The role of airports in city employment growth, 1950–2010

Journal of Urban Economics 116 (2020) 103240

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The role of airports in city employment growth, 1950–2010 Marquise J. McGraw1 Department of Economics, University of California, Berkeley, 530 Evans Hall, MC#3880, Berkeley, CA 94720, USA

a r t i c l e JEL classification: L9 N9: R1 R3 R4 Keywords: Airports Local labor markets Transportation Employment

i n f o

a b s t r a c t This study considers the effects of commercial airports on local economies over the post-World War II period (specifically, 1950–2010). To overcome endogeneity concerns, a pooled synthetic control event study strategy is employed on newly digitized historical aviation data to estimate treatment effects on a variety of employment, population, and wage outcomes. I find that airports have led to, on average, 3.9 percent growth in total employment (and 3.4% growth in population) per decade. Over the 30-year period for which wage and air traffic data are available, earnings per worker increased by 2%, and per-capita personal income increased by 3%, corresponding to growth rates of up to 1.2 percent per decade, respectively.

1. Introduction “We’re in a service business. You call, you write, but there is nothing like face to face”2 said the CEO of a Florida-based software firm in reflecting on the role of air service to his firm. A California-based fuel tank manufacturer surmised the end of air service at Chico airport in 2014 had hurt firm growth, noting that it had become more difficult to connect with outside companies to form partnerships, increase sales, and otherwise expand.3 More than ever, it appears that air service could play a critical role in the success of business, especially in smaller cities. In recent years, however, factors such as consolidation in the airline industry, increasing fuel costs, and changes in general economic conditions have converged, leading to fewer flights offered to such locations. Municipal governments have responded with subsidies and incentives such as revenue guarantees in a bid to counter these trends. Such expenditures are not trivial; for example, a 2013 study pegs such costs for 20 smaller communities in the contiguous United States at nearly $225 million dollars annually (Wittman and Swelbar, 2013). This response is consistent with the general belief that airports are vital contributors to their local economies. In particular, if a city’s economic

output depends on people located in distant cities collaborating through face-to-face meetings, air service could facilitate firm and regional level growth. Atack et al. (2014) argued that a similar process was at work as early as the 19th century via the railroad system. They found that banks located closer to railroad routes were more likely to have “good” banking outcomes; a primary channel for this appeared to be information flows that led banks to modify their asset compositions accordingly. In order to better understand whether intervention such as subsidies and other initiatives are warranted, this study aims to provide causal estimates of the contribution of airports to local economic outcomes. Estimation is complicated by the fact that airports, similar to other pieces of infrastructure such as roads, are not randomly assigned to cities, which could lead to biased estimates. In the case of airports, endogeneity is even more of a concern, given the law in the United States specifically stipulates that the construction and operation of airports is a local responsibility. Were airports strategically constructed in cities that were expected to thrive anyway, so that outcomes that might be attributed to airports potentially result from other factors? Or, alternatively, were airports built in places with relatively dim prospects in the hope of stimulating growth in those local economies, such that the true effect of aviation could actually be larger than that implied by a naive estimate? To

E-mail address: [email protected] Department of Public Administration and Policy, School of Public Affairs, American University, 4400 Massachusetts Avenue N.W., Washington, DC 20016, USA. 2 Chura, Hillary. Lacking Airlines, Small Cities’ Economies Suffer. The New York Times. January 9, 2009. https://www.nytimes.com/2009/01/10/business/economy/ 10airports.html. 3 Martin, Hugo. As Airlines Post Big Profits, Small Communities Lose Service. The Los Angeles Times. January 22, 2018. http://www.latimes.com/business/la-fismall-airport-service-cuts-20180116-story.html. 1

https://doi.org/10.1016/j.jue.2020.103240 Received 4 March 2017; Received in revised form 25 January 2020 0094-1190/© 2020 Elsevier Inc. All rights reserved.

M.J. McGraw

overcome this, instead of relying on traditional (and possibly endogenous) metrics of air traffic, such as enplanements or flights, the focus is on “treated” cities, that is, those that have an airport. I estimate the treatment effect of an airport on outcomes such as employment, population, and income, over the post-World-War-II period (here, 1950–2010). This approach is conceptually similar to BaumSnow (2007)’s work on highways and suburbanization adapted to the aviation context. Since airports receive traffic from multiple points in space, conceptualizing the treatment in this way is reasonable. To accomplish this, I collect novel historical data on airport placement and industry outcomes which are compiled into a long panel spanning 1900– 2010 at 10-year intervals. A set of historical landing field locations is used to identify a set of control non-airport cities. Since the largest cities have few, if any, credible counterfactual cities available, this research design necessitates a focus on mid-sized and smaller airports. A key advantage of conceptualizing the study in this way is the ability to estimate the long-run effects of aviation in the United States over its entire history. This provides a common framework that corroborates and unifies findings from previous work, such as Blonigen and Cristea (2015), Brueckner (2003), and Green (2007), which all used changes in air traffic to examine the effects of aviation over discrete time periods. Non-parallel trends in key population and employment measures between pre-period airport and non-airport control cities rule out a standard difference-in-differences approach. Thus, an event-time pooled synthetic control event study approach is employed to obtain causal estimates. First, counterfactual outcomes are first generated for each airport city. Then, an event study is run on the resulting case pairs (an approach similar to Liu, 2015). I find that airports have led to 3.9% growth in total employment (and 3.4% growth in population) per decade. Nontradable employment is estimated at approximately 2.9% growth over the average decade. Over the 1980–2010 period, airports increased earnings per worker by 2%, and per-capita personal income by 3%. These correspond to growth rates of up to 1.2 percent per decade, respectively. Airports have increased personal income growth by 3.5 percent per decade over the 30-year period, corroborating the causal growth estimated on total employment and population. Back-of-the-envelope calculations indicate the presence of an airport in a CBSA leads to average population growth of almost 1500 people per decade, almost 1,000 of whom are people of working age (ages 16–64). The average airport contributes to the creation of 916 jobs, 52 of which are in the transportation sector, and 660 of which are in the nontradable sector. Using payroll as a proxy for output, airports are responsible for an average of $77 million (in 2010 dollars) in output growth for a given decade over the study period. 2. Literature review This paper builds upon a number of recent papers have examined the relationship between aviation and economic development. Blonigen and Cristea (2015) exploit the market changes induced by the 1978 Airline Deregulation Act to examine the relationship between air traffic and local economic growth. Using time-series variation in local growth rates over a 20-year period centered around deregulation (1969–1991), they find that air service has a positive and significant effect on regional growth, with the size of these effects differing by the size of the MSA and its industrial mix. Their most conservative estimates suggest that a 50-percent increase in an average city’s air traffic growth rate generates an additional stream of income equal to 7.4% of real GDP over a 20year period. This corresponds to a 2.7 - 4.7% increase in the annual employment growth rate, and a 1.6 - 5.7% increase in total employment after a two-decade period. This paper similarly uses panel data, but over a much longer period, allowing for estimates to be estimated over the entire history of aviation in the United States inclusive of (and beyond) deregulation. Campante and Yanagizawa-Drott (2018) study the impact of international long-distance flights on the global spatial allocation of economic

