The Role of U.S. Airports in the National Economy (2015)

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28 C H A P T E R 5 The analysis of MFP examined how improvements in con- nectivity between the U.S. airports and between the U.S. and international airports can add to the national economy. The research approach was based on an empirical model that examines how changes in air service connectivity result in improved productivity. A set of variables was defined that capture differing aspects of connectivity provided by the air- line network. Next, a set of regression models were estimated that relate changes in connectivity measures to changes in multifactor productivity and, hence, the resulting changes in real economic or income growth. The data selected for exploring the relationship of airport connectivity to economic productivity was developed for the airports serving a sample of U.S. metropolitan regions (see Table 13), a set of foreign international hub airports that link the U.S. economy to the rest of the world, and 11 industry sectors (Table 14) based on NAICS codes for the years 1995, 2000, 2005, and 2010. Ten of the 11 industry sectors for which sector–specific models were developed were chosen on the basis of the likely role of air travel in sector productivity. The other industry sectors were combined into a single group labeled “other.” These airports were selected to include a variety of types and sizes: gateway airports; airline hubs; large, medium, and small hub airports; non-hub airports; and de-hubbed air- ports that were formerly airline hubs. Airports were also cho- sen to represent the geographic spread of the domestic U.S. travel market. The international airports used to measure international connectivity were: Amsterdam, London (Heathrow), Frank- furt, Munich, Paris (Charles de Gaulle), Madrid, Hong Kong, Singapore, Shanghai, Beijing, Dubai, Seoul (Incheon), Tokyo (Narita), Copenhagen, and Rome. These were chosen because they are the 15 largest international airline hubs outside the United States that provide air service connectivity to the rest of the world and are major international destinations in their own right. 5.1 Estimating Regional MFP Values Measures of MFP by industry over time at the national level are available from the U.S. Bureau of Labor Statistics (BLS). Data at the metropolitan statistical area level were assembled to allow calculations of a measure of labor productivity for each industry sector for a given MSA. As MFP measures are not available by industry at the MSA level, the research team needed to address how to translate the national MFP measures to the regional level. To do this, the research team defined the multifactor productivity measure for each industry sector at the national level and defined a measure of labor productivity for each industry by measures of value added and labor input (hours or numbers of employ- ees), and further defined labor productivity per industry sec- tor based on data for relevant MSAs. Values for the MFP for each sector at the national level were obtained from the BLS data on employment and the BEA for GDP by sector. Values for the MFP at regional levels for the 20 MSAs were obtained from Moody’s Analytics and the BLS Regional Economic Accounts. Several of the larger regions consist of more than one MSA, so the data at the MSA level were combined to give regional totals. A detailed description of how the multifactor productivity analysis was developed can be found in Appendix 1. A sum- mary of the method to calculate the regional MFP follows. Air Service and Other Variables The air service variables assembled for each airport in the 20 MSAs are shown in Table 15, with the means and standard deviations of these variables across the regions in the sample for each year. These variables were chosen to measure the level of air traffic activity at each airport, as well as provide different measures of airline network connectivity and to dis- tinguish between domestic and international connectivity. Most of the connectivity measures are self-explanatory. The Findings: MFP

