Do public transport investments cause agglomeration economies?

1. Do public transport investments cause agglomeration economies? Daniel G. Chatman, Department of City and Regional Planning, U.C. Berkeley Symposium on Transportation Investment and Economic Development April 2, 2012 at U.C. Berkeley

5. How increasing travel speed affects cities • Increases accessibility, decreasing the costs of accessing markets and of interactions between firms and households • UK def. of agglomeration; no spatial change • May lead to relocation of economic activity (or shaping of growth), creating or intensifying agglomerations • Depends on development/occupancy responses

14. How might transit affect agglomerations? • Mostly, by making already-central locations more accessible: • …By increasing the number of workers that can efficiently access/egress workplaces and other locations • …By reducing the amount of land required for roads and parking, allowing for other productive land uses

16. Agglomeration economies (AEs) and AE mechanisms • Increasing returns to agglomerating firms/ HHs, some of which are external to them. • e.g. higher productivity per worker • Various AE mechanisms e.g., firms join cluster to find workers; attract more workers, increasing labor pool size; other firms benefit • AE mechanisms are of interest because not all are likely to affected by travel, or travel by all modes

17. How might transit influence agglomeration economies? • Question: mere spatial redistribution, or (global) increase in productivity? • Agglomeration economies are positive externalities, so possibly undersupplied • Transit might facilitate walking-based interactions by increasing localized density near stops • Knowledge spillovers, transactions costs of vertical disaggregation

18. How might transit influence agglomeration economies?

19. How might transit influence agglomeration economies?

20. Estimating transit’s effects on productivity via agglomeration • Collected data from all US metro areas • Estimated the relationship between transit and agglomeration, and between agglomeration and productivity • Used multiple measures of transit, agglomeration, and productivity • Employed various methods to control for endogeneity and other causal factors • Found very strong net “effects”

21. Formalization: Agglomeration as a function of transit P: population X: population characteristics • ED: employment density • T: transit capacity • H: highway capacity;

22. Formalization: Productivity as a function of agglomeration Y: payroll or GMP L: labor supply Theta: rental price of capital A: agglomeration measure (employment density or population) H: human capital

23. Data sources • Initial approach: construct a panel of 366 metropolitan areas in the US (only 34 of which have any rail capacity: 17 commuter rail, 11 heavy rail, and 27 with light rail) • Data were messy and required cleaning • APTA, NTD, LEHD, Census, BEA, NTAD

24. Transit capacity measures • Rail route miles (total, per capita, and per urbanized area) • Seat capacity (all transit, and rail only; per capita, and per urbanized area) • Revenue miles (all transit, and rail only; total, per capita, and per urbanized area)

25. Agglomeration measures • Employment density in the urbanized portions of the Census-defined principal cities of the metropolitan area • Employment density in the urbanized portions of the metropolitan area • Population • NOTE: No time-based measures here; only distance based (and cruder)

28. Productivity measures • Gross metropolitan product (GDP for metro area), total and per capita • Payroll, total and per capita

30. Notes on transit and agglomeration models • Heavy rail most influential; light rail influential on central city employment density • Nonlinear effect: an additional mile of track in an already-dense area has a bigger absolute impact • Little difference in two or four year lags

31. Findings: Agglomeration and productivity • Principal city employment density significantly correlated with wages and GMP per capita • Population even more highly correlated • No significant relationships with urbanized area employment density • Strong evidence of smooth nonlinearity in productivity models

32. Industrial sub-sectors • Manufacturing (NAICS 31-33) and finance and insurance (52) payroll positively related to industry-specific principal city employment density – but only significant in the case of manufacturing • Health and social assistance (NAICS 62) per capita wages negatively related to own-industry employment density

34. Dollar value of elasticities • Marginal dollar value effects range between $5 and $50 per capita with variables held at means • Slightly more than one tenth percent increase in the wage rate • Across MSAs, multiplied across workers, net “effects” are from $10m to $500m per year

35. Implications for policy and future research • Large metropolitan areas with dense central cities might benefit more from rail investments • Constraints on employment densification in central cities would lower these benefits • Findings are subject to significant refinement as we improve the models