Measuring input-specific productivity change based on the principle of least action

In for-profit organizations, efficiency and productivity measurement with reference to the potential for input-specific reductions is particularly important and has been the focus of interest in the recent literature. Different approaches can be formulated to measure and decompose input-specific productivity change over time. In this paper, we highlight some problems within existing approaches and propose a new methodology based on the Principle of Least Action. In particular, this model is operationalized in the form of a non-radial Luenberger productivity indicator based on the determination of the least distance to the strongly efficient frontier of the considered production possibility sets, which are estimated by non-parametric techniques based upon Data Envelopment Analysis. In our approach, overall productivity change is the sum of input-specific productivity changes. Overall productivity change and input-specific changes are broken up into indicators of efficiency change and technical change. This decomposition enables the researcher to quantify the contributions of each production factor to productivity change and its components. In this way, the drivers of productivity development are revealed. For illustration purposes the new approach is applied to a recent dataset of Polish dairy processing firms.

[1]  Roman Urban,et al.  Polish Food Industry in 2008-2013 , 2014 .

[2]  Eduardo González,et al.  From efficiency measurement to efficiency improvement: The choice of a relevant benchmark , 2001, Eur. J. Oper. Res..

[3]  Joanna Wrzesińska-Kowal,et al.  Food production in Poland, compared to selected European Union Member States , 2014 .

[4]  O. H. Brownlee,et al.  ACTIVITY ANALYSIS OF PRODUCTION AND ALLOCATION , 1952 .

[5]  Léopold Simar,et al.  On Testing Equality of Distributions of Technical Efficiency Scores , 2006 .

[6]  Biresh K. Sahoo,et al.  Radial and non-radial decompositions of Luenberger productivity indicator with an illustrative application , 2011 .

[7]  A.G.J.M. Oude Lansink,et al.  Measurement of Input-Specific Productivity Growth with an Application to the Construction Industry in Spain and Portugal , 2014 .

[8]  Hristos Doucouliagos,et al.  The efficiency of the Australian dairy processing industry , 2000 .

[9]  Léopold Simar,et al.  Detecting Outliers in Frontier Models: A Simple Approach , 2003 .

[10]  Kaoru Tone,et al.  A slacks-based measure of efficiency in data envelopment analysis , 1997, Eur. J. Oper. Res..

[11]  Tim Coelli,et al.  A multi-stage methodology for the solution of orientated DEA models , 1998, Oper. Res. Lett..

[12]  Mikulas Luptacik,et al.  Productivity change in a multisectoral economic system , 2016 .

[13]  Xavier Irz,et al.  Competitiveness of Northern European dairy chains , 2014 .

[14]  Pekka Korhonen,et al.  Structural Comparison of Data Envelopment Analysis and Multiple Objective Linear Programming , 1998 .

[15]  R. Färe,et al.  Profit, Directional Distance Functions, and Nerlovian Efficiency , 1998 .

[16]  Gert van Dijk,et al.  Efficiency of Cooperatives and Investor Owned Firms Revisited , 2012 .

[17]  J. T. Pastor,et al.  A Quasi-Malmquist Productivity Index , 1998 .

[18]  Juan Aparicio,et al.  Closest targets and minimum distance to the Pareto-efficient frontier in DEA , 2007 .

[19]  Jesús T. Pastor,et al.  An enhanced DEA Russell graph efficiency measure , 1999, Eur. J. Oper. Res..

[20]  R. Solow TECHNICAL CHANGE AND THE AGGREGATE PRODUCTION FUNCTION , 1957 .

[21]  A. Lansink,et al.  Energy Productivity Growth in the Dutch Greenhouse Industry , 2006 .

[22]  Atsuhiko Kai,et al.  LEAST DISTANCE BASED INEFFICIENCY MEASURES ON THE PARETO-EFFICIENT FRONTIER IN DEA , 2012 .

[23]  Rolf Färe,et al.  Measuring the technical efficiency of production , 1978 .

[24]  Kristiaan Kerstens,et al.  Infeasibility and Directional Distance Functions with Application to the Determinateness of the Luenberger Productivity Indicator , 2009 .

