Impact of corruption on migration
The role of political conditions in the country of origin and in the country of destination in the level of migration between these countries. Impact of migration from poor countries on socio-economic development and corruption in high-income countries.
| Рубрика | Государство и право |
| Вид | дипломная работа |
| Язык | английский |
| Дата добавления | 17.07.2020 |
| Размер файла | 1,5 M |
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As it was revealed earlier, migrants tend to move to the high-income countries due to huge number of reasons including the smaller level of corruption and better quality of regulations. Now we would like to determine the category of income for which it is more important to enter a non-corrupt and law regulated government when we take into account that the government of destination is high-income. It is possible that both poor & medium and rich migrants enter rich government not only because of bigger amount of GDP per capita in the foreign country, but also because of the smaller than in the country of origin level of corruption and better law regulations. However, it is not clear for which category the CPI, Control of corruption and Rule of law in the country of destination are more important. In this work we suppose that for migrants from low- and medium-income to high-income countries the level of corruption and law regulations are out of primary importance. It can be explained by the idea that migrants from low- and medium- income countries migrate in order to find a shelter, working place and money, which they would send to home as remittances. However, migrants from high-income countries are already living in good conditions and the reasons for migration differ from the reasons of poor migrants. Rich migrants from high-income countries probably migrate because of better job opportunities or some norms, including political, which are better regulated in the country of destination, so they would feel better and freer.
So, we put forward a hypothesis that the significance of corruption for migration to high-income countries will be saved in the regressions for migration from high-income to high-income countries.
Now it is interesting to look at the results of the migration from low- & medium and high-income governments to high-income countries.
From high-income to high-income countries
First of all, we analyzed the migration from high-income to high-income countries (Table 7).
Table 7. The effect of corruption on migration from high-income to high-income countries; PPML regression
|
(I) |
(II) |
(III) |
||
|
VARIABLES |
1 |
2 |
3 |
|
|
CPI_d |
0.0320*** |
|||
|
(0.00571) |
||||
|
CPI_o |
0.0151 |
|||
|
(0.00487) |
||||
|
ControlofCorr _d |
0.0618*** |
|||
|
(0.0141) |
||||
|
ControlofCorr _o |
-0.00845 |
|||
|
(0.0147) |
||||
|
RuleofLaw_d |
0.0580*** |
|||
|
(0.0197) |
||||
|
RuleofLaw_o |
0.00635 |
|||
|
(0.0181) |
||||
|
GDPpcPPP_d |
0.117*** |
0.0542** |
0.0507** |
|
|
(0.0354) |
(0.0381) |
(0.0381) |
||
|
GDPpcPPP_o |
0.124*** |
0.0845** |
0.0737** |
|
|
(0.0385) |
(0.0353) |
(0.0346) |
||
|
Unemployment_d |
-0.00132 |
-0.00559*** |
-0.00664*** |
|
|
(0.00167) |
(0.00207) |
(0.00209) |
||
|
Unemployment_o |
0.00481*** |
0.00555*** |
0.00556*** |
|
|
(0.000860) |
(0.000926) |
(0.000961) |
||
|
Deathrate_d |
-0.0225*** |
-0.0303*** |
-0.0309*** |
|
|
(0.00576) |
(0.00654) |
(0.00663) |
||
|
Deathrate_o |
0.0158*** |
0.0153*** |
0.0156*** |
|
|
(0.00522) |
(0.00534) |
(0.00549) |
||
|
Schooltertiary _d |
0.00123*** |
0.00199*** |
0.00225*** |
|
|
(0.000362) |
(0.000362) |
(0.000361) |
||
|
Schooltertiary _o |
-0.00140*** |
-0.00150*** |
-0.00143*** |
|
|
(0.000285) |
(0.000276) |
(0.000285) |
||
|
Population _d |
0.147*** |
0.152*** |
0.153*** |
|
|
(0.00585) |
(0.00635) |
(0.00636) |
||
|
Population _o |
0.271*** |
0.269*** |
0.268*** |
|
|
(0.0624) |
(0.0474) |
(0.0480) |
||
|
Shadowecon_d |
0.00464*** |
0.00433*** |
0.00371** |
|
|
(0.00146) |
(0.00152) |
(0.00156) |
||
|
Shadowecon_o |
-2.76e-05 |
-0.00736*** |
-0.00719*** |
|
|
(0.00245) |
(0.00237) |
(0.00225) |
||
|
Urbanpopulation_d |
0.00111 |
0.000920 |
0.00132 |
|
|
(0.000835) |
(0.000858) |
(0.000842) |
||
|
Urbanpopulation_o |
-0.000942 |
-0.000467 |
-0.000311 |
|
|
(0.00130) |
(0.00134) |
(0.00132) |
||
|
Militaryex _d |
0.00491 |
0.000131 |
-0.000427 |
|
|
(0.00318) |
(0.00296) |
(0.00310) |
||
|
Militaryex _o |
0.00510* |
0.00815*** |
0.00849*** |
|
|
(0.00274) |
(0.00152) |
(0.00156) |
||
|
Governmenexp_d |
0.00639*** |
0.00906*** |
0.0100*** |
|
|
(0.00221) |
(0.00252) |
(0.00252) |
||
|
Governmenexp_o |
-0.00242 |
-0.00100 |
-0.00137 |
|
|
(0.00204) |
(0.00138) |
(0.00140) |
||
|
log_distw |
-0.0746*** |
-0.0758*** |
-0.0734*** |
|
|
(0.0118) |
(0.0119) |
(0.0119) |
||
|
com_border |
0.168*** |
0.178*** |
0.179*** |
|
|
(0.0304) |
(0.0322) |
(0.0325) |
||
|
comlang_off |
0.144*** |
0.156*** |
0.159*** |
|
|
(0.0297) |
(0.0327) |
(0.0330) |
||
|
comcur |
-0.0407** |
-0.0413* |
-0.0461** |
|
|
(0.0204) |
(0.0217) |
(0.0218) |
||
|
comrelig |
0.0569* |
0.0334 |
0.0383 |
|
|
(0.0306) |
(0.0323) |
(0.0325) |
||
|
period |
-0.0242*** |
-0.0122 |
-0.0153 |
|
|
(0.00910) |
(0.00996) |
(0.00962) |
||
|
Constant |
-7.455*** |
-6.007*** |
-5.914*** |
|
|
(1.412) |
(1.204) |
(1.202) |
||
|
Observations |
3,199 |
3,684 |
3,684 |
|
|
R-squared |
0.710 |
0.689 |
0.687 |
|
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
As we can see, the CPI, Control of corruption and Rule of law in the country of destination are important factors for a decision to enter a country for migrants from high-income to high-income countries (Table 7). It has a significant sign on the 5% significance level for CPI_d, Control of Corr_d and Rule of law_d. The values of the CPI, Control of corruption and Rule of law in the country of destination increased in comparison with the migration from all to rich countries meanings (Table 6).
