The tables below regress coverage, coverage-transfer, and status as of end 2020 on various variables.

1 Analyze missing data

The tables below show which of the vars that we use in the main analyses still have some missing data. I also show which specific countries we are missing social registry data for because that var is causing us to drop a few countries.

## [1] "Countries for which we are missing social registry data"
## character(0)

2 Graphs for May 3rd

3 New regressions from April 29th

3.1 Coverage / invisible as the dependent variable

Dependent variable:
cov_over_invis
high_vis 2.006***
(0.394)
gdpg_2020e -0.009
(0.014)
soc_reg_new -0.154
(0.284)
Constant 0.023
(0.126)
Observations 85
R2 0.298
Adjusted R2 0.272
Residual Std. Error 0.731 (df = 81)
F Statistic 11.475*** (df = 3; 81)
Note: p<0.1; p<0.05; p<0.01

3.2 Coverage as dependent variable

Dependent variable:
m21_bens_actual
govt_transfer 1.474***
(0.374)
pw_no_t 0.065
(0.453)
has_id_id4d 0.028
(0.172)
soc_reg_new 0.119
(0.109)
log_gdp 0.083
(0.052)
dig_id -0.010
(0.087)
Constant -0.674*
(0.346)
Observations 66
R2 0.458
Adjusted R2 0.403
Residual Std. Error 0.232 (df = 59)
F Statistic 8.317*** (df = 6; 59)
Note: p<0.1; p<0.05; p<0.01

3.3 Coverage as dependent variable and instrumenting for non-health fiscal

Dependent variable:
m21_bens_actual
(1) (2)
govt_transfer 0.848* 0.777
(0.468) (0.492)
pw_no_t -0.383 -0.490
(0.555) (0.599)
i_ATMs_pop 0.004** 0.004*
(0.002) (0.002)
has_id_id4d -0.109 -0.108
(0.177) (0.177)
soc_reg_new 0.279** 0.253**
(0.120) (0.121)
log_gdp 0.050
(0.069)
non_health_fiscal 0.051 0.050
(0.055) (0.052)
gov_effect -0.024 -0.045
(0.072) (0.082)
Constant 0.049 -0.335
(0.198) (0.581)
Observations 63 63
R2 0.436 0.447
Adjusted R2 0.364 0.365
Residual Std. Error 0.239 (df = 55) 0.239 (df = 54)
Note: p<0.1; p<0.05; p<0.01

4 Coverage regressions

Dependent variable:
m21_bens_actual
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
medium_vis 0.053 -0.100 0.066 0.022 0.295 0.292
(0.212) (0.211) (0.208) (0.202) (0.182) (0.179)
high_vis 0.526*** 0.602*** 0.495*** 0.504*** 0.112 0.113
(0.139) (0.138) (0.138) (0.140) (0.164) (0.167)
deaths 0.0003*** 0.0001 0.0002* 0.0001 0.00001 -0.00002
(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)
gdpg_2020e -0.009* -0.001 -0.009* -0.002 -0.001 0.001
(0.005) (0.005) (0.005) (0.005) (0.005) (0.005)
i_ATMs_pop 0.006*** 0.006*** 0.005*** 0.005***
(0.001) (0.001) (0.001) (0.001)
soc_reg_dummy 0.202** 0.273*** 0.165* 0.187** 0.118 0.122 0.120 0.111
(0.092) (0.083) (0.086) (0.079) (0.080) (0.075) (0.081) (0.076)
Constant 0.173*** 0.212*** 0.079* 0.084* 0.160*** 0.155*** 0.076* 0.074* 0.013 0.012 0.054 0.055
(0.048) (0.050) (0.043) (0.045) (0.048) (0.050) (0.042) (0.044) (0.048) (0.049) (0.040) (0.041)
Observations 85 85 85 85 85 85 85 85 85 85 85 85
R2 0.108 0.041 0.240 0.220 0.158 0.155 0.273 0.271 0.396 0.396 0.379 0.379
Adjusted R2 0.086 0.018 0.221 0.201 0.126 0.124 0.246 0.244 0.365 0.365 0.348 0.348
Residual Std. Error 0.284 (df = 82) 0.294 (df = 82) 0.262 (df = 82) 0.265 (df = 82) 0.277 (df = 81) 0.278 (df = 81) 0.258 (df = 81) 0.258 (df = 81) 0.236 (df = 80) 0.236 (df = 80) 0.240 (df = 80) 0.240 (df = 80)
F Statistic 4.948*** (df = 2; 82) 1.765 (df = 2; 82) 12.939*** (df = 2; 82) 11.563*** (df = 2; 82) 5.053*** (df = 3; 81) 4.948*** (df = 3; 81) 10.123*** (df = 3; 81) 10.014*** (df = 3; 81) 13.087*** (df = 4; 80) 13.086*** (df = 4; 80) 12.219*** (df = 4; 80) 12.202*** (df = 4; 80)
Note: p<0.1; p<0.05; p<0.01