Journal of Urban Economics 116 (2020) 103240

activity. They exploit discontinuities in flight regulations and technological constraints that lead to a discontinuity in connectedness between cities at a distance of around 6000 miles, which are in turn used to estimate the effect of an airport’s position on local and global economic development. They find that an increase in interconnectedness does generate increased activity at the local level, on an order of an annual increase of roughly 0.8% of GDP. The movement of people appears to lead to a movement of capital. However, these effects cannot be fully explained by movement of workers from one location to the airport city. One local case study by Button et al. (2010) uses a sample of 66 small airports in Virginia to estimate their effects on economic development, finding that a doubling of passenger traffic produces up to a 4% increase in per capita income. Sheard (2014) uses the Civil Aeronautics Administration’s 1944 National Airport Plan as an instrument for the current distribution of airports by size, as measured by air traffic in the U.S. He estimates that airport size has a positive effect on local employment share in tradable services, with an elasticity of approximately 0.1, and a negative effect on manufacturing employment share. He finds no measurable effect on non-tradable services. Although the National Airport Plan instrument is relevant to understanding movement in emplyment shares, it is endogenous if one is interested in understanding aggregate population or employment outcomes, since, by 1944, planners were basing their assessments and airport locations based on future needs and expected growth. Sheard (2019) constructs an instrument, similar in nature to the Bartik instrument, for changes in traffic at airports that could plausibly be unrelated to other factors for economic outcomes. He finds that the elasticity between airport size and employment is 0.02, and that between airport size and GDP to be 0.035. In both cases, the effects of interest are identified off of relatively short-run effects from variation on airport size. In contrast, this paper provides a different perspective, with identification based on medium- and long-run effects of airport presence. 3. Historical context, data, and sample selection 3.1. Historical background As noted by Bednarek (2001), the period between the conclusion of World War I and 1947 was formative in the development of the United States’ aviation system. The Air Commerce Act of 1926 stipulated that airports were a local level responsibility - cities themselves ultimately decided where, when, and how to build their airports. During the 1920s and 1930s, the Post Office and the military lobbied cities to build airports, as neither entity had the funds to do so on its own. The major goal of the Post Office was to create a network of airfields to facilitate its Air Mail service, while that of the Army Air Service was to create a network of airfields to facilitate military needs. In order to convince cities to participate in airport construction, these lobbying efforts emphasized the “winged gospel” - that airports were going to be essential to a city’s ability to continue to grow and to compete with urban rivals. Accordingly, many airports were ultimately located to facilitate the activities of at least one of these entities, resulting in airports located in a variety of cities. As it became more expensive for municipalities to develop airports, city leaders and airport operators lobbied Congress for financial assistance. The first major federal aid came through Congress, via the Federal Emergency Relief Act, and the Civil Works Administration created by President Roosevelt, in 1933. Starting in 1935, the Works Progress Administration (WPA) also provided some funding. However, soon after, cities realized that the funding provided by these temporary programs would be insufficient, leading to lobbying efforts from mayors and other local officials. In 1938, Congress passed the Civil Aeronautics Act, which repealed the ban on Federal funding of airports. Subsequently, in 1946, the Federal government established more long-term funding sources. This included establishment of the first National Airport Plan, borne out

M.J. McGraw

of a desire to help the Federal Government distribute this funding in what it deemed the most effective manner for the overall operational utility of the air system. Even after these changes, the final decision to establish and operate an airport remains in the hands of local municipalities; however, with the availability of Federal assistance came standards for the operation of airports. In 1923, the Army Air Service published the first comprehensive plan of airways and air routes deemed necessary for military navigation in Airways and Landing Facilities. Such airways would “promote commercial aviation, be an important transportation factor in the progress of civilization, and be available for national defense”. The Plan stipulated that airways would have main stations 200 miles apart, substations 100 miles apart with landing fields and basic services, and intermediate airfields 25 miles apart for emergency use. These airfields were envisioned as places where Army pilots, the National Guard and Reserve units could train. The network was also envisioned to connect parts of the Air Service located in disparate places. For example, one of the first routes connected New York City, Washington DC, and Rantoul, Illinois via Dayton, Ohio where the Air Service’s engineering division was located. Around the same time, the Postmaster General created Air Mail routes with specific objectives, such as connecting San Francisco to New York. Intermediate stops along these trunk lines were placed in large part due to the constraints of early aircraft. Routes were flown by a variety of contract air lines.4 Passenger service began in the 1930s as those contract air lines opened up seats on some of their larger planes to passengers. This was seen as a way to recoup losses incurred from servicing the Air Mail system. The first passenger service on the DC-3 was inaugurated in 1936. Such smaller, expensive-to-operate propeller aircraft were used by most major airlines from late 1930s through the early post-war period, limiting the availability of air travel. However, with the advent and proliferation of jet aircraft starting around 1960, air travel quickly became the de facto mode of choice for long distance travel with passenger traffic increasing accordingly. Other parts of the system, such as air traffic control, navigation, and radar technology, would also continue to evolve into the system present today. 3.2. Data set and covariates In order to estimate the effects of interest, a novel data set consisting of a balanced panel of Core Based Statistical Area (CBSA) level information for 1900–2010, inclusive, was constructed to estimate the effects of interest. In almost all cases, data were obtained at the county level and subsequently aggregated to the CBSA level according to 2010 definitions. Data were compiled on population, employment, geography, climate and human capital characteristics. The data set also includes previously unexploited historical information related to the development and creation of the aviation system. This includes locations of the following: cities on the Air Mail system as of 1938, intermediate air fields constructed by the Civil Aeronautics Administration (CAA), proposed airfields for military use as of 1922, and alternative landing fields based on earliest available known atlas of such compiled in 1926.5 Employment data were obtained for the following sectors, in addition to total employment: Mining; Construction; Manufacturing; Trans4

Reproductions of these maps are available in the Web Appendix. CBSAs consist of the county or counties or equivalent entities associated with at least one core (urbanized area or urban cluster) of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties associated with the core. “CBSAs” refers collectively to metropolitan statistical areas and micropolitan statistical areas. CBSAs were selected as the unit of observation for the analysis since the service areas of airports are generally diffuse. The Data Appendix gives more information on how the data were aggregated and adjusted, where necessary, to ensure consistent geography throughout. Using 2010 CBSAs allows for city growth over the time period under consideration. 5

Journal of Urban Economics 116 (2020) 103240

portation, Communications and Utilities; Wholesale Trade; Retail Trade; Finance, Insurance and Real Estate (FIRE); and Business, Professional and Other Services (“Services”). Manufacturing, Wholesale Trade and Mining are summed to generate a measure of “Tradable” Employment. Construction, Retail, FIRE and Services are summed to generate a measure of “Nontradable” employment. In general, data from 1900 to 1940 were obtained from the IPUMS database (Ruggles et al., 2010) by aggregating microdata to the county level; 1950–1970 data were obtained from aggregate county-level data found in the City and County Data Book; and the remainder was downloaded from National Historical Geographic Information System (NHGIS) at the county level. Population data were obtained from the NHGIS database for the entire period. Payroll data were obtained from the County Business Patterns (CBP) where available, and could be obtained for 1951–2010 only. For 1950, data were hand-entered from the 1951 CBP documents as available. Values from later time periods were obtained from NHGIS. Data on personal income, per-capita personal income, earnings, and earnings per worker, available from 1970 to 2010 only, were obtained from the U.S. Bureau of Economic Analysis.6 Additionally, data were collected on a variety of geographic, transportation, and climate characteristics used as controls.7 3.3. Selection of treated and control CBSAs 3.3.1. Selection of airports for main sample Given that virtually all U.S. commercial airports have remained in continuous operation since their opening, and the fact that most were open in 1950 or prior, I began with the set of currently-operating public use airports provided by the Federal Aviation Administration record data.8 This sample was further refined as follows. Role of Airport in the aviation network The presence of large airports in cities such as Atlanta, Chicago, New York, and San Francisco could be viewed as inevitable, given their size, geography, and dynamic economies even prior to the advent of aviation. Thus, the local level factors described previously are likely to be inapplicable in such cities. Moreover, in these extremely large locales, air traffic is often constrained by capacity; as a result, airlines often must be awarded slots in order to provide service. Growth beyond this, in many cases, is constrained due to a lack of available land for airport expansion or a lack of available space in the air for additional traffic. Given all the economic processes at work in these larger cities, including such airports could lead to bias in the estimated effects. The large size of these cities also means that as a practical matter, there would be very few, if any, control CBSAs that would provide a credible counterfactual in estimation. Given these complications, the largest airports - the 13 airports that were characterized as “large hubs” by the Federal Aviation Administration as of 1964 - were removed from the sample.9 On the other hand, it is reasonable that in order for an airport to have an effect on its local economy, it must be “large” enough to service commercial traffic. Given that certain technological capabilities are required for typical commercial aircraft to service an airport, virtually all airports providing commercial service must have an air traffic control tower. In light of this, the Federal Aviation Administration has established explicit 6 Tables CA5 and CA5N, Regional Economic Accounts, Bureau of Economic Analysis, U.S. Department of Commerce: http://www.bea.gov/regional/. 7 More details on the construction of the data set are provided in the Web Appendix. 8 Data from FAA Form 5010 (Airport Master Record), as well as the FAA Statistical Handbook, was used to derive the initial sample. There is little evidence, anecdotal or otherwise, indicating that airport closures over the period could introduce bias into this sample. 9 1964 was the first year in which a Federal agency classified airports by their size and relevance to the national aviation system. Large hubs carry more than one percent of total air traffic.

