Tsukuba Economics Working Papers
No. 2024-004

The Effect of Volatility of Unpredicted Exchange Rate Movement and Labor Market
RigiditybyShafiqullah Yousafzai
Hisahiro Naito
May 2024

UNIVERSITY OF TSUKUBA
Faculty of Humanities and Social Sciences
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANThe Effect of Volatility of Unpredicted Exchange Rate
Movement and Labor Market Regidity on Export
Shafiqullah Yousafzai *†Graduate School of Humanities and Social Sciences
University of Tsukuba

Hisahiro Naito *‡Graduate School of Humanities and Social Sciences
University of Tsukuba
May, 2024* This research was conducted while S.Y. was affiliated with the University of Tsukuba. S.Y. appreciates the financial

support from MEXT Scholarship. The authors appreciate the comments from Taiyo Fukai, Kennnichi Kashiwagi, Yoshinori
Kurokawa, Yuko Nakano, Mohammad Abdul Malek and Zeng Fei Yu. The authors are responsible for all remaining errors.
†
Email: Address: 1-1-1, Tennodai, Tsukuba City, Ibaraki Prefecture‡
Corresponding author. Email: naito@dpipe.tsukuba.ac.jp. Address: Tennodai 1-1-1, Tsukuba City, Ibaraki Prefecture,
Japan, Postal Code 305-0032

Abstract
Exchange rate is one of the most volatile macroeconomic price variables. The fluctuations in theexchange rate generate volatility in the profits of firms and reduce the incentives of firms to enter themarket or to expand their capacity. In response to exchange rate volatility firms may reduce laborcosts to mitigate the negative impact through firing workers, reducing the work hours, eliminating
severance pay, etc. However, labor market rigidity restricts a firm’s ability to implement such adjustment, consequently amplifying the negative impact of exchange rate volatility on economic outcomes.
This study contributes to literature by investigating the interaction effects of exchange rate volatility
and labor market rigidity on industrial exports growth. We utilized a country-industry-level disaggregated panel dataset covering 17 industries for the span of 14 years (2005-2018) across 62 developed
and developing countries. For our benchmark regression analysis, we employ the labor market regulation rigidity index developed by Campos et al. (2018), employing alternative indices constructedby Forteza and Rama (2006) and Botero et al. (2004) for robustness checks. Our unique measureof exchange rate volatility captures unpredictable fluctuations in the real exchange rate over the last
three months. We used the fixed effects model, analogous to the triple-D estimation for the empiricalanalysis. We find negative and statistically significant impact of the interaction between exchange
rate volatility and labor market rigidity on export growth. The findings indicate that in a country
where labor market rigidity is one standard deviation higher, a one standard deviation increases inexchange rate volatility reduce export growth by 3.45 percentage points. The estimated coefficientis economically significant as well. This implies that the estimated coefficient reduces annual export
growth by 3.45 percentage points relative to the annual average export growth of 3.2%. However,
the estimated coefficient is smaller relative to the 40% standard deviation of annual average export
growth. In subsample analysis, the results for developed and developing countries are consistent withthe main findings.

1

Introduction

Exchange rate is one of the most volatile macroeconomic price variables. Table 1 shows the standard
deviation of the three key macroeconomic price variables for Japan, the United Kingdom, Germany,and India. For instance, the standard deviation of the exchange rate between the Japanese yen and theUnited States dollar fluctuated at an average of 2.3% per month from 2005 to 2018. The exchange
rate has a higher volatility compared to the consumer price index and interest rate.
Table A1: Standard Deviation of Macroeconomic Price Variables
Countries
Variables
Standard Deviation
Japan
Exchange rate Growth
0.023
CPI Growth
0.003
Interest Rate Growth
0.000
United Kingdom
Exchange rate Growth
0.023
CPI Growth
0.003
Interest Rate Growth
0.002
Germany
Exchange rate Growth
0.022
CPI Growth
0.003
Interest Rate Growth
0.002
India
Exchange rate Growth
0.019
CPI Growth
0.008
Interest Rate Growth
0.003
Note: The authors calculated the standard deviation from the data.
These are standard deviations of the difference of log of variables.

When an exporting firm faces a sudden increase of the exchange rate, one possible response is to
fire workers. This adjustment can help exporting firms to cope with the negative effects of suddenchange of the exchange rate.
However, firms might have difficulty to take such action when there is restriction on firing workers
and in fact most countries introduce employment protection regulations and, to some extent, firing
working is restricted (OECD, 2020). For example, Colombia, France Turkey, and others mandate
an interview with the employee while Czech Republic, Finland, Norway, Sweden, Poland, Slovak
Republic, and others necessitate consultation with a third party before firing. Thus, it is important tostudy the interaction effect of the exchange rate movemnt and labor market rigidity.
On the other hand, all change of the exchange rate does not impose cost to exporting firms. For
example, if the change of the exchange rate can be predicted, the exporting firm can reduce the cost
imposed by being engaged in the hedging in the financial market. Normally, there is premium cost1

which arise from the uncertainly of the market exchange rate, but if the movement of the exchange
rate is perfeclty predictable, such a premium cost should be close to zero. This indicates that among
the movment of the exchange rate, what is important is the unpredictable movement of the exchange
rate. To quantify the unpredicted movement of the exchange rate, using the reqression technique, weclassify the preditable movement of the exchange rate and unpredictable movement of the exhcnage
rate. Then we define the standard deviation of the exchange rate as the volatility of the exchnage rate.
Thus, in this paper, we examine the interactin term of the volatity of the exchange rate and thelabor market rigidity on the economic outcome. More specifically, we study the interaction effects
of volatility of exchange rate growth and labor market rigidity on export growth. For this purpose,
we use country-industry panel data. In this panel data, we construct the trade-weighted real exchange
rate for different industries using the initial trade share as the weight for the exchange rate. For
example if the main export destinaiton of industry A in US is Canda and the main export destinationof indusry B is EU, we put the higher weight to Canadian dollar for the real exchnage rate for industry
A and to Euro for the real exchange rate for industry B. This allows us to generate the variation of
the real exchnage rate for different industry in the same country and make it possible to estimate theinteraction effect of the exchange rate volaity while controlling country fixed effect, industry fixed
effect, country times year fixed effect and industry times fixed effect.
There are two groups of literature related to our paper. The first group includes studies examining
the effect of exchange rate volatility on economic outcomes. [Small literature review] The second
group examines the effect of labor market rigidity on economic outcomes. [Small literature review:]
The studies most closely related to our paper are those by Alexandre et al. (2011); ? and (Alexandre et al., 2017) and Ishise (2019). Alexandre et al. (2011); ? and (Alexandre et al., 2017) examine theinteraction effect of real exchange rate differences and employment rigidity on employment differences in Portugal and OECD countries, respectively. Ishise (2019) investigates the interaction effectof exchange rate variability and wage rigidity, demonstrating that a country with a more volatile nominal exchange rate has a comparative advantage in industries with flexible wages, while those withless volatility benefit in industries with sticky wages.
In the first paper, authors examined the difference of the exchange rate on the ecoomic outcome
and, as the result, the difference of the exchange rate include the the predictable change. In Ishise(2019) , the author is interested in the nominal wage ridigy. In our paper, we are interested in the realfiring rigidity.

2

The remaining sections of the paper are organized as follows. This section extended to the volatility of exchange rate growth and labor market rigidity. Section 2 discusses data construction, Section
3 explains the estimation strategy, and Section 4, 5, and 6 present the empirical results, robust test,
and discussion, respectively. The paper concludes with a policy recommendation.
2
2.1

Literature Review
Volatility of Exchange Rate Growth

The volatility of the real exchange rate refers to the wide range of fluctuations of the exchange rate.
These fluctuations can produce uncertainty and further increase the risk of business and investment.
They can also have considerable adverse effects (Oaikhenan and Aigheyisi, 2015). In the existing
empirical works, three models have been presented for measuring the volatility of real exchange rates.
First, the model measures the volatility of rates by the annual standard deviation of the first differenceof the logarithm of the monthly effective exchange rate (Caglayan and Demir, 2014). Second, themodel measures the z-score of exchange rate volatility (Morina et al., 2020). Third, the generalized
autoregressive conditional heteroskedasticity (GARCH) model and it is centered on the prediction oferrors, which is important for time series analysis because it accounts for the fact that the volatility ofa time series can change over time (Morina et al., 2020).
2.2

Labor Market Regulations index

Labor market rigidity is often used as a synonym for ”employment protection legislation.” However,
employment protection legislation is not exactly the same as labor market rigidity. The provisionsof employment protection legislation can cause frictions in the labor market, and these frictions can
impede the flow of labor freely from one job to another and may also hinder workers from entering
the labor market. Therefore, labor market rigidity can be sourced from employment protection legislation (Bista and Sawyer, 2019). Every country has established regulations under the labor market
law for the protection of the interests of workers and to ensure a minimum standard of living for
its population. However, these regulations can have unintended consequences. For example, strict
regulations curb employment (Autor et al., 2006; Haltiwanger et al., 2014; Gielen and Tatsiramos,2012), trade (Bista and Sawyer, 2019), economic growth (Huang and Huang, 2013; Caparrós et al.,2013), productivity growth (Griffith and Macartney, 2014; Bassanini et al., 2009; Autor et al., 2007;3

Kang, 2015), investment (Bai et al., 2020), and efficiency (Alpysbayeva and Vanormelingen, 2022;
González and Miles-Touya, 2012).
3