29 Code Airport/region Multi-airport Regions SF Bay San Francisco Bay Area SFO, OAK, SJC Chicago Chicago metropolitan region ORD, MDW ATL Hartsfield-Jackson Atlanta International Airport CVG Cincinnati/Northern Kentucky International Airport STL Lambert-St. Louis International Airport PIT Pittsburgh International Airport RDU Raleigh-Durham International Airport DEN Denver International Airport Phoenix Phoenix metropolitan region PHX, AZA SLC Salt Lake City International Airport Boston Boston metropolitan region BOS, MHT, PVD PHL Philadelphia International Airport DTW Detroit Metropolitan Wayne County Airport SAN San Diego International Airport PDX Portland International Airport TPA Tampa International Airport MCI Kansas City International Airport TUL Tulsa International Airport SAT San Antonio International Airport BNA Nashville International Airport Airports in the Four Multi-Airport Regions SFO San Francisco International Airport OAK Oakland International Airport SJC Mineta San Jose International Airport ORD Chicago O’Hare International Airport MDW Chicago Midway Airport PHX Phoenix Sky Harbor International Airport AZA Phoenix-Mesa Gateway Airport BOS Boston Logan International Airport PVD Theodore Francis Green State Airport (Providence) MHT Manchester-Boston Regional Airport Table 13. Airports selected for the analysis. Table 14. Eleven industry sectors included in the modeling. NAICS Code Sector 31-33 Manufacturing 42 Wholesale Trade 51 Information 52 Finance and Insurance 53 Real Estate and Renting and Leasing 54 Professional, Scientific, and Technical Services 55 Management of Companies and Enterprises 56 Administrative and Support and Waste Management and Remediation Services 71 Arts, Entertainment, and Recreation 72 Accommodation and Food Services Other* A grouping of the following sectors: Agriculture, Forestry, Fishing and Hunting; Mining, Quarrying, and Oil and Gas Extraction; Utilities; Retail Trade; Transportation and Warehousing; Educational Services; Health Care and Social Assistance; Other Services (except Public Administration); and Public Administration *By themselves, sectors in “Other” did not show significant results. However, when combined, results for three of the connectivity measures were significant.

30 Translating National MFP for Regions The U.S. BLS provides measures of MFP by industry over time at the national level. The issue is how to translate the national measures to be meaningful at the MSA level. We use measures of labor productivity since these can be calculated for productivity for a specific industry for a specific MSA. The transformation was undertaken in the following way. Define MFPiN as the multifactor productivity for industry i at the national level, and define L Q Li i i ˆ = as labor productivity for industry i where Q is value added output and L is labor input (hours or numbers of employees). Further define Li kˆ as the labor productivity of industry i in MSA k. Consider the following relationship which states national MFP for industry i at the national level is a function of labor productivity plus some other factors that we have no information about that would be captured in a constant a and an error term ε: ˆ (1)iMFP LiN i N = α + β + ε For simplicity, and due to lack of information, rewrite (1) as: MFP LiN i Ni ˆ (2)= β which can be re-written as: MFP L N i N i Nˆ (3)β = where we expect bN >1. We could also reproduce (2) and (3) for MFP and labor productivity for an MSA, as MFP L k i k i kˆ (4)β = The relationship between bN and bk is unclear, but if we assume they are similar, then making an assumption, set: MFP L MFP L k N i N i N i k i kˆ ˆ (5)β = β = = set MFPki = X, the unknown in these equations. Manipulating (5) find X as X L L MFPi k i N i Ni (6)=   Equation 6 states that a measure of MFP for MSA k for industry i can be calculated by taking the ratio of labor productivity in industry i in MSA k to the productivity of labor at the national level for the same industry i and multiply this by the MFP for industry i at the national level. Essentially what we have done is to weight the labor productivity at the MSA level by the labor productivity of the industry at the national level; Lik may be ≤ or ≥ than LiN.

