metadata: authors: 0: author: Nguyen,Ha Minh
1: author: Rezaei,Shawheen
2: author: Agarwal,Divya
count: World,United States
docna: 0: docna: The great recession and job loss spillovers
            : impact of tradable employment shocks on supporting services
docty: Policy Research Working Paper
subtopic: Transport Services,Labor Markets,Employment and Unemployment,Transport and Trade Logistics,Trade Facilitation,Common Carriers Industry,Food & Beverage Industry,Construction Industry,Textiles, Apparel & Leather Industry,Plastics & Rubber Industry,Business Cycles and Stabilization Policies,Pulp & Paper Industry,General Manufacturing
teratopic: Social Protections and Labor,Transport,International Economics and Trade,Macroeconomics and Economic Growth,Industry
abstracts: cdata!: This paper explores the spillover
            effects of job losses via input-output linkages during the
            Great Recession. Exploiting exogenous variation in tradable
            employment shocks across U.S. counties, the paper finds that
            job losses in a county's tradable sectors cause further
            job losses in the county's supporting services. For a
            given county, a 10 percent exogenous decline in tradable
            employment reduces supporting industries' employment by
            3.8 percent. In addition, a county’s regional supporting
            services are relatively less affected by its tradable job
            losses than its local supporting services are, which
            reinforces the argument that the spillovers are due to
            input-output linkages.
geo_reg_key: 119228,517191
repnme: repnme: The great recession and job loss spillovers
            : impact of tradable employment shocks on supporting services
display_title: The great recession and job loss
            spillovers : impact of tradable employment shocks on
            supporting services
listing_relative_url: /research/2016/01/25818518/great-recession-job-loss-spillovers-impact-tradable-employment-shocks-supporting-services
topicv3: Manufacturing Sectors m1326521 1281,Transport m1326661 135,Warehousing and Storage m1326665 1174,Jobs m1503931 2670,Unemployment m1504051 2747
publishtoextweb_dt: 2016-01-22T00:00:00Z
historic_topic: Social Protections and Labor,Transport,International Economics and Trade,Macroeconomics and Economic Growth,Industry
no_of_pages: 36
author_keys: 0: author_key: 1057980
model_extractions: Policy Research Working Paper 7543 The Great Recession and Job Loss Spillovers Impact of Tradable Employment Shocks on Supporting Services _Ha Nguyen Shawheen Rezaei Divya Agarwal_ Development Research Group Macroeconomics and Growth Team January 2016
Policy Research Working Paper 7543

# **Abstract**

This paper explores the spillover effects of job losses via input-output linkages during the Great Recession. Exploiting exogenous variation in tradable employment shocks across U.S. counties, the paper finds that job losses in a county’s tradable sectors cause further job losses in the county’s supporting services. For a given county, a 10 percent exogenous decline in tradable employment reduces supporting industries’ employment by 3.8 percent. In addition, a county’s regional supporting services are relatively less affected by its tradable job losses than its local supporting services are, which reinforces the argument that the spillovers are due to input-output linkages.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at hanguyen@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
**<mark>THE GREAT RECESSION AND JOB LOSS SPILLOVERS:</mark>**

# **IMPACT OF TRADABLE EMPLOYMENT SHOCKS ON**

# **SUPPORTING SERVICES**

_Updated January 2021_ Ha Nguyen<sup>**1**</sup> Development Research Group The World Bank, Shawheen Rezaei Harvard University, and Divya Agarwal Columbia University Keywords: Unemployment, Job losses, Spillovers, Great Recession JEL code: E32, J46

> 1 We thank Luis Serven and Aart Kraay for very helpful comments and feedback. The views expressed in this paper are those of the authors only and do not necessarily reflect those of the World Bank or the countries it represents. Contact address: Ha Nguyen, Development Research Group, The World Bank, 1818 H St Washington D.C. 20433. Email hanguyen@worldbank.org

1
# **1 Introduction**

It is important to understand if, how, and to what extent, unemployment spreads across sectors and geographic areas during a recession. Such propagation, if proven true, could provide a basis for government intervention in times of economic recession to prevent the ripple effects of adverse employment shocks from cascading through the economy.

The Great Recession provides a unique context for studying this issue as it was the most devastating economic event in the world economy since the 1930s. During this time, unemployment in the U.S. reached even as high as 10%. Employment in many industries, such as automobile, oil, and gas, dropped as much as 30 to 40% between 2007 and 2010 (Nguyen, 2015). Such economic fluctuations have left economists puzzled about many aspects of unemployment: Why was it so high? Did job losses propagate from one sector to another, and if so, how?

