U.S. Department of Labor 

Bureau of International Labor Affairs 

Office of Trade and Labor Affairs 

Contract Research Program

Download this and other papers at 

http://www.dol.gov/ilab/media/reports/otla/

The views expressed here are those of the author(s) and do not 

necessarily represent the views or official positions of the U.S. 

Government or the U.S. Department of Labor. 
Analyzing the Impact of Trade in Services on the U.S. Labor Market: 

The Response of Service Sector Employment to Exchange Rate Changes1

J. Bradford Jensen 

McDonough School of Business 

Georgetown University 

Revised version submitted 

September 24, 2010 

 

1 This report was produced for the Department of Labor, Bureau of International Labor Affairs (ILAB). I 

thank Zhi George Yu for research assistance. This research was conducted at the Center for Economic 

Studies at the U.S. Census Bureau. Any opinions and conclusions expressed herein are those of the author 

and do not necessarily represent the views of the U.S. Census Bureau, the U.S. Department of Labor, or the 

U.S. Government. All results have been reviewed to ensure that no confidential information is disclosed. 
 2

1. Motivation 

 The service sector is large and growing. Table 1 shows U.S. employment in 2007. 

The service sector accounts for about 50 percent of employment. Business services 

(NAICS sectors 51, 52, 53, 54, 56) account for about 25 percent of employment and 

personal services (NAICS 61, 62, 71, 72, 81) account for another 25 percent of 

employment. Business services employment increased almost 30 percent between 1997 

and 2007; personal services employment increased over 20 percent over the same period. 

In contrast, employment in the manufacturing sector decreased by over 20 percent and 

manufacturing accounted for only 10 percent of U.S. employment in 2007. 

 U.S. service imports and exports both more than doubled over the past 10 years. 

Over the period 1992 to 2007, service exports have increased from $177 billion to $497 

billion to account for 30 percent of all U.S. exports; service imports have increased from 

$120 billion to $378 billion to account for 17 percent of all U.S. imports. 

 The size of the service sector and growing trade in services present the possibility 

that international trade in services could affect labor market outcomes in the U.S. Yet, 

very little is known about the impact of trade in services on employment or wages.2

 The 

objective of this report is to start to address this shortage of empirical evidence and 

investigate one possible channel – exchange rates – through which international trade in 

services might influence labor market outcomes. 

We are accustomed to thinking of the impact of exchange rates on manufacturing 

activity. When the dollar appreciates, U.S. produced goods tend to become more 

expensive (to the extent that the exchange rate change is passed on to foreign customers) 

and demand from abroad for U.S.-produced goods decreases. The decrease in demand for 

U.S. goods tends to result in a decrease in employment in the manufacturing sector. 

Previous research (reviewed later in the report) has documented the impact of exchange 

 

2

 The dearth of empirical research extends beyond examining the relationship between exchange rates and 

employment growth in the service sector. The focus of most of the empirical work on job creation and 

destruction has been the manufacturing sector (for example see the seminal work by Davis, Haltiwanger, 

and Schuh (1998)), though more recent research on gross employment flows has started to include the 

service sector. See, for example, Davis, Faberman, and Haltiwanger (2006). 
 3

rate changes on export shipments and employment in the manufacturing sector and 

generally finds these relationships hold. 

If trade in services is important and if services trade is responsive to exchange rate 

changes, we would expect that changes in the exchange rate should impact economic 

activity, and thus labor demand, in the service sector. The intuition for this is analogous 

to the impact a change in the exchange rate has on the demand for manufactured goods. If 

the dollar appreciates, U.S. exports become more expensive (if there is significant passthrough of the exchange rate change) and demand for U.S. exports decreases. (Similarly, 

if the dollar appreciates, imports become relatively less expensive, also causing a 

decrease in demand for U.S. products.) We can imagine that the mechanism would be 

similar for tradable services. If the dollar appreciates, U.S. produced software, movies, or 

trips to visit U.S. resort destinations (e.g. Disney World, Broadway, Rocky Mountain ski 

resorts) become more expensive relative to other countries products. Foreign (and 

domestic) purchasers of these products might switch out of purchasing U.S. services and 

purchase the services elsewhere. Intuitively, exchange rate appreciations (depreciations) 

serve as a negative (positive) demand shock. Bernard and Jensen (2004) find that the 

depreciation of the dollar in the late 1980s and early 1990s was an important factor in 

increasing exports and increasing exports to sales ratios at U.S. manufacturers. While 

Bernard and Jensen did not examine the impact of exchange rates on plant survival or 

employment growth, it seems reasonable to expect that a positive demand shock which 

increases exports would increase employment and improve plant survival rates. An open 

question is whether the service sector behaves similarly to the manufacturing sector. This 

paper examines whether exchange rate changes influence establishment employment and 

survival in the service sector. 

Over the past decade, the U.S. trade-weighted exchange rate experienced a 30 

percent increase over the period 1997-2002 and subsequently experienced a 30 percent 

decline over the period 2002-2007, see Figure 1. The exchange rate fluctuations seem 

large enough to induce a labor market response within the service sector if trade in 

services is having an impact on the labor market. 

The paper examines the employment and wage responses of service 

establishments and service sector establishment survival probabilities to changes in the 
 4

exchange rate using annual establishment level data from the U. S. Census Bureau’s 

Longitudinal Business Database (LBD) for the period 1997-2005.3 

The project examines the differential impacts of exchange rates on service 

industries that are tradable and those that are non-tradable. The paper uses an index of 

“tradability” to classify industries as tradable or non-tradable. The operating assumption 

is that exchange rate changes should only be a factor for tradable industries. The paper 

examines whether exchange rate changes are associated with service establishment 

survival, employment growth, or wage growth. In addition to difference across tradable 

and non-tradable industries, to explore whether industries that are more consistent with 

U.S. comparative advantage (high-wage, high-skill industries) respond differently than 

industries that do not seem aligned with U.S. comparative advantage, the paper 

investigates whether high-wage industries are more or less responsive than low-wage 

industries. Last, for select industries the paper examines whether exporters have a 

differential response than non-exporters. 

 The paper finds little evidence of exchange rates having an impact on service 

establishment survival, employment growth, or wage growth. To the extent that a 

correlation exists, the estimated coefficient is positive (opposite of the expected sign), but 

only marginally statistically significant and not robust. 

 In the next section, we review related literature. Section 3 describes the method 

used to identify tradable services. Section 4 provides information on the correlation 

between the tradability measure and measures of international trade in services. Section 5 

describes the empirical approach and section 6 describes the data used in the analysis. 

Section 7 presents the results and section 8 concludes. 

2. Related Literature 

 Previous research has examined the relationship between international trade, 

employment and wages, and firm performance in the manufacturing sector. 

 

3

 In addition, as part of this project we examined service sector establishment responses to exchange rate 

changes using two panels of data from the Census of Services from 1997-2002 and 2002-2007. These 

results were no more satisfactory than the results with the annual LBD data and, as a result, are not 

discussed here. 
 5

Exchange Rates, Employment, and Firm Performance in Manufacturing 

 

Revenga (1998) investigates the relationship between exchange rate changes and 

employment and wages in a subset of import competing manufacturing industries. She 

finds that for a subset of manufacturing industries facing significant import competition 

exchange rates have significant implications for employment and wages. 

Gourinchas (1998) examines the impact of exchange rate fluctuations in net and 

gross job flows in U.S. manufacturing. He finds that exchange rates do have a significant 

effect on net and gross employment flows in the traded goods sector. Gourinchas finds 

that job creation and job destruction rates are positively correlated following an exchange 

rate shock. 

