Aggregation of Malmquist productivity indexes allowing for reallocation of resources

In this paper we consider aggregate Malmquist productivity index measures which allow inputs to be reallocated within the group (when in output orientation). This merges the single period aggregation results allowing input reallocation of Nesterenko and Zelenyuk (2007) with the aggregate Malmquist productivity index results of Zelenyuk (2006) to determine aggregate Malmquist productivity indexes that are justified by economic theory, consistent with previous aggregation results, and which maintain analogous decompositions to the original measures. Such measures are of direct relevance to firms or countries who have merged (making input reallocation possible), allowing them to measure potential productivity gains and how these have been realised (or not) over time

Understanding Productivity Changes in Public Universities: Evidence from Spain

This paper describes the dynamic changes in productivity in Spanish public universities (SPU) in the period 1994 to 2008. The Malmquist index is used to illustrate the contribution of efficiency and technological change to changes in the productivity of university activities. The results indicate that annual productivity growth is attributable more to efficiency improvements than technological progress. Gains in scale efficiency appear to play only a minor role in productivity gains. The fact that technical efficiency contributes more than technological progress suggests that most universities are not operating close to the best-practice frontier.Garcia Aracil, A. (2013). Understanding Productivity Changes in Public Universities: Evidence from Spain. Research Evaluation. 22(5):351-368. doi:10.1093/reseval/rvt009S351368225Agasisti, T., Catalano, G., Landoni, P., & Verganti, R. (2012). Evaluating the performance of academic departments: an analysis of research-related output efficiency. Research Evaluation, 21(1), 2-14. doi:10.1093/reseval/rvr001Agasisti, T., & Pérez-Esparrells, C. (2009). Comparing efficiency in a cross-country perspective: the case of Italian and Spanish state universities. Higher Education, 59(1), 85-103. doi:10.1007/s10734-009-9235-8ARCELUS‡, F. J., & Coleman‡§, D. F. (1997). An efficiency review of university departments. International Journal of Systems Science, 28(7), 721-729. doi:10.1080/00207729708929431Athanassopoulos, A. D., & Shale, E. (1997). Assessing the Comparative Efficiency of Higher Education Institutions in the UK by the Means of Data Envelopment Analysis. Education Economics, 5(2), 117-134. doi:10.1080/09645299700000011Attewell, P., Heil, S., & Reisel, L. (2012). What Is Academic Momentum? And Does It Matter? Educational Evaluation and Policy Analysis, 34(1), 27-44. doi:10.3102/0162373711421958Balk, B. M. (1993). Malmquist Productivity Indexes and Fisher Ideal Indexes: Comment. The Economic Journal, 103(418), 680. doi:10.2307/2234540Beasley, J. E. (1990). Comparing university departments. Omega, 18(2), 171-183. doi:10.1016/0305-0483(90)90064-gBeasley, J. E. (1995). Determining Teaching and Research Efficiencies. Journal of the Operational Research Society, 46(4), 441-452. doi:10.1057/jors.1995.63Bessent, A. M., & Bessent, E. W. (1980). Determining the Comparative Efficiency of Schools through Data Envelopment Analysis. Educational Administration Quarterly, 16(2), 57-75. doi:10.1177/0013161x8001600207Bonaccorsi, A., Daraio, C., & Simar, L. (2006). Advanced indicators of productivity of universitiesAn application of robust nonparametric methods to Italian data. Scientometrics, 66(2), 389-410. doi:10.1007/s11192-006-0028-xBonaccorsi, A., Daraio, C., Lepori, B., & Slipersæter, S. (2007). Indicators on individual higher education institutions: addressing data problems and comparability issues. Research Evaluation, 16(2), 66-78. doi:10.3152/095820207x218141Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity. Econometrica, 50(6), 1393. doi:10.2307/1913388Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8Coelli, T., & Perelman, S. (1999). A comparison of parametric and non-parametric distance functions: With application to European railways. European Journal of Operational Research, 117(2), 326-339. doi:10.1016/s0377-2217(98)00271-9Cohn, E., Rhine, S. L. W., & Santos, M. C. (1989). Institutions