Now we can see that the increase of the CPI in the country of destination by 1 unit leads to the increase of the number of migrants by 3,20%, the increase of Control of corruption in the country of destination by 1 unit results in the increase of migrants by 6,18%, while the increase of the Rule of law in the country of destination by 1 unit results in the 5,80% increase of migrants (Table 7). As for the CPI, Control of Corruption and Rule of law in the country of origin, it is still out of importance for migrants. It should be also mentioned that the Shadow economy in the country of destination for the migrants from high-income to high-income countries (Table 7) became smaller and less significant than for migrants from all to all countries (Table 4) and from all to high-income countries (Table 6) because high-income people are probably less interested in illegal work.
From low- and medium- income to rich countries
Now we consider the migration from low- and medium-income countries to high-income countries (Table 8).
Table 8. The effect of corruption on migration from low- and medium-income to high-income countries; PPML regression
|
(I) |
(II) |
(III) |
||
|
VARIABLES |
1 |
2 |
3 |
|
|
CPI_d |
0.0207 |
|||
|
(0.00618) |
||||
|
CPI_o |
0.0156 |
|||
|
(0.00561) |
||||
|
ControlofCorr _d |
0.0153 |
|||
|
(0.0153) |
||||
|
ControlofCorr _o |
-0.00702 |
|||
|
(0.0128) |
||||
|
RuleofLaw_d |
-0.00962 |
|||
|
(0.0218) |
||||
|
RuleofLaw_o |
0.00208 |
|||
|
(0.0140) |
||||
|
GDPpcPPP_d |
0.328*** |
0.241*** |
0.245*** |
|
|
(0.0511) |
(0.0591) |
(0.0579) |
||
|
GDPpcPPP_o |
-0.0158 |
0.00360 |
0.00356 |
|
|
(0.0234) |
(0.0225) |
(0.0225) |
||
|
Unemployment_d |
-0.00586*** |
-0.0110*** |
-0.0115*** |
|
|
(0.00212) |
(0.00287) |
(0.00288) |
||
|
Unemployment_o |
6.44e-05 |
0.00186* |
0.00192* |
|
|
(0.00125) |
(0.00106) |
(0.00107) |
||
|
Deathrate_d |
-0.0337*** |
-0.0466*** |
-0.0475*** |
|
|
(0.00752) |
(0.00894) |
(0.00899) |
||
|
Deathrate_o |
-0.0101*** |
-0.0109*** |
-0.0112*** |
|
|
(0.00295) |
(0.00283) |
(0.00286) |
||
|
Schooltertiary _d |
0.000558 |
0.00153*** |
0.00174*** |
|
|
(0.000356) |
(0.000362) |
(0.000370) |
||
|
Schooltertiary _o |
-0.000163 |
0.000394 |
0.000429 |
|
|
(0.000488) |
(0.000459) |
(0.000460) |
||
|
Population _d |
0.179*** |
0.191*** |
0.190*** |
|
|
(0.00703) |
(0.00724) |
(0.00722) |
||
|
Population _o |
-0.114** |
-0.0373 |
-0.0379 |
|
|
(0.0560) |
(0.0484) |
(0.0483) |
||
|
Shadowecon_d |
0.00786*** |
0.00556*** |
0.00432** |
|
|
(0.00175) |
(0.00178) |
(0.00172) |
||
|
Shadowecon_o |
-0.00307** |
-0.00189* |
-0.00178 |
|
|
(0.00124) |
(0.00111) |
(0.00113) |
||
|
Urbanpopulation_d |
0.00129 |
0.00187** |
0.00229*** |
|
|
(0.000835) |
(0.000892) |
(0.000839) |
||
|
Urbanpopulation_o |
0.00128 |
0.000525 |
0.000458 |
|
|
(0.00141) |
(0.00126) |
(0.00125) |
||
|
Militaryex _d |
-0.00169 |
-0.00849** |
-0.0101*** |
|
|
(0.00321) |
(0.00342) |
(0.00366) |
||
|
Militaryex _o |
0.000365 |
0.000252 |
0.000259 |
|
|
(0.000606) |
(0.000607) |
(0.000612) |
||
|
Governmenexp d |
0.0133*** |
0.0177*** |
0.0181*** |
|
|
(0.00302) |
(0.00378) |
(0.00378) |
||
|
Governmenexp_o |
0.00111** |
0.00121** |
0.00124** |
|
|
(0.000500) |
(0.000483) |
(0.000483) |
||
|
log_distw |
-0.250*** |
-0.264*** |
-0.261*** |
|
|
(0.0202) |
(0.0196) |
(0.0196) |
||
|
com_border |
0.304*** |
0.230*** |
0.233*** |
|
|
(0.0800) |
(0.0772) |
(0.0764) |
||
|
comlang_off |
0.303*** |
0.309*** |
0.310*** |
|
|
(0.0207) |
(0.0211) |
(0.0211) |
||
|
comrelig |
0.0412 |
0.0350 |
0.0262 |
|
|
(0.0359) |
(0.0397) |
(0.0399) |
||
|
period |
-0.0344*** |
-0.0341*** |
-0.0388*** |
|
|
(0.0108) |
(0.0117) |
(0.0112) |
||
|
Constant |
0.450 |
-1.557 |
-1.570 |
|
|
(1.330) |
(1.006) |
(0.999) |
||
|
Observations |
4,537 |
5,357 |
5,357 |
|
|
R-squared |
0.700 |
0.687 |
0.687 |
|
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
The next step of our analysis is the determination of the CPI, Control of Corruption and Rule of law effect on the migration from low- and medium- income to high-income countries (Table 8).
As we can see, the effect of Control of Corr_d and Rule of law_d has disappeared. The effect of CPI_d became much less significant than for the regression with migration from all to high-income countries (Table 6). Now we can see that migrants from low- and medium- to rich countries definitely migrate not in order to find a less-corrupt government but in order to gain more money, working places, government help and other factors. So, our hypothesis was proved. We can conclude that the existence of the corruption impact in the country of destination on migration to high-income countries is carried out by the migrants from high- income countries.
