Christopher Cornwell

Estimating the Economic Model of Crime with Panel Data

Previous attempts at estimating the economic model of crime with aggregate data relied heavily on cross-section econometric techniques and, therefore, do not control for unobserved heterogeneity. This is even true of studies that estimated simultaneous equations models. Using a new panel data set of North Carolina counties, the authors exploit both single and simultaneous equations panel data estimators to address two sources of endogeneity: unobserved heterogeneity and conventional simultaneity. Their results suggest that both labor market and criminal justice strategies are important in deterring crime but that the effectiveness of law enforcement incentives has been greatly overstated. Copyright 1994 by MIT Press.

The Enrollment Effects of Merit-Based Financial Aid: Evidence from Georgia's HOPE Program

Introduced in 1993, Georgia’s HOPE Program sponsors a merit‐based scholarship for students attending in‐state colleges and a grant for those entering technical schools. There are no income restrictions. Comparing Georgia with other southeastern states over the 1988–97 period, HOPE increased freshmen enrollment by 5.9%, or 2,889 students per year, which amounts to only 15% of freshmen scholarship recipients. Four‐year colleges account for most of the gain; a reduction in students leaving the state explains two‐thirds of the 4‐year‐school effect attributable to freshmen who have recently graduated from high school. White and black enrollments increased because of HOPE.

Simultaneous equations and panel data

Abstract This paper considers a simultaneous equations model, with panel data and unobservable individual effects in each structural equation. The effects may be fixed or random. In the fixed effects case, a conditional likehood approach leads to the within transformation, just as in the single equation setting. In the random effects case, we allow an arbitrary number of the exogenous variables to be correlated with the effects, and provide efficient GMM estimators along the lines of two-stage and three-stage least squares. The case of different instruments in different equations is also considered.

Parametric Estimation of Technical and Allocative Inefficiency with Panel Data

The error-components approach to estimating allocative inefficiency imposes restrictive assumptions on the distributions of the errors and functional form. The parametric approach does not require special assumptions about the error distribution or technology but typically assumes technical efficiency or restrictive functional forms. The parametric approach also allows for systematic firm responses to shadow prices. The authors generalize the parametric approach to a panel data setting and show that input and firm-specific allocative inefficiency, as well as firm-specific technical inefficiency, can be identified and estimated using a flexible functional form. This is demonstrated empirically with an application to U.S. airlines. Copyright 1994 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Estimation of Output and Input Technical Efficiency Using a Flexible Form and Panel Data

The Farrell (1957) concept of technical efficiency can be measured in two ways--denoted output and input technical efficiency. The empirical literature does not distinguish between the two measures and confusion exists regarding the required restrictions on technology for the specification of either measure. With panel data, the authors show how both types of efficiency, when treated as fixed effects, are to be specified in the context of a translog cost system. Technical efficiency enters the share equations only in the output case when homotheticity is not imposed. The authors estimate their cost systems using panel data on U.S. airlines. Copyright 1994 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Student Responses to Merit Scholarship Retention Rules

A common justification for state-sponsored merit scholarships like Georgia’s HOPE program is to promote academic achievement. However, grade-based retention rules encourage other behavioral responses. Using longitudinal records of enrolled undergraduates at the University of Georgia between 1989 and 1997, we estimate the effects of HOPE on course-taking, treating nonresidents as a control group. First, we find that HOPE decreased full-load enrollments and increased course withdrawals among resident freshmen. Second, the scholarship’s influence on course-taking behavior is concentrated on students whose predicted freshmen GPAs place them on or below the scholarship-retention margin. Third, HOPE substantially increased summer school credits.

The Enrollment Effects of Merit-Based Financial Aid: Evidence from Georgia's Hope Scholarship

Introduced in 1993, Georgia’s HOPE Program sponsors a merit‐based scholarship for students attending in‐state colleges and a grant for those entering technical schools. There are no income restrictions. Comparing Georgia with other southeastern states over the 1988–97 period, HOPE increased freshmen enrollment by 5.9%, or 2,889 students per year, which amounts to only 15% of freshmen scholarship recipients. Four‐year colleges account for most of the gain; a reduction in students leaving the state explains two‐thirds of the 4‐year‐school effect attributable to freshmen who have recently graduated from high school. White and black enrollments increased because of HOPE.

Measuring and Decomposing Productivity Change: Stochastic Distance Function Estimation versus Data Envelopment Analysis

Measuring productivity change with Malmquist indices has become common practice, because they are easily computed using nonparametric programming techniques and can be readily decomposed into technical and efficiency change. However, this approach is nonstochastic and requires a constant returns to scale assumption to construct the reference technology. We propose estimating productivity change using a stochastic input distance frontier, imposing no restrictions on returns to scale. We derive the analogous decomposition of productivity change and develop a generalized method of moments strategy in which outputs or inputs may be endogenous. We compare two methods in an application to electric utilities.

Survey Response-Related Biases in Contingent Valuation: Concepts, Remedies, and Empirical Application to Valuing Aquatic Plant Management

Sample nonresponse and selection biases that may occur in survey research such as contingent valuation applications are discussed and tested. Correction mechanisms for these types of biases are demonstrated. Results indicate the importance of testing and correcting for unit and item nonresponse bias in contingent valuation survey data. When sample nonresponse and selection bias go uncorrected, welfare measures may be overestimated or underestimated contributing to potential errors in resource policy and management decisions. Copyright 2000, Oxford University Press.

Production Frontiers and Efficiency Measurement

The standard definition of a production function is that it gives the maximum possible output for a given set of inputs. This is a different concept than the regression function, which gives mean output for a given set of inputs. Thus the production function defines a boundary or “frontier.” Deviations of observed outputs from this frontier are in principle one-sided (non-positive) and can be taken to reflect inefficiency, since they represent failures to achieve maximum possible output given the inputs. Other types of frontiers exist. For example, a cost function gives the minimum possible cost for a given level of output and set of input prices, and defines a frontier from which deviations are in principle non-negative. In this chapter we will concentrate on the estimation of production frontiers and the measurement of technical inefficiency relative to them.