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Journal of Urban Economics 116 (2020) 103240

Fig. 1. Map of sample airports (𝑛 = 150).

criteria for the establishment and discontinuance of an air traffic control tower. Airports containing a tower must have passed a cost-benefit test for the establishment of that tower; that is, the benefits from avoided accidents at the airport must exceed the cost of building and operating the tower. An airport with little or no commercial traffic would not satisfy this condition. Using this measure as a proxy for an airport’s ability to sustain scheduled passenger service allows for the exclusion of extremely small airports that reasonably would not be expected to have an economic impact on their cities. Any airport lacking an air traffic control tower was thus removed from the sample. Multiple airport cities and closely located airports It is unclear why some cities have more than one airport while others do not. While most multiple airport cities drop out of the sample when large hub airports are removed, a few multiple airport cities remain, even among the set of smaller airport CBSAs considered here. One may be concerned that some unobservable process led to the presence of more than one airport serving the CBSA. This, in turn, could bias the estimated effects of interest. As a purely practical matter, allowing multiple airport cities to remain in the sample would also complicate the interpretation of the estimates produced by this study. Given the very small number of CBSAs in the sample (nine) having multiple airports, it is most straightforward to exclude them from the sample. Similarly, to reduce the potential of any bias from spillover effects, it is important that no single airport in the final sample is too close to another airport. As a result, one additional airport was dropped from the sample as it was not at least 40 miles away from an existing (larger) airport. The remaining airport cities thus contain one (and only one) airport that is fully capable of handling commercial flight activity. Treatment effects may now be interpreted as the effect of a single airport on its city. The final data set consists of 150 airport CBSAs. Fig. 1 shows their locations. Table 1 provides basic summary statistics.

3.3.2. Selection of control CBSAs To identify a suitable set of control CBSAs, the sample of non-airport CBSAs is reduced to those that (1) had some experience with aviation in the 1920s or (2) were slated to receive a first commercial-level airport under the Civil Aeronautics Administration’s National Airport Plan of 1944.10 For the former, I used the 1926 locations of emergency air fields, newly digitized from the Army Air Service’s Landing Fields in the United States, as a proxy for a set of places that could support an airport. These locations were selected based on land availability, engineering considerations, and local-level knowledge required to construct an airport (U.S. Army Air Service, 1923). In many (though certainly not all cases), it would have been rather easy to upgrade these facilities during the pre-period to full airport status if desired. In the second case, these sites were deemed capable of hosting an airport by Federal planners concerned with the placement of airports for maximizing the overall utility of the U.S. aviation network, but which for a variety of idiosyncratic reasons, never received one. In total, 379 CBSAs serve as controls.11 3.3.3. Initial comparison of treated and control CBSAs Table 2 gives characteristics of CBSAs with and without airports. Airport CBSAs are more likely to contain political capital cities and to have a land grant college. These factors could influence airport placement - for example, a political capital city might be more likely to have an airport due to its political importance. An area’s stock of human capital may also affect a city’s likelihood of receiving an airport. This is proxied here by whether the CBSA had a land-grant college or reseach university. The presence of a land grant college, in particular, has 10 More precisely, locations proposed to receive airports of Class 3 or greater in the National Airport Plan. 11 Appendix Figure A.3 shows the treated and control CBSAs.

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Journal of Urban Economics 116 (2020) 103240

Table 1 Sample means - airport characteristics. All Airports (𝑛 = 150) Variable

Mean

Median

SD

Activation Year Distance to nearest CBD (miles) Land Area of Airport (acres) Current length of longest runway (feet) Distance to nearest comm. airport (miles) Boardings/Enplanements [thousands] (1980) Boardings/Enplanements [thousands] (2010) Per Capita Boardings (1980) Per Capita Boardings (2010)

1943.68 5.49 2249.95 8886.54 88.03 361.28 831.31 0.95 1.12

1941 4 1797.5 8701 74 137.65 189.39 0.78 0.89

(8.64) (5.8) (1679.53) (1710.84) (39.77) (546.67) (1991.5) (0.89) (1.13)

Notes: Standard Deviations (SD) in parentheses. Boardings/enplanements include air carrier, air taxi (on-demand) and commuter flights.

Table 2 Covariates by Airport Status. Variable

Airport Predictors (Binary 0/1) On 1938 Air Mail Network On 1922 Army Air Service Defense Plan CBSA County on 1944 National Airport Plan Had CAA Intermediate Airfield (1930s) Transport Planned Highway Mileage as of 1947 Located near river Has Port 1887 Straight-Line Rail Mileage Geography/Climate Has Political Capital City NOAA Coastal County Has NRC Top-200 College/University Has Land Grant College Mean January Temperature CBSA and Region CBSA Land Area (std. to 2010) Region 1: Northeast Region 2: Midwest Region 3: South Region 4: West City Size (thousands) Population - 1900 Population - 1930 Population - 1950 Population - 2010 1950 Industry Shares (Percent) Manufacturing Transport/Comm/Utilities Wholesale Trade Retail Trade Finance/Insurance/Real Estate Services

been shown to be predictive of future growth (e.g. Liu, 2015; Moretti, 2004) Airport CBSAs are also more likely to have larger amounts of other transportation infrastructure such as roads, ports, or river access, and to be larger in overall land area as well. However, climate does not vary substantially between CBSAs with airports and those without. Additionally, the distribution of airports and non-airport CBSAs across regions are similar. Other findings are also consistent with the discussion given in Section 3.1. Airport CBSAs were more likely to have been located on the 1938 Air Mail network, and to have been home to a city listed in the 1922 Army Air Service Proposed System of Air Routes.

Treated (Airport) CBSAs

Control (Non-Airport) CBSAs

(𝑛 = 150)

(𝑛 = 355)

Mean

SD

Mean

SD

0.673 0.3 0.1 0.393

(0.471) (0.46) (0.301) (0.49)

0.079 0.023 0.135 0.318

(0.27) (0.149) (0.342) (0.466)

60.2 0.54 0.107 172.9

(57.0) (0.5) (0.31) (149.0)

18.3 0.344 0.031 82.7

(24.6) (0.476) (0.174) (64.2)

0.167 0.233 0.553 0.160 32.7

(0.374) (0.424) (0.499) (0.368) (12.0)

0.02 0.248 0 0.042 31.8

(0.139) (0.432) – (0.201) (10.6)

2583 0.12 0.327 0.393 0.16

(2020) (0.326) (0.471) (0.49) (0.368)

1421 0.135 0.397 0.301 0.166

(2221) (0.342) (0.49) (0.46) (0.373)

99.2 162.8 206.3 457.9

(116.3) (182.0) (219.1) (576.0)

36.7 50.3 58.8 103.1

(36.1) (54.6) (64.3) (130.0)

19.8 8.00 3.9 12.9 2.8 23.9

(12.2) (2.9) (1.8) (2.6) (1.1) (6.8)

20.3 7.1 2.3 11.5 1.8 20.7

(13.3) (3.9) (1.6) (2.7) (0.6) (6.8)

4. Estimation 4.1. Identification Availability of and proximity to public infrastructure such as airports may affect the economic activity of a metropolitan area by: (1) acting as an unpaid factor of production in a firm’s production function, (2) working to making other inputs more productive, (3) helping to attract other inputs from elsewhere, and/or (4) stimulating demand for more infrastructure (e.g. roads) and related services (Eberts and McMillen, 1999). By reducing the costs of production, airports allow

