\begin{document}


\title{State-Level Employment Implications of Trade Policy}
\author{David Riker}
\date{\vspace{1.5in}%
\today}

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{\Large \textbf{STATE-LEVEL EMPLOYMENT IMPLICATIONS}} \\ 
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{\Large \textbf{OF TRADE POLICY}} \\
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{\Large David Riker} \\

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{\large ECONOMICS WORKING PAPER SERIES}\\ 
Working Paper 2021--05--A \\ \vspace{0.5in}
U.S. INTERNATIONAL TRADE COMMISSION \\
500 E Street SW \\
Washington, DC 20436 \\
\vspace{0.5in}
May 2021 \\
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\noindent Office of Economics working papers are the result of ongoing professional research of USITC Staff and are solely meant to represent the opinions and professional research of individual authors. These papers are not meant to represent in any way the views of the U.S. International Trade Commission or any of its individual Commissioners. Working papers are circulated to promote the active exchange of ideas between USITC Staff and recognized experts outside the USITC and to promote professional development of Office Staff by encouraging outside professional critique of staff research.

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\noindent Thanks to Samantha Schreiber for helpful comments on this paper.

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State-Level Industry Employment Effects of Trade Policy \\
David Riker \\
Economics Working Paper 2021--05--A\\ 
May 2021 \\~\\
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\begin{abstract}
\noindent This paper provides an extension of the model of industry employment and wage effects of trade policy in \citeasnoun{Riker2021}. The model assumes that U.S. labor is immobile between states, at least in the short run, and this allows for significant differences in wage and industry employment changes in \textit{different parts of the country} in response to changes in national trade policy. We apply the model to the Other Fabricated Metal Product Manufacturing sector, NAICS code 3329, and simulate the effects of hypothetical tariff elimination on industry employment, wages, and the value of domestic shipments in each state over different time horizons.
\end{abstract}


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David Riker \\
Research Division\\
david.riker@usitc.gov \\
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\section{Introduction \label{sec: section1}}

This paper provides an extension of the partial equilibrium model of industry employment and wage effects of trade policy in \citeasnoun{Riker2021}. The model assumes that U.S. labor is immobile between states, at least in the short run. This allows for variation in wage and industry employment changes across the country in response to changes in national trade policy. The model incorporates data on fixed and variable labor inputs, inter-industry labor mobility, and the distribution of industry production across states.

We apply the model to the U.S. Other Fabricated Metal Product Manufacturing sector, NAICS code 3329, and simulate the effects of hypothetical tariff elimination on industry employment, wages, and the value of domestic shipments in each state in the short run and the long run. Model simulations demonstrate that industry employment and wage effects can vary significantly between the short run and the long run within the industry across states.

The rest of the paper is organized into four parts. Section \ref{sec: section2} describes how we extend the model to capture state-level labor market effects. Section \ref{sec: section3} discusses the data requirements of the state-segmented model and the approach to calibrating model parameters. Section \ref{sec: section4} applies the model to data for NAICS code 3329.  Section \ref{sec: section5} concludes.

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\section{Modeling Framework \label{sec: section2}}

In the model, there is monopolistic competition in industry $i$, with CES preferences for the differentiated products of firms within the industry and Cobb-Douglas preferences across industry composites. In the long run, the number of domestic firms in industry $i$ in sub-national region $r$, $N_{ir}$, is determined by free entry and exit and a zero profit condition. In the short run, $N_{ir}$ is fixed.\footnote{This distinction between the short run and the long run is explained in more detail in \citeasnoun{Riker2021}.} Labor supply pools are segmented into sub-national regions, indexed by $r$; on the other hand, the national product market is perfectly integrated.\footnote{In contrast, \citeasnoun{JITED2020} develops a sub-national model of trade policy that includes geographic segmentation of product markets, as well as segmentation of labor markets, due to inter-regional domestic shipping costs.}

