\begin{document} \title{Imports and the Location of Vehicle Production within the United States} \author{David Riker} \date{\vspace{1.5in}% \today} \thispagestyle{empty} { % set font to helvetica (arial) to make it 508-compliant \fontfamily{phv}\selectfont \begin{center} {\Large \textbf{IMPORTS AND THE LOCATION OF}} \\ \vspace{0.25in} {\Large \textbf{VEHICLE PRODUCTION WITHIN THE UNITED STATES}} \\ \vspace{0.75in} {\Large David Riker} \\ \vspace{0.75in} {\large ECONOMICS WORKING PAPER SERIES}\\ Working Paper 2024--02--C \\ \vspace{0.5in} U.S. INTERNATIONAL TRADE COMMISSION \\ 500 E Street SW \\ Washington, DC 20436 \\ \vspace{0.5in} February 2024 \\ \end{center} \vspace{0.5in} \vfill \noindent The author thanks David Coffin and Samantha Schreiber for helpful comments and suggestions on an earlier draft. 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. \newpage \thispagestyle{empty} % remove headers, footers, and page numbers from %cover page \begin{flushleft} Imports and the Location of Vehicle Production within the United States \\ David Riker \\ Economics Working Paper 2024--02--C \\ February 2024 \\~\\ \end{flushleft} \vfill \begin{abstract} \noindent Changes in tariffs or foreign production costs can alter the geographic concentration of motor vehicle production within the United States. I simulate the magnitude of these economic effects using a sub-national model of the motor vehicle industry that combines data on vehicle production at the level of individual plants and vehicle models with data on U.S. sales, multi-product ownership, and pricing. A new 25\% tariff on U.S. imports from outside of North America would reduce vehicle imports by 73.9\%, increase average prices of vehicles in the United States by 5.0\%, and increase variable profits from domestic production by 5.2\%. The resulting increase in North American vehicle production would be very unevenly distributed, with the largest percent increases in California, Tennessee, Georgia, and Alabama, moderate percent changes in Mexico and Canada, and the smallest percent increases in Missouri, Michigan, and Texas. \end{abstract} \vfill \begin{flushleft} David Riker \\ Research Division, Office of Economics \\ david.riker@usitc.gov \\ \vspace{0.5in} \end{flushleft} } % end of helvetica (arial) font \clearpage \newpage \doublespacing \setcounter{page}{1} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction \label{sec: section1}} The motor vehicle industry, a major contributor to the U.S. economy, is especially open to competition from imports. An increase in the cost of imported vehicles -- for example due to a new tariff, dollar depreciation, or other increases in the costs of foreign production -- would lead to a reduction in vehicle imports, an increase in prices paid by U.S. consumers, and an increase in sales of domestically produced vehicles. In 2019, the U.S. Department of Commerce recommended Section 232 national security tariffs as high as 25\% on all vehicle imports from outside of North America and 35\% on imports of Sports Utility Vehicles and Crossover Utility Vehicles.\footnote{The Commerce recommendations are described in \citeasnoun{DOC2019} and \citeasnoun{CRS2020}.} These tariffs were not ultimately applied by the President, but if they had been there likely would have been significant economic effects on the domestic industry. This is suggested by an the economics literature that estimates intricate oligopoly models of the U.S. motor vehicle industry and uses these models to estimate the economic effects of changes in trade policy. \citeasnoun{Goldberg1994} uses an econometric model developed in \citeasnoun{Goldberg1995} to estimate the effects of the Voluntary Export Restraints (VERs) on exports from Japan in the 1980s and the North American Trade Agreement (NAFTA) in the 1990s. She finds that the VERs increased average vehicle prices in 1983 and 1984 and shifted demand to the used car market and to large and luxury new cars. She also finds that NAFTA shifted demand away from vehicles imported from Japan and Germany but only had small effects on prices and domestic sales. \citeasnoun{BLP1999} use an econometric model that they develop in \citeasnoun{BLP1995} to estimate the economic effects of the same Japanese VERs. They find that the VERs increased market prices in the United States and increased sales and profits from domestic production. The VERs resulted in a significant net decline in domestic consumer welfare because, unlike tariffs, there was no additional government revenue collected by the VERs. More recently, \citeasnoun{Grieco2023} use an econometric model with data on second choice vehicle models, following the methodology in \citeasnoun{BLP2004}, to estimate the changes in price-cost margins and market power in the U.S. automotive industry between 1980 and 2018. They find that substitutability between vehicle models is significantly higher within vehicle class, and that U.S. prices rose while market concentration fell over the time period, mostly reflecting upgrading of product characteristics. Although \citeasnoun{Grieco2023} do not use their oligopoly model to directly estimate the effects of trade policy, they include a trade-related counterfactual simulation that estimates the economic effects of reassigning the ownership of foreign brands to U.S.