International Trade and Local Transportation Employment 

by David Riker 

Office of Competition and Economic Analysis 

March 2012 

Manufacturing and Services Economics Briefs are produced by the Office of Competition and Economic Analysis of 

the International Trade Administration’s Manufacturing and Services unit. A complete list of reports, along with 

links to other trade data and analysis, is available at www.trade.gov/mas/ian. 
Manufacturing and Services Economics Brief No. 6 

ii International Trade and Local Transportation Employment – March 2012

Acknowledgements 

I would like to thank Joseph Flynn, Martin Johnson, and Kenneth Reinert for helpful comments 

and suggestions. All remaining errors are my own. 
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International Trade and Local Transportation Employment iii 

Contents 

Acknowledgements ......................................................................................................................... ii

Contents ......................................................................................................................................... iii

Executive Summary ........................................................................................................................ v

Introduction ..................................................................................................................................... 1

Data on Transportation Employment and Exports.......................................................................... 3

Estimation of the Econometric Model ............................................................................................ 5

Employment Effects........................................................................................................................ 8

Concluding Remarks ..................................................................................................................... 10

References ..................................................................................................................................... 11

Technical Appendix ...................................................................................................................... 12

Tables 

Table 1 – Summary of Transportation Employment and Trade by State, 2003 and 2010 ............. 4

Table 2 – Fixed Effects Coefficient Estimates for the More Restricted Model .............................. 6

Table 3 – Fixed Effects Coefficient Estimates for the Less Restricted Model ............................... 7

Table 4 – Modeled Employment Effects and Recorded Changes in Employment ......................... 9

 
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iv International Trade and Local Transportation Employment – March 2012

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International Trade Administration 

International Trade and Local Transportation Employment v 

Executive Summary 

 The expansion of international trade can significantly increase the level of employment in 

a country’s transportation sector, since exports and imports require shipping, distribution, and 

warehousing. However, it is difficult to disentangle the contribution of international trade from 

the many other factors that affect transportation employment. There is an extensive economics 

literature that addresses the effects of international trade on labor market outcomes. However, 

most studies focuses on the effects of trade on employment or wages in the manufacturing sector 

rather than the transportation sector. 

 In this economics brief, I present an econometric model that quantifies the effect of U.S. 

exports on the level of transportation sector employment in different parts of the United States. I 

construct state-level monthly international trade flows based on port-level trade statistics of the 

U.S. Census Bureau, and I use state-level transportation employment data from the U.S. Bureau 

of Labor Statistics. I measure interstate commodity flows based on the 2007 Commodity Flow 

Survey. I consider several alternative specifications that vary in their assumptions about the state 

effects, interstate commodity flows, and the role of imports. 

 I estimate that the expansion of U.S. exports between 2003 and 2010 added between 

63,000 and 140,000 workers to the sector, with a central estimate of 101,000 workers. This 

positive contribution of U.S. exports to transportation sector employment offsets some of the 

national decline in transportation employment over this period. The 30.4 percent increase in the 

value of exports between 2003 and 2010 helped to limit the national decline in transportation 

employment to about one percent over this period. 

 The model can also be applied on a prospective basis, for example to project the increase 

in transportation sector employment that would result from doubling U.S. exports relative to 

2009 levels (the goal of the National Export Initiative). This calculation indicates that a doubling 

of exports (in constant dollars) could increase transportation employment by approximately 

270,000 to 603,000 workers nationwide, with a central estimate of 437,000 workers. 
Manufacturing and Services Economics Brief No. 6 

vi International Trade and Local Transportation Employment – March 2012

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International Trade Administration 

International Trade and Local Transportation Employment 1 

Introduction 

 The expansion of international trade can significantly increase the level of employment in 

a country’s transportation sector, since exports and imports require shipping, distribution, and 

warehousing. However, it is difficult to disentangle the contribution of international trade from 

the many other factors that affect transportation employment. Ideally, a calculation of the effect 

of trade on employment in the transportation sector would utilize data on the number of workers 

who are directly tied to the transportation of exports and imports, but to my knowledge there are 

no statistics that count the contributions of each transportation worker whose services facilitate 

international trade. The ideal data would include workers throughout the transportation network, 

including workers that transport the country’s exports and imports between different states 

within the country and also workers that warehouse the goods. It should not be too narrowly 

limited to port workers, for example. On the other hand, simply counting all transportation and 

warehousing workers in the transportation network, including those far from the ports and 

borders, would generate an estimate that is overly broad. 