This paper examines how job losses propagated across sectors via input-output linkages during the Great Recession. Although many economists would agree that job losses could propagate, few have successfully captured the magnitude of this phenomenon given the difficulty of pinpointing an exogenous source of employment declines. Since employment across various industries is interrelated, overcoming endogeneity bias is a formidable obstacle to accurately measuring this effect.

The identification strategy we utilize in this paper is the following: we exploit exogenous variation in _the change in tradable employment_ in a U.S. county and examine the subsequent spillover effect on its _local supporting service industries_ , i.e. those that provide support to 2
tradable production, such as warehousing, transportation, human resource management, marketing and IT support.<sup>2</sup> These exogenous job losses are _a county’s tradable job losses that are only driven by declines in national tradable employment_ , and not by county-specific issues. The idea is based on Bartik (1991). For brevity, we refer to this exogenous source of job losses as Bartik job losses. We find that counties with greater exogenous losses in tradable employment experienced more severe job losses in supporting service industries. Quantitatively, Bartik job losses of 10% led to a 3.76% decline in a county’s supporting services employment during the Great Recession.

Furthermore, we split the supporting services, based on two Herfindahl indices, into industries dealing with more local support and those dealing with more regional support. Supporting industries that have smaller Herfindahl indices are considered local, as their diffused market concentration suggests that they serve a more localized area. The job loss spillover is much more significant for the more local supporting sectors. Bartik job losses of 10% cause a 4.38% decline in local supporting services employment. As we will discuss in detail later, this may be due to more local industries’ greater dependence on the success of the given county’s tradable sector. In turn, we find that more regional supporting industries are less affected by the county’s tradable job losses given that these industries’ have greater geographic coverage.

The concept of job loss spillover via production linkages has not been a subject of many empirical studies. One reason for this may be the difficulty in distinguishing job loss propagation pathways in data, which does not readily point out causality. In other words, given that job losses in any industry may affect employment in other industries, it becomes exceedingly difficult to determine the direction in which a spillover travels. For example, unemployment in the

> 2 The detailed list of supporting service sectors is in section III.

3
automobile industry could reduce the need for freight, causing workers in the freight industry to lose their jobs. Laid-off workers in the freight industry, however, may be less inclined to purchase new cars, therefore causing demand-driven unemployment in the automobile industry. As demonstrated by the aforementioned scenarios, job loss spillovers oftentimes occur simultaneously and in multiple directions, making the determination of causality within a particular spillover channel quite a challenge.

We overcome this identification challenge by utilizing Bartik job losses to capture exogenous job losses. We argue that tradable job losses driven by aggregate shocks are exogenous to a county and are not driven by a county’s fundamentals. Mathematically, Bartik job losses are defined as ∑ T T <u>,2007</u> U , where is the employment of ~~�~~ ii � llcc ii ii TT ii∈T llTT llcc,2007 × ∆logll UU llcc,2007 tradable industry _i_ in county _c_ and is the employment of all tradable industries in county _c_ TT cc,2007 before the Great Recession. The national job losses of industry ll _i_ during the Recession are U . Since there are more than 3000 counties in the U.S, changes in national tradable ii UU ∆logllemployment are largely exogenous to a county; that is, such changes are little affected by any county’s changes in fundamentals, such as productivity shocks or an increase in minimum wages during the Recession.

Although supporting sectors only accounts for just a fraction of employment, this does not imply that job losses spillovers via input-output linkages are insignificant. We focus on the relationship between tradable and supporting sectors because the econometric identification is clearer. Spillovers via input-output linkages could operate via other channels, such as intracounty tradable-tradable linkages (e.g. car assembly plants and car parts producers within a county) or inter-county linkages. However, these channels are more difficult to identify and establish a relationship from.

4
The paper is organized in the following way: Section 2 gives a literature review; Section 3 presents the data; Section 4 describes our identification strategy in greater details; Section 5 examines the main results; Section 6 presents two extensions; and Section 7 concludes.

# **2 Literature Review**

Our paper is related to current literature on the severe decline in demand during the Great Recession. Most prominently, Mian, Sufi, and other co-authors have examined the effects of the decline in housing net-worth on consumption and employment during the crisis. Mian, Rao, and Sufi (2013) find that ZIP codes with higher pre-crisis household leverage (and steeper subsequent declines in housing net-worth in the Great Recession) cut their consumption more sharply during this period. Similarly, Mian and Sufi (2014) find that housing net worth declines were driving consumption cuts and job losses during the Recession. Nguyen (2015) examines job loss spillovers driven by the demand channel. In particular, he argues that job losses in the tradable sector caused further job losses in local retail and restaurant industries, via the aggregate demand channel.