The significance and the meaning of Unemployment both in the country of origin and in the country of destination has increased in comparison with the previous regressions for all and high-income migrants (Table 4, Table 7).
It should be mentioned that the effect of the Shadow economy in the country of destination in the case of migrants from low- and medium- income to rich countries (Table 8) is bigger than for the migrants from high-income countries (Table 7) both for the regressions with CPI, Control of Corruption and Rule of law. The increase of the Shadow economy in the country of destination by 1 unit results in the increase of the migrants from high-income to high-income countries by 0,464% for CPI regression, by 0,433% for the regression with Control of corruption and by 0,371% for the Rule of law regression while for migrants from medium and low-income to high-income countries the increase of the index of Shadow economy in the country of destination by 1 unit results in the increase of the number of migrants by 0,786% for the regression with CPI, by 0,556% for the regression with Control of Corruption index and by 0,432% for the regression with Rule of law. The smaller effect for high-income countries of origin can be explained by the fact that the migrants from rich countries would rather migrate to work in official spheres, so there are less reasons to migrate from developed government with good standards of living and do something illegal.
2.5 Robustness check
The next step was the robustness check in order to ensure that our results are stable and the dependence between political variables and the number of migrants would stay after we add some new regressors or replace some old variables.
Table 9. The robustness check for the CPI; PPML regression
|
(I) |
(II) |
(III) |
(IV) |
||
|
VARIABLES |
1 |
2 |
3 |
4 |
|
|
CPI_d |
0.0537*** |
0.0539*** |
0.0492*** |
0.0511*** |
|
|
(0.00371) |
(0.00380) |
(0.00366) |
(0.00422) |
||
|
CPI_o |
0.00614 |
0.00304 |
0.00175 |
0.00639 |
|
|
(0.00357) |
(0.00383) |
(0.00273) |
(0.00543) |
||
|
GDPpcPPP_d |
0.110*** |
0.113*** |
0.104*** |
0.0979*** |
|
|
(0.0141) |
(0.0142) |
(0.0127) |
(0.0144) |
||
|
GDPpcPPP_o |
0.0158 |
0.0197 |
0.0216 |
0.0166 |
|
|
(0.0191) |
(0.0197) |
(0.0138) |
(0.0265) |
||
|
Unemployment_d |
0.000438 |
0.00113 |
0.000512 |
-0.000307 |
|
|
(0.000861) |
(0.000857) |
(0.000859) |
(0.00108) |
||
|
Unemployment_o |
0.00170** |
0.00142* |
0.000893* |
-0.00122 |
|
|
(0.000706) |
(0.000771) |
(0.000530) |
(0.000962) |
||
|
Deathrate_d |
-0.00827*** |
-0.00887*** |
-0.00949*** |
-0.00931*** |
|
|
(0.00232) |
(0.00240) |
(0.00228) |
(0.00260) |
||
|
Deathrate_o |
-0.00350 |
0.000607 |
-0.00163 |
0.00674** |
|
|
(0.00245) |
(0.00262) |
(0.00190) |
(0.00338) |
||
|
Schooltertiary _d |
-0.000881*** |
-0.000988*** |
|||
|
(0.000292) |
(0.000295) |
||||
|
Schooltertiary _o |
-0.000694*** |
-0.000764*** |
|||
|
(0.000250) |
(0.000255) |
||||
|
Compulsoryeduc _d |
-0.0132*** |
-0.0149*** |
|||
|
(0.00243) |
(0.00286) |
||||
|
Compulsoryeduc _o |
0.00149 |
0.00127 |
|||
|
(0.00120) |
(0.00247) |
||||
|
Population _d |
0.141*** |
0.142*** |
0.140*** |
0.135*** |
|
|
(0.00398) |
(0.00394) |
(0.00370) |
(0.00406) |
||
|
Population _o |
-0.000173 |
0.0293 |
0.0689** |
0.109** |
|
|
(0.0313) |
(0.0338) |
(0.0268) |
(0.0486) |
||
|
Shadowecon_d |
0.00497*** |
0.00533*** |
0.00441*** |
0.00417*** |
|
|
(0.000706) |
(0.000713) |
(0.000664) |
(0.000720) |
||
|
Shadowecon_o |
-0.00225** |
-0.00210** |
-0.00198*** |
0.00119 |
|
|
(0.000941) |
(0.000965) |
(0.000654) |
(0.00124) |
||
|
Urbanpopulation_d |
-0.000535 |
-0.000627 |
8.45e-06 |
-0.000394 |
|
|
(0.000452) |
(0.000467) |
(0.000438) |
(0.000474) |
||
|
Urbanpopulation_o |
0.00123 |
0.00141 |
0.000699 |
-0.00121 |
|
|
(0.000863) |
(0.000974) |
(0.000703) |
(0.00127) |
||
|
Militaryex _d |
0.00534*** |
0.00542*** |
0.00370*** |
0.00389*** |
|
|
(0.00128) |
(0.00137) |
(0.00124) |
(0.00130) |
||
|
Militaryex _o |
-0.000234 |
-0.000528 |
-0.000536 |
0.00163 |
|
|
(0.000739) |
(0.000926) |
(0.000719) |
(0.00124) |
||
|
Governmenexp_d |
0.00866*** |
0.00895*** |
0.00787*** |
0.00852*** |
|
|
(0.00104) |
(0.00103) |
(0.000968) |
(0.00165) |
||
|
Governmenexp_o |
-0.000465 |
-0.000245 |
0.000140 |
-0.00115 |
|
|
(0.000586) |
(0.000604) |
(0.000351) |
(0.00136) |
||
|
log_distw |
-0.144*** |
-0.149*** |
-0.127*** |
-0.131*** |
|
|
(0.00682) |
(0.00685) |
(0.00680) |
(0.00726) |
||
|
com_border |
0.176*** |
0.179*** |
0.170*** |
0.173*** |
|
|
(0.0228) |
(0.0226) |
(0.0224) |
(0.0238) |
||
|
comlang_off |
0.269*** |
0.266*** |
0.260*** |
0.265*** |
|
|
(0.0163) |
(0.0161) |
(0.0150) |
(0.0160) |
||
|
comcur |
-0.00264 |
0.00173 |
-0.000496 |
-0.00243 |
|
|
(0.0207) |
(0.0208) |
(0.0218) |
(0.0247) |
||
|
comrelig |
0.0566*** |
0.0454** |
0.0778*** |
0.0672*** |
|
|
(0.0208) |
(0.0211) |
(0.0207) |
(0.0245) |
||
|
period |
0.0107** |
0.0101** |
-0.00430 |
0.00887 |
|
|
(0.00476) |
(0.00488) |
(0.00394) |
(0.00803) |
||
|
Economicfreedom_d |
0.000598 |
0.00167*** |
0.00165** |
||
|
(0.000548) |
(0.000512) |
(0.000644) |
|||
|
Economicfreedom_o |
0.000191 |
0.000150 |
-0.000556 |
||
|
(0.000474) |
(0.000321) |
(0.000734) |
|||
|
Int_rate_spread _d |
-5.77e-05 |
||||
|
(0.000307) |
|||||
|
Int_rate_spread _o |
-1.54e-05 |
||||
|
(0.000326) |
|||||
|
Constant |
-1.533*** |
-1.354* |
-2.036*** |
-2.702*** |
|
|
(0.505) |
(0.726) |
(0.572) |
(0.753) |
||
|
Observations |
11,454 |
10,795 |
13,557 |
8,533 |
|
|
R-squared |
0.546 |
0.550 |
0.548 |
0.537 |
|
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
The regression I is our basic regression for the CPI for all countries. For checking the robustness we've either drop of or add some regressors. As can be seen, in the regression II we decided to add the index of Economic freedom. It came out that this regressor does not change the significance of the CPI both in the country of origin and destination. In the regression III we replaced the School enrollment by Compulsory education. As can be seen, the significance of the CPI stays the same. Finally, in the regression IV, we added the Interest rate spread. As it was before, the significance and the sign of CPI and other regressors stays the same. The same results are obtained for the Control of corruption and the Rule of law (A. 4, A. 5).