 As a practical alternative to the ideal measure, I estimate the contribution of international 

trade flows to state-level transportation employment using a statistical model that is based on 

variation in transportation employment and international trade flows in different parts of the 

United States between 2003 and 2010. By measuring the conditional covariance of month-tomonth changes in state-level international trade flows and transportation employment, I identify 

the contribution of international trade flows to employment in the sector. 

 There is an extensive economics literature that addresses the effects of international trade 

on labor market outcomes. However, most studies focuses on the effects of trade on 

employment or wages in the manufacturing sector rather than the transportation sector. One 

branch of the literature uses cross-industry variation in national employment and wage data to 

estimate the contribution of trade to labor market outcomes. Examples include Beaulieu (2000) 

for Canada and Bernard, Jensen, and Schott (2006) for the United States. A second branch of the 

literature uses geographical variation in employment or wages at the state or local level, but 

again they focus on employment in the manufacturing sector. Examples include Leichenko and 
Manufacturing and Services Economics Brief No. 6 

2 International Trade and Local Transportation Employment—March 2012

Silva (2004), Chiquiar (2008), McLaren and Hakobyan (2010), and Martincus (2010), and Autor, 

Dorn, and Hanson (2011). None of these studies estimates the impact of international trade on 

transportation sector employment, even though the transportation sector is clearly tied to 

international trade and there is substantial cross-state variation in the level of employment in the 

transportation sector. 

 In this economics brief, I present an econometric model that quantifies the effect of U.S. 

exports on the level of transportation sector employment in different parts of the United States. I 

present a theoretical framework for analyzing the relationship between transportation 

employment and the value of international trade flows, based on an economic model of the 

provision of transportation services. In the model, transportation employment depends on the 

value of shipments. It varies with international trade flows to the extent that the trade flows add 

to, and do not displace, domestic shipments in each state. The model predicts that U.S. exports 

have a positive effect on transportation employment, because an expansion of exports adds to 

total shipments within the United States. On the other hand, U.S. imports may have little or no 

effect on U.S. transportation employment if they displace domestic shipments. I extend the 

model to include the shipment of international trade across multiple states, and I address the 

unobservable factors that are included in the error term of the fixed effects of the econometric 

analysis. See the Technical Appendix for the full development of the model. 

 I construct state-level monthly international trade flows based on port-level trade 

statistics of the U.S. Census Bureau, and I use state-level transportation employment data from 

the U.S. Bureau of Labor Statistics. I measure interstate commodity flows based on the 2007 

Commodity Flow Survey. The model includes state and month fixed effects to control for 

differences in the size of the transportation sector across states and over time that are 

independent of the variation in international trade flows. I consider several alternative 

specifications that vary in their assumptions about the state effects, interstate commodity flows, 

and the role of imports. 

 I use the econometric estimates to calculate the effect of exports on the level of 

transportation employment in each state. Overall, the econometric estimates indicate that exports 

from nearby ports had a significant positive effect on transportation employment. Exports from 

ports in the same state have the largest effect, followed by exports from ports in states that are 
International Trade Administration 

International Trade and Local Transportation Employment 3 

closely connected by interstate commodity flows. I estimate that the expansion of U.S. exports 

between 2003 and 2010 added between 63,000 and 140,000 workers to the sector, with a central 

estimate of 101,000 workers. This positive contribution of U.S. exports to transportation sector 

employment offsets some of the national decline in transportation employment over the period. 

The 30.4 percent increase in the value of exports between 2003 and 2010 helped to limit the 

national decline in transportation employment to about one percent. 

Data on Transportation Employment and Exports 

 In this section, I describe the monthly, state-level data that I use in the econometric 

analysis. The employment data are from the Employment, Hours, and Earnings State and Metro 

Area series published by the Bureau of Labor Statistics (BLS) Current Employment Statistics. I 

measure employment in each state’s transportation sector based on the Transportation and 

Warehousing statistics. For five of the smaller states (Arkansas, Delaware, New Mexico, South 

Dakota, and Wyoming), I use employment data at the next highest level of aggregation, which 

also includes employment by utilities, because BLS does not report employment at the level of 

Transportation and Warehousing for these states. I estimate the sector’s payroll in each state for 

each month using the sector’s average hourly earnings from BLS. I convert current dollar values 

to constant 2010 dollars using the monthly All Commodities Producer Price Index from BLS. 

