Wage inequality and trade reform:

productivity channel

Oleksandr Shepotylo∗and Volodymyr Vakhitov†

November 21, 2012

Abstract

We explore the productivity channel of rising wage inequality within

manufacturing industries. To solve the endogeneity problem, we use

the trade and services liberalization episode in Ukraine in 2001-2007

as a source of exogenous variation. We con�rm the main predictions

of new models that link productivity and wages. First, �rms that use

liberalized goods and services more extensively have higher gains in

productivity and and are more likely to export. These gains in turn

result in heterogeneous growth in wages and higher wage inequality.

Second, extensive margins of trade non-linearly in�uence industry in-

equality of wages. The share of exporters within an industry increases

∗Kyiv School of Economics and Kyiv Economic Institute, oshepotylo@kse.org.ua†Kyiv School of Economics and Kyiv Economic Institute, vakhitov@kse.org.ua

1

wage inequality when the share of exporters is low and reduces wage

inequality when the share of exporters is close to 1.

1 Introduction

Rising wage inequality over the last 20 years, which according to OECD

(2012) was the main contributor to the rising household income inequality

in both rich and poor countries, is often linked to globalization. However,

the evidence on the link is not clear cut. According to Goldberg & Pavcnik

(2007), trade liberalization in Chile in 70's, in Mexico in 80's, in Argentina,

Colombia, and India in 90's came along with increasing wage inequality.

OECD, on the other hand, reports that increased wage gap between bottom

10 and top 90 wage earners in the OECD countries came primarily through

the skill-biased technical change and through the institutional and regulatory

reforms (OECD, 2012).

The traditional Heckscher-Ohlin model fails to explain an increasing wage

gap between skilled and unskilled labor that occurred both in rich and poor

countries over the last two decades. In addition, it cannot account for large

variation of wages within a narrowly de�ned industry reported in the liter-

ature (Dunne et al., 2004). Recently, Helpman et al. (2010, hereafter HIR)

(and some others authors, including Egger & Kreickemeier (2012); Felber-

mayr et al. (2011); Amiti & Davis (2012)) suggested a new class of models

that link changes in wage inequality to the distributional changes in produc-

2

tivity within an industry. In their framework, heterogeneous �rms, facing a

labor market with search and matching frictions, in equilibrium pay wages

that increase with productivity. In addition, exporting �rms pay higher wages

relative to the �rms with the same productivity in the closed economy. As a

result, distribution of wages within the industry has the same shape as the

distribution of productivities. In HIR trade openness in�uences wage inequal-

ity within the industry through changes in the share of exporting �rms. The

relationship is bell-shaped � trade openness increases wage inequality when

trade openness is low and reduces wage inequality when trade openness is

high.

This paper tests the main theoretical predictions of this class of mod-

els, focusing mainly on the HIR model. First, the model predicts that the

average wage across �rms within an industry is an increasing function of

productivity with constant elasticity of wage with respect to productivity.

Second, exporters pay higher wages, which jumps up discontinuously at the

threshold productivity level separating exporters from non-exporters. Third,

wage inequality within the industry is increasing when the share of exporting

�rms is small and decreasing when the share of exporting �rms approaches

1.

Testing these predictions is hard for several reasons. First, the wage-

productivity relationship is plagued with endogeneity. In HIR, in equilibrium

more productive �rms employ workers with higher abilities. In addition, pro-

ductivity within a �rm depends on wages and bonuses that reward exerting

3

more e�ort.1. Hence, the unobserved average ability and e�ort are positively

correlated with productivity, causing an upward bias in the estimation of

the e�ect of productivity on wages. Therefore, a source of exogenous vari-

ation is needed to estimate the e�ect. We propose a well-known link from

deregulation to productivity as such variation. Recent studies of services

and trade liberalization (Amiti & Konings, 2007; Arnold et al., 2011; Fer-

nandes & Paunov, 2011; Khandelwal & Topalova, 2011) �nd a positive e�ect

of the liberalization on productivity of manufacturing �rms. The size of

the e�ect varies across �rms because of di�erences in intensity, with which

�rms use liberalized goods and services as inputs. We use the episode of

the Ukrainian trade and services liberalization in 2001-2007, isolated from

other major deregulatory changes and driven by political pressure imposed

by Ukraine's trading partners as a precondition for the Ukrainian WTO ac-

cession, as the source of the variation. Shepotylo & Vakhitov (2012) �nd

that, on average, the episode led to a 9.2 percent increase in productivity of

the downstream manufacturing �rms.

Second, price di�erences within an industry are embodied in the out-

put and productivity measures. Variation in prices may re�ect idiosyncratic

demand shocks or market power, leading to overestimation of technical e�-

ciency of �rms Foster et al. (2008). It is not possible to separate the variation

in productivity from the variation in markups directly, because we do not

1Lazear & Rosen (1981)on the e�ect of wage dispersion within a �rm on the level ofe�ort. Akerlof & Yellen (1990) on fair wages

4

observe �rm-speci�c prices in the data. We introduce a demand system into

the estimation of the production function, following De Loecker (2011), to

eliminate the e�ect of the demand shocks. In addition, we control for the

remaining variation in prices by computing the �rm-speci�c markups follow-

ing De Loecker & Warzynski (2009). As a result, we are able to mitigate the

measurement error problem by disentangling the �true� productivity pass-

through on wages from the markup pass-through.

Our main results are as follows. First, an increase in productivity leads

to an increase in the average wage per worker, but the e�ect is not as strong

as the OLS results suggest. The wage elasticity of productivity is 0.153 in

the baseline speci�cation. An increase in the markup also results in a higher

wage, but the pass-through is twice lower in the baseline speci�cation. Sec-

ond, exporters pay a 6 percent wage premium. Wage elasticity of exporters

is not statistically di�erent from the wage elasticity of non-exporters, as the

theoretical model suggests, which points that Pareto distribution of produc-

tivity and wages �ts reality quite well. Finally, we �nd evidence supporting

the bell-shaped relationship between the coe�cient of variation of wages and

the share of exporting �rms in the industry.

The rest of the paper proceeds as follows. Section 2 discusses a theoretical

mechanism that links liberalization of services and distribution of wages in

manufacturing industries. Section 3 describes the data. Section 4 outlines

the identi�cation strategy. Section 5 presents results. Section 6 concludes.