-owned vehicle manufacturers. While these intricate models of trade policy in the motor vehicle industry are informative about the market-wide effects of the trade policies on prices and total sales within the United States, they do not address how changes in trade policy affect production unevenly across states. The current geographic distribution of U.S. production of cars and light trucks is very concentrated, limited to 15 states in 2022, with a regional focus in the Midwest and Southeast. Changes in domestic production and employment due to a reduction in imports would be unevenly distributed across states, depending on the location of production of the vehicle models that most directly compete with the imports. In this paper, I build an industry-specific sub-national model of the motor vehicle industry to address this issue. Its main contribution is that it tracks the state-level effects of changes in trade policy in a detailed way. I start with a simplified version of the model that includes six single-product firms that produce vehicles in two different domestic locations and one foreign location and sell in a common national product market. This simplified model identifies several economic factors that determine how the production effects are distributed across states. When domestic production is initially distributed unevenly across sub-national regions, the reduction in imports due to a new tariff leads to a greater percent expansion in the region that is initially smaller, resulting in partial convergence of production levels across domestic regions. The uneven distribution of the percent increases in vehicle production is a result of the variable markups that characterizes the Bertrand oligopoly model. Joint ownership of multiple products and multinational production through FDI are also important factors. Inbound FDI generally magnifies the effects on vehicle production in domestic locations where there is foreign transplant production. Next, I use a more detailed and more realistic version of the model that is calibrated to plant-level data for the U.S. light vehicle industry. This full version of the model includes 313 distinct vehicle models, the complete pattern of multi-product ownership and market concentration, and the domestic or foreign location of assembly for each vehicle model. The resulting simulations provide estimates of economic effects that combine all of the factors highlighted by the simplified model. These simulations indicate that a new 25\% tariff on all U.S. imports from outside of North America would reduce these imports by 73.9\%, increase average prices of vehicles in the United States by 5.0\%, and increase variable profits from domestic production by 5.2\%. The resulting increase in North American production would be very unevenly distributed, with the largest percent increases in California, Tennessee, Georgia, and Alabama, moderate percent changes in Mexico and Canada, and the smallest percent increases in Missouri, Michigan, and Texas. I also run simulations that limit the new tariff to imports from a single foreign country. They show that the segment composition of the imports and of domestic production in each state, and the location of foreign transplant production combine to explain the geographic distribution of the changes in domestic vehicle production. The rest of the paper is organized in five parts. Section \ref{sec: section2} starts with summary statistics for the U.S. light vehicle industry in 2022. Section \ref{sec: section3} introduces the modeling framework. Section \ref{sec: section4} uses a simplified version of the model to illustrate the economic factors that shape how import shocks affect the level and location of domestic vehicle production. Section \ref{sec: section5} reports simulated effects of a new 25\% tariff using the full version of the model. Section \ref{sec: section6} offers caveats, conclusions, and ideas for further research. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Summary Statistics for the Light Vehicle Industry \label{sec: section2}} According to Ward's Intelligence, 21 manufacturers sold light vehicles in the United States in 2022.\footnote{Ward's Intelligence, "U.S. Vehicle Sales by Vehicle Type and Source, 1931-2022.”} All but one of these manufacturers sold multiple vehicle models, often under several different makes. For examples, Honda manufactures the Honda and Acura makes, and Ford manufactures the Ford and Lincoln makes. Examples of individual vehicle models include the Ford Escape and the Honda Accord. Table \ref{tab: by segment} reports the number of vehicles, makes, and models in eight distinct classes of light vehicles, as well as the class' expenditure shares of imports from outside of North America. Small Crossover Utility Vehicles (CUVs), Luxury Cars, and Small Cars are the most import-intensive, while Mid and Large Cars and Pickups are the least. \begin{table}[tbph] \centering \begin{threeparttable} \caption{Light Vehicle Market Segments in 2022} \label{tab: by segment} \begin{tabular}{p{4cm} r r r r} Vehicle & Number of & Number of & Number of & Quantity Share \\ Class & Vehicles & Makes & Vehicle Models & of Imports (\%) \\ \toprule Small Cars & 947,703 & 11 & 21 & 34.6 \\ Mid and Large Cars & 1,191,156 & 15 & 24 & 10.2 \\ Luxury Cars & 738,775 & 22 & 68 & 42.5 \\ Small CUVs & 1,639,542 & 23 & 40 & 46.8 \\ Mid and Large CUVs & 4,372,003 & 30 & 82 & 24.2 \\ SUVs & 1,388,875 & 14 & 25 & 13.9 \\ Vans & 616,048 & 12 & 17 & 10.6 \\ Pickups & 2,652,650 & 10 & 17 & 0.0 \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} Table \ref{tab: state shares} reports the distribution of U.S. production of these vehicles across states in 2022. Michigan is at the top of the list, accounting for 19.0\% of vehicles produced in North America in 2022, and the Upper Midwest states (Michigan, Indiana, Ohio, and Illinois) together account for 42.2\%. Table \ref{tab: state shares} also reports each state's share of foreign transplant production in the United States. Transplant production is the U.S. production of foreign-owned manufacturers.\footnote{These calculations do not count U.S. production of Stellantis as transplant production, even though Stellantis is a multinational firm.