Empirical Methodologies to Research Agglomeration Externalities

1. Empirical Methodologies to Research Agglomeration Externalities Frank van Oort 02-07-2009

2. Content Empirical methodologies of agglomeration externalities • Growth in cities Spatial dependence Spatial heterogeneity Related variety 2. Employment-population Causality 3. Scale issues MAUP Multilevel issues • Contexts Locations or networks ---------------------------------------------------------------------- 5. Policy analysis Indicators of clusters

3. Content Empirical methodologies of agglomeration externalities • Growth in cities Spatial dependence Spatial heterogeneity Related variety 2. Employment-population Causality 3. Scale issues MAUP Multilevel issues • Contexts Locations or networks ---------------------------------------------------------------------- 5. Policy analysis Indicators of clusters

4. Basic principles – externalities in cities “My purpose is to show that cities are primary economic organs” (Jacobs 1969, p.6). “Development is a process of continuously improving in a context that makes injecting improvisations feasible. Cities create that context. Nothing else does” (Jacobs 1984, p.155). “The city is not only the place where growth occurs, but also the engine of growth itself” (Duranton 2000, p.291-292). “Large cities have been and will continue to be an important source of economic growth” (Quigly 1998, p.137). “Agglomeration can be considered the territorial counterpart of economic growth” (Fujita and Thisse 2002, p.389).

5. Growth and innovation externalities Spillovers Agglomeration Clusters Regional Innovation System Knowledge Economy Knowledge Production Function

6. Knowledge spillovers are: • unpaid externalities, • causes of (cumulative) economic growth, • causes of innovation-diffusion, • in urban contexts: agglomeration-economies, • according to NEG-economists, not measurable, • according to many, not measurable in space, • according to some, related to specialised, diversified and urban localised industries, • according to many scale-free, • embedded in endogenous growth models, • embedded in evolutionary economic theory • often related to innovative firms, knowledge institutions, growing firms or the emergence of new firms, • often interpretable as location factors of firms (clusters), and hence interesting for policy

7. Example: the Dutch ICT-sector Spatial factors (externalities) that econometrically relate to firm growthin ICT-firms Theory: endogenous growth theory & externalities, evolutionary economic geography Hypotheses on agglomeration economies: specialisation, diversity, competition (Glaeser et al. 1992) 580 municipalities

8. Dynamic externalities Glaeser et al. (1992) – Growth in cities. JPE. Henderson et al. (1995) – Industrial development in cities. JPE. Their research results are highly suggestive for the relevance of urban environments for economic growth processes. Debate: localisation (Marshallian) economies versus urbanisation (Jacobs) economies BT-BS (63%), DV (9%), OV (11%), SCV (17%)

9. Agglomeration hypotheses

10. Many Empirical Studies, Different Findings Source: Rosenthal & Strange (2004) Lack of robustness across studies implies that different economies can exist next to each other and that not per definition one type of agglomeration externality leads to more concentration or economic growth than the other.

11. ICT-sector Netherlands:

12. Localised spillovers: spatial dependence Spatial proximity based clustering also called spatial autocorrelation or contiguous spatial dependence Spatial regimes on urban structures also called spatial heterogeneity or non-contiguous spatial dependence

13. Spatial proximity:Moran 1 - employment function ICT-firms (location quotients 1996)

14. Spatial proximity:Moran 2 - log employment growth all ICT-firms (1996-2000)

15. Spatial proximity:Moran 3 - log new firm formation (1996-2000)

16. urban regimes 1: national zoning

17. urban regime2: labour-markets

18. urban regimes 3: Municipal size

20. Conclusions: agglomeration hypotheses Growth in the Dutch ICT sector tends to be concentrated in urban areas that are already relatively specialized in this sector and that are relatively rich in the presence of other industries. These outcomes do not fully support or contradict any of the four theories of localized knowledge spillovers (MAR, Porter, Jacobs) Spatial policy should not be restricted to the local or regional environment alone. Spatial externalities relevant in local contexts can be at work at higher levels. Local policy makers should be open to the argument of spillover effects from nearby (not necessarily adjacent) agglomerations instead of promoting ‘own’ ICT-clusters

21. Summary Van Oort (2007)

22. Why, where and when does it matter? Duranton and Puga (2000) paper • Specialized and diversified cities co-exist • Larger cities tend to be more diversified • The distribution of city-sizes and specializations tend to be stable over time • City growth is related to specialization and diversity • Relocations are from diversified to specialized cities • Assumptions: crowding, agents, labour mobility, (endogenous) self-organisation, path-dependency, systems of cities (policentricity).

23. Do we measure knowledge spillovers? • Jacobs’ externalities  related variety • Space, knowledge and growth are more complex related than often thought: spatial dependence, spatial heterogeneity, scale, measurement units, timeframe, definitions • Localization versus urbanization economies too simple? • Innovation as source of growth (KPF) • We did not measure transactions or linkages (yet)! • Micro-foundations of growth • Causality • MAUP • Contexts of firms (networks, sectors, location)

24. Related and unrelated variety

25. Variety and urban economic growth • source externalities = localization: similar firms & products, intra-industry, incremental innovation -> productivity growth • source externalities = Jacobs externalities: inter-industry, radical innovation, new markets -> employment growth

26. Hypotheses • Jacobs externalities are positively related to employment growth • Localisation economies are positively related to productivity growth • Unrelated variety is negatively related to unemployment growth Analysis: • COROP (functional) regions • Netherlands – natural control location factors • 1996-2002 – base year approach • control variables • sensitivity analysis period • standardised variables • spatial econometrics (lag/error and regimes)