of Higher Education as Multi-Product Firms: Economies of Scale and Scope. The Review of Economics and Statistics, 71(2), 284. doi:10.2307/1926974COSTAS, R., & BORDONS, M. (2007). The h-index: Advantages, limitations and its relation with other bibliometric indicators at the micro level. Journal of Informetrics, 1(3), 193-203. doi:10.1016/j.joi.2007.02.001De Groot, H., McMahon, W. W., & Volkwein, J. F. (1991). The Cost Structure of American Research Universities. The Review of Economics and Statistics, 73(3), 424. doi:10.2307/2109566F�re, R., Grosskopf, S., & Lovell, C. A. K. (1992). Indirect productivity measurement. Journal of Productivity Analysis, 2(4), 283-298. doi:10.1007/bf00156471Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253. doi:10.2307/2343100Flegg, A. T., & Allen, D. O. (2007). Does Expansion Cause Congestion? The Case of the Older British Universities, 1994–2004. Education Economics, 15(1), 75-102. doi:10.1080/09645290601133928FLEGG, A. T., ALLEN, D. O., FIELD, K., & THURLOW, T. W. (2004). Measuring the efficiency of British universities: a multi‐period data envelopment analysis. Education Economics, 12(3), 231-249. doi:10.1080/0904529042000258590García-Aracil, A., & Palomares-Montero, D. (2009). Examining benchmark indicator systems for the evaluation of higher education institutions. Higher Education, 60(2), 217-234. doi:10.1007/s10734-009-9296-8García-Aracil, A., & Palomares-Montero, D. (2012). Indicadores para la evaluación de las instituciones universitarias: validación a través del método Delphi. Revista española de Documentación Científica, 35(1), 119-144. doi:10.3989/redc.2012.1.863Giménez, V. M., & Martínez, J. L. (2006). Cost efficiency in the university: A departmental evaluation model. Economics of Education Review, 25(5), 543-553. doi:10.1016/j.econedurev.2005.05.006Glass, J. C., McKillop, D. G., & O’Rourke, G. (1998). Journal of Productivity Analysis, 10(2), 153-175. doi:10.1023/a:1018607223276Grifell-Tatjé, E., & Lovell, C. A. K. (1999). A generalized Malmquist productivity index. Top, 7(1), 81-101. doi:10.1007/bf02564713Grosskopf, S., Margaritis, D., & Valdmanis, V. (1995). Estimating output substitutability of hospital services: A distance function approach. European Journal of Operational Research, 80(3), 575-587. doi:10.1016/0377-2217(94)00138-3Jiménez-Contreras, E., de Moya Anegón, F., & López-Cózar, E. D. (2003). The evolution of research activity in Spain. Research Policy, 32(1), 123-142. doi:10.1016/s0048-7333(02)00008-2Johnes, G. (1988). Determinants of research output in economics departments in British universities. Research Policy, 17(3), 171-178. doi:10.1016/0048-7333(88)90041-8JOHNES, J. (2008). EFFICIENCY AND PRODUCTIVITY CHANGE IN THE ENGLISH HIGHER EDUCATION SECTOR FROM 1996/97 TO 2004/5*. Manchester School, 76(6), 653-674. doi:10.1111/j.1467-9957.2008.01087.xJohnes, G., & Schwarzenberger, A. (2011). Differences in cost structure and the evaluation of efficiency: the case of German universities. Education Economics, 19(5), 487-499. doi:10.1080/09645291003726442JOHNES, J., & YU, L. (2008). Measuring the research performance of Chinese higher education institutions using data envelopment analysis. China Economic Review, 19(4), 679-696. doi:10.1016/j.chieco.2008.08.004Koshal, R. K., & Koshal, M. (1999). Economies of scale and scope in higher education: a case of comprehensive universities. Economics of Education Review, 18(2), 269-277. doi:10.1016/s0272-7757(98)00035-1Kortelainen, M. (2008). Dynamic environmental performance analysis: A Malmquist index approach. Ecological Economics, 64(4), 701-715. doi:10.1016/j.ecolecon.2007.08.001Laudel, G. (2005). Is external research funding a valid indicator for research performance? Research Evaluation, 14(1), 27-34. doi:10.3152/147154405781776300Lovell, C. A. K. (2003). Journal of Productivity Analysis, 20(3), 437-458. doi:10.1023/a:1027312102834Lucas, S. R., & Beresford, L. (2010). Naming and Classifying: Theory, Evidence, and Equity in Education. Review of Research in Education, 34(1), 25-84. doi:10.3102/0091732x09353578Madden, G., Savage, S., & Kemp, S. (1997). Measuring Public Sector Efficiency: A Study of Economics Departments at Australian Universities. Education