There are surprisingly few studies on cross-sector spillovers based on production linkages. Bodrin et al. (2013), using U.S. national data, examine job loss propagation from the construction sector to other sectors. They argue that this channel has important effects on aggregate employment. Li and Martin (2015), also using national data at the sector level, find the financial sector to be the “epicenter” during the Great Recession, from which emanates job loss spillovers into other sectors. Unlike those two papers, we utilize _sectoral_ data at the _county_ level, and for that reason, we are less concerned about the endogeneity issue.

In a different context, using national employment data at the sectoral level, Acemoglu et al. (2016) study the transmission of trade shocks across U.S. manufacturing industries, via input5
output linkages. The trade shock they refer to is the rising import competition from China. They find that inter-industry linkages do in fact magnify the employment effects of trade shocks.

Our paper is related to literature that uses the Bartik instrument to isolate exogenous changes in labor demand. Blanchard and Katz (1992) construct exogenous employment shocks at the state level from 1970 to 1989, and estimate the effects of the shocks on unemployment and wages. Since their Bartik instrument includes all industries in all sectors, they do not estimate the spillover effects of job losses. Moretti (2010) uses three years of data (1980, 1990 and 2000) and a Bartik-typed instrument to estimate the _long-term_ employment multiplier across U.S. cities. Although we use a similar approach to Moretti (2010), our focus is different. While Moretti’s focus is on the long-run “geography of jobs” (i.e. how jobs are allocated across the country), we are interested in identifying the input-output linkage channel and estimating the short-run job loss spillover from tradable industries to supporting industries.

Our paper is also related to a growing amount of literature on labor market dynamics during the Great Recession. Ohanian (2011) argues that the decline in economic output and income during the Great Recession was due to severe distortions in the labor market, and not due to declines in capital or productivity. Hoffman and Lemieux (2016) show that the collapse in construction employment was a major factor contributing to the fall in overall employment. <mark>Kroft et al. (2016) show that the massive 2008 employment collapse, combined with the slow recovery in demand prevented employment from picking up quickly. Similarly, Foster et al. (2016) find that job destruction in the 2008–2009 period was accompanied by a very large drop in the job creation rate, which limited the scope for re-allocation of jobs and pushed many laidoff workers into long-term unemployment.</mark> 6
# **3 Identification Strategy**

Our identification strategy involves the generation of Bartik job losses which capture exogenous changes in employment. We use Bartik job losses to isolate the exogenous change in tradable employment in every U.S. county. In using this identification strategy, we find that counties with larger Bartik job losses experience more severe job losses in their supporting services sector.

# **3.1 A simple model**

Our empirical regressions can be better understood as the derivatives of simple production functions, which illustrate the relationship between inputs and outputs. These functions are the following: where is supporting sector employment, is tradable employment, is tradable output, ss,cc TT,cc cc the subscript LL _c_ identifies a given county, and LL and are parameters. The underlying θθ assumption with these equations is that employment in the supporting service sector is θθ, ωω, αα ββ determined by productivity ( and tradable output ( ), which in turn is produced by tradable cc employment. θθ) θθ We can manipulate equations (1) and (2) to derive our desired regression. We first combine the two equations by substituting from equation (1) into equation (2) to get: cc θθ (3) ααββ ss,cc TT,cc LL = θθωω<sup>αα</sup> LL Next, if we take the log of both sides, equation (3) becomes: ) ss,cc TT,cc log (LL ) = log(θθωω<sup>αα</sup> ) + ααββlog(LL We can then use equation (4) to derive the change in employment from 2007 to 2010: 7
<!-- Start of picture text --> Alog(L )+ controls<br><!-- End of picture text --> <!-- Start of picture text --> )<br><!-- End of picture text -->
<!-- Start of picture text --> —-- )<br>(— )<br>m E )<br>“ HH H e<br>(— )<br><!-- End of picture text -->
The second term, ∑ <u>,2007</u> U , can be interpreted as tradable job ~~�~~ ii )� llcc ii ii TT ii∈TT llcc,2007 × (∆logllcc −∆logll UU losses driven by county-specific issues. It is the difference between actual tradable job losses and those that are driven by aggregate shocks.

Using Bartik job losses as an exogenous source of variation in tradable job losses, we can rewrite the regression as follows: Note that all of the regressions are weighted by the number of households in each county (following Mian and Sufi, 2014). In addition, all of the standard errors in this paper are robust and clustered at the state level.

