metadata: authors: 0: author: Ulate,Mauricio
1: author: Vasquez,Jose P.
2: author: Zarate Vasquez,Roman David
count: World
docna: 0: docna: Labor Market Effects of Global Supply Chain Disruptions
docty: Policy Research Working Paper
abstracts: cdata!: This paper examines the labor market
            consequences of recent global supply chain disruptions
            induced by COVID-19. Specifically, it considers a temporary
            increase in international trade costs similar to the one
            observed during the pandemic and analyzes its effects on
            labor market outcomes using a quantitative trade model with
            downward nominal wage rigidities. Even omitting any
            health-related impacts of the pandemic, the increase in
            trade costs leads to a temporary but prolonged decline in
            U.S. labor force participation. However, there is a
            temporary increase in manufacturing employment as the United
            States is a net importer of manufactured goods, which become
            costlier to obtain from abroad. By contrast, service and
            agricultural employment experience temporary declines.
            Nominal frictions lead to temporary unemployment when the
            shock dissipates, but this depends on the degree of monetary
            accommodation. Overall, the shock results in a 0.14 percent
            welfare loss for the United States. The impact on labor
            force participation and welfare across countries varies
            depending on the initial degree of openness and sectoral deficits.
display_title: Labor Market Effects of Global
            Supply Chain Disruptions
listing_relative_url: /research/2023/05/34054471/labor-market-effects-global-supply-chain-disruptions
topicv3: Trade m1326924 698,Supply and Distribution Chains m1504201 2870,Labor Market Analysis m1582757 4428
publishtoextweb_dt: 2023-05-02T00:00:00Z
dois: 10.1596/1813-9450-10434
author_keys: 0: author_key: NA
1: author_key: NA
2: author_key: 1560909
model_extractions: Policy Research Working Paper 10434

# Labor Market Effects of Global Supply Chain Disruptions

_Mauricio Ulate_ _Jose P. Vasquez Roman D. Zarate_ Development Economics Development Research Group May 2023
Policy Research Working Paper 10434

# **Abstract**

This paper examines the labor market consequences of recent global supply chain disruptions induced by COVID-19. Specifically, it considers a temporary increase in international trade costs similar to the one observed during the pandemic and analyzes its effects on labor market outcomes using a quantitative trade model with downward nominal wage rigidities. Even omitting any health-related impacts of the pandemic, the increase in trade costs leads to a temporary but prolonged decline in U.S. labor force participation. However, there is a temporary increase in manufacturing employment as the United States is a net importer of manufactured goods, which become costlier to obtain from abroad. By contrast, service and agricultural employment experience temporary declines. Nominal frictions lead to temporary unemployment when the shock dissipates, but this depends on the degree of monetary accommodation. Overall, the shock results in a 0.14 percent welfare loss for the United States. The impact on labor force participation and welfare across countries varies depending on the initial degree of openness and sectoral deficits.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rzaratevasquez@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Labor Market Effects of Global Supply Chain Disruptions<sup>*</sup>

Mauricio Ulate Jose P. Vasquez Roman D. Zarate

**JEL codes:** F10, F11, F16, F40, F66.

**Keywords:** Supply Chain Disruptions, Trade Costs, Downward Nominal Wage Rigidity.

> *Mauricio Ulate: Federal Reserve Bank of San Francisco, Jose P. Vasquez: LSE and CEPR, Roman Zarate: Development Research Group, The World Bank. We thank Marco Badilla, Lorenzo Caliendo, Juanma CastroVincenzi, Isabela Manelici, Joan Monras, Ishan Nath, Andres Rodriguez-Clare, Nicholas Sander and the whole department at the FRBSF for their help and useful comments. Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the Federal Reserve Bank of San Francisco, the Federal Reserve System, or the World Bank.
# **1 Introduction**

In the past few years, COVID-19 caused a number of disruptions in global supply chains. The pandemic strained trade flows due to port closures, reduced shipping capacity owing to lockdowns (for example, in China), fewer shipping workers being available for health-related reasons, and a shortage of shipping containers, among other challenges. These disruptions, in turn, caused an increase in the costs of international trade, potentially leading to substantial effects on production, prices, and labor markets, as well as reallocation within and across countries.

This studies the labor market of the increased in- paper quantitatively consequences ternational trade costs originating from the recent global supply chain disruptions induced by COVID-19. Specifically, we analyze an _x_ % increase in the iceberg trade costs of sending products across countries that reverts after _τ_ years. In our baseline exercise, we study a 12% shock that reverts after three years, which approximates the size and duration of the trade costs shock during the COVID-19 pandemic. However, we also examine how the effects of the shock depend on its size (i.e., _x_ = 6%, 18%, or 24%) or persistence (i.e., _τ_ = 2, 4, 5, or 6).

We employ the quantitative trade model developed by Rodriguez-Clare, Ulate, and Vasquez (2022, henceforth RUV) but with a focus on analyzing the effects of a trade-cost shock. The model features multiple sectors linked by an input-output structure, sectorlevel trade that satisfies the gravity equation, downward nominal wage rigidity (henceforth DNWR), and a home-production sector that leads to an upward sloping labor supply curve. Trade takes place between regions, and workers can move across sectors in a given region subject to mobility costs. Each period, workers draw idiosyncratic shocks to the utility of working in each sector. Based on these draws, the costs of switching sectors, and expected future real income adjusted for unemployment, workers choose the sector to participate.<sup>1</sup> We capture DNWR as in Schmitt-Grohe and Uribe (2016). This implies that the nom-

> 1Our baseline model does not feature movement between regions in a given country, but one of our extensions incorporates this feature. The consequences of allowing migration across regions are very small.