5

2 Fair wage hypothesis and its implications

2.1 Productivity and wages

In the Melitz model (Melitz, 2003), the productivity distribution of �rms is

�xed. The e�ect of trade liberalization on productivity comes only through

the re-distribution of resources from less productive �rms to more productive

�rms. However, the literature documents a positive e�ect of trade Amiti &

Konings (in Indonesia and India - 2007); Khandelwal & Topalova (in Indone-

sia and India - 2011) and services (in the Czech Republic, Chile, and Ukraine

� Arnold et al. (2011), Fernandes & Paunov (2011), and Shepotylo & Vakhi-

tov (2012)) deregulation on productivity of manufacturing �rms. Hence, in

addition to the redistribution e�ect, the industry level aggregates may change

due to a shift in the whole distribution of productivities. Therefore, a more

realistic model would recognize that as the economy opens up, distribution

of productivity changes. In particular, trade liberalization have a direct,

positive e�ect on productivity within a �rm, with exporting �rms bene�ting

disproportionally more.

2.2 Theoretical framework

In HIR framework, workers ex-post are heterogeneous. Heterogeneous �rms

invest in screening to cut-o� the least able workers. More productive �rms

have higher returns to screening, invest in screening to set a higher ability

cut-o�, and end up with workforce of higher average ability. Due to the

6

search frictions and more costly replacement of higher ability workers, more

productive �rms pay higher wages. When economy opens up, self-selection

of �rms into non-exporters and exporters raises the revenue of exporters

relative to non-exporters, further inducing exporters to invest in screening.

As a result, exporters pay higher wages for a given productivity level. The

resulting average wage per worker ω as the function of productivity θ and

export threshold θx is

ω = Υ(θ)λωd

(θ

θd

)η(1)

where

Υ(θ) =

1, θ < θx

Υx > 1, θ = θx

, θd is a zero pro�t productivity cut-o�, and ωd is a wage set by the least

productive �rm. The functional form of this equation holds for production

functions with either one or two factors of production, which allows us to

employ two measures of productivity in the empirical analysis � labor pro-

ductivity and total factor productivity � without changing the estimated

equation. Figure 4 illustrates how the average wage per worker depends on

productivity.

We extend the HIR set up by allowing the productivity distribution to

depend on the regulatory environment R.

Gθ(θ, R) = 1−(θminθ

)z(R)

7

Figure 1: Wage and productivity

where ∂z∂R

< 0 and z > 2.2 As a result of deregulation, �attening of the

productivity distribution, represented by lower z in Figure 2, increases the

coe�cient of variation as well as the mean and variance of the distribution

of productivities: ∂E(θ)∂R

= −θmin(z−1)2

∂z∂R

> 0, ∂var(θ)∂R

=−2(z2+z+1)θ2min(z−1)3(z−2)2

∂z∂R

> 0, and

∂CV (θ)∂R

= −z+1[z(z−2)]1.5

∂z∂R

> 0.

Alternatively, regulatory reforms can increase the lower bound of pro-

ductivities, θmin(Figure 3) , which would also lead to an increase in average

productivity and higher variance :E(θ) = zθminz−1 ,

∂E(θ)∂R

= zz−1

∂θmin∂R

> 0. and

var(θ) =zθ2min

(z−1)2(z−2) ,∂var(θ)∂R

= 2zθmin(z−1)2(z−2)

∂θmin∂R

> 0, z > 2. However, it will not

2Shepotylo & Vakhitov (2012) �nd evidence that �rms with higher productivity gainmore from the services liberalization, which can be modeled as the reduction in the shapeparameter z.

8

Figure 2: Shift in distribution of �rms due to regulatory changes. Increasein shape parameter

9

change the coe�cient of variation, CV (θ) = 1√z(z−2)

and other scale invari-

ant measures of inequality within an industry. This result crucially depends

on the assumptions of Pareto distributed productivity. Cut-o�s in HIR are

determined by the following equations

[Υ

δkλ

x − 1](θx

θd

) δkη

=fxfd

(2)

and ˆ ∞θmin

π(θ)dG(θ) = fe (3)

where fd, fx, and fe are �xed costs of producing, exporting, and entry.

k > 1 determines the shape of distribution of worker's ability and δ > 0 is a

parameter of the screening technology. Consider a partial equilibrium result,

when the reform does not e�ect aggregate demand. Clearly, the ratio θxθd

is

�xed and does not depend on θmin. It can be also shown that the ratio θdθmin

will not change as well. Therefore, the increase in minimum productivity

will proportionally increase the productivity cut-o�s and will not change the

inequality in wage distribution within an industry. The reduction in z, on

the other hand, will have the e�ect on wage inequality.

As HIR show, for closed economy with �rms' productivity distributed

Pareto, distribution of wages within an industry is also distributed Pareto

with minimum wage ωd and shape parameter 1+ 1µ(z)

. In the closed economy

µ(z) is the su�cient statistics for wage inequality. Moreover, ∂µ∂z< 0, mean-

ing that the regulatory reform that makes productivity distribution more

10

Figure 3: Shift in distribution of �rms due to regulatory changes. Change inminimum productivity

11

Figure 4: Within sector inequality and trade openness

dispersed (lower z) leads to higher wage inequality. In an open economy,

the wage inequality depends on µ and on extensive and intensive margins

of trade. In particular, HIR show that the within-industry wage inequality

increases when the share of exporters is small and decreases when the share

of exporters is close to 1, as Figure 4 illustrates. Therefore, the regulatory

reform in the open economy reinforces wage inequality relative to wage in-

equality in the closed economy when the trade openness is not very closed

to 1.

12

3 Data

The data for the study come from several statistical statements annually

submitted to the National Statistics O�ce (Derzhkomstat) by all commer-

cial �rms in the country. The sample covers seven years from 2001 to 2007.

We start with the sample of manufacturing �rms (NACE Section �D�) that

never switched to another sector over the period of study. Since the Sectoral

Expenditures Statement, required to construct the �rm-speci�c service lib-

eralization index, is submitted by only relatively large �rms, our sample is

restricted to the �rms with 300 employees on average. We further excluded

observations with zero or negative output, capital stock or employment as-

suming that they indicated non-operational �rms in a year. Based on the �les

accompanying the Enterprise Performance Statement and the Balance Sheet

Statement, we have created a comprehensive pro�le for every �rm which in-

cludes the industry NACE), ownership type, exporting and importing status

in every year.

As the measure of output, we used net sales after excise taxes from the

Financial Results Statement. The Balance Sheet Statement is the source of

the capital measure for which we used the end-of-year value of the tangible

assets. For the production function estimation we used investments in tangi-

ble assets which come from the Enterprise Performance Statement. The same

statement is also a source for our employment variable. It is measured as the

�year-averaged number of enlisted employees�, which is a rough estimate of

13

the full time equivalent of labor used. The material costs come from the same

statement in 2001-2004, whereas since 2005 they have been available from a

separate Sectoral Expenditures Statement. The statement provides detailed

information about the �rm's expenditures on purchases from 22 manufac-

turing sectors and 15 service sectors. Data from this statement were used

to construct an individual �rm-speci�c index of services liberalization and

�rm-speci�c index of import tari� liberalization. All variables were de�ated

by the appropriate price de�ators as described in the next sub-section. The

descriptive statistics for the sample are presented in Table 1.