Table 15. Summary statistics for airport air service variables used in regressions. 1995 2000 2005 2010 Air Service Variable Mean St Dev Mean St Dev Mean St Dev Mean St Dev Number of airlines 18.25 12.79 19.20 14.79 19.50 14.77 17.45 13.48 Percent of flights by dominant carrier 52% 21% 52% 22% 49% 22% 44% 20% Total nonstop departures Domestic 138,992 105,285 159,329 117,204 180,435 129,056 154,895 129,024 International 4,245 4,993 7,272 9,814 7,880 8,239 9,405 12,533 Airline hubs served (domestic) 24.95 9.49 28.50 11.63 28.55 11.43 28.95 10.66 Nonstop destinations Domestic 69.50 37.81 74.30 41.55 88.55 47.24 82.85 50.39 International 10.20 9.81 12.20 14.21 14.05 15.36 15.80 19.94 Percent of world GDP served by Nonstop flights 22% 24% 26% 25% 24% 23% 22% 22% At least daily nonstops 16% 21% 21% 22% 20% 22% 17% 20% 2 or more daily nonstops 7% 13% 10% 16% 11% 15% 9% 13% Total air cargo (metric tons) Enplaned domestic 26,200 28,764 24,715 25,691 116,503 113,613 92,384 75,869 Enplaned international 31,595 64,291 42,161 82,885 42,569 92,839 43,424 95,478 Deplaned domestic 26,103 25,927 25,695 26,194 124,665 108,816 98,510 73,129 Deplaned international 28,120 60,630 54,227 113,480 60,217 136,591 55,384 131,683 International hubs served At least daily nonstops 1.35 2.03 2.25 3.21 2.45 3.62 2.60 3.94 3 or more daily nonstops 0.35 0.81 0.55 1.23 0.55 1.23 0.50 1.10 Total passengers (000) Domestic 20,499 16,495 25,434 19,607 26,387 21,609 24,744 21,395 International 1,191 1,629 2,069 2,987 2,072 2,755 2,273 3,352 Domestic nonstop destinations 2 or more daily nonstops 53.20 32.16 60.70 37.05 70.15 41.47 60.10 41.38 5 or more daily nonstops 25.50 21.52 29.55 22.31 35.15 27.10 29.10 26.38

32 percentage of the world GDP served by flights at different frequencies is based on the total GDP of countries with air- ports served by nonstop flights at the frequency in question (irrespective of the geographical size of the country or the number of airports within each country that are served at the relevant frequency). Air service data only counted: (1) scheduled service and the number of nonstop departures in a market and (2) service by airlines operating at least 50 flights annually to a given desti- nation. This latter criterion was applied to exclude occasional seasonal service or flights that made unscheduled technical stops (e.g., diversions or refueling stops). Regional affili- ates were considered to be part of the mainline carrier when counting the number of airlines or the percentage of flights by the dominant carrier at an airport. Flights to Canada were included in international service. In addition to air service variables, the regression models of MFP included regional population to control for size dif- ferences between the regions and dummy variables for three of the four years used in the analysis to capture the effects of time.21 Regional population data was obtained from the U.S. BEA, which uses U.S. Census Bureau mid-year population estimates. 5.2 Model Results Table 16 lists the results of the regressions for the 11 industry sectors across the 20 MSA regions in the sample. Coefficients in bold are statistically significant at least at the 90th percentile confidence level (the fit of the variables in explaining GDP estimates are shown in the second to last row, titled “Adjusted R2”). The degree of explanatory power ranges from a low of 64% for the “Arts, Entertainment, and Recreation” sector to a high of 92% for the “Information” sector. The Stata econometric software package was used to generate these findings using standard ordinary least squares (OLS). In addition to the air service connectivity variables, the regression variables included the regional population to explore how market size may affect MFP and dummy variables for three of the four years of data to capture any time-related trends. In all cases, the estimated coefficients for the regional pop- ulation are positive and generally significant, indicating that the size of the region has an impact on multifactor productiv- ity. The coefficients for the time dummy variables are positive except in two cases (the values for which are not statistically significant) and generally significant. The values for 2010 are not always larger than for 2000 and 2005, showing that pro- ductivity growth has varied significantly across industries, as well as over time. Since the model was estimated