1
inal wage in any period must be no less than a factor _δ_ times the nominal wage in the previous period and that involuntary unemployment arises when wages are constrained by this lower bound.<sup>2</sup> Given the presence of the DNWR, the model also requires a nominal anchor that prevents nominal wages from rising enough to make the DNWR always non-binding.<sup>3</sup> We assume that world nominal GDP in dollars grows at a constant and exogenous rate _γ_ . This assumption captures the regularity that central banks are unwilling to allow inflation or unemployment to be too high (because of their related costs) while simultaneously keeping our model tractable. While this nominal anchor may not capture all the complexities of the real world, it allows us to incorporate a rich trade structure with multiple countries and sectors, intermediate inputs, and forward-looking mobility decisions into our trade framework while still being able to solve this otherwise-unwieldy model.<sup>4</sup> Our quantitative analysis requires data for sector-level input-output flows as well as trade flows across all pairs of regions in our sample. We combine multiple data sources, a set of proportionality assumptions, and implications from a gravity model to construct sector-level trade flows across all region pairs in our sample. The resulting dataset contains 87 regions (50 U.S. states, 36 additional countries, and an aggregate rest of the world region), and 15 sectors (home production, 12 manufacturing sectors, services, and agriculture), for our base year of 2019.

We quantify the effects of the shock in our model using the dynamic “exact-hat algebra” approach to counterfactual analysis (c.f., Caliendo et al., 2019). This methodology ensures that our model perfectly matches sector-level production, trade, and reallocation patterns in the base year. We then introduce an unexpected increase in trade costs that reverts after a certain number of years.

Besides the myriad parameters implicitly calibrated by the exact hat algebra approach

> 2See Dickens et al. (2007), Grigsby et al. (2019), and Hazell and Taska (2019) for papers that have found support for the presence of DNWR in the data. Labor-market frictions in the real world might go significantly beyond DNWR, but our model uses this modeling device as a parsimonious way to capture such frictions in a rich dynamic quantitative trade model.

> 3Our baseline analysis also assumes flexible exchange rates between the U.S. dollar and other countries’ currencies. However, we have also conducted an alternative analysis with fixed exchange rates, and the implications for the United States are similar. The results of this analysis are available upon request.

> 4Introducing other types of nominal anchors prevents us from using the efficient Alvarez-and-Lucas type algorithm developed in RUV to deal with the DNWR, increasing the computation time by several orders of magnitude. Implementing more realistic nominal anchors is left for future research.

2
using data from the base year, we only require an explicit calibration of four parameters. Namely: the DNWR parameter _δ_ , the growth in world nominal GDP in dollars _γ_ , the inverse elasticity of mobility across sectors _ν_ , and the trade elasticity 1 _− σ_ . We normalize _δ_ to one, indicating that nominal wages cannot fall, without loss of generality. We then set _γ_ to 4%, in line with the relatively high nominal growth rate observed in the post-pandemic period, but we explore the robustness of our results to different values. Finally, we obtain _ν_ directly from RUV, and we take _σ_ from the trade literature.

Our model-based analysis shows a temporary yet long-lasting decrease in U.S. labor force participation due to the shock. During the high trade cost period, engaging in the home production sector (which provides a constant utility flow) becomes more appealing, resulting in a decline in labor force participation. Once the shock dissipates, there is negative pressure on nominal wages as the economy adjusts to the lower trade costs, which in the presence of DNWR can generate unemployment. The manufacturing sector, where increase the most the trade cost the most unem- wages during high period, experiences ployment when the shock dissipates.

The impact of the trade cost shock on the labor market varies by sector. There is a temporary employment increase in manufacturing, a sector that is very tradable and where the United States is a net importer. By contrast, the service and agriculture sectors experience temporary employment reductions. We also find that the effect of the shock varies by state. States with larger service sectors (such as Alaska, Nevada, and Hawaii) tend to experience a more significant decline in labor force participation than those with larger manufacturing sectors (such as Ohio, Pennsylvania, and Wisconsin), but additional factors such as the exposure to other countries and states also play an important role in determining the extent of the impact.

The impact of the shock on labor force participation varies internationally and depends on size and trade openness. On the one hand, countries like China, the United States, Brazil, and India, which are relatively large and less reliant on international trade, experience a smaller decrease in labor force participation. On the other hand, small and open countries like Ireland, Estonia, the Slovakia Republic, and Cyprus, which rely heavily on foreign intermediate inputs and importing/exporting, experience a more signifi3
cant decline in labor force participation. Furthermore, countries that are net importers in a given sector tend to increase their participation relatively more in that sector due to a cross-country expenditure-switching effect.

We also investigate how different assumptions affect our conclusions. To do so, we consider alternative specifications where we vary the persistence of the shock, the size of the shock, or the nominal growth rate of world GDP in dollars. In addition, we examine how allowing for migration between U.S. states and varying the elasticity of moving across sectors change our results. Our conclusions are qualitatively robust across these different specifications. Quantitatively, one of the key lessons is that more monetary accommodation in the form of a higher _γ_ at the time the shock disappears (but not necessarily before) can mitigate the unemployment consequences of the shock.

It is important to highlight some limitations of our analysis. First, our model does not account for health-related concerns that could also affect participation decisions at the same time as the supply chain disruptions. Our quantitative results suggest that even abstracting from these forces, there is a long-lasting effect on labor participation. Second, our framework does not incorporate elements such as pent-up demand after post-pandemic reopening or domestic transportation disruptions, which also contributed to the recent increase in trade costs. Therefore, our analysis provides insights into specific aspects of the broader issue of global supply chain disruptions caused by the pandemic.

Our paper contributes to the literature studying the relationship between the COVID19 pandemic and global supply chains, which has highlighted supply disruptions as one of the main factors explaining the decline in output and increase in inflation. For instance, LaBelle and Santacreu (2022) exploit cross-industry variation in sourcing patterns and find that to chain is associated with increases in the exposure supply disruptions higher producer price index. Similarly, Meier and Pinto (2020) analyze the impact of Chinese lockdowns on the U.S. economy, finding that sectors with higher exposure to intermediate inputs from China experienced larger decreases in output, employment, and exports. Bonadio et al. (2020) investigate the role of global supply chains on GDP growth during the pandemic and find that disruptions explain one-quarter of the decline in GDP. Sforza and Steininger (2020) develop a multi-sector model to show that global production linkages 4
amplified the pandemic shock. We contribute to this literature by analyzing the effect of supply disruptions on local labor markets through the lens of a state-of-the-art trade model.

