International Trade and Unionization:

Theory and Evidence* Reshad N. Ahsan

University of Melbourne, Australia rahsan@unimelb.edu.au

Arghya Ghosh University of New South Wales, Australia

a.ghosh@unsw.edu.au

Devashish Mitra Syracuse University

dmitra@maxwell.syr.edu

March11, 2013

Abstract

Extending an efficient bargaining framework by allowing for a fixed cost of union formation and making the union formation decision endogenous, we look at the impact of trade liberalization on unionization. We test the predictions of our theory using a combination of National Sample Survey Organization (NSSO) and Annual Survey of Industries (ASI) data from India. We find that unionization indeed does go down with trade liberalization for high-skilled workers. For low-skilled workers, this effect is more pronounced for industry categories in which imports exceed exports. We also find that the deunionization effects of trade liberalization in net-importer industries were generally attenuated in flexible labor market states. Our evidence suggests that, in net-importer industries, trade liberalization raised union wages relative to nonunion wages. Accordingly, in net-importer industries, trade liberalization led to higher average wage rates in highly unionized industries relative to less unionized ones.

 

                                                            * We thank participants at the 2012 Econometric Society Australasian Meetings (ESAM) organized by Deakin University in Melbourne for useful comments. The standard disclaimer applies.

1 Introduction

In an important monograph, Baldwin (2003) discussed the decline of labor unions in the US,

as measured by a decline in union membership or the rate of unionization in manufacturing as well

as all sectors taken together since the 1970s. Between 1977 and 1997 in the US the unionization

rate for workers with only basic education fell from roughly 29 percent to about 14 percent, while

the unionization rate in the case of the relatively educated workers fell from roughly 19 percent to

13 percent. Baldwin finds that three-quarters of this decline is accounted for by within-industry

changes while just a quarter can be attributed to between-industry reallocation of labor (from

industries with high unionization rates to ones with low unionization rates). He then goes on to

find that only a quarter of the deunionization can be attributed to trade, with the effect of trade

concentrated on the unionization rates of the workers with only basic education. He finds only

negligible change in the union-nonunion wage differential in the US during this period 1977-97.

According to Baldwin, trade affects unionization through a change in factor prices that

changes the intensity of less educated to educated workers with the two categories differing in their

unionization rates. Trade also affects unionization through the expansion of the sectors that use

the relatively abundant type of labor (workers with basic education or with higher education) more

intensively. In addition, input trade is also important in this regard as such trade might be a result

of the offshoring of unionized jobs to developing countries. The important nontrade factors Baldwin

lists have to do with skill-biased and capital-biased technological change, change in union-related

legislation and the change in attitudes of employers and the general public towards unions.

A related argument has been made by Rodrik (1997). He argues that trade can make

domestic labor more replaceable directly and indirectly. Import competition, through importation

of final foreign goods, can make the products produced by domestic labor more substitutable and

hence can, in turn, make their services more replaceable. Input trade can make their services

directly more substitutable. Thus workers (and therefore, we believe, labor unions) lose their

bargaining power. This, we believe, can make the existence of labor unions less profitable. It also

impacts the membership decision of individuals in that it can reduce the attractiveness of union

membership.

Our model is an extension of the efficient bargaining framework used in Brock and Dobbe-

laere (2006) and McDonald and Solow (1981). We build on their framework by allowing for a fixed

1

cost of union formation and making the union formation decision endogenous. We look at the im-

pact of trade liberalization on unionization. Our model gives us two main predictions. Firstly, trade

liberalization makes a firm less likely to be penetrated by a union. As a result, a smaller proportion

of firms will experience union penetration after trade liberalization. Secondly, trade liberalization

has an ambiguous impact on the union wage rate and union-nonunion wage inequality.

We test the predictions of our theory using a combination of National Sample Survey

Organization (NSSO) and Annual Survey of Industries (ASI) data from India. Our analysis is

done at the level of 3-digit industry by state (over time). First, there is evidence that trade

liberalization, on average, led to greater deunionization for high-skilled workers. Second, for low-

skilled workers, the deunionization effects of trade liberalization were concentrated in net importer

industries. Third, the deunionization effects of trade liberalization in net importer industries were

generally attenuated in flexible labor market states. In addition, we find that, relative to other

industries, trade liberalization raised union wages in net importer industries for both high-skilled

and low-skilled workers. Accordingly, in net importer industries, trade liberalization led to higher

average wage rates in highly unionized industries relative to less unionized ones.

