load("D:/THESIS/Code/Data_Pilot (1).RData")library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondata_latin <- subset(data_country, region == "Latin America & Caribbean")data_rates <- data_latin %>%
mutate(
venezu_rate = (imm_stock_VEN / population) * 100,
venezu_rate_female = (imm_stock_fem_VEN / population) * 100,
venezu_rate_male = (imm_stock_male_VEN / population) * 100
)model1 <- lm(unemp ~ venezu_rate + gdp_percap_const_ppp + lf_part_1564, data = data_rates)
summary(model1)##
## Call:
## lm(formula = unemp ~ venezu_rate + gdp_percap_const_ppp + lf_part_1564,
## data = data_rates)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.651 -3.282 -1.085 2.714 12.406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.916e+00 4.354e+00 0.899 0.3701
## venezu_rate 1.132e+00 6.518e-01 1.736 0.0850 .
## gdp_percap_const_ppp 9.958e-05 4.549e-05 2.189 0.0304 *
## lf_part_1564 4.512e-02 6.536e-02 0.690 0.4912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.454 on 128 degrees of freedom
## (2514 observations deleted due to missingness)
## Multiple R-squared: 0.06809, Adjusted R-squared: 0.04625
## F-statistic: 3.117 on 3 and 128 DF, p-value: 0.02849model2 <- lm(unemp_fem ~ venezu_rate_female + gdp_percap_const_ppp + lf_part_1564_fem, data = data_rates)
summary(model2)##
## Call:
## lm(formula = unemp_fem ~ venezu_rate_female + gdp_percap_const_ppp +
## lf_part_1564_fem, data = data_rates)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.2322 -4.1437 -0.5702 3.6281 14.8228
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.271e+00 2.530e+00 0.502 0.61624
## venezu_rate_female 2.795e+00 1.474e+00 1.897 0.06010 .
## gdp_percap_const_ppp 2.280e-05 5.469e-05 0.417 0.67741
## lf_part_1564_fem 1.544e-01 4.853e-02 3.182 0.00183 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.228 on 128 degrees of freedom
## (2514 observations deleted due to missingness)
## Multiple R-squared: 0.1081, Adjusted R-squared: 0.08717
## F-statistic: 5.17 on 3 and 128 DF, p-value: 0.002095model3 <- lm(unemp_male ~ venezu_rate_male + gdp_percap_const_ppp + lf_part_1564_male, data = data_rates)
summary(model3)##
## Call:
## lm(formula = unemp_male ~ venezu_rate_male + gdp_percap_const_ppp +
## lf_part_1564_male, data = data_rates)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.579 -2.614 -1.008 2.378 11.749
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.942e+01 5.105e+00 5.762 5.87e-08 ***
## venezu_rate_male 7.969e-01 1.125e+00 0.708 0.4801
## gdp_percap_const_ppp 7.601e-05 3.923e-05 1.937 0.0549 .
## lf_part_1564_male -2.879e-01 6.132e-02 -4.695 6.75e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.706 on 128 degrees of freedom
## (2514 observations deleted due to missingness)
## Multiple R-squared: 0.2241, Adjusted R-squared: 0.206
## F-statistic: 12.33 on 3 and 128 DF, p-value: 3.894e-07model4 <- lm(vulnerable_emp ~ venezu_rate + gdp_percap_const_ppp + unemp + lf_part_1564 + poverty_count_215, data = data_rates)
summary(model4)##
## Call:
## lm(formula = vulnerable_emp ~ venezu_rate + gdp_percap_const_ppp +
## unemp + lf_part_1564 + poverty_count_215, data = data_rates)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.2892 -4.2719 -0.8646 3.7223 20.0777
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0961443 13.0268144 0.314 0.754210
## venezu_rate 5.1974135 1.3241335 3.925 0.000215 ***
## gdp_percap_const_ppp -0.0012264 0.0001997 -6.141 5.8e-08 ***
## unemp -0.8135423 0.2208900 -3.683 0.000476 ***
## lf_part_1564 0.7752711 0.1889456 4.103 0.000118 ***
## poverty_count_215 0.1264476 0.1718127 0.736 0.464443
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.065 on 64 degrees of freedom