Our also relates to the vast literature that studies the of trade on local paper impacts labor markets using quantitative trade models. Most recent papers have focused on the effects of the rise of China’s prevalence in international trade, also known as the “China Shock” (Rodriguez-Clare et al., 2022; Galle et al., 2020; Caliendo et al., 2019; Adao et al., 2020), but other quantitative papers also examine migration shocks (Caliendo et al., 2021, 2022) or automation (Galle and Lorentzen, 2022).<sup>5</sup> Among these papers, RUV is the closest to our work. They present a dynamic quantitative trade model with DNWR and use it to rationalize the empirical findings from the China-Shock literature. The shock itself is modeled as an increase in productivity in China from 2000 to 2007 that varies across sectors. In contrast, our paper applies the quantitative framework from RUV to study the labor market effects of the recent chain modeled as an global supply disruptions, unexpected increase in international trade costs.<sup>6</sup> Open-economy macroeconomics papers such as Gali and Monacelli (2005, 2008) or Clarida et al. (2002) have incorporated nominal rigidities into general equilibrium models with a simplified trade structure. Schmitt-Grohe and Uribe (2016) study optimal policies in the presence of DNWR in a small open economy and Guerrieri et al. (2021) analyze monetary policy after pandemic-induced sectoral shocks in a model with DNWR and a nominal anchor similar to ours. Our contribution relative to this literature is to incorporate a hint of these important nominal elements into a rich quantitative trade model to assess the impact of trade cost shocks.

The remainder of the paper is organized as follows. Section 2 provides a brief overview of the model. Section 3 describes our data construction and baseline calibration. Section 4 presents the results of our baseline analysis for U.S. states. Section 5 focuses on how the results vary across countries. Section 6 investigates the sensitivity of our results to changes in some of the key assumptions and Section 7 concludes.

> 5The list is not exhaustive. Other recent papers also study the impacts of trade shocks with unemployment effects generated via search and matching instead of DNWR (e.g. Kim and Vogel, 2020a,b; Dix-Carneiro et al., 2020; Carrere et al., 2020).

> 6Our paper also adds to the rapidly growing literature in trade that discusses or incorporates nominal elements (see RUV, Comin and Johnson, 2020; Costinot et al., 2022; Ahn et al., 2022; Fadinger et al., 2022).

5
# **2 Model Environment**

In order to study the effects of increases in international trade costs, we use a dynamic multi-sector quantitative trade model with nominal wage rigidities and an input-output structure based on RUV. In this section, we briefly discuss the main features of the model, A. relegating the details to appendix The model incorporates 87 regions (50 U.S. states, 36 other countries, and an aggregate rest of the world region) and 15 sectors (home production, 12 manufacturing sectors, services, and agriculture). The “home production” sector is meant to capture useful activities conducted at home (e.g., taking care of family members, cooking, cleaning, enjoying leisure) that are not remunerated as work. In the baseline model, we assume that there is no mobility across countries or across states of the U.S. In an extension, we allow for migration across U.S. states, but this makes very little difference for any of the results.

Total consumption is a Cobb-Douglas aggregate of consumption across all the market sectors with given time-invariant expenditure shares.<sup>7</sup> As in a multi-sector Armington trade model, consumption within a given market sector is a CES aggregate of the good produced by each of the regions, with an elasticity of substitution denoted by _σ_ . In principle, each region can produce the goods in all of the sectors, but they might have very low productivity in some of these sectors.

We denote the region _i_ , sector _s_ , and time _t_ triad as ( _i_ , _s_ , _t_ ). Production uses labor and intermediate inputs, but no capital. Specifically, production of the final good in ( _i_ , _s_ , _t_ ) takes the following Cobb-Douglas form: where _Ai_ , _s_ , _t_ is total factor productivity in ( _i_ , _s_ , _t_ ), _Li_ , _s_ , _t_ is employment in ( _i_ , _s_ , _t_ ), _Mi_ , _ks_ , _t_ is the quantity of intermediate inputs of sector _k_ used in ( _i_ , _s_ , _t_ ), _φi_ , _s_ is the time-invariant labor share in ( _i_ , _s_ ), and _φi_ , _ks_ is the share of inputs that sector _s_ uses from sector _k_ in region _i_ .

> 7This assumption is made for tractability and does not capture changes in consumption patterns that might have occurred during the pandemic.

6
Production has constant returns to scale, i.e. _φi_ , _s_ + ∑ _k φi_ , _ks_ = 1.

There are iceberg trade costs _τij_ , _s_ , _t ≥_ 1 for sending the product of sector _s_ from region _i_ to region _j_ at time _t_ . These _τ_ ’s will play an important role because they are the ones getting shocked when the economy faces an increase in trade costs generating the disruption.

There is perfect competition in production. Letting _Wi_ , _s_ , _t_ denote the wage in dollars in ( _i_ , _s_ , _t_ ) and _Pi_ , _k_ , _t_ denote the dollar price of the composite good of sector _k_ , in region _i_ , at time _t_ , then the dollar price in region _j_ of the ( _i_ , _s_ , _t_ ) good is: This expression describes all four factors affecting the price of sending the individual good of sector _s_ from region _i_ to region _j_ at time _t_ ; namely: iceberg trade costs, technology at the sector-region of origin, the wage at the sector-region of origin, and intermediate input prices (in all sectors) at the origin location.

We denote the number of agents participating in ( _i_ , _s_ , _t_ ) by _li_ , _s_ , _t_ , whose behavior is described below. In a standard trade model, employment in a sector-region has to equal labor supply in that sector-region, i.e. _Li_ , _s_ , _t_ = _li_ , _s_ , _t_ . We depart from this assumption and instead follow Schmitt-Grohe and Uribe (2016) by allowing for a downward nominal wage rigidity (DNWR) specifying that the nominal wage in ( _i_ , _s_ , _t_ ) has to be greater than _δ_ times the nominal wage in ( _i_ , _s_ , _t −_ 1), that is: Given this rigidity, employment does not necessarily have to equal labor supply, it could be strictly below it. This is captured by the following weak inequality: Importantly, there can only be unemployment if the wage is at its lower bound. Therefore, the previous two inequalities are augmented by a complementary slackness condition 7
indicating that at least one of them always has to hold with equality: The previous equation says that wage and employment are determined by supply and demand when the wage is away from its lower bound. By contrast, when the wage lower bound is binding, the market does not clear, and there is rationing (i.e., unemployment).

Returning to the determination of _li_ , _s_ , _t_ , agents in the model can either engage in home production (sector zero) or look for work in the labor market (sectors 1 through _S_ ). Particin home results in an and time-invariant real level of con- ipating production exogenous sumption which does not depend on labor market conditions. By contrast, a given market sector _s >_ 0 yields a real level of consumption _ci_ , _s_ , _t_ which is endogenous.

