Matching Teachers to Rural Schools in Peru: A Structural Challenge
Despite two decades of investment in education, rural schools in Peru continue to face acute shortages of qualified teachers. This imbalance is not merely the result of logistical barriers or scarce resources—it reflects deeper structural flaws in how teachers are assigned to schools. Increasingly, researchers and policymakers recognize that reducing educational inequality requires understanding the market dynamics and incentive structures that shape teacher-school matches.
A key contribution comes from Ederer (2023), who models teacher assignment as a dynamic two-sided matching problem. Unlike static models, his framework accounts for forward-looking behavior and the option to reapply, capturing how both teachers and schools act strategically over time. This approach is especially relevant in Peru, where centralized assignment mechanisms regularly fail to place strong candidates in rural schools.
Evidence from this framework shows that high value-added teachers disproportionately migrate to urban areas as their careers progress. Those who begin in rural schools are more likely to exit the profession or relocate to cities, while those who start in urban areas tend to remain. As a result, the urban–rural gap in teacher value-added widens within just two years beyond what initial placements alone would predict. But why do teachers have this preference? Non-pecuniary factors are a form of compensation. For example, teachers may prefer urban areas to stay closer to their families.
To explore policy solutions, Ederer estimates teachers’ and schools’ preferences and simulates dynamic contracts aimed at reducing rural attrition. He finds that requiring a minimum contract length for rural posts leads to adverse selection: high value-added teachers avoid these roles to preserve their ability to rematch. Retaining them would require a monthly wage increase of 24%, merely to reduce the urban–rural teacher quality gap by 0.02 standard deviations. These findings suggest that improving retention through financial incentives may be prohibitively expensive when rematching is easy, particularly for talented early-career teachers.
Bobba et al. (2021) extend this analysis by examining Peru’s national teacher assignment system. They show that unequal sorting of teachers across locations accounts for about one-quarter of the urban–rural achievement gap. Low rural salaries, combined with strong preferences for urban amenities, result in systematically weaker matches for remote schools—even those serving the most vulnerable populations. The authors suggest that better alignment between compensation and both teacher preferences and productivity could reduce these disparities.
Crucially, simulations show that salary increases must be integrated with assignment rules to be effective. Outcomes depend not only on pay but on how it interacts with application timing, priority structures, and reapplication windows. Without adapting the centralized matching mechanism, salary incentives alone are unlikely to change teacher distribution meaningfully.
The data also highlight dynamic inefficiencies. Teachers often treat rural posts as temporary placements, encouraged by annual reapplication opportunities. This behavior creates high turnover and weak community ties. Policy responses such as restricting reapplications or offering tenure-linked bonuses may better align incentives with long-term service in underserved areas.
The Peruvian case reveals a broader truth: educational inequality is often rooted not in funding shortfalls, but in the misallocation of talent. Teachers—like students and families—respond to incentives. When systems ignore these preferences, they generate persistent mismatches, not from unwillingness or inability, but from misaligned institutional design.
Addressing these issues requires more than raising wages. Reform must target the matching algorithm, reapplication dynamics, and compensation structures in tandem. Without such systemic changes, centralized assignment mechanisms risk perpetuating the very inequalities they were designed to correct.
References:
Bobba, M., Ederer, T., Leon-Ciliotta, G., Neilson, C., and Nieddu, M. G. (2021). Teacher Compensation and Structural Inequality: Evidence from Centralized Teacher School Choice in Peru. Technical Report, National Bureau of Economic Research.
Ederer, T. (2023). Labor Market Dynamics and Teacher Spatial Sorting. Technical Report, Technical Report.
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