} For example, all vehicles produced by Subaru in Indiana or by Honda in Ohio are U.S. transplant production. Alabama has the largest share at 22.9\%, followed by Indiana, Kentucky, and Ohio. Together, these four states account of approximately one-third of the vehicles produced by foreign transplants in the United States. \begin{table}[tbph] \centering \begin{threeparttable} \caption{Levels and Shares of Production by State in 2022} \label{tab: state shares} \begin{tabular}{p{4cm} r r r} & & Share of & Share of \\ \ & Number & U.S. Vehicle & U.S. Transplant \\ States & of Vehicles & Production (\%) & Production (\%) \\ \toprule Michigan & 1,465,854 & 19.3 & \\ Indiana & 866,775 & 11.8 & 20.4 \\ Kentucky & 880,854 & 11.6 & 12.6 \\ Alabama & 731,662 & 9.9 & 22.9 \\ Ohio & 655,866 & 8.6 & 10.0 \\ Texas & 388,817 & 5.1 & 0.2 \\ California & 456,100 & 6.0 & \\ Tennessee & 374,515 & 4.9 & 8.3 \\ South Carolina & 267,846 & 3.5 & 8.2 \\ Missouri & 445,498 & 5.9 & \\ Mississippi & 305,322 & 4.0 & 9.3 \\ Illinois & 289,280 & 3.8 & \\ Georgia & 257,047 & 3.6 & 8.3 \\ Kansas & 137,241 & 1.8 & \\ Arizona & 3,535 & 0.0 & \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} Overall, approximately 69\% of total North American light vehicle production was located in the United States in 2022, 23\% was located in Mexico, and the rest of North American production was located in Canada. Approximately 3 million new light vehicles were imported into the United States from outside of North America in 2022. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Economic Modeling Framework \label{sec: section3}} The economic modeling framework adopts many of the assumptions in \citeasnoun{Goldberg1994}, \citeasnoun{BLP1999}, and \citeasnoun{Grieco2023}, including logit demand with many differentiated products, Bertrand Nash competition in prices, and constant marginal costs of production. Consumer demand in \citeasnoun{BLP1999} and \citeasnoun{Grieco2023} includes an outside alternative to new car purchases, while demand in \citeasnoun{Goldberg1994} includes a decision about whether to purchase a new car in an earlier step in her sequential logit model. The analysis focuses on a short run equilibrium in which the set of producers and the vehicle models that they offer are pre-determined and therefore exogenous to the model. While there are many similarities between my model and these earlier models in the literature, there are also important differences. First, \citeasnoun{Goldberg1994} and \citeasnoun{BLP1999} focused on the national welfare effects of VERs in the 1980s and NAFTA in the 1990s, while the sub-national model in this paper simulates the impact on domestic vehicle production in individual states of a hypothetical new tariff.\footnote{\citeasnoun{Grieco2023} does not directly analyze the economic effects of trade policy.} Second, the earlier studies generate econometric estimates of demand parameters at the level of individual consumers, while the model in this paper calibrates its demand parameters to price-cost margins estimated by these earlier studies. Consumers have discrete choice logit demands. Equation (\ref{eq:1}) is the quantity market share of vehicle model $j$, $\mu_j$. \begin{equation}\label{eq:1} \mu_j = \frac {Exp \ \left(\beta_j \ - \ \alpha \ p_j \ \tau_j \right)} {1 \ + \ \sum_{k=1}^{N} Exp \ \left(\beta_{k} \ - \ \alpha \ p_{k} \ \tau_{k}\right)} \end{equation} \noindent $p_j$ is the producer price of $j$. The tariff factor $\tau_j$ is equal to one plus the tariff rate if product $j$ is imported from outside of North America. $k$ indexes the $N$ vehicle models sold in the U.S. market. The demand parameter $\beta_j$ represents a composite of product characteristics like vehicle size, engine power, and appearance. This demand parameter is calibrated to initial quantity shares in the market. If firm $j$ only sells a single product $j$, then equation (\ref{eq:2}) is the profits of oligopolist $j$, $\pi_j$. \begin{equation}\label{eq:2} \pi_j = ( p_j \ - \ c_j ) \ \Bar{Q} \ \mu_j \ - \ f_j \end{equation} \noindent $\Bar{Q}$ is the fixed total number of consumers in the market, including those who chose the outside option of purchasing a used vehicle or not purchasing any vehicle. $c_j$ is the constant marginal cost of product $j$, and $f_j$ is a fixed cost of production. Equation (\ref{eq:3}) is firm $j$'s first order condition for its own price, given demand in equation (\ref{eq:1}). \begin{equation}\label{eq:3} 1 \ - \ ( p_j \ - \ c_j ) \ \alpha \ \tau_j \ ( 1 \ - \ \mu_j ) = 0 \end{equation} \noindent The prices of firm $j$'s competitors enter equation (\ref{eq:3}) through $\mu_j$. If the firm that sells product $j$ also sells other products, then the profit function in equation (\ref{eq:2}) and the first order condition in equation (\ref{eq:3}) are replaced by the more generalized versions in equations (\ref{eq:4}) and (\ref{eq:5}). \begin{equation}\label{eq:4} \pi_{j} = \sum_{k=1} ^N \ \phi_{jk} \ ( p_k \ - \ c_k ) \ \Bar{Q} \ \mu_k \ - \ f_k \end{equation} \begin{equation} \label{eq:5} 1 \ - \ ( p_j \ - \ c_j ) \ \alpha \ \tau_j \ + \ \sum_{k=1} ^N \ \phi_{jk} \ ( p_{k} \ - \ c_{k}) \ \alpha \ \tau_{j} \ \mu_{k} = 0 \end{equation} \noindent Again, $k$ indexes all products in the market, and $\phi_{jk}$ is an indicator variable that is equal to one if the same firm owns and controls products $j$ and $k$ and is equal to zero otherwise. The model calibrates the $N$ marginal costs $c_j$ using the $N$ first order conditions. The demand parameter $\beta_j$ is calibrated according to equation (\ref{eq:6}). \begin{equation} \label{eq:6} \beta_j = \ln \left( \frac{\mu_j}{\mu_{out}} \right) \ + \ \alpha \ p_j \ \tau_j \end{equation} \noindent $\mu_{out}$ is the initial share of potential consumers who chose the outside option, either purchasing a used vehicle or no vehicle. The initial share of potential consumers who choose the outside goods $\mu_{out}$ is set equal to 0.90, since approximately 90 percent of households do not purchase a new car each year, and either purchase no car or a used car, according to \citeasnoun{Goldberg1994}. The price-sensitivity parameter $\alpha$ is set equal to 1.275. This is the value of $\alpha$ that fits the median price-cost markup in the full version of the model in Section \ref{sec: section5} to the median price-cost markup in 2018 from \citeasnoun{Grieco2023}. Finally, the model translates the changes in sales of each vehicle model into changes in the location of domestic vehicle production using data on the plant where each vehicle model is assembled.\footnote{The plant or plants where each vehicle model is assembled is exogenous to the model.