Analyze new beneficiaries data

Share of population covered vs various vars

Explanation of variables:

• gdp20 - Projected percentage increase in GDP per capita in 2020 in purchasing power parity terms from the IMF
• delta_2020 - Percentage point change in forecasted GDP growth for 2020 from January 2020 to June 2020 from the IMF Global Economic Prospects report.

Regression of share of population receiving ct

Results from linear regression of share of population receiving cash transfers on ease of id, ease of payment, and forecasted GDP growth for 2020.

Call:
lm(formula = share ~ ease_id + ease_pay + gdp20, data = subset(nb_merged, 
    subset = code != "TLS"))

Residuals:
     Min       1Q   Median       3Q      Max 
-0.45764 -0.15058 -0.02855  0.12182  0.62470 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.130879   0.168176   0.778 0.441817    
ease_id     -0.721777   0.369770  -1.952 0.059219 .  
ease_pay     0.981328   0.240113   4.087 0.000252 ***
gdp20       -0.003201   0.012876  -0.249 0.805185    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2343 on 34 degrees of freedom
  (22 observations deleted due to missingness)
Multiple R-squared:  0.3476,    Adjusted R-squared:  0.2901 
F-statistic: 6.039 on 3 and 34 DF,  p-value: 0.002067

Program status / started vs. ease of pay / ID

Coeffs and p-values from regression of started on various terms
Using 3 different definitions for ID penetration
term	estimate	p_value
ifc_i_branches_A1_pop	0.06	0.02
ifc_i_ATMs_pop	0.05	0.00
has_account	5.16	0.00
soc_reg_above_40	5.84	0.98
has_id_manual	1.77	0.02
has_id	1.60	0.07
has_id_id4d	0.06	0.00
soc_ins	5.78	0.00

Estimated impact on financial inclusion