Methodology for Determining the Economic Development Impacts of Transit Projects (2012) / Chapter Skim
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21 4. MSALEVEL TRANSITAGGLOMERATIONPRODUCTIVITY ANALYSIS In the first empirical part of our study, we used data from all of the metropolitan areas in the United States to estimate how transit capacity is correlated with agglomeration, and how in turn agglomeration is correlated with productivity.
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22 Next, we define a production function that includes a multiplier to account for any additional productivity effects from agglomeration. Similar to Graham (2007)
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23 Data We compiled data for the 366 US metropolitan statistical areas (MSAs) as defined by the US Census for an eleven year period from 1998 to 2008, although we were not able to use all observations for our analysis, as we describe below.
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24 evaluate total GDP data across the economy. Annual population and land area estimates by county were also obtained from the Census Bureau.
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25 FIGURE 1 Distribution of central city employment density (workers per square mile) FIGURE 2 Distribution of urbanized area employment density (workers per square mile)
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26 MSAs include Honolulu; New York City; Trenton, NJ; Philadelphia; Providence, RI; and Los Angeles. FIGURE 3 Number of metropolitan areas by share of area occupied by central cities FIGURE 4 Urbanized share of MSAs
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27 We also obtained some variables ("instruments") that were needed to control for mutual causality, as described below.
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28 physical capital as øj Our methodological strategy was to first model measures of agglomeration as a function of transit capacity; then to separately estimate productivity as a function of the agglomeration measures; and finally to trace the net effects of transit capacity on agglomeration-related productivity. We distinguished agglomeration-related productivity increases from the capitalization of transit travel time savings in wages or GDP by including transit capacity as an independent variable in our productivity models.
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29 tested a variation of this model in which we set predictions of track mileage that are less than zero to be equal to zero. This had differing but less statistically reliable results, and we do report those estimates.
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30 transit capacity in 2004 and 2002, along with control variables including road capacity (lagged similarly) and population (observed contemporaneously)
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31 Track mileage is associated with dispersion of employment at the urbanized area level, with a consistently negative and statistically significant coefficient (Table B-1)
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32 We omitted the New York metropolitan region from the final set of models to test their sensitivity to the inclusion of the MSA with the most pervasive rail transit and the highest central city employment density, exceeding the next highest MSA by 50 percent. We expected omission of the New York region to result in a reduced agglomeration effect.
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33 TABLE 4 Summary of track mile regression results 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 overidentified 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 TABLE 5 Track mile, population models Population (with and without NYC)
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34 Rail track mileage by type We also separately analyzed the impact of commuter rail (CR) , heavy or metro rail (HR)
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35 TABLE 6 Summary of rail track associations by rail type Urbanized area employment density Central city employment density Population Commuter rail Small positive effect Negative effect No effect Heavy rail Negative effect Positive effect Positive effect Light rail Positive effect (NS for track miles) Positive (except for CBSA density)
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36 B10 and Table B11. Population models show a high level of statistical significance and are shown in Table B12, although the instrumental variable model is over-identified.
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37 unsurprising that the results are more robust for average payroll than for GDP, since most agglomeration mechanisms are related to labor productivity. Results of our first estimates are shown in Table B16.
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38 actually shows a negative and statistically significant association between own-sector employment density and productivity. Finance and insurance was positive, but not statistically significant.
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39 consistent: youth population share was found to be negatively correlated with principal city density, while elderly population share was found to be negatively correlated with urbanized area density. Also, race was largely not a factor in the results, except that black population share was negatively correlated with urbanized area employment density.
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40 Elasticity estimates give the effect of a one percent change in different measures of transit capacity upon average wages and GDP per capita. These are shown for the sample mean values in TABLE 10 (below)
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41 additional mile of rail is larger in the Chicago region, which has a large population with an extensive rail network, than it is in Tampa-St. Petersburg, with less population and a minor streetcar system.
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42 TABLE 7 Density elasticities w.r.t. transit capacity measures, calculated at the sample mean Total track mile coefficient elasticity (4 year lag)
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43 TABLE 10 Estimated changes per unit based on mean elasticity estimates w.r.t transit capacity measures Change in average annual wage Change in GDP per capita Agglomeration mechanism Emp. Density (principal city)
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44 Several features of our elasticity estimates are worth noting. First, we show a range of estimates based on different model types (ordinary least squares [OLS]
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