## (2576 observations deleted due to missingness)
## Multiple R-squared: 0.6607, Adjusted R-squared: 0.6342
## F-statistic: 24.92 on 5 and 64 DF, p-value: 7.553e-14library(plm)##
## Attaching package: 'plm'## The following objects are masked from 'package:dplyr':
##
## between, lag, leaddata <- data_rates %>%
rename(id = country, time = year)data <- pdata.frame(data, index = c("id", "time"))model1 <- plm(unemp ~ venezu_rate + gdp_percap_const_ppp + lf_part_1564, data = data, model = "within")
summary(model1)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = unemp ~ venezu_rate + gdp_percap_const_ppp + lf_part_1564,
## data = data, model = "within")
##
## Balanced Panel: n = 22, T = 6, N = 132
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -5.48964 -1.36073 -0.24996 1.05352 8.50085
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## venezu_rate 1.9876e+00 4.5504e-01 4.3681 2.909e-05 ***
## gdp_percap_const_ppp -3.1725e-04 8.7553e-05 -3.6235 0.0004464 ***
## lf_part_1564 1.1782e-01 1.0723e-01 1.0988 0.2743131
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 872.18
## Residual Sum of Squares: 712.93
## R-Squared: 0.18259
## Adj. R-Squared: -0.00075406
## F-statistic: 7.9671 on 3 and 107 DF, p-value: 7.6346e-05model2 <- plm(unemp_fem ~ venezu_rate_female + gdp_percap_const_ppp + lf_part_1564_fem, data = data, model = "within")
summary(model2)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = unemp_fem ~ venezu_rate_female + gdp_percap_const_ppp +
## lf_part_1564_fem, data = data, model = "within")
##
## Balanced Panel: n = 22, T = 6, N = 132
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -7.6027 -1.8107 -0.3199 1.3886 10.4014
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## venezu_rate_female 3.85712371 0.99469549 3.8777 0.0001824 ***
## gdp_percap_const_ppp -0.00052491 0.00012055 -4.3542 3.069e-05 ***
## lf_part_1564_fem 0.23051539 0.08620499 2.6740 0.0086699 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1247.7
## Residual Sum of Squares: 1007
## R-Squared: 0.1929
## Adj. R-Squared: 0.011871
## F-statistic: 8.52458 on 3 and 107 DF, p-value: 3.9691e-05model3 <- plm(unemp_male ~ venezu_rate_male + gdp_percap_const_ppp + lf_part_1564_male, data = data, model = "within")
summary(model3)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = unemp_male ~ venezu_rate_male + gdp_percap_const_ppp +
## lf_part_1564_male, data = data, model = "within")
##
## Balanced Panel: n = 22, T = 6, N = 132
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -4.99346 -1.25787 -0.10091 0.85222 7.61390
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## venezu_rate_male 2.8534e+00 8.3741e-01 3.4074 0.0009251 ***
## gdp_percap_const_ppp -3.0742e-04 7.2934e-05 -4.2150 5.235e-05 ***
## lf_part_1564_male -1.9071e-01 7.9489e-02 -2.3991 0.0181624 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 720.74
## Residual Sum of Squares: 552.12
## R-Squared: 0.23395
## Adj. R-Squared: 0.062124
## F-statistic: 10.8924 on 3 and 107 DF, p-value: 2.6578e-06model4 <- plm(vulnerable_emp ~ venezu_rate + gdp_percap_const_ppp + unemp + lf_part_1564 + poverty_count_215, data = data, model = "within")
summary(model4)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = vulnerable_emp ~ venezu_rate + gdp_percap_const_ppp +
## unemp + lf_part_1564 + poverty_count_215, data = data, model = "within")
##
## Unbalanced Panel: n = 17, T = 1-6, N = 70
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -3.561780 -1.410300 -0.050456 0.971347 7.446958
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## venezu_rate 1.1331e+00 5.9607e-01 1.9009 0.06333 .