M.J. McGraw

firms to increase production, possibly make other inputs more productive, and possibly help attract opportunities from elsewhere, resulting in observable growth in employment. Appold (2015) classifies the employment that may result from the presence of an airport into three parts: transportation-providing employment, transportation-supporting employment, and transportation-using employment. The first case refers to the direct jobs created by the airport’s activities such as aircraft maintenance and airport operations. The second type of jobs serve the immediate needs of airport users, such as wholesale, accomodations, and some retail. This attracts businesses such as cargo shippers, who benefit from the proximity to the airport. The third type of jobs that indirectly result from this are from firms who are taking advantage of their proximity to the airport as an unpaid factor of production, such as jobs in finance, real estate, engineering, and information processing.12 One could expect to see one (or potentially all three) types of jobs resulting from the presence of the airport. Tourism undoubtedly plays some role as well; however, a lack of available data prevents further exploration of the relative effects of business travel relative to tourism-related travel in this study. Under ideal conditions - e.g. strong assumption of identical cities with identical populations and sectoral employment structures - if airports were randomly assigned to cities, estimating the treatment effect of airports would be trivially given by the difference in outcomes between airport and non-airport cities. Realistically, however, this is not the case. Even after controlling for differences in city size and employment, there are reasons to believe that airports were not randomly assigned to cities. Since the construction of airports is a local responsibility, cities chose whether to invest in their early airports. This selection gives rise to endogeneity concerns which could lead to omitted variable bias, the direction of which is unclear. While the effect between any unobserved determinants of city growth and output growth is likely to be positive, the relationship between the airport location decision and an omitted determinant of city growth is unclear. On the one hand, it is possible that cities with larger populations, or those expected to grow more quickly, could be more likely to establish airports. On the other hand, the opposite could be true as well - places that foresaw a loss in population or employment in key industries may have turned to airports as a way to reverse those trends in their cities. Accordingly, cities where policymakers believed in the “winged gospel” of aviation may have been more likely to put substantial local resources behind airport construction and maintenance (Bednarek, 2001). It is also likely that these cities had more engaged local chambers of commerce or other influence from the business community. Additionally, there is the threat of bias from simultaneous causality - air service could be leading to greater firm activity, or greater firm activity could attract more air service. Furthermore, suppose a small airport in the sample loses its scheduled air service during a given decade, e.g. the 1980s. The airport would remain in the sample, since the sample consists of airports open and able to serve passengers as of 1950. Hence, there is the potential for estimates to be biased as a result of this. In general, there is not enough historical data on service patterns at individual airports available to further investigate this. However, these concerns might be mitigated by the fact that most air carriers have focused, at least until the 2000s, on increasing their market share. As a result, it is unlikely that most commercial airports have gone without some sort of air service for an extended amount of time. Of course, frequency, destinations, number of seats offered, and other factors, could be affected for competitive, regulatory, or other reasons, and may change from time to time.

12 Certainly, this is not a fully unpaid factor of production. For example, firms pay for their employees to fly from these airports, or to ship goods from these airports to other locations. I only argue that the proximity to the airport, specifically, the reduced travel time and/or distance, might be considered an unpaid factor of production.

Journal of Urban Economics 116 (2020) 103240

Given the long panel of data available in this study, and the set of treated and control cities identified earlier, one may conceptualize the study as a classic setup for difference-in-differences. Although comprehensive data on opening dates is unavailable, airport activation dates are included in FAA records.13 In order for commercial aviation to affect the economy, not only did airports have to be built, but other pieces of infrastructure such as airways, beacons, and crucially, aircraft, had to be in place and capable of carrying significant numbers of passengers. Such technology did not exist until the post World War II period. As a result, the beginning of the post-war period (1947) becomes the relevant structural break. Coupled with the decadal nature of the data, 1950 is considered the base year for estimating treatment effects. One might believe it would be preferable to normalize each airport to its opening date, and to examine the evolution of outcomes from that point forward. However, this would confound estimates. Without the technology and conditions for the rest of the aviation network to function in place, such an analysis would fail to pick up the desired effects. Additionally, the effects of government efforts in fighting the second World War would be picked up in such a normalization (airport closures to civilian traffic, repurposing of some airfields as temporary military bases, etc.), biasing the estimates. As a result, the treatment may be thought of as the interaction of a dummy variable for the presence of an airport and a post-1950 year dummy variable. Airports that were activated post-1950 remain in the sample, with most airports active by 1960. Consistent with this, an initial estimation strategy following Reber (2005) and Mora and Reggio (2012) is implemented; an “eventtime” version of the differences-in-differences estimator (ET-DD). With 𝑘 = 0 normalized to 1950), if the parallel trends assumption is satisfied, Eq. 1 would identify the dynamic effects of the airports on their local economic outcomes. The model specification is: 𝑦𝑖𝑡 = 𝛼 + 𝜃𝑖 + 𝛾𝑡 +

6 ∑ 𝑘=−5

𝜆𝑘 𝛿𝑘,𝑖𝑡 + 𝜖𝑖𝑡

(1)

where 𝛼 is a constant, 𝜃 i is a CBSA fixed effect, and 𝛾 t is a decade fixed effect. 𝛿 k,it is an indicator variable equal to 1 if the CBSA is in decade k of having its airport, and 0 otherwise. In the regressions, 𝑘 = 0 is left out as the reference decade of 1950. The pattern of 𝜆k s describes the change in trend in the outcome of interest, yit , associated with having an airport. For example, 𝜆1 - 𝜆0 gives the change in the dependent variable associated with moving from 𝑘 = 0 (1950) to 𝑘 = 1 (1960). Assuming smooth growth in the treatment effect over time (which is borne out by the data), 𝜆6 identifies a coefficient of interest, the cumulative causal growth in the dependent variable (population or employment) attributable to airports over the sixty year period of the study. A similar specification with a single coefficient identifying effects from 𝑘 = 1 to 𝑘 = 5 can be used to identify average treatment effects. Eq. (1) allows for a partial test of the parallel trends condition; unfortunately, in practice, this assumption does not hold, as 𝜆k for k < 0 are statistically distinguishable from zero in all cases, as shown in Fig. 2. Given this, I adopt a version of this estimator that relies on airport city and counterfactual airport city outcomes created by a synthetic control estimator in the spirit of Liu (2015). 4.2. Pooled synthetic control event study estimator The use of synthetic controls was first proposed by Abadie and Gardeazabal (2003) and Abadie et al. (2010). It allows for the extension of the traditional differences-in-differences framework by allowing treatment effects to vary over time. In this case, the synthetic control is constructed as the weighted average of CBSAs in the “donor pool” - that is, the set of control counties selected by the criteria described 13 Activation dates indicate when the Federal government added the airport to the National Airport System. Given that these records were not maintained until 1926, airports opening earlier than 1926 are shown as being activated in 1926.

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Journal of Urban Economics 116 (2020) 103240

A. Population

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in Section 4.1. Under the identifying assumption that conditional on controls, a treated unit would have otherwise evolved as an estimated control unit does, the difference between the actual “treated” outcome and the synthetically constructed “control” outcome gives the causal treatment effect of airports on local employment and population. Suppose there is a sample of 𝐶 + 1 CBSAs, indexed by c, among which unit 𝑐 = 1 is the treated CBSA and 𝑐 = 2 to 𝑐 = 𝐶 + 1 are potential controls. Also assume a balanced panel with a positive number of pre-intervention periods, T0 , as well as a positive number of postintervention periods, T1 , with 𝑇0 + 𝑇1 = 𝑇 . Let Yct represent the outcome of unit c at time t. For a given t (with t ≥ T0 ), the synthetic control estimator of airport’s effect is given by the difference between the treatment and synthetic control at that period: 𝑌1𝑡 −

𝐶+1 ∑ 𝑐=2

𝑤∗𝑐 𝑌𝑐𝑡

where: 𝐖 = (𝑤2 , … , 𝑤𝐶+1 )𝑇 is a (C × 1) vector of positive weights that sum to 1; X1 is a (k × 1) vector containing a set of pre-intervention characteristic values; and X is a (k × C) matrix collecting the values of the same variables for the CBSAs in the set of airport potential CBSAs. The synthetic control algorithm chooses optimal weights W∗ that minimizes the mean square prediction error (MSPE) given by √ MSPE = ‖𝑋1 − 𝑋0 𝑊 ‖𝑉 = (𝑋1 − 𝑋0 𝑊 )𝑇 𝑉 (𝑋1 − 𝑋0 𝑊 ), where an optimal choice of variable weights V assigns weights to linear combinations of the variables in X0 and X1 . In practice, this estimation strategy is implemented using Abadie et al. (2011)’s R packageSynth. While this method potentially allows for estimation of causal effects in a wider variety of settings, it is not without some important drawbacks

and caveats. For example, as noted by Doudchenko and Imbens (2017), difference-in-differences allows for a non-zero intercept corresponding to permanent differences; the synthetic control method applied here does not. Also, the constraint that the set of weights W must sum to one is also not trivial; a more flexible estimation method could yield somewhat different results. Athey and Imbens (2017) note that in cases where a unit may be on the extreme end of a distribution of units, allowing for a different set of weights could be more ideal. I use the event time difference-in-differences (ET-DD) method from Section 4.1 and estimated by Eq. (1) to deal with intercept issues. Since each estimated control unit is estimated for medium and smaller cities with one and only one primary airport, pooled results can be interpreted as the average treatment effect of the airport on cities with airports. Finally, to reduce the likelihood of counterintuitive and/or otherwise inappropriate matches, for each airport city, including the restriction on weights, the donor pool is restricted to CBSAs within the same Census region. Standard errors are provided by the ET-DD estimator, which are clustered at the CBSA level. 4.3. Matching An additional way to assess the performance of the synthetic control is to compare its outcomes to those from a more conventional matching estimator. Let Y, the outcome variable, represent an outcome of interest. The group of treated CBSAs (𝐴 = 1) are the airport CBSAs. Under the conditional independence assumption, the treatment effect of interest would be free of any mean differences in outcomes that result from differences in the observed covariates X across the groups. Additionally, decade-by-decade interactions for pre-period population and/or employment growth are included.

