Chatman presentation.. - Nexus: Researching Networks, Economics
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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 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 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 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 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 How might transit influence agglomeration economies? Agglomeration mechanism Likely facilitated by transit projects? Input sharing No, unless transit projects reduce road congestion affecting freight Knowledge spillovers Indirectly (local firm concentrations; speed of business travel?) Labor market pooling Yes, by increasing the size of the labor pool within commuting distance Reduced transactions costs Indirectly, by facilitating local and walkaccessible firm concentrations How might transit influence agglomeration economies? Agglomeration mechanism Likely facilitated by transit projects? Input sharing No, unless transit projects reduce road congestion affecting freight Knowledge spillovers Indirectly (local firm concentrations; speed of business travel?) Labor market pooling Yes, by increasing the size of the labor pool within commuting distance Reduced transactions costs Indirectly, by facilitating local and walkaccessible firm concentrations 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” Formalization: Agglomeration as a function of transit EDi Ti Hi Pi X i Pi Ti Hi Pi ,t 1 X i • ED: employment density • T: transit capacity • H: highway capacity; • P: population • X: population characteristics Formalization: Productivity as a function of agglomeration ij11 ij ij ij H ij HL YYij A 1K11 H log logijj j ijAij ijlogA log Lij Lij Lij 1 Lij 1 Yij • Y: payroll or GMP • L: labor supply • Theta: rental price of capital • A: agglomeration measure (employment density or population) • H: human capital 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 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) 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) .0015 0 .001 0 500 1000 1500 2000 Employment density - urbanized area 2500 0 0 2000 4000 6000 Employment density - principal city 8000 Productivity measures • Gross metropolitan product (GDP for metro area), total and per capita • Payroll, total and per capita Urbanized area employment density Central city employment density Total track miles Negative Positive Track miles per CBSA area Positive Negative Freeway and Not arterial capacity statistically significant Positive Population Not statistically significant, positive for OLS Positive Urbanized area employment density, omitting NYC Not statistically significant Central city Regression employment diagnostics density, omitting NYC Not statistically significant Not statistically significant (except 1 case is negative) Positive Positive, larger value Positive, larger value Positive Not statistically significant, positive for OLS Good instruments, some overidentification Urbanized area over-identified 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 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 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 ownindustry employment density Average GDP per capita Average GDP per capita payroll (wages) payroll (wages) elasticity elasticity elasticity elasticity Agglomeration mechanism Total track miles Track mile per sqm CBSA area Track mile per capita Employment density (principal city) 0.00090 0.00529 0.0011 0.0091 0.00246 0.00627 0.0031 0.0108 Track mile per sqm UZA Rail revenue miles Total revenue miles Rail seat capacity per capita Motor bus seat capacity per capita 0.0015 0.0050 0.0056 0.0220 0.0013 0.0065 0.0119 0.0226 Population 0.004 0.0060 0.0153 0.0260 0.0036 0.0077 0.0327 0.0267 0.01262 0.02271 0.0145 0.0222 0.0214 0.047 0.0405 0.0831 0.0171 0.0466 0.0177 0.0142 0.01833 0.04180 0.0211 0.0409 0.0587 0.0556 0.1112 0.0984 0.0469 0.0552 0.0487 0.0168 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 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