Economics, 5(2), 153-168. doi:10.1080/09645299700000013Abbott, M., & Doucouliagos, C. (2001). Total factor productivity and efficiency in Australian colleges of advanced education. Journal of Educational Administration, 39(4), 384-393. doi:10.1108/eum0000000005497Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadistica, 4(2), 209-242. doi:10.1007/bf03006863Mamun, S. A. K. (2012). Stochastic estimation of cost frontier: evidence from Bangladesh. Education Economics, 20(2), 211-227. doi:10.1080/09645292.2010.494836Maniadakis, N., & Thanassoulis, E. (2004). A cost Malmquist productivity index. European Journal of Operational Research, 154(2), 396-409. doi:10.1016/s0377-2217(03)00177-2Molinero, C. M. (1996). On the Joint Determination of Efficiencies in a Data Envelopment Analysis Context. Journal of the Operational Research Society, 47(10), 1273-1279. doi:10.1057/jors.1996.154Molinero, C. M., & Tsai, P. F. (1997). Some mathematical properties of a DEA model for the joint determination of efficiencies. Journal of the Operational Research Society, 48(1), 51-56. doi:10.1057/palgrave.jors.2600327McLendon, M. K., Hearn, J. C., & Deaton, R. (2006). Called to Account: Analyzing the Origins and Spread of State Performance-Accountability Policies for Higher Education. Educational Evaluation and Policy Analysis, 28(1), 1-24. doi:10.3102/01623737028001001Monk, D. H. (1992). Education Productivity Research: An Update and Assessment of Its Role in Education Finance Reform. Educational Evaluation and Policy Analysis, 14(4), 307-332. doi:10.3102/01623737014004307Nishimizu, M., & Page, J. M. (1982). Total Factor Productivity Growth, Technological Progress and Technical Efficiency Change: Dimensions of Productivity Change in Yugoslavia, 1965-78. The Economic Journal, 92(368), 920. doi:10.2307/2232675Rodrı́guez-Álvarez, A., Fernández-Blanco, V., & Lovell, C. A. K. (2004). Allocative inefficiency and its cost: International Journal of Production Economics, 92(2), 99-111. doi:10.1016/j.ijpe.2003.08.012Salerno, C. (2006). Using Data Envelopment Analysis to Improve Estimates of Higher Education Institution’s Per‐student Education Costs1. Education Economics, 14(3), 281-295. doi:10.1080/09645290600777485Sarafoglou, N., & Haynes, K. E. (1996). University productivity in Sweden: a demonstration and explanatory analysis for economics and business programs. The Annals of Regional Science, 30(3), 285-304. doi:10.1007/bf01580523Schmoch, U., Schubert, T., Jansen, D., Heidler, R., & von Görtz, R. (2010). How to use indicators to measure scientific performance: a balanced approach. Research Evaluation, 19(1), 2-18. doi:10.3152/095820210x492477New, B. (1997). The rationing debate: Defining a package of healthcare services the NHS is responsible for The case for. BMJ, 314(7079), 498-498. doi:10.1136/bmj.314.7079.498Sinuany-Stern, Z., Mehrez, A., & Barboy, A. (1994). Academic departments efficiency via DEA. Computers & Operations Research, 21(5), 543-556. doi:10.1016/0305-0548(94)90103-1Tomkins, C., & Green, R. (1988). AN EXPERIMENT IN THE USE OF DATA ENVELOPMENT ANALYSIS FOR EVALUATING THE EFFICIENCY OF UK UNIVERSITY DEPARTMENTS OF ACCOUNTING. Financial Accountability and Management, 4(2), 147-164. doi:10.1111/j.1468-0408.1988.tb00066.xUri, N. D. (2003). Technical efficiency in telecommunications in the United States and the impact of incentive regulation. Applied Mathematical Modelling, 27(1), 53-67. doi:10.1016/s0307-904x(02)00098-7Uri, N. D. (2003). The adoption of incentive regulation and its effect on technical efficiency in telecommunications in the United States. International Journal of Production Economics, 86(1), 21-34. doi:10.1016/s0925-5273(03)00002-1Vidal, J. (2003). Quality Assurance, Legal Reforms and the European Higher Education Area in Spain. European Journal of Education, 38(3), 301-313. doi:10.1111/1467-3435.00149Williams, J. D., & Kerckhoff, A. C. (1995). The Challenge of Developing New Educational Indicators. Educational Evaluation and Policy Analysis, 17(1), 113-131. doi:10.3102/01623737017001113Worthington, A. C., & Lee, B. L. (2008). Efficiency, technology and productivity change in Australian universities, 1998–2003. Economics of Education Review, 27(3), 285-298. doi:10.1016/j.econedurev.2006.09.01

