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.
Here is the output with the formula:
~~~
Linear mixed model fit by REML ['lmerMod']
Formula: Migration ~ Rich_Poor_Indicator + (1 + Gini | Country.Name) +
AbroadIncome + (AbroadIncome | Country.Name)
Data: final_data
Control: lmerControl(check.nobs.vs.nRE = "ignore")
REML criterion at convergence: 4655.9
Scaled residuals:
Min 1Q Median 3Q Max
-5.7835 -0.1890 -0.0457 0.1119 6.5108
Random effects:
Groups Name Variance Std.Dev. Corr
Country.Name (Intercept) 3.033e+11 550753.08
Gini 3.810e+09 61729.20 -0.42
Country.Name.1 (Intercept) 2.471e+11 497112.89
AbroadIncome 1.364e+03 36.93 1.00
Residual 1.780e+11 421957.35
Number of obs: 153, groups: Country.Name, 89
Fixed effects:
Estimate Std. Error t value
(Intercept) 1.203e+06 3.084e+05 3.901
Rich_Poor_IndicatorMid 2.070e+05 2.922e+05 0.708
Rich_Poor_IndicatorHigh 1.884e+05 3.599e+05 0.524
AbroadIncome 4.978e+00 5.961e+01 0.084
Correlation of Fixed Effects:
(Intr) R_P_IM R_P_IH
Rch_Pr_IndM -0.546
Rch_Pr_IndH -0.652 0.635
AbroadIncom 0.037 0.004 0.077
fit warnings:
Some predictor variables are on very different scales: consider rescaling
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see ?isSingular
~~~
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!