Evolution of mean and standard deviation of productivity, markups, and

wages in 2001-2007 is presented in Table 2. Over the investigated period,

productivity has almost doubled, while dispersion of productivity has been

moderately growing. Markups have also increased, but without noticeable

trend in variation. Increase in markups during the trade and services liberal-

ization episode is consistent with �ndings of De Loecker et al. (2012) � during

the trade liberalization episode in India, marginal costs have been reduced

by 40 percent, while prices fall by 16.8 percent, leading to higher markups.

Average wage has increased by 221 percent, but wage dispersion has fallen.

The reduction in the wage dispersion goes against our prior expectations and

against the predictions of the HIR model. Perhaps, the reduction in wage

dispersion is related to privatization of state-owned enterprises, which set

wages di�erently from the private companies as we discuss in the robustness

14

Variable Observations Mean Std. deviation

ωit, thsd. UAH 2001 46515 4.885 3.94Yit, thsd. UAH 2001 46530 23521 180168Lit, workers 46530 297.1 1360Kit, thsd. UAH 2001 45923 8138 49820Mit, thsd. UAH 2001 45924 15265 147687Iit, thsd. UAH 2001 34321 2368 21711ln(TFPi,t) 44243 1.139 1.027Importeri,t 46530 0.3131 0.4637Exporteri,t 46530 0.3647 0.4813Foreigni,t 46530 0.08764 0.2828Exiti,t 46530 0.04008 0.1962Entryi,t 46530 0.09654 0.2953Urbani 46530 0.678 0.4673Serv. Libit (EBRD) 46530 0.3966 0.6553Serv. Libit (FDI) 46530 0.3784 0.7961Privatei,t 46530 0.893 0.3092Single plantit 46530 0.9356 0.2454Input tari�it 46530 5.693 3.2Markupi,t 46515 5.468 153.8

Table 1: Descriptive statistics

15

Year ln(TFPit) ln(pit/cit) ln(ωit)Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

2001 0.923 0.993 0.41 1.047 0.887 0.732002 0.995 0.99 0.361 1.057 1.135 0.662003 1.036 1.008 0.455 1.095 1.272 0.6212004 1.177 1.003 0.535 1.064 1.403 0.5872005 1.224 0.995 0.495 1.028 1.579 0.5622006 1.364 1.057 0.696 1.049 1.751 0.5492007 1.527 1.099 0.995 1.031 2.054 0.535Total 1.139 1.027 0.516 1.066 1.371 0.695

Table 2: Productivity, markups, and wages

checks.

4 Empirical strategy

There are three features of the model that we test in the empirical analysis.

First, wages are positively linked to productivity with constant elasticity η

that does not depend on the export status. Second, exporters pay a positive

wage premium. Third, wage inequality is higher within an industry where

the share of exporters is small and is lower within an industry where the

share of exporters is close to 1.

Our �rst objective is to test whether higher productivity is associated with

a higher average wage across �rms in the same industry. We allow ωd and θd

16

to vary across regions and industries and change over time.3 The �rm level

productivity is unobservable to the researcher and measured with error due

to unobservable prices. In addition, we allow wages in�uence productivity

through the average ability of workers. Υx is determined by transportation

cost and foreign market potential. We assume that transportation cost does

not vary across industries and local market is growing at the same rate as

the world economy hence Υx does not vary across industries and over time.

The empirical counterpart of the equation 1 in logarithmic form is

lnωit = α + η ln θit + µ× exporterit +Xitγ (4)

+Dstµ+Drr + εit

where ωit is �rm i′s average wage at time t. θit is �rm i′s measured productiv-

ity at time t. exporterit is the dummy variable that takes the value of one if

�rm i exports in year t and zero otherwise. Dst are industry-year �xed e�ects,

and Dr are region �xed e�ects. X represents a vector of additional controls

that in�uence the reference wage ωd, including importer status, employment,

whether the �rm is located in urban area.

There is also an important distinction between �rms that exit at t+1 and

surviving �rms. There is a lag between the decision to exit and the actual

3ωd depends on the tightness of the labor market and on the �xed costs of operatingdomestically, while θd depends on the �xed costs of operating domestically. Since labormobility in Ukraine is low, it is natural to assume that the tightness varies across regions.Fixed costs are industry speci�c and can change over time with changes in the regulatoryenvironment, R.

17

closing down. This information is typically known by managers at time t,

but is not observed by econometricians.

4.1 Productivity measures

In the empirical analysis we use two measures of productivity � labor pro-

ductivity and total factor productivity. Labor productivity is constructed

in the straightforward manner as the value added de�ated by the industry

price de�ator divided by the number of workers. To recover the TFP mea-

sure, we estimate the production function for each manufacturing industry

(1-digit NACE classi�cation) by the Olley-Pakes procedure (Olley & Pakes,

1996), controlling for the sub-industry-speci�c demand and price shocks as

suggested by De Loecker (2011). We identify the demand and price shocks by

exploiting variation in sub-industry (4-digit NACE classi�cation) output at

time t and by controlling for sub-industry and time �xed e�ects. Under the

constant elasticity of substitution (CES) demand system, unobserved prices

are picked up by the variation in inputs and by aggregate demand and do

not re�ect di�erences in technology within an industry.

Technology and market structure

Consider a production technology of a single-product �rm i at time t de-

scribed by a production function

Yit = LαlitKαkit M

αmit exp(ωit + uit), (5)

18

where Yit units of output are produced using Lit units of labor, Kit units of

capital, andMit units of material and services inputs. ωit is �rm-speci�c pro-

ductivity, unobservable by an econometrician, but known to the �rm before

it chooses a variable input Lit. uit is an idiosyncratic shock to production

that also captures measurement error introduced due to unobservable input

and output prices.

Yit is not known, because we do not observe �rm-speci�c prices, pit. As

a result observable sales, Rit = pitYit, may re�ect di�erences in physical

quantities and di�erences in mark-ups across �rms within the same industry.

Therefore, use of Rit as the dependent variable in estimation of produc-

tion function parameters, without controlling for prices, determined among

other things by market structure and demand shocks, would bias estimates

of the production function if prices are correlated with inputs. Even more

importantly, generated productivity estimates containing demand variation

introduces a relationship between services liberalization and measured pro-

ductivity through the impact of the liberalization on prices and demand.