10
# **4 Data Analysis**

The primary source of our data is the Census Bureau. We use employment data in March 2007 and March 2010 from the County Business Pattern (CBP) dataset, given that these dates represent the lowest and highest points of the U.S. unemployment rate during the Great Recession. This data comes with flags representing employment ranges, which we replace with average employment values. We use the NAICS codes obtained from the CBP dataset at the sixdigit level for supporting industries and at the four-digit level for tradable employment as provided by data from Mian and Sufi (2014). In later parts of the paper, we also use employment data by firm size from the CBP dataset in order to examine potential problems related to credit.

What classifies an industry as tradable or supporting? For the purposes of this paper, we follow Mian and Sufi (2014)’s classification of the tradable sector based on global trade data: a 4-digit NAICS industry is defined as _tradable_ if it has imports plus exports equal to at least $10,000 per worker, or if total exports plus imports exceed $500M. Regarding the supporting industries, we base our categorization of a NAICS code on whether the industry aids the tradable sector, be it through local or regional support. In order to be identified as a supporting industry for our purposes, the given industry should support tradable industries, but it cannot also deal with the residential population. In determining the suitability of an industry, we examine each NAICS code provided on the NAICS Association’s website (www.naics.com). Table 3.2 presents a full listing of supporting industries. A more detailed description of this methodology is provided in the Appendix.

A factor prevents us from using the input-output table. We use six-digit NAICS codes to classify our industries, whereas the input-output table provided by the Bureau of Economic Analysis (BEA) mainly uses NAICS codes up to two or three digits. For example, the BEA 11
input-output table includes the code 562 (Waste management and remediation services). Note that this could mean residential or commercial waste management. Our focus only includes 562112 (Hazardous Waste Collection) and 526221 (Hazardous Waste Treatment and Disposal) from the broader 562 category, because we are more confident that Hazardous Waste Collection serves the tradable sectors rather than local residents.

We also use Mian and Sufi (2014)’s data for several of our regressions. Most notably, we use several pre-crisis county characteristics in 2007 as controls: percentage white, the log of median household income, the log of median home value, percentage of homes occupied by owners, percentage of population with less than high school diplomas, percentage with only high school diplomas, percentage with college degrees, unemployment rate, poverty rate, and percentage urban. We have also included pre-crisis household leverage in 2006 as it serves as a strong proxy for household cuts in consumption (see Mian and Sufi, 2010). Aside from this, we also use their data for the robustness checks we conduct later in the paper, in which we test the influence of credit constraints on the change in supporting sector employment.

Summary statistics for the variables used in our regressions are provided in Table 4.1. Supporting sector employment makes up 3.5% of the total employment in 2007, while tradable employment makes up around 14.5%. On average, a given county lost a staggering 16.1% of tradable employment between 2007 and 2010. In fact, tradable job losses driven by aggregate shocks (i.e. Bartik) for a county between 2007 and 2010 are 18.3% on average.

Most of the variables have full coverage. The only exception is pre-crisis leverage. This variable represent how indebted households in a county were before the crisis. Mian and Sufi (2010) show that pre-crisis household leverage is strongly connected to the decline in 12
consumption during the Great Recession. This is an important control variable, and so we have also included it in some of the regressions.

|**Statistics**|**N**|**mean**|**sd**|**p10**|**p90**|
|---|---|---|---|---|---|
|Tradable employment/Population, 2007|3108|0.050|0.047|0.008|0.103|
|Supporting sector employment/Population, 2007|3093|0.012|0.017|0.004|0.023|
|Tradable employment/Employment, 2007|3102|0.145|0.107|0.030|0.288|
|Supporting sector employment/Employment, 2007|3146|0.035|0.037|0.012|0.064|
|Log change in tradable employment, 2007-2010|3013|-0.161|0.421|-0.580|0.186|
|Log change in supporting sector employment, 2007-2010|3131|-0.040|0.506|-0.600|0.505|
|Bartik|3128|-0.183|0.083|-0.282|-0.073|
|% White, 2007|3135|86.997|15.017|65.834|98.827|
|Median household income, 2007|3135|35597.430|9147.112|26312.470|46608.320|
|Median home value, 2007|3135|82862.160|45629.080|45377.850|127120.500|
|% Owner occupied, 2007|3135|74.063|7.541|64.320|81.818|
|% With less than high school diploma, 2007|3135|22.565|8.705|12.584|34.965|
|% With only high school diploma, 2007|3135|34.706|6.571|26.398|42.903|
|% With college education, 2007|3135|10.994|4.961|5.924|17.494|
|Unemployment rate, 2007|3135|5.820|2.730|2.998|9.071|
|Poverty rate, 2007|3135|14.154|6.454|7.261|22.605|
|% Urban, 2007|3135|39.318|30.881|0.000|84.608|
|Leverage, 2006|2219|1.573|0.584|0.971|2.366|