Campa and Goldberg (2001) examine the employment and wage responses in the 

U.S. manufacturing sector to exchange rate changes. For the 1972-1995 period, they 

investigate the relationship between exchange rate changes and industry employment and 

wages in the manufacturing sector. They find that exchange rates have statistically 

significant effects on employment and wages, especially in low price-over-cost markup 

industries. 

Klein, Schuh, and Triest (2003) examine the net and gross employment effects of 

a real exchange rate change in the U.S. manufacturing sector. They find that increases in 

the real exchange rate decrease net employment growth. They find that the decrease in 

net employment growth is due primarily to an increase in job destruction in affected 

industries (as opposed to decreases in job creation). 

Cross-Country and International Studies 

 Burgess and Knetter (1998) examine the industry-level employment response in 

manufacturing industries in the G-8 countries to exchange rate changes. They find that 

employment changes are smaller in Germany, France, and Japan. They find the net 

employment response is more rapid in the U.S., Canada, and the U.K. 

 Galindo, Izquierdo, Montero (2006) examine the impact of exchange rate 

fluctuations on manufacturing employment in 9 Latin American countries. They find that 
 6

real exchange rate depreciations can impact net employment growth positively, but also 

find that the level of liabilities in dollars can overturn this effect. 

International Trade and Producer Dynamics in Manufacturing 

While there is very little empirical work covering establishment survival and 

growth in the service sector, there is a large literature on the role of plant characteristics 

in determining manufacturing plant survival. Dunne, Roberts, and Samuelson (1988, 

1989) identified the positive correlation between industry exit and entry rates and the 

relationship between plant characteristics (e.g. plant age, size, and multi-unit status) and 

survival. Dunne, Roberts, and Samuelson find that larger manufacturing plants and plants 

owned by multi-unit firms are more likely to survive. Dunne, Roberts, and Samuelson 

also emphasize the importance of sunk entry costs in determining death rates. 

Bernard and Jensen (2007) examine the role of firm structure on manufacturing 

plant closure and confirm the Dunne, Roberts, and Samuelson results that plants owned 

by multi-unit firms are more likely to survive. However, they find that plants owned by 

multi-unit firms have systematically different characteristics and that conditional on other 

plant characteristics associated with plant survival, plants owned by multi-unit firms are 

actually more likely to close. 

Bernard and Jensen (2004) examine the response of U.S. manufacturers to 

exchange rate changes and foreign income growth. They find that the weakening dollar 

and rising foreign incomes induced a significant number of manufacturing plants to start 

exporting. They also found that changes in export shipments were largely concentrated at 

existing exporters who had already paid the sunk cost to enter the export market. 

Bernard, Jensen, and Schott (2006a) use data on both tariff and transportation 

costs to examine the implications of falling trade costs on US manufacturers. They find 

when trade costs in an industry fall, plants are more likely to close. They find that low 

productivity plants are more likely to die. 

In separate but related work, Bernard, Jensen, and Schott (2006b) examine the 

role of import-competition from low-wage countries on the reallocation of U.S. 

manufacturing within and across industries. They develop a measure of import 

competition that focuses on where imports originate (rather than their overall level), 
 7

motivated in part by the significant increases in import shares from low-wage countries 

like China. They focus on import penetration from very low-wage countries. They show 

that low-wage country import shares and overall penetration vary substantially across 

both industries and time. Both components tend to be higher and to increase more rapidly 

among labor-intensive industries. Capital- and skill-intensive sectors experience little or 

no increase in the share of imports from low-wage countries. They find that plant survival 

and growth are negatively associated with industry exposure to low-wage country 

imports. 

 

 

3. Identifying Tradable Services 

 An important issue in examining the response of service sector establishments to 

exchange rate changes is whether service activities are actually “tradable” – i.e. can be 

provided at a distance – and, if so, which activities are tradable. Part of the difficulty in 

answering this question is there is a paucity of empirical work on the service sector in 

general and trade in services in particular. The lack of empirical work derives in part 

from the fact that the data infrastructure in services is less developed than that for goods. 

In this section we examine the geographic concentration of productive activity within the 

U.S. to identify which activities are traded within the U.S. and are thus at least potentially 

tradable internationally. 

 There is a long tradition among economists of using the geographic concentration 

of economic activity to identify a region's "export base" or "manufacturing base." The 

thinking was that if a region specialized in a manufacturing activity -- think Boeing and 

airplanes in Seattle -- it was likely to export the product in which it specializes. Seattle 

has a disproportionate share of U.S. aircraft manufacturing employment. This is not 

because people in Seattle consume more airplanes than other parts of the country; they 

export the planes in exchange for other goods and services. 

 This same type of logic applies to services. Economists have long thought of 

many services as "non-tradable" because services seem to require face-to-face interaction. 

The quintessential services are personal services like haircuts or divorce lawyers. These 
 8

service activities tend to be distributed in proportion to the population in a region (and 

thus we don't see big concentrations of these types of service activities in one place). But 

increasingly, there are services that don't seem to require face-to-face interaction and thus 

might be tradable. We can use this feature to distinguish between service activities that 

are tradable and those that require face-to-face interaction (and thus are far less likely to 

be traded). 

This intuition is described in Krugman (1991), where he notes "In the late 

twentieth century the great bulk of our labor force makes services rather than goods. 

Many of these services are nontradable and simply follow the geographical distribution of 

the goods-producing population -- fast-food outlets, day-care providers, divorce lawyers 

surely have locational Ginis pretty close to zero. Some services, however, especially in 

the financial sector, can be traded. Hartford is an insurance city; Chicago the center of 

futures trading; Los Angeles the entertainment capital; and so on. .... The most 

spectacular examples of localization in today's world are, in fact, services rather than 

manufacturing. .... Transportation of goods has not gotten much cheaper in the past 

eighty years... But the ability to transmit information has grown spectacularly, with 

telecommunications, computers, fiber optics, etc." (p. 65) 

 Let's go back to Seattle. Figure 2 shows the location quotient for several 

industries in Seattle.4

 Indeed, Seattle has a disproportionate share of U.S. aircraft 

manufacturing employment (about 9 times Seattle's share of employment). We are 

accustomed to thinking of Seattle exporting aircraft. But, Seattle also has a 

disproportionate share of U.S. employment in software publishing (about 18 times 

Seattle's share of the population). Again, this is not because consumers in Seattle demand 

more software than other parts of the country, Microsoft and other software publishers 

based in Seattle produce software and then export it in exchange for other goods and 

services. Software is a service that is traded with other regions. 

 Jensen and Kletzer (2006) generalize this approach to make up for the lack of 

detailed data on trade in services and identify which activities are vulnerable to services 

offshoring by looking at services that are geographically concentrated and traded 

 

4

 The location quotient is the share of an industry's employment in a region relative to its share of total 

employment. These calculations are made using the 2000 Decennial Public Use Micro Sample. 
 9

domestically. They use the geographic concentration of service employment across 

metropolitan areas within the U.S. to identify service activities that are tradable.5

 They find that a significant number of service industries (and occupations) exhibit 

levels of geographic concentration consistent with the activity being traded within the 

U.S. Figure 3 shows a graph of Gini coefficients (the measure used to identify geographic 

concentration) by industry.6

 While industries in the manufacturing sector tend to have higher levels of 

geographic concentration than the service sector, many service industries exhibit levels of 

geographic concentration consistent with them being traded within the U.S. In addition, 

the industries that do exhibit high levels of geographic concentration conform to our 

priors about what service activities might be tradable. For example, software publishing, 

sound recording, motion picture production, securities and commodities trading all 

exhibit high levels of geographic concentration. In addition, service industries identified 

as non-tradable also conform to our notions of industries that are likely to be non-tradable. 

For example, retail banking and video tape rental exhibit low levels of geographic 

concentration. 