## gdp_percap_const_ppp 9.2056e-05 1.6561e-04 0.5559 0.58089
## unemp 1.4050e-01 1.3958e-01 1.0066 0.31917
## lf_part_1564 -1.0093e-01 1.6654e-01 -0.6060 0.54737
## poverty_count_215 1.0743e-01 7.6347e-02 1.4071 0.16583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 363.29
## Residual Sum of Squares: 300.03
## R-Squared: 0.17414
## Adj. R-Squared: -0.18717
## F-statistic: 2.02431 on 5 and 48 DF, p-value: 0.091957model1_robust <- plm(unemp ~ venezu_rate + gdp_percap_const_usd + lf_part_15p, data = data, model = "within")
summary(model1_robust)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = unemp ~ venezu_rate + gdp_percap_const_usd + lf_part_15p,
## data = data, model = "within")
##
## Balanced Panel: n = 23, T = 6, N = 138
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -5.23539 -1.29378 -0.36313 1.09303 8.72463
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## venezu_rate 1.93727427 0.44985983 4.3064 3.574e-05 ***
## gdp_percap_const_usd -0.00053050 0.00013154 -4.0328 0.0001009 ***
## lf_part_15p 0.10069458 0.10380296 0.9701 0.3341093
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 907.6
## Residual Sum of Squares: 730.51
## R-Squared: 0.19512
## Adj. R-Squared: 0.015461
## F-statistic: 9.05049 on 3 and 112 DF, p-value: 2.0438e-05model2_robust <- plm(unemp_fem ~ venezu_rate_female + gdp_percap_const_usd + lf_part_15p_fem, data = data, model = "within")
summary(model2_robust)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = unemp_fem ~ venezu_rate_female + gdp_percap_const_usd +
## lf_part_15p_fem, data = data, model = "within")
##
## Balanced Panel: n = 23, T = 6, N = 138
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -7.05326 -1.64260 -0.61101 1.41986 10.85092
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## venezu_rate_female 3.76170963 0.99829501 3.7681 0.0002641 ***
## gdp_percap_const_usd -0.00082939 0.00018461 -4.4926 1.721e-05 ***
## lf_part_15p_fem 0.20945977 0.09267235 2.2602 0.0257409 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1362.5
## Residual Sum of Squares: 1101.4
## R-Squared: 0.19163
## Adj. R-Squared: 0.011191
## F-statistic: 8.85017 on 3 and 112 DF, p-value: 2.5828e-05model3_robust <- plm(unemp_male ~ venezu_rate_male + gdp_percap_const_usd + lf_part_15p_male, data = data, model = "within")
summary(model3_robust)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = unemp_male ~ venezu_rate_male + gdp_percap_const_usd +
## lf_part_15p_male, data = data, model = "within")
##
## Balanced Panel: n = 23, T = 6, N = 138
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -4.956809 -1.100960 -0.079383 0.728023 7.784892
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## venezu_rate_male 2.80165881 0.82716794 3.3870 0.0009755 ***
## gdp_percap_const_usd -0.00051281 0.00011502 -4.4583 1.972e-05 ***
## lf_part_15p_male -0.13814205 0.07072285 -1.9533 0.0532798 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 733.88
## Residual Sum of Squares: 563.7
## R-Squared: 0.23189
## Adj. R-Squared: 0.060436
## F-statistic: 11.2708 on 3 and 112 DF, p-value: 1.6141e-06model4_robust <- lm(vulnerable_emp ~ venezu_rate + gdp_percap_const_usd + unemp + lf_part_15p + poverty_count_365, data = data_rates)
summary(model4_robust)##
## Call:
## lm(formula = vulnerable_emp ~ venezu_rate + gdp_percap_const_usd +
## unemp + lf_part_15p + poverty_count_365, data = data_rates)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.4203 -3.6634 -0.1731 3.3342 12.1100
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.197e+01 1.023e+01 -1.170 0.2464
## venezu_rate 4.275e+00 1.001e+00 4.271 6.58e-05 ***
## gdp_percap_const_usd -2.117e-03 2.686e-04 -7.882 5.21e-11 ***
## unemp -4.435e-01 1.682e-01 -2.637 0.0105 *
## lf_part_15p 1.070e+00 1.504e-01 7.112 1.18e-09 ***
## poverty_count_365 -1.144e-01 8.630e-02 -1.326 0.1897
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.353 on 64 degrees of freedom
