Estimating and explaining efficiency in a multilevel setting: A robust two-stage approach

Various applications require multilevel settings (e.g., for estimating fixed and random effects). However, due to the curse of dimensionality, the literature on non-parametric efficiency analysis did not yet explore the estimation of performance drivers in highly multilevel settings. As such, it lacks models which are particularly designed for multilevel estimations. This paper suggests a semi-parametric two-stage framework in which, in a first stage, non-parametric a effciency estimators are determined. As such, we do not require any a priori information on the production possibility set. In a second stage, a semiparametric Generalized Additive Mixed Model (GAMM) examines the sign and significance of both discrete and continuous background characteristics. The proper working of the procedure is illustrated by simulated data. Finally, the model is applied on real life data. In particular, using the proposed robust two-stage approach, we examine a claim by the Dutch Ministry of Education in that three out of the twelve Dutch provinces would provide lower quality education. When properly controlled for abilities, background variables, peer group and ability track effects, we do not observe differences among the provinces in educational attainments.Productivity estimation; Multilevel setting; Generalized Additive Mixed Model; Education; Social segregation

Role of benchmark technology in sustainable value analysis : an application to Finnish dairy farms

Sustainability is a multidimensional concept that entails economic, environmental, and social aspects. The sustainable value (SV) method is one of the most promising attempts to quantify sustainability performance of firms. SV compares performance of a firm to a benchmark, which must be estimated in one way or another. This paper examines alternative parametric and nonparametric methods for estimating the benchmark technology from empirical data. Reviewed methods are applied to an empirical data of 332 Finnish dairy farms. The application reveals four interesting conclusions. First, the greater flexibility of the nonparametric methods is evident from the better empirical fit. Second, negative skewness of the regression residuals of both parametric OLS and nonparametric CNLS speaks against the average-practice benchmark technology in this application. Third, high positive correlations across a wide spectrum of methods suggest that the findings are relatively robust. Forth, the stochastic decomposition of the disturbance term to filter out the noise component from the inefficiency term yields more realistic efficiency estimates and performance targets

Modelling Financial High Frequency Data Using Point Processes

In this chapter written for a forthcoming Handbook of Financial Time Series to be published by Springer-Verlag, we review the econometric literature on dynamic duration and intensity processes applied to high frequency financial data, which was boosted by the work of Engle and Russell (1997) on autoregressive duration modelsDuration, Intensity, Point process, High frequency data, ACD models

Adapting image processing and clustering methods to productive efficiency analysis and benchmarking: A cross disciplinary approach

This dissertation explores the interdisciplinary applications of computational methods in quantitative economics. Particularly, this thesis focuses on problems in productive efficiency analysis and benchmarking that are hardly approachable or solvable using conventional methods. In productive efficiency analysis, null or zero values are often produced due to the wrong skewness or low kurtosis of the inefficiency distribution as against the distributional assumption on the inefficiency term. This thesis uses the deconvolution technique, which is traditionally used in image processing for noise removal, to develop a fully non-parametric method for efficiency estimation. Publications 1 and 2 are devoted to this topic, with focus being laid on the cross-sectional case and panel case, respectively. Through Monte-Carlo simulations and empirical applications to Finnish electricity distribution network data and Finnish banking data, the results show that the Richardson-Lucy blind deconvolution method is insensitive to the distributio-nal assumptions, robust to the data noise levels and heteroscedasticity on efficiency estimation. In benchmarking, which could be the next step of productive efficiency analysis, the 'best practice' target may not perform under the same operational environment with the DMU under study. This would render the benchmarks impractical to follow and adversely affects the managers to make the correct decisions on performance improvement of a DMU. This dissertation proposes a clustering-based benchmarking framework in Publication 3. The empirical study on Finnish electricity distribution network reveals that the proposed framework novels not only in its consideration on the differences of the operational environment among DMUs, but also its extreme flexibility. We conducted a comparison analysis on the different combinations of the clustering and efficiency estimation techniques using computational simulations and empirical applications to Finnish electricity distribution network data, based on which Publication 4 specifies an efficient combination for benchmarking in energy regulation.  This dissertation endeavors to solve problems in quantitative economics using interdisciplinary approaches. The methods developed benefit this field and the way how we approach the problems open a new perspective

Empirical Calibration of a Least-Cost Conservation Reserve Program

Mechanism design models typically conclude by characterizing an optimal allocation schedule based on the principal's beliefs regarding agent value functions and the distribution of agent types. This article addresses the question of how a principal can develop these beliefs given a standard cross-sectional data set in which agents' input-output choices are observable, but their underlying heterogeneity is not. I employ the methodology to evaluate strategies for reducing the cost of a voluntary program that reduces cultivation on environmentally-sensitive farmland.Environmental Economics and Policy,

What Determines the Technical Efficiency of a Firm? The Importance of Industry, Location, and Size

This paper investigates the factors that explain the level of technical efficiency of a firm. In our empirical analysis, we use a unique sample of about 35,000 firms in 256 industries from the German Cost Structure Census over the years 1992-2004. We estimate the technical efficiency of the firms and relate it to firm- and industry-specific characteristics. One third of the explanatory power is due to industry effects. Size accounts for another 25 percent and the headquarters? location explains ten percent of the variation in efficiency. Most other firm characteristics such as ownership structure, legal form, age of the firm and outsourcing activities have an extremely small explanatory power. R&D activity does not exert any positive influence on technical efficiency.Frontier analysis, determinants of technical efficiency, firm     performance, industry effects, regional effects.

Regulatory Benchmarking with Panel Data

This paper considers panel data procedures for regulatory benchmarking that allow for both latent heterogeneity and inefficiency, encapsulating the regulatory dilemma in comparative efficiency analysis for incentive regulation. It applies a distance function model with appropriate concavity properties for econometric estimation to a panel of electricity distribution utilities in Turkey, since electricity industry reform is a major policy issue there. The results confirm the importance of allowing simultaneously for heterogeneity and inefficiency and emphasise the need for specific time-invariant heterogeneity information, such as geographical data, on regulated utilities in different regions.efficiency and productivity analysis, regulation, electricity distribution.

Age-structured Human Capital and Spatial Total Factor Productivity Dynamics

This paper models total factor productivity (TFP) in space and proposes an empirical model for TFP interdependence across spatial locations. The interdependence is assumed to occur due to age-structured human capital dynamics. A semi-parametric spatial vector autoregressive framework is suggested for modeling spatial TFP dynamics where the role of demographic state and technological change are explicitly incorporated in the model to influence their spatial TFP co-movements. Empirical scrutiny in case of Asian countries suggests that cross-country human capital differences in their accumulation and appropriation pattern significantly influenced TFP volatility interdependence. The finding of complementarity in TFP in spatial locations calls for joint policy program for improving aggregate and individual country welfare.Total factor productivity, Spatial growth, Non-linearity, Human capital, Age-structure, Semi-parametric VAR