Implementation 

Jensen and Kletzer (2006) implemented these measures using employment 

information from the 2000 Decennial Census of Population Public Use Micro Sample 

(PUMS) files.7

 In this project, we construct these measures for the initial year of the 

study using employment information from the U.S. Census Bureau's 1997 Business 

Register, the 1997 Census of Manufactures, and the 1997 Census of Services. We 

construct a Gini coefficient of employment in service industries using labor market areas 

defined by the Bureau of Economic Analysis as the unit of geography. The labor market 

 

5

 If a service is non-tradable and demand for the service is concentrated (the industries that use the nontraded service are geographically concentrated), the service industry will be geographically concentrated 

and the analysis would incorrectly infer that the service is tradable. Jensen and Kletzer adjust their measure 

of geographic concentration to correct for this possibility and construct region specific measures of demand 

for each industry using the input-output use tables produced by the Bureau of Economic Analysis. 

6

 This figure is taken from Jensen and Kletzer (2006). 

7

 Jensen and Kletzer used the Consolidated Metropolitan Statistical Area or the Metropolitan Statistical 

Area where an individual reports working as the geographic entity. 
 10

areas are metropolitan areas and surrounding counties; there are 183 labor market areas in 

the U.S. 

Figure 4 shows a plot of industry geographic concentration measures by 2-digit 

NAICS sector for 2002.8

 While Gini coefficients for industries in the manufacturing 

sector tend to be higher than for service sector industries, there are a number of service 

sector industries with relatively large Gini coefficients. The high Gini coefficients are 

evidence of significant geographic concentration of employment within the U.S., enough 

concentration to suggest that these service activities are traded within the U.S. In addition, 

the industries within both services and manufacturing that have high Ginis conform to 

our priors about what service activities are likely to be tradable and which manufacturing 

industries are less tradable (i.e., they have high trade costs to value ratios). For example, 

within manufacturing, the 5 industries with the lowest Ginis are Other Concrete Product 

Manufacturing, Fabricated Structural Metal Manufacturing, Concrete Block and Brick 

Manufacturing, Wood Container and Pallet Manufacturing, and Ready-Mix Concrete 

Manufacturing -- all characterized by low value to weight ratios and thus less likely to be 

traded. 

Within the Information sector, the industries with the lowest Ginis are Newspaper 

Publishers, Motion Picture Theaters (except Drive-Ins), Television Broadcasting, Radio 

Stations, and Wired Telecommunications Carriers. These all tend to have a heavy 

reliance on local inputs or require a physical presence to provide the service. The 

Information industries with the highest Ginis are Record Production, Music Publishers, 

Cable and Other Subscription Programming, Integrated Record Production/Distribution, 

and Other Motion Picture and Video Industries. Within Professional, Scientific and 

Technical services some of the low Gini industries are Photography Studios, Portrait and 

Veterinary Services. High Gini Professional, Scientific, and Technical service industries 

are Payroll Services and Research and Development in the Social Sciences and 

Humanities. 

 

8

 The analysis uses demand adjusted Gini measures constructed from 1997 data; please see Jensen and 

Kletzer (2006) for a description of the demand adjusted Gini construction. In an interest to limit disclosure 

requests, we include a graph of the Gini coefficients constructed from 2002 data (which had already been 

released). The graphs are qualitatively similar. 
 11

The share of employment within sectors that is classified as tradable varies 

considerably across sectors. In manufacturing, approximately 89 percent of employment 

is in industries classified as tradable, within business services the share is 64 percent. 

These results are consistent with priors about the ability to provide these services over 

distance. Industries within the Educational, Health Services, and Other Services (except 

Public Administration) sectors tend to have low Gini coefficients, suggesting low 

tradability. In contrast to business services, the share of employment in tradable 

industries in education and health services is 15 percent. 

These results suggest that a number of business service industries are tradable 

within the U.S. Jensen and Kletzer (2006) report that the employment in tradable service 

activities is a significant share of total civilian employment – for example they find 

tradable business services employ more people than the manufacturing sector. For at least 

some service industries and for a significant share of the service workforce, international 

trade seems technologically feasible.9

4. Tradability and Trade 

A number of business service industries exhibit levels of geographic 

concentration high enough to suggest that these activities are being traded within the U.S. 

and are thus in principle tradable internationally. While there may exist a number of 

impediments to international trade in services that do not apply to domestic trade in 

services (e.g. language, culture, regulation), we expect that service activities that are 

tradable within the U.S. are more likely to be traded internationally. In this section we 

examine the relationship between our measure of tradability and official U.S. statistics on 

trade in services. 

The Gini coefficient measures the geographic concentration of employment in 

industries. It seems reasonable that once the Gini coefficient reaches a threshold, it 

represents an activity that is not produced everywhere (as non-tradables are) and is thus 

 

9

 This abstracts from the issue of policy or regulatory impediments to trade in services (e.g. national or 

local licensing requirements). 
 12

tradable. We are interested in examining the producer survival and employment growth 

in tradable and non-tradable activities. An important issue in this empirical approach is to 

identify the level of geographic concentration that indicates that an industry or occupation 

is “tradable.” Jensen and Kletzer (2006) explore where to impose the tradable/nontradable threshold with industries because we have a much better sense of which 

industries are tradable – particularly for goods producing industries. They choose a Gini 

coefficient of 0.10 for the data used in that paper and report robustness checks on the 

threshold. In this paper, because the detail available in the industrial classification system 

is different and the level of geography is different, the Gini coefficient threshold for 

“tradability” must be adjusted. In this paper for the Gini coefficients constructed using 

the Census establishment data, industries with a Gini coefficient level greater than or 

equal to 0.12 are classified as tradable; those with a Gini coefficient below 0.12 are 

classified as non-tradable.10

To make this comparison of tradability and trade, we use publicly available 

information from the Census of Services collected by the Census Bureau. The Census of 

Service Industries contains information on principal industry, location, employment, 

payroll, and sales across all sectors in scope. There are also sector specific questions, one 

of which is whether the establishment exports. Establishments in the Information sector 

(51), Professional, Scientific, and Technical services (54), and Administrative and 

Support and Waste Management and Remediation services (56) are asked to report their 

export sales for services. The service sectors where the export question is asked provide 

direct evidence on the correlation between the classification of "tradability" and exports. 

Information from the Bureau of Economic Analysis provides information on the 

correlation between the “tradability” classification and imports. 

 

10 The threshold is calibrated to leave the same (or the closest analog) categories of manufacturing 

industries in the same tradable and non-tradable classifications as those used in Jensen and Kletzer (2006). 

In manufacturing, industries with Ginis below the threshold are Other Concrete Product Manufacturing, 

Fabricated Structural Metal Manufacturing, Concrete Block and Brick Manufacturing, Wood Container and 

Pallet Manufacturing, and Ready-Mix Concrete Manufacturing -- all characterized by low value to weight 

ratios and thus less likely to be traded. 
 13

Tradability and Exporting Across Industries 

To examine the relationship between our tradable measure and exporting, we 

examine industry level export information from published aggregates of the 2002 Census 

of Service Industries.11 Table 2 reports the share of establishments that export and exports 

to sales ratios in service industries classified as tradable and non-tradable. Industries 

classified as tradable have significantly higher shares of establishments that export 

(almost an order of magnitude larger, though it is a relatively small share) and 

significantly higher exports to sales ratios (again, an order of magnitude larger than nontradable industries). These results indicate that the tradable classification is positively 

correlated with export participation, confirming that geographic concentration is 

concentrated with tradability in services. It is also interesting to note that industry average 

wages are much higher in tradable industries than non-tradable industries. Tradable 

service activities are high wage, high skill activities. 

Tradability and Importing Across Industries 

To examine the relationship between our tradable measure and importing, we 

draw on information for the Bureau of Economic Analysis's benchmark input-output 

tables.12 We recognize and acknowledge that the BEA data on imports are imperfect due 

to the classification systems used on the collection instruments not corresponding directly 

to the NAICS classification system (they are collected at a much more aggregated level). 