} The change in production in each location is the sum of the changes in production of all vehicle models with plants in that location. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Simplified Illustrative Model \label{sec: section4}} To illustrate the many economic effects of a new tariff without the complexity of the plant-level data, I first present a simplified version of the simulation model with a hypothetical initial distribution of vehicle production across regions. In this illustrative model, domestic production occurs in two regions, North and South. Vehicle production in North is always larger or the same size as vehicle production in South. There are two domestic producers of vehicles in each of the two domestic regions and two foreign producers who import into the domestic market. Each producer sells a single vehicle model, so there are six products in the simplified model. \subsection{Non-Nested Logit Model \label{subsec: section4.1}} Consumer demand for the six products has the non-nested logit form in equation (\ref{eq:1}). The six products are differentiated, but they are not grouped into classes in the non-nested model. The illustrative model is calibrated to summary statistics for the U.S. market in 2022, including total sales of light vehicles (13.5 million vehicles), the import penetration rate in the United States (21.1\%), and the average vehicle price (\$40,195). The model simulates the expansion in domestic production and other economic effects of a new 25\% tariff on imported vehicles. Table \ref{tab: illustrative} reports the simulated increase in the number of vehicles and the corresponding percent changes. To illustrate how asymmetry in the initial distribution of domestic production across regions shapes the distribution of production effects, the rows of the table report simulation results for three alternative assumptions about the initial share of domestic production in North. \begin{table}[tbph] \centering \begin{threeparttable} \caption{Changes in the Number of Vehicles Produced, Non-Nested Logit} \label{tab: illustrative} \begin{tabular} {l c c c} Initial North Share of & Change in North & Change in South & Change in Imports \\ Domestic Production (\%) & (\%) & (\%) & (\%) \\ \toprule 50 & 71,604 & 71,604 & -1,810,770 \\ & (1.343) & (1.343) & (-63.510) \\ & & & \\ 60 & 85,565 & 57,530 & -1,810,760 \\ & (1.338) & (1.349) & (-63.510) \\ & & & \\ 70 & 99,416 & 43,336 & -1,810,760 \\ & (1.332) & (1.355) & (-63.510) \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} In response to the new tariff and the increase in import prices, imports decline and vehicle production rises in both domestic regions, North and South. The changes in domestic production are unevenly distributed. The simulated increase in the number of vehicles is larger in the North when North initially accounts for more than half of domestic production.\footnote{The estimated changes in the number of vehicles produced assumes that a combined 13,512,553 vehicles are produced in the two regions in the initial equilibrium.} The difference between the change in the number of vehicles produced in the two regions is increasing in the initial relative size of North. On the other hand, the \textit{percent changes} are larger in South when North initially accounts for more than half of domestic production, and this difference between the two regions is increasing with the initial relative size of North. When there is initial asymmetry, South partially catches up as a result of the overall expansion of the domestic industry, and the production levels in the two regions partially converge. The asymmetries in the simulation results in Table \ref{tab: illustrative} are a result of the variable markups in the Bertrand oligopoly model. If domestic producers were perfectly competitive and set prices equal to marginal costs with no markups, then producers in North and South would both expand at the same rate in response to the new tariff on imports, since there would be no change in their constant marginal costs and therefore no change in their relative prices. This reflects the Independence of Irrelevant Alternatives (IIA) property of the non-nested logit model and the modeling assumption that marginal costs are constant. In a demand system with IIA, an increase in the price of one product will increase demand for a second and a third product at the same rate, so that the demand for the second relative to the third remains the same.\footnote{Constant elasticity of substitution demand also has the IIA property.} To demonstrate this point, Table \ref{tab: PC illustrative} reports simulations using a more restricted version of the illustrative model without markups. In this case, the percent change in domestic production is the same in North and South, regardless of the initial relative size of the two regions. In contrast, the variable markups in the oligopoly model are responsible for the asymmetric growth rates across regions in Table \ref{tab: illustrative}. \begin{table}[tbph] \centering \begin{threeparttable} \caption{Changes in Vehicles Produced Without Variable Markups, Non-Nested Logit} \label{tab: PC illustrative} \begin{tabular} {l c c c} Initial North Share of & Change in North & Change in South & Change in Imports \\ Domestic Production (\%) & (\%) & (\%) & (\%) \\ \toprule 50 & 82,500 & 82,500 & -2,047,130 \\ & (1.548) & (1.548) & (-71.800) \\ & & & \\ 60 & 99,000 & 66,000 & -2,047,130 \\ & (1.548) & (1.548) & (-71.800) \\ & & & \\ 70 & 115,500 & 49,500 & -2,047,130 \\ & (1.548) & (1.548) & (-71.800) \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} The simulations reported in Table \ref{tab: FDI illustrative} extend the simpified model to focus on the implications of multi-product firms, and specifically the impact of joint ownership of foreign and domestic production within the same firm through foreign direct investment (FDI). In this case, the first order conditions for the multi-product firms are based on equation (\ref{eq:5}) rather than equation (\ref{eq:3}). This extension of the simplified