[25]  Theodoros Skevas,et al.  Reducing pesticide use and pesticide impact by productivity growth: the case of dutch arable farming , 2014 .

[26]  W. Cooper,et al.  RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA , 1999 .

[27]  S. Grosskopf,et al.  PRODUCTIVITY GROWTH IN APEC COUNTRIES , 1996 .

[28]  Walter Briec,et al.  Hölder Distance Function and Measurement of Technical Efficiency , 1999 .

[29]  Kevin J. Fox,et al.  A decomposition of US business sector TFP growth into technical progress and cost efficiency components , 2018, Journal of Productivity Analysis.

[30]  Lawrence M. Seiford,et al.  Data envelopment analysis (DEA) - Thirty years on , 2009, Eur. J. Oper. Res..

[31]  Juan Aparicio,et al.  A note on "A directional slacks-based measure of technical inefficiency" , 2010 .

[32]  Magdalena Kapelko,et al.  Effect of Food Regulation on the Spanish Food Processing Industry: A Dynamic Productivity Analysis , 2015, PloS one.

[33]  W. Cook,et al.  Sales performance measurement in bank branches , 2001 .

[34]  Juan Aparicio,et al.  The determination of the least distance to the strongly efficient frontier in Data Envelopment Analysis oriented models: Modelling and computational aspects ☆ , 2016 .

[35]  Kazuyuki Sekitani,et al.  Distance optimization approach to ratio-form efficiency measures in data envelopment analysis , 2014 .

[36]  Kaoru Tone,et al.  A slacks-based measure of super-efficiency in data envelopment analysis , 2001, Eur. J. Oper. Res..

[37]  Bernhard Mahlberg,et al.  Eco-Efficiency and Eco-Productivity Change Over Time in a Multisectoral Economic System , 2013 .

[38]  M. Bergstrom Statistical yearbook of agriculture 1973. , 1973 .

[39]  Juan Aparicio,et al.  A well-defined efficiency measure for dealing with closest targets in DEA , 2013, Appl. Math. Comput..

[40]  Juan Aparicio,et al.  Decomposing technical inefficiency using the principle of least action , 2014, Eur. J. Oper. Res..

[41]  Kazuyuki Sekitani,et al.  Input-output substitutability and strongly monotonic p-norm least distance DEA measures , 2014, Eur. J. Oper. Res..

[42]  Magdalena Kapelko,et al.  Investment Age and Dynamic Productivity Growth in the Spanish Food Processing Industry , 2016 .

[43]  E. Tillard,et al.  Efficiency in milk production on Reunion Island: dealing with land scarcity. , 2009, Journal of dairy science.

[44]  Emmanuel Thanassoulis,et al.  Finding Closest Targets in Non-Oriented DEA Models: The Case of Convex and Non-Convex Technologies , 2003 .

[45]  Juan Aparicio,et al.  Families of linear efficiency programs based on Debreu’s loss function , 2012 .

[46]  Jesús T. Pastor,et al.  Units invariant and translation invariant DEA models , 1995, Oper. Res. Lett..

[47]  Juan Aparicio,et al.  Closest targets and strong monotonicity on the strongly efficient frontier in DEA , 2014 .

[48]  Elvira Silva,et al.  CO2 and Energy Efficiency of Different Heating Technologies in the Dutch Glasshouse Industry , 2003 .

[49]  Biresh K. Sahoo,et al.  Examining the drivers of total factor productivity change with an illustrative example of 14 EU countries , 2011 .

[50]  J. Florens,et al.  Nonparametric frontier estimation: a robust approach , 2002 .

[51]  William L. Weber,et al.  A directional slacks-based measure of technical inefficiency , 2009 .

[52]  R. Färe,et al.  The measurement of efficiency of production , 1985 .

[53]  J. Zofío,et al.  Evaluating Effectiveness in Public Provision of Infrastructure and Equipment: The Case of Spanish Municipalities , 2001 .

[54]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[55]  A. S. Camanho,et al.  Measurement of input-speci fi c productivity growth with an application to the construction industry in Spain and Portugal , 2015 .

[56]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[57]  Jin-Li Hu,et al.  The sources of bank productivity growth in China during 2002-2009: A disaggregation view , 2012 .