Because the classification systems used are different, BEA must allocate imports across 

NAICS categories. While we acknowledge this shortcoming, we think it is useful to 

compare the data as a crude robustness check. 

Table 2 also reports industry level imports and industry import penetration. 

Industries classified as tradable have higher levels of imports and higher import 

penetration levels. These results indicate that the tradable classification is positively 

correlated with imports, suggesting that geographic concentration is concentrated with 

 

11 See http://www.census.gov/econ/census02/. 

12 See http://www.bea.gov/industry/io_benchmark.htm#2002data. 
 14

tradability in services. The data presented in Table 2 on imports and exports suggest that 

the tradability index is a useful proxy for identifying industries that are more likely to be 

traded internationally. 

5. Empirical Approach 

 As described above, the data available on the service sector are not as rich as the 

data available for the manufacturing sector. This presents significant challenges for the 

empirical analysis. To examine the relationship between exchange rate changes and 

employment and wages at U.S. service sector establishments, we pursue an empirical 

strategy that is similar in spirit to that described above (Revenga (1998), Campa and 

Goldberg (2001), and Bernard and Jensen (2004)), but with far less rich data. Because 

this paper examines employment changes at the plant level, it is more similar to the work 

by Bernard and Jensen. 

 Based on the results for the manufacturing sector from Revenga (1998), Campa 

and Goldberg (2001) and Bernard and Jensen (2004), we expect that exchange rate 

changes will have an impact on economic activity in industries where international trade 

is important. We do not expect that all service activities will be responsive to exchange 

rate changes. We expect that only tradable service activities will be affected by exchange 

rate changes. To investigate whether establishments in tradable service activities are more 

responsive to exchange rate changes, we will estimate the following specification: 

 Empj,i,t = +  XRt +  GDPt +  World GDPt +  Interest Ratet + 

 NASDAQt + Tradablei +  XRt*Tradablei + Urbanj + Region Fixed Effectj

where 

 Empj,i,t is the annual change in employment t-1 to t at establishment j in industry i. 
 15

 XRt is the change in the trade-weighted exchange rate t-1 to t. 

 GDPt is the change in U.S. GDP t-1 to t. 

 World GDPt is the change in World GDP t-1 to t. 

 Interest Ratet is the change in the interest rate t-1 to t. 

 NASDAQt is the change in the NASDAQ stock market index t-1 to t. 

Tradablei is an indicator variable of whether industry i is classified as tradable using the 

methodology described above. 

Urbanj is an indicator of whether the establishment is located in an urban county. 

Region Fixed Effectj is a fixed effect for the Bureau of Economic Analysis Labor Market 

Area in which the establishment is located. 

This specification is similar in spirit to that used by Campa and Goldberg 

(2001).13 One difference is the unit of observation. Campa and Goldberg use industry 

level data, while in the paper we examine establishment level responses. There are other 

differences in the independent variables, as well. Camp and Goldberg use the price of oil 

as a control in their analysis; this project will drop oil and include a measure of the 

NASDAQ stock market to capture the telecommunications and internet investment boom 

(and subsequent dot.com bust) in the late 1990s. This specification will investigate 

whether service establishments on average are responsive to exchange rate changes. 

Similar specifications will be estimated for changes in establishment level average wage 

and establishment survival. 

 

13 Campa and Goldberg (2001) also include import penetration. While it would be desirable to include 

import penetration in the regressions, lack of industry detail on services flows precludes including this in 

the analysis. 
 16

While the macro-economic variables are primarily included as controls, we have 

some general notions about how the should affect establishment employment, average 

wages, and survival. We expect that when aggregate demand is strong, this will increase 

demand for services and tend to increase establishment level employment, and survival 

probabilities. We have no strong priors regarding the impact of these variables on average 

wages. If establishments add new workers that have lower wages than the average of 

current workers, the average wage would tend to decrease. 

Controls are also included for local demand conditions. An indicator variable is 

included to identify whether the county is classified as an urban county using the 1993 

Rural-Urban Continuum Code produced by the U.S. Department of Agriculture.14

Counties with a code of 0-3 were classified as Urban. We also include region fixed 

effects for the Bureau of Economic Analysis Labor Market Area in which the 

establishment is located.15 

 Following previous research, the project will also examine whether there are 

differential responses to exchange rates across tradable service industries with different 

characteristics. Campa and Goldberg (2001) find that industries with low price-cost 

margins are more responsive to exchange rate changes. This project will investigate 

whether industries with higher average wages (industries more consistent with U.S. 

comparative advantage) are more insulated from exchange rate changes. The project will 

separately estimate the empirical specifications for high-wage and low-wage industries.16 

 

14 See http://www.ers.usda.gov/Data/RuralUrbanContinuumCodes/ for additional information. 15Seehttp://www.bts.gov/programs/commodity_flow_survey/methods_and_limitations/national_transportati

on_analysis_regions/ for additional information. 16 Jensen and Kletzer (2008) in preliminary work find that establishments in tradable industries are more 

likely to close over the 1997-2002 period than establishments in non-tradable industries, though high-wage 

industries have lower closure rates. 
 17

6. Data 

Longitudinal Business Database (LBD) 

 The principal data source for examining the impact of exchange rate changes on 

service establishment survival, employment growth, and wage growth is the Longitudinal 

Business Database (LBD), developed and maintained by the Center for Economic Studies 

at the U.S. Census Bureau. The LBD contains the universe of all U.S. business 

establishments with paid employees that are in scope for the Economic Census (i.e., all 

manufacturing; mining; construction; retail; wholesale; service; transportation, 

communications and utilities (TCU) and finance, insurance, and real estate (FIRE) 

establishments). In constructing the LBD, great care was taken to provide researchers 

with the most complete and accurate set of longitudinal establishment linkages possible 

which substantially reduces the number spurious establishment births and deaths 

observed in the data. (See Jarmin and Miranda (2002) for more information on the 

contents and construction of the LBD.) The LBD contains basic information on 

establishment size, payroll, the industry in which it operates, its geographical location 

and ownership that permit researchers to analyze establishment demographics. 

 The LBD is a rich and highly reliable source of information on establishment 

survival and employment and wage changes at annual frequencies. One problem with the 

LBD is that industry coding information (like other variable son the LBD) is derived 

from the Standard Statistical Establishment List (SSEL). The SSEL is the basis for the 

Census Bureau’s sampling frame and is derived from a variety of sources – both 

administrative data and survey data. One particular issue with the LBD/SSEL data is that 

industry classification information lags for newly formed establishments. Because new 

establishments are included in the SSEL before they have been surveyed (often coming 

from administrative data), the industry classification information is not always complete, 

i.e. establishments sometimes have an incomplete industry code (for example 540000 or 

541200 instead of 541214). 

 To resolve this issue, we link establishments from the LBD to observations in the 

Economic Census (which occurs every five years) and use industrial classification 
 18

information from the Economic Census where possible. For some new establishments 

appearing in the LBD only after 2002, no Economic Census record is available. For these 

establishments, we leave the industry code as the partial code and classify the 

establishment as being in a non-tradable industry. We construct a sample of all service 

sector establishments in scope for the Economic Census for the period 1997 – 2005 (the 

most recent year the LBD is available). 

 The high wage industry indicator is constructed by taking (employment weighted) 

average wages within an industry and comparing that to the average across industries. 

Industries above the mean are classified as high wage industries. 

Outside measures 

Exchange rate: US trade weighted exchange rate (broad range of currencies). The data are 

downloaded from St. Louis Fed website: 

http://research.stlouisfed.org/fred2/categories/105. 