model assumes that two of the firms manufacture one product in South and import a second product. In other words, there is foreign transplant production in the South owned by firms that also import. Table \ref{tab: FDI illustrative} reports simulation results for this model extension with FDI, again for the same 25\% tariff on imported vehicles. In this case, since production in South is owned and controlled by foreign producers, there is a larger percent increase in production in South than in North \textit{even if} production levels in the two regions are initially equal. Foreign-owned domestic producers increase their prices less in response to the increase in import prices, to offset some of their loss of import sales. There are much larger increases in vehicle production in South compared to North in Table \ref{tab: FDI illustrative}. In addition, the increases in production in South in the FDI extension of the model (Table \ref{tab: FDI illustrative}) are much larger than the increases in South in the basic model that does not have multi-product firms (Table \ref{tab: illustrative}). \begin{table}[tbph] \centering \begin{threeparttable} \caption{Changes in Vehicles Produced with FDI, Non-Nested Logit} \label{tab: FDI illustrative} \begin{tabular} {l c c c} Initial North Share of & Change in North & Change in South & Change in Imports \\ Domestic Production (\%) & (\%) & (\%) & (\%) \\ \toprule 50 & 69,973 & 111,732 & -1,811,270 \\ & (1.313) & (2.096) & (-63.528) \\ & & & \\ 60 & 84,011 & 89,781 & -1,811,160 \\ & (1.313) & (2.105) & (-63.524) \\ & & & \\ 70 & 98,067 & 67,637 & -1,811,060 \\ & (1.314) & (2.115) & (-63.520) \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} \subsection{Nested Logit Model \label{subsec: section4.2}} The independence of irrelevant alternatives (IIA) property of logit models is a limitation that is usually addressed in the literature on competition in the motor vehicle industry. For example, \citeasnoun{Goldberg1994} and \citeasnoun{Goldberg1995} develop a sequential nested logit framework to allow for more complex patterns of substitution among pairs of vehicle models.\footnote{\citeasnoun{BLP1995} and \citeasnoun{BLP1999} develop a random coefficients logit model that also avoids the IIA property.} In the same spirit, this section considers a nested logit extension of the illustrative model. There are two classes of vehicles, and two corresponding nests in the logit demand system. One product from each class is produced in each of the three locations, so there are still six differentiated products. Equation (\ref{eq:7}) is the share equation for the nested logit model, with a nesting parameter $\theta$ that ranges from zero to one and is lower when the products are more similar. \begin{equation}\label{eq:7} \mu_j = \left( \frac{Exp \ \left( \theta \ \gamma_c \right)}{1 \ + \ \sum_d Exp \ \left(\theta \ \gamma_d \right)} \right) \left( \frac {Exp \ \left(\frac{\beta_j \ - \ \alpha \ p_j \ \tau_j }{\theta} \right)} {1 \ + \ \sum_{k \in c} Exp \ \left(\frac{\beta_k \ - \ \alpha \ p_k \ \tau_k }{\theta} \right) } \right) \end{equation} \noindent Class $c$ is the nest that includes product $j$, and $d$ indexes both of the classes. $\gamma_c$ is defined in equation (\ref{eq:8}). \begin{equation}\label{eq:8} \gamma_c = Log \ \left( \sum_{j \in c} Exp \ \left(\frac{\beta_j \ - \ \alpha \ p_j \ \tau_j }{\theta}\right) \right) \end{equation} \noindent The first order condition for single-product firms in equation (\ref{eq:3}) still applies, though $\mu_j$ is defined differently, according to equations (\ref{eq:7}) and (\ref{eq:8}). Table \ref{tab: Nested illustrative} illustrates the importance of nesting by varying the nesting parameter $\theta$ across the rows of the table. All three simulations assume that domestic production is initially distributed unevenly, with 60\% in North and 40\% in South, and that each firm produces a single product. The estimates with $\theta = 1$ are identical to the middle row in the non-nested simulations reported in Table \ref{tab: illustrative}. Lowering $\theta$ increases the magnitude of the estimated effects on domestic production and imports. Nesting does not change the signs of these effects. \begin{table}[tbph] \centering \begin{threeparttable} \caption{Changes in Vehicles Produced, Nested Logit} \label{tab: Nested illustrative} \begin{tabular} {l c c c} Nesting Parameter $\theta$ & Change in North & Change in South & Change in Imports \\ & (\%) & (\%) & (\%) \\ \toprule 1.00 & 85,565 & 57,530 & -1,810,760 \\ & (1.338) & (1.349) & (-63.510) \\ & & & \\ 0.75 & 350,894 & 236,683 & -2,078,600 \\ & (5.485) & (5.550) & (-72.904) \\ & & & \\ 0.50 & 720,465 & 495,289 & -2,435,310 \\ & (11.404) & (11.614) & (-85.415) \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} \subsection{Market-Wide Effects \label{subsec: section4.3}} The four versions of the illustrative model also generate estimates of market-wide effects. Table \ref{tab: marketwide} reports the percent changes in total market demand, the quantity of imports, average vehicle prices in the market, and variable profits from domestic production for the four versions of the model. These market-wide effects are not especially sensitive to the initial share of North in domestic production, so all of the simulations reported in Table \ref{tab: marketwide} assume that this initial share is equal to 60\%. \begin{table}[tbph] \centering \begin{threeparttable} \caption{Percent Changes in Market-wide Aggregates} \label{tab: marketwide} \begin{tabular} {l c c c c} Estimated & Non-Nested & Non-Nested Model & Non-Nested Model & Nested with \\ Effect & Model & without Markups & with FDI & $\theta = 0.75$ \\ \toprule Total Market Demand & -12.3 & -13.9 & -12.1 & -11.0 \\ & & & \\ Imports & -63.5 & -71.8 & -63.5 & -72.9 \\ & & & \\ Average Prices & 4.2 & 5.3 & 4.2 & 4.2 \\ & & & \\ Variable Profits from & 1.4 & n/a & 1.4 & 5.6 \\ Domestic Production & & & & \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} In all four simulations, the new tariff reduces total quantity demanded in the market and reduces the number of imported vehicles, while it increases the average price in the market and the variable profits from domestic production. The changes in market-wide aggregates are generally magnified when the simulations do not include variable markups or when there is nested logit demand. The illustrative model isolates the effects of several different factors, one at a time, including the initial location of domestic vehicle production, variable markups from Bertrand competition in prices, and joint ownership and control over multiple vehicle models. In fact, the economic effects on the industry are a complex mix of all of these factors. The full version of the model in the next section brings them all together. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Simulations Using the Full Version of the Model \label{sec: section5}} The full version of the model is much more elaborate. It incorporates detailed data on plant-level production and sales in 2022 and 313 specific vehicle models. It is similar to the detailed calibrated simulation models of the light vehicle industry in \citeasnoun{USITC2019} and \citeasnoun{USITC2023}. These two reports develop vehicle model-level simulation models that estimate the economic effects of the automotive rules of origin in the U.S.-Mexico-Canada Agreement (USMCA).\footnote{The two reports find that the change in the USMCA rules of origin increased U.S. employment in parts production while slightly reducing U.S. employment in vehicle assembly. The rules led to a small increase in average vehicle prices, a reduction in U.S. vehicle imports from Mexico and Canada, and an increase in U.S. imports from outside of North America.} The full version of our model has a more elaborate nested logit demand system with eight distinct classes of vehicles represented by eight nests.\footnote{Table \ref{tab: by segment} lists the eight market segments.} Another distinction is that our model tracks the sub-national location of vehicle production and the uneven distribution of production effects across states. Vehicle production occurs in 18 distinct production locations (15 states, Canada, Mexico, and the Outside of North America).\footnote{The 15 states are Alabama, Arizona, California, Georgia, Illinois, Indiana, Kansas, Kentucky, Michigan, Mississippi, Missouri, Ohio, South Carolina, Tennessee, and Texas.} The full version of the model uses detailed sales and production data from Ward's Intelligence.\footnote{Ward's Intelligence, "North America Vehicle Production by State and Plant, 2017–2021" and "U.S. Vehicle Sales by Vehicle Type and Source, 1931-2022.”} It uses prices of the most popular trim of each vehicle model in 2022 from the Kelley Blue Book at the level of individual vehicle models.\footnote{\url{https://www.kbb.com/car-prices/}.} \subsection{A New Tariff on All Imports from Outside North America \label{subsec: section5.1}} Table \ref{tab: All Countries Market-Wide} reports the market-wide effects of a new 25\% tariff on all U.S. imports from outside of North America for alternative assumptions about the nesting parameter $\theta$. The first column of estimates, with $\theta = 1.00$, is non-nested logit. In every column, the new tariff reduces total demand and imports and increases average prices and variable profits from domestic production. In the case with moderate segmentation of the market into vehicle classes ($\theta = 0.75$), the new tariff reduces these imports by 73.9\%, increases average prices of vehicles in the United States by 5.0\%, and increases the variable profit from domestic production by 5.2\%. As $\theta$ is lowered, the percent changes in total demand and average prices are dampened or the same, while the percent changes in imports and the profits of domestic producers are amplified. \begin{table}[tbph] \centering \begin{threeparttable} \caption{Simulated Market-Wide Effects -- All Countries } \label{tab: All Countries Market-Wide} \begin{tabular}{p{5cm} r r r} Estimated Changes (\%) & $\theta = 1.00$ & $\theta = 0.75$ & $\theta = 0.50$ \\ \toprule Total Market Demand & -12.7 & -11.2 & -8.9 \\ & & & \\ Imports & -66.0 & -73.9 & -84.6 \\ & & & \\ Average Prices & 5.0 & 5.0 & 5.0 \\ & & & \\ Variable Profits from & 1.4 & 5.2 & 10.4 \\ Domestic Production & & & \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} \newpage \noindent Table \ref{tab: shift in segment shares} indicates that the new tariff also results in an overall shift in U.S. light vehicle sales toward Pickups, Mid and Large Cars, Vans, and SUVs. These are the four vehicle classes with the lowest import penetration rates in 2022 (Table \ref{tab: by segment}). \begin{table}[tbph] \centering \begin{threeparttable} \caption{Shifts in Class Shares and Average Prices ($\theta = 0.75$)} \label{tab: shift in segment shares} \begin{tabular}{p{6cm} r r} & Initial Share & New Share \\ Vehicle Classes & (\%) & (\%) \\ \toprule Small Cars & 7.0 & 6.7 \\ Mid and Large Cars & 8.8 & 9.5 \\ Luxury Cars & 5.2 & 4.1 \\ Small CUVs & 12.1 & 10.4 \\ Mid and Large CUVs & 32.4 & 31.5 \\ SUVs & 10.3 & 10.6 \\ Vans & 4.6 & 4.9 \\ Pickups & 19.6 & 22.4 \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} Table \ref{tab: All Countries} reports the simulated changes in the total number of vehicles produced for the U.S. market by location (state or country in North America). With moderate segmentation of the market into vehicle classes ($\theta = 0.75$), the largest percent increases in vehicle production are in California, followed by Tennessee, Georgia, and Alabama.\footnote{It is important to keep in mind that the model is estimating the expansion of production at existing plants in specific states and not the addition in new plants or new states.} This ranking reflects many factors. These states generally focus on production of Luxury Cars and Mid and Large CUVs, and most have significant foreign transplant production. Alternatively, the production effects can be ranked by the share of the total increase in North American vehicle production that is contributed by each state and country in North America. According to this second ranking, Mexico accounts for 21.4\% of the total increase in North American production, while Canada accounts for 9.9\%. The largest contributing state is Alabama (accounting for 9.6\%), followed by Michigan (8.5\%), California (8.4\%), and Indiana (8.1\%).