Productivity in services twenty years on. A review of conceptual and measurement issues and a way forward

Griliches' seminal contribution on "Output measurement in the service sectors" (1992) is now more than twenty years old. The aim of this paper is to review and systematise the scholarship that has been produced since, to identify any step forward in the conceptualisation of service output, the measurement of service productivity and the account of technical change in affecting productivity in services that might have occurred. An agenda for both innovation and service scholars is proposed

Comprehensive assessment of the efficiency and sustainability of the regional health care system

Оценка деятельности системы здравоохранения в современных условиях - актуальное направление исследований; труды отечественных ученых показывают отсутствие системного подхода к определению эффективности и устойчивости российского здравоохранения. Зарубежный опыт показывает, что, несмотря на широкомасштабную научную работу, по данным Европейского регионального бюро Всемирной организации здравоохранения, на сегодняшний день разработано недостаточно показателей эффективности работы системы здравоохранения. Рассмотрена многогранность оценки эффективности отрасли, дано определение устойчивости применительно к системе здравоохранения. С использованием метода многомерного сравнительного анализа, метода детерминированного факторного анализа, структурного анализа, метода экспертных оценок, статистических методов моделирования и прогнозирования разработан методический аппарат комплексной оценки эффективности и устойчивости региональной системы здравоохранения. Методический инструментарий включает комплексную оценку относительной эффективности и комплексную оценку относительной устойчивости региональной системы здравоохранения на основе разработанных интегральных показателей. Методический инструментарий апробирован на примере системы здравоохранения Свердловской области в 2017-2018 гг. На интегральные коэффициенты относительной эффективности региональной системы здравоохранения оказывают непосредственное влияние показатели медико-социальной результативности, которые во многом зависят от уровня финансирования и управления отрасли. В то же время показатели относительной эффективности могут быть высокими даже при недостижении нормативного уровня относительной устойчивости. Предложен интегральный коэффициент уровня риска, учитывающий необходимость сохранения устойчивости системы здравоохранения для стабильного функционирования и развития. Построена интерактивная модель, позволяющая определить зону риска и безопасную зону для региональной системы здравоохранения. Разработанный методический инструментарий является универсальным и позволяет объективно оценить уровень эффективности и состояние устойчивости региональной системы здравоохранения. Дальнейшие исследования в области оценки эффективности и устойчивости российского здравоохранения позволят совершенствовать предложенный методический аппарат с точки зрения расширения охвата факторов, влияющих на развитие отрасли.Nowadays, the sustainability of the health care system is a relevant research topic. The works of Russian scientists demonstrate the lack of a systematic approach to determining the efficiency and sustainability of the Russian health care. International experience and the data of the Regional Office for Europe of the World Health Organization (WHO) show that, despite extensive research, efficiency indicators of the health care system have been insufficiently developed. Using the methods of multidimensional comparative analysis, determined factor analysis, structural analysis, expert assessment, statistical modelling and forecasting methods, we developed a methodology for the comprehensive assessment of the efficiency and sustainability of the regional health care system. The methodological toolkit includes the comprehensive assessment of the relative efficiency and relative sustainability of the regional health care system based on the established integral indicators. We tested this methodology on the example of the health care system of Sverdlovsk Oblast in the period 2017-2018. Integral indicators of the relative efficiency are directly influenced by the indicators of medical and social performance, which largely depend on the funding and management of the health care system. Simultaneously, the indicators of the relative efficiency can be high even if the indicators of the relative sustainability did not reach the established threshold. An integral indicator of the risk level considers the need to maintain the sustainability of the health care system for its functioning and development. Further, we constructed an interactive model for determining the risk zone and safe zone of the health care system. Due to its versatility, the proposed methodological toolkit allows an objective assessment of the efficiency and sustainability of the regional health care system.Исследование проведено при поддержке Российского фонда фундаментальных исследований, грант №19–010–00396 «Эффективность системы здравоохранения как фактор устойчивого социально-экономического развития регионов».The article has been prepared with the support of Russian Foundation for Basic Research, the grant No. 19–010–00396 “Effectiveness of health system as a factor of sustainable social and economic development of regions