In this context, it is important to mention a recent paper by Bastos, Kreickemeier and

Wright (2010) who look at how unionization and union wages respond to product market com-

petition. Theoretically, they find that for low levels of unionization, product market competition

increases union bargaining power and therefore leads to higher union wages. Their empirical work

for the UK confirms their findings.

There are three other related papers that need to be mentioned here. Magnani and Prentice

(2003) look at the impact of import penetration, among many factors, on unionization across 3-

digit industries in the US for the period 1993-1994. They are unable to find an effect that is robust

in sign and significance to alternative specifications. In fact, controlling for a time trend always

renders the coefficients of the shares of imports and exports in output insignificant. Thus, they

are unable to arrive at a firm conclusion about the effects of trade on unionization. Dreher and

Gaston (2007) study the variation of the overall unionization rate at the country level for 17 OECD

countries for the 1980s and 1990s. Using alternative broad measures of globalization and their

various components, they find that social integration (“spread of ideas, information, images and

people” leading to the “Americanization of institutions”) rather than economic integration has led

2

to deunionization. Another important paper that is related to our work is by Gaston and Trefler

(1995). They show theoretically that higher protection or lower levels of import penetration need

not lead to higher union wage rates. Empirically as well, they find a negative relatioship between

tariffs and union wage rates.

To our knowledge, our paper is the first to look at the relationship between unionization, union

wage and trade policy for a developing country. In fact, unlike other papers in the literature, we

look at this relationship in the context of a major trade reform. A couple of other distinguishing

features of our paper are that we look at this relationship for different skill categories and that we

look at how these relationships change with labor market institutions which vary across the Indian

states.

2 Theory

The model we present in this section is an extension of Brock and Dobbelaere (2006) and

McDonald and Solow (1981). We build on their framework by allowing for a fixed cost of union

formation and making the union formation decision endogenous. We look at the case of perfect

competition in the product market (and then briefly discuss an extension to the case of imperfect

competition by assuming that firms have some monopoly power). Our goal is to look at the impact

of trade liberalization on unionization.

We consider a setup in which a representative firm in an industry and a workers’ union

bargain over both the wage w and employment N . Consider a firm with the following production

function:

Q = F (N, v) (1)

where N is the firm’s employment of labor and v is the vector of all other factor inputs that we

will assume to be fixed for tractability. Labor is the only variable input. The prices of the other

(fixed) inputs will be taken as given by the firm. The above production function given by (1) is

assumed to be constant returns to scale and thus it exhibits diminishing marginal product of labor.

The firm’s utility function (commonly known as the profit function) is:

π(w,N, v) = PQ− wN − pvv (2)

3

where w is the wage paid by the firm, pv is the vector of prices of other factor inputs and P is

the output price, and Q is the quantity. The import tariff is denoted by τ , so that P = P ∗(1 + τ)

where P ∗ is the world price.

There is also a risk-neutral labor union with the following utility function:

U = Nw + (N −N)wa (3)

where N is union membership and wa is alternative or outside wage.

The Nash bargaining problem is represented by the following maximization problem:

Maxw,N (Nw + (N −N)wa −Nwa)β(PQ− wN)1−β (4)

where β is the bargaining power of workers (of the union). After a few manipulations, the first-order

condition of the firm with respect to w can be written as:

w = wa +β

1 − β

(PQ− wN

N

)(5)

The first-order condition with respect to N , with (5) substituted into it gives us:

wa = PFN (6)

Let us assume that there is a fixed cost z the union has to incur before it can be operational or

come into existence (can start negotiating with the firm). From (5) and (6) above, we can arrive at

the following expression for the payoff of the union, which is its net gain from becoming operational

or coming into existence:

U = (w − wa)N −z = β(1 − εQ,N )PQ−z (7)

where εQ,N = NFNQ is the elasticity of output with respect to employment of labor. Note that this

is the gain over what these N workers would otherwise get, which would be their outside wage.