CONCLUSION
Migration is a process that exists from oldest time. When the borders of the countries became freer in terms of liberty of movements the migration flow has increased even more. While some governments gain from the foreign newcomers, the other countries suffer from the brain drain or outflow of the working power. That is why nowadays the government authorities are considerably concerned with the migration factors determination. One of such factors that can directly impact the migrant decision is the political background. This feature can serve either as a pull or a push factor.
The purpose of this study was to evaluate the effect that the political conditions both in the country of origin and the country of destination have on the number of migrants between these two countries. As we revealed, migrants care about the level of the corruption and rule regulations in the country of destination while the corruptness and rule regulations in the country of origin is out of importance for them. It should be mentioned that there is a bigger effect of a corruption in the country of destination for females than for males. When we considered the effect of migration from all to high-income countries, we observed an existence of the positive effect of CPI, Control of corruption and Rule of law in the country of destination on the number of migrants. Then we found out that this effect is positively significant only for the migrants from high-income countries while for the migrants from low- and medium-income countries the CPI, Control of Corruption and Rule of law effects are not significant both in the country of origin and in the country of destination.
This study is a major asset to the field of migrant's movements across the whole world. The attained findings can be implemented into the studies dedicated to the corruption or politics in general. The government authorities can use the reached conclusions if they are interested in the attraction of the new labor in the person of new migrants or if they are trying to make the native population stay in their home country and increase the total level of production.
REFERENCES
1. Ahmad, N., & Arjumand, S. (2016). Impact of corruption on GDP per capita through international migration: an empirical investigation. Quality & Quantity, 50(4), 1633-1643.
2. Ariu, A., & Squicciarini, P. (2013). The balance of brains: corruption and high skilled migration. Catholic University of Louvain, IRES Discussion Paper, (10).
3. Carling, J., Paasche, E., & Siegel, M. (2015). Finding connections: the nexus between migration and corruption. Migration Information Source.
4. CEPII Gravity Database. (2020). Retrieved from http://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele.asp
5. Cooray, A., & Schneider, F. (2016). Does corruption promote emigration? An empirical examination. Journal of Population Economics, 29(1), 293-310.
6. Dimant, E., Krieger, T., & Meierrieks, D. (2013). The effect of corruption on migration, 1985-2000. Applied Economics Letters, 20(13), 1270-1274.
7. Docquier, F., Lodigiani, E., Rapoport, H., & Schiff, M. (2016). Emigration and democracy. Journal of Development Economics, 120, 209-296.
8. Jain, A. K. (Ed.). (2001). The political economy of corruption (Vol. 2). Routledge.
9. Lapshyna, I. (2014). Corruption as a driver of migration aspirations: The case of Ukraine. Economics & Sociology, 7(4), 113.
10. McKenzie, D. (2007). Paper walls are easier to tear down: Passport costs and legal barriers to emigration. World Development, 35(11), 2026-2039.
11. Medina, L., & Schneider, F. (2018). Shadow economies around the world: what did we learn over the last 20 years?.
12. Metelev, S. E. (2014). Labor migration in Russia as the reflection of macroeconomic trends. Life Science Journal, 11(10), 709.
13. Moore, W. H., & Shellman, S. M. (2004). Fear of persecution: Forced migration, 1952-1995. Journal of Conflict Resolution, 48(5), 723-745.
14. Morano Foadi, S. (2006). Key issues and causes of the Italian brain drain. Innovation, 19(2), 209-223.
15. Poprawe, M. (2015). On the relationship between corruption and migration: empirical evidence from a gravity model of migration. Public Choice, 163(3-4), 337-354.
16. Radnitz, S. (2006). Weighing the political and economic motivations for migration in post-soviet space: the case of Uzbekistan. Europe-Asia Studies, 58(5), 653-677.
17. Ritchey, P. N. (1976). Explanations of migration. Annual review of sociology, 2(1), 363-404.
18. The Global Economy. (2018) Shadow economy. Retrieved from https://www.theglobaleconomy.com/rankings/shadow_economy/
19. Transparency International. (2020). Corruption Perception index. Retrieved from https://www.transparency.org/en/
20. United Nations, Department of Economic and Social Affairs. Population Division. (2019). International Migrant Stock 2019. Retrieved from https://www.un.org/development/desa/en/
21. Verуnico, J. P. A. (2018). How Corruption affects Migration: a Gravity Model Approach.
22. World Bank, World Development Indicators. (2020). Retrieved from https://databank.worldbank.org/source/world-development-indicators
23. Zhang, S. X., & Pineda, S. L. (2008). Corruption as a causal factor in human trafficking. In Organized crime: Culture, markets and policies (pp. 41-55). Springer, New York, NY.