To deal with this issue, we introduce a constant elasticity of substitution

demand system

Yit = Yst

(pitPst

)σsexp(ξit), (6)

where Yst is total expenditures on goods produced by manufacturing industry

s, in which �rm i operates. Pst is industry-wide price at time t. ξit is demand

shock which is not observed by the �rm when it chooses variable inputs in

19

production, but determines di�erences in mark-ups and measured produc-

tivity. Assuming monopolistic competition, this demand structure implies a

constant mark-up price-setting rule, which depends on the industry-speci�c

elasticity of substitution σs. It further implies the following expression for

the revenue function

Rit = (Yit)σs+1σs (Yst)

− 1σs Pst

(exp(ξit)

)− 1σs. (7)

Substituting (5) into (7), moving Pst to the left-hand-side and taking logs

yields

rit = βllit + βkkit + βmmit + βsyst + ωit + ξit + uit, (8)

where rit = ln(Rit/Pst) is log of revenue de�ated by corresponding industry (2

digit NACE) price de�ator, which is provided by the national statistics o�ce,

and other lower-case letters represent upper-case variables in the log form.

βf = σs+1σs

αf , where f = {l, k,m}. The elasticity of substitution in industry

s can be retrieved as σs = −1/βs. Finally, ωit = σs+1σs

ωit, ξit = − 1σsξit, and

uit = σs+1σs

uit are error terms.

This procedure allows us estimating parameters of production function

consistently, without having information on �rm-speci�c prices. Here we rely

only on the assumption that the demand function is of CES form, which is

common in the literature.

20

Estimation of production function

We estimate

rit = βllit + βkkit + βmmit + βsygt + ωit + ξit + uit, (9)

separately, for 11 manufacturing sectors (1 digit NACE classi�cation). In

what follows we suppress the sector index for clarity of presentation. Capital

and materials are de�ated by the production price index. Instead of using

overall output of industry, we use more disaggregated sub-industry g output

(NACE 4 digit), ygt, to add more variability to estimation of σs. It is valid

since we assume that the elasticity of substitution is constant within the

manufacturing sector.

We decompose the overall demand shock into the following components

ξit = ξt + ξg + ξit, (10)

where ξt is industry-speci�c shock common to all �rms at time t, ξg is de-

mand factor a�ecting only �rms producing in sub-industry g, and ξit is an

idiosyncratic shock. Plugging in (10) in (9), we have the following equation

rit = βllit + βkkit + βmmit + βygt + δtDt + δgDg + ωit + εit (11)

where Dt is a a year �xed e�ect and Dg is a sub-industry �xed-e�ect. εit =

ξit+uit is the error term which is not correlated with inputs and productivity.

21

Industry ln(K) ln(L) ln(M) ln(Y ) Firms NβK αK βL αL βM αM βs

Food and Tobacco 0.032 0.033 0.199*** 0.207 0.737*** 0.766 0.038 2685 12165(0.018) (0.016) (0.013) (0.026)

Textile and Leather 0.089* 0.090 0.426*** 0.430 0.482*** 0.487 0.010 884 3569(0.036) (0.024) (0.018) (0.046)

Wood and Paper 0.032 0.040 0.172*** 0.217 0.703*** 0.885 0.206*** 571 2357(0.023) (0.023) (0.037) (0.062)

Printing 0.061 0.061 0.407*** 0.404 0.511*** 0.507 -0.008 936 3939(0.033) (0.033) (0.022) (0.043)

Coke, chemistry 0.108** 0.120 0.157*** 0.175 0.683*** 0.761 0.102*** 843 3967plastics (0.036) (0.018) (0.032) (0.026)Non-metallic minerals 0.036 0.037 0.151*** 0.156 0.788*** 0.812 0.029 795 3585

(0.029) (0.021) (0.019) (0.033)Metallurgy 0.097** 0.105 0.173*** 0.186 0.676*** 0.728 0.072* 779 3243

(0.030) (0.027) (0.038) (0.032)Machinery and 0.031 0.031 0.363*** 0.365 0.567*** 0.570 0.006 1085 4684equipment (0.018) (0.025) (0.024) (0.032)High-tech machinery 0.065 0.070 0.225*** 0.241 0.591*** 0.633 0.067 756 3531

(0.035) (0.027) (0.021) (0.051)Vehicles and transport -0.041 -0.047 0.254*** 0.294 0.562*** 0.650 0.136* 321 1483

(0.042) (0.047) (0.041) (0.059)Furniture and others 0.078 0.079 0.329*** 0.334 0.548*** 0.557 0.016 604 2458

(0.044) (0.046) (0.045) (0.083)Notes: * p<0.05, ** p<0.01, *** p<0.001. Bootstrap standard errors are presented in parentheses. Table

reports point estimates of revenue function parameters, β and production function paramters α = σsσs+1

β,

where σs = −1/βs for Ukrainian manufactruing �rms for 2001-2007. Each row in the table represents

Olley-Pakes estimation of production function for eleven manufacturing industries, de�ned according to

NACE classi�cation. Each estimation is performed with year and sub-industry dummies, which are not

reported for brevity.

Table 3: Estimation of production function by Olley-Pakes procedure

22

We estimate (11) by the Olley-Pakes methodology. Results are presented

in Table 3. Total factor productivity net of price and demand e�ects is

recovered as

ln(TFPit) = (rit − βllit − βkkit − βmmit − βsyst)σs

σs + 1. (12)

4.2 Endogeneity of productivity measure

In the HIR framework, productivity is split into two components � technical

e�ciency θit and average ability of workers, a, which is increasing with the

screening intensity. Since we do not have information on a, our productivity

measure includes both components. Unobserved average ability is positively

correlated with both wage and productivity. It means that corr(a, θ) >

0, and leads to an upward bias in estimation of η. Alternatively, in the

framework of Egger & Kreickemeier (2012) and (Amiti & Davis, 2012) based

on the fair wage hypothesis (Akerlof & Yellen, 1990), a wage rate below

some level ω∗, considered as a fair wage by workers, would lower their level

of e�ort, e, leading to lower productivity. The positive correlation between

an unobserved e�ort and productivity, corr(e, θ) > 0, leads to a positive bias

in the OLS estimation of the coe�cient η.

To estimate equation 4 consistently, a source of exogenous variation in

productivity is needed. We propose a well-known link from deregulation to

productivity as such variation. Recent studies of services and trade liberal-

ization Amiti & Konings (2007); Arnold et al. (2011); Fernandes & Paunov

23

(2011); Khandelwal & Topalova (2011) �nd positive e�ect of the liberaliza-

tion on productivity of manufacturing �rms. The size of the e�ect varies

across �rms because of di�erences in intensity, with which �rms use liberal-

ized goods and services as inputs.

