# Trade Policy Uncertainty and the Labor Market: State-Level Evidence

Céline Poilly, Fabien Tripier

► To cite this version:

Céline Poilly, Fabien Tripier. Trade Policy Uncertainty and the Labor Market: State-Level Evidence. American Economic Review Papers and Proceedings, 2025, 115., pp.172-76. 10.1257/pandp.20251009. hal-05099742

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# Trade Policy Uncertainty and the Labor Market:
## State-Level Evidence

Céline Poilly and Fabien Tripier*

January 16, 2025

## Abstract

This short paper provides U.S. state-level evidence regarding the effects of trade policy uncertainty on the labor market. We show that a higher exposure to trade policy uncertainty generates a contraction in total hours worked at the state level. The extensive margins of labor is the primary margin of employment adjustment. State-level employment is more strongly impacted by trade policy uncertainty in goods-producing industries, and more particularly in the durable goods industry. States which are more specialized in goods industries, when they face higher uncertainty, tends to postpone hiring by more, which explains the drop in total employment. JEL classification: E32, E52, F41.

Keywords: Uncertainty Shocks, Tariffs, Labor Market.

## 1 Introduction

The return of the Trump Administration to the White House in January 2025 is likely to be accompanied by new import tariffs, echoing the trade war initiated in 2018. Major changes in tariffs volatility as well as renegotiation in trade agreements foster trade policy uncertainty (TPU). The seminal paper by Handley (2014) highlights the detrimental effects of trade policy uncertainty on trade. However, trade policy uncertainty has broader impacts on the economy through product entry, prices or investment decisions (see Handley and Limão (2022) for a survey). Evidence on the effects of trade policy uncertainty on the labor market is scarce, one exception being Pierce and Schott (2016), which attributes the sharp decline in U.S. manufacturing

<footnote>*Céline Poilly: Aix-Marseille Univ, CNRS, AMSE, Marseille, France. Email: celine-poilly@univ-amu.fr. Fabien Tripier: Université Paris Dauphine, PSL Research University, LEDa, France, & CEPREMAP. Email: fabien.tripier@dauphine.psl.eu. Acknowledgements: The project leading to this publication has received funding from the french government under the "France 2030" investment plan managed by the French National Research Agency (reference :ANR-17-EURE-0020) and from Excellence Initiative of Aix-Marseille University - A*MIDEX. It was also supported by French National Research Agency Grant ANR-20-CE26-DEMUR. Céline Poilly thanks the Institut Universitaire de France for its financial support.</footnote>
employment after 2000 to the reduction in trade policy uncertainty following China's WTO accession.

In this short paper, we contribute to the literature by providing a set of state-level evidence about the labor market effects of trade policy uncertainty in the U.S. economy.<fnref n="1" /> To do so, we combine the state-specific measure of TPU shocks provided by Poilly and Tripier (2025) with a rich labor market database available at the industry-state level. This enable a detailed analysis of the impact of trade policy uncertainty on the extensive and intensive margins of labor, employment dynamics by industry, and labor market flows.

## 2 Data

The U.S. state-level TPU series provided by Poilly and Tripier (2025) is constructed by first estimating the time-varying volatility of U.S. import tariffs at the sectoral level. We then compute the exposure of U.S. states to uncertainty shocks by weighting sector-based tariff volatility by the share of sectoral imports in total imports within each state. All methodological details are provided in Poilly and Tripier (2025). In this paper, we combine the TPU series with labor market-related data. First, using the Current Employment Statistics program (CES-BLS), we decompose total hours worked into extensive margins of labor—i.e., the total number of employees—and intensive margins of labor—i.e., the average weekly hours worked by all employees. Data are available at the industry level, and the dataset includes private-sector industries categorized as either goods-producing or service-providing industries, based on the NAICS classification. Second, we extract state-level data on hires and separations using the Job Openings and Labor Turnover Survey (JOLTS-BLS).

## 3 Intensive and Extensive Margins

We investigate the effects of TPU shocks on both employment margins by estimating the following panel local projection regression

$$ \Delta y_{i,s,t+h} = \delta_{is,h} + \delta_{t,h} + \beta_h TPU_{s,t} + \gamma_h(L)X_{s,t}(L) + v_{i,s,t+h}, \quad (1) $$

where $\beta_h$ corresponds to the cumulative response of the outcome variable $\Delta y_{i,s,t+h}$ for industry i at time t and state s, $h \ge 0$ quarters after the realization of the TPU exposure shock. Thus, $\beta_h$ measures the relative change in the outcome variable between two states in response to an

<footnote n="1">¹Focusing on regional data enhances the identification of causal relationships compared to macroeconomic data, which typically aggregates multiple simultaneous events, such as the trade war initiated by the Trump Administration in 2018. See for instance Nakamura and Steinsson (2014) or Chodorow-Reich (2019).</footnote>
increase in TPU exposure in a treated (or shocked) region relative to an untreated one; see Jordà (2023). We consider total employment, as well as the extensive and intensive labor margins as outcome variables. The outcome variables are expressed in logarithms.