 I use monthly values of U.S. commodity exports and imports from the U.S. Census 

Bureau. I aggregate port-level data to the state level. The data represent the state of the port and 

not necessarily the state of origin of the exports. The trade dataset is available on a monthly 

basis from 2003 to 2010. 

 Table 1 reports the 2003 and 2010 transportation sector employment and average 

monthly values of exports from each state. In total, U.S. transportation sector employment 

declined by approximately 42,000 workers over the period, while the average monthly value of 

U.S. commodity exports increased by approximately $23.2 billion in 2010 dollars. 
Manufacturing and Services Economics Brief No. 6 

4 International Trade and Local Transportation Employment—March 2012

Table 1: Summary of Transportation Employment and Trade by State, 2003 and 2010 

Employment in Number of Workers 

Exports in Thousands of 2010 Constant Dollars per Month

 Employment 

 in 2003 

 Employment 

 in 2010 

 Exports 

 in 2003 

 Exports 

 in 2010 

Total 4,205,408 4,163,417 76,533,017 99,769,494 

By State 

Alaska 18,808 19,100 939,733 1,202,083 

Alabama 52,117 51,483 289,005 428,959 

Arkansas 65,825 58,725 8,710 28,664 

Arizona 65,392 68,350 556,155 733,402 

California 425,192 407,258 12,596,979 14,010,690 

Colorado 64,958 62,025 72,563 54,970 

Connecticut 39,958 40,925 50,821 49,676 

Delaware 12,725 11,867 83,706 142,363 

Florida 208,700 201,800 3,607,634 6,082,747 

Georgia 154,408 159,267 1,892,488 3,391,776 

Hawaii 24,117 23,092 278,537 616,262 

Iowa 48,633 53,300 1,664 6,525 

Idaho 16,833 17,758 93,106 156,311 

Illinois 229,925 228,983 2,364,726 2,987,045 

Indiana 107,400 109,508 303,786 35,246 

Kansas 45,158 44,633 49,144 60,883 

Kentucky 80,608 81,650 94,006 72,236 

Louisiana 72,392 67,758 3,613,286 5,462,339 

Massachusetts 74,350 72,092 727,259 630,024 

Maryland 66,883 65,983 671,212 1,226,462 

Maine 16,117 15,008 299,185 315,808 

Michigan 105,275 93,475 9,863,374 9,372,234 

Minnesota 80,125 77,375 354,256 434,675 

Missouri 92,058 82,725 14,242 24,724 

Mississippi 37,775 38,958 218,603 305,833 

Montana 12,583 13,608 414,317 626,493 

North Carolina 110,625 101,750 195,631 432,774 

North Dakota 9,742 12,442 975,037 1,751,796 

Nebraska 44,492 49,817 1,546 1,351 

New Hampshire 12,950 11,800 9,258 46,481 

New Jersey 162,633 147,675 774,522 1,281,238 

New Mexico 22,783 21,700 47,654 408,573 

Nevada 36,700 45,425 29,119 27,252 

New York 223,250 219,758 11,624,419 14,774,058 

Ohio 161,817 160,617 1,523,419 1,999,101 

Oklahoma 42,783 41,958 4,223 1,782 

Oregon 50,533 47,517 361,211 351,544 

Pennsylvania 196,967 209,358 710,032 1,013,543 

Rhode Island 9,992 9,017 6,913 24,326 

South Carolina 48,692 45,733 1,514,834 1,627,214 
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International Trade and Local Transportation Employment 5 