With a Cobb-Douglas production function, εQ,N is a constant. With trade liberalization (which

means τ goes down), P = P ∗(1 + τ) falls and so does Q.1 Thus PQ goes down as well and from

(7), the payoff of the union falls.

1From (5), dNdP

= −FNPFNN

. Thus dQdP

= FNdNdP

= −(FN )2

PFNN> 0.

4

If, instead, we have a CES production function of the form,

Q =

[θNN

σ−1σ +

∑i

θivσ−1σ

i

] σσ−1

(8)

then we have

εQ,N = θN

(N

Y

)σ−1σ

= θσN

(P

wa

)σ−1

(9)

Clearly, then εQ,N is non-increasing in P when σ ≤ 1 (with σ = 1 being the Cobb-Douglas

case). This condition in turn makes the union payoff from (7) unambiguously increasing in P. Trade

liberalization reduces P = P ∗(1 + τ) and that in turn reduces the union payoff.2 Even when σ > 1,

we can have U going down with trade liberalization since the effect of trade liberalization on PQ

can counteract the effect on 1 − εQ,N . In fact, we will impose the restriction that the latter effect

is not the dominant one.3 Note that the union will be in place only if U > 0. If U is decreasing in

P = P ∗(1 + τ), then trade liberalization makes union formation less likely or alternatively, makes

deunionization more likely.

If we have in an industry a continuum of firms that are identical in all respects but vary

continuously in their resistance to unions, then z will vary across firms. Let us index firms in

increasing order of fixed costs of a union penetrating the firm. In other words, we now have a

function, z(n) (with z´(n) > 0) which is a union’s fixed cost of penetrating the nth firm. It’s net

2At first, it might seem counterintuitive that in a model with factors other than labor held fixed, the elasticity

of substitution has an important role to play. To understand this, suppose we have in the model only two factor

inputs, namely labor and capital, and that capital is held fixed. What the elasticity of substitution determines is the

responsiveness of capital intensity (capital-labor ratio) to a change in the wage rate. So for example in the perfect

complements case where the elasticity of subsitution is zero, the capital intensity will be fixed (Leontief case). In

other words, it is just the fixed factor that will determine the amount of labor to be used. With σ > 0, as wage goes

down the firm will be willing to use more and more labor and will be less constrained by the amount of the fixed

capital it owns (or has access to). If capital and labor are perfect substitutes (σ goes to infinity), the amount of the

fixed capital possessed is not a constraint at all in the firm’s expansion in response to a fall in the wage. In other

words, σ tells us how much of a constraint the fixed factors are for a firm and how easily it can keep adding more

labor to expand output as wage keeps falling.

3This restriction seems reasonable to us, especially in light of few other channels mentioned below while discussing

extensions.

5

payoff from penetrating the nth firm is

U(τ, n) = β(1 − εQ,N )PQ−z(n),∂U

∂n= −z(n) < 0,

∂U

∂τ> 0

If U(τ, n) > 0, then the nth firm will be penetrated by a union. In equilibrium, the number of firms

unionized (experience union penetration), n∗will be given by the solution to the following equation:

U(τ, n∗) = 0 (10)

Totally differentiating (10) with respect to τ, we have

dn∗

dτ=

1

z(n∗)

∂U

∂τ> 0

Thus, as τ goes down with trade liberalization, we will have a smaller proportion of firms in the

industry that are unionized. This leads to our main hypothesis that trade liberalization leads to

deunionization. This will be especially true in import-competing industries.

What happens to the unionized wage rate, w? We can write it as follows:

w = wa

[1 + β

(1 − εQ,NεQ,N

)](11)

For a given wa, we have from (11), an inverse relationship between w and εQ,N . We know from (9)

thatdεQ,NdP ≷ 0 as σ ≷ 1. Therefore, trade liberalization will increase w for given wa when σ > 1

and decrease w for given wa when σ < 1. Another way of stating this is that wwa

will increase with

trade liberalization when σ > 1 and decrease with trade liberalization when σ < 1. Thus, how trade

liberalization will affect the union wage and union-nonunion wage differential becomes an empirical

question. To present the intuition behind this result, can write (5) as

w = (1 − β)wa + β

(PQ

N

)This means that the union wage rate is increasing in the revenue per worker, PQ

N . For a given

average product, QN , the union wage rate goes down as P goes down with trade liberalization.