As reported on the St. Louis Fed website: “Averages of daily figures. A weighted average 

of the foreign exchange value of the U.S. dollar against the currencies of a broad group of 

major U.S. trading partners. Broad currency index includes the Euro Area, Canada, Japan, 

Mexico, China, United Kingdom, Taiwan, Korea, Singapore, Hong Kong, Malaysia, 

Brazil, Switzerland, Thailand, Philippines, Australia, Indonesia, India, Israel, Saudi 

Arabia, Russia, Sweden, Argentina, Venezuela, Chile and Colombia.”17 The original data 

are monthly, and the annual rate is calculated as the simple average of the monthly rate. 

NASDAQ: NASDAQ composite index for March 15 of each year. Source: 

“Daily Stock Price Record, NASDAQ” Standard & Poor's Corp. 

Real interest rate: calculated from the nominal interest rate and inflation rate. (i) The 

nominal interest rate is the 5-year T-bill rate, which is downloaded from the Federal 

 

17 For more information about trade-weighted indexes see 

http://www.federalreserve.gov/pubs/bulletin/2005/winter05_index.pdf.
 19

Reserve Board website: http://www.federalreserve.gov/releases/h15/data.htm. (ii) 

inflation rate is calculated from CPI index, which is downloaded from IMF International 

Financial Statistics. Both the original nominal interest rate and CPI data are monthly. The 

annual nominal interest rate is calculated as the simple average of the monthly nominal 

interest rate, and the annual CPI is the CPI for December in each year. 

US GDP: downloaded from IMF International Financial Statistics. The original data are 

quarterly, and the annual GDP is calculated as the sum of the quarterly GDP in each year. 

World GDP: Sum of GDP of all countries in the world. GDP data of each country are 

also downloaded from IMF International Financial Statistics. The original data are 

quarterly, and the annual GDP of each country is calculated as the sum of the quarterly 

GDP of the country. 

7. Results 

Establishment Employment Growth 

 Table 3 reports the regression results for the base specification. The first two 

columns report the regression coefficients and standard errors for the all industry sample. 

Changes in the exchange rate between t-1 and t have a positive and statistically 

significant impact on employment growth at the average service establishment. Changes 

in U.S. GDP have a negative and statistically significant impact on employment growth 

at service establishments. This is not intuitive. World GDP has a positive and statistically 

significant impact on service establishment employment growth. Changes in real interest 

rates have a negative and statistically significant affect on employment growth. Changes 

in the NASDAQ index have a positive and statistically significant affect on service 

establishment employment. Service establishments in tradable industries do not, on 

average, have employment growth different from the average service sector 

establishment. When we examine the interaction term between tradability and the change 
 20

in the exchange rate, the coefficient is positive and statistically significant. The expected 

sign for the exchange rate is negative (appreciation of the dollar should lead to a decrease 

in demand for tradable service activities), so this is unexpected. 

 The second set of columns in Table 3 presents results for establishment 

employment growth in high wage industries. Changes in the exchange rate between t-1 

and t have a positive and statistically significant impact on employment growth at the 

average service establishment. Changes in US and world GDP also have a positive and 

statistically significant impact on service establishment employment growth and the 

coefficient estimates are larger than for the full sample (especially US GDP growth 

which is negative in the full sample). Changes in the NASDAQ index have a negative 

and statistically significant affect on service establishment employment. This is counterintuitive and difficult to explain. Employment growth in service establishments in 

tradable industries is lower than the average high-wage service sector establishment. 

When we examine the interaction term between tradability and the change in the 

exchange rate, the coefficient is positive (and larger than the full sample estimate) and 

statistically significant. The expected sign for the exchange rate is negative (appreciation 

of the dollar should lead to a decrease in demand for tradable service activities), so this is 

unexpected. 

 The third set of columns in Table 3 presents results for establishment employment 

growth for low wage industries. The qualitative results for low wage industries are 

similar to the overall results with the exception of the Tradable indicator. In low wage 

industries, establishments in tradable industries have higher employment growth on 

average. The sign on the interaction between the exchange rate term and the tradability 

indicator is unexpected as in the full sample and the high wage industry sample. 

Establishment Wage Growth 

 Table 4 reports regression results for average wage changes at establishments 

over the sample period. The first set of columns reports regression coefficients and 

standard errors for the full sample of establishments. Increases in the exchange rate are 

associated with increasing average wages at service establishments over the period. This 
 21

is contrary to previous results in the manufacturing sector, where an appreciation of the 

dollar was associated with negative labor market outcomes. 

 Increases in US GDP are associated with rising average wages at service 

establishments. Increases in world GDP are associated with negative average wage 

changes at service establishments. Increases in real interest rates are associated with 

increases in average wages. 

 Service establishments in tradable industries have, on average, lower average 

wage growth than establishments in non-tradable industries. The interaction between the 

exchange rate and the tradable industry indicator is negative and statistically significant, 

suggesting that an appreciation of the dollar is associated with lower average wage 

growth at the average service establishment in a tradable industry. 

 The results for the high wage industry sample are presented in the second set of 

columns in Table 4. Changes in World GDP are associated with positive changes in 

average wages in high wage service industries (in contrast to the effect in the full sample 

of establishments). The interaction between the exchange rate and the tradability 

indicator is positive and statistically significant (again, in contrast to the overall results). 

For the low wage industry sample, the results are qualitatively similar to the overall 

results with the exception of the exchange rate variable. 

Establishment Survival 

 Table 5 reports regression results for annual service establishment survival over 

the sample period. An establishment is considered to have exited if it appears in year t-1

and does not appear in year t. The first set of columns reports regression coefficients for 

establishment survival for the full sample. Exchange rate changes have negative and 

statistically significant impact on survival probabilities. Surprisingly, US GDP growth 

and World GDP growth are negatively (and statistically significantly) associated with 

service establishment survival probabilities. Increases in interest rates, contrary to 

expectations, are associated with higher survival probabilities. Increases in the NASDAQ 

index are positively and statistically associated with service establishment survival. 

Establishments in tradable industries are less likely to survive than the average 
 22

establishment. Increases in the exchange rate increase the likelihood of survival for 

establishments in tradable industries. This is the opposite of what was expected. 

 The results for high wage industries and low wage industries are qualitatively 

similar. 

Different Lag Relationships 

 In this section, we explore alternative lag structures for the macro variables. 

Tables 6 and 7 report the employment change and survival probability results for 

specifications using the change from t-2 to t-1 for all the macro variables (i.e. exchange 

rates, US GDP, world GDP, real interest rates, NASDAQ index). The results for the 

employment change regression are sensitive to the choice of lag structure – the 

coefficient estimates of all the macroeconomic control variables (with the exception of 

the NASDAQ index) change sign and are statistically significant compared to the base 

specification for the full sample. The lagged changes in U.S. GDP are positive and 

statistically significant. The lagged changes in World GDP are negative and statistically 

significant. The coefficient on the real interest change is negative and significant; the 

coefficient on the change in the NASDAQ index is negative and statistically significant. 

The coefficient on the tradable indicator is negative and the coefficient on the interaction 

between the exchange rate change and the tradability indicator is positive. The results for 

the survival regression using the lagged changes are qualitatively similar to the base 

specification. 

 

Including Initial Establishment Employment 

 Previous research examining establishment level employment growth and survival 

probabilities have included initial plant characteristics as controls.18 To control for initial 

establishment characteristics, we include initial establishment employment in the base 

specifications. Tables 8, 9, and 10 report the results. The estimated coefficients are 

qualitatively similar to the base regression results. We see that initial employment is 

 

18 See Dunne, Roberts, and Samuelson (1988, 1989) and Bernard and Jensen (1999, 2007). 
 23

negatively associated with employment growth (some evidence of mean reversion), while 

larger establishments are more likely to have higher than average wage growth and have 

higher survival probabilities. 