We include time fixed effects, $\delta_{t,h}$, to absorb any common shocks in the U.S., as well as industry-state fixed effects, $\delta_{is,h}$, to control for unobservable industry-state-specific factors that might affect the outcome variable. We control for the level of tariffs by including the exposure to level shocks in the set of control variables $X_{s,t}$. Additionally, we account for the business cycle by using the growth rate of the coincident index of economic activity provided by the Federal Reserve Bank of Philadelphia at time $t$ and with two lags. We also include, as control variable, the growth rate of the dependent variable $\Delta y_{i,s,t}$ with two lags. Equation (1) is estimated by weighting the observations by the population share of each state in 2008. Our sample includes 51 U.S. states, 9 industries, and spans from 2008q1 to 2020q4.

Figure 1 shows the point estimates of $\beta_h$ for $h = \{1, \dots, 8\}$. A higher exposure to trade policy uncertainty generates a contraction in total hours worked at the state level.

<figure><img src="image_2.png" /><figcaption>Figure 1: Effects of a TPU shock on total hours worked through the intensive and extensive margins.</figcaption></figure>

Note: The figure displays $\beta_h$, the point estimates of the effects of a TPU shock on (a) total hours worked, (b) number of employees, and (c) hours per employee. Grey bands represent 95% confidence intervals. The estimation is based on regression (1).

If we consider a shock of 0.006, corresponding to the 75th percentile of the shock distribution, given the estimate of $\beta_4 = -0.264$, this reduces total employment by -0.16% after a year and it is highly significant. The extensive margin of labor explains most of this result, notably because the effect on hours is weak and statistically significant only at impact while the effect on the number of employees is negative and persistent. This finding is consistent with the firm-level evidence provided by Pierce and Schott (2016) for the manufacturing sector. We also complement Choi, Furceri and Yoo (2024) who resort to a cross-country analysis and stress that the extensive margin of labor is more sensitive than the intensive one to financial market related uncertainty. As the extensive margin is more subject to irreversibility than the intensive margin (due to the specific adjustment costs like recruitment, training, or separation costs), firms are more likely to adopt a wait-and-see behavior when it comes to adjust the number of employees (e.g., Bernanke (1983)
and Bloom (2009)).<fnref n="2" />

# 4 Employment by Industry

Since sectors may differ in terms of employment adjustment costs, we go further by investigating the impact of trade policy uncertainty on employment at the industry level. We estimate the following industry-by-industry regression

$$ \Delta y_{i,s,t+h} = \delta_{s,h}^i + \delta_{t,h}^i + \beta_h^i TPU_{s,t} + \gamma_h^i(L) X_{i,s,t}(L) + v_{i,s,t+h}, \quad (2) $$

where $y_{i,s,t}$ is the total number of hours worked in state $s$, industry $i$ and at time $t$. We introduce state fixed effects for each industry regression as well as time fixed effects. The set of controls is the same as for regression (1). The coefficient of interest $\beta_h^i$ is industry specific. We first consider the two main categories of the private industry sector: Goods and Services.

<figure><img src="image_3.png" /><figcaption>Figure 2: Effects of a TPU shock on total hours worked by industry.</figcaption></figure>

*Note:* The figure displays $\beta_{i,h}$, the point estimates of the effects of a TPU shock on total hours worked in industry $i$ at $h = 4$ quarters after the shock (red bullets). Mining stands for “Mining, logging and construction”. Trade stands for “Trade, transportation and utilities”. Business stands for “Professional and business services”. Education stands for “Private education and health services”. Leisure stands for “Leisure and Hospitality”. Black lines represent 95% confidence intervals. The estimation is based on regression (2).