Data Construction

3.1

Export Data

This study utilized disaggregated country-industry level panel data of 62 developed and developing
countries 1 . In this empirical study, having 17 manufacturing industries indicates a sectoral heterogeneity of industries, which allows us to observe how the interaction effects of exchange rate volatility
and labor market rigidity differentiate across industries and how the coefficient estimators might bedifferent depending on the nature of the industry. Export growth is the outcome variable. The twodigit industry-level annual data of exports from 2005 to 2018 were obtained from the Trade Map.
We included 17 manufacturing industries as sub-cross sections within the countries for the span of
14 years, from 2005 to 2018. Industries were added based on the availability of two-digit industrylevel data for export. The list of industries used in the study, along with their International Standard
Industrial Code (ISIC) and Harmonized Commodity (HS) code information, is presented in Table 1.
3.2

Exchange Rate Volatility

In this empirical analysis, the exchange rate volatility is constructed based on the following procedure.
First, to exploit the fact that different industry even in the same countries face the relatively different
exchange rate movement due to the fact that different industries in the same country have different
export destination, we calculate the shares of the top five partners of an exporting country for eachindustry. To avoid the endogeniety, we use the trade share of the initial three yeas as the weight
and we exclude the data of those year from the main regression. Second, we convert the nominal
exchange rate to the real exchange rate and calculate the difference of the log of the trade weighted
real exchange rate and its first lag, the second lag and the third lag. Finally, we regressed the differenceof the log of the traded weight real exchnage rate on its first lag, second lag, the thrid lag and calculate
the residual. We use the standard deviation of this residual in each year as the volatilty.
More specicially, we calcluate as follows. The industry-specific export weight can be expressed
1

Table 1C presents the list of sample countries for the study in Appendix C.

4

Table 1: Names of ISIC Two-Digit Industries Used in the Study#Name of Industry
1 Food and Beverages
2 Tobacco Products

HS 2-digit Code22

ISIC 2-digit Code
15

24

16

3 Textile
4 Wearing Apparels, fur

53

17

61

18

5 Leather, leather products and footwear
6 Wood Products (excluding furniture)

42

19

44

20

7 Paper and Paper Products
8 Printing and Publishing

48

21

49

22

9 Chemicals and Chemical Products
10 Rubber and Plastics Products

38

24

40

25

11 Non-metallic Mineral Products
12 Basic Metals

27

26

83

27

13 Fabricated Metal Products
14 Machinery and Equipment n.e.c.

72

28

84

29

15 Electronic Machinery and Apparatus
16 Motor Vehicles, Trailers, Semi-Trailers

85

31

87

34

17 Furniture; manufacturing n.e.c.

9436

Note: The ISIC abbreviation stands for International Standard Industrial Classification
for all economic activities. The HS stands for Harmonized Commodity description andcoding system.

as follows:
EXikjk∈top 5 EXikj

EXWikj = P(1)

where EXWikj is the export weight of industry i to destination country k by exporting country j isdenoted by . The average export of industry i from 2001 to 2004 to destination country k by exporting
country j is denoted by EXikj .
Second, we converted the nominal exchange rate of national currency per United States dollar
to the nominal exchange rate of the national currency in terms of United States dollar for exporting
country j and as well as for destination country k. As the export data is in United States dollars, so
we need to convert it to the currency of exporting country j, so we have:
NERjkmt =

Ekmt
Ejmt

(2)

where NERjkmt denotes the nominal exchange rate of destination country k in terms of the currency

5

of exporting country j at month m and year t, where Ekmt and Ejmt denote the nominal exchange
rates in terms of US dollar of destination country k and exporting country j, respectively. Second,
we convert the nominal exchange rates in terms of the currency of exporting country j to the realexchange rates in terms of the currency of exporting country j using country-specific Consumer Price
Index (CPI)2 .
The real exchange rate of exporting country j to destination country k in month M and year t canbe written as:
RERkjmt = NERkjmt ×

CPIkmtCPIjmt(3)

The real exchange rate of destination country k in terms of exporting country j at month m and year
t is denoted by RERkjmt . The CPI of destination country k and exporting country j at month m andyear t are denoted by CP Ikmt and CP Ijmt , respectively.
Step 3: Export weighted real exchange rate (EWRER)
Export weighted real exchange rate (EWRER) can be described as:
EWRERikjmt =X

EXWikj × RERkjmt(4)k∈top 5The export-weighted real exchange rate of industry i to destination country k from exporting country j in month m and year t is denoted by EWRERikjmt . The export weight is multiplied by thereal exchange rate, which varies by industry i and destination country k. This leads to considerable
heterogeneity in trade partners by the specific industry. For example, if one industry has five trade
partners, then one country for 17 industries may have 85 trade partners.
Step 4: Computed export-weighted real exchange rate volatility
To compute export-weighted real exchange rate volatility, we first took the logarithm of theexport-weighted real exchange rate (EWRERikjmt ), and calculated the difference of the logarithmof 12 months of export-weighted real exchange rates. We then generated the first, second, and thirdlags of the difference of logarithmic export-weighted real exchange rates, and regressed the differenceof logarithmic export-weighted real exchange rates on the first, second, and third lags of the differenceof logarithmic export-weighted real exchange rate. We obtained the residual from this regression, and2

To use more adequate CPI data, we calculated country-specific CPI from the inflation rate (which is the percentagechange in the monthly CPI from the previous year) by considering that CP It+1 is a function of πt , πt+1 ,. . . and CP It(where, π stands for inflation and t denotes time trend). We also assumed that the initial CPI is equivalent to one.

6

calculated the standard deviation of the residual to get the exchange rate volatility.
EWRER Volatilityikjt = SD( Residual of difference of logarithm-EWRERikjmt )(5)Finally, we standardized the volatility of exchange rate growth to address the unit of analysis problem
in aggregation when interpreting the estimated coefficients.
We used the monthly nominal exchange rate of national currency per US dollar and inflation rate
(percentage change in CPI from the previous period) for the period 2005-2018 from International
Financial Statistics (IFS) 3 . Once we obtain the monthly real exchange rate for each industry in eachcountry over time, we calculate difference of the log of the real exchnage rate, its first lag, the secondlag and the third lag. Then, we regress the difference of the log traded real exchange rate on its firstlag, the second lag and the third lag and calcualte the residual.
3.3

Labor Market Rigidty Index

According to Campos et al. (2018), most of the indices don’t reflect by any means all dimensions of
the labor market such as wage flexibility, team production, job rotation, social parts, different types ofpension plans and workers use of courts etc., which have affect economic outcomes in various ways.
However other indices capture a limited dimension of labor market regulation and used them as ameasure of strictness of labor market for firms. Therefore, this study used three different effective
datasets as a proxy for labor market rigidity for the empirical analysis. First, we use Labor Market
Legislation Rigidity Index (LAMRIG) that was constructed by Campos et al. (2018) 4 . Second, we
use Employment Law Rigidity Index developed by Botero et al. (2004). And third, we use Aggregate
Labor Market Rigidity index constructed by Forteza and Rama (2006).
This study use Labor Market Legislation Rigidity Index (LAMRIG) as a benchmark for measuring
as a proxy of labor market rigidity for several reasons. First, this is the most recent and comprehensive
data, covering more than 140 developed and developing countries. However, other indices cover only
richer countries. Second, this index is cross-sectional and time-variant, with each average period offive years from 1950-54 to 2000-2004, while other indices covering the post 1995 period. Third, this3

The monthly nominal exchange rate and inflation rate of Taiwan are obtained from the website of the National Statistics
Republic of China (Taiwan). The inflation rate data for missing periods in the International Monetary Fund (IMF)’s databaseis filled from the Food and Agriculture Organization (FAO). The annual inflation rate data of Bermuda from 2005 to 2018,
Uzbekistan from 2005 to 2010, and Yemen from 2015 to 2018 are obtained from the World Bank. The inflation rate data
for some missing periods of Syria and Venezuela are obtained from their national statistics office websites.
4
We thank Nauro F. Campos for providing the Labor Market Regulation Rigidity Index (LAMRIG) dataset.