in log-linear form, the coef- ficients can be interpreted as giving the percentage change in MFP for a one-percent change in the selected airport variable. As an example, the results for the manufacturing sector show that a 1% increase in the number of airlines serving a region would lead to a 0.044% (0.044 of 1%) increase in the MFP for manufacturing, while a 1% increase in the number of domes- tic nonstop flight departures would increase manufacturing MFP by 0.024%. A 1% increase in the number of nonstop domestic and international destinations served will increase manufacturing MFP by 0.082%. Table 17 shows the average elasticity22 for each statisti- cally significant connectivity measure across industries for each of the airport variables included in the model. On average, considering only values that were statistically sig- nificant, two or more daily domestic nonstop flights is the most important connectivity measure affecting productiv- ity. The second most important measure is the number of international nonstop destinations, the third is the num- ber of domestic nonstop destinations, and the fourth is the percentage of the world GDP accounted for by countries that are served by daily international flights. This last vari- able points out that while adding flights or destinations is important, the flights should be to destinations important in terms of the overall level of economic activity (as mea- sured by GDP) in the regions or countries served by those destinations. The relative impact of each connectivity measure is illus- trated in the fourth column of Table 17. The elasticity of each measure is compared to that of the measure with the greatest impact on MFP, which is the number of domestic destina- tions having two or more daily nonstop flights. The measure with the highest elasticity is two or more daily nonstop flights. If we take the measure with the second highest elasticity (the number of international nonstop destinations), this latter measure would have to increase from its current value by 2.5 times as the former to have the same impact on MFP. For example to match a 1% increase in the “number of domestic destinations having two or more daily nonstop flights,” the “number of international nonstop destinations” would have to increase by 2.5%. These results also imply that destinations and departures provide about the same amount of connectivity and that frequency is important. The remaining variables have about 20% of the impact of the connectivity measure with the greatest impact on MFP. Using the average values displayed in Table 17 is useful to gauge the overall effects of each con- nectivity measure. However, they can be misleading for any particular industry and assessing which variables matter and their relative importance should be based on the elasticity values in Table 16.

Table 16. Estimation results for multifactor productivity regressions. Industry Sector NAICS 31-33 NAICS 42 NAICS 51 NAICS 52 NAICS 53 NAICS 54 Dependent Variable: Ln MFP for Region Independent Variable Manufacturing Wholesale Trade Information Finance & Insurance Real Estate, Rental & Leasing Professional Scientific & Technical Services Constant -1.0913 6.4783 9.1860 0.5697 9.0946 4.5121 Year 2000 Dummy -0.0546 0.0395 -0.0004 0.0601 0.0223 0.0689 Year 2005 Dummy 0.0608 0.0657 0.2552 0.1264 0.6151 0.0151 Year 2010 Dummy 0.2107 0.2486 0.3492 0.2622 0.4221 0.0115 Ln Regional Population 0.0037 0.0015 0.0013 0.0433 0.0252 0.0447 Ln Number of Airlines 0.0439 0.0215 0.0596 0.0048 0.0797 0.0435 Ln Domestic Nonstop Departures 0.0237 0.0257 0.0192 0.0479 0.0213 0.0182 Ln Airline Hubs Served-Domestic 0.0423 0.6624 0.0151 0.0716 0.0316 0.0361 Ln Domestic Nonstop Destinations 0.0344 0.0152 0.0074 0.0711 0.0397 0.0504 Ln Two or More Daily Nonstop Domestic Flights 0.0991 0.0607 0.0121 0.0312 0.0406 0.0112 Ln Five or More Daily Nonstop Domestic Flights 0.0531 0.0318 0.0192 0.0697 0.0256 0.0096 Ln International Nonstop Departures 0.0163 0.0003 0.0244 0.0132 0.0039 0.0262 Ln International Nonstop Destinations 0.0479 0.0191 0.0144 0.0375 0.0532 0.0275 Ln Percent of the World GDP Served Nonstop 0.0174 0.0117 0.0147 0.0911 0.0246 0.0107 Ln Percent of the World GDP Served Daily 0.0263 0.0214 0.0257 0.0107 0.0612 0.0491 Ln Percent of the World GDP Served Twice or More Daily 0.0157 0.0032 0.0201 0.0072 0.0079 0.0205 No Observations 80 80 80 80 80 80 Adjusted R2 0.74 0.79 0.92 0.89 0.84 0.81 Log-Likelihood 633.25 449.62 345.76 318.98 329.87 366.77 (continued on next page)

