1 Analyse 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.

2 Basic regressions

The first regression uses the same specification as the one that you sent me.

The second regression adds in a few variables (log deaths as of end of 2020, ATM penetration, and social registry coverage) which seemed useful in the last set of regressions.

The third regression adds in COVID-19 spending but instruments for this variable using log deaths and change in real GDP per capita.

Overall, it seems like log deaths might be a useful addition to the regression but that the other variables (and the IV spec) don’t add much predictive value.

Dependent variable:
Coverage
OLS instrumental
variable
(1) (2) (3)
Pre-CovCov 0.643*** 0.621*** 0.333
(0.131) (0.136) (0.314)
rGDPg2020 -0.008* -0.008*
(0.004) (0.004)
digIDcov 0.069 0.021 -0.059
(0.073) (0.080) (0.105)
Digital registration 0.235*** 0.213*** 0.122
(0.052) (0.056) (0.102)
FSI -0.002 0.005 -0.022
(0.015) (0.016) (0.021)
EAP dummy 0.177*** 0.280*** -0.001
(0.063) (0.073) (0.120)
log_deaths 0.038**
(0.015)
soc_reg_coverage -0.001 -0.0004
(0.001) (0.001)
i_ATMs_pop -0.00001 0.0001
(0.001) (0.001)
Cov spend 28.744
(18.621)
Constant 0.017 -0.127 0.223
(0.146) (0.165) (0.196)
Observations 82 76 67
R2 0.693 0.739 0.679
Adjusted R2 0.669 0.703 0.635
Residual Std. Error 0.182 (df = 75) 0.171 (df = 66) 0.189 (df = 58)
F Statistic 28.227*** (df = 6; 75) 20.762*** (df = 9; 66)
Note: p<0.1; p<0.05; p<0.01

3 Analysis of fit

The first figure below shows predicted versus actual coverage. I wasn’t able to see any pattern in the outliers but I could be missing something. The next few graphs are partial adjustment graphs which are sometimes useful for checking if there are any important non-linearities that we should be taking into account. (There don’t seem to be any.)