7index is developed with more intention on employment protection for instance, it is constructed on
the basis of two pillars: (1) the employment law rigidity index constructed by Botero et al. (2004),
which developed based on core indicators of employment protection and (2) National Labour, Social
Security and Related Human Rights Legislation (NATLEX). National Labour, Social Security and
Related Human Rights Legislation (NATLEX) is a depository of labor law of the International Labour
Organization (ILO), which contains more than 20 legislation’s since the 1940s for 150 countries.
Therefore, this index captures various dimensions of labor market rigidity. Campos et al. (2018)
focused on the categories of National Labour, Social Security and Related Human Rights Legislationin constructing the labor market regulation rigidity index. For example, they included conditions ofwork (such as hours of work, weekly rest and paid leave), security of job, termination of employment,
conditions of employment (such as labor contract, wages and personal management), and generalprovisions (such as labor codes, general labor, and employment acts).
This index has considerable variation across countries since 1950, and It ranges between 0 to 3.5,
with higher values indicating a more rigid employment protection law. For instance, the United States,
Canada, Japan, the United Kingdom and Malaysia exhibited an average rigidity of labor market withvalues of 0.26, 0.32, and 0.51, 0.56 and 0.82 from 1950-54 to 2000-2004, respectively, which reflectsa more flexible employment protection law. Meanwhile, Spain, Poland, Slovakia, Mexico, Brazil,
Germany,the Netherlands, Estonia, Russia, China, Colombia, and Turkey experienced labor market
rigidity with values around 2 in the same period. In the data of labor market rigidity, there is adecreasing trend over time in the mean and increasing changes in the standard deviation of labormarket rigidity. Furthermore, there are heterogeneous changes in employment protection regulation
across countries. For example, some countries exhibit significant changes in the labor market rigidity
index, while other countries experience slight changes.
As the second measre of the labor market rigidity, we use Employment Law Rigidity Index:
The employment law rigidity index is an aggregate cross-section time-invariant index constructedby Botero et al. (2004). It covers 85 countries and the index values vary between 0 and 1, withhigher values reflecting stricter regulation of employment protection. The index is an average of four
subindices:, such as 1) firing costs of workers, 2) firing procedures, 3) cost of increasing hours worked,
and 4) alternative employment contracts. These subindices are themselves averages of several factors5
51) Cost of Firing Workers: This measure quantifies the expenses associated with terminating employees. It considers
factors such as the notice period, severance pay, and any mandatory penalties imposed by law or collective agreements. For
workers with three years of tenure, the index calculates the cost of firing. In cases where a worker’s firing is unlawful, theindex equates the cost of firing to the annual wage. The new wage bill encompasses both the regular wages of the remaining

8

As the third index, we use Aggregate Labor Market Rigidity index. This index is developed by
Forteza and Rama (2006) from the ratified labor laws of a country under the International Labor
Organization’s (ILO) conventions. The measure of this labor market rigidity is based on four pairsof more narrowly specified indicators such as minimum wages, mandated benefits, trade unions, andgovernment employment.
3.4

Computation of Export-Weighted Real Exchange Rate Volatility

We computed the export-weighted real exchange rate volatility in the following steps:
3.5

Descriptive Statistics

Table 2 presents the mean and standard deviation of the key variables used in this study. The large
number of countries and industries over a span of 14 years enabled us to have sufficient variationin the data and cover a massive proportional share of exports of manufacturing commodities in the
globe. The outcome variable is two-digit industry-level export growth. The mean of annual export
growth is 0.032, which indicates positive annual export growth with a standard deviation of 0.40. The
main explanatory variable is the interaction of exchange rate volatility and labor market rigidity. In
Panel A, the statistics of the interaction term and standardized labor market rigidity belong to the labormarket rigidity regulation index developed by Campos et al. (2018). Using this index, the number ofobservations is 12,567, covering 62 countries.
Control variables include the volatility of exchange rate growth, the growth of export-weighted
real exchange rate, and the first lag of the growth of real exchange rate. Panel B and Panel C report thestatistics of the interaction of exchange rate volatility and labor market rigidity, and standardized labormarket rigidity index constructed by Botero et al. (2004) and Forteza and Rama (2006), respectively.
The number of observations for these two indices are 10,787 and 9,764, and the number of countries
are 53 and 48, respectively. This indicates a lower number of observations and countries under these
two rigidity indices relative to the benchmark index. This is unbalanced panel data due to missing
data for specific industries (such as tobacco, etc.) or periods for some countries.
workforce and the cost of firing employees. Additionally, this index assesses the cost of firing workers by comparing the
new wage bill to the previous one.

9

Table 2: The Summary Statistics
Variable
Mean Std. Dev.
Panel A: LAMRIG index of Campos & Nugent (2018):
Dependent Variable
Export (in thousands USD)
Differece of logarithm of Export
Independent Variable
Interaction of Volatility and Rigidity
Controls & other Variables
Volatility of Exchange Rate Growth7437.697 29561.764
0.032
0.40
0.07
0.022

Max

0.001 664425.06
-5.71
7.17

0.90

-4.70

28.58

1.08

-0.32

35.73

0.991 -2.12
0.14 -1.32
0.11 -1.32
0.53 -4.07
23.19
22
3.45
2007
62/12567

1.78
2.20
1.66
6.90
94
2018

Interaction of Volatility and Rigidity-0.08
0.94 -23.54
Standardized Labor Market Rigidity-0.0011.00 -1.90
Countries/Observations
53/10, 585
Panel C: Aggregate Regulation Index of Forteza & Rama et al. (2006):

5.76
1.71

Interaction of Volatility and Rigidity
Standardized Labor Market Rigidity
Countries/Observations

4.14
2.32

Standardized Labor Market Rigidity
Difference of log-EWRER
First lag of differece of logarithmic EWRER
Export-Weighted GDP Growth Rate

0.014
0.007
0.001
0.44
56.11
2012.499

Industry
Year
Countries/Observations
Panel B: Employment Law Index of Botero et al. (2004):

4Min-0.026-0.0020.40 -8.16
1.00 -1.64
48/9, 764

Estimation Strategy

To quantify the interaction effect of exchange rate volatility and labor market rigidity on export
growth, the following econometric model is employed:∆yijt = β0 + β1 σijt + β2 LM Rj × σijt + β3 Zijt + ηj + ηi + ηt + ηjt + ηit + ηji + ijt

(6)

j=1, 2,. . . ,62, i=1, 2,. . . ,17, t=2005, 2006,. . . ,2018
where j, i, and t are the indices of country, industry, and year, respectively. ∆yijt is the logarithmic
export growth at industry level. σijt is industry-specific volatility of residualized logarithmic growthof 12 months averaged export-weighted real exchange rates. LM Rj is standardized time-invariant
labor market rigidity of country j. Zijt is a set of control variables, including exchange rate volatility
10growth, real exchange rate growth, and first lag of real exchange rate growth. ηj is country fixed effect,
ηi is industry fixed effect, ηt is time fixed effect, ηjt is country-time fixed effect, ηit is industry-time
fixed effect and ηji is country-industry fixed effect. The higher correlation among right-hand side
variables, such as volatility of exchange rate growth, interaction term, growth of real exchange rate,
and first lag of real exchange rate growth, may cause the multicollinearity problem. However, byexploiting fixed effects regression, this issue is treated appropriately.
Fixed effects models can measure an observation repeatedly over time for a specific industry
and a specific country. This identification strategy is analogous to the difference-in-difference-indifferences (DDD) estimation. In this model, country fixed effects are used to control for countrylevel factors that affect the export growth, independent of changes in industry and time trend. For
example, different countries have different GDP, population, legislative, and institutional policies,
all of which can affect export growth. Country fixed effects control for all of these observed andunobserved country-level factors affecting export growth. Industry fixed effect account for industry
level various factors that influence the exports of industries irrespective to the changes in country andtime. We have several industries and the availability of many industries may explore the different
industry-level characteristics in terms of size, use of technology, access to loan, policy changes etc.
The inclusion of such specifications control industry-level factors that affect the export growth ofindustries heterogeneously. Time fixed effects are included to control for time trend shocks, suchas business cycle fluctuations, over time, regardless of country or industry. For example, a global
economic recession would affect export growth in all countries and industries. Time fixed effects
control for these time-specific shocks. We also include country-time fixed effects and industry-time
fixed effects. Country-time fixed effects control for country-level factors that affect the growth ofexports over time trend, regardless of industry. For example, a change in government policy in acountry could affect export growth over time, regardless of the industry. Industry-time fixed effects
control for industry-level factors that affect the growth of exports in time-specific shocks, such as
trade costs and boom or bust specific to an industry. For example, a change in trade policy could
affect the export growth of a specific industry over time. Finally, we included country-industry fixed
effects with the intention of absorbing the factors that affect the growth of exports relative to variationin country and industry, regardless of variation over time period. For example, a country with amore rigid labor market may be more sensitive to exchange rate volatility than a country with a more
flexible labor market. The country-industry fixed effects will control for this country-industry specific11effect.
5

Empirical Results

Figure 1 shows the probability distribution of volatility of exchange rate growth after controllingfor various fixed effects. To obtain the volatility of real exchange rate, we regressed the growth of
exchange rate volatility on control variables and used all fixed effect specifications of equation (6),and obtained the residual. Figure 1 depicts the identification of variation in exchange rate volatility,
after partialling out the effects of fixed effect specifications and control variables on exchange rate
volatility. The histogram of exchange rate volatility has a normal distribution and depicts the variationin exchange rate volatility that is unexplained by fixed effects and control variables. This eliminatesconcerns about the inclusion of so many fixed effect specifications in the model, which might identify
the whole variation in export growth and leave no room for the volatility effects of real exchange rate
growth on export.
Figure 1 : Histogram of exchange rate volatility after controlling of fixed effects specifications.

0.05

Probability

.1

.15

Histogram of Exchange Rate Volatility

-.04

-.02
0
.02
Exchange Rate Volatility After Controlling Fixed Effects.04

The above histogram is obtained after regressing the real exchange rate volatility on country-specific effects, industry-specific effects, time-specific effects, country-time-specific effects, industry-time-specific effects, country-industry-specific effects, and controls. The controls include growth of real exchange rate andfirst lag of growth of real exchange rate. The volatility residuals are derived from a panel regression using dataof 62 countries from 2005 to 2018.