Table 1 (continued): Summary of Transportation Employment and Trade by State 

Employment in Number of Workers 

Exports in Thousands of 2010 Constant Dollars per Month 

 Employment 

 in 2003 

 Employment 

 in 2010 

 Exports 

 in 2003 

 Exports 

 in 2010 

South Dakota 11,492 12,517 97 14,007 

Tennessee 135,417 127,392 114,194 180,796 

Texas 339,592 370,125 12,551,702 18,699,470 

Utah 39,933 42,958 23,821 611,674 

Virginia 106,917 101,367 1,458,628 2,260,690 

Vermont 6,775 6,667 339,598 264,295 

Washington 84,492 83,383 4,745,848 5,533,380 

Wisconsin 93,992 91,208 62,810 5,350 

West Virginia 25,058 25,342 5 369 

Wyoming 9,467 11,183 0 3 

 I use data from the 2007 Commodity Flow Survey to identify states that are connected by 

significant interstate commodity flows. For each state, I classify a second state as “closely 

connected” if the second state accounts for at least two percent of the inbound or outbound 

commodity flows of the first state. In general, the intensity of interstate commodity flows is 

increasing in the size of the two states and decreasing in the distance between them, as a basic 

gravity model of interstate trade would predict. 

Estimation of the Econometric Model 

 Table 2 reports the econometric estimates for two versions of the specification derived in 

the Technical Appendix.1

 All of the models include state and month fixed effects, and they are 

corrected for first-order autocorrelation using the method developed in Bhargava, Franzini, and 

Narendranathan (1982), Baltagi and Li (1991), and Baltagi and Wu (1999). The F tests of 

 

1

 Before estimating the coefficients of the model, I tested for unit roots in the panel data series to ensure that the 

monthly time series are stationary. I reject unit roots for the monthly, state-level employment and trade series using 

Levin-Lin-Chu, Harris-Tzavalis, and Breitung panel unit-root tests. However, there is significant first-order 

autocorrelation in the series. 
Manufacturing and Services Economics Brief No. 6 

6 International Trade and Local Transportation Employment—March 2012

Table 2: Fixed Effects Coefficient Estimates for the More Restricted Model 

Point Estimates, with Standard Error in Parentheses 

The Dependent Variable is Transportation Sector Payroll by State and Month. 

Explanatory Variables Model 1 Model 2 

 

Exports from the 

Same State 

2.7180 

(0.3090) 

2.9981 

(0.3206) 

Exports from the 

Closely Connected States 

 0.3146 

(0.0984) 

Rho Parameter for the State Panels 0.9174 0.9182 

ܨ Test for the State Fixed Effects 816.49 792.04 

ܨ Test for the Month Fixed Effects 31.11 38.81 

ܨ Test that ߛൌ1 75.51 

 

R-Squared (Within) 0.4056 0.4069 

Akaike Information Criterion 163194 163116 

Number of Observations 4,750 4,750 

parameter restrictions indicate that the state and month fixed effects account for a significant 

amount of the variation in transportation sector payrolls. In both versions of the model, the 

estimated coefficients on exports are positive and significantly different from zero. Within each 

model, the relative magnitudes of the coefficients are increasing in the intensity of interstate 

commodity flows. The Akaike information criterion and the t-test of the coefficient on exports 

from closely connected states indicate that Model 2 is a better fit for the data. 

 Table 3 reports the fixed effects estimates for two versions of a less restricted 

specification. The estimated coefficients on import flows are all negative. Since the theory 

indicates that these estimates will be upward-biased, it is unlikely that the coefficients on imports 

are equal to zero. The error terms in these models should be independent of the export values, 

since the specification controls for the import values. Therefore, the estimated coefficients on 

export values in Table 3 should be unbiased. The estimated coefficients on exports are all 

positive, and they are larger than their counterparts in Table 2. 

 
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International Trade and Local Transportation Employment 7 

Table 3: Fixed Effects Coefficient Estimates for the Less Restricted Model 

Point Estimates, with Standard Error in Parentheses 

The Dependent Variable is Transportation Sector Payroll by State and Month. 