Innovation Systems and Technical Efficiency in Developing-Country Agriculture

The paper uses a stochastic frontier analysis of production functions to estimate the level of technical efficiency in agriculture for a panel of 29 developing countries in Africa and Asia between 1994 and 2000. In addition, the paper examines how different components of an agricultural innovation system interact to determine the estimated technical inefficiencies. Results show that the mean level of technical efficiency among the sampled countries was about 86 percent, with some modest increases during the period in question. These results suggest that there is room for significant increases of production through reallocations of existing resources. Despite significant variation among countries, these results also indicate quite a number of least developed countries have high mean efficiency scores, implying a need to focus on investment that pushes the production frontier outward in these countries. Several measures of agricultural R&D achievement and intensity, along with educational enrollment, are found to enhance agricultural efficiency. On the other hand, countries with higher levels of official development assistance, foreign direct investment, and a greater share of land under irrigation are found to be performing poorly in their agricultural efficiency score.agricultural innovation systems, technical efficiency, developing country agriculture, Agricultural and Food Policy, Crop Production/Industries, Food Security and Poverty, International Development, Production Economics, Productivity Analysis,

Structural change in developing countries:patterns, causes and consequences

The main objective of the thesis is to investigate the patterns, causes, and (labor market) consequences of structural change in developing countries especially those in Africa. The thesis begins with the premise that our understanding of structural change in Africa is limited by a great statistical problem. Building on the existing work, the thesis produces a new sectoral database for Africa. Using this database, which reflects current sectoral development in Africa, the thesis establishes many empirical regularities about the patterns, causes and consequences of structural change in Africa

The portuguese public hospitals performance evolution before and during the SARS-CoV-2 Pandemic (2017–2022)

COVID-19 is a disease caused by SARS-CoV-2, which has spread worldwide since the beginning of 2020. Several pharmaceutical and non-pharmaceutical strategies were proposed to contain the virus, including vaccination and lockdowns. One of the consequences of the pandemic was the denial or delay of access to convenient healthcare services, but also potentially the increase in adverse events within those services, like the number of hospital infections. Therefore, the main question here is about what happened to the performance of Portuguese public hospitals. The main goal of this work was to test if the Portuguese public hospitals' performance has been affected by the SARS-CoV-2 pandemic. We used the Benefit-of-Doubt method integrated with the Malmquist Index to analyze the performance evolution over time. Then, we employed a multiple regression model to test whether some pandemic-related variables could explain the performance results. We considered a database of 40 Portuguese public hospitals evaluated from January 2017 to May 2022. The period 2017 to 2019 corresponds to the baseline (pre-pandemic), against which the remaining period will be compared (during the pandemic). We also considered fourteen variables characterizing hospital quality, divided into three main performance definitions (efficiency and productivity; access; safety and care appropriateness). As potential explanatory variables, we consider seven dimensions, including vaccination rate and the need for intensive care for COVID-19-infected people. The results suggest that COVID-19 pandemic features help explain the drop in access after 2020, but not the evolution of safety and appropriateness of care, which surprisingly increased the whole time.info:eu-repo/semantics/publishedVersio