27. Related and unrelated variety

28. Employment growth

29. Conclusions Hypothesis 1 (related/Jacobs/employment) – confirmed Hypothesis 2 (localisation/productivity) – unconfirmed (traditional) Hypothesis 3 (unrelated/unemployment) – confirmed (but sensitive) Classic studies (Glaeser cs.) measure unrelated variety and conclude on related variety Agglomeration or cities (urbanisation) per se are not enough for stimulating economic growth Spatial regimes more important than spatial aurocorrelation patterns

30. Content Empirical methodologies of agglomeration externalities • Growth in cities Spatial dependence Spatial heterogeneity Related variety 2. Employment-population Causality 3. Scale issues MAUP Multilevel issues • Contexts Locations or networks ---------------------------------------------------------------------- 5. Policy analysis Indicators of clusters

31. Employment-population dynamics in the Dutch Randstad Frank van Oort Pecs, 2-7-2009

32. Urbanisation Act (1976, p.132) “Little is known on the way spatial development and social-economic development influence each other. There exists uncertainty especially on the mutual dependence of choices for places of living (of people) and working (of firms)”

33. Introduction • Employment-population dynamics: • “Do people follow jobs, or do jobs follow people?” • International research: • Mixed results • Limited Dutch research: • “jobs follow people” • Levels of analysis: NUTS3 and zip-codes

34. International research • Marlon Boarnet (1994): • “The monocentric model and employment location” • Employment is endogenous to population changes • Donald Steinnes (1977): • “Causality and intraurban location” • Causality runs from residence to employment • Donald Steinness (1982): • “Do people follow jobs” A causality issue in urb. ec. • Measurement issues!!

35. Research questions • Research question: • What is the relation between population growth and employment growth at the level of Dutch municipalities? • Special attention for: • Industrial composition • Influence of policy • Spatial differentiation: Randstad Holland

36. Why important for policy? • Population-employment dynamics is primarily an issue on the municpal level (Nota Ruime) • ‘For the accommodation of space for population and employment over Dutch municipalities, the Dutch government wants municipalities to offer space for the existing population and its growth, as well as the existing population of local firms and their growth potential’. • ‘Other services related to spatial planning (governmental, retail, etc.) should be accommodated timely and in the right numbers, related to the local demand of citizens and entrepreneurs’. • No policy regarding spatial heterogeneity: • Does the Randstad encounter the same dynamics as municipalities in the Intermediate Zone and in the National Periphery?

37. Model • dPi,t =Xβ + dWEi,t + WEi,t-1 – λP Pi,t-1 + other P-factors • dEi,t =Yδ + dWPi,t + WPi,t-1 – λE Ei,t-1 + other E-factors

38. What follows what? Jobs follow People • cf. earlier research • But: • Especially personal services follow popualtion • Industrial sectors and business services to a much lesser extent • Distribution does not follow poulation • C.P. other local growth factors population (environment) and jobs growth (clusters)

39. Spatial differentiation

40. Spatial differentiation

41. Spatial differentiation • National population-employment dynamics is mainly determined by dynamics in the Randstad (‘jobs follow people’) • In the Intermediate Zone complex dynamics (‘jobs follow people’ and ‘people follow jobs’) • Little dynamics in the National periphery (and when so, ‘people follow jobs’)

42. Conclusions • In general: • ‘jobs follow people’ • However, differences for industries, policy and density zones: • Personal service jobs follow people • Jobs in the North-wing of the Randstad follow people • In VINEX-municipalities jobs follow people • Population-employment dynamics in the Netherlands: • Restrictions on location choice of people (Vinex municipalities in the North-wing Randstad) • Outside the Randstad more opportunities for choice (where people follow jobs more easily)

43. Conclusions Dutch jobs follow people; but only because the latter have no choice

44. Policy implications • Attracting population outside the North-wing of the Randstad is no guarantee for job growth. • In the National Periphery, policy aiming at only employment growth, e.g. by providing business sites, only to a very limited (to no) extent leads to population growth. Policy aimed at only population growth does not lead to employment growth. • How about shrinking regions!

45. Content Empirical methodologies of agglomeration externalities • Growth in cities Spatial dependence Spatial heterogeneity Related variety 2. Employment-population Causality 3. Scale issues MAUP Multilevel issues • Contexts Locations or networks ---------------------------------------------------------------------- 5. Policy analysis Indicators of clusters

46. Scale-dependency • Traditional: functional region unit of observation • The choice of this level as spatial unit of analysis is arbitrary and foremost a result of data limitations. • Other problems: • Agglomeration externalities may well reach beyond the regional level or be present at a lower scale • Most often agglomeration externalities are treated as spatially fixed (agglomeration externalities as club good); this isunsatisfactory

47. Analysis • Accordingly, the spatial scope of agglomeration externalities remains opaque • Moreover, their effects seem to depend on the spatial scale they are studied • It is the geographical scale and scope of agglomeration externalities that will be the focus of our analysis (holding sector, time, area, and measurement constant)

48. Data • Aggregated plant-level data on employment (N=647000, 5-digit sectors) • Three spatial levels of analysis • Neighborhood (N=3957, +/- 9 km2) • Municipality (N=483, +/- 70 km2) • Functional Region (N=40, +/- 850 km2) • Spatial autoregression to evaluate scale and scope of agglomeration externalities

49. Very preliminary outcomes Dependent Variable: Absolute Employment Growth (1996-2004), estimated with constant, control variables (wage, competition, investments), controlled for fixed and random effects, and spatial dependency. To control for endogeneity, we used lagged levels of past conditions.

50. Synthesis If agglomeration externalities turn out to be scale- and scope-dependent: • Question the external validity of past studies on agglomeration externalities • Focus on the micro-foundations of agglomeration externalities • Take the firm or plant seriously in analysis by taking it as unit of analysis.