12

This paper conducts a country-industry level analysis of the interaction effects of exchange rate
volatility and labor market rigidity on export growth. We begin our empirical analysis by employing
the aforementioned benchmark index of labor market regulation rigidity, expanded by Campos et al.
(2018), as a proxy for labor market rigidity in Table 3, 4, and 5.
Table 3: The Effects of Interaction of Labor Market Rigidity and RealExchange Rate Volatility on Export Growth : Using Campus & Nugent (2018)'s LMR Index
Export Growth

Dependent Variable
Interaction of LMR & Exchange Rate Volatility
Volatility of Exchange Rate GrowthDifference of Log-EWRER

(1)

(2)

(3)

(4)

-0.0193-0.0335**-0.0129-0.0341**(0.0118)(0.0142)(0.0132)(0.0139)0.0173*0.0309**0.01320.0327**(0.00983)(0.0142)(0.0111)(0.0141)-0.0104

0.146-0.01200.123(0.0338)(0.0968)(0.0361)(0.0959)First Lag Differece of Log-EWRER

0.0674**-0.06190.0715**-0.0245(0.0326)(0.135)(0.0334)(0.132)

Constant

0.0332***0.107***

0.05590.105**(0.00230)(0.0314)(0.0401)(0.0476)

62/12,567

62/12,567

62/12,567

62/12,567

0.099

0.257

0.170

0.290

Yes
Yes

Yes

Yes
Yes
Yes

Countries/Observations
R-squared
Country-Industry FE
Country-Time FE

Yes

Industry-Time FE

Note: All columns included country fixed effects, industry fixed effects and time fixed effects.
Clustered robust standard errors are reported in parentheses, assuming that error terms arecorrelated within each country industry. * indicates significant at 1% level, ** indicates significant
at 5% level and *** indicates significant at 10% level.

To quantify the impact of the interaction of exchange rate volatility and labor market rigidity on
industry’s export growth, we controlled for the covariates e.g. volatility of real exchange rate growth,difference of logarithmic real exchange rate, first lag of the difference of logarithmic real exchange
rate, country fixed effect, industry fixed effect, and time fixed effect in all regression estimationsin Tables 3, 4, and 5. We also included the Country-industry fixed effect in columns 2, 3, and 4,
country-time fixed effect in columns 2 and 4, industry-time fixed effect in columns 3 and 4 in specific regression estimations. The reported standard errors are robust clustered errors in all regression
estimations.
Table 3 presents the regression estimations for all 62 countries in the sample. The dependent13

Figure 2: The Association between Labor Market Rigidity and the Estimated Effect of the Exchange Rate
Volatility on Export Growth
Scatter plot of LMR and Estimated Export Growth
LUX

4

CAN

Coefficients of Export Growth

BHR

2

NZL
AUS JPN

0

USA

CHE
GBR
IRL

SAU
GRC
ISR
TUR
KAZ
SWESVKCHL
PRTITA
BGRTHAMYS ZAF
TWD VNM UKR
RUS
FRA KOR
LTU
URY
CHN
ECU
IRN
COL
MEX
FIN
ISL
IDN
LVA
ROU EGY SVN
KWT IND
NLD
HUN
JOR
DEU
BEL
CZE
DNK
AUT
NOR
KGZ

BRA
EST
BLRESP
POL

CRI

-2
-2

-1
0
1
Standardized Labor Market Rigidity2

Note: The labor market rigidity regulation index developed by Campos et al. (2018) is used as a proxy forlabor market rigidity. The slope, standard error, and R-squared of the fitted line are -0.226, 0.119, and 0.056,
respectively.

Figure 3: The Association between Labor Market Rigidity and the Estimated Effect of the Exchange Rate
Volatility on Export Growth
Scatter plot of LMR and Estimated Export Growth

Coefficients of Export Growth

4

2

NZL
AUS JPN

0

USA

CHE
GBR
IRL

SAU
GRC
ISR
TUR
KAZ
SWESVKCHL
PRTITA
BGRTHAMYS ZAF
TWD VNM UKR
RUS
FRA KOR
LTU
URY
ECU
IRN
COL
MEXCHN
FIN
ISL
SVN
IDN
LVA
ROU
EGY
KWT IND
NLD
HUN
JOR
DEU
BEL
CZE
DNK
AUT
NOR

BRA
EST
BLRESP
POL

KGZ
CRI

-2
-2

-1
0
1
Standardized Labor Market Rigidity

2

Notes: The labor market regulations rigidity index developed by Campos et al. (2018) is used as a proxy forlabor market rigidity. In this figure, Bahrain, Canada, and Luxembourg are dropped from the sample. The
slope, standard error, and R-squared of the fitted line are -0.126, 0.054, and 0.086, respectively.

14

variable is export growth, and the main independent variable is the interaction of real exchange rate
volatility and labor market rigidity. The regression estimation in column 4 shows that the estimatedcoefficient of the interaction of volatility of exchange rate growth and labor market rigidity is negativeand statistically significant at the 5% level. This indicates that for a country where the rigidity of thelabor market is one standard deviation higher, one standard deviation increase in real exchange rate
volatility reduces export growth by 3.41 percentage points. The estimated coefficients are economically significant. The result implies that the interaction of exchange rate volatility and labor market
rigidity reduces annual export growth by 3.41 percentage points. The annual average export growthis 3.2 percentage points, so the economic effect of the interaction term is much larger. However,
the estimated coefficient is smaller relative to the 40% standard deviation of annual average export
growth.
Table 4: The Effects of Interaction of Labor Market Rigidity and RealExchange Rate Volatility on Export Growth : Using Campus & Nugent (2018)'s LMR Index
Export Growth

Dependent Variable
Interaction of LMR & Exchange Rate Volatility
Volatility of Exchange Rate GrowthDifference of Log-EWRER
First Lag Differece of Log-EWRER
Constant
Countries/Observations
R-squared
Country-Industry FE
Country-Time FE

(1)

(2)

(3)

(4)

-0.0211*-0.0330**-0.0153-0.0345**(0.0121)(0.0144)(0.0135)(0.0141)0.0192*0.0296**0.0156

0.0318**(0.00990)(0.0143)(0.0112)(0.0141)-0.00930

0.137-0.01170.110(0.0338)(0.0971)(0.0360)(0.0960)

0.0569*-0.0406

0.0615*0.00286(0.0325)(0.136)(0.0333)(0.132)0.0323***0.102***

0.05490.0975*(0.00235)(0.0315)(0.0417)(0.0502)

59/11,959

59/11,959

59/11,959

59/11,959

0.105

0.262

0.178

0.298

Yes
Yes

Yes

Yes
Yes
Yes

Industry-Time FE
Yes
Note: In this regression model we dropped Bahrain, Canada and Luxembourg. All columns
included country fixed effects, industry fixed effects and time fixed effects. Clustered robust
standard errors are reported in parentheses, assuming that error terms are correlated within eachcountry Íindustry. * indicates significant at 1% level, ** indicates significant at 5% level and ***indicates significant at 10% level.

Figure 2 presents a scatter plot of the coefficient effects of exchange rate volatility on export
15

growth 6 and labor market rigidity. The vertical axis shows the coefficient effects of exchange rate
volatility on export growth, and the horizontal axis shows standardized labor market rigidity. The
fitted line depicts a negative slope, indicating that the coefficient effects of exchange rate volatility on
export growth are negatively correlated with labor market rigidity.
Table 4 evidences the negative and statistically significant effect of the interaction of exchange rate
volatility and labor market rigidity on export growth, even after removing outliers (see Figure 2). The
number of countries in this sample decreased to 59 after dropping Bahrain, Canada, and Luxembourg.
The estimated coefficient remained consistent with the main results of Table 3. Empirical results showthat for a country with one standard deviation higher labor market rigidity, an increase in the volatilityof real exchange rate growth by one standard deviation decreases export growth by 3.45 percentage
points. The magnitude of the interaction effect of exchange rate volatility and labor market rigidity
is economically significant as well. This implies that the annual decrease in export growth due to theinteraction term is larger relative to the annual average export growth of 3.2%. However, the estimatedcoefficient is smaller relative to the 40% standard deviation of annual average export growth.
Figure 3 depicts the graphical association between the coefficient effects of exchange rate volatility on export growth 7 and labor market rigidity. The vertical axis denotes the coefficient effects of
exchange rate volatility on export growth, and the horizontal axis is the standardized labor market
rigidity. There is a negative relationship between the coefficient effects of exchange rate volatility
on export growth and labor market rigidity, even after dropping Bahrain, Canada, and Luxembourg,
which appear to be outliers in Figure 2.
Table 5 presents the sub-sample regression estimation for developed and developing countries.
The results suggest that the interaction effect of exchange rate volatility and labor market rigidity on
export growth is not significantly different between developed and developing countries. However,the size of the interaction effect is larger for developing countries (5.8 percentage points) than fordeveloped countries (3.88 percentage points). This suggests that exchange rate volatility and labormarket rigidity have a more pronounced negative impact on export growth in developing countries
6
First, we conducted a regression of the difference of the logarithm of exports on export-weighted real exchange rate
volatility, the difference of the logarithm of export-weighted real exchange rate, and the first lag of the difference of thelogarithm of export-weighted real exchange rate. We included industry-specific effects and time-specific effects for eachindividual country, and obtained the coefficients of the effects of exchange rate volatility on export growth.
7First, we conducted a regression of the difference of the logarithm of exports on export-weighted real exchange rate
volatility, the difference of the logarithm of export-weighted real exchange rate, and the first lag of the difference of thelogarithm of export-weighted real exchange rate. We included industry-specific effects and time-specific effects for eachindividual country, and obtained the coefficients of the effects of exchange rate volatility on export growth.