Table 16. (Continued). Industry Sector NAICS 55 NAICS 56 NAICS 71 NAICS 72 NAICS Other Dependent Variable: Ln MFP for Region Independent Variable Management of Companies & Enterprises Administration & Support Waste Management Services Art, Entertainment, & Recreation Accommodation & Food Services Otherb Constant 1.9440 3.9618 3.9720 5.8501 1.2294 Year 2000 Dummy 0.0318 0.0983 0.0416 0.0163 0.8700 Year 2005 Dummy 0.1104 0.0130 0.0263 0.0541 0.0812 Year 2010 Dummy 0.0287 0.0112 0.0693 0.1218 0.5798 Ln Regional Population 0.0185 0.0004 0.0113 0.0529 0.0981 Ln Number of Airlines 0.0152 0.0519 0.0562 0.0161 0.1004 Ln Domestic Nonstop Departures 0.0843 0.0104 0.0817 0.0001 0.0004 Ln Airline Hubs Served-Domestic 0.0106 0.0226 0.0093 0.0456 0.0285 Ln Domestic Nonstop Destinations 0.0321 0.0301 0.0132 0.0229 0.0371 Ln Two or More Daily Nonstop Domestic Flights 0.0151 0.0269 0.0191 0.0153 0.0227 Ln Five or More Daily Nonstop Domestic Flights 0.0749 0.0074 0.0197 0.0885 0.0452 Ln International Nonstop Departures 0.0091 0.0211 0.0217 0.0691 0.0142 Ln International Nonstop Destinations 0.0227 0.0877 0.0215 0.0526 0.0136 Ln Percent of the World GDP Served Nonstop 0.0203 0.0472 0.0576 0.0231 0.0946 Ln Percent of the World GDP Served Daily 0.0579 0.0357 0.0399 0.0222 0.0129 Ln Percent of the World GDP Served Twice or More Daily 0.0779 0.0176 0.0291 0.0883 0.0907 No Observations 80 80 80 80 80 Adjusted R2 0.85 0.71 0.64 0.74 0.62 Log-Likelihood 352.81 444.81 338.91 282.95 227.13 Notes: a) Bold coefficients are significant at the 90 th percentile confidence level or higher. b) “Other” sector includes Agriculture, Forestry, Fishing and Hunting; Mining, Quarrying, and Oil and Gas Extraction; Utilities; Retail Trade; Transportation and Warehousing; Educational Services; Health Care and Social Assistance; Other Services (except Public Administration); and Public Administration.

35 Table 18 illustrates how these elasticities can be used to mea- sure the impact on GDP, measured for year 2010. Based on data for the 11 industries and aggregating across the 20 regions, the increase in each industry’s value added is calculated for a one- percent change in those connectivity measures that were sta- tistically significant for that industry sector. The last row in the table reports the value added change for the aggregate of the 20 regions (across all 11 industry sectors) for a change in each connectivity measure. The coefficients in Table 18 are the calculated changes in value added per industry based on a 1% change in selected airport connectivity variables. As an example, in Column 3, a 1% increase in the number of airlines serving the 20 MSAs would lead to an increase of GDP across the regions of $201 million, of which $158 million is an increase in value added for manufacturing. In Column 4, a 1% increase in the number of nonstop domestic departures would increase GDP by $453 million, but in this case manufacturing value added accounts for $85 million of the total. A 1% increase in the number of nonstop domestic destinations served (Col- umn 6) will increase GDP by $686 million, with value added in the manufacturing sector contributing $123 million.23 Results shown in Table 18 identify which connectivity mea- sures have the strongest effect on economic output for different industries. In Table 17, the number of airlines is ranked 11th in terms of its effect on productivity based on the average elastic- ity values. However, this measure has a fairly strong effect on the output of the manufacturing sector, which forms a large proportion of total GDP. The number of airline hubs served strongly affects the finance and insurance sector. If the num- ber of domestic airline hubs served across the airline network (represented in this analysis by the sample of 20 regions and their airports) were to increase by 1% (31,000 flights), the change in value added generated by increased MFP from the enhanced connectivity across all sectors for which this vari- able was significant (Table 4) would be about $374 million. Moreover, if the number of hubs served increased by 10%, the MFP value would be about $3.7 billion. Full Economic Impacts of MFP—Connectivity It is possible to construct a variety of different scenarios for national investment in airports that can lead