A trade hierarchy of cities based on transport cost thresholds

Empirical evidence is lagging behind in explaining trade agglomerations in short distances. Departing from a novel micro-database on road freight shipments within Spain for the period 2003-2007, we decompose cities (municipal) trade flows into the extensive and intensive margins to assess trade frictions and trade concentration by way of a unique generalized transport cost measure and three internal borders, NUTS-5 (municipal), NUTS-3 (provincial) and NUTS-2 (regional). We discover a stark accumulation of trade flows up to a transport cost value of e189 (170km approx.) and conclude that this high density is not explained by the existence of administrative borders effects but to significant changes in the trade-to-transport costs relationship. To support this hypothesis, we propose and adopt an endogenous Chow test to identify significant thresholds at which trade flows change structurally with distance. These breakpoints allow us to split the sample when controlling for internal borders, and define trade market areas corresponding to specific transport costs values that consistently portrait an urban hierarchical system of cities, thereby providing clear evidence of the predictions made by the central place theory.JRC.B.3 - Territorial Developmen

Who leads research productivity growth? Guidelines for R&D policy-makers

[EN] This paper evaluates to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. Malmquist productivity indices are applied in the case of the Spanish Food Technology Program, finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. While identifying these groups as benchmarks, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research groups, have not succeeded in reorienting them in time so as to overcome their limitations. We suggest that this methodology offers relevant conclusions to policy evaluation methods, helping policy-makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro-level).Jiménez Saez, F.; Zabala Iturriagagoitia, JM.; Zofio, JL. (2013). Who leads research productivity growth? Guidelines for R&D policy-makers. Scientometrics. 94(1):273-303. doi:10.1007/s11192-012-0763-0S273303941Abbring, J. H., & Heckman, J. J. (2008). Dynamic policy analysis. In L. Mátyás & P. Sevestre (Eds.), The econometrics of panel data (3rd ed., pp. 795–863). Heidelberg: Springer.Acosta Ballesteros, J., & Modrego Rico, A. (2001). 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(1987). Technology policy and economic performance: Lessons from Japan. London: Printer Publishers.García-Martínez, M., & Briz, J. (2000). Innovation in the Spanish food & drink industry. International Food and Agribusiness Management Review, 3, 155–176.Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage Publications.Grammatikopoulos, V., Kousteiios, A., Tsigilis, N., & Theodorakis, Y. (2004). Applying dynamic evaluation approach in education. Studies in Educational Evaluation, 30, 255–263.Grifell-Tatjé, E., & Lovell, C. A. K. (1999). A generalized Malmquist productivity index. Top, 7(1), 81–101.Grimpe, C., & Sofka, W. (2007). Search patterns and absorptive capacity: A comparison of low- and high-technology firms from thirteen European countries. Discussion paper no. 07-062. 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Working paper, Department of Economics, University of Georgia, Athens, GA 30602, USA.Zofio, J. L., & Lovell, C. A. K. (2001). Graph efficiency and productivity measures: An application to US agriculture. Applied Economics, 33(10), 1433–1442.Zofio, J. L., & Prieto, A. M. (2006). Return to dollar, generalized distance function and the Fisher productivity index. Spanish Economic Review, 8, 113–138

Modelos de estimacion de eficiencia tecnica Una aplicacion a los sectores industriales de la OCDE

Centro de Informacion y Documentacion Cientifica (CINDOC). C/Joaquin Costa, 22. 28002 Madrid. SPAIN / CINDOC - Centro de Informaciòn y Documentaciòn CientìficaSIGLEESSpai

Drivers of changes in Spanish accessibility for the 1960–2010 period

Purpose The accessibility of a certain place can evolve either as the direct result of transport changes or as a consequence of the spatial redistribution of economic activities. These two factors are often indistinguishable—especially at regional level—since improved infrastructure stimulates relocation of activities. Moreover, infrastructure investment choices tend to follow population and economic activity patterns, distorting the cause and effect relationship between infrastructure and accessibility even further. The methodology and results presented here decompose the impact of both factors in terms of accessibility using Spanish data between 1960 and 2010. During this period, Spain experienced profound changes in transport infrastructure and economic activity. Methods We use the potential accessibility indicator and resort to index number theory to disentangle the contribution of transport infrastructure from that of land-use changes. Detailed historical data on road infrastructure and population is used to represent the transport and land-use components of accessibility. Results Our results show that changes in transport infrastructure had a relevant impact on accessibility, as expected, but changes in the spatial distribution of population had an even greater effect. This outcome may be used as an argument for sustainable accessibility, a concept that advocates integration of transport and land use planning.JRC.C.6-Economics of Climate Change, Energy and Transpor