Explanatory Variables Model 1 Model 2 

 

Exports from the 

Same State 

4.9943 

(0.3456) 

5.3535 

(0.3589) 

Exports from the 

Closely Connected States 

 0.4306 

(0.1182) 

 

Imports from the 

Same State 

-3.4145 

(0.2459) 

-3.6110 

(0.2632) 

Imports from the 

Closely Connected States 

 -0.2240 

(0.0973) 

Rho Parameter for the State Panels 0.9106 0.9112 

ܨ Test for the State Fixed Effects 845.50 803.71 

ܨ Test for the Month Fixed Effects 30.44 29.55 

R-Squared (Within) 0.4294 0.4311 

Akaike Information Criterion 162988 162917 

Number of Observations 4,750 4,750 

 In the next section, I use the estimated coefficients in Model 2 in Table 3 to calculate the 

contribution of the growth of U.S. exports to the change in transportation sector employment 

between 2003 and 2010. I do not calculate the effect of the change in imports over this period, 

since the model in Table 3 does not necessarily provide an unbiased estimate of the coefficients 

on imports, as I discuss in the Technical Appendix. 
Manufacturing and Services Economics Brief No. 6 

8 International Trade and Local Transportation Employment—March 2012

Employment Effects 

 Nationwide, transportation sector employment declined between 2003 and 2010, but the 

trends vary significantly across the states. Transportation employment declined in California and 

New York and expanded in Texas and Pennsylvania. In this section, I use the econometric 

model to estimate the contribution of exports to the experiences of each of the states. 

 First, I calculate the change in the average monthly values of exports (in the same state 

and in closely connected states) between 2003 and 2010. Then I multiply these changes by the 

estimates of the corresponding coefficients from Model 2 in Table 3 and divide by the 

transportation sector wage rate in 2003. By fixing the sector wage at its constant-dollar value in 

2003, the calculation assumes that the supply of transportation labor was highly elastic such that 

shifts in transportation labor demand due to the changes in exports did not have a discernible 

impact on this wage. (An increase in wages would reduce the magnitude of the change in 

employment.) Nationwide, I estimate that the change in exports increased transportation 

employment by approximately 101,000 workers, with a 95 percent confidence interval that 

ranges from approximately 63,000 to 140,000. 

 Table 4 reports the modeled change in transportation employment in each of the fifty 

states. The largest transportation employment effects from exports were in Texas, New York, 

Florida, Georgia, and Louisiana. The table compares the simulated changes in the state’s 

transportation employment to the actual (recorded) change in the state’s transportation 

employment between 2003 and 2010. 

Overall, transportation employment declined by nearly 42,000 workers (approximately 1 

percent) between 2003 and 2010, despite the additional 101,000 transportation workers 

associated with the increase in U.S. exports. In some states like Texas the employment effect of 

the export expansion magnified an increase in transportation employment over the period. In 

other states like New York and Florida the employment effect of the export expansion offset part 

of the decline in transportation employment. 
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International Trade and Local Transportation Employment 9 