Equation (\ref{eq:1}) is the variable component of labor demand associated with the domestic shipments of each firm in industry $i$ in region $r$, $E_{vir}$.
\begin{equation}\label{eq:1}
E_{vir} = A_i \ (P_i)^{\sigma_i - 1} \ (p_{ir})^{-\sigma_i} \ \theta_{ir}
\end{equation}

\noindent $A_i$ is total expenditures on the products of industry $i$ in the integrated domestic product market.\footnote{This is a constant share of aggregate expenditures and is an exogenous variable. The model treats the exports of the domestic industry as exogenous and focuses on employment associated with the domestic shipments.} $\sigma_i$ is the elasticity of substitution between the differentiated products of domestic and foreign firms in the industry. $\theta_{ir}$ is the unit labor requirement for domestic production in industry $i$ in region $r$. $P_i$ is the CES price index for industry $i$. The right-hand side of (\ref{eq:1}) is implicitly a decreasing function of wages through the price $p_{ir}$ and the price index $P_{i}$.
\begin{equation}\label{eq:2}
P_i = \left( \sum_r N_{ir} \ (p_{ir})^{1-\sigma_i} \ + \ b_i \ (p_i^* \ \tau_i)^{1-\sigma_i} \right)^{\frac{1}{1-\sigma_i}}
\end{equation}

\noindent $N_{ir}$ is the number of symmetrically differentiated domestic firms in industry $i$ in region $r$. $b_i$ is a calibrated demand asymmetry parameter that controls for the number of foreign varieties, home bias, and international quality differences. $\tau_i \geq 1$ is the power of the tariff, which is equal to one plus the tariff rate. $p_i^*$ is the foreign producer price.\footnote{Foreign producer prices and tariffs are treated as exogenous variables in the model.} $p_{ir}$, the producer price of domestic firms in industry $i$ who are located in region $r$, is a fixed mark-up over variable labor costs.
\begin{equation}\label{eq:3}
p_{ir} = \left( \frac{\sigma_i}{\sigma_i - 1} \right) \ \theta_{ir} \ w_r
\end{equation}

\noindent $w_r$ is the wage rate in the region. 

$\pi_{ir}$ is the profits of each of the $N_{ir}$ symmetrically differentiated domestic firms in industry $i$ and region $r$. 
\begin{equation}\label{eq:4}
\pi_{ir} = \frac{1}{\sigma_i} \ A_i \ (P_i)^{\sigma_i - 1} \ (p_{ir})^{1-\sigma_i} \ - \ w_r \ f_{ir}
\end{equation}

\noindent Profits are equal to zero in the long run, though not necessarily in the short run. $w_r f_{ir}$ is the fixed cost of each firm, for example the labor cost of overhead. 

$E_{ir}$ is total domestic employment in industry $i$ in region $r$. It is the sum of the firms' variable labor inputs, $N_{ir} \ E_{vir}$, and their fixed labor inputs, $N_{ir} \ f_{ir}$.
\begin{equation}\label{eq:5}
E_{ir} = N_{ir} \ E_{vir} \ + \ N_{ir} \ f_{ir}
\end{equation}

Finally, equation (\ref{eq:6}) represents a set of labor market clearing conditions, one equation for each of the sub-national regions indexed by $r$. The equations sum over all other industries (indexed by $k$) in the same regional labor supply pool as industry $i$. Workers are mobile across industries, and this arbitrages the wage rate \textit{within the region}, but only across industries that use similar skills and are therefore in the same labor supply pool.\footnote{The relationship between labor mobility and labor supply pools is discussed in more detail in \citeasnoun{Riker2020}.}
\begin{equation}\label{eq:6}
E_{ir} \ + \ \sum_{k \neq i} \ E_{kr} = E_r
\end{equation}

\noindent $E_r$ is the total supply of labor in the relevant regional pool.\footnote{It is straightforward to extend the model to allow for some wage elasticity of $E_r$ rather than assuming that it is constant, as in (\ref{eq:6}).} As noted above, workers are not mobile across regions, at least in the short run.