We use the episode of the Ukrainian trade and services liberalization in

2001-2007, isolated from other major deregulatory changes and driven by

political pressure imposed by Ukraine's trading partners as a precondition

for the Ukrainian WTO accession. Concerning services, the government de-

veloped new laws and amended existing ones that regulated activities of TV

and broadcasting, information agencies, banks and banking activities, in-

surance, telecommunications, and business services. It led to di�erentiated

but positive e�ect on productivity of downstream manufacturing �rms (She-

potylo & Vakhitov, 2012). The results indicate that a standard deviation

increase in services liberalization is associated with a 9.2 percent increase in

TFP. In parallel with the services liberalization, the WTO negotiations also

led to further liberalization of trade in goods. To capture this e�ect in the

empirical analysis we control for the e�ect of exporting on productivity and

interact it with the e�ect of services liberalization to control for potential

complementarities. In addition, we control for the positive spillovers from

the liberalization of trade in inputs on productivity found, among others by

Amiti & Konings (2007).

The index of services liberalization is �rm-speci�c, re�ecting the variation

in �rm-level intensity of usage of various services inputs. Similarly to Arnold

24

et al. (2011), but using �rm level data, the index is computed according to

the following formula

serv libit =∑j

ajit × indexjt (13)

where ajit is the share of input sourced from the services sub-sector j in the

total input for a �rm i at time t, and indexjt is the measure of liberaliza-

tion in the service sub-sector j at time t. The constructed index of services

liberalization is further divided by a standard deviation for normalization.

We proxy for indexjt by structural change indicators provided by the Euro-

pean Bank for Reconstruction and Development (EBRD) and by the share

of output of services sub-sector j at time t produced by the foreign-owned

�rms.4

The third instrument captures the �rm-speci�c measure of trade liberal-

ization. We compute an index of input tari� liberalization following Amiti

& Konings (2007):

input tariffit =∑s

bsit × tariff st (14)

where input tariffit is the �rm-speci�c input tari� measure, bsit is the share

of input sourced from the two-digit NACE industry s in the total input for

4EBRD structural change indicators are available athttp://www.ebrd.com/pages/research/economics/data/macro.shtml. The mappingfrom the structural change indicators to sub-sectors of services is explained in theappendix. The foreign ownership is de�ned at the 10 percent threshold.

25

�rm i at time t, and tariff st is the trade-weighted average MFN import tari�

in industry s at time t.

Our identi�cation strategy would fail if we do not account for a general

equilibrium e�ect of services and trade liberalization on the labor market. We

control for this by including industry-time speci�c e�ects, capturing struc-

tural changes in the economy. The identi�cation strategy would fail only if

liberalization has systematic and di�erentiated impact on wages within the

industry that comes through channels other than the productivity channel.

4.3 Measurement error and omitted variable bias

The measured TFP su�ers from several shortcomings, including measurement

errors due to imprecise input de�ators, variability of mark-ups across �rms

located within the same industry and variation in quality which is impossible

to measure directly in our sample (Van Beveren, 2012). Consider for example,

the measurement error due to the price variability. It introduces a bias in the

measured productivity proportional to pit − pst, where pit is the log of �rm-

speci�c price and pst is the industry price de�ator. Since corr(yit, pit−pst) < 0

and corr(yit, lit) > 0, it introduces a negative correlation between labor and

the error term, leading to a downward bias in the estimation of βl (as well

as biased estimation of βk and βm). As a result, the measured productivity

overestimate the technological e�ciency of a �rm.

Price variation within the industry may also re�ect di�erences in quality

across �rms, producing similar goods. Depending whether di�erences in

26

prices fully capture variation in quality or not, corr(yit, qualityit) can be

positive or negative, leading to the measurement error, but the direction of

the bias is undetermined.

To control for unobserved price variation, we recover the �rm-speci�c

markups from the �rm-level data following, the procedure developed by

De Loecker & Warzynski (2009). The markups are computed as

pitc(θit)

=βl

ωitLit/pitYit(15)

where c(θit) is the marginal cost.

It should be noted that the constructed markup is endogenously deter-

mined as a function of wages. Since corr(pitθit, εit) < 0, the OLS estimation of 4

would lead to the downward bias in the estimation of the markup coe�cient.

Fortunately, we can instrument the markups by the same instruments as the

productivity. For example, De Loecker et al. (2012) �nd that after trade

liberalization �rms do not fully adjust prices to reduction in the marginal

costs. The price declines are small relative to the declines in marginal costs,

leading to a positive correlation between markups and productivity.

27

5 Results

5.1 OLS

We �rst estimate equation 4 by OLS with industry-time and regional �xed

e�ects. The errors are corrected for heteroskedasticity at the �rm level. The

results are presented in Table 4. First, we regress lnωit on the measured

labor productivity and other controls, ignoring variation in the markups.

Column 1 of Table 4 shows that a 10 percent increase in labor productivity

is associated with 2.4 percent increase in wage. Once we include markups

in column 2, the coe�cient on labor productivity is almost doubled, while

the coe�cient on markups is negative and signi�cant. Comparing results

in column 1 and 2, points to the omitted variable bias in column 1. At

the same time, column 2 has two endogenous variables � productivity and

markup � causing an upward bias in the OLS estimation of the coe�cient

on productivity, while the estimation of the coe�cient on markup is biased

downward. Inclusion of �rm-speci�c e�ects in column 3 of the table alleviate

endogeneity, resulting in the coe�cient on labor productivity almost the

same as in column 1, but the coe�cient on the markup remains negative.

Column 3 shows that �rms that start exporting pay a wage premium of 3.4

percent, which is well in agreement with the theoretical predictions. Adding

the interaction between export status and productivity, exporterit× ln θit, in

column 4 demonstrates that elasticity of wage with respect to productivity

is not statistically di�erent for exporters and non-exporters, which is also

28

corresponds well with the model predictions. At the same time the coe�cient

on export jumps to 0.08, indicating much higher export premium.

Results in column 4 also indicate that �rms that start importing some of

their inputs pay 4.8 percent more, which corresponds well with Amiti & Davis

(2012), who found 3.2 percent increase in wage after the �rm starts importing

in Indonesia. Firms that switch ownership from domestic to foreign, de�ned

as foreign ownership greater than of equal to 10 percent, pay 3.8 percent

higher wages. Exiting �rms pay 9.5 percent lower wage. We have not found

any scale e�ect, measured by total employment, in the model that includes

�rm �xed e�ects, but when we compare �rms of di�erent sizes in columns 1

and 2, larger �rms pay higher wages even after controlling for productivity

and export status.

Columns 4 - 8 of Table 4 show results of the regression of lnωit on TFP

and other controls. The results with two di�erent productivity measures are

quite similar for exogenous controls. However, the positive e�ect of TFP

on wages is smaller and the negative e�ect of markup on wages is much

smaller in the absolute value. These results are expected because the model

with labor productivity overestimates productivity for �rms with high value

of capital, leading to positive correlation of errors with productivity and

negative correlation of errors with markups.