Given the existence of downward nominal wage rigidity, agents must take into account the possibility of unemployment when deciding which sector to participate in. To simplify the analysis, we assume a representative agent in each sector-region.<sup>8</sup> Since a fraction _Li_ , _s_ , _t_ / _li_ , _s_ , _t_ of agents is actually employed in ( _i_ , _s_ , _t_ ), and employed agents obtain a nominal wage of _Wi_ , _s_ , _t_ , the real level of consumption _ci_ , _s_ , _t_ from participating in market sector _s_ is given by: where _Pi_ , _t_ is the aggregate price index in region _i_ .

Agents choose their sector while facing idiosyncratic preference shocks, switching costs, and incorporating into their decision the expected future income in all sectors (i.e., the _ci_ , _s_ , _t_ ’s) with perfect foresight. The idiosyncratic preference shocks are assumed to have a Gumbel distribution, making the participation decision tractable and allowing for closedform expressions (see appendix A for additional details on the derivations). Importantly, there is an elasticity 1/ _ν_ of moving across different sectors within any given region.

Since the model contains nominal elements (namely the DNWR) it is also important

> 8This is equivalent to assuming that the income generated in a sector-region is equally shared between all agents in that sector-region.

8
to introduce a “nominal anchor”, preventing nominal wages from rising so much in each period as to make the DNWR always non-binding. We implement a relatively simple nominal rule that captures the idea that central banks are unwilling to allow inflation or unemployment to be too high (because of their related costs which, in the case of inflation, are outside the model) while at the same time being amenable for quantification.<sup>9</sup> Specifically, we assume that world nominal GDP in dollars grows at a constant rate _γ_ across years: Although this assumption is useful to solve the model, it has limitations and might not reflect the optimal monetary policy of any given country. This implies that the nominal implications of the model are to be taken with a grain of salt. Consequently, we will refrain from discussing the implications of the trade-cost shock for inflation since the model is not well suited to study this aspect. Nevertheless, the model can provide valuable insights into the behavior of relative prices, which we discuss in our results section.

As mentioned above, the main objective of the paper is to examine the effects of an trade-cost shock. To do so in a tractable we unanticipated computationally way, employ a technique known as “dynamic exact hat algebra”, which allows the model to implicitly match production, trade, and reallocation patterns in a given base year. By doing so, one can then introduce a percentage change in any of the model’s fundamentals, such as trade costs, without knowing the initial levels of these fundamentals, and study the economy’s dynamic response to such a shock.

To analyze the effects of the trade cost shock, we assume that the base year is 2019. At that point, the shock has not hit the economy and the model perfectly matches production, trade, and sectoral flow patterns as they occurred in the real world. Then, the shock is introduced in 2020, and the agents in the model learn the full path of the shock (recall that the agents in the model have “perfect foresight”). As the shock hits, employment, prices, production, and trade respond accordingly.

> 9The tractability of this nominal anchor allows us to solve our model using a fast contraction mapping algorithm in the spirit of Alvarez and Lucas (2007) developed in RUV to deal with the complementary slackness condition implied by the DNWR. A similar nominal anchor is used in Guerrieri et al. (2021).

9
# **3 Data, Calibration, and Shocks**

## **3.1 Data Sources**

Our data construction process follows steps similar to those in RUV but uses 2019 as the base year (whereas in RUV the base year is 2000). A summary of the data-construction B. process is provided here, with additional details available in Appendix _Labor, input, and consumption shares:_ Data from the BEA (for U.S. states) and from ICIO (OECD’s Inter-Country Input-Output Database) is used to compute the value-added share in gross output (which is used as the labor share in the model) and the input-output coefficients for each region. Consumption shares can be inferred based on trade flows, labor shares, and intermediate input shares.

_Bilateral flows:_ The model requires bilateral trade flows in all sectors between any pair B.2. of regions in the sample. These are constructed in 4 steps outlined in Appendix _Labor flows across sectors and regions:_ Data for intersectoral mobility for each U.S. state is obtained from the Current Population Survey (CPS), while frictionless mobility is assumed for other countries. In the extension allowing for migration within the U.S., data for interstate mobility is obtained from the American Community Survey (ACS).

_Labor supply:_ Data on employment by sector (including home production) comes from the WIOD Socio Economic Accounts, the International Labor Organization, the U.S. Census, and the ACS. As in RUV, only observations for people aged between 25 and 65 are kept. Labor force participation is measured as the share of individuals in that age group who are either employed or unemployed.

## **3.2 Parameter Calibration**

Table 1 describes the parameters used in the baseline specification. Notice that _γ_ (the nominal growth rate of world GDP in U.S. dollars) and _δ_ (the downward nominal wage rigidity parameter) are not separately identified. For a given _δ_ , if _γ_ is higher, then the DNWR is less likely to bind. Likewise, for a given _γ_ , if _δ_ is lower, then the DNWR is less likely to bind. Therefore, we require a normalization and set _δ_ = 1, indicating that nominal 10
**Table 1: Parameter values used**

|Parameter|Value|Description|Source|
|---|---|---|---|
|_δ_|1|Lower bound in DNWR|Normalization|
|_γ_|4%|Growth rate of world nominal GDP in $|Suggestive|
|_ν_|0.55|Inverse elasticity of moving across sectors|RUV|
|_σ_|6|Trade elasticity|Trade Literature|

**Notes:** This table contains the parameter values used in the baseline specification, together with their description and the source where they are taken from.

wages in dollars cannot fall, and putting the burden of the nominal adjustment on _γ_ .

We set _γ_ = 4%, due to the high nominal growth rate in the post-pandemic period. We discuss the implications of different _γ_ ’s (between 2% and 6%) in Section 6. The implications go in the direction expected, the higher the _γ_ , the less binding the DNWR is, and the less unemployment is generated in the model. For a high _γ_ of 6% or higher, the model has essentially the same behavior as the model without DNWR. The outcomes of the model unrelated to unemployment are only slightly affected by the choice of _γ_ .