APPENDIX
APPENDIX 1
A. 1. Descriptive statistics
|
N |
Mean |
min |
max |
||
|
migrants |
34900 |
21129.82 |
1 |
1.22e+07 |
|
|
CPI_d |
30758 |
5.842 |
1.2 |
10 |
|
|
CPI_o |
26725 |
4.78 |
1.2 |
10 |
|
|
ControlofCorr _d |
34760 |
.684 |
-1.766 |
2.443 |
|
|
ControlofCorr _o |
34281 |
.172 |
-1.766 |
2.443 |
|
|
RuleofLaw_d |
34765 |
.691 |
-1.905 |
2.063 |
|
|
RuleofLaw_o |
34424 |
.187 |
-1.905 |
2.063 |
|
|
GDPpcPPP_d |
33805 |
21929.05 |
422.978 |
107000 |
|
|
GDPpcPPP_o |
32240 |
15981.29 |
422.978 |
107000 |
|
|
Unemployment_d |
33695 |
8.319 |
.299 |
37.075 |
|
|
Unemployment_o |
31995 |
7.85 |
.299 |
37.075 |
|
|
Deathrate_d |
33940 |
8.811 |
2.327 |
20.204 |
|
|
Deathrate_o |
32310 |
8.748 |
2.327 |
20.204 |
|
|
Schooltertiary _d |
29744 |
48.929 |
.352 |
122.402 |
|
|
Schooltertiary _o |
26702 |
35.741 |
.352 |
122.402 |
|
|
Population_d |
33805 |
4.05e+07 |
75304 |
1.37e+09 |
|
|
Population_o |
32240 |
7.36e+07 |
75304 |
1.37e+09 |
|
|
Shadowecon_d |
31227 |
25.143 |
8.56 |
62.21 |
|
|
Shadowecon_o |
28650 |
29.658 |
8.56 |
69.4 |
|
|
Urbanpopulation _d |
33940 |
68.237 |
7.211 |
100 |
|
|
Urbanpopulation _o |
32310 |
58.344 |
7.211 |
100 |
|
|
Militaryex _d |
29561 |
5.654 |
.566 |
58.203 |
|
|
Militaryex _o |
26938 |
7.568 |
.566 |
58.203 |
|
|
Governmenexp _d |
33309 |
17.537 |
1.166 |
71.754 |
|
|
Governmenexp _o |
30523 |
15.642 |
1.166 |
71.754 |
|
|
distw |
29580 |
6556.12 |
114.637 |
19539.48 |
|
|
com_border |
29580 |
.047 |
0 |
1 |
|
|
comlang off |
29580 |
.171 |
0 |
1 |
|
|
comcur |
29580 |
.046 |
0 |
1 |
|
|
comrelig |
29580 |
.196 |
0 |
.988 |
|
|
period |
34900 |
3 |
1 |
5 |
APPENDIX 2
A. 2. The effect of Control of Corruption on migration from all to all countries for all migrants; RE regression
|
(I) |
(II) |
(III) |
(IV) |
||
|
VARIABLES |
1 |
2 |
3 |
4 |
|
|
ControlofCorr _d |
0.0900*** |
0.0929*** |
0.106*** |
0.106*** |
|
|
(0.00705) |
(0.00734) |
(0.00937) |
(0.00946) |
||
|
ControlofCorr _o |
0.00470 |
0.000601 |
-0.0157* |
-0.0157* |
|
|
(0.00474) |
(0.00681) |
(0.00955) |
(0.00954) |
||
|
GDPpcPPP_d |
0.0720*** |
0.0632*** |
0.0817*** |
0.0813*** |
|
|
(0.00924) |
(0.00965) |
(0.0131) |
(0.0143) |
||
|
GDPpcPPP_o |
0.0264*** |
0.0347*** |
0.0269 |
0.0300 |
|
|
(0.00798) |
(0.0121) |
(0.0181) |
(0.0184) |
||
|
Unemployment_d |
-0.00310*** |
-0.00356*** |
-0.00313*** |
-0.00314*** |
|
|
(0.000728) |
(0.000808) |
(0.000910) |
(0.000913) |
||
|
Unemployment_o |
0.000349 |
0.000768* |
0.00148** |
0.00149** |
|
|
(0.000375) |
(0.000455) |
(0.000656) |
(0.000656) |
||
|
Population _d |
0.134*** |
0.127*** |
0.139*** |
0.139*** |
|
|
(0.00295) |
(0.00335) |
(0.00409) |
(0.00407) |
||
|
Population _o |
0.0845*** |
0.0524** |
0.0321 |
0.0368 |
|
|
(0.0142) |
(0.0225) |
(0.0266) |
(0.0275) |
||
|
log_distw |
-0.167*** |
-0.132*** |
-0.152*** |
-0.152*** |
|
|
(0.00633) |
(0.00650) |
(0.00702) |
(0.00712) |
||
|
com_border |
0.172*** |
0.191*** |
0.174*** |
0.174*** |
|
|
(0.0222) |
(0.0218) |
(0.0228) |
(0.0227) |
||
|
comlang_off |
0.323*** |
0.275*** |
0.284*** |
0.284*** |
|
|
(0.0129) |
(0.0142) |
(0.0167) |
(0.0167) |
||
|
comcur |
0.0255 |
0.0232 |
0.0132 |
0.0132 |
|
|
(0.0233) |
(0.0222) |
(0.0206) |
(0.0208) |
||
|
comrelig |
0.0932*** |
0.0926*** |
0.0572*** |
0.0572*** |
|
|
(0.0204) |
(0.0204) |
(0.0221) |
(0.0221) |
||
|
period |
0.00116 |
0.00410 |
0.0135*** |
0.0135*** |
|
|
(0.00299) |
(0.00390) |
(0.00511) |
(0.00511) |
||
|
Deathrate_d |
-0.00571*** |
-0.00883*** |
-0.00881*** |
||
|
(0.00190) |
(0.00237) |
(0.00244) |
|||
|
Deathrate_o |
-0.00546*** |
-0.00312 |
-0.00307 |
||
|
(0.00178) |
(0.00258) |
(0.00258) |
|||
|
Militaryex _d |
0.00575*** |
0.00521*** |
0.00520*** |
||
|
(0.000957) |
(0.00122) |
(0.00122) |
|||
|
Militaryex _o |
-4.11e-05 |
0.000458 |
0.000440 |
||
|
(0.000536) |
(0.000696) |
(0.000697) |
|||
|
Governmenexp d |
0.00716*** |
0.00941*** |
0.00941*** |
||
|
(0.000966) |
(0.00114) |
(0.00118) |
|||
|
Governmenexp_o |
