An efficiency and productivity study in the presence of the 'No Child Left Behind Act' in Pennsylvania school districts

This study investigated the efficiency and variation of 492 Pennsylvania school districts. Five questions were addressed in this study: (1) What are the levels of efficiency measures in school districts? (2) How widely do efficiency measures vary across school districts? (3) What are the common factors presented by efficient school districts? (4) Has the efficiency of school districts improved since the 2001-02 school year? What explains the efficiency change of school districts? (5) Has the productivity of school districts improved since the 2001-02 school year?First, using the stochastic frontier analysis, the efficiency measures of districts were calculated from the Battese & Coelli model. Efficiency measures of this basic model were compared with other variants of stochastic frontier analysis in order to deal with the unobserved heterogeneity problem and, in doing so, obtain reliable efficiency measures. The stochastic frontier models represent that the average efficiency of school districts ranged from 77.48 percent to 82.98 percent in math proficiency rate and from 85.10 percent to 87.40 percent in reading proficiency rate. On average, school districts had an average inefficiency of 22.52 percent to 17.12 percent in math and 14.90 percent to 12.60 percent in reading.Next, this study estimated the education production frontier and its relationship with input variables. The correlation between institutional factors and inefficiency was explored in order to explain the monitoring and competition effects on inefficiency. The efficiency of 492 districts was descriptively analyzed according to geographic location, locale type, and AYP status component of the NCLB Act.Environmental variables (NIEP and NECO) had a greater impact on the educational production than traditional inputs (INST and SUP), which is consistent with a theoretical background. Teachers' salary (SALARY) was positively associated with the proficiency rate.In the case of inefficiency model estimation, SIZE (population per square mile) had a positive impact on school district inefficiency. As a monitoring factor, the aid ratio factor (MV/PI AR) had a positive impact on the school district inefficiency statistically significant at the five percent level.In the case of competition factors, the Herfindahl index (HERF) had a negative association with district inefficiency. As a school district dominates the county education system in terms of enrollment shares, district inefficiency will be reduced. On the other hand, a lagged value of private school enrollments (LagPRIV) showed a positive impact on district inefficiency.Third, to answer RQ 3, this study identified the similarities of the most efficient school districts. The efficient districts came from the highest group (5 percent) of all school districts. These efficient school districts were compared with the results of Pennsylvania's costing out study.The efficient school districts of this study presented more diversity in differences rather than evident similarities under the stochastic frontier analysis. Eight and ten of the efficient school districts were located in southwest region in math and reading, respectively. Also, 15 and 12 of the efficient school districts were located in large suburb in both math and reading proficiency rates, respectively.Based on the costing out study, there was no clear relationship between school district needs and efficiency measures. However, school districts of higher needs had better efficiency measures. Therefore, adequate funding for higher needs school districts obtained the rationality of their adequate funding under the framework of efficiency. However, nine of the 25 highest needs school districts were ranked below 400.Fourth, in the case of RQ 4, average efficiency measures of each school district from the 2001-02 to 2005-06 school years were obtained in order to explore the relationship between the efficiency change and four determinants. The efficiency changes were regressed on regressors such as the status of warning, improvement, and corrective action of school districts, the percentage of state aid (school district's dependence on the state), equalized mills (school district's tax effort), and the percentage of expenditures dedicated to salaries and benefits spending (fixed cost) in order to obtain the critical determinants of the school district efficiency change.State share had a positive impact on the efficiency change statistically significant at the five percent level. School district tax effort, equalized mills, was positively related to the efficiency change. Salary share had a negative association with the efficiency change.There has been a critical expectation that school districts have to transform inputs into outputs efficiently under the NCLB accountability regime. The positive relationship between the AYP status and the efficiency change of this study provided a positive evidence of the AYP status component in the NCLB Act.Last, answering RQ 5, following Coelli et al. (2005) and Orea (2002), the total factor productivity index of this study was decomposed into a technical efficiency change, technical change, and scale change of school districts. (Abstract shortened by UMI.)...