## (2576 observations deleted due to missingness)
## Multiple R-squared: 0.8053, Adjusted R-squared: 0.79
## F-statistic: 52.93 on 5 and 64 DF, p-value: < 2.2e-16| Dataset | Observations | Variables |
|---|---|---|
| data | 2646 | 262 |
| data_country | 13671 | 259 |
| data_latin | 2646 | 259 |
| data_rates | 2646 | 262 |
key_variables <- data %>%
select(
venezu_rate, venezu_rate_female, venezu_rate_male,
gdp_percap_const_ppp, gdp_percap_const_usd,
lf_part_1564, lf_part_15p,
unemp, unemp_fem, unemp_male,
vulnerable_emp, poverty_count_215, poverty_count_365
)
summary(key_variables)## venezu_rate venezu_rate_female venezu_rate_male gdp_percap_const_ppp
## Min. : 0.0000 Min. :0.0000 Min. :0.0000 Min. : 2685
## 1st Qu.: 0.0048 1st Qu.:0.0026 1st Qu.:0.0021 1st Qu.: 8695
## Median : 0.0256 Median :0.0133 Median :0.0120 Median :12898
## Mean : 0.3572 Mean :0.1778 Mean :0.1794 Mean :16163
## 3rd Qu.: 0.0970 3rd Qu.:0.0352 3rd Qu.:0.0448 3rd Qu.:20261
## Max. :15.9497 Max. :7.8632 Max. :8.0865 Max. :84870
## NA's :2436 NA's :2436 NA's :2436 NA's :1491
## gdp_percap_const_usd lf_part_1564 lf_part_15p unemp
## Min. : 1157 Min. :48.50 Min. :40.00 Min. : 1.200
## 1st Qu.: 3183 1st Qu.:62.80 1st Qu.:59.22 1st Qu.: 5.016
## Median : 5629 Median :66.64 Median :62.63 Median : 7.609
## Mean : 8874 Mean :66.48 Mean :62.12 Mean : 8.656
## 3rd Qu.: 9757 3rd Qu.:70.37 3rd Qu.:65.67 3rd Qu.:11.363
## Max. :96280 Max. :82.52 Max. :79.24 Max. :24.540
## NA's :564 NA's :1654 NA's :1654 NA's :1654
## unemp_fem unemp_male vulnerable_emp poverty_count_215
## Min. : 1.200 Min. : 1.200 Min. : 5.367 Min. : 0.000
## 1st Qu.: 5.694 1st Qu.: 4.317 1st Qu.:20.489 1st Qu.: 2.200
## Median : 9.415 Median : 6.375 Median :29.844 Median : 6.050
## Mean :10.222 Mean : 7.635 Mean :31.631 Mean : 8.534
## 3rd Qu.:13.820 3rd Qu.:10.043 3rd Qu.:40.860 3rd Qu.:13.200
## Max. :28.431 Max. :22.622 Max. :76.170 Max. :49.700
## NA's :1654 NA's :1654 NA's :1654 NA's :2182
## poverty_count_365
## Min. : 0.40
## 1st Qu.: 7.60
## Median :15.45
## Mean :18.10
## 3rd Qu.:26.43
## Max. :69.90
## NA's :2182The dataset analysis begins with the data_country dataset, which comprises 13,671 observations and 259 variables. From this dataset, a subset named data_latin is created by filtering the data for the “Latin America & Caribbean” region. This subset includes 2,646 observations and retains 259 variables. The next step involves creating data_rates, which adds three new variables related to Venezuelan immigration rates to the existing 259 variables, resulting in 262 variables. Finally, the data is transformed into a panel data frame named data, maintaining the same number of observations (2,646) and variables (262) to facilitate fixed effects modeling.
To establish an overview, descriptive statistics for key variables were calculated. These variables include Venezuelan immigrant stock (total, female, and male), GDP per capita (constant PPP and constant USD), labor force participation rates (ages 15-64 and 15+), unemployment rates (total, female, and male), vulnerable employment, and poverty headcount ratios at $2.15 and $3.65 a day.
The summary statistics reveal that the average Venezuelan immigration rate is 0.357%, with female and male immigration rates averaging 0.178% and 0.179%, respectively. GDP per capita (constant PPP) has a mean of $11,616.13, while GDP per capita (constant USD) averages $8,874. Labor force participation rates for individuals aged 15-64 average 66.48%, whereas the overall participation rate (ages 15+) averages 62.12%. Unemployment rates show substantial variation, with an average of 8.66% for the total population, 10.22% for females, and 7.39% for males.
Vulnerable employment averages 31.63%, highlighting significant economic insecurity within the region. The poverty headcount ratio at $2.15 a day shows a mean of 8.53%, while at $3.65 a day, it averages 18.10%. These statistics indicate a considerable proportion of the population living in poverty.