-0.000557 |
-0.000428 |
-0.000401 |
||
|
(0.000396) |
(0.000520) |
(0.000523) |
|||
|
Schooltertiary _d |
-0.000454 |
-0.000456 |
|||
|
(0.000306) |
(0.000306) |
||||
|
Schooltertiary _o |
-0.000345 |
-0.000347 |
|||
|
(0.000242) |
(0.000242) |
||||
|
Shadowecon_d |
0.00466*** |
0.00466*** |
|||
|
(0.000730) |
(0.000750) |
||||
|
Shadowecon_o |
-0.00216** |
-0.00206** |
|||
|
(0.000891) |
(0.000879) |
||||
|
Urbanpopulation_d |
2.68e-05 |
||||
|
(0.000469) |
|||||
|
Urbanpopulation_o |
-0.000442 |
||||
|
(0.000828) |
|||||
|
Constant |
-1.431*** |
-1.423*** |
-0.728 |
-0.815 |
|
|
(0.245) |
(0.393) |
(0.611) |
(0.620) |
||
|
Observations |
26,743 |
19,500 |
13,316 |
13,316 |
|
|
R-squared |
0.535 |
0.524 |
0.525 |
0.525 |
|
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
APPENDIX 3
A. 3. The effect of the Rule of Law on migration from all to all countries for all migrants; RE regression
|
(I) |
(II) |
(III) |
(IV) |
||
|
VARIABLES |
1 |
2 |
3 |
4 |
|
|
RuleofLaw_d |
0.0813*** |
0.0888*** |
0.110*** |
0.110*** |
|
|
(0.00806) |
(0.00824) |
(0.0114) |
(0.0114) |
||
|
RuleofLaw_o |
-0.00357 |
-0.0104 |
-0.00398 |
-0.00428 |
|
|
(0.00536) |
(0.00744) |
(0.0105) |
(0.0106) |
||
|
GDPpcPPP_d |
0.0849*** |
0.0710*** |
0.0819*** |
0.0722*** |
|
|
(0.00961) |
(0.00999) |
(0.0132) |
(0.0146) |
||
|
GDPpcPPP_o |
0.0260*** |
0.0343*** |
0.0282 |
0.0320* |
|
|
(0.00800) |
(0.0121) |
(0.0183) |
(0.0184) |
||
|
Unemployment_d |
-0.00344*** |
-0.00409*** |
-0.00401*** |
-0.00427*** |
|
|
(0.000742) |
(0.000818) |
(0.000903) |
(0.000904) |
||
|
Unemployment_o |
0.000234 |
0.000607 |
0.00144** |
0.00146** |
|
|
(0.000366) |
(0.000452) |
(0.000656) |
(0.000655) |
||
|
Population _d |
0.131*** |
0.126*** |
0.139*** |
0.140*** |
|
|
(0.00292) |
(0.00334) |
(0.00414) |
(0.00411) |
||
|
Population _o |
0.0886*** |
0.0537** |
0.0404 |
0.0434 |
|
|
(0.0141) |
(0.0227) |
(0.0272) |
(0.0281) |
||
|
log_distw |
-0.164*** |
-0.128*** |
-0.147*** |
-0.148*** |
|
|
(0.00639) |
(0.00657) |
(0.00705) |
(0.00716) |
||
|
com_border |
0.178*** |
0.198*** |
0.181*** |
0.180*** |
|
|
(0.0221) |
(0.0218) |
(0.0229) |
(0.0229) |
||
|
comlang_off |
0.332*** |
0.283*** |
0.292*** |
0.290*** |
|
|
(0.0130) |
(0.0142) |
(0.0168) |
(0.0168) |
||
|
comcur |
0.0261 |
0.0227 |
0.00824 |
0.0102 |
|
|
(0.0234) |
(0.0223) |
(0.0208) |
(0.0209) |
||
|
comrelig |
0.0867*** |
0.0892*** |
0.0593*** |
0.0589*** |
|
|
(0.0205) |
(0.0203) |
(0.0221) |
(0.0220) |
||
|
period |
-0.00433 |
-0.000894 |
0.00887* |
0.00995* |
|
|
(0.00291) |
(0.00381) |
(0.00507) |
(0.00508) |
||
|
Deathrate_d |
-0.00610*** |
-0.00948*** |
-0.00876*** |
||
|
(0.00189) |
(0.00237) |
(0.00244) |
|||
|
Deathrate_o |
-0.00539*** |
-0.00290 |
-0.00307 |
||
|
(0.00179) |
(0.00262) |
(0.00261) |
|||
|
Militaryex _d |
0.00605*** |
0.00604*** |
0.00579*** |
||
|
(0.000943) |
(0.00120) |
(0.00120) |
|||
|
Militaryex _o |
-4.17e-05 |
0.000329 |
0.000311 |
||
|
(0.000541) |
(0.000708) |
(0.000706) |
|||
|
Governmenexp d |
0.00816*** |
0.0104*** |
0.0101*** |
||
|
(0.000948) |
(0.00113) |
(0.00118) |
|||
|
Governmenexp_o |
-0.000624 |
-0.000490 |
-0.000487 |
||
|
(0.000401) |
(0.000542) |
(0.000542) |
|||
|
Schooltertiary _d |
-0.000270 |
-0.000330 |
|||
|
(0.000307) |
(0.000307) |
||||
|
Schooltertiary _o |
-0.000280 |
-0.000274 |
|||
|
(0.000241) |
(0.000242) |
||||
|
Shadowecon_d |
0.00475*** |
0.00466*** |
|||
|
(0.000787) |
(0.000792) |
||||
|
Shadowecon_o |
-0.00170* |
-0.00161* |
|||
|
(0.000911) |
(0.000899) |
||||
|
Urbanpopulation_d |
0.000720 |
||||
|
(0.000466) |
|||||
|
Urbanpopulation_o |
-0.000421 |
||||
|
(0.000834) |
|||||
|
Constant |
-2.217*** |
-1.528*** |
-0.967 |
-0.974 |
|
|
(0.332) |
(0.393) |
(0.621) |
(0.630) |
||
|
Observations |
26,765 |
19,500 |
13,316 |
13,316 |
|
|
R-squared |
0.531 |
0.520 |
0.522 |
0.522 |
|
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
APPENDIX 4
A. 4. The robustness check for the Control of corruption; PPML regression