Parameter _ν_ for the inverse elasticity of moving across sectors is taken directly from RUV and set to 0.55. In that paper, the inverse elasticity of moving across sectors is set to match the evidence shown in Autor et al. (2013) on how more exposure to the China shock across U.S. commuting zones affects labor force participation. As we will discuss in Section 6, our results do not change substantially when using other reasonable values of _ν_ . Finally, we set _σ_ to 6 in all sectors, which implies a trade elasticity of -5, consistent with the trade literature (see, e.g., Costinot and Rodriguez-Clare, 2014).

# **3.3 Trade Shock**

As indicated in the introduction, the baseline exercise examines a 12% increase in the iceberg trade costs of sending products across countries that reverts after three years. We also explore, in alternative exercises described in Section 6, how the effects of the shock depend on its size (i.e., 6%, 18%, or 24% instead of 12%) or persistence (i.e., 2, 4, 5, or 6 years instead of 3).

The choice of a 12% magnitude for the increase in trade costs in our baseline specifi11
<!-- Start of picture text --> Producer Price Index by Industry: Deep Sea Freight Transportation: Deep Sea Freight Transportation<br>Services<br>380<br>360<br>340<br>320<br>300<br>280<br>260<br>240<br>Jan 2015 Jan 2016 Jan 2017 Jan 2018 Jan 2019 Jan 2020 Jan 2021 Jan 2022<br>Shaded areas indicate U.S. recessions. Source: U.S. Bureau of Labor Statistics fred.stlouisfed.org<br>Index Jun 1988=100<br><!-- End of picture text -->

**Figure 1:** PPI for deep sea freight transportation services between January 2015 and January 2022, taken directly from FRED.

cation is motivated by the behavior of the producer price index for deep sea freight trans1. portation services, depicted in figure This index increased by approximately 12% from its pre-pandemic level of 330 to around 370 in December 2021.

It is worth noting that the aforementioned producer price index may not capture the full increase in the cost of cross-border trade, as other trade cost components could have also risen. Nevertheless, iceberg trade costs in trade models also include non-physical factors such as marketing or research costs for entry into a foreign country, which may not have risen as much as physical costs. Overall, we view the 12% increase in the iceberg trade cost as a suggestive benchmark and assess the sensitivity of our results to different values of this parameter.

The choice of a 3-year duration for the shock is based on current evidence suggesting that global supply chain disruptions have largely subsided by early 2023. Thus, we assume that high trade costs are in effect for 2020, 2021, and 2022, but then dissipate by 2023.

12
In Section 6, we examine how different shock duration impacts the results. We find that longer-lasting shocks lead to more severe outcomes because, given the costs of moving between sectors, it makes more sense to pay the moving cost for more extended shocks than it does for more transitory ones.

# **4 Baseline Results**

This section investigates the effects of a 12% increase in the iceberg trade costs of sending products across countries on labor outcomes for the United States. Our baseline exercise uses a model where there is no migration across U.S. states, world nominal GDP in dollars grows at 4% per year, and the trade cost shock lasts for 3 years (2020-2022). We discuss the effects on labor force participation, employment, and unemployment in the aggregate, as well as on labor supply to the broad sectors of manufacturing, services, and agriculture. We also assess how these effects vary across different U.S. states. International results are discussed in the next section.

Figure 2 displays the effects of the shock on employment-related outcomes for the United States as a whole. The blue line depicts the cumulative percentage change in employment since 2019, the green line shows the cumulative percentage change in labor supply since 2019, and the red line shows the level of unemployment (in percent).

We find that aggregate U.S. labor supply decreases during the years when the trade shock is active, with the largest cumulative decline of 0.7% occurring in 2022. The fall in labor supply occurs because, while trade costs are high, participating in the home-production sector (which offers a constant real utility flow) temporarily becomes more attractive than participating in the market sectors. This is due to the fact that high trade costs make the market sectors less productive because intermediate inputs from other countries are more expensive, which is akin to a fall in productivity. Although aggregate nominal wages increase at a faster-than-normal pace during the period of high trade costs, aggregate prices increase even faster, resulting in a decrease in real wages (see appendix figure C.1), which is what causes some individuals to exit the labor force.

The recovery of labor supply once the shock disappears is slow, by 2027 labor supply 13
<!-- Start of picture text --> Employment, labor supply, and unemployment<br>0.5<br>0<br>-0.5<br>Employment Change<br>Labor Supply Change<br>Unemployment<br>-1<br>2020 2025 2030 2035<br> Year<br> Percentage<br><!-- End of picture text -->

**Figure 2:** Paths of relevant variables for the U.S. on aggregate. The cumulative percentage change in employment since 2019 is in blue, the cumulative percentage change in labor supply since 2019 is in green, and the level of unemployment (in percent) is in red. The years in the _x_ -axis go from 2019 until 2035.

is still 0.2% lower than pre-shock. In the model, the costs of switching between market sectors and entering/exiting the labor force are calibrated based on pre-pandemic mobility patterns. These patterns suggest that there are substantial costs associated with reallocating across sectors, which explains why agents return to the labor force gradually. Although the pandemic may have altered the costs of switching between sectors, the model does not account for such changes.<sup>10</sup>

> 10Another limitation of our model is that it does not feature an income effect on labor supply, because the agents do not have a level of wealth or assets that enters their labor decision. Agents simply compare their real consumption of participating in home production (which is constant) against the real consumption they obtain from participating in a given market sector (which is given by the real wage in that sector adjusted for unemployment). Moreover, the agents cannot choose their level of employment (or their “hours”); they simply choose their sector and supply all their units of labor inelastically to that sector.

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Unemployment is generated mostly when the shock dissipates, with the peak a-mount of unemployment of 0.32% occurring in 2023. As mentioned above, during the years with high trade costs nominal wages are increasing faster than usual, so the downward wage rigidity mostly does not bind. By contrast, when the shock dissipates nominal wages need to fall in some region-sectors.<sup>11</sup> Consequently, those locations hit the DNWR and experience some temporary unemployment.

The blue line in figure 2, which shows the cumulative change in employment since 2019, is essentially the combination of the green and red lines (labor supply and unemployment). Importantly, even though the trough for labor supply occurs in the year 2022, the trough for employment occurs in 2023, due to the additional unemployment generated when the trade-cost shock dissipates.

