These estimates conflate the short- and medium-term effects of mobility, however. Although it could be that all effects of mobility are due to the disruption in the year of the move itself, it is also possible that effects of mobility due to changes in curriculum or peers take longer to materialize and are therefore only observed in the medium term, or it could be that mobility had both short-term and medium-term effects. To gain a further understanding of when mobility matters relative to the year of the move itself, we parse the short-term and medium-term effects of mobility. As shown in table 5, we see that structural moves have significant negative impacts on student performance for both ELA and math in the medium term, and an additional negative impact on short-term ELA performance.23 In ELA, students perform an additional 0.052–0.059 worse in the year of the move itself with small negative effects in the years following the move. In math, there is no differential impact in the year of the move itself but students perform significantly worse in all years following a structural move (0.176–0.186). By contrast, nonstructural moves appear to have lasting positive effects in ELA (0.156–0.275) with no additional impact experienced in the year of the move itself. Nonstructural moves have no significant impact on math performance, however. Thus, these estimates provide consistent evidence that structural moves harm student math and ELA performance in the medium-term, and nonstructural moves appear to have a positive effect in ELA in the medium-term. Furthermore, these results highlight the importance of separating the short-term versus medium-term impacts of mobility, as table 4 masks the result that the impacts of nonstructural mobility may take longer to appear.
| ELA | Math | |||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Post-summer move | ||||
| Structural | −0.033 | −0.047*** | −0.186*** | −0.176*** |
| (0.024) | (0.018) | (0.034) | (0.024) | |
| Nonstructural | 0.275** | 0.156* | 0.115 | −0.040 |
| (0.109) | (0.083) | (0.149) | (0.107) | |
| Move in year t | ||||
| Structural | −0.059*** | −0.052*** | 0.022 | −0.001 |
| (0.019) | (0.015) | (0.022) | (0.015) | |
| Nonstructural | −0.005 | −0.024 | 0.242* | 0.074 |
| (0.113) | (0.067) | (0.127) | (0.071) | |
| Instruments | ||||
| Building sale | Y | Y | Y | Y |
| Terminal and entry grade | ||||
| Quadratic | Y | N | Y | N |
| Nonparametric | N | Y | N | Y |
| Observations | 342,685 | 342,685 | 343,832 | 343,832 |
| Unique students | 88,241 | 88,241 | 88,254 | 88,254 |
| ELA | Math | |||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Post-summer move | ||||
| Structural | −0.033 | −0.047*** | −0.186*** | −0.176*** |
| (0.024) | (0.018) | (0.034) | (0.024) | |
| Nonstructural | 0.275** | 0.156* | 0.115 | −0.040 |
| (0.109) | (0.083) | (0.149) | (0.107) | |
| Move in year t | ||||
| Structural | −0.059*** | −0.052*** | 0.022 | −0.001 |
| (0.019) | (0.015) | (0.022) | (0.015) | |
| Nonstructural | −0.005 | −0.024 | 0.242* | 0.074 |
| (0.113) | (0.067) | (0.127) | (0.071) | |
| Instruments | ||||
| Building sale | Y | Y | Y | Y |
| Terminal and entry grade | ||||
| Quadratic | Y | N | Y | N |
| Nonparametric | N | Y | N | Y |
| Observations | 342,685 | 342,685 | 343,832 | 343,832 |
| Unique students | 88,241 | 88,241 | 88,254 | 88,254 |
Notes: Robust standard errors, clustered by first-grade school and middle school by cohort, in parentheses. Post-summer move is equal to 1 in all years after a student moves schools between June and October. Summer moves made after the completion of a terminal grade are structural moves and summer moves made after the completion of a nonterminal grade are nonstructural moves. Move in t is equal to 1 in the year that a student makes a particular type of move and 0 in all other years. All models include controls for poverty, English proficiency, home language, participation in special education services, mid-year moves, building type, residence borough, grade, and year. Models in columns (1) and (3) use the number of years between a student's grade in t and the completion of the terminal grade of his first-grade school (YearsPre) and this number squared, the number of years between the beginning of a student's grade in year t and the completion of the grade after the terminal grade of a student's first-grade school (YearsPost), and an indicator equal to one in the summer following the completion of the terminal grade of a student's first-grade school (Terminal) as grade span instruments. These models also include the number of years between a student's grade in t and the entry grade of his closest ZIP code (YearsPreMS) and this number squared, the number of years between a student's current grade and when he would have entered the lowest grade of his middle school (YearsPostMS), and an indicator equal to one in the summer before a student would enter the closest middle school if he started on time (Entry). Models in columns (2) and (4) use a vector of indicators that are the interaction between a student's current grade and the terminal grade of his first-grade school (
gT) and a vector of indicators that are the interaction between a student's current grade and the entry grade of his closest middle school (ηgE). All models use the interaction between an indicator of whether a student's current building of residence was sold between t − 2 and t − 1 and an indicator for the building type as instruments for school moves.
*p < 0.1; ***p < 0.01.