Transferability of disaggregate mode choice models

Predictions of aggregate transport mode split for inter-city trips are derived from a disaggregate model of travel demand. A series of tests are performed to assess the limitations of the prediction methodology for disaggregate models, and it is shown that disaggregate models are capable of predictions across diverse travel situations. The disaggregate model predictions are compared with predictions derived from aggregate models such as are currently used in urban transportation planning, and it is shown that disaggregate models based on much smaller data sets predict better than aggregate models while requiring no more information about the predicted population.     

Measuring efficiency in education: The influence of imprecision and variability in data on DEA estimates

Many studies devoted to efficiency performance evaluation in the education sector are based on measures of central tendency at school level as, for example, the average values of students belonging to the same school. Although this is a common and accepted way of summarizing data from the original observations (students), it is not less true that this approach neglects the existing dispersion of data, which may become a serious problem if variability across schools is high. Additionally, imprecision may arise when experts on each evaluated subject select the battery of questions, with different levels of difficulty, which will be the base for the final questionnaires completed by students. This paper uses data from US students and schools participating in PISA (Programme for International Student Assessment) 2015 to illustrate that schools' efficiency measures based on aggregate data and imprecision may reflect an inaccurate picture of their performance if they are compared to measures estimated accounting for broader information provided by all students of the same school. In order to operationalize our approach, we resort to Fuzzy Data Envelopment Analysis. This methodology allows us to deal with the notion of fuzziness in some variables such as the socio-economic status of students or test scores. Our results indicate that the estimated measures of performance obtained with the fuzzy DEA approach are highly correlated with those calculated with traditional DEA models. However, we find some relevant divergences in the identification of efficient units when we account for data dispersion and vagueness.     

Does class matter more than school? Evidence from a multilevel statistical analysis on Italian junior secondary school students

This paper assesses the differences in educational attainments between students across classes and schools they are grouped by, in the context of Italian educational system. The purpose is to identify a relationship between pupils' reading test scores and students' characteristics, stratifying for classes, schools and geographical areas. The dataset contains detailed information about more than 500,000 students at the first year of junior secondary school in the year 2012/2013. By means of multilevel linear models, it is possible to estimate statistically significant school and class effects, after adjusting for pupil's characteristics, including prior achievement. The results show that school and class effects are very heterogeneous across macro-areas (Northern, Central and Southern Italy), and that there are substantial discrepancies between and within schools; overall, class effects on achievement tend to be larger than school ones.     

Aggregate vs. disaggregate data analysis—a paradox in the estimation of a money demand function of Japan under the low interest rate policy

We use Japanese aggregate and disaggregate money demand data to show that conflicting inferences can arise. The aggregate data appears to support the contention that there was no stable money demand function. The disaggregate data shows that there was a stable money demand function. Neither was there any indication of the presence of a liquidity trap. Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and disaggregate analysis are attributed to the neglected heterogeneity among micro units. We provide necessary and sufficient conditions for the existence of a cointegrating relation among aggregate variables when heterogeneous cointegration relations among micro units exist. We also conduct simulation analysis to show that when such conditions are violated, it is possible to observe stable micro relations, but unit root phenomena among macro variables. Moreover, the prediction of aggregate outcomes, using aggregate data, is less accurate than the prediction based on micro equations, and policy evaluation based on aggregate data ignoring heterogeneity in micro units can be grossly misleading. Copyright © 2005 John Wiley & Sons, Ltd.     

Linear prediction of temporal aggregates under model misspecification

This paper aims to demonstrate a possible aggregation gain in predicting future aggregates under a practical assumption of model misspecification. Empirical analysis of a number of economic time series suggests that the use of the disaggregate model is not always preferred over the aggregate model in predicting future aggregates, in terms of an out-of-sample prediction root-mean-square error criterion. One possible justification of this interesting phenomena is model misspecification. In particular, if the model fitted to the disaggregate series is misspecified (i.e., not the true data generating mechanism), then the forecast made by a misspecified model is not always the most efficient. This opens up an opportunity for the aggregate model to perform better. It will be of interest to find out when the aggregate model helps. In this paper, we study a framework where the underlying disaggregate series has a periodic structure. We derive and compare the efficiency loss in linear prediction of future aggregates using the adapted disaggregate model and aggregate model. Some scenarios for aggregation gain to occur are identified. Numerical results show that the aggregate model helps over a fairly large region in the parameter space of the periodic model that we studied.     