Next, we log-linearize the model and express (\ref{eq:1}) through (\ref{eq:6}) in percent changes in the short run. We assume that all exogenous variables other than tariff rates remain constant, including $A_i$, $p_i^*$, $\theta_{ir}$, and $f_{ir}$. On the other hand, employment, prices, shipments, and wages adjust in response to the tariff change.

In the short run, fixed costs are sunk, irreversible expenditures that have not yet recurred. With prices a constant mark-up over marginal costs, variable profits are always positive, and incumbent firms will not exit, even if a tariff reduction leads to negative total profits. Likewise, in the short run even if a tariff increase leads to positive total profits, firms cannot enter for a while due to a time-to-build. In the long run, when these fixed costs would recur and new entry is feasible, firms decide whether to exit the market, remain in the industry, or enter based on the total profits they would earn.

In the short run, $\hat N_{ir} = 0$, the zero profit condition is not necessarily binding, and the changes in equilibrium prices and quantities are characterized by (\ref{eq:7}) through (\ref{eq:11}).\footnote{For a variable $x$, $\hat x$ is the proportional change in $x$, equal to $\frac{dx}{x}$.}

\begin{equation}\label{eq:7}
\hat E_{vir} = \left(\sigma_i \ - \ 1 \right) \ \hat P_i \ -\sigma_i \ \hat p_{ir}
\end{equation}

\begin{equation}\label{eq:8}
\hat P_i = (1 - m_i) \ \sum_r \left( \frac{D_{ir}}{D_i} \right) \hat p_{ir} \ + \ m_i \ \hat \tau_i
\end{equation}

\begin{equation}\label{eq:9}
\hat p_{ir} = \hat w_r
\end{equation}

\begin{equation}\label{eq:10}
s_{ir} \ \hat E_{vir} \ + \ \sum_{k \neq i}  \ s_{kr} \ \hat E_{vkr} = 0
\end{equation}

\begin{equation}\label{eq:11}
\hat E_{ir} = \left( \frac{E_{vir}}{E_{ir}} \right) \ \hat E_{vir}
\end{equation}

\noindent $m_i$ is the initial import penetration rate in industry $i$. $s_{ir}$ is the share of the total labor supply pool in region $r$ that is initially employed in industry $i$, $\frac{E_{ir}}{E_r}$. $D_{ir}$ is the value of domestic shipments from producers in industry $i$ in region $r$, and $D_i$ is the national value of domestic shipments from producers in the industry. In the short run, the amount of variable labor inputs, $E_{vir}$, adjusts in proportion to the change in production, but total employment adjusts less than proportionally, since fixed labor inputs do not adjust in the short run.

In the long run, the zero profit condition is binding, both variable labor inputs and total industry employment adjust in proportion to the change in production, and the size of each firm is invariant to the tariff rate. With free entry and a perfectly integrated national product market, price changes are the same across regions, and this implies that wage changes are also the same, regardless of whether labor is mobile between the regions. In the long run of this model, the changes in industry employment, wages, and the value of domestic shipments are indeterminate at the regional level.\footnote{This is the case in the long run whether there is inter-regional labor mobility or not.} For this reason, changes in industry employment, wages, and the value of domestic shipments can be calculated at the \textit{national} level using the simpler long-run model in \citeasnoun{Riker2021}.

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\section{Data Inputs and Calibration of the Model \label{sec: section3}}

The data requirements of the model are the initial import penetration rate in industry $i$ ($m_i$), the initial share of workers in industry $i$ that are variable inputs in the short run ($\frac{E_{vi}}{E_{i}}$), and the share of the region's labor supply pool that is initially employed in the industry ($s_{ir}$). For the model application that follows, we calculate $m_i$ as the ratio of the landed duty paid value of imports to the sum of the value of these imports and the value of domestic shipments of the industry. $\frac{E_{vir}}{E_{ir}}$ is the ratio of production workers to total employment in the industry in each region. $s_{ir}$ as the industry's share of total employment in the relevant regional labor supply pool.