Table 4.1: Summary statistics 13
|**NAICS**|**Industry Name**|
|---|---|
|484110|General Freight Trucking, Local|
|484220|Specialized Freight (except Used Goods) Trucking, Local|
|488210|Support Activities for Rail Transportation|
|488510|Freight Transportation Arrangement|
|488991|Packing and Crating|
|493110|General Warehousing and Storage|
|493120|Refrigerated Warehousing and Storage|
|493130|Farm Product Warehousing and Storage|
|493190|Other Warehousing and Storage|
|541214|Payroll Services|
|541219|Other Accounting Services|
|541511|Custom Computer Programming Services|
|541512|Computer Systems Design Services|
|541513|Computer Facilities Management Services|
|541519|Other Computer Related Services|
|541611|Administrative Management and General Management Consulting Services|
|541612|Human Resources Consulting Services|
|541613|Marketing Consulting Services|
|541614|Process, Physical Distribution, and Logistics Consulting|
|561110|Office Administrative Services|
|561410|Document Preparation Services|
|561499|All Other Business Support Services|
|561910|Packaging and Labeling Services|
|562112|Hazardous Waste Collection|
|562211|Hazardous Waste Treatment and Disposal|
|811212|Computer and Office Machine Repair and Maintenance|
|811213|Communication Equipment Repair and Maintenance|
|811219|Other Electronic and Precision Equipment Repair and Maintenance|
|811310|Commercial and Industrial Machinery and Equipment (except Automotive and Electronic) Repair|

Table 4.2: Supporting industries 14
# **5 Main Results**

This section explores the relationship between job losses in a county’s tradable and supporting service sectors, showing that the two are positively correlated. This finding is robust to the pre-crisis controls.

Table 5.1 below presents the baseline results underlying the job loss spillover effect from the tradable sector to supporting industries. It includes four regressions: the first reveals the relationship between supporting sector employment and tradable sector employment, the second including the county level controls, the third includes leverage, and the fourth shows the relationship with both controls and leverage. The positive coefficient of our explanatory variable indicates that a greater decrease in tradable employment leads to a greater decline in employment in supporting industries. Column 1 reveals that across counties, a 10% decrease in tradable jobs driven by Bartik jobs, on average, caused a 3.37% decrease in supporting sector employment between 2007 and 2010. This relationship is significant at a 5% level. The relationship is stable when other county level demographic and income characteristics are included. It is also stable with pre-crisis household leverage. Household leverage has been identified by Mian and Sufi (2010) as an important explanation for the drop of non-tradable employment during the Great Recession. Given this, it is an important control variable to include. Column 3 includes just leverage and shows that its inclusion that does affect our results. Given the additions of control variables, the coefficient for Bartik jobs losses gradually increases from 0.337 (in column 1) to 0.376 (in column 4).

15
|VARIABLES|Log chan|ge in supportin|g employme|nt, 2007-2010|
|---|---|---|---|---|
||[1]|[2]|[3]|[4]|
|Bartik job change|0.337**|0.348**|0.367**|0.376**|
||[0.153]|[0.142]|[0.163]|[0.152]|
|% White, 2007||0.001||0.001|
|||[0.001]||[0.001]|
|Log of median household income, 2007||0.055||0.043|
|||[0.123]||[0.160]|
|Log of median homevalue, 2007||-0.058||-0.058*|
|||[0.035]||[0.057]|
|% Owner occupied, 2007||-0.005***||-0.005***|
|||[0.001]||[0.002]|
|% With less than high school diploma, 2007||0.000||0.000|
|||[0.002]||[0.002]|
|% With only high school diploma, 2007||-0.001||-0.001|
|||[0.002]||[0.002]|
|Unemployment rate, 2007||0.014**||0.015**|
|||[0.006]||[0.007]|
|Poverty rate, 2007||-0.006||-0.007|
|||[0.005]||[0.006]|
|% Urban, 2007||-0.001||-0.001|
|||[0.000]||[0.001]|
|Leverage, 2006|||-0.008|0.003|
||||[0.013]|[0.018]|
|Constant|0.030|0.400|0.051|0.534|
||[0.027]|[1.091]|[0.039]|[1.263]|
|Observations|3,035|3,035|2,218|2,218|
|R-squared|0.004|0.016|0.005|0.018|

Robust standard errors in brackets (*** p<0.01, ** p<0.05, * p<0.1).

All regressions in this paper are weighted by number of households. Standard errors are clustered at the state level.

