1 Changes from previous version

  • Added correlation matrix of digital ID vars
  • Added fractional regression for coverage

2 Variable descriptions

Variable Description
coverage % pop living in hh reached by COVID-19 cash transfer
spending Total marginal spending on COVID-19 cash transfers as % GDP
spending_pc Marginal COVID-19 cash transfer spending as share of GDP per capita (ratio of spending over coverag)
pre_coverage % pop receiving cash transfers prior to COVID-19
pre_spending Spending on cash transfers as share of GDP prior to COVID
dig_reg Dummy for whether existing databases used for horizontal expansion
fsi Public service delivery component (P2) of Fragile States Index
non_ct_spending Total COVID-19 spending (excluding spending on cash transfers) as share of GDP
dig_id % pop with a digital ID
dig_xcheck Use of digital ID for cross-checking databases
deaths Total COVID-19 deaths per million up to 31-12-2020
region Country region
eap Dummy for region == East Asia and Pacific
atm Number of ATMs per 100,000 adults
soc_reg_coverage % population included in a social registry
pop_2018 Country population in 2018
rGDPg2020 Real GDP growth per capita in 2020
gdp_per_capita GDP per capita, PPP (constant 2011 international $)
log_gdp Log of GDP per capita

3 Correlation matrix of digital ID vars

##              dig_reg    dig_id dig_xcheck
## dig_reg    1.0000000 0.4794406  0.8617094
## dig_id     0.4794406 1.0000000  0.4521009
## dig_xcheck 0.8617094 0.4521009  1.0000000

4 Coverage regressions

In the regressions below the dependent variable is the share of the population in a household that received a COVID-19 cash transfer.

4.1 OLS

Dependent variable:
coverage
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
rGDPg2020 -0.023*** -0.018** -0.017** -0.007 -0.011* -0.011* -0.006 -0.009 -0.008 -0.005
(0.006) (0.007) (0.007) (0.007) (0.006) (0.006) (0.007) (0.006) (0.006) (0.007)
dig_id 0.001 0.002** 0.0001
(0.001) (0.001) (0.001)
eap 0.279*** 0.168* 0.266*** 0.228** 0.276*** 0.234***
(0.091) (0.087) (0.089) (0.092) (0.084) (0.090)
log_gdp 0.175*** 0.103** 0.080
(0.036) (0.046) (0.053)
dig_reg 0.319*** 0.317*** 0.235***
(0.074) (0.063) (0.083)
dig_xcheck 0.317*** 0.338*** 0.256***
(0.071) (0.059) (0.093)
Constant 0.240*** 0.201* 0.088 -1.212*** 0.156*** 0.107*** -0.716** 0.185*** 0.129*** -0.507
(0.033) (0.110) (0.076) (0.270) (0.047) (0.041) (0.342) (0.041) (0.038) (0.401)
Observations 83 66 66 66 67 67 67 66 66 66
R2 0.151 0.133 0.256 0.421 0.351 0.469 0.521 0.368 0.494 0.521
Adjusted R2 0.140 0.106 0.220 0.383 0.330 0.444 0.490 0.348 0.470 0.489
Residual Std. Error 0.293 (df = 81) 0.284 (df = 63) 0.266 (df = 62) 0.236 (df = 61) 0.252 (df = 64) 0.230 (df = 63) 0.220 (df = 62) 0.243 (df = 63) 0.219 (df = 62) 0.215 (df = 61)
F Statistic 14.375*** (df = 1; 81) 4.848** (df = 2; 63) 7.105*** (df = 3; 62) 11.066*** (df = 4; 61) 17.282*** (df = 2; 64) 18.575*** (df = 3; 63) 16.874*** (df = 4; 62) 18.353*** (df = 2; 63) 20.194*** (df = 3; 62) 16.559*** (df = 4; 61)
Note: p<0.1; p<0.05; p<0.01

4.2 Linear regression diagnostics

Plot suggested by Alan for a regression of coverage on rGDPg2020, dig_id, eap, and log_gdp.

