Results indicate that remittances have strong and positive feedback effect in South Asian countries which is indicated by the coefficient of 0.8632959 for lag remittance-GDP ratio with 1 percent level of significance.
Remittance inflows are negatively related inflation rate of home country. Result of pooled OLS regression shows that one unit increase in inflation rate reduces remittance-GDP ratio by 0.0351227 units.
The impact of official exchange rate is found to be negative though it is statistically insignificant. Although host country’s income positively impact on remittance inflows, it remains statistically insignificant.
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Number of migrant to population ratio has a positive and significant impact on remittance inflows in this region. Results indicate that one unit change in migrant population ratio will increase remittance-GDP ratio by almost 219 units.
Broad money to GDP has a positive impact on remittance inflows with 10 percent level of significance which implies that wider and improved financial development attracts more remittances.
Political rights in domestic country play a positive and significant impact on remittances inflows in South Asian countries which indicate that improved political institutions and stable political environment is conducive to receive more remittances.
The coefficient of time dummy for 9/11, 2001 is found to be positive and statistically significant which implies that remitters are remitting more remittances through formal channel after 2001 attack.
Results of FEM and REM are presented in Table 8.6. In case of FEM, the value of F- statistic of 244.61 is statistically significant at one percent level. In case of REM, the value of Wald (χ2) statistic of 3334.40 is also statistically significant at one percent level.
Therefore, in both models, the regressors used in the model jointly and significantly determine the remittance inflows in South Asian countries.
In both FEM and REM, we find that remittances have strong feedback effect with one percent level of significance. Inflation rate of domestic country negatively impact on remittance inflows though it is significant in REM while insignificant in FEM.
In Both FEM and REM, we see that remittances has a shock absorbing role as coefficients of home country’s income are found to be negative and significant with one percent level.
Although host country’s income shows a positive impact in both models they found to be statistically insignificant. Coefficients of official exchange rate are found to be insignificant which implies that it does not have any impact on remittance inflows in South Asian countries.
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Remittance inflows in South Asian countries are positively and significantly related with migrant to population ratio in both models. However, impact of REM is found to be higher than that of FEM.
In both models, coefficients of political rights are found to be positive with one percent level of significance implies that improved political situation attracts more remittances.
Coefficients of broad money to GDP and time dummy are found to be positive in both models but they are significant in REM while insignificant in FEM.
Table 8.6: Empirical Results of FEM and REM on Remittances Determinant Model
FEM REM
Variable Coefficient Probability
Value
Coefficient Probability Value Lag1 Remittances to GDP
Ratio
0.8520626*** 0.000 0.8632959*** 0.000
Inflation rate -0.029237 0.145 -0.0351227* 0.073 Official exchange rate -0.0085986 0.261 -0.003113 0.608 Home-country income -0.0011378*** 0.010 -0.001546*** 0.000 Host-country income 0.0000303 0.144 0.0000271 0.154 Broad money to GDP 0.0176579 0.119 0.014828* 0.088 Number of migrants to
Population in Home Country
206.2757*** 0.001 218.5936*** 0.000
Political rights 0.2728349*** 0.001 0.2220031*** 0.001 Dummy for September 11,
2001
0.588927 0.112 0.6931617** 0.048
Constant term -0.850886 0.162 -0.5909857 0.227
Number of observations Number of Cross Section F-Statistic/Wald (χ2)
153 5
244.61*** 0.000
153 5
3334.40*** 0.000
Note: *, ** and *** indicate significance at 10 percent, 5 percent and 1 percent respectively.
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In choosing between these two models we run Housman specification test. Housman χ2 value is found to be 1.94 with the probability value of 0.7464. This means that our null hypothesis of appropriateness of REM cannot be rejected as we get insignificant p-value.
Therefore we interpret REM in estimating the macroeconomic determinants of remittances in South Asian countries.
Results of determinants of remittances in South Asian countries using system GMM estimation technique are presented in Table 8.7.