However, N goes down as P goes down. This reduction in N will result in a decline in output, Q

but an increase in the average product. This increase in the average product will counter the direct

effect of the reduction in P . Depending on which effect dominates, the average revenue per worker

and, therefore, the union wage rate may go up or down.

6

Next we consider some extensions. If we introduce imperfect competition in that the firm

is allowed to have some monopoly power in the goods market, then the payoff function of the union

will be modified to

Um = β(1 −εQ,Nµ

)PQ−z (12)

where µ is markup given by the ratio of price to marginal cost and is an additional variable that is

expected to decrease with trade liberalization. The markup is a measure of monopoly power and to

the extent that trade liberalization destroys the monopoly power of domestic firms, it also reduces

markups. This reduction in µ also contributes to a reduction in Um in equation (12). In addition,

note that there may be a reduction in bargaining power, β due to globalization, as argued first

by Rodrik (1997). The reason is that trade liberalization makes domestic labor more replaceable

through the increase in the possibilities for substitution of the products it produces with foreign

products as well as through the imports of imported inputs.

Finally, also note that the union wage equation will get modified to w = wa

[1 + β

(µ−εQ,NεQ,N

)]under imperfect competition. As σ ≷ 1, w goes up or down due to the reasons explained in the

perfect competition case above. However, the markup going down (and the bargaining power going

down), will put a downward pressure on the union wage.

From our above analysis, we arrive at the following hypotheses:

Hypothesis 1: Trade liberalization leads to deunionization: a firm is less likely to

be penetrated by a union in that a smaller proportion of firms will experience union

penetration after trade liberalization.

Hypothesis 2: Trade liberalization has an ambiguous effect on the union wage rate

and the union-nonunion wage ratio.

3 Data

The unionization measures used in our empirical work were constructed using data from

the “employment-unemployment” household surveys conducted by India’s National Sample Survey

Organisation (NSSO). We use three rounds of these nationally-representative surveys: round 50

7

(1993-1994), round 55 (1999-2000), and round 61 (2003-2004).4 Unfortunately data on unionization

were not collected for previous rounds. As a result, we are unable to examine unionization patterns

using these data for the pre-1993 period.

The “employment-unemployment” household surveys collect demographic and employ-

ment information on all household members. Apart from standard employment information, these

surveys also ask respondents about unions in their activity. In particular, individuals were asked

whether there was any union/association in their activity (union presence). In addition, individuals

were asked, conditional on there being a union/association in their activity, whether they were a

member (union membership). We aggregated individual responses to both questions to the 3-digit

industry and state level.5 The union presence aggregate captures the fraction of individuals in a

given industry and state that work in unionized activities. Similarly, the union membership aggre-

gate captures the fraction of individuals in a given industry and state that are members of a union.

When calculating these aggregates, we accounted for each individual’s sample weights. In addition,

we restricted the sample to individuals that were in the labor force, worked in manufacturing in-

dustries, and were between the ages of 14 and 65. In addition, we also restricted the sample to the

fifteen major states in India. Both measures of unionization vary by 3-digit industry, state, and

year. The correlation coefficient between them is 0.88.

Table 1 lists the top five and bottom five industries according to both measures of union-

ization. The reported numbers have been averaged over the period 1993 to 2004. As the numbers

suggest, there is a large degree of cross-industry variation. For example, in the “manufacture of

railway wagons” industry, 81.6% of individuals report being in activities where there is a union

present. On the other hand, in the “manufacture of musical instruments” industry, only 5.6% of

individuals report being in activities where there is a union present. There is similar cross-industry

variation in the union membership measure. For example, in the “manufacture of railway wagons”

industry, 79.8% of individuals report being members of a union. On the other hand, in the “man-

ufacture of wooden and cane boxes” industry, only 3% of individuals report being members of a

union.