Figure 3 displays the cumulative percentage change since 2019 of activity (i.e., the number of people engaged) in four different broad sectors for the U.S. as a whole. Home production is depicted in blue, manufacturing in green, services in red, and agriculture in purple. While home production and manufacturing show an increase during the period when the shock is active, services and agriculture decline.

The rise in home production has already been explained above, so we now focus on the employment changes in the other sectors. Several forces affect employment in each market sector. First, there is a general decrease in demand due to the aggregate fall in employment (with people out of work there is less spending power). Second, intermediate inputs become more expensive, making production less efficient and decreasing labor demand. Third, there is an expenditure-switching effect across countries: imports become more expensive and tend to be substituted with local production, increasing labor demand in net-importing sectors.

The United States is a net exporter of services and has balanced trade in agriculture, in these sectors the first two effects mentioned in the previous paragraph dominate, de-

> 11While high trade costs are active, agents are flowing out of the labor force, which forces nominal wages to increase faster than usual to attain the _γ_ growth of nominal GDP embedded in the nominal anchor. Once trade costs return to their normal level, agents flow back into the labor force, which implies nominal wages must grow more slowly than usual (or in some cases fall) to satisfy the nominal anchor. This is reminiscent of central banks around the world withdrawing monetary accommodation once the pandemic and supply disruptions subsided and the labor market normalized.

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<!-- Start of picture text --> Change in participation by broad sector<br>2<br>Home production<br>Manufacturing<br>1.5<br>Services<br>Agriculture<br>1<br>0.5<br>0<br>-0.5<br>-1<br>-1.5<br>2020 2025 2030 2035<br> Year<br> Percentage change in participation<br><!-- End of picture text -->

**Figure 3:** Paths of aggregate participation for different broad sectors in the U.S. The percentage change in home production from the baseline year of 2019 is in blue, the percentage change in labor supply to the manufacturing sector since 2019 is in green, the same concept for the service sector is in red, and for the agricultural sector is in purple.

creasing participation. By contrast, the United States is a net manufacturing importer. In this sector, the expenditure-switching effect dominates and participation increases. Since manufacturing is the sector that experiences an increase in participation (and the highest increase in nominal wages) during the years when the shock is active, it is also the sector that suffers most of the unemployment when the shock dissipates.

Appendix figure C.2 depicts the evolution of relative sectoral prices (nominal sectoral prices divided by the aggregate price index). Manufacturing experiences the highest increase in relative price (up to 4%). Agriculture’s relative price increases slightly (up to 1%), while the relative price of services decreases (up to -1%).

Turning to regional results, figure 4 presents a map of the cumulative percentage 16
<!-- Start of picture text --> (-.5,0]<br>(-.75,-.5]<br>(-1,-.75]<br>[-1.6,-1]<br><!-- End of picture text -->

**Figure 4:** Map for the cumulative percentage change in participation between 2019 and 2022 across U.S. states. Darker shades of blue represent smaller falls in participation.

change in labor force participation between 2019 and 2022 for U.S. states. Some of the states where labor participation falls the most are Alaska, Nevada, and Hawaii, while some states where it falls the least are Pennsylvania, Ohio, and Wisconsin.

While the states where participation falls the most tend to have a relatively large service sector and the ones where participation falls the least tend to have a larger manufacturing sector, the way the shock affects each state is not immediately apparent. This is because the fall in participation and the amount of unemployment generated depend on several factors, such as the distribution of labor across sectors, deficits in the pre-pandemic period, and exposure to trade with other countries and other U.S. states, among others.

# **5 International Results**

In this section, we turn our attention to how the impacts of the trade shock vary across countries. The shock itself is uniform in how it affects the iceberg trade costs of sending products across countries. Nevertheless, countries’ exposures to the shock vary due to differences in their openness levels, size, and sectoral compositions. Figure 5 illustrates this idea by displaying the change in home production participation between 2019 and 17
<!-- Start of picture text --> Home production participation change between 2019 and 2022<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br> Country<br>CHNJPNUSABRAINDITAAUSIDNRUSTURRoWGBRFRAKORESPDEUFINGRCROUCANMEXSWEPRTTWNDNKPOLAUTBELNLDBGRLTUCZESVNCYPSVKESTHUNIRL<br> Change between 2019 and 2022, in %<br><!-- End of picture text -->

**Figure 5:** Percentage change in home production participation between 2019 and 2022 B.1. across countries, in percent. For country abbreviation codes see appendix

2022 for all 38 countries in our The shows that countries such as sample. figure large the United States, China, Japan, Brazil, and India, which are less reliant on international trade due to the size of their domestic market, experience relatively smaller increases in home production participation due to the trade shock. In contrast, smaller and more open countries such as Ireland, Hungary, Estonia, the Slovakia Republic, and Cyprus, suffer a greater increase in home production participation (akin to a fall in the labor force).

Adao, Arkolakis, and Esposito (2020) (henceforth AAE), presents a formula for the first-order approximation of the exposure of a region to a trade shock under certain simplifying assumptions. We use their formula to understand how regions (and therefore countries) are differentially exposed to the increase in international trade costs. As expressed in equations (16) and (17) of AAE, the formula for the change in excess labor demand in 18
region _i_ after a change in trade costs _τ_ ˆ is given by: In the previous expression, (1 _− σ_ ) is the trade elasticity, _li_ , _s_ ,0 is the share of labor in market _i_ employed in sector _s_ in the base year (denoted with a zero even though in our implementation it will be the year 2019), and _θi_ , _s_ ( ˆ _τ_ ) is the shift in demand for the sector _s_ good of region _i_ : The variable _rij_ , _s_ ,0 denotes the share of market _i_ ’s sales in sector _s_ that go to market _j_ in the base year, _λqj_ , _s_ ,0 denotes the share of market _j_ ’s purchases in sector _s_ that come from market ˆ _q_ in the base year, and _τij_ , _s_ = ln( _τij_ , _s_ ,2022) _−_ ln( _τij_ , _s_ ,2019) denotes the log of the change in the iceberg trade costs between the base year and the high-trade-cost years (2020, 2021, and 2022).