Table 4: Modeled Employment Effects and Recorded Changes in Employment 

 Modeled Effect of Exports on 

Transportation Employment 

Recorded Change in 

Transportation Employment 

Total 101,424 -41,992 

By State 

Alaska 1,680 292 

Alabama 1,980 -633 

Arkansas 1,617 -7,100 

Arizona 1,265 2,958 

California 3,009 -17,933 

Colorado 1,309 -2,933 

Connecticut 1,736 967 

Delaware 1,595 -858 

Florida 5,088 -6,900 

Georgia 3,814 4,858 

Hawaii 0,889 -1,025 

Iowa 0,946 4,667 

Idaho 1,620 925 

Illinois 1,926 -942 

Indiana 1,395 2,108 

Kansas 0,983 -525 

Kentucky 1,466 1,042 

Louisiana 3,678 -4,633 

Massachusetts 1,525 -2,258 

Maryland 1,557 -900 

Maine 0,653 -1,108 

Michigan 0,934 -11,800 

Minnesota 1,659 -2,750 

Missouri 1,974 -9,333 

Mississippi 1,797 1,183 

Montana 1,356 1,025 

North Carolina 2,336 -8,875 

North Dakota 2,059 2,700 

Nebraska 0,995 5,325 

New Hampshire 1,871 -1,150 

New Jersey 2,547 -14,958 

New Mexico 1,433 -1,083 

Nevada 1,676 8,725 

New York 5,832 -3,492 

Ohio 2,001 -1,200 

Oklahoma 0, 994 -825 

Oregon 0 ,881 -3,017 

Pennsylvania 2,215 12,392 

Rhode Island 1,827 -975 

South Carolina 2,043 -2,958 
Manufacturing and Services Economics Brief No. 6 

10 International Trade and Local Transportation Employment—March 2012

Table 4 (continued): Modeled Effects and Recorded Changes in Employment 

 Modeled Effect of Exports on 

Transportation Employment 

Recorded Change in 

Transportation Employment 

South Dakota 1,078 1,025 

Tennessee 2,060 -8,025 

Texas 9,195 30,533 

Utah 1,960 3,025 

Virginia 3,134 -5,550 

Vermont 1,261 -108 

Washington 2,339 -1,108 

Wisconsin 1,228 -2,783 

West Virginia 1,989 283 

Wyoming 1,088 1,717 

Concluding Remarks 

 I have estimated the contribution of exports to transportation sector employment in each 

state, based on an econometric model fitted to monthly, state-level employment and international 

trade data. The retrospective analysis indicates that the expansion of U.S. exports between 2003 

and 2010 added approximately 101,000 workers to the transportation sector and offset part of the 

national decline in transportation employment over this period. 

 The model can also be applied on a prospective basis, for example to project the increase 

in transportation sector employment that would result from doubling U.S. exports relative to 

2009 levels (the goal of the National Export Initiative). I multiply the coefficient estimates from 

Model 2 in Table 3 by the average monthly value of exports in 2009 and divide by the 

transportation sector wage in 2009. This calculation indicates that a doubling of exports (in 

constant dollars) could increase transportation employment by approximately 270,000 to 603,000 

workers nationwide, with a central estimate of 437,000 workers. This is close to the estimate of 

500,000 jobs that are supported by U.S. exports of goods in 2008, according to Tschetter (2010). 
International Trade Administration 

International Trade and Local Transportation Employment 11 

References 

Autor, D.H., D. Dorn, and G.H. Hanson (2011). The China Syndrome: Local Labor Market 

Effects of Import Competition in the United States. Manuscript. 

Baltagi, B.H. and Q.Li (1991). A Transformation That Will Circumvent the Problem of 

Autocorrelation in an Error-Component Model. Journal of Econometrics, 48, pp. 385-393. 

Baltagi, B.H. and P.X. Wu (1999). Unequally Spaced Panel Data Regressions with AR(1) 

Disturbances. Econometric Theory, 15, pp. 814-823. 

Beaulieu, E. (2000). The Canada-U.S. Free Trade Agreement and Labour Market Adjustment in 

Canada. Canadian Journal of Economics, 33, pp. 540-563. 

Bernard, A.B., J.B. Jensen, and P.K. Schott (2007). Survival of the Best Fit: Exposure to LowWage Countries and the (Uneven) Growth of U.S. Manufacturing Plants. Journal of 

International Economics, 68, pp. 219-237. 

Bhargava, A., L. Franzini, and W. Narendranathan (1982). Serial Correlation and the Fixed 

Effects Model. Review of Economic Studies, 49, pp. 533-549. 

Borjas, G.J., R.B. Freeman, and L.F. Katz (1997). How Much Do Immigration and Trade Affect 

Labor Outcomes, Brookings Papers on Economic Activities. 

Chiquiar, D. (2008). Globalization, Regional Wage Differentials and the Stolper-Samuelson 

Theorem: Evidence from Mexico. Journal of International Economics, 74, pp. 70-93. 

Eaton, J. and S. Kortum (2002). Technology, Geography, and Trade. Econometrica, 70, pp. 

1741-1779. 

Helpman, E., M. J. Melitz, and S.R. Yeaple (2004). Exports Versus FDI with Heterogeneous 

Firms. American Economic Review, 94, pp. 300-316. 

Hillberry, R. and D. Hummels (2008). Trade Responses to Geographic Frictions: A 

Decomposition Using Micro-Data. European Economic Review, 52, pp. 527-550. 

Leichenko, R. and J. Silva (2004). International Trade, Employment and Earnings: Evidence 

from US Rural Counties. Regional Studies, 38, pp. 355-374. 

McLaren, J. and S. Hakobyan (2010). Looking for Local Labor Market Effects of NAFTA. 

NBER Working Paper No. 16535. 

Martincus, C.V. (2010). Spatial Effects of Trade Policy: Evidence from Brazil. Journal of 

Regional Science, 50, pp. 541-569. 