THE QUALITY AND EFFICIENCY OF PRIVATE AND PUBLIC EDUCATION: A CASE-STUDY OF URBAN INDIA†

Recent evidence of a substantial link between quality of schooling and individual productivity suggests that, from an economic efficiency perspective, quality aspects of education deserve attention. This paper presents empirical evidence on the relative quality and efficiency of private and government-funded schools in urban India, using data from Uttar Pradesh. The results suggest that standardizing for home background and controlling for sample selectivity greatly reduces the raw average achievement advantage of private school students over public school students, but does not wipe it out. Private schools' standardized achievement advantage (or better quality) is complemented by their lower unit costs to enable them to be more efficient. The results support much of the existing international evidence on the relative efficiency of private and public schools.     

An Analysis of WellBeing in Secondary School with Multilevel Growth Curve models and Multilevel Multivariate Models

This paper reports on an exploration of student wellbeing in secondary school. A wellbeing questionnaire was administered four times to the same students. Multilevel models were applied in which measurements are grouped within students within schools. Differences between students are large, but there are only minor differences between schools regarding the wellbeing. Two methods of analysis of longitudinal data are compared: a multilevel multivariate approach and a multilevel growth curve analysis. It is shown that the estimation of individual growth curves is an elegant and parsimonious way of modelling. The multivariate approach on the other hand is a more modest model. The assumptions, advantages and disadvantages of both perspectives are listed.     

Maximum likelihood estimation of seemingly unrelated stochastic frontier regressions

This paper explores the changes in value added (VA) of a sample of schools for cohorts of students finishing secondary education between 2005 and 2008. VA estimates are based on distance measures obtained from DEA models. These measures are computed for each pupil in each school, and evaluate the distance between the school frontier in a given year and a pooled frontier comprising all schools analysed. The school VA is then computed by aggregating the VA scores for the cohort of pupils attending that school in a given year. The ratio between VA estimates for two consecutive cohorts, that attended the school in different years, is taken as the index of VA change. However, the evolution of school performance over time should consider not only the movements of the school frontier, but should also take into account other effects, such as the proximity of the students to the best-practices, represented by the school frontier, observed over time. For that purpose we developed an enhanced Malmquist index to evaluate the evolution of school performance over time. One of the components of the Malmquist index proposed measures VA change, and the other measures the ability of all school students to move closer to their own school best practices over time. The approach developed is applied to a sample of Portuguese secondary schools.     

Comparisons of aggregate variable forecasts using aggregate and disaggregate models

When forecasting aggregate variables, a choice must often be made to either add up individual forecasts made at a disaggregate level or to simply forecast at the aggregate level. The presence of heterogeneity introduces aggregation bias and makes the disaggregates approach more preferable, while the presence of data and specification errors introduces relatively large variances in the disaggregate forecasts, making the aggregate approach more preferable. It is suggested that the mean square error is useful in evaluating the combined effects of heterogeneity and specification and data errors, and in facilitating comparisons between aggregate and disaggregate approaches to aggregate variable forecasting.     

Multicomponent Efficiency Measurement and Shared Inputs in Data Envelopment Analysis: An Application to Sales and Service Performance in Bank Branches

In most applications ofDEA presented in the literature, the models presented are designedto obtain a single measure of efficiency. In many instances however,the decision making units involved may perform several differentand clearly identifiable functions, or can be separated intodifferent components. In such situations, inputs, in particularresources, are often shared among those functions. This sharingphenomenon will commonly present the technical difficulty ofhow to disaggregate an overall measure into component parts.In the present paper, we extend the usual DEA structure to onethat determines a best resource split to optimize the aggregateefficiency score. The particular application area investigatedis that involving the sales and service functions within thebranches of a bank. An illustrative application of the methodologyto a sample of branches from a major Canadian bank is given.     

A Predictive Estimator of the Mean with Missing Data

One of the most difficult problems confronting investigators who analyze data from surveys is how treat missing data. Many statistical procedures can not be used immediately if any values are missing. This paper considers the problem of estimating the population mean using auxiliary information when some observations on the sample are missing and the population mean of the auxiliary variable is not available. We use tools of classical statistical estimation theory to find a suitable estimator. We study the model and design properties of the proposed estimator. We also report the results of a broad-based simulation study of the efficiency of the estimator, which reveals very promising results.     