Figure 2 shows that higher exposure to trade policy uncertainty significantly reduces total employment in both industries, but the magnitude is twice as large for the goods industry as for

<footnote n="2">²Several papers quantify the size of the labor adjustment costs, often resorting to plants-level data (see for instance Varejão and Portugal (2007) or Cooper, Haltiwanger and Willis (2015)). However, the literature is silent regarding the comparison between employment-related and hours-related adjustments costs.</footnote>
the services industry (−0.48 versus −0.24, respectively). Looking at sub-categories of industries, the labor market effects are strongest in the durable goods sector and not significantly different from zero in several services industries, such as trade, business services, information, and finance. Using firm-level data, Caldara et al. (2020) show that higher trade policy uncertainty reduces investment in the manufacturing sector but they are silent about employment. Choi, Furceri and Yoo (2024) find a higher sensitivity of manufacturing activity to uncertainty shocks, relative to services, which is explained by the higher level of employment protection (proxied by the layoff rate) in manufacturing.

# 5 Labor Market Flows

Our final examination concerns job flows. Given the importance of the extensive margin in labor market adjustments, it is important to understand the respective roles of job creation and job destruction when the number of employees changes. Unfortunately, data on job flows at the state level are not available at the industry level, which, as shown above, plays a crucial role in the impact of TPU shocks. To circumvent this difficulty, we adjust regression (1) as following

$$ \Delta y_{s,t+h} = \delta_{s,h} + \delta_{t,h} + \beta_h TPU_{s,t} + \eta_h (\omega_{s,t} \times TPU_{s,t}) + \mu_h \omega_{s,t} + \gamma_h(L) X_{s,t}(L) + v_{s,t+h}, \quad (3) $$

where $\omega_{s,t}$ is the share of total employment in the goods-producing sector over total private sector employment in state $s$. In order to capture the heterogenous effects of TPU exposure on job flow at the industry level, we include an interaction term between the degree of industrial specialization $\omega_{s,t}$ and $TPU_{s,t}$. We consider the following dependent variables for $y_{s,t}$: hires and total separations (which includes quits and layoffs). The set of controls is the same as for regression (1).

Panels (a) and (b) of Figure 3 show the estimates for the median state, that is, $(\beta_h + \eta_h \times \omega^{p^{50}})$ where $\omega^{p^{X}}$ is the $X^{th}$ percentile of $\omega_{s,t}$. Hires decline after one year, with a large and significant fall 6 and 7 quarters after the shock. For separations, the estimates are positive, but not significantly different from zero. The negative estimated values of $\eta_h$ for hires and separations indicate a differentiated effect between states depending on their specialization. Panels (c) and (d) of Figure 3 show the differences in estimates between states specialized in goods-producing industry versus states specialized in services, i.e. $\eta_h (\omega^{p^{75}} - \omega^{p^{25}})$. We find that the relative effect of trade policy uncertainty on hires is negative and significant. If we consider a shock of 0.006, corresponding to the 75th percentile of the shock distribution, the cumulative growth rate of hires is −0.42 points of percentage lower in states specialized in goods than in states specialized
in services after one year. On contrary, total separation does not seem to be affected differently
across states' degree of specialization since the relative effect is negative and significant only for
$h = 6$. Our estimates stress that total separation hardly reacts to higher exposure to trade policy
uncertainty, irrespective of the degree of specialization of the state. This finding is consistent
with the view that firms, when they face trade policy uncertainty, adopt a wait-and-see behavior
that leads them to postpone hiring. Therefore, hiring appears to be the primary margin of
employment adjustment, especially in states that are specialized in good-producing industries,
those which are more sensitive to uncertainty (see Figure 2).

<figure><img src="image_4.png" /><figcaption>Figure 3: Effects of a TPU shock on job flows.</figcaption></figure>

Note: The figure displays ($\beta_h + \eta_h \times \omega^{p50}$), the point estimates of the effects of a TPU shock on (a) hires and (b) separations for the median state, and $\eta_h \times (\omega^{p75} - \omega^{p25})$, the point estimates of relative effects of a TPU shock on (c) hires and (d) separations in states highly specialized in goods relative to states highly specialized in services. Grey bands represent 95% confidence intervals. The estimation is based on regression (3).

# 6 Conclusion

We have shown that higher trade policy uncertainty has adverse effects on employment at the U.S. state level. This primarily goes through a reduction in the number of employees rather than in hours worked per employee. Looking at the heterogeneous effects at the industry level, we find that state-level employment in goods industries is more sensitive to a rise in trade policy uncertainty than in services industries, which is partly driven by employment dynamics in the durable goods industry. States which are more specialized in good-producing industries, when
they face higher uncertainty, tends to postpone hiring by more, which explains the drop in employment.

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