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Meta-Analysis on Agglomeration Externalities

Meta-Analysis on Agglomeration Externalities. Henri L.F. de Groot Dept. of Spatial Economics VU University Amsterdam. This presentation. Brief introduction in meta-analysis Basics, ‘what’ and ‘why’, caveats Applications in the field of agglomeration Melo et al. on productivity-density

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Meta-Analysis on Agglomeration Externalities

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  1. Meta-Analysis on Agglomeration Externalities Henri L.F. de Groot Dept. of Spatial Economics VU University Amsterdam

  2. This presentation • Brief introduction in meta-analysis • Basics, ‘what’ and ‘why’, caveats • Applications in the field of agglomeration • Melo et al. on productivity-density • Update of De Groot, Poot and Smit on MAR, Jacobs and Porter • Discussion DIME Workshop - Pecs

  3. A brief ‘history’ of economic research • Exponential growth of studies, and knowledge (?) • Competition of ideas • Mostly narrative reviews of empirical research • Need for ‘stylized facts’ where available and location- and time-specific evidence where relevant • Policy makers in need of information to underpin policy initiatives DIME Workshop - Pecs

  4. Why meta-analysis? • Answer to the “flood of numbers” (Heckman, 2001) • Take stock of knowledge and accumulate instead of duplicate (answer to the “lack of collective memory”) • Methodology of plausible inference (Goldfarb, 1995) • Tool to make sense of ‘hotch potch’ of research findings • Application of value transfer • Potential to yield ‘new’ insights DIME Workshop - Pecs

  5. What is meta-analysis? Meta-analysis refers to the statisticalanalysis of a large collection of results from individual studies for the purpose of integrating the findings. It connotes a rigorous alternative to the causal, narrative discussions of research studies which typify our attempt to make sense of the rapidly expanding research literature. Glass (1976) DIME Workshop - Pecs

  6. Pivotal elements of the definition • Statistical: toolbox • Rigorous or ‘objective’ or ‘systematic’ • Integration of large collection of studies • Alternative for narrative reviews … or complement DIME Workshop - Pecs

  7. A brief history of meta-analysis • Strong tradition in medicine and psychology • Natural to apply in experimental sciences • Increase sample size by combining experiments • “Tool to save lives” DIME Workshop - Pecs

  8. Meta-analysis in economics • Relatively recent (since early 1980s) • Started in environmental economics • More recently used in labor economics, industrial organization, marketing, macroeconomics • Characteristics and trends of meta-analysis in economics (from Florax and Poot, Sønderborg, 2007) DIME Workshop - Pecs

  9. Exponential growth – 22% annually DIME Workshop - Pecs

  10. Citations (evidence from 2007) • Within business and economics broadly, meta-analysis remains more cited in marketing and finance than in economics • Most cited (515 GSC) is Sheppard et al. (1988) in J. of Consumer Research, on the theory on reasoned action • In mainstream economics, 15 meta-analyses have more than or close to 100 GSC • The average number of GSC is 38.5 (with st. dev. of 56.8) DIME Workshop - Pecs

  11. Distribution across fields DIME Workshop - Pecs

  12. How to do a meta-analysis? • Selection of topic • Construct representative sample of studies • Construct a database • Perform the statistical analysis • Description of effect sizes • Exploratory analysis (e.g., with visual tools) • Analysis of variance • Test for heterogeneity and dependence • Meta-regression DIME Workshop - Pecs

  13. By means of an intermediate conclusion • Useful methodology • Needs theoretical underpinning (no empiricism) • Reasonably objective (at least transparent) • Several applications in applied and policy-oriented research • Time-consuming but rewarding exercise • Several problems and pitfalls, but in many cases surmountable • May provide guidance for future theory development and empirics DIME Workshop - Pecs

  14. Agglomeration… DIME Workshop - Pecs

  15. Context: ‘Death of distance debate’ • Advent of ICT: reduction of transaction costs • Are cities dying? • End of industrial complexes? • Convergence of rich and poor regions and countries? DIME Workshop - Pecs

  16. Agglomeration forces In equilibrium: balance between these forces Centripetal forces Centrifugal forces Market size (‘linkages’) Location of inputs ‘Thick’ labour markets Land rents Pure external economies of scale Pure external diseconomies of scale Source: Krugman (1999; IRSR); also Glaeser (1998) DIME Workshop - Pecs

  17. Determinants of urban growth Dynamic externalities Marshall-Arrow-Romer (scale, specialisation) Jacobs (diversity) Porter (competition) Theoretical background technology as residual (Solow, 1956) endogenous growth (cf. Romer) new economic geography (agglomeration) evolutionary economics (diversity) DIME Workshop - Pecs

  18. Seminal contribution Glaeser et al. (1992), Growth in Cities, JPE employment growth as proxy for technological progress growth in sector-region combination as unit of observation cities in USA over long period: 1956-1987 effect of three externalities MAR: location-quotient Porter: average firm size relative to national average Jacobs: employment share of five largest sectors DIME Workshop - Pecs

  19. Developments after Glaeser et al. (1992) Many papers ( > 300) Differences: type of dependent variable operationalisationof specialisation, competitionand diversity country / region considered spatial aggregation level estimation technique sectors considered Need for synthesis: meta-analysis DIME Workshop - Pecs

  20. Characterisation meta-database Elimination of irrelevant/non-empirical studies 73 studies remaining (1992–2009) 392 estimated equations yielding 786 effect sizes of which 358 effect sizes at aggregate level from 45 studies DIME Workshop - Pecs

  21. Analysis Ordinal variable (from significantly negative to positive) Step 1: vote counting first impression of dataset and variation therein Step 2: meta-regression analysis explaining observed heterogeneity DIME Workshop - Pecs

  22. Vote counting 316 170 301 DIME Workshop - Pecs

  23. Results – I DIME Workshop - Pecs

  24. Results – II DIME Workshop - Pecs

  25. Results – III DIME Workshop - Pecs

  26. Results – IV DIME Workshop - Pecs

  27. Meta-regression – Summary • Operationalisationof dependent variable matters • Results are heterogeneous across • Space • Tme • Sector • Operationalisation explanatory variables matters • Impact of correlation specialisation, competition and diversity • Impact of inclusion of physical and human capital • Use of microdate • Evidence for selection effects DIME Workshop - Pecs

  28. Conclusions – I • Meta-analysis as a useful tool • Need for synthesis and replication • For different locations and different time periods • In view of mounting evidence from micro-data • Evidence for selection effects in literatures DIME Workshop - Pecs

  29. Conclusions – II Diversityand competition important drivers Operationalization of variables matters Role of agglomerative forces changing over time Role of agglomerative forces varying across space But also need for more primary research DIME Workshop - Pecs

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