Table 8.7: Results of SGMM Estimation on Remittances Determinant Model
Variable Coefficient Probability Value
Lag1 Remittances GDP Ratio 0.8250098*** 0.000
Inflation rate -0.0282266* 0.090
Official exchange rate -0.0079307 0.174
Home-country income -0.001472*** 0.000
Host-country income 0.000024 0.148
Broad money to GDP 0.0185221* 0.037
Number of migrants to Population in Home Country
285.7256*** 0.000
Political rights 0.2859274*** 0.000
Dummy for September 11, 2001 0.9007509*** 0.002
Constant term -0.8755116* 0.067
Number of observations Number of Cross Section Wald (χ2)
148 5
4949.11*** 0.000
Note: *, ** and *** indicate significance at 10 percent and 1 percent respectively.
A one percent level of statistical significance of the Wald statistics shows that the explanatory variables jointly explain the dependent variable in the models. The positive statistical value of lagged remittance GDP ratio indicates that remittances have strong feedback effects on attracting more remittances. Results show that 1 unit past year remittance GDP ratio contributes 0.825 units current year’s remittance GDP ratio.
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The rate of inflation affects remittance inflows negatively and significantly. Result shows that one unit changes in inflation rate reduces remittance GDP ratio by 0.0282266 units.
This indicates that inflation is regarded as a symptom of economic instability in home country to the migrant population. That is why, they remit less amount of remittances rather they prefer to remit later for not to afford the inflation effect. Insurance motivation may work here in sending remittances.
The coefficient of official exchange rate in remittance determinants model is found to be -0.0079307 with a probability value of 0.174. Therefore, official exchange rate does not affect remittance inflows significantly in South Asian region. However, the negative sign attached to official exchange rate indicates that the investment and insurance motivations are the dominant motivation in the remitting decision. The impact of an appreciation of the local currency in the case of insurance motivation would be the same as the impact of inflation. The migrant would prefer to remit more later to offset the impact of the appreciation of the local currency (because he need to send more money in the foreign currency). In the case of investment motives, especially for the investment in housing, the migrant is expected to decrease the amount of remittances in the case of an appreciation of the origin country’s currency. This is because, the cost of the construction increases in the currency of his host country.
Remittances to South Asian countries do seem to play a shock-absorbing role. The coefficient of per capita GDP in the home country’s per capita GDP is significantly negative. It shows that if per capita GDP of home country decreases by one unit, remittance GDP ratio rises by 0.001472 units. This suggests that when adverse economic shocks decrease incomes in their home country, migrants would remit more to protect their family from those shocks. Another way of interpreting this result is that migrants send remittances so that those left behind can maintain a certain quality of life. In that case, migrants must send more if those who receive remittances become poorer. That is, migrants are altruistically motivated to send remittances.
As expected, the coefficient of host country’s per capita GDP is found to be positive, 0.000024, which means that the location of migrant communities matters—the wealthier the country where migrants are located, the higher the remittances they send back home.
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However, the coefficient is found to be insignificant, probably as a result of not including all host countries where migrants are residing.
The coefficient of political rights is found to be 0.2859 with 0.000 probability value. It indicates that countries with better institutions or a more stable political system would receive more remittances relative to GDP. Institutional quality, political rights and governance situation can be viewed as reflecting the business environment, which in turn should influence the amount of remittances driven by the investment motive. This reflects the portfolio approach to send remittances in this region.
The coefficient of broad money (M2) to GDP ratio as an index of financial development is found to be significantly positive. SGMM result shows that one unit change in broad money to GDP ratio attracts 0.0185221 unit remittance-GDP ratio. This suggests that remittances are positively correlated with financial deepening. Countries with more developed financial markets would attract more remittances relative to GDP. Because financial development ease the process of money transfers and reduce the fee associated with sending remittances through competition, so that it can raise the amount or share of remittances transferred through official channels. Our finding is consistent with those of Freund and Spatafora (2005) and Singh et al. (2010).
Number of migrants to population ratio is positively correlated with the level of remittance to GDP ratio implying that growing stock of migrants abroad contributes to higher level of remittances. Results indicate that if migrant population ratio changes by one unit remittances GDP ratio rise by 285.7256 units. This result complies with those of Singh et al. (2010) and Barua et al (2007).
The coefficient of dummy variable (D1) is significantly positive. This indicates that there is an upward shift in the flow of remittances from abroad, in the aftermath of September 11, 2001. Probably, this happens because of tighter regulations of international money transfers, clampdown on the use of informal transfer channels and channeling more remittances through formal channel.
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8.4 Empirical Results of the Model of Macroeconomic Impact of