A toolbox for calculating and decomposing Total Factor Productivity indices

Total Factor Productivity Toolbox is a new set of functions to calculate the main Total Factor Productivity (TFP) indices and their decompositions, based on Shephard's distance functions, and using Data Envelopment Analysis (DEA) programming techniques. The package includes code for the standard Malmquist, Moorsteen-Bjurek, price-weighted and share-weighted TFP indices, allowing for the choice of orientation (input or output), reference period (base, comparison, geometric mean), returns to scale (variable or constant), and specific decompositions (aggregate, or identifying scale effects, as well as input and output mix effects). Classic definitions of TFP corresponding to the Laspeyres, Paasche, Fisher, or Törnqvist formulas can also be calculated as particular cases. This paper describes the methodology and implementation of the productivity functions in MATLAB. We compare the results corresponding to the different definitions by studying productivity trends in the US agriculture at the individual state level.JRC.B.3-Territorial Developmen

Benchmarking performance through efficiency analysis trees:Improvement strategies for colombian higher education institutions

We introduce benchmarking analysis based on state-of-the-art machine learning techniques applied to the measurement of efficiency to assess the performance of Higher Education Institutions (HEIs). We rely on Efficiency Analysis Trees (EAT) and its Convexified frontier counterpart (CEAT) to assess the efficiency of 144 private HEIs in Colombia and compare the results with those achieved with classical Data Envelopment Analysis (DEA). Both EAT and CEAT show a higher discriminatory power than DEA when determining efficiency scores. Our results identify the different splits of the production frontier, corresponding to each node of the efficiency tree, which groups HEIs according to specific management models. By identifying relevant peers for inefficient observations at the node level, we show which strategic guidelines can be adopted to improve the performance of each HEI. This process encourages mutual learning and suggests potential changes within each node leading to efficiency improvements.</p

Benchmarking performance through efficiency analysis trees:Improvement strategies for colombian higher education institutions

We introduce benchmarking analysis based on state-of-the-art machine learning techniques applied to the measurement of efficiency to assess the performance of Higher Education Institutions (HEIs). We rely on Efficiency Analysis Trees (EAT) and its Convexified frontier counterpart (CEAT) to assess the efficiency of 144 private HEIs in Colombia and compare the results with those achieved with classical Data Envelopment Analysis (DEA). Both EAT and CEAT show a higher discriminatory power than DEA when determining efficiency scores. Our results identify the different splits of the production frontier, corresponding to each node of the efficiency tree, which groups HEIs according to specific management models. By identifying relevant peers for inefficient observations at the node level, we show which strategic guidelines can be adopted to improve the performance of each HEI. This process encourages mutual learning and suggests potential changes within each node leading to efficiency improvements.</p

The influence of bottlenecks on innovation systems performance:Put the slowest climber first

This paper contributes to the literature with a methodology that helps identify the functions that constrain the overall performance of an innovation system, hence providing clear guidelines to policymakers on the direction of their interventions. This methodology relies on the notion of penalty for bottleneck, which is defined as the weakest link or the binding constraint that holds back system performance. These penalty bottlenecks are applied to all the indicators that characterize innovation systems, and consider its input-output mix when assessing their performance through a Productivity Innovation Index. The data provided by the 2021 edition of the European Innovation Scoreboard are used to illustrate the utility of the method introduced in the paper. We first identify the input and output bottlenecks for every country. Second, we report the productivity loss due to the existence of these bottlenecks. Third, we evidence the responsiveness of the Productivity Innovation Index to bottleneck alleviation, from three different perspectives: (i) application of a 10 % alleviation to the input bottleneck; (ii) application of a 10 % alleviation to the output bottleneck; and (iii) application of a 5 % alleviation to both the input and output bottlenecks, respectively