|
(I) |
(II) |
(III) |
(IV) |
||
|
VARIABLES |
1 |
2 |
3 |
4 |
|
|
ControlofCorr _d |
0.106*** |
0.106*** |
0.0905*** |
0.0987*** |
|
|
(0.00946) |
(0.00958) |
(0.00910) |
(0.0110) |
||
|
ControlofCorr _o |
-0.0157* |
-0.0115 |
-0.00611 |
0.0134 |
|
|
(0.00954) |
(0.00977) |
(0.00687) |
(0.0149) |
||
|
GDPpcPPP_d |
0.0813*** |
0.103*** |
0.0930*** |
0.0854*** |
|
|
(0.0143) |
(0.0140) |
(0.0129) |
(0.0148) |
||
|
GDPpcPPP_o |
0.0300 |
0.00439 |
0.0153 |
0.0302 |
|
|
(0.0184) |
(0.0179) |
(0.0126) |
(0.0250) |
||
|
Unemployment_d |
-0.00314*** |
-0.00143 |
-0.00209** |
-0.00294*** |
|
|
(0.000913) |
(0.000888) |
(0.000869) |
(0.00108) |
||
|
Unemployment_o |
0.00149** |
0.000907 |
0.000588 |
-0.00189** |
|
|
(0.000656) |
(0.000668) |
(0.000476) |
(0.000941) |
||
|
Deathrate_d |
-0.00881*** |
-0.0103*** |
-0.0105*** |
-0.0117*** |
|
|
(0.00244) |
(0.00244) |
(0.00226) |
(0.00263) |
||
|
Deathrate_o |
-0.00307 |
0.000379 |
-0.00113 |
0.00323 |
|
|
(0.00258) |
(0.00248) |
(0.00174) |
(0.00325) |
||
|
Schooltertiary _d |
-0.000456 |
-0.000867*** |
|||
|
(0.000306) |
(0.000303) |
||||
|
Schooltertiary _o |
-0.000347 |
-0.000481** |
|||
|
(0.000242) |
(0.000241) |
||||
|
Compulsoryeduc _d |
-0.0159*** |
-0.0173*** |
|||
|
(0.00253) |
(0.00305) |
||||
|
Compulsoryeduc _o |
0.00139 |
0.00192 |
|||
|
(0.00114) |
(0.00244) |
||||
|
Population _d |
0.139*** |
0.139*** |
0.136*** |
0.131*** |
|
|
(0.00407) |
(0.00391) |
(0.00372) |
(0.00422) |
||
|
Population _o |
0.0368 |
0.0167 |
0.0537** |
0.0451 |
|
|
(0.0275) |
(0.0309) |
(0.0247) |
(0.0461) |
||
|
Shadowecon_d |
0.00466*** |
0.00552*** |
0.00417*** |
0.00390*** |
|
|
(0.000750) |
(0.000735) |
(0.000671) |
(0.000730) |
||
|
Shadowecon_o |
-0.00206** |
-0.00166* |
-0.00173*** |
0.00174 |
|
|
(0.000879) |
(0.000866) |
(0.000569) |
(0.00118) |
||
|
Urbanpopulation_d |
2.68e-05 |
-0.000522 |
0.000469 |
-0.000300 |
|
|
(0.000469) |
(0.000470) |
(0.000442) |
(0.000487) |
||
|
Urbanpopulation_o |
-0.000442 |
0.000600 |
0.000173 |
-0.00255** |
|
|
(0.000828) |
(0.000892) |
(0.000640) |
(0.00123) |
||
|
Militaryex _d |
0.00520*** |
0.00623*** |
0.00392*** |
0.00410*** |
|
|
(0.00122) |
(0.00132) |
(0.00120) |
(0.00128) |
||
|
Militaryex _o |
0.000440 |
-0.000309 |
0.000453 |
0.00223* |
|
|
(0.000697) |
(0.000999) |
(0.000608) |
(0.00124) |
||
|
Governmenexp d |
0.00941*** |
0.00997*** |
0.00905*** |
0.0108*** |
|
|
(0.00118) |
(0.00115) |
(0.00104) |
(0.00168) |
||
|
Governmenexp_o |
-0.000401 |
-0.000573 |
-0.000213 |
-7.26e-05 |
|
|
(0.000523) |
(0.000578) |
(0.000286) |
(0.00135) |
||
|
Economicfreedom_d |
0.00128** |
0.00247*** |
0.00230*** |
||
|
(0.000590) |
(0.000572) |
(0.000739) |
|||
|
Economicfreedom_o |
4.94e-05 |
-0.000225 |
-0.000481 |
||
|
(0.000435) |
(0.000286) |
(0.000718) |
|||
|
log_distw |
-0.152*** |
-0.157*** |
-0.136*** |
-0.137*** |
|
|
(0.00712) |
(0.00705) |
(0.00708) |
(0.00749) |
||
|
com_border |
0.174*** |
0.172*** |
0.160*** |
0.169*** |
|
|
(0.0227) |
(0.0223) |
(0.0224) |
(0.0236) |
||
|
comlang_off |
0.284*** |
0.275*** |
0.261*** |
0.264*** |
|
|
(0.0167) |
(0.0164) |
(0.0155) |
(0.0166) |
||
|
comcur |
0.0132 |
0.0190 |
0.0153 |
0.0139 |
|
|
(0.0208) |
(0.0206) |
(0.0225) |
(0.0248) |
||
|
comrelig |
0.0572*** |
0.0477** |
0.0937*** |
0.0813*** |
|
|
(0.0221) |
(0.0219) |
(0.0219) |
(0.0261) |
||
|
period |
0.0135*** |
0.0188*** |
0.00390 |
0.0113 |
|
|
(0.00511) |
(0.00494) |
(0.00406) |
(0.00792) |
||
|
Int_rate_spread _d |
3.12e-05 |
||||
|
(0.000306) |
|||||
|
Int_rate_spread _o |
-0.000227 |
||||
|
(0.000326) |
|||||
|
Constant |
-0.815 |
-1.205** |
-1.270** |
-0.995 |
|
|
(0.620) |
(0.528) |
(0.537) |
(0.980) |
||
|
Observations |
13,316 |
12,075 |
15,231 |
9,068 |
|
|
R-squared |
0.525 |
0.537 |
0.534 |
0.523 |
|
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
APPENDIX 5
A. 5. The robustness check for the Rule of law; PPML regression
|
(I) |
(II) |
(III) |
(IV) |
||
|
VARIABLES |
1 |
2 |
3 |
4 |
|
|