\footnote{For brevity, the table does not report the small effects on vehicle production in Kansas (5,149 vehicles, 0.9\% of the North American total increase) or Arizona (532 vehicles, 0.1\%).} This second ranking reflects the large initial production levels in Mexico, Canada, Michigan, and Indiana. \begin{table}[tbph] \centering \begin{threeparttable} \caption{Simulated Changes In Production Due to Tariff Changes on All Imports} \label{tab: All Countries} \begin{tabular}{p{8cm} r r r} States and Countries & $\theta = 1.00$ & $\theta = 0.75$ & $\theta = 0.50$ \\ \toprule Alabama & 11,476 & 55,487 & 123,395 \\ & 1.524\% & 7.369\% & 16.387\% \\ \midrule California & 6,321 & 48,533 & 106,215 \\ & 1.386\% & 10.641\% & 23.288\% \\ \midrule Canada & 15,160 & 57,134 & 116,150 \\ & 1.489\% & 5.612\% & 11.409\% \\ \midrule Georgia & 4,142 & 20,728 & 46,456 \\ & 1.517\% & 7.591\% & 17.013\% \\ \midrule Illinois & 4,021 & 14,080 & 26,731 \\ & 1.390\% & 4.867\% & 9.240\% \\ \midrule Indiana & 13,701 & 46,474 & 92,304 \\ & 1.523\% & 5.166\% & 10.260\% \\ \midrule Kentucky & 13,272 & 45,058 & 88,262 \\ & 1.510\% & 5.128\% & 10.045\% \\ \midrule Mexico & 30,158 & 123,503 & 271,112 \\ & 1.456\% & 5.963\% & 13.089\% \\ \midrule Michigan & 20,589 & 48,860 & 86,261 \\ & 1.404\% & 3.333\% & 5.884\% \\ \midrule Mississippi & 4,597 & 13,327 & 27,214 \\ & 1.504\% & 4.360\% & 8.902\% \\ \midrule Missouri & 6,205 & 8,597 & 11,653 \\ & 1.395\% & 1.933\% & 2.619\% \\ \midrule Ohio & 9,096 & 28,327 & 53,639 \\ & 1.389\% & 4.326\% & 8.191\% \\ \midrule South Carolina & 4,018 & 16,869 & 34,353 \\ & 1.496\% & 6.281\% & 12.790\% \\ \midrule Tennessee & 5,335 & 29,601 & 65,686 \\ & 1.424\% & 7.904\% & 17.539\% \\ \midrule Texas & 5,753 & 14,784 & 25,681 \\ & 1.480\% & 3.802\% & 6.605\% \\ \midrule Imports from the & -1,867,790 & -2,089,770 & -2,394,010 \\ Rest of the World & -66.017\% & -73.863\% & -84.616\% \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} A comparison of the estimates for the different columns in Table \ref{tab: All Countries} demonstrates the impact of nesting on the geographic dispersionf of production effects. In the first column ($\theta = 1$), there is no nesting and there is much less heterogeneity in the percent changes across states and countries in North America. In this case, the coefficient of variation of the percent changes for the 13 states in the table is 0.04. In contrast, when $\theta=0.50$, the coefficient of variation of the percent changes is 0.50. Table \ref{tab: All Countries Sensitivity Analysis} examines the sensitivity of the simulation model to incorporating the pattern of joint ownership across individual vehicle models.\footnote{All of the simulations in Table \ref{tab: All Countries Sensitivity Analysis} assume that $\theta$ is equal to 0.75.} The simulation in the last column removes joint ownership and multi-product profit maximization by setting $\phi_{jk}$ equal to one if $j$ is the same as $k$ and equal to zero otherwise. This restriction has only a small effect on the percent changes in vehicle production due to the new tariff. Indiana, Alabama, Kentucky, Georgia, Mississippi, and Texas have the largest reductions in their percent changes when the pattern of joint ownership is removed from the model. They are top states for transplant production according to Table \ref{tab: state shares}. \begin{table}[tbph] \centering \begin{threeparttable} \caption{Simulated Changes without Joint Ownership } \label{tab: All Countries Sensitivity Analysis} \begin{tabular}{p{7cm} r r} States and Countries & With Joint Ownership & Without Joint Ownership \\ \toprule Alabama & 55,487 & 54,481 \\ & 7.4\% & 7.2\% \\ \midrule California & 48,533 & 48,769 \\ & 10.6\% & 10.7\% \\ \midrule Canada & 57,134 & 56,245 \\ & 5.6\% & 5.5\% \\ \midrule Georgia & 20,728 & 20,398 \\ & 7.6\% & 7.5\% \\ \midrule Illinois & 14,080 & 14,161 \\ & 4.9\% & 4.9\% \\ \midrule Indiana & 46,474 & 45,258 \\ & 5.2\% & 5.0\% \\ \midrule Kentucky & 45,058 & 43,998 \\ & 5.1\% & 5.0\% \\ \midrule Mexico & 123,503 & 122,574 \\ & 6.0\% & 5.9\% \\ \midrule Michigan & 48,860 & 49,005 \\ & 3.3\% & 3.3\% \\ \midrule Mississippi & 13,327 & 13,012 \\ & 4.4\% & 4.3\% \\ \midrule Missouri & 8,597 & 8,686 \\ & 1.9\% & 2.0\% \\ \midrule Ohio & 28,327 & 28,578 \\ & 4.3\% & 4.4\% \\ \midrule South Carolina & 16,869 & 16,630 \\ & 6.3\% & 6.2\% \\ \midrule Tennessee & 29,601 & 29,606 \\ & 7.9\% & 7.9\% \\ \midrule Texas & 14,784 & 14,404 \\ & 3.8\% & 3.7\% \\ \midrule Imports from the & -2,089,770 & -2,090,660 \\ Rest of the World & -73.9\% & -73.9\% \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} \newpage \subsection{Country-Specific Tariff Changes \label{subsec: section5.2}} The final simulations examine the effects of a new tariff that is limited to imports from a single country outside of North America, either Japan, Korea, or Germany. The estimated percent increases in production vary across the states depending on the segment concentration of imports from the country, the vehicle class concentration of each state's production, and the location of the transplant domestic production of Japanese, Korean, or German manufacturers. Table \ref{tab: segment and country} indicates that vehicle imports from Japan were relatively concentrated in the Mid and Large CUV sales in 2022. In contrast, vehicle imports from Korea were relatively concentrated in the Small CUVs, while imports from Germany were relatively concentrated in Luxury Cars. \begin{table}[tbph] \centering \begin{threeparttable} \caption{Vehicle Class Shares by Country of Origin} \label{tab: segment and country} \begin{tabular}{p{6cm} r r r r} & All & Japan & Korea & Germany \\ Vehicle Class & (\%) & (\%) & (\%) & (\%) \\ \toprule Small Cars & 11.6 & 13.5 & 16.7 & \\ Mid