5 Coverage - transfer regressions

Dependent variable:
cov_minus_transfer
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
medium_vis -0.154 -0.278 -0.141 -0.169 -0.015 -0.018
(0.179) (0.177) (0.173) (0.168) (0.167) (0.164)
high_vis 0.311** 0.399*** 0.276** 0.290** 0.038 0.041
(0.123) (0.123) (0.120) (0.122) (0.149) (0.152)
deaths 0.0002** 0.0001* 0.0001 0.0001 0.00001 0.00001
(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)
gdpg_2020e -0.005 -0.001 -0.005 -0.001 0.00001 0.0002
(0.005) (0.005) (0.004) (0.004) (0.004) (0.004)
i_ATMs_pop 0.003*** 0.003*** 0.003** 0.003**
(0.001) (0.001) (0.001) (0.001)
soc_reg_dummy 0.203*** 0.243*** 0.185** 0.208*** 0.157** 0.160** 0.157** 0.159**
(0.077) (0.069) (0.075) (0.069) (0.073) (0.069) (0.073) (0.069)
Constant 0.102** 0.139*** 0.018 0.024 0.089** 0.088** 0.015 0.014 0.007 0.009 0.001 0.002
(0.041) (0.042) (0.038) (0.040) (0.040) (0.042) (0.037) (0.038) (0.044) (0.045) (0.036) (0.037)
Observations 85 85 85 85 85 85 85 85 85 85 85 85
R2 0.110 0.051 0.167 0.134 0.182 0.178 0.226 0.222 0.283 0.283 0.284 0.283
Adjusted R2 0.089 0.028 0.147 0.113 0.151 0.148 0.197 0.193 0.247 0.247 0.248 0.248
Residual Std. Error 0.239 (df = 82) 0.247 (df = 82) 0.231 (df = 82) 0.236 (df = 82) 0.231 (df = 81) 0.231 (df = 81) 0.224 (df = 81) 0.225 (df = 81) 0.217 (df = 80) 0.217 (df = 80) 0.217 (df = 80) 0.217 (df = 80)
F Statistic 5.087*** (df = 2; 82) 2.221 (df = 2; 82) 8.248*** (df = 2; 82) 6.371*** (df = 2; 82) 5.988*** (df = 3; 81) 5.854*** (df = 3; 81) 7.862*** (df = 3; 81) 7.690*** (df = 3; 81) 7.895*** (df = 4; 80) 7.891*** (df = 4; 80) 7.914*** (df = 4; 80) 7.911*** (df = 4; 80)
Note: p<0.1; p<0.05; p<0.01

6 Status end 2020 regressions

Dependent variable:
status_binary
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
medium_vis -0.266 -0.399 -0.238 -0.181 0.096 0.165
(0.360) (0.344) (0.346) (0.326) (0.316) (0.308)
high_vis 1.007*** 1.012*** 0.940*** 0.820*** 0.465* 0.388
(0.230) (0.226) (0.224) (0.226) (0.278) (0.282)
deaths 0.0004** 0.0001 0.0002 -0.00002 -0.0001 -0.0001
(0.0001) (0.0001) (0.0002) (0.0001) (0.0002) (0.0001)
gdpg_2020e -0.022** -0.009 -0.021** -0.011 -0.010 -0.008
(0.009) (0.009) (0.008) (0.008) (0.008) (0.008)
i_ATMs_pop 0.008*** 0.007*** 0.006*** 0.005**
(0.002) (0.002) (0.002) (0.002)
soc_reg_dummy 0.427*** 0.488*** 0.361** 0.365*** 0.304** 0.295** 0.306** 0.281**
(0.153) (0.133) (0.140) (0.128) (0.139) (0.130) (0.137) (0.128)
Constant 0.513*** 0.515*** 0.276*** 0.266*** 0.485*** 0.411*** 0.270*** 0.248*** 0.270*** 0.229*** 0.243*** 0.227***
(0.082) (0.081) (0.072) (0.073) (0.079) (0.081) (0.069) (0.070) (0.084) (0.085) (0.067) (0.069)
Observations 85 85 85 85 85 85 85 85 85 85 85 85
R2 0.093 0.095 0.260 0.262 0.173 0.224 0.316 0.329 0.353 0.364 0.374 0.376
Adjusted R2 0.071 0.073 0.242 0.244 0.142 0.195 0.291 0.305 0.320 0.332 0.343 0.345
Residual Std. Error 0.481 (df = 82) 0.480 (df = 82) 0.434 (df = 82) 0.434 (df = 82) 0.462 (df = 81) 0.447 (df = 81) 0.420 (df = 81) 0.416 (df = 81) 0.411 (df = 80) 0.408 (df = 80) 0.404 (df = 80) 0.404 (df = 80)
F Statistic 4.228** (df = 2; 82) 4.312** (df = 2; 82) 14.421*** (df = 2; 82) 14.522*** (df = 2; 82) 5.638*** (df = 3; 81) 7.787*** (df = 3; 81) 12.480*** (df = 3; 81) 13.267*** (df = 3; 81) 10.900*** (df = 4; 80) 11.440*** (df = 4; 80) 11.949*** (df = 4; 80) 12.070*** (df = 4; 80)
Note: p<0.1; p<0.05; p<0.01

7 Regression tree approach

Rather than use a linear model with no interactions, the code below fits the data using a simple regression tree using all of the variables above except for log GDP per capita. The way to read the final output is that the model predicts coverage of 63% for countries with low_vis < 53% and GDP growth higher than -9.5%. The model is kind of interesting on its own since it implies that the most important predictor is the share of adults who are low vis and that the next most important predictors are variables related to need (deaths and GDP growth). The model also gives us a rough sense of how accurate a more complex model which allows interactions between variables and higher-order terms could be. The cross-validated R squared from this model is about .5.

## 
## Regression tree:
## rpart(formula = m21_bens_actual ~ govt_transfer + low_vis + medium_vis + 
##     high_vis + i_ATMs_pop + gdpg_2020e + deaths + soc_reg_dummy, 
##     data = infra, method = "anova", control = list(maxdepth = 2))
## 
## Variables actually used in tree construction:
## [1] deaths     gdpg_2020e low_vis   
## 
## Root node error: 7.5647/89 = 0.084996
## 
## n= 89 
## 
##         CP nsplit rel error  xerror    xstd
## 1 0.357357      0   1.00000 1.02102 0.14777
## 2 0.122924      1   0.64264 0.83608 0.13961
## 3 0.079975      2   0.51972 0.80225 0.13021
## 4 0.010000      3   0.43974 0.73860 0.12998