As discussed in \citeasnoun{Riker2021}, there are two alternatives for calibrating the remaining parameters of the model. If the model user has a reliable estimate of $\frac{E_{vi}}{E_{i}}$, the share of variable labor inputs in the industry at the national level, then the elasticity of substitution $\sigma_i$ can be calibrated by setting $\sigma_i$ equal to $\frac{E_{i}}{E_{i} \ - \ E_{vi}}$. If the model user \textit{instead} has a reliable estimate of $\sigma_i$ but is uncertain about $\frac{E_{vi}}{E_{i}}$, then $\frac{E_{vi}}{E_{i}}$ can be calibrated by setting it equal to $\frac{\sigma_i \ - 1}{\sigma_i}$.

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\section{Application to a Specific Industry \label{sec: section4}}

To illustrate the model, we apply it to the Other Fabricated Metal Product Manufacturing sector (NAICS code 3329), which includes producers of metal valve and pipe fittings, ball and roller bearings, arms, and ammunition.\footnote{The model is coded in a Mathematica notebook file available on request from the author.} We use annual state-level data for the sector from the 2019 Annual Survey of Manufactures.\footnote{These data are publicly available at \url{census.gov/programs-surveys/asm/data.table.html}.} We define each state's labor supply pool as a group of NAICS four-digit industries within the same NAICS three-digit code. We aggregate together all other industries in the state's labor supply pool into a "rest of 332" aggregate.\footnote{Alternatively, the relevant labor supply pool would be the entire manufacturing sector if workers' skills are more broadly transferable across industries. This alternative is considered in Section 5 below.} We use annual trade data for 2019 from the ITC/DOC Trade Dataweb.\footnote{These data are publicly available at \url{dataweb.usitc.gov}.}

Tables 1 through 5 report the inputs of the extended model. These include national imports, exports, and total shipments of the industry, its initial tariff rate, and its implied elasticity of substitution and import penetration rate. The tables also include state-level data on employment of production and non-production workers in the modeled industry and in the rest of the industries in the relevant labor supply pool.

Tables 6 and 7 report the short-run percent changes in domestic industry employment, wage, and the value of domestic shipments from tariff elimination, estimated by applying (\ref{eq:7}) through (\ref{eq:11}) to the model inputs in Tables 1 through 5. Across the 48 states in the model, the median percent change in variable employment in the industry in the short run is -5.39\%. There is a wide range of employment effects, with New Mexico the largest in absolute value (at -5.98\%) and New Hampshire the smallest (at -4.03\%). The percent changes in wages ranged from -1.14\% to -0.61\%, with a median of -0.77\%.

Table 8 reports the estimated long-run effects, based on the national model in \citeasnoun{Riker2021}. As expected, the long-run effects in Table 8 are larger than the median short-run effects in Tables 6 and 7. However, there is no longer variation across the states in the long run, for the reasons explained in Section 2. 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\section{Conclusions \label{sec: section5}}

We extend the model in \citeasnoun{Riker2021} by assuming that workers cannot move between sub-national regions, and this generates estimates of short-run local labor market effects while keeping the equations of the model tractable and the data requirements modest. The simulations in our application of the extended model show that the magnitudes of the effects on industry employment and wages can vary significantly between the short run and the long run within the industry across states.

The model can be applied to less aggregated industries and geographic areas, as long as there are available data for each industry-location pair. The Annual Survey of Manufactures is a useful source of data for NAICS four-digit industry-by-state modeling of the U.S. manufacturing sector. For finer product disaggregation or for other sectors of the economy, model users will probably need to develop a method for constructing the input data by allocating available aggregated data.

Another potential extension of the model would allow for segmentation of product markets as well as labor markets. Assuming a perfectly integrated national product market greatly simplifies the model, and it is a conventional assumption in the literature on trade and local labor market effects, including \citeasnoun{ADH2013} and \citeasnoun{Hak2016}. However, it is probably an unrealistic description of the geographically diverse U.S. economy, as discussed in \citeasnoun{JITED2020}.