and Large Cars & 4.3 & 4.8 & 5.2 & 6.1 \\ Luxury Cars & 11.1 & 3.2 & 3.2 & 58.5 \\ Small CUVs & 27.1 & 21.2 & 45.0 & 5.9 \\ Mid and Large CUVs & 36.6 & 42.5 & 27.5 & 29.4 \\ SUVs & 6.8 & 14.9 & & \\ Vans & 2.3 & & 2.4 & 0.2 \\ Pickups & & & & \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} Table \ref{tab: location and nationality} indicates the location of North American foreign transplant production owned by Japanese, Korean, and German manufacturers. For Japan, the top states and countries in North America are Indiana, Canada, Mexico, and Kentucky. For Korea, they are Alabama, Georgia, and Mexico. For Germany, they are Mexico, South Carolina, and Alabama. \begin{table}[tbph] \centering \begin{threeparttable} \caption{Production Location by Foreign Nationality} \label{tab: location and nationality} \begin{tabular}{p{5cm} r r r} Location in & Japan & Korea & Germany \\ North America & (\%) & (\%) & (\%) \\ \toprule Canada & 16.2 & & \\ Mexico & 14.8 & 22.0 & 42.8 \\ \midrule Alabama & 9.1 & 51.8 & 11.6 \\ Georgia & & 26.2 & \\ Indiana & 19.7 & & \\ Kentucky & 12.1 & & \\ Mississippi & 9.0 & & \\ Ohio & 9.7 & & \\ South Carolina & & & 37.0 \\ Tennessee & 6.2 & & 8.6 \\ Texas & 3.2 & & \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} \newpage These vehicle class and production location shares of manufacturers from the three countries help to explain the simulation results for country-specific tariff changes in Table \ref{tab: Specific Countries}.\footnote{The first column of estimates repeats the $\theta = 0.75$ column from Table \ref{tab: All Countries}.} In the simulation with the new tariff applied only to imports from Japan, the largest percent increase in North American vehicle production are in Illinois, followed by Tennessee, Texas, Georgia, and Alabama. In the simulation that focuses on a tariff on imports from Korea, the largest percent increase in North American vehicle production are in Georgia, followed by Alabama, Tennessee, and Mexico. Finally, in the simulation that focuses on imports from Germany, the largest percent increase in North American vehicle production are in California, followed by Kentucky, South Carolina, and Tennessee. \begin{table}[tbph] \centering \begin{threeparttable} \caption{Simulated Changes in Vehicles Produced -- Specific Countries } \label{tab: Specific Countries} \begin{tabular}{p{4cm} r r r r} States and Countries & All & Japan & Korea & Germany \\ \toprule Alabama & 55,487 & 18,883 & 16,976 & 4,202 \\ & 7.4\% & 2.5\% & 2.3\% & 0.6\% \\ \midrule California & 48,533 & 10,003 & 5,724 & 21,484 \\ & 10.6\% & 2.2\% & 1.3\% & 4.7\% \\ \midrule Canada & 57,134 & 23,160 & 13,276 & 5,868 \\ & 5.6\% & 2.3\% & 1.3\% & 0.6\% \\ \midrule Georgia & 20,728 & 6,898 & 6,639 & 1,696 \\ & 7.6\% & 2.5\% & 2.4\% & 0.6\% \\ \midrule Illinois & 14,080 & 8,855 & 1,577 & 905 \\ & 4.9\% & 3.1\% & 0.5\% & 0.3\% \\ \midrule Indiana & 46,474 & 19,106 & 10,461 & 4,709 \\ & 5.2\% & 2.1\% & 1.2\% & 0.5\% \\ \midrule Kentucky & 45,058 & 16,326 & 7,681 & 11,928 \\ & 5.1\% & 1.9\% & 0.9\% & 1.4\% \\ \midrule Mexico & 123,503 & 42,146 & 37,759 & 10,781 \\ & 6.0\% & 2.0\% & 1.8\% & 0.5\% \\ \midrule Michigan & 48,860 & 24,360 & 8,378 & 6,109 \\ & 3.3\% & 1.7\% & 0.6\% & 0.4\% \\ \midrule Mississippi & 13,327 & 6,007 & 4,147 & 1,112 \\ & 4.4\% & 2.0\% & 1.4\% & 0.4\% \\ \midrule Missouri & 8,597 & 1,986 & 2,389 & 863 \\ & 1.9\% & 0.4\% & 0.5\% & 0.2\% \\ \midrule Ohio & 28,327 & 13,076 & 5,188 & 3,794 \\ & 4.3\% & 2.0\% & 0.8\% & 0.6\% \\ \midrule South Carolina & 16,869 & 5,852 & 3,447 & 2,517 \\ & 6.3\% & 2.2\% & 1.3\% & 0.9\% \\ \midrule Tennessee & 29,601 & 10,396 & 7,821 & 3,167 \\ & 7.9\% & 2.8\% & 2.1\% & 0.8\% \\ \midrule Texas & 14,784 & 10,453 & 1,396 & 704 \\ & 3.8\% & 2.7\% & 0.4\% & 0.2\% \\ \midrule Imports from the & -2,089,770 & -804,261 & -523,249 & -316,464 \\ Rest of the World & -73.9\% & -28.4\% & -18.5\% & -11.2\% \\ \bottomrule \end{tabular} \end{threeparttable} \end{table} \newpage %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Conclusions \label{sec: section6}} A new tariff on all U.S. light vehicle imports from outside of North America would increase domestic production and the average prices of vehicles in the United States. The production effects would be unevenly distributed, with the largest percent increases in California, Tennessee, Georgia, and Alabama, moderate percent changes in Mexico and Canada, and especially small percent increases in Missouri, Michigan, and Texas. This distribution of economic effects reflects the vehicle class of imports and vehicle production in each state, as well as the location of foreign transplant production. There are important caveats and limitations of the model that are worth noting. First, the model estimates changes in vehicle production levels, but not changes in employment levels, due to data limitations at the plant level. Direct employment effects will likely be closely linked to production effects, especially if there are constant returns to labor and wages are determined on a broader economy-wide labor market. There may be substantial indirect employment effects as well, as the shifts in labor demand spill over to the supply chain, affecting manufacturers of engines, transmissions, brakes, electronics, steel, and aluminum producers, though these effects are also not included in the model due to data limitations. Second, as noted by \citeasnoun{Goldberg1994} and \citeasnoun{BLP1999}, it would be very useful to expand the analysis into a dynamic framework, but this will be difficult due to the complexity of the capital-intensive motor vehicle industry. \citeasnoun{Hashmi2016} provides an interesting example of a dynamic model of the motor vehicle industry; however, their dynamic model is at the firm level rather than the vehicle model level, and it focuses on how market structure affects incentives to innovate rather than trade policy. \newpage %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \bibliographystyle{dcu} \bibliography{biblio} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \end{document}