Competition and public high school performance

Increasing the level of school competition has been suggested as a way to improve school performance. This study examines one of the most extreme examples of such reform using data from New Zealand public high schools. In the 1990s school zoning was abolished in New Zealand and public schools competed for students, not just with private schools, but also with each other. A categorical Data Envelopment Analysis model using data on school resources and student academic performance, stratified using student socio-economic characteristics, is used to calculate efficiency scores for schools. A regression model is then used to analyse differences in these efficiency scores and their relationship to different levels of competition.     

Barriers to skill acquisition in Brazil: Public and private school students performance in a public university entrance exam

This paper uses a novel data set to quantify the difference in performance of public and private school students in an entrance test exam of the major public university in Brazilian Northeast (Universidade Federal de Pernambuco – UFPE). Although there are many public universities in Brazil, from our knowledge, there is no study that uses data on entrance test scores at such universities to evaluate the determinants of students’ performance and the barriers for public school students to get in the good universities. The data set has detailed information on individual and school characteristics, and family background. We found that test scores of public school students are on average about 4.2–17% lower than those taken by private school students, depending on the set of controls. This result is robust when we address problems related to attrition, omitted variables (e.g., cognitive ability), and unobservable selectivity. We also show that once students get into the university, those from public schools perform as well as those from private schools. In addition, the proportion of public school students that gets into the university is roughly the same as the proportion of students doing the entrance exam. However, there is a strong barrier for public school students to get into high competitive majors. The fraction of students from public schools that gets into high competitive majors such as law, medicine, and electronic engineering is almost null. Our findings provide quantitative evidence to the common view that the Brazilian elitist high education system is an important channel for inequality persistence.     

Mathematical programming for nominating exchange students for international universities: The impact of stakeholders’ objectives and fairness constraints on allocations

We consider the problem of nominating exchange students to attend international universities where places are limited. We take into account three objectives: The sending university aims to maximize the number of nominations, the students seek nomination for a highly preferred university and, finally, the receiving universities strive for excellent incoming students. Pairwise comparison of students should guarantee the following fairness: A student with higher academic achievements should be preferred over a student with lower academic achievements. We provide mathematical programming models of the nomination problem which maximize the overall objectives and guarantee different types of pairwise fairness. Several years of real data from a major school are employed to evaluate the models’ performance including a benchmark against the heuristic that is used by the school. We show analytically and experimentally that the heuristic approach fails to guarantee some pairwise fairness. Our results reveal the following four insights: First, compared to the current approach, up to 6.6% more students can be nominated with our optimization model while ensuring all pairwise fairness perspectives. Second, on average, students are nominated with better academic achievements. Third, the problem instances can be solved to optimality within a fraction of a second even for large-size instances comprising more than 500 students and about 150 schools offering nearly 450 exchange places. This is important for its use in practice. Last, up to 17.9% more students can be nominated when considering the overall objective to maximize nominations.     

Sample Size Considerations for Multilevel Surveys

Social and economic data commonly have a nested structure (for example, households nested within neighborhoods). Recently techniques and computer programs have become available for dealing with such data, permitting the formulation of explicit multilevel models with hypotheses about effects occurring at each level and across levels. If data users are planning to analyze survey data using multilevel models rather than concentrating on means, totals, and proportions, this needs to be accounted for in the survey design. The implications for determining sample sizes (for example, the number of neighborhoods in the sample and the number of households sampled within each neighborhood) are explored.     

Disaggregate evidence on the persistence of consumer price inflation

This paper uses disaggregate inflation data spanning all of consumption to examine: (i) the persistence of disaggregate inflation relative to aggregate inflation; (ii) the distribution of persistence across consumption sectors; and (iii) whether persistence has changed. Assuming mean inflation to be unchanged, disaggregate persistence inflation is consistently below aggregate persistence. Taking into account an early 1990s shift in mean inflation identified by break tests yields much lower estimates of both aggregate and disaggregate persistence for 1984–2002. But with the mean break, average disaggregate persistence is actually as great as aggregate inflation persistence. A factor model provides a natural framework for interpreting the relationship between aggregate and disaggregate persistence. Copyright © 2006 John Wiley & Sons, Ltd.     