8. Discussion and Conclusions 

 While it seems intuitive that exchange rate changes should translate into demand 

shocks for tradable service producers, the results from this analysis do not support the 

hypothesis that exchange rates are associated with employment changes, average wage 

changes, or survival in the service sector over the period of study. There are a number of 

potential explanations for why the empirical results do not identify this effect: 

1. The tradability classification is not accurate. If the tradability classification is not 

accurate, then it would be difficult to identify the effect of exchange rate changes 

on establishment level employment change, average wage chance, and survival. 

The correlations between the tradability classification and measures of 

international trade in services suggest that the classification is appropriately 

capturing some variation across industries. 

2. Related to the above, if only a small share of service establishments within a 

tradable industry are exporters it is likely to be difficult to identify this effect. For 

the service industries where establishment level export information is available, it 

is true that a small share of establishments export (roughly 5 percent). If this is 

true across all tradable industries, it would be difficult to identify the affect of 

exchange rate changes using an industry-level identifier. 19 

3. The exchange rate change measure is poorly measured. We use a single trade 

weighted measure of exchange rates constructed by the Federal Reserve for all 

 

19 In this project, we did examine whether exporters’ employment, wages, and survival are responsive to 

changes in the exchange rate for the select industries where exporting information is available. These 

results did not provide any clarification to the previous results, so we chose not to include these results. 
 24

industries.20 The trade weights include merchandise trade (and, because services 

trade accounts for about 30 percent of trade, the weights are dominated by 

merchandise trade patterns). If the countries with which the US engages in 

services trade differ from the countries with which the US conducts merchandise 

trade, the exchange rate measure would not accurately reflect the demand shock. 

4. There is relatively inelastic demand for US tradable services. If, because of the 

nature of services (e.g. they are small in terms of budget share, there is little 

possibility for inter-temporal substitution for services, services are fixed costs 

types of activities like payroll processing) they have relatively inelastic demand, 

gradual changes in the exchange rate will not be large enough to affect demand. 

5. Exchange rates are a demand shock to revenue, but increased revenue does not 

translate directly (or rapidly) into increased employment or wages. It is possible 

for activities like motion pictures and software (and other services in the 

information sector) that revenues increase as a result of exchange rate changes but 

this does not translate into measurable increases in employment. 

6. Secular trends swamp cyclical or transitory trends. It is possible that secular 

forces like comparative advantage and increasing specialization across countries 

is driving changes in employment and survival in service industries and that these 

effects are quantitatively more important than any effects from exchange rate 

changes. 

7. We don’t understand service producer employment dynamics well enough to 

tease out the exchange rate effect. The manufacturing sector studies built upon a 

large body of work regarding employment dynamics in the manufacturing sector. 

Currently little is known regarding employment dynamics in the service sector 

and it is possible that we are not adequately modeling other influences on service 

sector employment growth and survival to identify the exchange rate effect. 

 

20 In contrast, Bernard and Jensen (2004) construct 4-digit SIC level trade weighted exchange rates. 
 25

The results of the study do not support the hypothesis that exchange rates influence 

employment, wages, or survival in the service sector, but do point to directions for future 

research. 

References: 

Bernard, Andrew B. and J. Bradford Jensen. “Exceptional Exporter Performance: Cause, 

Effect, or Both?” Journal of International Economics, 1999, vol. 47, no. 1, pp. 1-

25. 

Bernard, Andrew B. and J. Bradford Jensen. 2004. “Entry, Expansion, and Intensity in 

the U.S. Export Boom, 1987-1992,” Review of International Economics, 12(4), pp. 

662-675. 

Bernard, Andrew B. and J. Bradford Jensen. 2007. “Firm Structure, Multinationals, and 

Manufacturing Plant Deaths,” Review of Economics and Statistics, vol. 89, no. 2, 

pp. 193-204. 

Bernard, Andrew B., J. Bradford Jensen and Peter K. Schott. 2006a. “Survival of the Best 

Fit: Exposure to Low Wage Countries and the (Uneven) Growth of US 

Manufacturing Plants,” Journal of International Economics, vol. 68, no. 1, pp. 

219-237. 

Bernard, Andrew B., J. Bradford Jensen and Peter K. Schott. 2006b. “Trade Costs, Firms, 

and Productivity,” Journal of Monetary Economics, 2006, vol. 53, no. 5, pp. 917-

937. 

Burgess, Simon and Michael Knetter. 1998. “An International Comparison of 

Employment Adjustment to Exchange Rate Fluctuations,” Review of International 

Economics, 6, pp. 151-163. 

Campa, Jose M. and Linda S. Goldberg. 2001. “Employment versus Wage Adjustment 

and the U.S. Dollar.” Review of Economics and Statistics, 83(3), pp. 477-489. 

Davis, Steven J., R. Jason Faberman and John C. Haltiwanger. 2006. “The Flow 

Approach to Labor Markets: New Data Sources and Micro-Macro Links,” Journal 

of Economic Perspectives, vol. 20(3), 3-26. 

Davis, Steven J., John C. Haltiwanger, and Scott Schuh. 1998. Job Creation and 

Destruction. MIT Press. 
 26

Dunne, Timothy, Mark J. Roberts, and Larry Samuelson, (1988) “Patterns of firm entry 

and exit in US manufacturing industries”, Rand Journal of Economics, Vol. 19(4), 

495-515. 

Dunne, Timothy, Mark J. Roberts, and Larry Samuelson. (1989) ‘The Growth and Failure 

of US Manufacturing Plants’, Quarterly Journal of Economics, Vol. 104, No. 4, pp. 

671-698. 

Galindo, Arturo Jose, Alejandro Izquierdo, and Jose Manuel Montero. (2006) “Real 

Exchange Rates, Dollarization, and Industrial Employment in Latin America,” Bank 

of Spain Research Paper No. WP-0601. 

Gourinchas, Pierre-Olivier. 1998. “Exchange Rates and Jobs: What Do We Learn from 

Job Flows?” NBER Macroeconomics Annual, MIT Press, Cambridge. 

Jarmin, Ronald S. and Javier Miranda, 2002. “The Longitudinal Business Database,” 

Center for Economic Studies Working Paper Series, 02-17, U.S. Census Bureau. 

Jensen, J. Bradford and Lori G. Kletzer. 2006. “Tradable Services: Understanding the 

Scope and Impact of Services Offshoring,” Offshoring White-Collar Work – 

Issues and Implications, Brookings Trade Forum 2005, ed. Lael Brainard and 

Susan M. Collins, 2006. 

Jensen, J. Bradford and Lori G. Kletzer. 2008 “Tradability and Service Producer 

Dynamics,” mimeo, 2008. 

Klein, Michael W., Scott Schuh, and Robert K. Triest. 2003. “Job Creation, Job 

Destruction, and the Real Exchange Rate,” Journal of International Economics, 

vol. 59, pp. 239-265. 

Krugman, Paul R. 1991. Geography and Trade. Cambridge: MIT Press. 

Revenga, A. 1992. “Exporting Jobs? The Impact of Import Competition on Employment 

and Wages in U.S. Manufacturing,” Quarterly Journal of Eonomics, 107(1), pp. 

