Labor Market Agglomeration Economies Shihe Fu fush@swufe

1. Labor Market Agglomeration Economies Shihe Fu fush@swufe.edu.cn Southwestern University of Finance and Economics Summer School on Socioeconomic Inequality 2016 · Jinan University

2. Outline • Concepts of business agglomeration and labor market agglomeration economies • Micro-foundations of labor market agglomeration economies • Empirical evidence • Causal identification in empirical agglomeration economies studies • New research topics

3. Part 1: Concept • Cities are areas with high-density population (or concentration of people and firms in limited geographic areas) • The benefit of such concentration is called agglomeration economies • The reason why cities exist

4. Business agglomeration economies • Localization Economies: the benefit from the concentration of same-industry firms in a city (economies of scale external to a firm but internal to an industry) • Urbanization Economies: the benefit from the concentration of different-industry firms in a city (economies of scale external to an industry but internal to a city) Hoover (1937) (Location Theory and the Shoe and Leather Industries)

5. Localization economies • Input sharing • Labor market pooling (matching and statistical economies) • Information or knowledge spillovers (learning) • Specialization (Smithian economies) • Competition (Porter) In dynamic context: Marshallian-Arrow-Romer (MAR) externalities (Glaeser et al., 1992) (Growth in cities, JPE)

6. Urbanization economies • First stage: scale economies external to any industry but internal to a city, resulting from the general level of city economy. Measured by city size (population). (Hoover, 1937, 1971) , Henderson (1986) • Second stage: resulting from overall local urban scale and diversity (Henderson et al., 1995) • Third stage: resulting from industrial diversity • In dynamic context: Jacobs externalities, Jane Jacobs (1961,1969); Glaeser et al. (1992)

7. Urbanization economies: micro-foundations • Jacobs externalities (effect of industrial diversity) • Cross-industry fertilization • Promote innovation and urban growth • Statistical economies in product and labor markets • Unemployment stability • Lower frictional unemployment • Input sharing • Economies of scope

8. Three important survey articles • Duranton and Diego, 2004, Microfoundations of urban agglomeration economies, Handbook of Regional and Urban Economics, vol.4 • Rosenthal and Strange, 2004, Evidence on the nature and sources of agglomeration economies, vol.4 • Combes and Gobillon, 2014, The empirics of agglomeration economies, vol.5

9. Labor market agglomeration economies • Benefits from the concentration of workers (employment) in cities. Such benefits increase workers’ productivity and therefore wages • Labor market localization economies: Benefits from concentration of workers in the same industry (occupation) in a city. In dynamic context, Marshallian externalities in labor markets • Labor market urbanization economies: Benefits from concentration of workers in different industries (occupations) in a city. In dynamic context, Jacobs externalities in labor markets

10. Part 2: Micro-foundations for labor market agglomeration economies • Labor market pooling effect: a large, dense labor market increases matching quality, decreases search friction and frictional unemployment

11. Learning • Human capital externalities (knowledge spillovers): information exchange and knowledge spillovers through formal and informal social interactions, such as communication, imitation, peer effects, learning by doing

12. Lucas (1988): On the mechanism of economic development

13. Networking • Social network (social capital): help reaching better information and resources, such as weak ties and strong ties

14. Part 3:Empirical evidence of labor market agglomeration economies • Urban wage premium: Glaeser and Mare (2001), Moretti (2004), Rosenthal and Strange (2006), Combes et al. (2008)… • Doubling city size increases wage by 4.5-11%, conditional on observed characteristics

15. Why do cities pay more? • Yanknow (2006): Why do cities pay more? An empirical examination of some competing theories of the urban wage premium) • Cost of living • Ability sorting • Firm-level productivity (business agglomeration) • Learning (human capital accumulation, wage growth) • Coordination or matching (between-job wage growth) • uses NLSY79 data, 19% wage premium, 2/3 due to sorting

16. Empirical evidence of Marshallian labor market externalities • Boston MSA (Fu, 2007): significant effect, semi-elasticity of occupation specialization in a county is 0.17 • Netherlands (Groot, et al. 2014): doubling the local share of a (two-digit) industry employment results in a 2.9 percent higher productivity. • Italy (Andini, et al., 2012, Marshallian Labor Market Pooling: Evidence From Italy): focus on labor market pooling, find evidence of high turn-over, on-the-job learning in dense labor market, but overall magnitude is small.

17. Empirical evidence on human capital externalities • Moretti (2004, Human capital externalities in cities, vol. 4): a one year increase in average education in a city increases individual wage by 3-5%; a one percentage point increase in city college share raises average wage by 0.6-1.2% • Rosenthal and Strange (2006) : the elasticity of wage with respect to the number of workers within five miles is roughly 4.5 percent, mainly due to the presence of college graduates

18. Spatial decay of labor market agglomeration economies (localized agglomeration effect) • Business agglomeration economies decay with distance (Rosenthal and Strange, 2003,Geography, Industrial Organization, and Agglomeration, RESTAT; Duranton and Overman, 2005 ) • Labor market agglomeration economies decay with distance: • Fu (2007): human capital externalities decay rapidly after 6 miles away from a block centriod • Rosenthal and Strange (2006): decay rapidly after 5 miles

19. Evidence from firm productivity, city size • Supportive evidence • Sveikauskas (1975): productivity is higher in larger cities • Segal (1976): productivity is 8% higher in cities above 2 millions • Moomaw (1985): 7% • Baldwin et al. (2007): 7.7% in Canada • No or weak evidence • Carlino (1979): net diseconomies of city size • Nakamura (1985): small urbanization economies in Japan • Henderson (1986): little urbanization economies • Baldwin et al. (2008): negative effect of city size