# Table 5.1: Baseline results

16
Figure 5.1: Scatter plot of regression [2] in Table 5.1 Figure 5.1 shows the scatter plot between the residuals of the log change in supporting sector employment and the residuals of Bartik job losses (i.e. column 2 in table 5.1). It shows that the positive relationship is quite robust and not dependent on any outliers (see figure A.1 in the Appendix for the full sample). This figure depicts a scatter plot of the data when three outliers are removed. For the rest of the paper we use this smaller sample.

# **6. Extensions**

In this section we will provide further evidence that the transmission mechanism for job loss spillovers takes place via input-output linkages. In order to do so, we conduct two exercises. In the first exercise, we argue that credit-led spillovers were not likely the driving force of the spillover. In the second exercise, we split a county’s supporting sectors into those that serve a more local geographic area and those that serve a larger regional area. We show that those that serve locally are more affected by the county’s Bartik job losses—further supporting the input-output linkage channel.

17
# **6.1 Credit-led spillovers?**

Spillovers from the tradable sectors to supporting industries could occur by means of credit channels. For instance, when an underwater tradable firm is late in repaying debt, it affects bank lending to firms in supporting industries. If this were the case, supporting firms that are smaller in size should experience greater declines in employment when faced with an exogenous tradable employment shock, on the ground that that they are likely to have less access to credit.

If this is the case, it would weaken our argument for the spillovers based on input-output linkages.

|VARIABLES|1 to 4|5 to 9|10 to 19|20 to 49|50 to 99|
|---|---|---|---|---|---|
|||Aggreg|ate supportin|g services||
||[1]|[2]|[3]|[4]|[5]|
|Bartik|0.016|0.017|-0.082|0.278|0.826**|
||[0.101]|[0.171]|[0.162]|[0.312]|[0.410]|
|Controls|Yes|Yes|Yes|Yes|Yes|
|Constant|-0.479|1.907|-0.868|-1.992|-4.206|
||[0.758]|[1.390]|[1.187]|[1.742]|[3.286]|
|Observations|2,084|1,257|970|778|481|
|R-squared|0.025|0.011|0.025|0.023|0.040|

Table 6.1: Testing credit supply constraints We find, however, that this is not the case. To test this hypothesis, we use an approach similar to that introduced in Mian and Sufi (2014). We divide supporting firms based on firm size (1 to 4 workers, 5-9 workers, 10-19 workers, 20-49 workers, 50-99 workers). Subsequently, we regress the change in the number of firms (by firm size) against Bartik job losses over six regressions. If credit constraints were a problem, the coefficients of Bartik job losses would decrease as firm size increases, signifying that smaller supporting service firms (i.e., those with presumably less access to credit) would be hit harder by the tradable employment shock. Table 18
6.1 shows that this is not the case. Many of the coefficients are not significant. Further, when they are, they do not decrease in magnitude as assumed under the credit constraint hypothesis.

# **6.2 ‘Local’ versus ‘regional’ supporting sectors**

In this section, we will further divide the supporting industries into two separate subgroups. As can be seen in Table 3.2, the supporting sector includes a diverse array of industries, which may react differently to changes in tradable employment given that the nature of their workforces—the supply of workers available, the skillsets necessary, etc.—is inherently different. In addition, the scope of services might also be different across supporting sectors.

Some industries serve more locally, while some others serve broader geographical areas.

The standard index is commonly used in the literature. It measures how spread out a supporting industry is across counties. 1 is the standard Herfindahl index of supporting sector _i._ ii HH is the share of supporting sector _i_ in county _c_ , calculated as the ratio between employment of U llcc<sup>ii</sup> ii ll UU sector _i_ in county _c_ and total employment of sector _i_ . The standard index for industry _i,_ 1, ii would take the smallest value if industry _i_ is perfectly spread out across counties; this would HH indicate that industry _i_ serves locally. Conversely, if industry _i_ is all concentrated in a single county, 1 would take the value of 1, which is the largest possible value for 1. ii ii HH HH 19
In addition to the standard Herfindahl index, we also create a modified Herfindahl index by subtracting the share of tradable industries from the standard measure in order to measure market concentration while also considering the relative share of tradable industries. is the U llcc<sup>ii</sup> ii ll UU share of the supporting sector _i_ in county _c_ . is the share of tradable employment in county _c_ . U llcc<sup>TT</sup> TT ll UU If a supporting sector _i_ is located in perfect proportion to the counties’ shares of tradable industries, the value of the modified Herfindahl index would be zero. This would imply that supporting industry _i_ is located where tradable industries are—an indication that the supporting industry serves local tradable industries. In other words, those supporting industries that have a small modified Herfindahl index values are considered local. The standard and modified Herfindahl indices yield very similar results as the correlation between the two indices is .98. We present the results with the modified Herfindahl index here, and leave those with the standard index in the Appendix. The classification of each industry into local and regional supporting sectors is shown below in Table 6.2b. We classify the top 14 industries as local, and the bottom 15 as regional.