16

Table 5: The Effects of Interaction of Labor Market Rigidity and Volatility on Export Growth:
Using Campus & Nugent (2018)'s LMR Index for Developed and Developing Countries
Panel A: Developed Countries
Export Growth
Dependent Variable
Interaction of LMR & Exchange Rate Volatility

(1)

(2)

(3)

(4)

-0.0201***-0.0545**-0.0181***-0.0388**(0.00521)(0.0214)(0.00529)(0.0197)

Constant

0.0218***0.0475*0.0567***

0.0920***Countries/Observations(0.00246)
32/6,504(0.0247)
32/6,504(0.0149)
32/6,504(0.0313)
32/6,504

0.198

0.303

0.312

0.379

Yes
Yes

Yes
Yes

Yes
Yes
Yes
-0.0580***

R-squared
Country-Industry FE
Country-Time FE
Industry-Time FE
Panel B: Developing Countries
Interaction of LMR & Exchange Rate Volatility-0.0245-0.0401**-0.0281(0.0199)(0.0204)(0.0236)(0.0223)

Constant

0.0428***0.04250.01840.000502

Countries/Observations(0.00394)
30/6,063(0.0366)
30/6,063(0.0816)
30/6,063(0.0801)
30/6,063

0.072

0.240

0.152

0.285

Yes
Yes

Yes

Yes
Yes
Yes

R-squared
Country-Industry FE
Country-Time FE

Yes

Industry-Time FE

Note: All columns controls the volatility of exchange rate growth, the difference of logarithmic realexchange rate, the first lag of difference of logarithmic real exchage rate, country fixed effects,
industry fixed effects and time fixed effects. Clustered robust standard errors are reported inparentheses, assuming that error terms are correlated within each country Íindustry. * indicatessignificant at 1% level, ** indicates significant at 5% level and *** indicates significant at 10% level.

than in developed countries and the estimated coefficients are economically significant for both developed and developing countries.
6

Robustness Checks

In the robustness checks, we extended our empirical analysis by employing labor market rigidity
regulation indices constructed by Botero et al. (2004) and Forteza and Rama (2006) as a proxy forlabor market rigidity. We included various fixed effects specifications, such as country fixed effects,

17industry fixed effects, and time fixed effects in regression estimations of Table 6, 7, 8 and 9. Specifically, we included country-industry fixed effects in columns 2, 3, and 4; country-time fixed effects
in columns 2 and 4; and industry-time fixed effects in columns 3 and 4. The control variables werevolatility of real exchange rate growth, difference of logarithmic real exchange rate, and first lag ofthe difference of logarithmic real exchange rate. The reported standard errors were robust clusterederrors in all regression estimations.
Table 6: The Effects of Interaction of Labor Market Rigidity and RealExchange Rate Volatility on Export Growth : Using Botero et al. (2004)'s LMR Index
Dependent Variable
Export Growth
(1)
(2)
(3)
(4)
Interaction of LMR & Exchange Rate Volatility-0.0276*** -0.00660 -0.0308*** -0.0121(0.00733) (0.0126) (0.00738) (0.0116)
Volatility of Exchange Rate Growth-0.0116** -0.000739 -0.0130** -0.00294(0.00571) (0.0102) (0.00595) (0.00971)
Difference of Log-EWRER-0.0499*0.0609-0.0588**0.0393(0.0284)(0.0712)(0.0292)(0.0689)
First Lag Differece of Log-EWRER
0.02100.02820.02410.0495(0.0291)(0.123)(0.0292)(0.122)
Constant
0.0316*** 0.101***
0.06750.107**(0.00228) (0.0312)(0.0450)(0.0457)
Countries/Observations
R-squared

52/10,585
0.145Country-Industry FE
Country-Time FE

52/10,585
0.299

52/10,585
0.226

52/10,585
0.342

Yes
Yes

Yes

Yes
Yes
Yes

Industry-Time FE
Yes
Note: All columns included country fixed effects, industry fixed effects and time fixed effects.
Clustered robust standard errors are reported in parentheses, assuming that error terms arecorrelated within each country industry. * indicates significant at 1% level, ** indicatessignificant at 5% level and *** indicates significant at 10% level.

Table 6 shows the regression estimation by employing Botero et al. (2004)’s labor market rigidity
index and with 53 countries in the sample. The interaction term is negative and statistically significant
in columns 1, 2, and 3. However, the coefficient is statistically insignificant in column 4. Thissuggests that for a country with one standard deviation higher labor market rigidity, a one standard
deviation increase in real exchange rate volatility reduces export growth by 1.21 percentage points.
Although the estimated coefficient is statistically insignificant, Figure 4 shows a negative association18

between the coefficient effect of exchange rate volatility on export growth and labor market rigidity.
The estimated coefficient size is 1.21 percentage point, very close to the 3.2% annual average export
growth, which implies that the interaction effect reduces export growth by 1.21 percentage points ineach single year. This is economically significant. However, the estimated coefficient is smaller than
the 40% standard deviation of export growth.
Figure 4: The Association between Labor Market Rigidity and the Estimated Effect of the Exchange Rate
Volatility on Export Growth
Scatter plot of LMR and Estimated Export Growth
CAN

Coefficients of Export Growth

4

2

NZL
JPN
MYS
USA

0

GRC
AUS TUR
SWEKAZPRT
CHL
ITA
SVK
VNM
THAKORZAF
TWD BGRRUS
FRA
CHE
LTU UKRURYGBR ROUESPCOL ECUCHN
MEX
FIN
BRA
IRL
SVN
IDN LVA
IND
EGY
POL
NLD
HUN
JOR
DEU
BEL
CZE DNK
AUT
NOR
KGZ
ISR

-2
-2

-1

0
1
Standardized Labor Market Rigidity

2

Notes: The employment law index developed by Botero et al. (2004) is used as a proxy for labor market rigidity.
The slope, standard error, and R-squared of the fitted line are -0.20, 0.087, and 0.092, respectively.

In Figure 4, the vertical axis shows the estimated coefficient of the effect of exchange rate volatility on export growth8 , and the horizontal axis shows the standardized labor market rigidity. The slopeof the fitted line indicates a negative association between labor market rigidity and the coefficienteffects of exchange rate volatility on export growth.
Table 7 presents the regression estimation by using Botero et al. (2004)’s index for labor market
rigidity after removing outliers observed in Figure 4. The estimated results are consistent with thosein Table 6, and the estimated coefficient sizes are approximately the same. The regression estimatesshow negative but statistically insignificant effects of the interaction of exchange rate volatility andlabor market rigidity on export growth. However, Figure 5 depicts a negative relationship between the
8First, we conducted a regression of the difference of the logarithm of exports on export-weighted real exchange rate
volatility, the difference of the logarithm of export-weighted real exchange rate, and the first lag of the difference of thelogarithm of export-weighted real exchange rate. We included industry-specific effects and time-specific effects for eachindividual country, and obtained the coefficients of the effects of exchange rate volatility on export growth.

19Figure 5: The Association between Labor Market Rigidity and the Estimated Effect of the Exchange Rate
Volatility on Export Growth
Scatter plot of LMR and Estimated Export Growth

Coefficients of Export Growth

4

2

NZL
JPN
MYS
USA

0

GRC
AUS TUR
SWEKAZPRT
CHL
ITA
SVK
VNM
THAKORZAF
TWD BGRRUS
FRA
CHE
LTU UKRURYGBR ROUESPCOL ECUCHN
MEX
FIN
BRA
IRL
SVN
IDN LVA
IND
EGY
POL
NLD
HUN
JOR
DEU
BEL
CZE DNK
AUT
NOR
KGZ
ISR

-2
-2

-1

0
1
Standardized Labor Market Rigidity2

Note: The employment law index developed by Botero et al. (2004) is used as a proxy for labor market rigidity. Canada isdropped from this figure. The slope, standard error, and R-squared of the fitted line are -0.098, 0.05, and 0.071, respectively.

coefficient effects of exchange rate volatility on export growth and labor market rigidity. Compared tothe annual average export growth of 3.2%, the estimated coefficient size is economically significant,which shows a 1.21 percentage point annual decrease in export growth. However, the estimatedcoefficient is smaller relative to the 40% standard deviation of export growth.
Figure 5 shows a scatter plot of the negative relationship between the coefficients of export growthand labor market rigidity in different countries. The vertical axis is the estimated coefficient of theeffects of exchange rate volatility on export growth 9 , and the horizontal axis is the standardized
rigidity of the labor market. The slope of the fitted line shows a negative association between thecoefficient effects of exchange rate volatility on export growth and labor market rigidity.
Table 8 implies the regression estimation by employing Forteza and Rama (2006)’s labor market
rigidity index with the 48 countries in the sample. The regression estimates in column 4 show that,after controlling for all fixed effects specifications, the coefficient is negative but statistically insignificant. This suggests that for a country with one standard deviation higher labor market rigidity, a one
standard deviation increase in the volatility growth of the real exchange rate reduces export growth by
9First, we conducted a regression of the difference of the logarithm of exports on export-weighted real exchange rate
volatility, the difference of the logarithm of export-weighted real exchange rate, and the first lag of the difference of thelogarithm of export-weighted real exchange rate. We included industry-specific effects and time-specific effects for eachindividual country, and obtained the coefficients of the effects of exchange rate volatility on export growth.

