Linganisha mbinu

	Utafiti wa Tukio la Paneli katika Utafiti wa Elimu	Utafiti wa Tukio la Paneli
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	1993 (general method); 2000s (education applications)	1990s–2020s (modern panel formulation)
Mwanzilishi	Jacobson, LaLonde & Sullivan (1993); widely adopted in education economics from 2000s onward	Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation
Aina	Causal inference / panel regression	Quasi-experimental / causal panel design
Chanzo asilia	Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings Losses of Displaced Workers. American Economic Review, 83(4), 685-709.	Freyaldenhoven, S., Hansen, C., Perez-Orive, J., & Shapiro, J. M. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper 29170. National Bureau of Economic Research.
Majina mbadala	education event study, panel event-study design, education policy event study, school event study	event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model
Zinazohusiana	4	4
Muhtasari	The panel event study is a causal-inference design that tracks outcomes for a panel of educational units — students, teachers, schools, or districts — across relative time periods around a well-defined event such as a policy change, school reform, or staffing transition. By estimating period-by-period treatment effects, it reveals not only whether an intervention mattered but also when effects appeared and how long they persisted, making it especially valued in education economics.	A panel event study estimates the dynamic causal effect of a treatment or policy by regressing an outcome on a full set of relative-time indicators — one for each period before and after the event — while controlling for unit and time fixed effects. The resulting coefficient plot shows how the treated units diverged from untreated units at each point in calendar time relative to their treatment date, making both pre-treatment trend violations and post-treatment effect trajectories immediately visible.