Summary statistics for a county’s local and regional industries are provided in Table 6.2a. While the local portion of supporting services comprises of a slightly larger portion of workforce, employing a bit over 2.3% of the total employment in 2007, the regional portion makes up slightly over 1.3%.

|**Statistics**|**N**|**mean**|**sd**|**p10**|**p90**|
|---|---|---|---|---|---|
|Local supporting sector employment/Population, 2007|3074|0.08|0.011|0.002|0.015|
|Local supporting sector employment/Employment, 2007|3114|0.023|0.024|0.008|0.045|
|Regional supporting sector employment/Population, 2007|2710|0.005|0.014|0.001|0.009|
|Regional supporting sector employment/Employment, 2007|2761|0.013|0.031|0.003|0.025|

Table 6.2a: Summary statistics for local and regional industries 20
|||**Modified**|**Local (l)**|
|---|---|---|---|
|**NAICS**|**Industry Name**|**Herfindahl**|**Regional (r)**|
|484220|Specialized Freight (except Used Goods) Trucking, Local|0.001201|l|
|484110|General Freight Trucking, Local|0.00134|l|
|541219|Other Accounting Services|0.001874|l|
|493110|General Warehousing and Storage|0.002287|l|
|561110|Office Administrative Services|0.003353|l|
||Other Electronic and Precision Equipment Repair and|||
|811219|Maintenance|0.003672|l|
||Commercial and Industrial Machinery and Equipment|||
|811310|(except Automotive and Electronic) Repair|0.004667|l|
|561410|Document Preparation Services|0.004911|l|
|493130|Farm Product Warehousing and Storage|0.005773|l|
|493190|Other Warehousing and Storage|0.005910|l|
|561499|All Other Business Support Services|0.006142|l|
|561910|Packaging and Labeling Services|0.006186|l|
|541614|Process, Physical Distribution, and Logistics Consulting|0.006207|l|
|493120|Refrigerated Warehousing and Storage|0.006388|l|
|541612|Human Resources Consulting Services|0.006618|r|
|541511|Custom Computer Programming Services|0.006821|r|
|811213|Communication Equipment Repair and Maintenance|0.007847|r|
|541613|Marketing Consulting Services|0.007908|r|
|488510|Freight Transportation Arrangement|0.008280|r|
|541512|Computer Systems Design Services|0.008515|r|
|562112|Hazardous Waste Collection|0.008640|r|
|488991|Packing and Crating|0.009334|r|
|488210|Support Activities for Rail Transportation|0.009852|r|
||Administrative Management and General Management|||
|541611|Consulting Services|0.010226|r|
|541519|Other Computer Related Services|0.010330|r|
|811212|Computer and Office Machine Repair and Maintenance|0.011539|r|
|541513|Computer Facilities Management Services|0.019112|r|
|562211|Hazardous Waste Treatment and Disposal|0.023096|r|
|541214|Payroll Services|0.037047|r|

Table 6.2b: Supporting industries classified as local or regional 21
Tables 6.2c and 6.2d depict the results of the two separate categories of regressions when the supporting sector is split into local and regional industries, respectively, based on the modified Herfindahl index. In each table, the first column depicts the relationship between a county’s Bartik job losses and the log change in the county’s supporting sector employment without controls. The second regression then adds controls, while the third adds leverage, and the fourth regression includes both controls and leverage. Adding control variables especially increases the magnitude of the local baseline regression, and notably impacts significance.

The positive coefficients of tradable employment in each regression indicate that greater Bartik job losses lead to a greater decrease in a county’s supporting employment. Across counties and holding control variables constant, a 10% decline in Bartik jobs causes a 4.38% decrease in a county’s local supporting jobs. The relationship is significant at a 5% level. Bartik job losses, however, do not seem to affect a county’s regional supporting jobs. In the regional regressions the coefficients of the explanatory variable, while positive, are not significant. In other words, exogenous tradable employment shocks in a county during the Great Recession did not possess a significant spillover effect for the county’s regional supporting industries.

The findings reinforce the argument for the input-output linkage channel. Since both local supporting industries and regional supporting industries are located in the same county, the different impacts of local tradable job losses on them indicate that transmission mechanisms relying on county-specific factors did not play an important role. If a local county-specific factor, such as a credit crunch, played a dominant role, both types of supporting industries should have reacted similarly. The fact that a county’s local supporting industries are more affected by its tradable industries than the county’s regional supporting industries suggests the spillovers occur via the input-output linkage channel.