29

Dependent variable:ln(wagei,t) ln(vai,t/Li,t) ln(TFPi,t)

OLS Markups FE Exp × OLS Markups FE Exp ×prod. prod.

(1) (2) (3) (4) (5) (6) (7) (8)

Productivity 0.240*** 0.457*** 0.247*** 0.252*** 0.264*** 0.351*** 0.223*** 0.229***(0.008) (0.022) (0.024) (0.029) (0.009) (0.011) (0.011) (0.012)

Markup -0.384*** -0.323*** -0.323*** -0.119*** -0.175*** -0.175***(0.017) (0.021) (0.022) (0.006) (0.009) (0.009)

Exporteri,t 0.025** 0.036*** 0.034*** 0.080* 0.061*** 0.073*** 0.048*** 0.071***(0.009) (0.007) (0.006) (0.035) (0.010) (0.009) (0.006) (0.012)

Exporteri,t× -0.017 -0.020*productivityi,t (0.013) (0.008)Importeri,t 0.020* 0.080*** 0.048*** 0.048*** 0.114*** 0.155*** 0.060*** 0.061***

(0.010) (0.008) (0.005) (0.005) (0.010) (0.010) (0.006) (0.006)Foreigni,t 0.156*** 0.128*** 0.037** 0.038** 0.219*** 0.226*** 0.051*** 0.051***

(0.017) (0.014) (0.013) (0.013) (0.019) (0.018) (0.015) (0.015)ln(Li,t) 0.115*** 0.072*** -0.006 -0.006 0.106*** 0.091*** -0.024** -0.025**

(0.004) (0.003) (0.007) (0.007) (0.004) (0.004) (0.008) (0.008)Exiti,t -0.063*** -0.121*** -0.096*** -0.095*** -0.143*** -0.193*** -0.128*** -0.128***

(0.019) (0.016) (0.015) (0.015) (0.021) (0.021) (0.018) (0.018)Urbani 0.094*** 0.064*** 0.015 0.014 0.132*** 0.132*** 0.025 0.024

(0.009) (0.008) (0.020) (0.020) (0.011) (0.011) (0.025) (0.026)Industry × Year FE Yes Yes Yes Yes Yes Yes Yes YesRegion FE Yes Yes Yes Yes Yes Yes Yes YesFirm FE No No Yes Yes No No Yes YesObservations 43985 43985 43985 43985 44236 44236 44236 44236R2 0.583 0.715 0.601 0.602 0.509 0.529 0.522 0.522Standard errors clustered by �rms in parentheses. * p<0.05, ** p<0.01, *** p<0.001

Standard errors in parentheses

Table 4: OLS

30

5.2 IV results

In this section we report results of estimation of equation (4) by IV, where we

use three instruments � services liberalization measured by EBRD, services

liberalization measured by the share of output produced by foreign-owned

services companies, and the input tari� liberalization measure, computed ac-

cording to equations (13) and (14). The errors are corrected for heteroskedas-

ticity at the �rm level, all regressions include industry-time and regional �xed

e�ects.

The results are presented in Table 5. We regress lnωit on the measured

TFP, markups, and other controls. Column 1 of Table 5 shows that a 10

percent increase in productivity is associated with 1.5 percent increase in

wage, while a 10 percent increase in markup leads to 0.8 percent increase

in wage. Comparing this results with OLS results in column 5 of Table 4

con�rms our theoretical priors that the OLS estimation of the coe�cient on

productivity is biased upward and the OLS estimation of the coe�cient on

the markup is biased downward. The coe�cient on productivity is more than

halved, while the coe�cient on markup �ips the sign, becoming positive and

signi�cant.

Exporters pay a 6.1 wage premium relative to non-exporters, in accor-

dance to the wage setting rule in equation (1). Firms that import their

inputs pay 9.6 percent more relative to �rms that do not import. Foreign-

owned �rms pay 22 percent more relative to domestic �rms. Elasticity of

wage with respect to employment is positive and signi�cant 0.11, even after

31

controlling for productivity, export and import status, and ownership. Ex-

iting �rms pay 11 percent less than surviving �rms. In urban areas �rms

pay extra 13.8 percent, probably to compensate for the higher cost of living

relative to the rural areas.

One of the concerns with the speci�cation in column (1) is that wage is

often set in advance and responds slowly to changing economic environment.

Column 2 reports results of the model speci�cation

lnωit = α + η ln θit−1 + µexporterit−1 +Xit−1γ +Dstµ+Drr + εit

which remarkably are very close to the results in column 1.

In column 3 we include an interaction term exporterit × ln θit. Elasticity

of wage with respect to productivity is not statistically di�erent for exporters

and non-exporters, which is also corresponds well with the model predictions.

At the same time the coe�cient on export becomes insigni�cant, indicating

that we cannot discriminate between the models where wage of exporting

�rms has a discrete jump or a di�erent slope.

It should be noted that private �rms in Ukraine pay part of the salary

in cash and do not report it as the wage bill to evade the social security tax

ranging from 32.6 to 49.7 percent of the wage bill. State-owned companies

do not have this incentive, reporting all labor-related expenses in wage bills.

This feature of the tax system leads to under-reporting of wages by the

private companies and can bias our results. Column 4 report the results

32

with additional variable that control for state- vs. private-owned �rms. As

expected, private �rms pay 16 percent lower wage, but it does not change

our main conclusions.

Literature emphasizes distinction between multi-plant and single-plant

�rms. Related to this, distinction between multi-product and single-product

�rms is important when modeling �rm's reaction to trade liberalization Bernard

et al. (2011). We control for single- vs. multi-plant �rms in column 5, which

has no impact on our conclusions. In column 6 we control for new �rms,

which pay 8.5 percent lower wage than incumbents.

Our identi�cation strategy would fail if exclusion restrictions are not valid.

For instance, trade and services liberalization can have a general equilibrium

e�ect on wages which in�uences more �rms that use imported goods and

liberalized services more intensively. In that case our excluded variables

in�uence wages not only through productivity, but also directly. We are quite

con�dent that our estimation is valid for several reasons. First, we control

for the industry-time speci�c trends directly. Second, the overidenti�cation

test does not reject validity of our instruments. Finally, when we include

additional controls for input tari�s at sub-industry level. It does not change

our results as column 7 indicates.

When we include all variables that we use in the robustness checks, it

only strengthens our results. Column 8 of Table 5 shows that 10 percent

increase in productivity leads to 1.67 percent higher wage, while 10 percent

increase in the markup leads to 1.37 percent increase in wage. Coe�cients

33

on exporterit and exporterit × ln(θit) are jointly signi�cant, but we can not

distinguish which model � with a jump or with a change in the slope � better

represents the data.