\newpage
\bibliographystyle{dcu}
\bibliography{biblio}

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\newpage
\begin{table}[tbph]
\centering
\begin{threeparttable}
	\caption{National Inputs for the Model}
	\begin{tabular}{l r r}
	\toprule
\ & NAICS 3329 & Rest of NAICS 332 \\
\midrule \\
Total Shipments & 5,418,502,000 & 370,601,818,000 \\
Exports & 321,016,231 & 41,914,226,632 \\
Imports & 1,552,989,638 & 81,673,450,645 \\
Import Penetration Rate $m_i$ & 23.4\% & 19.9\% \\
Elasticity of Substitution $\sigma_i$ & 3.86 & 4.13 \\
\midrule \\
Initial Tariff & 15.6\% & \ \\
\bottomrule
\end{tabular}
\end{threeparttable}
\end{table}

\begin{table}[tbph]
\centering
\begin{threeparttable}
	\caption{State-Level Inputs for the Model, NAICS 3329}
	\begin{tabular}{l r r r}
	\toprule
State & Total & Production & Industry \\
\ & Employment & Workers & Employment \\
\ & \ & \ & Share \\
\midrule \\
Alabama & 3,982 & 3,044 & 0.20 \\
Arizona & 5,550 & 2,843 & 0.37 \\
Arkansas & 3,945 & 3,451 & 0.46 \\
California & 17,838 & 11,885 & 0.16 \\
Colorado & 940 & 735 & 0.08 \\
Connecticut & 4,955 & 3,595 & 0.22 \\
Delaware & 305 & 181 & 0.19 \\
Florida & 4,910 & 3,587 & 0.17 \\
Georgia & 4,679 & 3,768 & 0.19 \\
Idaho & 1,774 & 1,503 & 0.42 \\
Illinois & 17,022 & 13,451 & 0.26 \\
Indiana & 10,613 & 8,045 & 0.24 \\
Iowa & 3,951 & 3,019 & 0.25 \\
Kansas & 2,071 & 1,582 & 0.15 \\
Kentucky & 3,093 & 2,482 & 0.17 \\
Louisiana & 3,963 & 3,111 & 0.30 \\
Maine & 702 & 542 & 0.18 \\
Maryland & 623 & 471 & 0.09 \\
Massachusetts & 4,386 & 3,106 & 0.18 \\
Michigan & 9,955 & 7,227 & 0.15 \\
Minnesota & 6,223 & 4,671 & 0.18 \\
Mississippi & 2,580 & 2,220 & 0.26 \\
Missouri & 5,349 & 4,153 & 0.23 \\
Montana & 404 & 311 & 0.24 \\
\bottomrule
\end{tabular}

\end{threeparttable}
\end{table}
\begin{table}[tbph]
\centering
\begin{threeparttable}
	\caption{State-Level Inputs for the Model, NAICS 3329}
	\begin{tabular}{l r r r}
	\toprule
State & Total & Production & Industry \\
\ & Employment & Workers & Employment \\
\ & \ & \ & Share \\
\midrule \\
Nebraska & 1,610 & 1,406 & 0.24 \\
Nevada & 693 & 563 & 0.15 \\
New Hampshire & 5,002 & 3,164 & 0.65 \\
New Jersey & 3,414 & 2,260 & 0.19 \\
New Mexico & 126 & < 126 & 0.06 \\
New York & 7,995 & 6,268 & 0.21 \\
North Carolina & 8,763 & 7,099 & 0.31 \\
North Dakota & 226 & 172 & 0.10 \\
Ohio & 15,842 & 12,367 & 0.19 \\
Oklahoma & 5,295 & 3,716 & 0.28 \\
Oregon & 1,820 & 1,302 & 0.14 \\
Pennsylvania & 11,755 & 8,369 & 0.18 \\
Rhode Island & 1,011 & 768 & 0.11 \\
South Carolina & 8,413 & 6,524 & 0.63 \\
South Dakota & 1,117 & 861 & 0.30 \\
Tennessee & 11,116 & 4,526 & 0.44 \\
Texas & 25,525 & 18,690 & 0.27 \\
Utah & 2,567 & 1,891 & 0.28 \\
Vermont & 432 & 164 & 0.24 \\
Virginia & 1,654 & 1,318 & 0.11 \\
Washington & 2,356 & 1,677 & 0.11 \\
West Virginia & 578 & 415 & 0.14 \\
Wisconsin & 9,196 & 7,029 & 0.15 \\
Wyoming & 300 & 240 & 0.29 \\
\bottomrule
\end{tabular}
\end{threeparttable}
\end{table}