22
|VARIABLES|Log change|in local supp<br>20|orting emplo<br>10|yment, 2007-|
|---|---|---|---|---|
||[1]|[2]|[3]|[4]|
|Bartik|0.366**|0.425***|0.408**|0.438**|
||[0.139]|[0.152]|[0.153]|[0.165]|
|% White, 2007||0.001||0.001|
|||[0.001]||[0.001]|
|Log of median household income, 2007||0.032||0.038|
|||[0.152]||[0.202]|
|Log of median homevalue, 2007||-0.153*||-0.160|
|||[0.080]||[0.117]|
|% Owner occupied, 2007||-0.004***||-0.004*|
|||[0.001]||[0.002]|
|% With less than high school diploma, 2007||0.001||0.000|
|||[0.003]||[0.003]|
|% With only high school diploma, 2007||-0.002||-0.002|
|||[0.004]||[0.004]|
|Unemployment rate, 2007||0.017*||0.020**|
|||[0.008]||[0.009]|
|Poverty rate, 2007||-0.013***||-0.014**|
|||[0.005]||[0.006]|
|% Urban, 2007||-0.001||-0.001|
|||[0.001]||[0.001]|
|Leverage, 2006|||-0.031**|0.005|
||||[0.014]|[0.025]|
|Constant|-0.001|1.829|0.064|1.866|
||[0.034]|[1.144]|[0.047]|[1.241]|
|Observations|3,014|3,014|2,219|2,219|
|R-squared|0.003|0.017|0.006|0.019|
|Robust standard errors in brackets (*** p<0.0|1, ** p<0.05,|* p<0.1).|||

Table 6.1c: Impacts on local supporting sector employment 23
|VARIABLES|Log chang<br>|e in regiona<br>200|l supporting<br>7-2010|employment|
|---|---|---|---|---|
||[1]|[2]|[3]|[4]|
|Bartik|0.182|0.181|0.186|0.189|
||[0.250]|[0.238]|[0.260]|[0.245]|
|% White, 2007||0.000||0.001|
|||[0.002]||[0.002]|
|Log of median household income, 2007||-0.024||0.094|
|||[0.188]||[0.227]|
|Log of median homevalue, 2007||0.015||-0.026|
|||[0.051]||[0.078]|
|% Owner occupied, 2007||-0.003||-0.004|
|||[0.002]||[0.002]|
|% With less than high school diploma, 2007||-0.002||-0.003|
|||[0.003]||[0.003]|
|% With only high school diploma, 2007||0.002||0.003|
|||[0.003]||[0.003]|
|Unemployment rate, 2007||0.005||0.004|
|||[0.008]||[0.009]|
|Poverty rate, 2007||0.002||0.003|
|||[0.007]||[0.008]|
|% Urban, 2007||-0.001||-0.001|
|||[0.001]||[0.001]|
|Leverage, 2006|||0.003|0.020|
||||[0.017]|[0.031]|
|Constant|0.036|-0.268|0.031|-0.526|
||[0.043]|[1.878]|[0.050]|[2.018]|
|Observations|2,582|2,582|2,134|2,134|
|R-squared|0.000|0.004|0.001|0.005|

Robust standard errors in brackets (*** p<0.01, ** p<0.05, * p<0.1).

Table 6.1d: Impacts on regional supporting sector employment 24
# **7 Conclusion**

The Great Recession was a painful period in world economic history. Behind the dry numbers are actual people and communities that suffer from job losses and the resulting hardship. It is important to understand the impacts of the Great Recession, in particular, how shocks transmit across economic sectors.

This paper is an effort to better understand the transmission of job losses during Great Recession. We focus only on the transmission via input linkages. We find that counties with higher tradable job losses driven by aggregate shocks experience greater declines in supporting service employment. The results reveal that across counties, a 10% decrease in Bartik jobs, on average, caused a 3.76% decrease in supporting sector employment between 2007 and 2010. These results are statistically significant and are robust across numerous control variables.

Additionally, disaggregating the supporting sectors based on the modified Herfindahl index into two separate subsectors—local and regional—reveals that the impact of the exogenous shock was much greater and more significant for the supporting services that serve local markets compared to those that serve larger geographic areas. Holding the controls constant, across counties, a 10% decline in Bartik jobs causes a 4.38% decrease in a county’s local supporting jobs. We argue that this is due to local industries’ high dependence on local tradable employment. Furthermore, we find that these results are not influenced by household leverage or credit supply constraints. The results hence are argued to reflect the impact of cross-sectoral job loss spillovers during the Great Recession.

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