Tschetter, J. (2010). Exports Support American Jobs. International Trade Research Report no. 

1, U.S. Department of Commerce, International Trade Administration. 
Manufacturing and Services Economics Brief No. 6 

12 International Trade and Local Transportation Employment—March 2012

Technical Appendix 

 I use a simple model of the relationship between transportation sector employment and 

the value of international trade flows to derive the econometric specification. The model 

recognizes that transportation inputs may be used more intensively when shipping relatively 

valuable commodities. ܹ௦௧ is the price of each unit of transportation labor in state ݏ in month ݐ. 

The total cost of the labor input in the state and month is equal to ܹ௦௧ܮ௦௧. ܸ௦௧ is the value per 

unit of the transported commodity if it is not lost or damaged, and ݌ሺܮ௦௧ሻ is the probability that 

the shipment is not lost or damaged. Equation (1) represents the expected profit on each 

commodity shipment. 

(1) ܧሾߨ௦௧ሿ ൌ ݌ሺܮ௦௧ሻ ܸ௦௧ െ ܹ௦௧ܮ௦௧

I assume that the probability ݌ሺܮ௦௧ሻ is increasing in the transportation labor input, ܮ௦௧, but at a 

diminishing rate. Specifically, I assume that the probability has the functional form in Equation 

(2). 

(2) ݌ሺܮ௦௧ሻ ൌ ߚ ݈݊ሺܮ௦௧ሻ 

In this case, Equation (3) is the transportation labor demand that maximizes expected profits. 

(3) ܮ௦௧ ൌ ߚ ܸ௦௧⁄ܹ௦௧ 

The linear relationship between employment levels and the value of shipments in Equation (3) 

holds for individual shipments and also for aggregates of these shipments. It holds when the 

shipments contain an assortment of commodities with different unit values, because the 

transportation labor input increases in proportion to the unit value of each commodity. The 

applicability of Equation (3) does not depend on the product composition of the shipments. 

 The total value of shipments is related to international trade flows and aggregate 

consumption by two accounting identities. Equation (4) states that the value of shipments 

through the U.S. transportation system is the sum of the value of U.S. exports from state ݏ) ܺ௦௧), 

U.S. imports into state ݏ) ܯ௦௧), and domestic shipments in the state (ܦ௦௧). 
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International Trade and Local Transportation Employment 13 

(4) ܸ௦௧ ൌ ܺ௦௧ ൅ ܯ௦௧ ൅ ܦ௦௧ 

Equation (5) states that the value of total domestic consumption (ܥ௦௧) is the sum of imports and 

domestic shipments. 

(5) ܥ௦௧ ൌ ܯ௦௧ ൅ ܦ௦௧ 

Equations (4) and (5) imply Equation (6). 

(6) ܸ௦௧ ൌ ܺ௦௧ ൅ ܥ௦௧ 

Imports do not enter the right-hand side of Equation (6) except as a component of total domestic 

consumption ܥ௦௧. 

Next, I extend the model to include the shipment of exports and imports across multiple 

states. I use the parameter ߛ to represent the share of the value of international trade through a 

state’s ports that is also shipped through the states that are closely connected by interstate 

commodity flows (for example Oklahoma and Texas, are closely connected, as are Oregon and 

California). I estimate the magnitude of ߛ in the econometric analysis below. I expect that ߛ is 

significantly less than one but greater than zero.2

 

 Together, Equations (3) and (6) imply that the transportation sector payroll in state ݏ in 

month ݐ depends on the value of exports from ports in state ݏ and in connected states. 

(7) ܹ௦௧ ܮ௦௧ ൌ ߙ௧ ൅ ߚ ܺ௦௧ ൅ ߚ ߛ ܺ௖௖,௦௧ ൅ ߜ௦ ൅ ߝ௦௧ 

The variable ܺ௦௧ represents the value of U.S. exports from ports in state ݏ in month ݐ. ܺ௖௖,௦௧

represents the value of U.S. exports from ports in states that are closely connected to state ݏ by 

interstate commodity flows. If the international trade flows through a state’s ports are not

shipped through multiple states, then ߛൌ0 in Equation (7). The variable ߝ௦௧ is the error term of 

the model, which I discuss below. 