to direct changes in productivity, and those changes can lead to broader impacts on national competitiveness, capital investment, labor force utilization, output growth, and export changes. Depending on the specifics of those scenarios, different airports and mixes of associated activities may benefit. To illustrate the potential magnitude of broader impacts, the study team simply applied a national input/output model to portray how growth associ- ated with productivity benefits can potentially spread across the economy.24 For instance, the earlier analysis showed that a 1% change in the aviation connectivity variables will generate between $68 million (for “percent of GDP served nonstop”) and $686 million (for “number of nonstop destinations”) in direct productivity (value added) across industries, depend- ing on which variable is affected. In turn, the direct impact of these additional contributions to U.S. GDP are equivalent to 500 to 6,900 jobs; $43 million to $369 million in labor income (gross payroll); and $168 million25 to $1.7 billion in total economic output.26 The direct impacts per variable are summarized in Table 19. National economic growth associated with those pro- ductivity changes can potentially lead to further indirect and induced effects. In this illustration using the IMPLAN national model, the broader impact on total value added is estimated to range from $221 million to $2.1 billion by vari- able, which in turn yield estimates of: • 2,000 to 19,000 jobs • $100 million to $1.2 billion in labor income • $247 million to $4.5 billion in total economic output Connectivity Measure Elasticity (average) Rank Relative Weight Two or More Daily Nonstop Domestic Flights 0.0915 1 1.00 International Nonstop Destinations 0.0375 2 0.41 Domestic Nonstop Destinations 0.0284 3 0.31 Percent of the World GDP Served Daily 0.0259 4 0.28 Five or More Daily Nonstop Domestic Flights 0.0258 5 0.28 Airline Hubs Served–Domestic 0.0254 6 0.28 International Nonstop Departures 0.0182 7 0.20 Percent of the World GDP Served Nonstop 0.0169 8 0.18 Domestic Nonstop Departures 0.0164 9 0.18 Percent of the World GDP Served Twice or More Daily 0.0161 10 0.18 Number of Airlines 0.0160 11 0.17 Table 17. Average values of air service elasticities across industries.

Industry GRP over 20 MSAs (3) Number of Airlines Domestic Nonstop Departures Airline Hubs Served-Domestic Domestic Nonstop Destinations Two or More Daily Nonstop Domestic Flights Manufacturing $358,857.91 $157.54 $85.05 $123.45 $355.63 Wholesale Trade $199,956.26 $42.99 $51.39 $30.39 Information $158,156.77 $23.88 $19.14 Finance & Insurance $315,875.87 $151.30 $226.17 $98.55 Real Estate, Rental & Leasing $444,512.52 $94.68 $176.47 $180.47 Professional Scientific & Technical Services $311,416.85 $56.68 $112.42 Management of Companies & Enterprises $80,042.52 $8.48 $25.69 Administration & Support Waste Management Services $108,779.27 $11.31 $32.74 Art, Entertainment & Recreation $34,213.83 $3.18 $4.45 Accommodation & Food Services $87,114.85 $0.09 $19.95 Other* $734,242.98 $2.94 $272.40 Total $2,833,169.64 $200.53 $453.44 $374.14 $685.55 $653.79 Industry Five or More Daily Nonstop Domestic Flights International Nonstop Departures International Nonstop Destinations Percent of World GDP Served Nonstop Percent of the World GDP Served Daily Percent of the World GDP Served with Two or More Daily Flights Manufacturing $171.89 $56.34 Wholesale Trade $63.59 $38.19 $6.40 Information $38.59 $22.77 $40.65 Finance & Insurance $41.70 $33.80 Real Estate, Rental & Leasing $48.90 $236.48 Professional Scientific & Technical Services $81.59 $152.91 Management of Companies & Enterprises $7.28 $18.17 $16.25 $14.33 Administration & Support Waste Management Services $22.95 $95.40 $51.34 Art, Entertainment & Recreation $6.74 $13.65 Accommodation & Food Services $19.34 Other* $99.86 $94.72 Total $119.22 $192.11 $682.77 $67.59 $361.46 $70.67 *“Other” represents a grouping of the following sectors: Agriculture, Forestry, Fishing and Hunting; Mining, Quarrying, and Oil and Gas Extraction; Utilities; Retail Trade; Transportation and Warehousing; Educational Services; Health Care and Social Assistance; Other Services (except Public Administration); and Public Administration. Table 18. Impact of 1% changes in different connectivity measures on industry value added aggregated across all 20 regions (millions of 2010 dollars).

