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

Knowledge spillovers

Labor market pooling

Reduced transactions costs

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 walk-accessible 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

• ED: employment density

• T: transit capacity• H: highway capacity;

• P: population• X: population

characteristics

i i i iiED T H P X

, 1i i i i itP T H XP

Formalization: Productivity as a

function of agglomeration

1

1 1ij ij

j ijij ij

Y Hlog log logA log

L L

• Y: payroll or GMP• L: labor supply• Theta: rental price

of capital

• A: agglomeration measure (employment density or population)

• H: human capital

1ij ij ij ij ijY A K H L 1 1

1ij ijj ij

ij ij

Y HA

L L

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)

05.0

e-0

4.0

01

.001

5D

ensi

ty

0 500 1000 1500 2000 2500Employment density - urbanized area

02.

0e-0

44.

0e-0

46.

0e-0

48.

0e-0

4D

ens

ity

0 2000 4000 6000 8000Employment density - principal city

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

Urbanized area employment density, omitting NYC

Central city employment density, omitting NYC

Regression diagnostics

Total track miles Negative Positive Not statistically significant

Positive, larger value

Good instruments, some over-identification

Track miles per CBSA area

Negative Positive Not statistically significant

Positive, larger value

Urbanized area over-identified

Freeway and arterial capacity

Not statistically significant

Positive Not statistically significant (except 1 case is negative)

Positive

Population Positive Not statistically significant, positive for OLS

Positive Not statistically significant, positive for OLS

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 own-industry employment density

Average payroll (wages)

elasticity

GDP per capita elasticity

Average payroll (wages)

elasticity

GDP per capita elasticity

Agglomeration mechanism Employment density (principal city)

Population

Total track miles 0.00090 - 0.00529

0.00246 - 0.00627

Track mile per sqm CBSA area

0.0011 - 0.0091

0.0031 - 0.0108

Track mile per capita 0.01262 -0.02271

0.01833 -0.04180

Track mile per sqm UZA 0.0145 -0.0222

0.0211 -0.0409

Rail revenue miles 0.0015 - 0.0050

0.004 - 0.0060

0.0214 - 0.047

0.0587 - 0.0556

Total revenue miles 0.0056 - 0.0220

0.0153 - 0.0260

0.0405 - 0.0831

0.1112 - 0.0984

Rail seat capacity per capita 0.0013 - 0.0065

0.0036 - 0.0077

0.0171 - 0.0466

0.0469 - 0.0552

Motor bus seat capacity per capita

0.0119 - 0.0226

0.0327 - 0.0267

0.0177 - 0.0142

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