Efficiency aggregation with enhanced Russell measures in data envelopment analysis

In aggregation for data envelopment analysis (DEA), a jointly determined aggregate measure of output and input efficiency is desired that is consistent with the individual decision making unit measures. An impasse has been reached in the current state of the literature, however, where only separate measures of input and output efficiency have resulted from attempts to aggregate technical efficiency with the radial measure models commonly employed in DEA. The latter measures are “incomplete” in that they omit the non-zero input and output slacks, and thus fail to account for all inefficiencies that the model can identify. The Russell measure eliminates the latter deficiency but is difficult to solve in standard formulations. A new approach has become available, however, which utilizes a ratio measure in place of the standard formulations. Referred to as an enhanced Russell graph measure (ERM), the resulting model is in the form of a fractional program. Hence, it can be transformed into an ordinary linear programming structure that can generate an optimal solution for the corresponding ERM model. As shown in this paper, an aggregate ERM can then be formed with all the properties considered to be desirable in an aggregate measure—including jointly determined input and output efficiency measures that represent separate estimates of input and output efficiency. Much of this paper is concerned with technical efficiency in both individual and system-wide efficiency measures. Weighting systems are introduced that extend to efficiency-based measures of cost, revenue, and profit, as well as derivatives such as rates of return over cost. The penultimate section shows how the solution to one model also generates optimal solutions to models with other objectives that include rates of return over cost and total profit. This is accomplished in the form of efficiency-adjusted versions of these commonly used measures of performance.     

A simple estimator for nonlinear error in variable models

We propose a simple estimator for nonlinear method of moment models with measurement error of the classical type when no additional data, such as validation data or double measurements, are available. We assume that the marginal distributions of the measurement errors are Laplace (double exponential) with zero means and unknown variances and the measurement errors are independent of the latent variables and are independent of each other. Under these assumptions, we derive simple revised moment conditions in terms of the observed variables. They are used to make inference about the model parameters and the variance of the measurement error. The results of this paper show that the distributional assumption on the measurement errors can be used to point identify the parameters of interest. Our estimator is a parametric method of moments estimator that uses the revised moment conditions and hence is simple to compute. Our estimation method is particularly useful in situations where no additional data are available, which is the case in many economic data sets. Simulation study demonstrates good finite sample properties of our proposed estimator. We also examine the performance of the estimator in the case where the error distribution is misspecified.     

Measuring efficiency in Australian Schools: A preliminary analysis

In this paper, we apply a public sector Data Envelopment Analysis model to estimate the efficiency of Australian primary and secondary schools. Standard microeconomic production theory showing the transformation of inputs into outputs is extended to allow nondiscretionary environmental variables characteristic of educational production. Failure to properly control for the socioeconomic environment leads to inappropriate comparisons and biased efficiency estimates. We employ a conditional estimator that does not allow a school with a better environment to serve as a benchmark for a school with a worse environment. The results suggest that Australian schools are moderately inefficient and that efficiency increases for the quintile of schools with the most favorable environment. Further, efficiency gains are realized with increasing enrollment.     

Closing schools in a shrinking district: Do student outcomes depend on which schools are closed?

In the last decade, many cities around the country have needed to close schools due to declining enrollments and low achievement. School closings raise concerns about the possible negative impacts on student achievement, neighborhoods, families, and teaching staff. This study examines an anonymous urban district that, faced with declining enrollment, chose to make student achievement a major criterion in determining which schools would be closed. The district targeted low-performing schools in its closure plan, and sought to move their students to higher-performing schools. We estimate the impact of school closures on student test scores and attendance rates by comparing the growth of these measures among students differentially affected by the closures. We use residential assignment to school as an instrument to address non-random sorting of students into new schools. We also statistically control for the contemporaneous effects of other reforms within the district. Results show that students displaced by school closures can experience adverse effects on test scores and attendance, but these effects can be minimized when students move to schools that are higher-performing (in value-added terms). Moreover, the negative effect on attendance disappears after the first year in the new school. Meanwhile, we find no adverse effects on students in the schools that are receiving the transferring students.