To sum up, we �nd a robust positive e�ect of productivity on wages, with

elasticity in the range 0.14-0.17. The markups also positively contribute to

wages, but the e�ect is weaker. Exporting �rms pay a wage premium of 3-6

percent, depending on the model speci�cation. The result is not robust to

inclusion of an interaction term exporterit × ln(θit), meaning that we can

not distinguish between the model with a di�erent intersection for exporters

vs the model with a di�erent intersection and di�erent slope. Overall, our

results con�rm the predictions of HIR at the �rm level.

5.3 Industry level results

Having con�rmed the positive causal e�ect from productivity to wages, we

further test predictions of HIR at industry level. We aggregate our data to

the level of NACE 3 digit sub-industries. We proxy ρ = θd/θx by the share of

exporters, Nxjt/Njt, where N

xjt is the number of exporters and Njt is the total

number of �rms in sub-industry j at time t. Figure 4 presents a scatterpolot

of the average wage as a function of the share of exporters. The solid line is

the quadratic �t that minimizes the total sum of squared errors of the follow-

ing optimization: mincm,αm,βm[∑

j,t(lnωjt − cm − αmexpsharejt − βmexpshare2jt)2].

Clearly, the average wage increases with the share of exporters within the

sub-industry.

34

Dependent variable:ln(wageit) (1) (2) (3) (4) (5) (6) (7) (8)

Base Lagged Inter- Private Single Entry Input Allaction plant tari�

ln(TFPi,t) 0.153*** 0.155*** 0.142*** 0.165*** 0.156*** 0.157*** 0.145*** 0.167***(0.033) (0.037) (0.041) (0.033) (0.033) (0.033) (0.034) (0.044)

Markupi,t 0.083*** 0.074*** 0.083*** 0.089*** 0.083*** 0.085*** 0.140*** 0.137***(0.014) (0.016) (0.014) (0.014) (0.014) (0.014) (0.024) (0.024)

Exporteri,t 0.061*** 0.050*** 0.032 0.056*** 0.060*** 0.060*** 0.047*** 0.042(0.012) (0.013) (0.022) (0.012) (0.012) (0.012) (0.013) (0.023)

Importeri,t 0.096*** 0.089*** 0.094*** 0.102*** 0.096*** 0.097*** 0.073*** 0.085***(0.016) (0.017) (0.015) (0.016) (0.016) (0.016) (0.018) (0.016)

Foreigni,t 0.220*** 0.220*** 0.220*** 0.232*** 0.221*** 0.221*** 0.211*** 0.228***(0.020) (0.022) (0.020) (0.020) (0.020) (0.020) (0.021) (0.020)

ln(Li,t) 0.115*** 0.119*** 0.115*** 0.111*** 0.118*** 0.110*** 0.122*** 0.115***(0.005) (0.006) (0.005) (0.005) (0.005) (0.005) (0.006) (0.005)

Exiti,t -0.117*** -0.119*** -0.115*** -0.115*** -0.119*** -0.096*** -0.099***(0.024) (0.025) (0.024) (0.024) (0.024) (0.026) (0.026)

Urbani 0.138*** 0.138*** 0.138*** 0.141*** 0.138*** 0.140*** 0.137*** 0.142***(0.012) (0.013) (0.012) (0.012) (0.012) (0.012) (0.013) (0.012)

Exporteri,t× 0.026 0.001ln(TFPi,t) (0.023) (0.025)Privatei,t -0.164*** -0.176***

(0.017) (0.017)Single planti,t 0.073*** 0.091***

(0.018) (0.019)Entryi,t -0.085*** -0.096***

(0.013) (0.016)Input tari�j,t -0.009** -0.007*

(0.003) (0.003)Industry × Year FE Yes Yes Yes Yes Yes Yes Yes YesRegion FE Yes Yes Yes Yes Yes Yes Yes YesObservations 44236 33222 44236 44236 44236 44236 44236 44236Hansen J statistic 0.055 1.263 0.047 0.000 0.044 0.058 0.049 0.001χ2(1) p-value 0.815 0.261 0.829 0.999 0.834 0.810 0.824 0.978R2 0.470 0.440 0.469 0.472 0.471 0.471 0.436 0.447Standard errors clustered by �rms are reported in parentheses. * p<0.05, ** p<0.01, *** p<0.001

Table 5: IV

35

Figure 5: Average wage and share of exporters

Figure 5 presents a scatterpolot of the coe�cient of variation, cv(lnωjt) =

σ(lnωjt)

E(lnωjt), as a function of the share of exporters. The solid line is the quadratic

�t that minimizes the total sum of squared errors of the following optimiza-

tion: minccv ,αcv ,βcv[∑

j,t(cv(lnωjt)− csd − αsdexpsharejt − βsdexpshare2jt)2].

As predicted by the HIR model, the coe�cient of variation of wages within

a sub-industry is �rst rising and then declining with the increase in trade

openness.

The observed regularities might be driven by variation of wages across

industries and by other factors. To test the predictions on the relationship

between sub-industry wage statistics and trade openness, we estimate the

36

Figure 6: Coe�cient of variation and trade openness

following equations

lnωjt = cm + ηm ln θjt + αmexpsharejt + βmexpshare2jt +Xjtγm + ujt

sd(lnω)jt = csd +ηsdσ(ln θ)jt +αsdexpsharejt +βsdexpshare2jt +Xjtγsd + vjt

and

cv(lnω)jt = ccv + ηcvσ(ln θ)jt +αcvexpsharejt +βcvexpshare2jt +Xjtγcv + vjt

37

where sd(lnω)jt and cv(lnω)jt are standard deviation and coe�cient of vari-

ation of wages within the sub-industry j at time t. We estimate these equa-

tions with and without industry e�ects (industry is de�ned according to 1

digit NACE classi�cation), to explore the relationship within and between

industries. The results indicate that the average wage within sub-industry

is positively linked to average productivity, while higher variation of wages

within industry is positively linked to higher variation of productivity. The

coe�cients on exporter shares have expected sign and is signi�cant. Overall,

we can conclude that the results do not reject the hypothesis on inverse U-

shaped relationship between trade openness in the industry and variation of

wages.