\begin{table}[tbph]
\centering
\begin{threeparttable}
	\caption{State-Level Inputs for the Model, Rest of NAICS 332}
	\begin{tabular}{l r r}
	\toprule
State & Total & Production \\
\ & Employment & Workers \\
\midrule \\
Alabama & 20,003 & 15,102 \\
Arizona & 15,079 & 11,587 \\
Arkansas & 8,485 & 6,161 \\
California & 108,228 & 83,166 \\
Colorado & 11,286 & 8,663 \\
Connecticut & 22,803 & 17,399 \\
Delaware & 1,642 & 1,177 \\
Florida & 29,323 & 21,567 \\
Georgia & 25,131 & 18,755 \\
Idaho & 4,188 & 3,201 \\
Illinois & 65,040 & 49,167 \\
Indiana & 44,919 & 35,424 \\
Iowa & 15,573 & 12,089 \\
Kansas & 14,151 & 10,523 \\
Kentucky & 18,252 & 14,401 \\
Louisiana & 13,254 & 10,111 \\
Maine & 3,885 & 3,084 \\
Maryland & 6,711 & 4,925 \\
Massachusetts & 24,053 & 18,364 \\
Michigan & 67,683 & 52,358 \\
Minnesota & 33,765 & 25,848 \\
Mississippi & 9,872 & 7,890 \\
Missouri & 23,625 & 18,252 \\
Montana & 1,685 & 1,277 \\
\bottomrule
\end{tabular}

\end{threeparttable}
\end{table}
\begin{table}[tbph]
\centering
\begin{threeparttable}
	\caption{State-Level Inputs for the Model, Rest of NAICS 332}
	\begin{tabular}{l r r}
	\toprule
State & Total & Production \\
\ & Employment & Workers \\
\midrule \\
Nebraska & 6,641 & 4,892 \\
Nevada & 4,720 & 3,610 \\
New Hampshire & 7,715 & 5,321 \\
New Jersey & 17,939 & 13,014 \\
New Mexico & 2,037 & 1,692 \\
New York & 38,036 & 28,345 \\
North Carolina & 28,504 & 22,263 \\
North Dakota & 2,273 & 1,752 \\
Ohio & 83,080 & 63,343 \\
Oklahoma & 19,236 & 15,063 \\
Oregon & 13,176 & 9,532 \\
Pennsylvania & 65,981 & 50,001 \\
Rhode Island & 8,958 & 7,257 \\
South Carolina & 13,439 & 10,244 \\
South Dakota & 3,681 & 2,684 \\
Tennessee & 25,235 & 20,063 \\
Texas & 93,021 & 72,051 \\
Utah & 9,034 & 6,717 \\
Vermont & 1,803 & 1,401 \\
Virginia & 15,032 & 10,853 \\
Washington & 20,776 & 16,100 \\
West Virginia & 4,234 & 3,341 \\
Wisconsin & 61,988 & 47,226 \\
Wyoming & 1,035 & 823 \\
\bottomrule
\end{tabular}
\end{threeparttable}
\end{table}

