Estimation Of Education Production Functions Research Articles

The rise of endogeneity in multilevel models: A theoretical assessment of the role of stratification

This paper studies the role of stratification in the rise of endogeneity bias in multilevel models. The theory is illustrated using educational stratification and its implications on the estimation of multilevel education production functions. Educational stratification results from the functioning of the education market; it transforms a continuum of student characteristics into a continuum of tuition fees. These fees enter students' utility functions and determine the school they attend and its quality. In other words, student characteristics are the major determinants of school quality and the two are correlated. In this paper, I analyze how these correlations arise and what their implications are for multilevel estimation of education production functions. The major problem posed by such correlations is cross-level endogeneity bias. The theory developed in this paper can be extended to any economic phenomenon that exhibits stratification or nesting of smaller units within larger units (employees within firms, residents within neighborhoods, etc.).

Solving endogeneity problems in multilevel estimation: an example using education production functions

This paper explores endogeneity problems in multilevel estimation of education production functions. The focus is on level 2 endogeneity which arises from correlations between student characteristics and omitted school variables. Theses correlations are mainly the result of student stratification between schools. From an econometric point of view, the correlations between student and school characteristics imply that the omission of some variables may generate endogeneity bias. Therefore, an estimation approach based on the Mundlak [20] technique is developed in order to tackle bias and to generate consistent estimates. Note that our analysis can be extended to any multilevel-structured data (students nested within schools, employees within firms, firms within regions, etc). The entire analysis is undertaken in a comparative context between three countries: Germany, Finland and the UK. Each one of them represents a particular system. For instance, Finland is known for its extreme comprehensiveness, Germany for early selection and the UK for its liberalism. These countries are used to illustrate the theory and to prove that the level of bias arising from omitted variables varies according to the characteristics of education systems.

CORRECTING FOR SAMPLING BIAS IN EDUCATION PRODUCTION FUNCTIONS

AbstractSample selectivity bias arises in the estimation of education production functions when the process of selecting the sample is not independent of the value of the dependent variable. In this study, the dependent variable was a student's retention of microeconomics knowledge after a period of 6 weeks following the final examination. Potential sampling bias arises from a student's decision to participate or not to participate in the research. Results obtained from the Heckman selection model indicated minor sampling bias. Student ability and the male dominance in economics performance were overestimated in the OLS model. Student effort was underestimated.

Violence in European Schools: Victimization and Consequences

Violence at schools is a well-known problem in many societies. This paper assesses the degree of school violence in 11 European countries and analyzes the determinants of being a victim and its effect on student performance. The study draws on the international TIMSS 2003 and the British longitudinal NCDS data. The level of school violence is high in most countries but seems not to increase over time. Besides gender, social and migration background and the appearance of students determine being bullied, hurt or stolen from by fellow students. Being a victim has a small but significantly negative impact on contemporary and later student performance and the level of educational attainment and thereby affects earnings. It is hence an important peer effect that should not be omitted in the estimation of educational production functions.

The Effects of Curriculum Reform on Economics Education in A Spanish College

The aim of this study is to analyze the effects of changes in curricula brought about as a consequence of the reform carried out in university education in Spain during the first half of the 1990s. The study concentrates on first-cycle undergraduate economics students and is based on the estimation of education production functions. This estimation has the feature that two dimensions of education output are considered: the affective part and the cognitive part. Our results show that there have not been significant differences in education/training as a consequence of the reform of the curriculum.The estimates obtained highlight the importance of peer-group effects in the educative process.

Estimating educational production functions: The problem of multicollinearity

This article attempts to address the problem of multicollinearity in the estimation of educational production functions. Past studies consistently indicate the seeming lack of importance of school input variables such as teacher salary, class size, and expenditure per student in influencing educational outcomes. Our ordinary least squares results were consistent with these past studies, but collinearity diagnostics revealed the presence of at least four potentially serious sources of collinearity within the data, possibly degrading the coefficients on the school input variables. Ridge regression and principal components regression techniques were employed in an effort to “solve” the collinearity problem. The coefficients of the school input variables continue to have the hypothesized signs, but with smaller standard errors. The results indicate that researchers should be cautioned against reaching the conclusion that school input variables don't matter on the basis of ordinary least squares estimates of these parameters.

School District Inputs and Biased Estimation of Educational Production Functions

This study offers some highly tentative statistical evidence on a potential problem, noted by Eric Hanushek in 1979, in estimating educational production functions, particularly at the precollege level. Hanushek observed that it is frequently inappropriate to include school-system variables in equations using the individual student as the unit of observation. This study offers limited evidence supporting this concern.

School District Inputs and Biased Estimation of Educational Production Functions

School District Inputs and Biased Estimation of Educational Production Functions

Conceptual and Empirical Issues in the Estimation of Educational Production Functions

Measuring educational performance and understanding its determinants are important for designing policies with respect to such varying issues as teacher accountability, educational finance systems, and school integration. Unfortunately, past analyses of student achievement and educational production relationships have been plagued by both a lack of conceptual clarity and a number of potentially severe analytical problems. As a result, there is considerable confusion not only about what has been learned, but also about how such studies should be conducted and what can be learned. This review considers each of these issues and also relates knowledge from these studies to research about areas other than just school operations and performance.