20Table 7: The Effects of Interaction of Labor Market Rigidity and RealExchange Rate Volatility on Export Growth : Using Botero et al. (2004)'s LMR Index
Export Growth
Dependent Variable
(1)
(2)
(3)
(4)
Interaction of LMR & Exchange Rate Volatility-0.0277*** -0.00656 -0.0310*** -0.0121(0.00736) (0.0126) (0.00740) (0.0116)
Volatility of Exchange Rate Growth-0.0121** -0.000943 -0.0136** -0.00335(0.00592) (0.0106) (0.00616) (0.0100)
Difference of Log-EWRER-0.0514*0.0610-0.0606**
0.0385(0.0285)(0.0712)(0.0294)(0.0691)First Lag Differece of Log-EWRER
0.01660.02890.02000.0526(0.0292)(0.123)(0.0293)(0.123)
Constant
0.0324*** 0.101***
0.06820.107**(0.00232) (0.0313)(0.0459)(0.0463)
Countries/Observations
R-squared

51/10,583
0.145Country-Industry FE
Country-Time FE

51/10,583
0.299

51/10,583
0.226

51/10,583
0.341

Yes
Yes

Yes

Yes
Yes
Yes

Yes

Industry-Time FE

Note: In this regression model, we dropped Canada. All columns included country fixed
effects, industry fixed effects and time fixed effects. Clustered robust standard errors arereported in parentheses, assuming that error terms are correlated within each countryÍindustry. * indicates significant at 1% level, ** indicates significant at 5% level and *** indicatessignificant at 10% level.

5.69 percentage points. The results are economically significant as well, as the estimated coefficientis much larger than the 3.2% annual export growth. However, the estimated coefficient is smaller
than the 40% standard deviation of export growth. Although the results are statistically insignificant,Figure 6 depicts a negative relationship between the coefficient effects of exchange rate volatility on
export growth and labor market rigidity.
Figure 6 shows a scatter plot of the estimated coefficients of the effects of exchange rate volatility
on export growth against labor market rigidity. The vertical axis is the estimated coefficient of theeffects of exchange rate volatility on export growth10 , and the horizontal axis is the standardized labormarket rigidity. The fitted line has a negative slope, which indicates a negative relationship between
10First, we conducted a regression of the difference of the logarithm of exports on export-weighted real exchange rate
volatility, the difference of the logarithm of export-weighted real exchange rate, and the first lag of the difference of thelogarithm of export-weighted real exchange rate. We included industry-specific effects and time-specific effects for eachindividual country, and obtained the coefficients of the effects of exchange rate volatility on export growth.

21the estimated coefficients of export growth and labor market rigidity.
Table 8: The Effects of Interaction of Labor Market Rigidity and RealExchange Rate Volatility on Export Growth : Using Forteza & Rama (2000)'s LMR Index
Export Growth

Dependent Variable
Interaction of LMR & Exchange Rate Volatility
Volatility of Exchange Rate Growth

(1)

(2)

(3)

(4)

-0.0199-0.0553-0.0173-0.0569(0.0233)(0.0376)(0.0251)(0.0376)

0.00334-0.0106

0.00548-0.00924(0.00703)(0.0141)(0.00764)(0.0143)-0.0738*0.120-0.0822**0.104(0.0394)(0.0774)(0.0403)(0.0749)

First Lag Differece of Log-EWRER

0.0824*-0.07150.0826*-0.0679(0.0495)(0.174)(0.0498)(0.173)

Constant

0.0284***0.0841***

0.04420.0766(0.00252)(0.0319)(0.0494)(0.0511)

Countries/Observations

48/9,764

48/9,764

48/9,764

48/9,764

R-squared

0.104

0.276

0.181

0.311

Yes
Yes

Yes

Yes
Yes
Yes

Difference of Log-EWRERCountry-Industry FE
Country-Time FE

Yes

Industry-Time FE

Note: All columns included country fixed effects, industry fixed effects and time fixed effects.
Clustered robust standard errors are reported in parentheses, assuming that error terms arecorrelated within each country industry. * indicates significant at 1% level, ** indicates significant
at 5% level and *** indicates significant at 10% level.

Table 9 presents the empirical results by using Forteza and Rama (2006)’s labor market rigidity
index after dropping Bahrain, Canada, and Luxembourg from the sample, which appear to be outliersin Figure 6. The number of countries in the sample declined to 45. The results suggest that thecoefficient estimate of the interaction term has a negative relationship with export growth, with avalue of 6.07 percentage points. Although the estimated coefficient is statistically insignificant, Figure
7 shows a negative association between the coefficient effect of exchange rate volatility on export
growth and labor market rigidity.
Figure 7 explores the relationship between the coefficient effects of exchange rate volatility on export growth and labor market rigidity. The vertical axis shows the estimated coefficient of the effects
of exchange rate volatility on export growth11 , and the horizontal axis shows the standardized labor
11First, we conducted a regression of the difference of the logarithm of exports on export-weighted real exchange rate
volatility, the difference of the logarithm of export-weighted real exchange rate, and the first lag of the difference of the22

Figure 6: The Association between Labor Market Rigidity and the Estimated Effect of the Exchange Rate
Volatility on Export Growth
Scatter plot of LMR and Estimated Export Growth
LUX
CAN

4
Coefficients of Export Growth

BHR

2
GRC
NZL
ISR AUSTUR JPN
CHL
PRT
ITA
BGR
ZAF THAMYSVNM
TWD
RUS
KOR
FRA
CHE
CHNIDN
ECU
ESP
COL
MEX
FINGBR URY
BRA
BLR
IRL
USA
IND
EGY NLDPOL
JOR
DEU
BEL HUNDNKNOR AUT
KGZ

0

SWE

-2
-2

-1

0
1
Standardized Labor Market Rigidity2

Note: Forteza and Rama (2006)’s aggregate rigidity index is used as a measurement of labor market rigidity.
The slope, standard error, and R-squared of the fitted line are -0.21, 0.15, and 0.041, respectively.

Figure 7: The Association between Labor Market Rigidity and the Estimated Effect of the Exchange Rate
Volatility on Export Growth
Scatter plot of LMR and Estimated Export Growth

Coefficients of Export Growth

4

2

GRC
NZL
ISR AUSTUR JPN
CHL
PRT
ITA
VNM
BGR
ZAF THAMYSTWD
RUS
KOR
FRA
CHE
CHNIDN
ECU
ESP
COL
MEX
FINGBR URY
BRA
BLR
IRL
USA
IND
EGY NLD
POL
HUN
JOR
DEU
BEL
DNK
NOR AUT
KGZ

0

SWE

-2
-2

-1

0
1
Standardized Labor Market Rigidity2

Note: Forteza and Rama (2006)’s aggregate rigidity index is used as a measurement of labor market rigidity.
Bahrain, Canada and Luxembourg have been dropped from this figure. The slope, standard error, and R-squaredof the fitted line are -0.079, 0.054, and 0.046, respectively.

logarithm of export-weighted real exchange rate. We included industry-specific effects and time-specific effects for eachindividual country, and obtained the coefficients of the effects of exchange rate volatility on export growth.

23market rigidity. The fitted line has a negative trend, which reflects a negative association between theestimated coefficients of export growth and standardized cross-sectional labor market rigidity.
Table 9: The Effects of Interaction of Labor Market Rigidity and RealExchange Rate Volatility on Export Growth : Using Forteza & Rama (2000)'s LMR Index
Export Growth

Dependent Variable
Interaction of LMR & Exchange Rate Volatility
Volatility of Exchange Rate GrowthDifference of Log-EWRER
First Lag Differece of Log-EWRER

(1)

(2)

(3)

(4)

-0.0229-0.0587-0.0194-0.0607(0.0246)(0.0400)(0.0263)(0.0399)

0.00349-0.0138

0.00597-0.0132(0.00794)(0.0157)(0.00860)(0.0157)-0.0674*

0.105-0.0777*0.0815(0.0395)(0.0776)(0.0403)(0.0753)0.0542-0.0367

0.0551-0.0205(0.0495)(0.175)(0.0499)(0.173)0.0265***0.0760**0.04190.0653(0.00257)(0.0319)(0.0522)(0.0543)

Countries/Observations

45/9,156

45/9,156

45/9,156

45/9,156

R-squared

0.112

0.286

0.193

0.324

Yes
Yes

Yes

Yes
Yes
Yes

Constant

Country-Industry FE
Country-Time FE

Yes

Industry-Time FE

Note: In this regression estimation, we dropped Bahrain, Canada and Luxembourg. All
columns included country fixed effects, industry fixed effects and time fixed effects. Clusteredrobust standard errors are reported in parentheses, assuming that error terms are correlated
within each country Íindustry. * indicates significant at 1% level, ** indicates significant at 5%
level and *** indicates significant at 10% level.