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Journal of Urban Economics 116 (2020) 103240

Fig. 3. Springfield airport case study. Notes: Graphs show sectoral employment and population outcomes for the treated airport CBSA, a control unit identified by using a one-to-one matching strategy, and a synthetic control unit. Please see Section 4 for more details.

Fig. 4. Elmira airport case study. Notes: Graphs show sectoral employment and population outcomes for the treated airport CBSA, a control unit identified by using a one-to-one matching strategy, and a synthetic control unit. Please see Section 4 for more details.

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Journal of Urban Economics 116 (2020) 103240

B. Total Employment OLS (SC Sample) Estimated Outcomes, 1910-2010

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Fig. 5. Evolution of estimated effects - total employment. Note: This figure plots the results of level regressions, for each decade indicated, of the relevant outcome (here, total employment). For years 1950 and prior, population and employment controls are included only up to the decade prior. For example, the 1940 regressions for total employment would control for population and employment from 1900 to 1930. All controls are included in all specifications, as detailed in the text. Dotted lines indicate 95% confidence intervals.

A distance metric is used to determine the matches. This metric includes the propensity score as well as those covariates that are particularly good predictors of the outcome (in addition to the treatment). Since this distance metric has many components, usually a Mahalanobis distance (MD) is used to compute the distance between the treated and the controls (see Rosenbaum and Rubin, 1985). This is even more important than usual in this case because of the limited overlap on propensity scores between the treated and control groups, and the potential for misspecification of the propensity score 𝜌(X) to lead to biased estimates. The MD between the X covariates for two units i and j is 𝑀𝐷(𝑋𝑖 , 𝑋𝑗 ) =

√ (𝑋𝑖 − 𝑋𝑗 )𝑇 𝐶̂ −1 (𝑋𝑖 − 𝑋𝑗 )

where 𝐶̂ is the sample covariance matrix of X and XT is its transpose. I include the vector of covariates X, as well as the propensity score, in the match function. Given the need for enforcing an optimal pre-treatment balance of treated and control units, a caliper is applied as well. Observations which are outside of the caliper are dropped. The caliper is enforced only for pre-period population/employment levels. It is set to a standard distance of 0.3 standard deviations for population/employment levels from 1900 through 1930. For values in 1940 and 1950, the caliper is enforced at 0.2 standard deviations. Any observations outside of these tolerances are dropped from the sample. While it is true that dropping observations outside the caliper generally changes the quantity being estimated, this is consistent with the goal of finding sets of treated and control cities, which yield, on average, similar population/employment outcomes in

the pre-period. While results from this exercise are instructive, the key disadvantage of this estimator are that estimates rely on, and are highly sensitive to, the choice of caliper.14 4.4. Case study Here, the performance of the matching and synthetic control estimators are examined via two case studies illustrating the links between a region’s airport and its economy: Springfield, Missouri (SpringfieldBranson National Airport) and Elmira, New York (Elmira Corning Regional Airport). The Springfield case study illustrates how an airport, coupled with a vibrant local economy, can benefit a metropolitan area, while the Elmira case illustrates how an airport could fail to substantially impact a city’s fortunes over the long term. Each case considers population and employment outcomes. Springfield–Branson National Airport (SGF) opened in 1945 and the Springfield metropolitan area has since boomed. SGF, according to an economic impact analysis, generated 4454 jobs, $154 million in payroll, and $402 million in total output as of 2012, accounting for 2.48% of total metropolitan area output.15 Fig. 3 compares actual outcomes with estimated counterfactuals. According to both the matching and synthetic control estimators, Springfield has experienced large levels of 14

The caliper matching routine was implemented using Jaskeet Sekhon’s

Matching package for R (Sekhon, 2011). 15 MO Statewide Airports Economic Impact Study: http://www.modot.org/ othertransportation/aviation/documents/Missouri-2012-Economic-Impact.pdf.

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Journal of Urban Economics 116 (2020) 103240

Table 3 Results: average 60-year effects attributable to airports. (1) OLS (All)

(2) OLS (SC Sample)

(3) ET-DD (All)

(4) ET-DD (SC Sample)

(5) Matching

(6) SC

0.094∗ ∗ ∗ (0.034) 501

0.065 (0.041) 425

0.13∗ ∗ ∗ (0.029) 11492

0.064∗ (0.032) 9730

0.193∗ ∗ ∗ (0.04) 134

0.178∗ ∗ ∗ (0.042) 152

150 0.097∗ ∗ ∗ (0.032) 502

74 0.134∗ ∗ ∗ ∗ (0.038) 396

150 0.117∗ ∗ ∗ (0.028) 11494

74 0.01 (0.031) 9012

87 0.186∗ ∗ ∗ (0.032) 156

76 0.148∗ ∗ ∗ (0.033) 92

150 0.045 (0.04) 499

44 0.03 (0.069) 387

150 0.098∗ ∗ ∗ (0.028) 11492

44 -0.009 (0.036) 8866

97 0.197∗ ∗ ∗ (0.037) 134

46 0.12∗ ∗ (0.045) 80

150 0.107∗ ∗ ∗ (0.035) 499

38 0.078 (0.049) 410

150 0.124∗ ∗ ∗ (0.03) 11488

38 0.032 (0.032) 9390

80 0.146∗ ∗ ∗ (0.025) 101

40 0.196∗ ∗ ∗ (0.043) 124

n

150 0.076∗ (0.043) 430

60 -0.002 (0.074) 321

150 0.125∗ ∗ ∗ (0.031) 11426

60 0.038 (0.049) 8686

60 0.445∗ ∗ ∗ (0.039) 57

62 0.258∗ ∗ ∗ (0.082) 68

No. Airports

150

33

150

33

33

34

A. Total Employment n No. Airports B. Population n No. Airports C. Tradable Employment n No. Airports D. Nontradable Employment n No. Airports E. Transport/Comm/Util Emp.

Notes: This table summarizes results from four estimation strategies - ordinary least-squares regression (OLS), event-time difference-in-differences run on treated and control cities (ET-DD), one-to-one distance matching with caliper (Matching), and event-time difference-in-differences results run on pairs of treated units and synthetically constructed counterfactuals (SC). Columns (1), (3), (5) and (6) represent outcomes using the entire available sample, where columns (2) and (4) are restricted to the same group of airports ultimately included in the preferred synthetic control estimator in Column (6). All controls are included in all specifications, as described in Section 5, and standard errors are clustered at the CBSA level. CBSA and year fixed effects are also included in all cases, except for the matching estimator. ∗ p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗ p < 0.01.

population and employment increase since 1950, with only the tradable sector seeing employment declines limited to more recent years. Pittsfield, Massachusetts, a city with similar pre-1950 population and employment levels but no airport, is identified by the matching estimator as the closest match. It experienced similar economic fundamentals with peak population during the 1960s with subsequent decline, and a relatively flat employment profile overall. Taking total employment as an example, the synthetic Springfield unit is comprised of the OttawaStreator, IL Micro Area (with its employment outcomes weighted at 0.7, or 70 percent of the synthetic control unit), Ann Arbor, MI Metro area (weight 0.13) and much smaller weights of other midwestern CBSAs experiencing otherwise similar economic fundamentals, the sum of which comprise the remaining 17% of the synthetic control unit. In sum, it appears that Springfield’s ability to attract and subsequently retain tradable sector jobs, coupled with its ability to capitalize on tourism and boost its non-tradable sector employment, was due in no small part to the presence of SGF. In contrast to Springfield, Elmira, New York, also a heavily manufacturing based economy in the pre-period, has experienced continued population and employment decline since the 1960s, a trend that Elmira’s airport was unable to help reverse. Elmira’ s county-owned airport, Elmira-Corning Regional (ELM), opened in 1945. Danville, Ohio, the city identified as the best counterfactual by the matching estimator, was similarly positioned to Elmira before 1950, sharing many similarities with Elmira. However, Danville never received an airport. In the past, both Danville and Elmira were major thoroughfares for rail freight. A look at Fig. 4 reveals that in both places, total employment has been essentially flat since 1950. Additionally, tradable sector employment, driven by manufacturing increased, then declined starting in the 1950s, never to recover. Both places were positioned similarly before Elmira’s airport opened, and have followed similar trajectories since, indicating that ELM did not have a substantial effect on its local economy. Focusing on total employment, its synthetic control unit is comprised of