37 The most robust aggregate impacts across industries are gen- erated by an increase in the number of destinations with two or more daily nonstop flights. The variable with the smallest return differs according to jobs, labor income, economic output, or value added, based on the industry mix affected. The smallest total value added and economic output increases result from changes in percent of the world GDP served daily. The smallest total impacts on jobs and labor income occur from changes in destinations with five or more daily nonstop domestic flights. Potential total impacts by variable are summarized in Table 20. Similar to the industry/connectivity variable results pre- sented for value added (Table 18), jobs per industry sector vary by type of connectivity improvement. For example, as seen in Table 18, the employment impact of a 1% change in the number of airlines serving airports directly benefits only the manufacturing and wholesale trade sectors. How- ever, looking at the variable domestic airline hubs served (the number of hubs served from a given airport), shows that a 1% increase is primarily beneficial to the finance and insurance sector. Table 21 shows the percent of direct jobs Variable Jobs Labor Income Output Value Added Number of Airlines 1,300 $106 $651 $201 Domestic Nonstop Departures 3,100 $218 $858 $453 Airline Hubs Served-Domestic 2,900 $228 $582 $374 Domestic Nonstop Destinations 6,900 $369 $1,270 $686 Two or More Daily Nonstop Domestic Flights 4,000 $268 $1,749 $654 Five or More Daily Nonstop Domestic Flights 800 $46 $168 $119 International Nonstop Departures 1,900 $129 $293 $192 International Nonstop Destinations 6,400 $317 $1,357 $683 Percent of World GDP Served Nonstop 1,300 $56 $99 $68 Percent of the World GDP Served Daily 4,300 $257 $543 $362 Percent of the World GDP Served Two or More Daily 500 $43 $232 $71 Jobs rounded to the nearest 100. Dollars in Millions $2010. Calculations based on the MFP calculations previously presented. Table 19. Aggregated direct economic impacts for the 20 MSAs driven by a 1% change in each variable (in millions $2010). Table 20. Aggregated total economic impacts for the 20 MSAs driven by a 1% change in each variable including direct, indirect, and induced effects (in millions $2010). Variable Jobs Labor Income Output Value Added Number of Airlines 7,500 $471 $1,725 $794 Domestic Nonstop Departures 9,900 $614 $2,025 $1,118 Airline Hubs Served-Domestic 7,600 $493 $1,340 $831 Domestic Nonstop Destinations 17,400 $963 $3,030 $1,676 Two or More Daily Nonstop Domestic Flights 19,200 $1,161 $4,455 $2,135 Five or More Daily Nonstop Domestic Flights 1,900 $106 $336 $221 International Nonstop Departures 4,400 $267 $689 $429 International Nonstop Destinations 17,500 $949 $3,240 $1,742 Percent of World GDP Served Nonstop 2,300 $108 $247 $156 Percent of the World GDP Served Daily 9,100 $517 $1,291 $807 Percent of the World GDP Served Two or More Daily 2,800 $176 $635 $291 Jobs rounded to the nearest 100. Dollars in Millions $2010. Calculations based on the MFP calculations shown in Appendix 1.