RuleofLaw_d |
0.110*** |
0.110*** |
0.0868*** |
0.0927*** |
|
|
(0.0114) |
(0.0116) |
(0.0109) |
(0.0138) |
||
|
RuleofLaw_o |
-0.00428 |
0.00405 |
-0.000424 |
-0.00685 |
|
|
(0.0106) |
(0.0108) |
(0.00776) |
(0.0158) |
||
|
GDPpcPPP_d |
0.0722*** |
0.0967*** |
0.0921*** |
0.0866*** |
|
|
(0.0146) |
(0.0142) |
(0.0133) |
(0.0154) |
||
|
GDPpcPPP_o |
0.0320* |
0.00494 |
0.0149 |
0.0337 |
|
|
(0.0184) |
(0.0179) |
(0.0126) |
(0.0249) |
||
|
Unemployment_d |
-0.00427*** |
-0.00246*** |
-0.00269*** |
-0.00334*** |
|
|
(0.000904) |
(0.000877) |
(0.000876) |
(0.00109) |
||
|
Unemployment_o |
0.00146** |
0.000960 |
0.000549 |
-0.00208** |
|
|
(0.000655) |
(0.000670) |
(0.000472) |
(0.000922) |
||
|
Deathrate_d |
-0.00876*** |
-0.0104*** |
-0.0103*** |
-0.0119*** |
|
|
(0.00244) |
(0.00244) |
(0.00225) |
(0.00264) |
||
|
Deathrate_o |
-0.00307 |
0.000679 |
-0.00111 |
0.00379 |
|
|
(0.00261) |
(0.00252) |
(0.00175) |
(0.00325) |
||
|
Schooltertiary _d |
-0.000330 |
-0.000781** |
|||
|
(0.000307) |
(0.000304) |
||||
|
Schooltertiary _o |
-0.000274 |
-0.000400* |
|||
|
(0.000242) |
(0.000241) |
||||
|
Compulsoryeduc _d |
-0.0159*** |
-0.0176*** |
|||
|
(0.00256) |
(0.00304) |
||||
|
Compulsoryeduc _o |
0.00113 |
0.00167 |
|||
|
(0.00114) |
(0.00248) |
||||
|
Population _d |
0.140*** |
0.141*** |
0.138*** |
0.133*** |
|
|
(0.00411) |
(0.00396) |
(0.00376) |
(0.00432) |
||
|
Population _o |
0.0434 |
0.0232 |
0.0550** |
0.0522 |
|
|
(0.0281) |
(0.0315) |
(0.0250) |
(0.0459) |
||
|
Shadowecon_d |
0.00466*** |
0.00576*** |
0.00416*** |
0.00388*** |
|
|
(0.000792) |
(0.000777) |
(0.000697) |
(0.000772) |
||
|
Shadowecon_o |
-0.00161* |
-0.00125 |
-0.00147*** |
0.00181 |
|
|
(0.000899) |
(0.000877) |
(0.000570) |
(0.00116) |
||
|
Urbanpopulation_d |
0.000720 |
7.38e-05 |
0.00103** |
0.000233 |
|
|
(0.000466) |
(0.000471) |
(0.000442) |
(0.000492) |
||
|
Urbanpopulation_o |
-0.000421 |
0.000674 |
0.000233 |
-0.00238** |
|
|
(0.000834) |
(0.000897) |
(0.000640) |
(0.00121) |
||
|
Militaryex _d |
0.00579*** |
0.00707*** |
0.00450*** |
0.00469*** |
|
|
(0.00120) |
(0.00132) |
(0.00120) |
(0.00129) |
||
|
Militaryex _o |
0.000311 |
-0.000406 |
0.000375 |
0.00228* |
|
|
(0.000706) |
(0.000992) |
(0.000606) |
(0.00123) |
||
|
Governmenexp d |
0.0101*** |
0.0108*** |
0.00979*** |
0.0116*** |
|
|
(0.00118) |
(0.00116) |
(0.00103) |
(0.00169) |
||
|
Governmenexp_o |
-0.000487 |
-0.000618 |
-0.000249 |
0.000105 |
|
|
(0.000542) |
(0.000599) |
(0.000287) |
(0.00135) |
||
|
log_distw |
-0.148*** |
-0.153*** |
-0.133*** |
-0.133*** |
|
|
(0.00716) |
(0.00709) |
(0.00711) |
(0.00752) |
||
|
com_border |
0.180*** |
0.179*** |
0.166*** |
0.175*** |
|
|
(0.0229) |
(0.0225) |
(0.0226) |
(0.0237) |
||
|
comlang_off |
0.290*** |
0.281*** |
0.267*** |
0.272*** |
|
|
(0.0168) |
(0.0165) |
(0.0155) |
(0.0166) |
||
|
comcur |
0.0102 |
0.0160 |
0.0148 |
0.0163 |
|
|
(0.0209) |
(0.0208) |
(0.0226) |
(0.0249) |
||
|
comrelig |
0.0589*** |
0.0480** |
0.0919*** |
0.0809*** |
|
|
(0.0220) |
(0.0218) |
(0.0217) |
(0.0259) |
||
|
period |
0.00995* |
0.0151*** |
0.000227 |
0.00199 |
|
|
(0.00508) |
(0.00486) |
(0.00397) |
(0.00770) |
||
|
Economicfreedom_d |
0.00161*** |
0.00301*** |
0.00286*** |
||
|
(0.000587) |
(0.000572) |
(0.000735) |
|||
|
Economicfreedom_o |
-1.20e-05 |
-0.000250 |
-0.000302 |
||
|
(0.000439) |
(0.000288) |
(0.000701) |
|||
|
Int_rate_spread _d |
0.000118 |
||||
|
(0.000308) |
|||||
|
Int_rate_spread _o |
-0.000285 |
||||
|
(0.000327) |
|||||
|
Constant |
-0.974 |
-1.386*** |
-1.411*** |
-1.535 |
|
|
(0.630) |
(0.534) |
(0.540) |
(1.045) |
||
|
Observations |
13,316 |
12,075 |
15,231 |
9,068 |
|
|
R-squared |
0.522 |
0.535 |
0.532 |
0.520 |
|
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
APPENDIX 6
A. 6. VIF for the regression with CPI
APPENDIX 7
A. 7. VIF for the regression with Control of Corruption
APPENDIX 8
A. 8. VIF for the regression with Rule of law
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