255-84. 
 27

Figure 1 

Trade Weighted Dollar Exchange Rate 

(Broad, 1997=1)

80.0000

85.0000

90.0000

95.0000

100.0000

105.0000

110.0000

115.0000

120.0000

125.0000

130.0000

Jan-95

Jul-95

Jan-96

Jul-96

Jan-97

Jul-97

Jan-98

Jul-98

Jan-99

Jul-99

Jan-00

Jul-00

Jan-01

Jul-01

Jan-02

Jul-02

Jan-03

Jul-03

Jan-04

Jul-04

Jan-05

Jul-05

Jan-06

Jul-06

Jan-07

Jul-07

Jan-08

Source: Board of Governors of the Federal Reserve System, 

http://research.stlouisfed.org/fred2/series/TWEXBMTH/
 28

Figure 2 

Location Quotients for Seattle, Washington 

0

2

4

6

8

10

12

14

16

18

20

Software

publishing

Aircraft and parts Aerospace product

and parts

Ship and boat

building

Fishing, hunting,

trapping

Toys, amusements,

and sporting goods

Other information

services

Electronic

shopping and

mail-order houses

Location Quotient
 29

Figure 321

Geographic Concentration of Industries

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 100 200 300 400 500 600 700 800 900 1000

NAICS Industry

Gini Coefficient

Ag Mining Utilities Construction Manufacturing Wholesale Retail Transportation Services Public Admin

 

21 This figure is taken from Jensen and Kletzer (2006). 
 30

Figure 4 

Gini Coefficients by NAICS Industry (from establishment data), 2002 

0

0.2

0.4

0.6

0.8

1

30 40 50 60 70 80 90

2-digit NAICS Industry Groups

Gini Coefficient
 31

Table 1 

NAICS Code Sector

Employment 

2007

Share of Total 

Employment 

2007

Employment 

Growth 

1997-2007

21 Mining 703,129 0.5% 38%

22 Utilities 632,432 0.5% -10%

23 Construction 7,399,047 5.5% 31%

31-33 Manufacturing 13,333,390 9.9% -21%

42 Wholesale trade 6,295,109 4.7% 9%

44-45 Retail trade 15,610,710 11.5% 12%

48-49 Transportation and warehousing 4,435,760 3.3% 52%

51-56 Business Services 33,430,809 24.7% 29%

51 Information 3,428,262 2.5% 12%

52 Finance and insurance 6,562,546 4.9% 12%

53 Real estate and rental and leasing 2,249,353 1.7% 32%

54 Professional, scientific, and technical services 8,121,171 6.0% 51%

55 Management of companies and enterprises 2,915,644 2.2% 11%

56 Administrative and support and waste remediation services 10,153,833 7.5% 38%

61-81 Personal Services 34,595,857 25.6% 23%

61 Educational services 562,210 0.4% 75%

62 Health care and social assistance 16,859,513 12.5% 24%

71 Arts, entertainment, and recreation 2,070,524 1.5% 30%

72 Accommodation and food services 11,587,814 8.6% 23%

81 Other services (except public administration) 3,515,796 2.6% 8%

Federal Government 2,462,000 1.8% --

State and Local Government 16,400,000 12.1% --

Table 2 

Variable N Mean Std Dev

Non-Tradable Exports/Sales 21 0.001291 0.001997

Shr of Estabs that Export 21 0.008853 0.006773

Import Penetration 15 0.003924 0.00605

Imports ($ millions) 15 264.26 347.0536

Average Wage 22 27849.35 11832.14

Tradable Exports/Sales 99 0.024838 0.036448

Shr of Estabs that Export 101 0.074616 0.091129

Import Penetration 77 0.012065 0.016373

Imports ($ millions) 77 592.0325 704.5372

Average Wage 102 43853.9 16701.23

Business Service Sector (NAICS 51, 54, 56) 

Industry Level Descriptive Statistics
 32

Table 3 

Dependent Variable:  log(employmentf,t)

ALL INDUSTRIES HIGH WAGE INDUSTRIES LOW WAGE INDUSTRIES

Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error

 log(XRt) 0.1544 *** (0.0057) 0.0633 *** (0.0091) 0.2138 *** (0.0073)

 log(GDPt) -0.1359 *** (0.0168) 0.2987 *** (0.0260) -0.4638 *** (0.0219)

 log(World GDPt) 0.0248 *** (0.0070) 0.1129 *** (0.0109) 0.2726 *** (0.0092)

 (Real Interest Ratet) -0.0015 *** (0.0002) -0.0013 *** (0.0003) -0.0018 *** (0.0002)

 log(NASDAQt) 0.0068 *** (0.0005) -0.0046 *** (0.0008) 0.0153 *** (0.0007)

Tradable Industryt -0.0003 (0.0002) -0.0048 *** (0.0003) 0.0017 *** (0.0003)

 log(XRt) * Tradable Industryt 0.0964 *** (0.0040) 0.1524 *** (0.0059) 0.0262 *** (0.0061)

Urban Areat 0.0009 (0.0007) 0.0011 (0.0011) 0.0006 (0.0008)

Fixed Effect Region Region Region

N 23,317,590 10,139,003 13,178,587

R2 0.0004 0.0006 0.0005

Note: Standard errors are robust and clustered on year. 

Impact of Exchange Rate Change on Establishment Performance: Employment

Table 4 

Dependent Variable:  log(wagef,t)

ALL INDUSTRIES HIGH WAGE INDUSTRIES LOW WAGE INDUSTRIES

Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error

 log(XRt) 0.1397 *** (0.0064) 0.2693 *** (0.0104) -0.0022 (0.0080)

 log(GDPt) 0.3887 *** (0.0188) 0.2318 *** (0.0297) 0.5049 *** (0.0241)

 log(World GDPt) -0.0261 *** (0.0079) 0.3192 *** (0.0125) -0.2925 *** (0.0101)

 (Real Interest Ratet) 0.0123 *** (0.0002) 0.0200 *** (0.0003) 0.0063 *** (0.0003)

 log(NASDAQt) -0.0105 *** (0.0006) -0.0081 *** (0.0009) -0.0122 *** (0.0007)

Tradable Industryt -0.0095 *** (0.0002) -0.0214 *** (0.0003) -0.0018 *** (0.0003)

 log(XRt) * Tradable Industryt -0.0254 *** (0.0044) 0.1086 *** (0.0067) -0.0895 *** (0.0067)

Urban Areat -0.0033 *** (0.0007) -0.0077 *** (0.0012) -0.0008 (0.0009)

Fixed Effect Region Region Region

N 23,317,586 10,139,002 13,178,584

R2 0.0008 0.0015 0.0009

Note: Standard errors are robust and clustered on year. 

Impact of Exchange Rate Change on Establishment Performance: Average Wage
 33

Table 5 

Dependent Variable: Survival

ALL INDUSTRIES HIGH WAGE INDUSTRIES LOW WAGE INDUSTRIES

Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error

 log(XRt) -0.1433 *** (0.0035) -0.2167 *** (0.0056) -0.1014 *** (0.0045)

 log(GDPt) -0.7328 *** (0.0104) -0.6715 *** (0.0159) -0.7754 *** (0.0137)

 log(World GDPt) -0.2261 *** (0.0043) -0.2676 *** (0.0067) -0.1952 *** (0.0057)

 (Real Interest Ratet) 0.0014 *** (0.0001) 0.0006 *** (0.0002) 0.0019 *** (0.0001)

 log(NASDAQt) 0.0127 *** (0.0003) 0.0113 *** (0.0005) 0.0137 *** (0.0004)

Tradable Industryt -0.0200 *** (0.0001) -0.0299 *** (0.0002) -0.0115 *** (0.0002)

 log(XRt) * Tradable Industryt 0.1209 *** (0.0024) 0.1848 *** (0.0036) 0.0575 *** (0.0038)

Urban Areat -0.0052 *** (0.0004) -0.0105 *** (0.0007) -0.0017 *** (0.0005)

Fixed Effect Region Region Region

N 25,976,635 11,322,508 14,654,127

R2 0.0036 0.0060 0.0027

Note: Standard errors are robust and clustered on year. 