Bradford, PA (weight 0.47), Utica-Rome NY (weight 0.16), and Altoona, PA (weight, 0.12), with other similar northeastern cities weighted at smaller levels. Bradford, PA, in particular, is a nearby city with similar industrial origins to Elmira. The synthetic control unit implies that Elmira ultimately performed worse than expected, even in light of its airport. In contrast to Springfield, Elmira’s airport was not able to stem the region’s gradual decline, resulting from unanticipated regional shocks. Additional details on the composition of the synthetic control units for outcomes presented here are provided in the Web Appendix. 5. Results 5.1. Effects on employment and population Results are presented for the entire sample based on four different identification strategies: Ordinary Least Squares (OLS), event-time difference-in-differences (ET-DD), matching, and the pooled synthetic control approach (SC). The primary outcome variables considered are total employment, population, tradable and nontradable employment, and employment in transportation/communication/utilities (TCU), which serves as the best available measure of direct airport employment. In the OLS estimates, pre-period employment and population controls, as well as a set of dummy variables grouping 1950 population into a set of 10 bins, are included in all specifications to allow cities of different sizes, regardless of whether they are treated or not, to grow at a different average rate over the period 1950–2010. This, in turn, allows flexibility in using population/employment histories to account for non-linear trends. The addition of employment shares for mining, construction, manufacturing, TCU, wholesale trade, retail trade, finance/insurance/real estate and business and professional services allows cities with, for example, a large manufacturing base in 1950, to grow at a differential average rate than cities with a large services base in 1950. These shares also help capture any long-run

M.J. McGraw

Journal of Urban Economics 116 (2020) 103240

Table 4 Results: growth in average decade attributable to airports.

A. Total Employment n No. Airports B. Population n No. Airports C. Tradable Employment n No. Airports D. Nontradable Employment n No. Airports E. Transport/Comm/Util Emp. n No. Airports

(1) OLS (All)

(2) OLS (SC Sample)

(3) ET-DD (All)

(4) ET-DD (SC Sample)

(5) Matching

(6) SC

0.025∗ ∗ ∗ (0.009) 501

0.019∗ (0.01) 425

0.04∗ ∗ ∗ (0.008) 11492

0.02∗ ∗ (0.009) 9730

0.047∗ ∗ ∗ (0.009) 134

0.039∗ ∗ ∗ (0.01) 152

150 0.026∗ ∗ ∗ (0.008) 502

74 0.035∗ ∗ ∗ ∗ (0.01) 396

150 0.034∗ ∗ ∗ (0.008) 11494

74 0.001 (0.009) 9012

87 0.04∗ ∗ ∗ (0.008) 156

76 0.034∗ ∗ ∗ (0.009) 92

150 0.012 (0.011) 499

44 0.013 (0.019) 387

150 0.026∗ ∗ ∗ (0.008) 11492

44 -0.006 (0.01) 8866

97 0.064∗ ∗ ∗ (0.009) 134

46 0.034∗ ∗ ∗ (0.011) 80

150 0.027∗ ∗ ∗ (0.009) 499

38 0.021∗ (0.012) 410

150 0.036∗ ∗ ∗ (0.008) 11488

38 0.009 (0.009) 9390

80 0.029∗ ∗ ∗ (0.006) 101

40 0.029∗ ∗ ∗ (0.009) 124

150 0.014 (0.011) 430 150

60 0.004 (0.02) 321 33

150 0.034∗ ∗ ∗ (0.008) 11426 150

60 0.012 (0.014) 8686 33

60 0.099∗ ∗ ∗ (0.009) 57 33

62 0.068∗ ∗ ∗ (0.02) 68 34

Notes: This table summarizes results from four estimation strategies - ordinary least-squares regression (OLS), event-time difference-in-differences run on treated and control cities (ET-DD), one-to-one distance matching with caliper (Matching), and event-time difference-in-differences results run on pairs of treated units and synthetically constructed counterfactuals (SC). Columns (1), (3), (5) and (6) represent outcomes using the entire available sample, where columns (2) and (4) are restricted to the same group of airports ultimately included in the preferred synthetic control estimator in Column (6). All controls are included in all specifications, as described in Section 5, and standard errors are clustered at the CBSA level. CBSA and year fixed effects are also included in all cases, except for the matching estimator. ∗ p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗ p < 0.01.

industry-specific trends such as the general decline in manufacturing employment over time. Additional geographic controls include controls for Census division, land area, whether a city is a political capital city, has a port, rail mileage pre-1900, highway mileage, January temperature, July humidity, a measure of land typography, the share of the CBSA that is water, whether the CBSA has a county that is coastal or located on a river. Many of these controls could influence airport placement - for example, a political capital city might be more likely to have an airport due to its political importance. Finally, controls for an area’s human capital, proxied here by whether the CBSA had a land-grant college or reseach university. The land grant college variable, in particular has been shown to be predictive of future city growth (e.g. Liu, 2015; Moretti, 2004).16 These factors are incorporated into the one-to-one matching algorithm and the estimation of the synthetic control units as well. CBSA and year fixed effects are included directly in the OLS specifications. For the event-time difference-in-differences and pooled synthetic control estimators, CBSA and year fixed effects are included. Outcomes from the caliperized one-to-one matching models do not include CBSA or year fixed effects directly; however, the effect of the matching estimator is to create estimates that control for unobservable trends over time and CBSAs exactly as in the other models with fixed effects. In order to examine whether the estimation strategies were able to achieve pre-treatment balance, Fig. 5 shows the evolution of effects over time for total employment for the OLS (entire sample), OLS (restricted to the synthetic control sample), matching and synthetic control estimates. Generally, it appears that pre-treatment balance was achieved across the various estimation strategies, and that the estimated growth in these effects has evolved relatively smoothly over time. 16 Results examining changes in estimates as a result of systematically adding various groups of covariates to model specifications are provided in the Web Appendix.

5.1.1. Average outcomes Table 3 considers the average level effect of an airport in a region relative to 1950. Column (1) presents OLS results based on the entire sample of airports. Column (2) presents the same outcome, but restricted to the sample of airports used in the preferred synthetic control estimator for comparative purposes. Column (3) presents the event-time difference-in-differences (ET-DD) result using the entire sample, while column (4) presents that restricted to the synthetic control airport sample. Column (5) presents the results of the caliper matching exercise, while column (6) presents the results of the preferred synthetic control estimator. The results in Columns (2) and (4) are presented to address a potential concern with the estimates. As a result of lack of common support, particularly when considering larger airports in the sample, both the matching and synthetic control estimates are unable to use all 150 airports in their estimates. Thus, there may be concern that the synthetic control estimates are biased upward simply due to how the resultant sample is constructed. At the same time, the OLS and ET-DD estimates could also be biased, even with all airports included in their sample. If the estimates in Columns (2) and (4) are systematically larger than those in columns (1) and (3), then it is entirely plausible that higher synthetic control estimates could be due to the resultant sample rather than improved identification. If, however, these estimates are generally smaller, then it appears more plausible that the synthetic control is working as desired. Overall, Table 3 provides evidence of the latter. Smaller airports comprise the synthetic control estimates. These smaller airports are more likely to be located in cities which, due to their size, are experiencing lower levels of economic growth at the outset. This leads to downward bias in the restricted sample OLS and ET-DD estimates, bias which the synthetic control estimate is able to reduce. Additionally, the one-to-one matching estimator, which also by construction ends up using a smaller set of cities, yields estimates similar to, if not slightly larger than, the synthetic control estimates. This provides corroborating evi-