20. Evidence from firm productivity, diversity • Supportive evidence • Glaeser et al. (1992): diversity promotes urban employment growth • Henderson et al. (1995): attract new industries • No or weak evidence • Henderson (2003): no urbanization economies

21. Summary of empirical evidence • mostly from developed countries • mostly on effect of city size (urbanization economies or city-size wage premium) • mostly on urban workers

22. Part 4: Causal identification strategies

23. Standard cross-sectional model specification • Y：wage ( or firm productivity) • X：vector of worker i’s characteristics • U：vector of city j’s characteristics • unobserved worker characteristics, city characteristics, and random error term • workers’ sorting across cities based on unobservables

24. 1. Control for individual fixed effects using panel data (Glaeser and Mare, 2001, Cities and skills)

25. 2. Exogenous geographic feature as IV (Rosenthal and Strange, 2008, The Attenuation of Human Capital Spillovers: A Manhattan Skyline Approach ):the fraction of land underlain by sedimentary rock, designated as seismic hazard or landslide hazard as IV for total employment The presence of a Land-grant college in a city (in 1862) as IV for college share (Moretti, 2005) Long-lagged city size or college share as IV

26. 3. Observationally equivalent individuals; micro-geographic unit (census block) (Bayer and Ross, 2006, Identifying Individual and Group Effects in the Presence of Sorting: A Neighborhood Effects Application): there is no block-level correlation in unobserved attributes among block residents, after taking into account the broader neighborhood reference group such as block group.

27. 4. Residential location (census tract fixed effects) proxy for unobserved individual characteristics (Fu and Ross, 2013)

30. Part 5. New research topics on labor market agglomeration economies • New identification strategies • What kind of social interaction generates knowledge spillovers? • Urban sprawl, commuting, and social interaction • Agglomeration economies, innovation, entreprenuership • Agglomeration economies by groups (CEO, rural migrants…) • Agglomeration economies, social interaction, and ICT • Internal migration, spatial equilibrium and quality of life in cities • Agglomeration economies in cities of developing countries

31. What is inside the black box of social interaction? • Charlot and Duranton, 2004, Communication externalities in cities, JUE: In larger and more educated cities, workers communicate more and in turn this has a positive effect on their wages. 13 to 22% of the effects of a more educated and larger city on wages percolate through this channel. • (survey questions about communications within a firm, outside of a firm, and usage of media)

32. Cities and skills • O*NET data • Skill concentration in cities: large cities are more skilled • Return to skill varies across cities: higher wage premium for stronger cognitive and people skills but not for motor skills and physical strength in large cities • (Bacolod, Blume, and Strange, 2009, Skills in the city, JUE)

33. Why do African Americans benefit less from labor market agglomeration economies than do whites? • Ananat, Fu, and Ross, 2013, Race-specific agglomeration economies: social distance and the black-white wage gap, NBER Working Paper #18933

34. Main ideas • Blacks benefit much less from agglomeration economies in urban labor markets than do whites • Black workers lack same-race peers or same-race skilled peers in workplace. • Firms with racial composition that differs from local racial composition have lower productivity. • Blacks feel much greater social distance from whites than from blacks, even for blacks working in white-dominant firms.

35. Data • 2000 U.S. census data, long form: individual and household information, residential and workplace down to block level.(select only primary-age male workers aged 30-59) • workplace is defined at PUMA level (100,000 population) • 1997 Manufacturing firm survey data • General Social Survey data

36. Residential location (census tract) proxy for unobserved individual characteristics (Fu and Ross, 2013)

37. Labor market agglomeration economies

38. Agglomeration economies by race

39. Fully interactive model

40. Fully interactive wage model

41. Fully interactive wage model with different fixed effects

42. African-Americans tend to make different residential, workplace, and commuting choices than do whites? • (Estimate the slope model by:) • Central city vs. suburban residents • Share of blacks in a residential tract above/below MSA average • Work in central city vs. in suburbs • Workplace employment density above/below MSA average • Workplace college share above/below MSA average • Work in high spillover (or low spillover ) industries • Mass transit users, automobile users

43. Why do African Americans benefit less from labor market agglomeration economies? • Unobserved lower ability • Spatial mismatch • Residential segregation • Workplace segregation or race-specific social network (Bokenblom and Ekblod, 2007; Mas and Moretti, 2006; Hellerstein et al. 2008, 2009): social interactions are race specific

44. Agglomeration model with own race share controls

45. Agglomeration model with own race share controls, robustness

46. Total Factor Productivity Models • 1997 Census of Manufactures establishments data • Use the decennial census to estimate the fraction of workers in 3 digit industry-zip code cells with a four year college degree and the fraction that fall into each race and ethnicity category. • Calculate average worker exposure to own race workers at other firms in the workplace PUMA for each firm type (industry-zip code cell) for all workers and for college educated workers • Verify that racial differences and own race effects hold for a subsample manufacturing workers and TFP models

47. Average exposure to same races at other firms in workplace PUMA (given other racial composition, if own race share at PUMA is higher, benefit more) • (whitehedupcshare*cellwhiteshare+blackhedupcshare*cellblackshare+hisphedupcshare*cellhispanicshare+asianhedupcshare*cellasianshare)

48. TFP models with agglomeration and race exposure Dependent variable: log value added

49. TFP models with agglomeration and race exposure, robustness check

50. TFP models by research intensity