4.3 Fractional regression

Dependent variable:
coverage
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
rGDPg2020 -0.108*** -0.103*** -0.100*** -0.036 -0.062** -0.062* -0.030 -0.055* -0.049 -0.029
(0.033) (0.035) (0.035) (0.036) (0.029) (0.032) (0.033) (0.029) (0.031) (0.033)
dig_id 0.014* 0.018*** 0.003
(0.007) (0.006) (0.006)
eap 1.616*** 0.968** 1.533*** 1.280*** 1.726*** 1.354***
(0.444) (0.400) (0.539) (0.485) (0.496) (0.517)
log_gdp 1.069*** 0.688*** 0.591**
(0.216) (0.239) (0.286)
dig_reg 1.992*** 2.010*** 1.453***
(0.410) (0.359) (0.461)
dig_xcheck 1.848*** 2.049*** 1.395***
(0.363) (0.325) (0.515)
Constant -1.178*** -2.271*** -2.866*** -10.669*** -2.183*** -2.466*** -7.988*** -1.935*** -2.309*** -7.003***
(0.194) (0.634) (0.483) (1.628) (0.326) (0.308) (1.762) (0.263) (0.285) (2.140)
Observations 83 82 82 81 83 83 82 82 82 81
Log Likelihood -45.942 -43.362 -38.693 -33.280 -39.249 -35.032 -32.177 -37.614 -32.706 -31.267
Akaike Inf. Crit. 95.884 92.724 85.385 76.560 84.499 78.064 74.354 81.228 73.412 72.534
Note: p<0.1; p<0.05; p<0.01

Regression diagnostics for fractional regresssion.

5 Table 3 - Average benefit regressions

The dependent variable is the average benefit size divided by GDP (calculated by dividing spending as a share of GDP divided by coverage).

Dependent variable:
avg_ben
(1) (2) (3) (4) (5)
rGDPg2020 0.0004 0.0003 -0.0002 -0.0003 -0.0002
(0.001) (0.001) (0.0005) (0.001) (0.0004)
non_ct_spending 0.042 0.043 0.046 0.109
(0.106) (0.097) (0.100) (0.114)
dig_xcheck -0.014*** -0.010* -0.011*
(0.005) (0.006) (0.006)
log_gdp -0.003 -0.003
(0.004) (0.005)
regionECA 0.011
(0.007)
regionLAC -0.0002
(0.006)
regionMENA 0.019**
(0.008)
regionSAR 0.004
(0.008)
regionSSA 0.002
(0.007)
Constant 0.024*** 0.022*** 0.028*** 0.053* 0.050
(0.003) (0.004) (0.005) (0.031) (0.040)
Observations 69 60 60 60 60
R2 0.014 0.006 0.128 0.140 0.227
Adjusted R2 -0.0002 -0.029 0.082 0.077 0.088
Residual Std. Error 0.020 (df = 67) 0.019 (df = 57) 0.018 (df = 56) 0.018 (df = 55) 0.018 (df = 50)
F Statistic 0.984 (df = 1; 67) 0.182 (df = 2; 57) 2.748* (df = 3; 56) 2.230* (df = 4; 55) 1.634 (df = 9; 50)
Note: p<0.1; p<0.05; p<0.01

6 Table 4 - Spending regressions

The dependent variable is spending as a share of GDP.

Dependent variable:
spending
(1) (2) (3) (4) (5)
rGDPg2020 -0.0003** -0.0002 -0.0001 -0.0001 -0.00003
(0.0001) (0.0002) (0.0002) (0.0002) (0.0002)
dig_xcheck 0.004* 0.003 0.003* -0.001
(0.002) (0.002) (0.002) (0.004)
non_ct_spending 0.107 0.081 0.084
(0.077) (0.068) (0.069)
eap 0.005* 0.003
(0.003) (0.003)
log_gdp 0.003*
(0.002)
Constant 0.005*** 0.004*** 0.002 0.002 -0.023*
(0.001) (0.001) (0.002) (0.002) (0.014)
Observations 79 78 69 69 68
R2 0.047 0.092 0.162 0.209 0.261
Adjusted R2 0.035 0.068 0.123 0.159 0.201
Residual Std. Error 0.008 (df = 77) 0.008 (df = 75) 0.008 (df = 65) 0.008 (df = 64) 0.007 (df = 62)
F Statistic 3.805* (df = 1; 77) 3.809** (df = 2; 75) 4.184*** (df = 3; 65) 4.217*** (df = 4; 64) 4.376*** (df = 5; 62)
Note: p<0.1; p<0.05; p<0.01