Equation (7) is the first specification in the econometric analysis. The parameter ߙ௧ is a 

month fixed effect that absorbs any time-varying factors that are common across states, including 

 

2 Hillberry and Hummels (2008) uses micro-data from the Commodity Flow Survey to 

demonstrate that shipments within the United States usually travel only short distances.
Manufacturing and Services Economics Brief No. 6 

14 International Trade and Local Transportation Employment—March 2012

the downward national trend in transportation sector employment as well as aggregate business 

cycle fluctuations.3 The parameter ߜ௦ is a state fixed effect that absorbs any state characteristics 

that are fixed over the seven-year period. For example, some states have a significantly larger 

transportation infrastructure, and this factor explains some of the large differences in the level of 

transportation employment across the states independent of the variation in international trade 

flows. 

 Finally, I consider the components of the error term ߝ௦௧ to determine whether it is 

reasonable to assume in the econometric analysis that ߝ௦௧ is independent of the value of the 

export flows. Based on Equations (3) and (6), the error term in Equation (7) includes ܥ௦௧, the 

total domestic consumption in state ݏ and any other states served by transportation workers in 

state ݏ in month ݐ. It may also include random measurement error in the payroll data. It is 

common in industry-level models of international trade to assume that the value of total domestic 

consumption for the industry as a whole is a constant share of aggregate expenditures in the 

country. Prominent examples of trade models that include this assumption include Eaton and 

Kortum (2002), Helpman, Melitz, and Yeaple (2004), and Bernard, Redding, and Schott (2007). 

Under this assumption, total domestic consumption (ܥ௦௧) depends on aggregate expenditures in 

the United States but not on fluctuations in the relative price competitiveness of U.S. imports. In 

contrast, the value of U.S. exports (ܺ௦௧) depends on aggregate expenditures in foreign markets, 

international shipping costs, and the relative price competitiveness of the countries. U.S. exports 

should not be correlated with total domestic consumption in the United States, except in an 

indirect way through economy-wide resource constraints. 

In Equation (7), the value of U.S. imports does not affect transportation employment for a 

given level of total domestic consumption. However, this is not the case if the ߚ coefficients on 

the import values are different than the coefficients on domestic shipments. To allow for this 

possibility, Equation (8) is a generalization of Equation (7) that allows for differences in the ߚ

coefficients for exports, imports, and domestic shipments. 

 

3 The month effects are a more flexible functional form than a linear trend.
International Trade Administration 

International Trade and Local Transportation Employment 15 

(8) ݓ௦௧ ܮ௦௧ ൌ ߙ௧ ൅ ߚ௑൫ ܺ௦௧ ൅ ߛ ܺ௖௖,௦௧൯

 + ሺߚெ െ ߚ஽ሻ൫ ܯ௦௧ ൅ ߛ ܯ௖௖,௦௧൯൅ߜ௦ ൅ ߝ௦௧ 

The variable ܯ௦௧ represents the value of U.S. imports through ports in states ݏ in month ݐ. 

ܯ௖௖,௦௧ represents the value of U.S. imports through ports in states that are closely connected to 

state ݏ by interstate commodity flows. The last term in Equation (8) is the error term, and it is 

again proportional to total domestic consumption, ܥ௦௧. Equation (8) is identical to Equation (7) 

if ߚெ ൌ ߚ ஽and ߚ ௑ൌ ߚ.

 I expect that imports will be positively correlated with total domestic consumption, since 

both are increasing in aggregate expenditures in the United States, and I expect that exports will 

not be correlated with total domestic consumption, for the reasons discussed above. If ߚெ ൌ ߚ஽

then estimates of the coefficients on the export values based on the specification in Equation (7) 

will provide an unbiased estimate of ߚ௑. If ߚெ ൏ ߚ஽, then the estimates of the coefficients on 

exports in Equation (7) will provide a downward-biased estimate of ߚ௑, since the error term will 

include omitted import terms as well as a term for total domestic consumption, and U.S. exports 

and imports are positively correlated in the monthly, state-level data. The advantage of the 

specification in Equation (8), which controls for the value of state and month import flows, is 

that it provides an unbiased estimate of ߚ ௑even when ߚெ ് ߚ஽. 
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