_η_ ˆ _i_ ( ˆ _τ_ ) represents market _i_ ’s “revenue shock exposure”. It is the sum across sectors of the shock to the demand for the good of region _i_ in each sector, _θi_ , _s_ ( ˆ _τ_ ), weighted by that sector’s share in _i_ ’s employment in the base year _li_ , _s_ ,0. The sector-level demand shock, _θi_ , _s_ ( ˆ _τ_ ), is itself the sum across destinations _j_ of the impact of market _i_ ’s own trade shock on the demand for its good minus the demand shift caused by competitors’ trade shocks in that sector, weighted by the revenue importance of each destination in the base year _rij_ , _s_ ,0. Note that all components of _η_ ˆ _i_ ( ˆ _τ_ ) can be computed with information on bilateral trade flows in the base year plus measures of the bilateral trade shocks. In our implementation, ˆ ˆ _τij_ , _s ≈_ 12% if _i_ and _j_ are regions located in different countries, while _τij_ , _s_ = 0 if _i_ = _j_ or if _i_ and _j_ are regions of the same country (e.g., two U.S. states).

The AAE exposure measure provides a useful way to assess the impact of shocks on a given region by taking into account how it competes with all other regions in all possible destination markets, including its own. If a region is in autarky, a change in the _τ_ ’s has no ˆ effect and _θi_ , _s_ ( ˆ _τ_ ) = 0 for all _s_ , resulting in _ηi_ ( ˆ _τ_ ) = 0. Regions that are more open or have 19
<!-- Start of picture text --> Participation change vs exposure<br>14<br>IRL<br>12<br>10<br>8 CYP<br>SVN<br>LTU BGR<br>6<br>4<br>DEU<br>ESP<br>FRA<br>RUS<br>AUS<br>2 BRA<br>CHNUSAJPN<br>0<br>-0.5 0 0.5 1 1.5 2<br> AAE exposure to shock<br> Participation change, in %<br><!-- End of picture text -->

**Figure 6:** Scatter plot of the percentage change in home production participation between 2019 and 2022 against the AAE exposure measure across countries. For country abbreviation codes see appendix B.1, for the definition of AAE exposure see the text.

higher labor shares in more open sectors are more exposed to a change in trade costs.

Figure 6 gives a scatter plot between the AAE exposure measure on the _x_ -axis and the change in participation in home production between 2019 and 2022 on the _y_ -axis. The plot reveals a tight relationship between the exposure measure and the change in participation. Although the AAE exposure measure is a first-order approximation and it does not allow for differences in labor shares or input-output coefficients, it provides a reasonably good fit. A regression of participation change on the AAE exposure plus a constant has an R- squared of 72%, and the exposure measure has a p-value substantially below 1%.

The reduction in welfare due to the increase in trade costs is almost perfectly correlated 20
<!-- Start of picture text --> Manufacturing employment change between 2019 and 2022<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br> Country<br>HUNSVKIRLBELSVNESTCZETWNAUTBGRNLDFINLTUSWEPOLDNKMEXPRTDEUKORCANITAFRAINDIDNCHNTURGRCROUJPNESPRoWBRAAUSCYPUSARUSGBR<br> Change between 2019 and 2022, in %<br><!-- End of picture text -->

**Figure 7:** Percentage change in manufacturing employment between 2019 and 2022 B.1. across countries, in percent. For country abbreviation codes see appendix

with the fall in labor force participation between 2019 and 2022, because it is driven exactly the same factors. Appendix figure C.4 displays the welfare change across countries.<sup>12</sup> The welfare loss for the United States is approximately 14 basis points. The minimum welfare loss of 13 basis points occurs in China, while the maximum one of 97 basis points occurs in Ireland. The unweighted average welfare loss across the 38 countries in the sample (including the “rest of the world” region) is 37 basis points, while the weighted one is 19 basis points. The fact that the unweighted welfare loss is almost twice as high as the weighted one provides another illustration that countries with smaller populations do much worse

> 12The welfare change is measured as the equivalent variation in consumption required by agents in the model in the base year to be indifferent between the counterfactual economy where the trade costs increase and the baseline economy where they do not. The formula is given in RUV, it is a present value sum where we use an annual discount factor of _β_ = 0.95.

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in response to the shock than countries with larger populations.

Turning to results in specific sectors, figure 7 illustrates the change in manufacturing employment across countries between 2019 and 2022.<sup>13</sup> In addition to the aforementioned factors that influence the overall change in labor participation, the change in manufacturalso on the initial in Countries that are ing employment depends deficit manufacturing. net manufacturing importers, such as the United States, Great Britain, the Russian Federation, or Cyprus, need to substitute foreign imports with domestic production, driving up the change in manufacturing employment.

# **6 Alternative Assumptions**

This section explores how our results change if we make different assumptions regarding the persistence or size of the shock or the nominal growth rate of world GDP in dollars. We also touch briefly on the consequences of allowing for migration between U.S. states or changing the elasticity of moving across sectors.

To begin, we focus on persistence. Different amounts of persistence are captured by having the shock revert after 2, 4, 5, or 6 years instead of 3 years (which is the baseline assumption). Figure 8 illustrates the cumulative change in participation in the broad sectors of home production, manufacturing, and services (agriculture is excluded for simplicity and because it is a small sector) for different values of persistence.

A higher persistence leads to a greater increase in home production and a more substantial decrease in services. Manufacturing, however, is much less sensitive to changes in persistence. This is due to the fact that, out of the three factors affecting sectoral reallocation (fall in aggregate demand, lower productivity, and expenditure switching), a longer shock exacerbates the fall in aggregate demand without increasing the expenditure switching effect. The amount of unemployment generated across different amounts of persistence is essentially the same (i.e., around 0.32%).

We can also explore the impact of varying the size of the shock. Figure 9 depicts the

> 13Similarly, appendix figures C.5 and C.6 show changes in service and agricultural employment across countries between 2019 and 2022.