In the robustness check estimations, we also controlled the export-weighted GDP growth of the
top five partners of an exporting country. The estimated coefficient is negative and statistically significant. After including export-weighted GDP growth, the estimated results are approximately thesame as the main findings. Table B1 and B2 present the regression estimations in the appendix.
Figure 8 presents the estimated coefficient plot of the interaction term of exchange rate volatility
and labor market rigidity on export growth for 17 industries. The labor market regulation rigidity
index developed by Campos et al. (2018) is used in this graph, and the sample contains 62 countries
for the period 2005-2018. More than half of the industries have negative estimated coefficients, andthe confidence intervals are narrower for most industries except for textiles and printing and publish24

Figure 8: The Estimated Coefficient effects of interaction term on export growth across industries

Estimated Coefficients of Interaction effects on Export Growth20

Furniture
Vehicles, Trailers etc
Electronics and Apparatus
Machinery and Equipments

Industry

Basic Metals
Fabricated Metal
Wearing Apparels
TextilePrinting and Publishing
Paper
Wood
LeatherRubber and Plastic
Chemicals
Non-Metallic Minerals
TabaccoFood & Beverage
0

-.4

-.2

0
Estimated Coefficients

LCI/UCI.2.4

Estimated Coefficients

Notes: In the above coefficient plot, we first regressed export growth on the interaction term of exchange rate
volatility and labor market rigidity, volatility of exchange rate growth, difference of logarithmic real exchange
rate, and first lag of difference of logarithmic real exchange rate for each industry separately. Additionally,
country fixed effects and time fixed effects specifications were included. Then, we calculated estimated coefficients and confidence intervals for each industry. The labor market regulation rigidity index developed byCampos et al. (2018) was utilized and the sample contained 62 countries for the period 2005-2018.

ing. The narrower confidence intervals for most industries indicate that we have higher confidence
in the estimated coefficients. The result of the graph is consistent with our main empirical findings.
The magnitude of the estimated coefficients vary across industries. In the presence of labor market
rigidity industries such as vehicle, basic metals, fabricated metal, leather, non-metallic, tobacco, andfood & beverage are more sensitive to the exchange rate volatility. However, furniture, electronics
and apparatus, machinery and equipment, printing and publishing, paper, rubber and plastic, chemical
and wood industries are less sensitive to exchange rate volatility in presence of labor market rigidity.

25

7

Discussion

Previous literature focused on exchange rate shocks and interacted them with labor market rigidity to
examine its effects on employment for a single country (Bação and Cerejeira, 2010) or specific groupof countries (such as OECD countries) (Alexandre et al., 2017). Ishise (2019) interacted exchange ratevariability with wages rigidity and examined its effects on exports. Whereas, this study empiricallyinvestigated how the impact of exchange rate volatility on export growth depends on the firing rigidity
in the labor market.
This study builds on various indices of labor market regulation rigidity developed by Campos
et al. (2018), Botero et al. (2004), and Forteza and Rama (2006). The benchmark result is based on
the labor market regulation rigidity index developed by Campos et al. (2018). Table 10 summarizes
the estimated coefficients of interaction term, standard errors, and R-squared for different labor market rigidity indices with all countries in the sample and after dropping countries seems to be outliers.
The regression estimation in Panel A, which is based on Campos et al. (2018) labor market rigidity
index indicates that for a country where labor market rigidity is one standard deviation higher, one
standard deviation increase in exchange rate volatility growth depress export growth by 3.41 percentage points and after dropping the outlier, the estimated coefficient size become slightly larger to3.45 percentage point. The effects of exchange rate volatility on export growth in presence of labormarket rigidity is economically significant as well. The estimated coefficient of interaction terms is
economically significant as well. The economic magnitude of interaction terms implies that exchange
rate volatility reduce export growth annually by 3.41 percentage points of countries with higher labormarket rigidity, which is larger than 3.2% annual average export growth, whereas, relative to the 40%
standard deviation of export growth, the estimated coefficient is much smaller. Panel B and Panel C
evidence the negative interaction effect of exchange rate volatility and labor market rigidity on export
growth. The estimated coefficients are negative and statistically insignificant, however, the coefficients are consistent with main results and the scatter plots of the coefficient effects of exchange rate
volatility on export growth and labor market rigidity in Figure 4, 5, 6 and 7 also describe negative
association. In sub-sample analysis, the interaction effect is statistically significant and negative forboth developed countries and developing countries, but the effect is more pronounced for developing
countries.

26

Table 10: Summarized Empirical Results of Interaction Term
LMR Dataset

Outlier(s) Countries/
Estimated
Removed Observations CoefficientSE

R-squared

62/12,567-0.0341**-0.0140.290

59/11,959-0.0345**-0.0140.298

52/10,585-0.0121(0.0116)0.342

51/10,583-0.0121(0.0116)

0.341

48/9,764-0.0569-0.0380.311

45/9,156-0.0607-0.04

Panel A: Labor Market Regulation Index,
Expanded by Campos & Nugent (2018)

Yes
Panel B: Employment Law Index,
Constructed by Botero et al. (2004)
Yes
Panel C: Aggregate Rigidity Index,

Developed by Forteza & Rama (2006)

0.324
Yes
Note: This table summarizes the estimated coefficients of interaction term of exchange rate volatility
growth and labor market rigidity over different indices of rigidity. The estimated coefficients,
clustered robus errors and r-squared obtained from the regression model, after using control
variables and all fixed effects specifications of equation (6).

8Conclusion

In the theoretical and empirical research, the volatility impact of exchange rate on trade (Arize et al.,
2000; Caporale and Doroodian, 1994), productivity (Aghion et al., 2009; Baggs et al., 2009; Caglayan
and Demir, 2014), investment (Kandilov and Leblebicioğlu, 2011; Atella et al., 2003; Kiyota and
Urata, 2004) and economic growth (Morina et al., 2020) has been explored intensively. However,there is little about how the fluctuations in real exchange rate affect the export growth in responseto the rigidity in labor market. We implement our analysis by using comprehensive country-industry
panel data of 17 industries for the span of 14 years from 2005 to 2018 in 62 developed and developing countries. The labor market rigidity index developed by Campos et al. (2018) is used as thebenchmark index. This index builds not only on the four components introduced by Botero et al.
(2004)’s employment law rigidity index, but also considers the conventions of the International Labor
Organization approved by countries. This is the most recent index, covers more than 140 countries
with having variation over periods. The labor market rigidity indices developed by Forteza and Rama(2006) and Botero et al. (2004) are used for the robustness checks estimation. We exploited thevolatility of real exchange rate growth uniquely by imposing specific assumption. The assumption isthat firms’ decisions are negatively affected by the unpredictable fluctuations in exchange rate during
the past three months. We calculated exchange rate by regressing difference of logarithmic export-

27

weighted real exchange rate on the first, second and third lags of the difference of logarithmic exportweighted real exchange rate, and obtained the residual. In order to get volatility of real exchange
rate growth, we calculated the standard deviation of monthly residualized difference of logarithmic
export-weighted real exchange rate. Thus, exchange rate volatility is calculated by fluctuations in realexchange rate of the past three months.
The fixed effects panel model is employed for regression estimation, which is analogous todifference-in-difference-in-differences and is considered as a potential empirical model for such detailed panel data. The identification of variation in the model is coming from the country, industry andtime trend. We controlled the volatility of real exchange rate growth, the difference of the logarithm ofexport-weighted real exchange rate and the first lag of the difference of logarithm of export-weighted
real exchange rate. Furthermore, by inclusion of various fixed effects specifications, for example
fixed effects, industry fixed effects, time fixed effects, country-time fixed effects, industry-time fixed
effects and country-industry fixed effects, we controlled all country level, industry level and timetrend factors that affects the export growth of industries.
The most striking finding of this paper is that the interaction of exchange rate volatility and labormarket rigidity has an economically and statistically negative impact on industrial export growth.
Empirical result implies that for a country where labor market rigidity is one standard deviation
higher, one standard deviation increase in volatility of exchange rate growth reduces industrial exports
growth by 3.41 percentage points. This indicates that 3.41 percentage points decrease in annual
exports is much considerable, because the annual average export growth for the sample countries is
3.2 percent and the estimated coefficient size is larger than annual average export growth. However,standard deviation of export growth is 40 percent and the estimated coefficient is smaller in relative tothe standard deviation of export growth. Furthermore, in sub-sample empirical results for developed
and developing countries results are also negative and statistically significant alike main findings. The
robustness check results also support the main findings. Moreover, we find that there is heterogeneous
effects of the interaction of exchange rate volatility and labor market rigidity on export growth ofindustries. The estimated coefficients are vary across industries and the export growth of the mostindustries are more sensitive to the effects of exchange rate volatility in the presence of labor market
rigidity.
Policy recommendations have also evolved towards a more flexible and balanced labor market.
A flexible labor market regulation is needed that not only protects employees from labor market risk,

28

but also allows firms to respond to macroeconomic shocks effectively and efficiently. Accordingly,
as suggested by the Oecd (2004), the flexibility expressed by firms should not be opposed to theimportance of protecting workers against labor market risk.

29

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3

9
9.1

Appendix
Appendix A
Table A1: Standard Deviation of Macroeconomic Price Variables
Countries
Variables
Standard Deviation
Japan
Exchange rate Growth
0.023
CPI Growth
0.003
Interest Rate Growth
0.000
United Kingdom
Exchange rate Growth
0.023
CPI Growth
0.003
Interest Rate Growth
0.002
Germany
Exchange rate Growth
0.022
CPI Growth
0.003
Interest Rate Growth
0.002
India
Exchange rate Growth
0.019
CPI Growth
0.008
Interest Rate Growth
0.003
Note: The authors calculated the standard deviation from the data.
These are standard deviations of the difference of log of variables.

