Table 1.
Effects of remote learning on dropout risk and test scores
| Q4 2020 − Q4 2019 | (Q4 2020 − Q4 2019) − (Q4 2019 − Q4 2018) | (Q4 2020 − Q1 2020) − (Q4 2019 − Q1 2019) | |||
|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | |
| Panel A: High dropout risk | 0.0662 | 0.0691 | 0.0621 | 0.0621 | 0.0621 |
| (0.0002) | (0.0002) | (0.0002) | (0.0002) | (0.0002) | |
| P < 0.001 | P < 0.001 | P < 0.001 | P < 0.001 | P < 0.001 | |
| Mean for Q4 of 2019 | 0.017 | 0.017 | 0.017 | 0.017 | 0.017 |
| N | 4,271,928 | 6,724,744 | 8,543,588 | ||
| Panel B: Standardized test scores | 0.652 | 0.523 | −0.314 | −0.301 | −0.319 |
| (0.0001) | (0.0001) | (0.0001) | (0.0001) | (0.0001) | |
| P < 0.001 | P < 0.001 | P < 0.001 | P < 0.001 | P < 0.001 | |
| In-person learning equivalent | 0.44 | 0.44 | 0.44 | 0.44 | 0.44 |
| N | 3,688,042 | 6,367,375 | 7,097,042 | ||
| Grade fixed effects | Yes | Yes | Yes | Yes | Yes |
| Matching | No | No | No | Yes | Yes |
| Inverse probability weighting | No | No | No | No | Yes |
Notes: The table displays treatment effects of remote learning on educational outcomes. Column 1 compares Q4 of 2020 with Q4 of 2019. Column 2 compares the variation between Q4 of 2019 and Q4 of 2020 with that between Q4 of 2018 and Q4 of 2029. Columns 3–5 show estimated differences-in-differences comparing the variation in outcomes between Q1 and Q4 of 2020 with that between Q1 and Q4 of 2019. In panel A, the dependent variable is high dropout risk (=1 if the student had no maths or Portuguese grades on record for that school quarter, and 0 otherwise). In panel B, the dependent variable is scores from quarterly standardized tests (AAPs), averaging maths and Portuguese scores for that school quarter. All columns include grade fixed effects and an indicator variable equal to 1 for municipalities that authorized schools to reopen from September 2020 onwards, and 0 otherwise (allowing its effects to vary at Q4). In columns 4 and 5, we control for the propensity score of selection into examinations (see Supplementary Section E) with a third-degree polynomial. In column 5, we also re-weight observations by the inverse of their propensity score. All columns are OLS regressions, with standard errors clustered at the school level. P values are computed from two-sided t-tests that each coefficient is equal to zero.