M.J. McGraw

Journal of Urban Economics 116 (2020) 103240

A. Total Employment (OLS) 60-Year Average Estimates

B. Total Employment (OLS) 60-Year Growth Estimates

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Fig. 6. Total employment estimates by miles from airport to CBSA edge. Note: This figure plots the results of average level estimates over the 60-year period, estimated for CBSAs within 5 to 50 miles distance from the airport, in buckets of five miles. For example, 10 miles on the graph includes CBSAs that are more than 5 but less than or equal than 10 miles away from the airport in straight-line distance as treated units. Distance is measured from the center of the airport to the nearest CBSA edge. Control units consist of CBSAs that are farther away from the airport; for example, any CBSA located more than 10 miles away from the CBSAs would be included. All controls are included in all specifications, as detailed in the text. Dotted lines indicate 95% confidence intervals. It is expected that the airport effect should decline smoothly with distance.

dence that the synthetic control estimate appears to be working well, as the weighting scheme of the synthetic control estimation is more flexible than that of one-to-one matching. Another pattern emerges in Table 3 as well. The average 60-year effects attributable to airports is larger for matching and synthetic control estimates than it is for the OLS and ET-DD estimates. This is likely due to the fact that the matching and synthetic control estimators are better able to overcome selection bias occurring in the pre-period than are the standard OLS and ET-DD estimators. This pre-period bias, resulting from the fact that these estimators suffer from a lack of parallel trends even after the inclusion of fixed effects and other available covariates, is observed most acutely in Fig. 2. Additionally, the smaller airports included in the matching and the synthetic control estimates are more likely to be locations where the naive estimate of the airport could be muted due to other factors such as location, industrial mix, and smaller population size, as well as other unobserved factors. At the same time, these factors also mean that there actually could be the potential for a smaller city airport to have an outsized effect on its city’s outcomes compared to those for a larger city. Taken together, these factors imply the bias-corrected estimates provided by the synthetic control estimate (and to a lesser degree, matching) are higher. Focusing on the results from the preferred synthetic control estimator, the estimated (level) treatment effect on total employment is 0.178, population 0.148, tradable employment 0.12, nontradable employment 0.196, and transportation/communications/utilities (TCU) employment of 0.258. Of these, there appears to be consistency in results across the

various estimation strategies for total employment, population, nontradable employment and TCU employment. Taken together, this provides evidence that airports have contributed to employment growth over the post-war period. 5.1.2. Growth outcomes Table 4 considers the mean per-decade growth in employment and population outcomes attributable to airports over the 60 year period. It appears that, regardless of estimation strategy or sample used, decadal growth in the total employment measure is estimated to be between 1.9 and 4.7 percent per decade, with the preferred synthetic control estimate estimating 3.9% growth. Population growth is similar, with an estimate of 3.4%. The estimates for tradable employment are less consistent across estimation strategies. While the synthetic control estimate gives 3.4% growth, neither of the OLS estimates pick up any growth in this measure. This may be a result of noisier data for tradable employment, leading to fewer airport cities being included in the estimates. On nontradable employment, five of the six strategies pick up growth ranging from 2.1 to 3.6%, with a preferred estimate of 2.9%. Finally, the estimators disagree on the size of TCU employment growth. Again, this could be due to data quality; the synthetic control estimator estimates 6.8% growth. Interestingly, the estimated growth outcomes shown in Table 4 are more consistent across the estimators than the estimated average quantities were. Recall that the growth estimate simply measures the difference between the 2010 value and the 1950 value (normalized to zero

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Journal of Urban Economics 116 (2020) 103240

Table 5 Results: average wage increases attributable to airports.

A. Earnings n No. Airports B. Earnings Per Worker n No. Airports C. Personal Income n No. Airports D. Personal Income Per Capita n No. Airports E. Payroll n No. Airports F. Payroll Per Worker n No. Airports

(1) OLS (All)

(2) OLS (SC Sample)

(3) ET-DD (All)

(4) ET-DD (SC Sample)

(6) SC

0.191∗ ∗ ∗ (0.049) 501

0.167∗ ∗ ∗ (0.062) 425

0.13∗ ∗ ∗ (0.031) 5050

0.1∗ ∗ (0.039) 4290

0.151∗ ∗ ∗ (0.038) 152

150 0.01 (0.013) 501

74 0.02 (0.016) 425

150 0.014 (0.01) 5050

74 0.029∗ ∗ (0.013) 4290

76 0.02∗ (0.011) 152

150 0.128∗ ∗ ∗ (0.046) 501

74 0.097∗ (0.057) 425

150 0.09∗ ∗ ∗ (0.028) 5050

74 0.054 (0.035) 4290

76 0.093∗ ∗ (0.036) 152

150 0.227∗ ∗ ∗ (0.057) 501

74 0.307∗ ∗ ∗ (0.076) 425

150 0.023∗ ∗ (0.01) 5050

74 0.045∗ ∗ ∗ (0.011) 4290

76 0.031∗ ∗ ∗ (0.01) 152

150 0.215∗ ∗ ∗ ∗ (0.052) 501

74 0.187∗ ∗ ∗ (0.064) 425

150 0.135∗ ∗ ∗ (0.039) 5050

74 0.082∗ (0.048) 4290

76 0.267∗ ∗ ∗ (0.055) 152

150 0.08∗ ∗ ∗ ∗ (0.023) 501 150

74 0.089∗ ∗ ∗ (0.029) 425 74

150 0.042∗ ∗ (0.02) 5050 150

74 0.047∗ (0.025) 4290 74

76 0.122∗ ∗ ∗ (0.027) 152 76

Notes: This table summarizes results from four estimation strategies - ordinary least-squares regression (OLS), event-time difference-in-differences run on treated and control cities (ET-DD), one-to-one distance matching with caliper (Matching), and event-time difference-in-differences results run on pairs of treated units and synthetically constructed counterfactuals (SC). Columns (1), (3), and (5) represent outcomes using the entire available sample, where columns (2) and (4) are restricted to the same group of airports ultimately included in the preferred synthetic control estimator in Column (6). All controls are included in all specifications, as described in Section 5, and standard errors are clustered at the CBSA level. CBSA and year fixed effects are also included in all cases. ∗ p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗ p < 0.01.

in all cases except for the OLS model) divided by six. The fact that the averages are much more variable across models than the growth estimates speaks to the fact that although the different estimators have varying levels of success in correcting for the pre-treatment conditions (through behavior of fixed effects as intercepts and/or a matching-style process), the estimators are consistently identifying the rate at which airports have caused growth. From a policymaker’s perspective, the growth rate is arguably the more relevant metric in assessing the role of an airport in a community. Thus, the agreement across estimation strategies on these estimates, particularly for total employment, population, and nontradable employment, segments where the data is of comparatively high quality, is reassuring. 5.1.3. Performance of the synthetic control estimator Fig. 6 provides an additional check on the validity of the synthetic control estimates. If the treatment effects estimated are indeed from the airports themselves, then the treatment effect should decrease as distance from the airport increases. Panels A and C show a graph of level effects for CBSAs within five-mile bins, while panels B and D show the same for the growth effects. Panels A and B consider OLS effects, while panels C and D consider synthetic control effects. This shows that as distance from the airport to the nearest CBSA edge increases, estimated treatment effects do indeed decrease smoothly. In summary, it appears that airports have contributed to growth in total population, total employment, and nontradable employment in airport cities. This growth is consistent with information flows facilitated by business travel, as well as tourism. Unfortunately, the data does not allow for disentangling which of these mechanisms might dominate.17 17

Census data do not allow me to examine tourism in a rigorous way. For example, I attempted to construct an analogous series for hotels and lodging,

5.2. Extensions 5.2.1. Effects on wage outcomes Here, I focus on six outcomes of interest: payroll, payroll per worker, earnings, earnings per worker, personal income, and personal income per capita. Unlike the employment and population outcomes considered in the previous section, there are no complete histories available over the 1900–2010 period for any of these outcomes. Payroll data are available from 1950, and the other data are available are available from 1970 to 2010. Personal income, which measures gross earnings from all income sources, is widely used as a measure in similar studies as a proxy for local area GDP. Earnings allow for a measure of wages deriving directly from employment. Table 5 summarizes results across all available identification strategies. Estimates are derived from examining the 1980–2010 period only. While the OLS specifications include controls over the entire period prior to 1980, the ET-DD estimates and synthetic control estimates use only 1970 and 1980 data in the pre-period. These results indicate over the 30-year time period, earnings per worker increased by 2%, and personal income per capita increased by 3.1%. Total earnings and personal income closely mirror total employment growth. Payroll and payroll per worker values are even higher; however, these estimates could be unreliable because of changing definitions over time regarding which firms are captured in the underlying County Business Patterns data, especially in earlier years of the survey. Growth effects can be computed for all identification strategies using the same models presented earlier, with estimates shown in Table 6. but the quality of the available data was low, and, data was not available at all for much of the study period. Similar issues were encountered with other smaller sectors that could be relevant, such as amusements and recreation.