Table 21. Differences in direct job impacts by connectivity variable and industry sector. Industry Number of Airlines Domestic Nonstop Departures Airline Hubs Served- Domestic Domestic Nonstop Destinations Two or More Daily Nonstop Domestic Flights Five or More Daily Nonstop Domestic Flights International Nonstop Departures International Nonstop Destinations Percent of World GDP Served Nonstop Percent of the World GDP Served Daily Percent of the World GDP Served Two or More Daily Manufacturing 79% 19% 18% 54% 25% 80% Wholesale Trade 21% 11% 4% 53% 6% 2% Information 6% 3% 20% 3% 11% Finance & Insurance 33% 60% 15% 22% 9% Real Estate, Rental & Leasing 21% 26% 28% 41% 35% Professional Scientific & Technical Services 12% 30% 42% 42% Management of Companies & Enterprises 2% 4% 4% 3% 24% 20% Administration & Support Waste Management Services 2% 5% 12% 14% 76% Art, Entertainment & Recreation 1% 1% 6% 4% Accommodation & Food Services 0.02% 3% 5% Other* 1% 40% 15% 26% Total 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% Impacts are driven by direct value added in specific sectors and then filtered through the IMPLAN model to calculate direct employment change in those sectors. *“Other” represents the grouping of Agriculture, Forestry, Fishing and Hunting; Mining, Quarrying, and Oil and Gas Extraction; Utilities; Retail Trade; Transportation and Warehousing; Educational Services; Health Care and Social Assistance; Other Services (except Public Administration); and Public Administration.

39 by industry sector according to a 1% change in each con- nectivity variable. 5.3 MFP Air Cargo Air cargo is well developed in the United States; however, the analysis shows only a small impact of air cargo activity on MFP. Part of the explanation may lie in the (two-digit NAICS) major industry delineation being too aggregated to measure the importance of air cargo. For example, medical devices, fresh seafood, and fresh cut flowers are dependent on air cargo connectivity, but manufacturing and agricul- ture products also move by truck, rail, inland waterway, and marine modes. Calculations of the effects of air cargo services are based on manufacturing and wholesale trade sectors, which rely on transport of commodities, and are analyzed for the 20 MSAs. Based on the MFP analysis, variables that have significant impacts on the statistical relationship of air cargo to productiv- ity for the highly aggregated industries in the sample and the years selected are: • Manufacturing (only the amount of domestic enplaned air cargo) • Wholesale trade (international enplaned air cargo) An assumed 1% increase in enplaned air cargo reflects $173 million boost in direct value added, as shown in Table 22. (This is in addition to the $136 billion in value added from current air cargo services previously shown in Table 8.) Tons of air cargo reflect the production and sales of commodi- ties by U.S. industries. Cargo movements, as shown in the MFP analysis, are outcomes from the additional sales by U.S. industries and mode choices to utilize air cargo. They are not necessarily a catalytic impact of airports. The $173 million in value added is the equivalent of $490 million in direct economic output,27 which supports 1,200 jobs and $94 million in labor income in the 20 MSAs (see Table 23). These direct impacts lead to indirect and induced effects that support an additional 4,100 jobs that pay $228 million in labor income to workers, and generate an additional $411 million in value added and $769 million in output across the national economy. Sector Significant Variables Productivity Impacts as Value Added ($millions) Manufacturing Domestic enplaned air cargo $100.46 Wholesale Trade International enplaned air cargo $72.36 Total $172.82 Table 22. Estimated value added generated by a 1% increase in air cargo tonnage. Impact Type Employment Labor Income Output Value Added Direct Effect 1,200 $94 $490 $173 Total “Spinoff” (multiplier) Effect 4,100 $228 $769 $411 Indirect Effect 2,000 $128 $464 $228 Induced Effect 2,100 $100 $306 $183 Total Effect 5,300 $321 $1,259 $583 Employment is rounded to nearest thousand, and dollars are rounded to nearest million. Numbers may not add due to rounding. Table 23. Direct economic impacts of a 1% increase in air cargo in the 20 sample MSAs (dollars in 2010 value).