9.2

Appendix B

4

Table B1: The Effects of Interaction of Labor Market Rigidity and RealExchange Rate Volatility on Export Growth : Using Campus & Nugent (2018)'s LMR Index
Export Growth

Dependent Variable
(1)

(2)

(3)

(4)

-0.0192*-0.0336**-0.0131-0.0341**(0.0116)(0.0143)(0.0130)(0.0140)0.0184*0.0307**0.01470.0326**(0.00968)(0.0143)(0.0109)(0.0141)

Difference of Log-EWRER-0.00329

0.147-0.00299

0.123(0.0340)(0.0967)(0.0364)(0.0957)First Lag Differece of Log-EWRER

0.0678**-0.06220.0736**-0.0248(0.0326)(0.135)(0.0334)(0.132)

Export-Weighted GDP Growth Rate

0.0320***-0.00547

0.0387***-0.00408(0.0114)(0.0151)(0.0148)(0.0158)

Constant

0.0192***0.112***

0.02450.109**(0.00557)(0.0341)(0.0419)(0.0499)

62/12,567

62/12,567

62/12,567

62/12,567

0.100

0.257

0.17

0.290

Yes
Yes

Yes

Yes
Yes
Yes

Interaction of LMR & Exchange Rate Volatility
Volatility of Exchange Rate Growth

Countries/Observations
R-squared
Country-Industry FE
Country-Time FE

Industry-Time FE
Yes
Note: All columns included country fixed effects, industry fixed effects and time fixed effects.
Clustered robust standard errors are reported in parentheses, assuming that error terms arecorrelated within each country industry. * indicates significant at 1% level, ** indicatessignificant at 5% level and *** indicates significant at 10% level.

9.3

Appendix C

5

Table B2: The Effects of Interaction of Labor Market Rigidity and RealExchange Rate Volatility on Export Growth : Using Campus & Nugent (2018)'s LMR Index
Export Growth

Dependent Variable
Interaction of LMR & Exchange Rate Volatility
Volatility of Exchange Rate GrowthDifference of Log-EWRER

(1)

(2)

(3)

(4)

-0.0210*-0.0331**-0.0152-0.0345**(0.0119)(0.0145)(0.0133)(0.0142)0.0200**0.0295**0.0166

0.0318**(0.00977)(0.0144)(0.0110)(0.0141)-0.00293

0.137-0.00405

0.110(0.0340)(0.0970)(0.0364)(0.0958)

First Lag Differece of Log-EWRER

0.0577*-0.0407

0.0636*0.00286(0.0324)(0.136)(0.0333)(0.132)

Export-Weighted GDP Growth Rate

0.0273**-0.00175

0.0312**-5.75e-05(0.0114)(0.0148)(0.0148)(0.0155)

Constant
Countries/Observations
R-squared
Country-Industry FE
Country-Time FE

0.0203***0.104***

0.0297

0.0976*(0.00564)(0.0339)(0.0433)(0.0522)59/11,959

59/11,959

59/11,959

59/11,959

0.105

0.262

0.179

0.298

Yes
Yes

Yes

Yes
Yes
Yes

Industry-Time FE
Yes
Note: In this regression model we dropped Bahrain, Canada and Luxembourg. All columns
included country fixed effects, industry fixed effects and time fixed effects. Clustered robust
standard errors are reported in parentheses, assuming that error terms are correlated within eachcountry Íindustry. * indicates significant at 1% level, ** indicates significant at 5% level and ***indicates significant at 10% level.

6

Table C1: List of Countries used in this Paper
Panel A: Developed Countries
Panel B: Developing Countries
1 𝐵𝑎ℎ𝑟𝑎𝑖𝑛 ,17 𝐾𝑢𝑤𝑎𝑖𝑡
1 𝐴𝑢𝑠𝑡𝑟𝑎𝑙𝑖𝑎 , ,17 𝐽𝑎𝑝𝑎𝑛 , ,18 𝐿𝑎𝑡𝑣𝑖𝑎 ,2 𝐵𝑒𝑙𝑎𝑟𝑢𝑠 ,18 𝐾𝑦𝑟𝑔𝑦𝑧𝑠𝑡𝑎𝑛 , ,2 𝐴𝑢𝑠𝑡𝑟𝑖𝑎 , ,3 𝐵𝑒𝑙𝑔𝑖𝑢𝑚 , ,4 𝐶𝑎𝑛𝑎𝑑𝑎 , ,5 𝐶𝑧𝑒𝑐ℎ 𝑅𝑒𝑝𝑢𝑏𝑙𝑖𝑐 ,6 𝐷𝑒𝑛𝑚𝑎𝑟𝑘 , ,7 𝐸𝑠𝑡𝑜𝑛𝑖𝑎
8 𝐹𝑖𝑛𝑙𝑎𝑛𝑑 , ,9 𝐹𝑟𝑎𝑛𝑐𝑒 , ,10 𝐺𝑒𝑟𝑚𝑎𝑛𝑦 , ,11 𝐺𝑟𝑒𝑒𝑐𝑒 , ,12 𝐻𝑢𝑛𝑔𝑎𝑟𝑦 , ,13 𝐼𝑐𝑒𝑙𝑎𝑛𝑑
14 𝐼𝑟𝑒𝑙𝑎𝑛𝑑 , ,15 𝐼𝑠𝑟𝑎𝑒𝑙 , ,16 𝐼𝑡𝑎𝑙𝑦 , ,19 𝐿𝑖𝑡ℎ𝑢𝑎𝑛𝑖𝑎 ,20 𝐿𝑢𝑥𝑒𝑚𝑏𝑜𝑢𝑟𝑔 ,21 𝑁𝑒𝑡ℎ𝑒𝑟𝑙𝑎𝑛𝑑𝑠 , ,22 𝑁𝑒𝑤 𝑍𝑒𝑎𝑙𝑎𝑛𝑑, ,23 𝑁𝑜𝑟𝑤𝑎𝑦 , ,24 𝑃𝑜𝑙𝑎𝑛𝑑 , ,25 𝑃𝑜𝑟𝑡𝑢𝑔𝑎𝑙 , ,26

𝑆𝑙𝑜𝑣𝑎𝑘𝑖𝑎 ,27 𝑆𝑙𝑜𝑣𝑒𝑛𝑖𝑎 ,28 𝑆𝑝𝑎𝑖𝑛 , ,29 𝑆𝑤𝑒𝑑𝑒𝑛 , ,30

𝑆𝑤𝑖𝑡𝑧𝑒𝑟𝑙𝑎𝑛𝑑 , ,31 𝑈𝑛𝑖𝑡𝑒𝑑 𝐾𝑖𝑛𝑔𝑑𝑜𝑚 , ,32 𝑈𝑛𝑖𝑡𝑒𝑑 𝑆𝑡𝑎𝑡𝑒𝑠 𝑜𝑓 𝐴𝑚𝑒𝑟𝑖𝑐𝑎 , ,3 𝐵𝑟𝑎𝑧𝑖𝑙 , ,4 𝐵𝑢𝑙𝑔𝑎𝑟𝑖𝑎 , ,19 𝑀𝑎𝑙𝑎𝑦𝑠𝑖𝑎 , ,20 𝑀𝑒𝑥𝑖𝑐𝑜 , ,5 𝐶ℎ𝑖𝑙𝑒 , ,6 𝐶ℎ𝑖𝑛𝑎 , ,21 𝑅𝑜𝑚𝑎𝑛𝑖𝑎 ,22 𝑅𝑢𝑠𝑠𝑖𝑎𝑛 𝐹𝑒𝑑𝑒𝑟𝑎𝑡𝑖𝑜𝑛 , ,7 𝐶𝑜𝑙𝑜𝑚𝑏𝑖𝑎 , ,8 𝐶𝑜𝑠𝑡𝑎 𝑅𝑖𝑐𝑎

23 𝑆𝑎𝑢𝑑𝑖 𝐴𝑟𝑎𝑏𝑖𝑎
24 𝑆𝑜𝑢𝑡ℎ 𝐴𝑓𝑟𝑖𝑐𝑎 , ,9 𝐸𝑐𝑢𝑎𝑑𝑜𝑟 , ,10 𝐸𝑔𝑦𝑝𝑡 , ,25 𝑇𝑎𝑖𝑤𝑎𝑛 , ,26 𝑇ℎ𝑎𝑖𝑙𝑎𝑛𝑑 , ,11 𝐼𝑛𝑑𝑖𝑎 , ,12 𝐼𝑛𝑑𝑜𝑛𝑒𝑠𝑖𝑎 , ,27 𝑇ü𝑟𝑘𝑒𝑦 , ,28 𝑈𝑘𝑟𝑎𝑖𝑛𝑒 ,13 𝐼𝑟𝑎𝑛
14 𝐽𝑜𝑟𝑑𝑜𝑛 ,29 𝑈𝑟𝑢𝑔𝑢𝑎𝑦 , ,30 𝑉𝑖𝑒𝑡 𝑁𝑎𝑚 , ,15 𝐾𝑎𝑧𝑎𝑘ℎ𝑠𝑡𝑎𝑛 ,16 𝐾𝑜𝑟𝑒𝑎, 𝑅𝑒𝑝𝑢𝑏𝑙𝑖𝑐 𝑜𝑓 , ,Note: The countries are selected based on the availability of data.
a denotes list of countries in the sample of labor market rigidity index developed by Campus & Negunt (2018).

b denotes list of countries in the sample of labor market rigidity index constructed by Botero et al. (2004).
c denotes list of countries in the sample of of labor market rigidity index expanded by Forteza and Rama (2006).

7