Impact of Exchange Rate Change on Establishment Performance: Survival
 34

Table 6 

Dependent Variable:  log(employmentf,t)

ALL INDUSTRIES HIGH WAGE INDUSTRIES LOW WAGE INDUSTRIES

Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error

 log(XRt-1) -0.1882 *** (0.0058) -0.0775 *** (0.0093) -0.2725 *** (0.0074)

 log(GDPt-1) 0.7354 *** (0.0164) 0.5969 *** (0.0254) 0.8418 *** (0.0215)

 log(World GDPt-1) -0.2707 *** (0.0071) -0.1137 *** (0.0110) -0.3912 *** (0.0092)

 (Real Interest Ratet-1) -0.0029 *** (0.0002) -0.0014 *** (0.0002) -0.0041 *** (0.0002)

 log(NASDAQt-1) -0.1850 *** (0.0005) -0.0110 *** (0.0008) -0.0243 *** (0.0007)

Tradable Industryt -0.0004 ** (0.0002) -0.0049 *** (0.0003) 0.0018 *** (0.0003)

 log(XRt-1) * Tradable Industryt 0.0202 *** (0.0038) 0.0375 *** (0.0056) -0.0130 ** (0.0058)

Urban Areat -0.0017 *** (0.0003) -0.0013 *** (0.0005) -0.0022 *** (0.0004)

Fixed Effect Region Region Region

N 23,317,590 10,139,003 13,178,587

R2 0.0002 0.0003 0.0003

Note: Standard errors are robust and clustered on year. 

Impact of Lagged Exchange Rate Change on Establishment Performance: Employment

Table 7 

Dependent Variable: Survival

ALL INDUSTRIES HIGH WAGE INDUSTRIES LOW WAGE INDUSTRIES

Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error

 log(XRt-1) -0.1955 *** (0.0036) -0.2261 *** (0.0057) -0.1896 *** (0.0047)

 log(GDPt-1) -0.2514 *** (0.0102) -0.2928 *** (0.0157) -0.2194 *** (0.0135)

 log(World GDPt-1) -0.2780 *** (0.0044) -0.2410 *** (0.0067) -0.3069 *** (0.0058)

 (Real Interest Ratet-1) 0.0011 *** (0.0001) 0.0022 *** (0.0001) 0.0002 (0.0001)

 log(NASDAQt-1) 0.0154 *** (0.0003) 0.0164 *** (0.0005) 0.0146 *** (0.0004)

Tradable Industryt -0.0212 *** (0.0001) -0.0325 *** (0.0002) -0.0114 *** (0.0002)

 log(XRt-1) * Tradable Industryt 0.0977 *** (0.0023) 0.1807 *** (0.0034) 0.0132 *** (0.0036)

Urban Areat -0.0047 *** (0.0002) -0.0111 *** (0.0003) -0.0007 *** (0.0002)

Fixed Effect Region Region Region

N 25,976,635 11,322,508 14,654,127

R2 0.0025 0.0046 0.0016

Note: Standard errors are robust and clustered on year. 

Impact of Lagged Exchange Rate Change on Establishment Performance: Survival
 35

Table 8 

Dependent Variable:  log(employmentf,t)

ALL INDUSTRIES HIGH WAGE INDUSTRIES LOW WAGE INDUSTRIES

Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error

log(Employmentf,t-1) -0.0612 *** (0.0001) -0.0615 *** (0.0001) -0.0617 *** (0.0001)

 log(XRt) 0.1433 *** (0.0056) 0.0503 *** (0.0090) 0.2063 *** (0.0072)

 log(GDPt) -0.2106 *** (0.0165) 0.2338 *** (0.0257) -0.5527 *** (0.0216)

 log(World GDPt) 0.2100 *** (0.0069) 0.1091 *** (0.0108) 0.2864 *** (0.0091)

 (Real Interest Ratet) -0.0010 *** (0.0002) -0.0010 *** (0.0003) -0.0011 *** (0.0002)

 log(NASDAQt) 0.0080 *** (0.0005) -0.0034 *** (0.0008) 0.0167 *** (0.0006)

Tradable Industryt -0.0181 ** (0.0002) -0.0166 *** (0.0003) -0.0125 *** (0.0003)

 log(XRt) * Tradable Industryt 0.1165 *** (0.0039) 0.1678 *** (0.0058) 0.0464 *** (0.0060)

Urban Areat 0.0054 ** (0.0007) 0.0042 *** (0.0011) 0.0073 *** (0.0008)

Fixed Effect Region Region Region

N 23,317,590 10,139,003 13,178,587

R2 0.0291 0.0290 0.0296

Note: Standard errors are robust and clustered on year. 

Impact of Exchange Rate Change on Establishment Performance: Employment

Table 9 

Dependent Variable:  log(wagef,t)

ALL INDUSTRIES HIGH WAGE INDUSTRIES LOW WAGE INDUSTRIES

Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error

log(Employmentf,t-1) 0.0494 *** (0.0001) 0.0473 *** (0.0001) 0.0525 *** (0.0001)

 log(XRt) 0.1487 *** (0.0063) 0.2792 *** (0.0104) 0.0042 (0.0080)

 log(GDPt) 0.4490 *** (0.0186) 0.2817 *** (0.0295) 0.5805 *** (0.0239)

 log(World GDPt) -0.0303 *** (0.0078) 0.3221 *** (0.0124) -0.3042 *** (0.0100)

 (Real Interest Ratet) 0.0119 *** (0.0002) 0.0198 *** (0.0003) 0.0057 *** (0.0003)

 log(NASDAQt) -0.0115 *** (0.0006) -0.0090 *** (0.0009) -0.0134 *** (0.0007)

Tradable Industryt -0.0048 *** (0.0002) -0.0123 *** (0.0003) 0.0103 *** (0.0003)

 log(XRt) * Tradable Industryt -0.0417 *** (0.0044) 0.0968 *** (0.0067) -0.1066 *** (0.0066)

Urban Areat -0.0069 *** (0.0007) -0.0101 *** (0.0012) -0.0065 *** (0.0009)

Fixed Effect Region Region Region

N 23,317,586 10,139,002 13,178,584

R2 0.0157 0.0143 0.0182

Note: Standard errors are robust and clustered on year. 

Impact of Exchange Rate Change on Establishment Performance: Average Wage
 36

Table 10 

Dependent Variable: Survival

ALL INDUSTRIES HIGH WAGE INDUSTRIES LOW WAGE INDUSTRIES

Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error

log(Employmentf,t-1) 0.0299 *** (0.0001) 0.0294 *** (0.0001) 0.0310 *** (0.0001)

 log(XRt) -0.1318 *** (0.0035) -0.2026 *** (0.0055) -0.0923 *** (0.0045)

 log(GDPt) -0.6871 *** (0.0103) -0.6339 *** (0.0158) -0.7191 *** (0.0135)

 log(World GDPt) -0.2179 *** (0.0043) -0.2545 *** (0.0066) -0.1914 *** (0.0057)

 (Real Interest Ratet) 0.0013 *** (0.0001) 0.0007 *** (0.0002) 0.0018 *** (0.0001)

 log(NASDAQt) 0.0120 *** (0.0003) 0.0106 *** (0.0005) 0.0128 *** (0.0004)

Tradable Industryt -0.0111 *** (0.0001) -0.0238 *** (0.0002) -0.0045 *** (0.0002)

 log(XRt) * Tradable Industryt 0.1094 *** (0.0024) 0.1739 *** (0.0036) 0.0476 *** (0.0037)

Urban Areat -0.0072 *** (0.0004) -0.0118 *** (0.0007) -0.0049 *** (0.0005)

Fixed Effect Region Region Region

N 25,976,635 11,322,508 14,654,127

R2 0.0197 0.0215 0.0198

Note: Standard errors are robust and clustered on year. 

Impact of Exchange Rate Change on Establishment Performance: Survival