6 Conclusions

We �nd that the productivity channel plays an important role in shaping

the wage distribution within manufacturing industries. We �nd a robust

positive e�ect of productivity on wages, with elasticity in the range 0.14-

0.17. The markups also positively contribute to wages, but the e�ect is

weaker. Exporting �rms pay a wage premium of 3-6 percent, depending on

the model speci�cation. This channel can be one of the important drivers of

the increasing wage gap between high- and low-paid jobs, observed in the last

two decades in both developed and developing countries. We also con�rm

that the e�ect of the extensive margins of trade on wage inequality has a

38

Dependent variable Average wagej,t St. Dev. Wagej,t Coef. Var. Wagej,t(1) (2) (3) (4) (5) (6)OLS FE OLS FE OLS FE

ln(TFPj,t) 0.054*** 0.523***(0.015) (0.054)

σ(ln(TFPj,t)) 0.089* 0.101* 0.135** 0.128*(0.040) (0.043) (0.050) (0.050)

Share of exportersj,t -0.399 -0.147 0.657*** 0.694*** 0.460** 0.384*(0.267) (0.280) (0.133) (0.148) (0.164) (0.186)

Share of exporters2j,t 0.368 0.072 -0.831*** -0.856*** -0.607*** -0.536***(0.252) (0.245) (0.121) (0.130) (0.137) (0.155)

Share of importersj,t 0.493*** 0.410*** -0.004 0.003 -0.230* -0.371***(0.129) (0.121) (0.067) (0.068) (0.095) (0.100)

Share of foreignj,t 0.659*** 0.481*** 0.242* 0.265** -0.033 -0.047(0.161) (0.136) (0.107) (0.099) (0.083) (0.101)

ln(Lj,t) 0.061* 0.101*** -0.017 -0.010 0.010 0.063**(0.027) (0.025) (0.012) (0.014) (0.015) (0.020)

Share of exiting �rmsj,t -1.954*** -1.389*** 0.265 0.277 0.808* 0.786*(0.294) (0.258) (0.243) (0.246) (0.318) (0.309)

Share of urbanj,t 0.346*** 0.302*** 0.035 0.101 -0.153** -0.154*(0.084) (0.088) (0.038) (0.055) (0.058) (0.076)

Industry FE No Yes No Yes No YesObservations 683 683 681 681 681 681R2 0.369 0.618 0.206 0.251 0.196 0.269Standard errors clustered by industries in parentheses. * p<0.05, ** p<0.01, *** p<0.001

Table 6: Sub-industry (NACE 3 digit) level results

39

bell-shaped form, which is consistent with the theoretical model developed

by Helpman et al. (2010). From the methodological point, we demonstrate

that the OLS estimation of the wage-productivity relationship is biased. The

suggested IV approach, which also corrects for the measurement error, allows

to eliminate the bias.

References

Akerlof, G. & Yellen, J. (1990). The fair wage-e�ort hypothesis and unem-

ployment. The Quarterly Journal of Economics, 105(2), 255�283.

Amiti, M. & Davis, D. (2012). Trade, �rms, and wages: Theory and evidence.

The Review of Economic Studies, 79(1), 1�36.

Amiti, M. & Konings, J. (2007). Trade liberalization, intermediate inputs,

and productivity: Evidence from indonesia. American Economic Review,

97(5), 1611 � 1638.

Arnold, J., Javorcik, B., & Mattoo, A. (2011). Does services liberalization

bene�t manufacturing �rms?: Evidence from the czech republic. Journal

of International Economics, 85(1), 136�146.

Bernard, A., Redding, S., & Schott, P. (2011). Multiproduct �rms and trade

liberalization. The Quarterly Journal of Economics, 126(3), 1271�1318.

De Loecker, J. (2011). Product di�erentiation, multiproduct �rms, and esti-

40

mating the impact of trade liberalization on productivity. Econometrica,

79(5), 1407�1451.

De Loecker, J., Goldberg, P., Khandelwal, A., & Pavcnik, N. (2012). Prices,

Markups and Trade Reform. Technical report, National Bureau of Eco-

nomic Research.

De Loecker, J. & Warzynski, F. (2009). Markups and �rm-level export status.

Technical report, National Bureau of Economic Research.

Dunne, T., Foster, L., Haltiwanger, J., & Troske, K. (2004). Wage and pro-

ductivity dispersion in united states manufacturing: The role of computer

investment. Journal of Labor Economics, 22(2), 397�429.

Egger, H. & Kreickemeier, U. (2012). Fairness, trade, and inequality. Journal

of International Economics, 86(2), 184 � 196.

Felbermayr, G., Prat, J., & Schmerer, H. (2011). Globalization and labor

market outcomes: wage bargaining, search frictions, and �rm heterogene-

ity. Journal of Economic Theory, 146(1), 39�73.

Fernandes, A. M. & Paunov, C. (2011). Foreign direct investment in services

and manufacturing productivity: Evidence for chile. Journal of Develop-

ment Economics, In Press, Corrected Proof, �.

Foster, L., Haltiwanger, J., & Syverson, C. (2008). Reallocation, �rm

turnover, and e�ciency: Selection on productivity or pro�tability? Amer-

ican Economic Review, 98(1), 394�425.

41

Goldberg, P. & Pavcnik, N. (2007). Distributional e�ects of globalization in

developing countries. Journal of Economic Literature, 45, 39�82.

Helpman, E., Itskhoki, O., & Redding, S. (2010). Inequality and unemploy-

ment in a global economy. Econometrica, 78(4), 1239�1283.

Khandelwal, A. & Topalova, P. (2011). Trade liberalization and �rm produc-

tivity: The case of india. Review of Economics and Statistics, forthcoming.

Lazear, E. & Rosen, S. (1981). Rank-order tournaments as optimum labor

contracts. The Journal of Political Economy, 89(5), 841�864.

Melitz, M. J. (2003). The impact of trade on intra-industry reallocations and

aggregate industry productivity. Econometrica, 71(6), 1695 � 1725.

OECD (2012). Divided We Stand Why Inequality Keeps Rising: Why In-

equality Keeps Rising. OECD Publishing, Paris.

Olley, G. S. & Pakes, A. (1996). The dynamics of productivity in the telecom-

munications equipment industry. Econometrica, 64(6), 1263 � 1297.

Shepotylo, O. & Vakhitov, V. (2012). Services liberalization and productivity

of manufacturing �rms: evidence from ukraine. Policy Research Working

Paper Series.

Van Beveren, I. (2012). Total factor productivity estimation: a practical

review. Journal of Economic Surveys.

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7 Appendix

7.1 Mapping EBRD indices to services sub-sectors

For four services sub-sectors � Transport, Telecom, Finance, and Other

business-related services (hotels and restaurants, real estate, rent, informa-

tization, R&D, agencies) � we map the sub-sector with EBRD indices of

reforms as follows:

I: Transportation 1/2(rail + roads)

I1: Telecom (telecom)

J: Finance 1/2(banking + �nancial )

H+K: Other business-related services (hotels and restaurants, real estate,

rent, informatization, R&D, agencies) 1/5( small scale privatization + price

liberalization + trade liberalization+ competition reform+ �nancial reform)

43