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It is possible that moves are disproportionately made to better (or worse) schools, in which case our estimate of the impact of mobility may, in part, reflect changes in school quality such that isolating the impact of mobility (as distinct from improvements in school quality) requires controlling for these changes. Thus, we add a measure of school quality to our regression models (see table 7, columns 2 and 5). Specifically, we use the average, regression-adjusted value added for each school/grade in the previous year as a measure of school quality.24 Overall, results are robust. Signs and significance of coefficients generally remain, with the exceptions that the medium-term effects of nonstructural moves in ELA are no longer significant and the effect of structural moves on math performance is small, positive, and significant in the year of the move itself. This slight attenuation suggests that uncontrolled impacts may have been due, in part, to school quality improvements in the case of nonstructural moves and due to decreases in school equality in the case of structural moves. This finding is entirely consistent with the notion of parents making strategic, nonstructural moves in an effort to improve their child's outcomes.

Table 7. 
Robustness Checks, Instrumental Variable Specifications, School Quality, and Move Pre-trends
ELAMath
Main resultsSchool QualityPre-trendsMain resultsSchool QualityPre-trends
(1)(2)(3)(4)(5)(6)
Post-summer move       
Structural −0.047*** −0.042** −0.037* −0.176*** −0.140*** −0.148*** 
 (0.018) (0.017) (0.021) (0.024) (0.023) (0.029) 
Nonstructural 0.156* 0.121 0.249 −0.040 −0.063 −0.257 
 (0.083) (0.082) (0.167) (0.107) (0.102) (0.196) 
Summer move in year t       
Structural −0.052*** −0.039** −0.052*** −0.001 0.031** 0.016 
 (0.015) (0.015) (0.016) (0.015) (0.014) (0.015) 
Nonstructural −0.024 0.000 −0.054 0.074 0.077 0.126* 
 (0.067) (0.069) (0.065) (0.071) (0.067) (0.072) 
1 Year Prior to       
Structural move   0.029   0.058*** 
   (0.017)   (0.020) 
Nonstructural move   0.141*   −0.039 
   (0.075)   (0.085) 
Instruments       
Building sale 
Terminal & entry grade       
Nonparametric 
Observations 342,685 342,685 342,685 343,832 343,832 343,832 
Unique students 88,241 88,241 88,241 88,254 88,254 88,254 
ELAMath
Main resultsSchool QualityPre-trendsMain resultsSchool QualityPre-trends
(1)(2)(3)(4)(5)(6)
Post-summer move       
Structural −0.047*** −0.042** −0.037* −0.176*** −0.140*** −0.148*** 
 (0.018) (0.017) (0.021) (0.024) (0.023) (0.029) 
Nonstructural 0.156* 0.121 0.249 −0.040 −0.063 −0.257 
 (0.083) (0.082) (0.167) (0.107) (0.102) (0.196) 
Summer move in year t       
Structural −0.052*** −0.039** −0.052*** −0.001 0.031** 0.016 
 (0.015) (0.015) (0.016) (0.015) (0.014) (0.015) 
Nonstructural −0.024 0.000 −0.054 0.074 0.077 0.126* 
 (0.067) (0.069) (0.065) (0.071) (0.067) (0.072) 
1 Year Prior to       
Structural move   0.029   0.058*** 
   (0.017)   (0.020) 
Nonstructural move   0.141*   −0.039 
   (0.075)   (0.085) 
Instruments       
Building sale 
Terminal & entry grade       
Nonparametric 
Observations 342,685 342,685 342,685 343,832 343,832 343,832 
Unique students 88,241 88,241 88,241 88,254 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. All models use a vector of indicators that are the interaction between a student's current grade and the terminal grade of his first-grade school and a vector of indicators that are the interaction between a student's current grade and the entry grade of his closest middle school. All models 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). School quality is the regression adjusted average ELA performance in that school-grade the prior year. Pre-trends are captured through a series of indicators controlling for one year pre-move.

*p < 0.1; **p < 0.05; ***p < 0.01.

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