Identification strategy
| Variable | Coefficient | (t-test) |
|---|---|---|
| Age | 0.000 | (−1.58) |
| Marital_status | 0.000 | (−0.37) |
| Education | −0.005 | (−1.60) |
| Employed | −0.014 | (−1.42) |
| Retired | −0.009 | (−1.54) |
| Gender | 0.005 | (1.05) |
| _cons | 0.190** | (2.91) |
| N | 211096 | |
| R-squared | 0.0014 | |
| F-test | 0.66 |
| Variable | Coefficient | ( |
|---|---|---|
| Age | 0.000 | (−1.58) |
| Marital_status | 0.000 | (−0.37) |
| Education | −0.005 | (−1.60) |
| Employed | −0.014 | (−1.42) |
| Retired | −0.009 | (−1.54) |
| Gender | 0.005 | (1.05) |
| _cons | 0.190** | (2.91) |
| 211096 | ||
| 0.0014 | ||
| | 0.66 |
Source: Instituto Nacional de Estadística (INE). Here we present an exogeneity test of the treatment group. The model used to test this regression is a linear probability one, using the treatment variable as our dependent variable on all household characteristics and control variables. We use the F-test to check for exogeneity, under the null that all estimated regressors are equal to zero. Given that the F-test is 0.66, we fail to reject the null hypothesis and the identifying assumption of the paper holds. We compute robust standard errors. t-statistics in parentheses: *p < 0.05, **p < 0.01 and ***p < 0.001
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