I am an economics grad student and I am in the process of writing a paper disproving using the Gini coefficient as a solitary measure of income inequality in migration determinants analysis.
I have taken Gini and migration rates from different countries for two time periods and ran a mixed model where *random effects was Gini* and *AbroadIncome*, and *fixed effect was whether the country was 'Rich_Poor_Indicator'*.
The idea was that Gini coefficient is **not** a sufficient measure of income inequality as a determinant for migration (I have done lit. review prior), so I expected some garbage results, but I feel like my model is too bizarre.
Formula here:
> *Migration_it= β_o+α_i Indicator+Z_it Gini+I_it AbroadIncome+ ε_it*
Here is my ANOVA:
<pre> <table style="text-align:center"><caption><strong>Regression Results</strong></caption>
<tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
<tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
<tr><td style="text-align:left"></td><td>Migration</td></tr>
<tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr>
<tr><td style="text-align:left">Rich_Poor_IndicatorMid</td>. <td>206,974.300</td>.</tr>
<tr><td style="text-align:left">.</td><td>(292,230.400)</td></tr>
<tr><td style="text-align:left"></td><td></td></tr>
<tr><td style="text-align:left">Rich_Poor_IndicatorHigh</td>. <td>188,436.900</td></tr>
<tr><td style="text-align:left"></td><td>(359,936.000)</td></tr>
<tr><td style="text-align:left"></td><td></td></tr>
<tr><td style="text-align:left">AbroadIncome</td>. <td>4.978</td></tr>
<tr><td style="text-align:left"></td><td>(59.609)</td></tr>
<tr><td style="text-align:left"></td><td></td></tr>
<tr><td style="text-align:left">Constant</td>. <td>1,203,225.000<sup>***</sup></td></tr>
<tr><td style="text-align:left"></td><td>(308,437.300)</td></tr>
<tr><td style="text-align:left"></td><td></td></tr>
<tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>153</td></tr>
<tr><td style="text-align:left">Log Likelihood</td>. <td>-2,327.963</td></tr>
<tr><td style="text-align:left">Akaike Inf. Crit.</td>. <td>4,677.926</td></tr>
<tr><td style="text-align:left">Bayesian Inf. Crit.</td>. <td>4,711.261</td></tr>
<tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
</table> </pre>
But, my CIs and Random Effects per each country are more comprehensible! But Gini does not show up in Random Effects...
CIs
~~~ confint.merMod(mix_model, method = "boot")
Computing bootstrap confidence intervals ...
383 message(s): boundary (singular) fit: see ?isSingular
252 warning(s): Model failed to converge with max|grad| = 0.264562 (tol = 0.002, component 1) (and others)
2.5 % 97.5 %
.sig01 1.256697e+01 2.401887e+06
.sig02 -9.928984e-01 6.604565e-01
.sig03 4.208632e+04 9.651804e+04
.sig04 7.887378e+01 1.627314e+06
.sig05 -1.000000e+00 1.000000e+00
.sig06 1.215940e+00 4.086320e+02
.sigma 3.244136e+05 4.811134e+05
(Intercept) 6.679768e+05 1.873740e+06
Rich_Poor_IndicatorMid -3.883818e+05 7.826973e+05
Rich_Poor_IndicatorHigh -5.643505e+05 7.930619e+05
AbroadIncome -1.591774e+02 1.744238e+02
~~~
Random Effects
~~~
ranef(mix_model)$Country.Name
(Intercept) Gini (Intercept) AbroadIncome
Algeria -7446.2412 4451.2294 8980.157 0.6670574
Argentina 43191.8751 -18340.8448 -25998.904 -1.9312313
Armenia 38682.2221 -18581.0017 -29973.432 -2.2264643
Australia 39926.3675 -29014.5905 -65750.935 -4.8840623
Austria 34477.4962 -26956.5430 -65238.233 -4.8459781
Bangladesh -125521.7158 111794.9842 291254.949 21.6347845
Belarus -95005.4210 26977.6087 6192.779 0.4600074
Belgium 34126.4423 -33087.1346 -94934.288 -7.0518385
Belize 60286.3676 -23623.0023 -27433.462 -2.0377921
Benin 39787.0007 -21281.5020 -39571.309 -2.9394067
Bolivia 46788.4767 -18622.8722 -22439.365 -1.6668243
Botswana 58262.5126 -22310.0691 -24148.621 -1.7937900
Brazil 51117.2733 -13322.0661 2669.639 0.1983042
Bulgaria 27963.1360 -17092.4417 -35819.130 -2.6606901
Burkina Faso 55388.0338 -2956.0938 43594.547 3.2382579
Burundi 31971.0802 -26061.9075 -65023.664 -4.8300397
Cameroon 50944.1843 -24683.1290 -40962.994 -3.0427828
Canada 887.1362 -5137.1674 -15053.915 -1.1182238
Chad 50077.7077 -25918.0053 -45854.910 -3.4061605
~~~
Sorry for the messy formatting, I am still learning how to use this platform! Any help is appreciated! Thanks in advance!