M.J. McGraw

Journal of Urban Economics 116 (2020) 103240

Table 6 Results: growth rates (average decade) wage increases attributable to airports.

A. Earnings n No. Airports B. Earnings Per Worker n No. Airports C. Personal Income n No. Airports D. Personal Income Per Capita n No. Airports E. Payroll n No. Airports F. Payroll Per Worker n No. Airports

(1) OLS (All)

(2) OLS (SC Sample)

(3) ET-DD (All)

(4) ET-DD (SC Sample)

(5) SC

0.023∗ (0.012) 501

0.016 (0.015) 425

0.042∗ ∗ ∗ (0.012) 5050

0.032∗ ∗ (0.015) 4290

0.054∗ ∗ ∗ (0.015) 152

150 −0.004 (0.005) 501

74 −0.005 (0.006) 425

150 0.009∗ (0.005) 5050

74 0.009∗ (0.006) 4290

76 0.008 (0.005) 152

150 0.019∗ (0.011) 501

74 0.01 (0.013) 425

150 0.034∗ ∗ ∗ (0.011) 5050

74 0.02 (0.013) 4290

76 0.035∗ ∗ (0.013) 152

150 00 (0.005) 501

74 -0.002 (0.006) 425

150 0.007 (0.004) 5050

74 0.014∗ ∗ ∗ (0.005) 4290

76 0.012∗ ∗ ∗ (0.004) 152

150 0.038∗ ∗ ∗ (0.014) 501

74 0.023 (0.017) 425

150 0.045∗ ∗ ∗ (0.014) 5050

74 0.036∗ ∗ (0.017) 4290

76 0.002 (0.004) 152

150 0.01 (0.008) 501 150

74 0.004 (0.011) 425 74

150 0.013∗ (0.007) 5050 150

74 0.02∗ ∗ (0.009) 4290 74

76 -0.004 (0.004) 152 76

Notes: This table summarizes results from four estimation strategies - ordinary least-squares regression (OLS), event-time difference-in-differences run on treated and control cities (ET-DD), one-to-one distance matching with caliper (Matching), and event-time difference-in-differences results run on pairs of treated units and synthetically constructed counterfactuals (SC). Columns (1), (3), and (5) represent outcomes using the entire available sample, where columns (2) and (4) are restricted to the same group of airports ultimately included in the preferred synthetic control estimator in Column (6). All controls are included in all specifications, as described in Section 5, and standard errors are clustered at the CBSA level. CBSA and year fixed effects are also included in all cases. ∗ p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗ p < 0.01.

Findings are mixed, with growth in earnings observed across many of the specifications. The preferred synthetic control specification indicates growth in personal income per capita at the rate of 1.2 percent per decade; however, this is not corroborated by the earnings per worker or payroll per worker measures. These estimates suggest that workers may have been able to capture some of the gains provided by the airport as well. The positive wage premium supports the hypothesis that an airport acts as a productive amenity for cities. It is certainly possible that these wage gains come entirely from the selection of workers into different areas, induced by the choice of businesses requiring highly-skilled labor to locate near airports. However, this may be less important to a local policy maker who, for example, could still find an investment in an airport beneficial due to higher wages and additional jobs. From a national perspective, however, it is possible that an airport might cause increased spatial inequality. While the data and methods employ here do not allow for this type of assessment, Campante and YanagizawaDrott (2018) conclude that, in their global assessment of airports and economic development, increased air links generates economic activity at the local level, but could also give rise to these inequities nationally and globally. 5.2.2. Costs and benefits of airports and job creation The quantities estimated previously allow for back-of-the-envelope calculations on the costs and benefits of airports, as well as the number of jobs created by the typical airport over time. In order to generate these figures, I first compute decade-by-decade growth in their actual amounts (counts or 2010 dollars). Then, I determine the average amount of growth over the period, which is then multiplied by the coefficient estimated for growth based on the synthetic control estimator with all controls included. Applying this process to the measures of interest, I find that in the average decade, population is estimated to grow, on

average, by almost 1500, with almost 1000 people at or near working age (ages 16–64) per decade. 916 jobs are created overall, 52 of which are in the transportation sector, and 660 of which are in the nontradable sector. Using payroll as a proxy for output, airports are responsible for $77 million in output growth in a given decade over the study period. In terms of costs, comprehensive data on the cost of building and maintaining airports is difficult to obtain, given that each municipality makes their own financing decisions regarding their airport(s). Airlines are often involved as well. However, as a reference case, consider Branson Airport, the first all-new major commercial airport to open in the United States in recent years. The airport, which opened in 2009, cost $155 million to build in 2010 dollars.18 Branson airport was estimated to generate $77.5 million in payroll and 3299 jobs for the Branson Lakes Regional Economy. While their payroll estimate is extremely close to that found in this study, I find only a fraction of those jobs would be expected to materialize. That said, it appears that the typical airport will have paid for itself over the study period, the costs of environmental externalities, renovations, upgrades, and federally provided services such as air traffic control notwithstanding. It must be stressed that these are averages, and that a particular airport may generate little if any economic boost for its local economy, or, on the contrary, could generate an outsized impact as well. 6. Conclusion The primary goal of this paper was to consider the relationship between the presence of an airport in a metropolitan area and pertinent

18 Branson Airport Fact Sheet, http://flybranson.com/docs/BransonAirport FactSheet.pdf.

M.J. McGraw

local economic outcomes. This was accomplished by collecting historical data on airport placement and industry outcomes, exploiting key events in the development of the system to estimate treatment effects robust to endogeneity concerns. Focusing on the end of the World War II period as a key structural break in the formation of the U.S. aviation system, I considered how airports have affected cities post-1950. In order to conduct this study, a rich, detailed dataset of metropolitanarea-level employment outcomes, geographical, transportation, and human capital characteristics was compiled. I focused on airports in midsized and smaller estimates in order to generate credible estimates. A pooled synthetic control event study approach was adopted to generate causal estimates. Results indicate airports have led to 3.9% growth in total employment (and 3.4% growth in population) per decade. All identification strategies imply that some part of this growth pertains to nontradable employment (2.9% growth) but, they disagree on whether tradable employment growth plays a role, and if so, how much of a role. Effects on wages and job creation in airport cities were also observed, on the order of 1 to 3 percent per decade. Taken together, these results show, on balance, that infrastructure investment does appear to stimulate growth in the economy. However, the effect is likely heterogeneous across airport sizes and air traffic levels, so further research is needed to better understand these distributional outcomes. These caveats notwithstanding, it appears worthwhile for communities to continue to invest in airports, given their mean historical return, to ensure that firms and individuals who can benefit from the aviation system can do so, at least until the next innovation in long-distance transportation infrastructure comes along. Funding This work was supported by funding from the Graduate Research Fellowship of the National Science Foundation (NSF DGE 1106400) and the Chancellor’s Fellowship for Graduate Study provided by UC Berkeley. CRediT authorship contribution statement Marquise J. McGraw: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration, Funding acquisition. Acknowledgements I would like to thank Enrico Moretti, David Card, and Victor Couture for their expert guidance and advice on this project. I also thank Bob Helsley, Pat Kline, Dan Chatman, Mark Hansen, Severin Borenstein, Charles Becker, Nicholas Sheard, and Janet Kohlhase, as well as seminar participants at the 2016 AEA/ASSA Transportation and Public Utilities Group meeting, the University of Mannheim MAYBE Workshop, the Federal Reserve Bank of Chicago and the 2014 meeting of the Western Regional Science Association for helpful comments. I thank Alice Wang for excellent research assistance. This project was conducted while I was a NSF Graduate Research Fellow at UC Berkeley (NSF Grant Number DGE 1106400), and I gratefully acknowledge their financial support. I also

Journal of Urban Economics 116 (2020) 103240

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