\begin{table}[tbph]
\centering
\begin{threeparttable}
	\caption{Short Run Effects with Geographic Segmentation of Labor (\% Change)}
	\begin{tabular}{l r r r}
	\toprule
State & Variable & Wage Rate & Value of \\
\ & Employment & \ & Shipments \\
\midrule \\
Alabama & -5.37 & -0.78 & -6.15 \\
Arizona & -4.77 & -0.94 & -5.71 \\
Arkansas & -4.48 & -1.02 & -5.50 \\
California & -5.51 & -0.74 & -6.25 \\
Colorado & -5.87 & -0.64 & -6.51 \\
Connecticut & -5.30 & -0.80 & -6.09 \\
Delaware & -5.42 & -0.76 & -6.18 \\
Florida & -5.50 & -0.74 & -6.24 \\
Georgia & -5.42 & -0.76 & -6.18 \\
Idaho & -4.60 & -0.98 & -5.59 \\
Illinois & -5.13 & -0.84 & -5.97 \\
Indiana & -5.22 & -0.82 & -6.04 \\
Iowa & -5.16 & -0.83 & -5.99 \\
Kansas & -5.59 & -0.72 & -6.30 \\
Kentucky & -5.49 & -0.74 & -6.23 \\
Louisiana & -5.00 & -0.88 & -5.88 \\
Maine & -5.44 & -0.76 & -6.20 \\
Maryland & -5.83 & -0.65 & -6.48 \\
Massachusetts & -5.44 & -0.76 & -6.19 \\
Michigan & -5.58 & -0.72 & -6.30 \\
Minnesota & -5.43 & -0.76 & -6.19 \\
Mississippi & -5.13 & -0.84 & -5.97 \\
Missouri & -5.26 & -0.81 & -6.07 \\
Montana & -5.21 & -0.82 & -6.03 \\
\bottomrule
\end{tabular}
\end{threeparttable}
\end{table}

\begin{table}[tbph]
\centering
\begin{threeparttable}
	\caption{Short Run Effects with Geographic Segmentation of Labor (\% Change)}
	\begin{tabular}{l r r r}
	\toprule
State & Variable & Wage Rate & Value of \\
\ & Employment & \ & Shipments \\
\midrule \\
Nebraska & -5.20 & -0.82 & -6.02 \\
Nevada & -5.59 & -0.72 & -6.30 \\
New Hampshire & -4.03 & -1.14 & -5.17 \\
New Jersey & -5.40 & -0.77 & -6.17 \\
New Mexico & -5.98 & -0.61 & -6.59 \\
New York & -5.33 & -0.79 & -6.11 \\
North Carolina & -4.97 & -0.88 & -5.85 \\
North Dakota & -5.80 & -0.66 & -6.46 \\
Ohio & -5.40 & -0.77 & -6.17 \\
Oklahoma & -5.08 & -0.85 & -5.94 \\
Oregon & -5.62 & -0.71 & -6.33 \\
Pennsylvania & -5.45 & -0.75 & -6.21 \\
Rhode Island & -5.74 & -0.68 & -6.41 \\
South Carolina & -4.08 & -1.13 & -5.20 \\
South Dakota & -4.98 & -0.88 & -5.86 \\
Tennessee & -4.55 & -1.00 & -5.55 \\
Texas & -5.08 & -0.85 & -5.94 \\
Utah & -5.05 & -0.86 & -5.91 \\
Vermont & -5.21 & -0.82 & -6.03 \\
Virginia & -5.75 & -0.67 & -6.42 \\
Washington & -5.73 & -0.68 & -6.41 \\
West Virginia & -5.63 & -0.71 & -6.34 \\
Wisconsin & -5.58 & -0.72 & -6.30 \\
Wyoming & -5.03 & -0.87 & -5.90 \\
\bottomrule
\end{tabular}
\end{threeparttable}
\end{table}

\begin{table}[tbph]
\centering
\begin{threeparttable}
	\caption{Model Outputs without Geographic Segmentation of Labor}
	\begin{tabular}{l r}
	\toprule
\ & Long Run \\
\ & (\% Change) \\
\midrule \\
Employment for Domestic Shipments & \ \\
Variable Labor Inputs & -8.21 \\
Fixed Labor Inputs & -8.21 \\
\midrule \\
Wages & -1.08 \\
\midrule \\
Value of Domestic Shipments & -9.30 \\
\bottomrule
\end{tabular}
\end{threeparttable}
\end{table}

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