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<!-- Start of picture text --> Change in home production, manufacturing, and services across persistences<br>2 years, Home<br>4 2 years, Manu<br>2 years, Serv<br>3 years, Home<br>3 years, Manu<br>3 years, Serv<br>3<br>4 years, Home<br>4 years, Manu<br>4 years, Serv<br>5 years, Home<br>2<br>5 years, Manu<br>5 years, Serv<br>6 years, Home<br>6 years, Manu<br>1 6 years, Serv<br>0<br>-1<br>-2<br>2020 2025 2030 2035<br> Year<br> Percentage change in participation<br><!-- End of picture text -->

**Figure 8:** Paths of aggregate U.S. participation for home production, manufacturing, and services across different values for the persistence of the shock. Blue depicts a 2- year persistence, green 3, yellow 4, orange 5, and red 6. A solid line depicts home production, a dashed line depicts manufacturing, and a line with circle markers depicts services.

cumulative change in participation in home production, manufacturing, and services for four different values of the shock: 6%, 12%, 18%, and 24%. A larger shock tends to amplify the effects discussed earlier and generates more unemployment for a given value of _γ_ (the growth rate of world nominal GDP in dollars). In turn, more unemployment discourages participation in the manufacturing sector.

Next, we consider the effects of assuming different values for the annual growth rate of world nominal GDP in dollars ( _γ_ ). Figure 10 depicts the cumulative change in participation in the broad sectors of home production, manufacturing, and services for different values of _γ_ .

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<!-- Start of picture text --> Change in home production, manufacturing, and services for different shock sizes<br>6% shock, Home<br>6 6% shock, Manu<br>6% shock, Serv<br>12% shock, Home<br>5<br>12% shock, Manu<br>12% shock, Serv<br>4 18% shock, Home<br>18% shock, Manu<br>18% shock, Serv<br>3 24% shock, Home<br>24% shock, Manu<br>24% shock, Serv<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>2020 2025 2030 2035<br> Year<br> Percentage change in participation<br><!-- End of picture text -->

**Figure 9:** Paths of aggregate U.S. participation for home production, manufacturing, and services across different sizes of the shock. Blue depicts a 6% shock, green 12%, orange 18%, and red 24%. A solid line depicts home production, a dashed line depicts manufacturing, and a line with circle markers depicts services.

A higher _γ_ makes the DNWR less likely to bind and decreases the unemployment generated by the shock. In addition, it leads to a smaller participation increase in home production, a larger increase in manufacturing, and a smaller fall in services. The manufacturing sector is particularly sensitive to the value of _γ_ (as it is to the size of the shock) because it is the sector where most of the unemployment occurs when the shock dissipates. If agents realize that a lot of unemployment will be generated, they will hesitate to go into manufacturing in the first place.

The amount of unemployment generated across values of _γ_ differs substantially. While for _γ_ = 4% unemployment reaches 0.32% at its peak, this peak can reach 1.3% if _γ_ = 3%, 24
<!-- Start of picture text --> Change in home production, manufacturing, and services across values of<br>4 = 6%, Home<br> = 6%, Manu<br> = 6%, Serv<br> = 5%, Home<br> = 5%, Manu<br>3<br> = 5%, Serv<br> = 4%, Home<br> = 4%, Manu<br> = 4%, Serv<br>2<br> = 3%, Home<br> = 3%, Manu<br> = 3%, Serv<br> = 2%, Home<br>1<br> = 2%, Manu<br> = 2%, Serv<br>0<br>-1<br>2020 2025 2030 2035<br> Year<br> Percentage change in participation<br><!-- End of picture text -->

**Figure 10:** Paths of participation for home production, manufacturing, and services across different values for the growth of world nominal GDP in dollars ( _γ_ ). Blue depicts 6% growth, green 5%, yellow 4%, orange 3%, and red 2%. A solid line depicts home production, a dashed one depicts manufacturing, and a line with circle markers depicts services.

and almost 3% if _γ_ = 2%. In this sense, the model is highly non-linear due to the one-sided nature of the DNWR. More monetary policy accommodation (i.e., a higher _γ_ ) when the shock disappears can help alleviate the unemployment consequences of the shock.

Allowing for migration within U.S. states has a negligible impact on the results discussed so far. Note that when migration across U.S. states is allowed, there is a new elasticity 1/ _κ_ of moving across states that comes into play. RUV calibrate _κ_ to 12 in order to match the evidence found by Autor et al. (2013) on how population across states responds to the China shock. Given that the population response is small, the elasticity of moving across states is calibrated to be low. Additionally, since U.S. states are similarly exposed to the shock we study, the need for reallocation is not large.

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Finally, changes in the inverse elasticity of moving across sectors ( _ν_ ) do not have a major impact on the results. Naturally, the higher the _ν_ , the lower the elasticity with which agents move across sectors, and hence the smaller the reallocation that occurs due to the trade-cost shock. However, for reasonable values of _ν_ between 0.35 and 0.75 (recall that the baseline value is 0.55), the results discussed above do not change substantially.

# **7 Conclusion**

In this paper, we use a dynamic quantitative trade model with an input-output structure and downward nominal wage rigidity to study the effects of a 12% increase in the costs of sending products across countries that reverts after three years. This exercise is meant to capture some of the recent global supply chain disruptions caused by the COVID-19 pandemic.

Our analysis reveals three main results for the United States. First, there is a temporary but persistent decline in labor force participation: while the high trade cost is active, participating in the home production sector (which has a constant real utility flow) temporarily becomes more attractive. Second, there is a temporary increase in manufacturing employment, a highly tradable sector for which the United States is a net importer. By contrast, there are temporary reductions in service and agricultural employment. Third, unemployment is generated when the shock _disappears_ because this is the moment when the downward nominal wage rigidity binds. The bulk of the unemployment is generated in manufacturing, where wages increase during the period of high trade costs and must decrease when the shock dissipates. In general, labor force participation tends to fall more in states with a larger service sector (such as Alaska, Nevada, and Hawaii) and less in states with larger manufacturing sectors (such as Ohio, Pennsylvania, and Wisconsin).

Our country-level analysis finds that large or closed countries experience smaller declines in labor participation following the shock, while small or open economies experience larger ones. Although this result may seem intuitive, our model provides quantitative estimates of the changes in participation in home production derived from a state-of-the-art trade model, as well as changes in employment in specific sectors such as manufacturing, 26
# services, and agriculture.

The framework we offers a valuable tool for the propose policymakers estimating potential impacts of changes in trade costs across countries or sectors lasting different amounts of time. Given the increased risk of chain our model can supply disruptions, be useful in devising effective policy responses. However, it is important to note that our model does not feature explicit costs of inflation, or other pandemic-induced issues such as pent-up demand, fiscal support, or an unanchoring of inflation expectations. Therefore, our model is not ideal to study optimal monetary policy.

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