4In the remainder of this paper, we refer to each of the three survey years using the first year of the survey. In

other words, we refer to 1993-1994 as 1993.

5Throughout this paper, industries are classified according to the 1987 National Industrial Classification (NIC).

8

Table 2 displays the cross-state variation in both measures of unionization. Kerala is

the most unionized state with 47.4% of individuals working in unionized activities while 34.3% of

individuals are members of a union. On the other hand, Uttar Pradesh is the least unionized state

with 20.5% of individuals working in unionized activities while 14.6% of individuals are members

of a union. Next, Table 3 lists the trends in unionization by year for workers in different skill

categories. The second column suggests that union presence declined by 19% from 34.9% to 28.2%

between the period 1993 and 2004. This percentage decline was lower for workers with at least

a secondary education (high-skilled) as compared to workers without a secondary education (low-

skilled). A similar pattern holds for the union membership measure.

The “employment-unemployment” household surveys also collected wage data for both

unionized and nonunionized workers. These wages represent each respondent’s earnings during

the week prior to the survey date. We use these data to construct aggregated measures of union

and nonunion wages. In particular, for each industry, state, and year cell in our sample, we

calculate the median wage among all unionized workers in that cell. A unionized worker in this

instance is a worker that is a member of a union. This is the measure of union wages we use in

our subsequent analysis. Similarly, to construct nonunion wages, we calculate the median wage

among all nonunionized workers in a particular industry, state, and year cell. Both wage measures

were deflated using an industry-level wholesale price index. Note that these aggregated measures

vary by industry, state, and year. However, unlike the unionization measures above, these wage

measures were constructed at the 2-digit industry level. This is because the NSSO wage data

were not collected for self-employed survey respondents. As a result, we restricted the sample to

just wage employees (both regular and casual) for our NSSO-based wage analysis. Since wage

employees represented between 42% and 55% of the sample across the three rounds of data, the

above restriction significantly diminished our sample sizes. As a result, there were many 3-digit

industry and state pairs in which there were no unionized workers. This forced us to construct our

aggregated wage measures at the 2-digit industry level. A second limitation of the wage data is

due to a change in the definition of wages in round 55 (1999). In particular, in rounds 50 (1993)

and 61 (2004), the NSSO’s definition of wages excluded ‘overtime’ payments for additional work

done beyond normal working hours. However, in round 55 (1999), the wage data included these

‘overtime’ payments. Given that there was no information provided on ‘overtime’ hours worked, we

9

were unable to adjust the round 55 wage data to make it comparable to the other rounds. Instead,

we omitted round 55 from our NSSO-based wage analysis.

Fig. 1 depicts the trends in the ratio of median weekly union wages and the median

weekly nonunion wages. As the figure demonstrates, this ratio has increased dramatically from

1.09 in 1993 to 2.37 in 2004 for the sample including all workers. Fig. 1 also suggests that the ratio

of weekly union wages to nonunion wages increased for both high-skilled and low-skilled workers. In

particular, this ratio increased from 0.76 to 2.26 for high-skilled workers over the period 1993–2004.

On the other hand, for low-skilled workers, this ratio increased from 1.25 to 1.79 over the same

period.

Our wage analysis also uses industry-level data from the Annual Survey of Industries (ASI)

for the period 1989 to 2004. These data are representative of formal sector manufacturing firms in

India. We construct our data by combining the industry-level ASI data used in Hasan, Mitra, and

Ramaswamy (2007) and Gupta, Hasan, and Kumar (2009) respectively. The combined ASI data

are at the 2-digit industry and state level. As with the NSSO data, we restricted the sample to the

fifteen major states in India. These states are listed in Table 2. We use these data to construct

industry and state level wage rates by dividing total wages by the total number of workers. These

wage rates were then deflated using an industry-level wholesale price index. Fig. 2 depicts the

trends in the average wage rate for both high union presence and low union presence industries in

1993. Industries were classified into these two groups as follows. First, we calculated the median

industry-level union presence in 1993. Then, we classified industries with a union presence in

1993 above this median as high union presence. All other industries were classified as low union

presence. As Fig. 2 demonstrates, the gap between the average annual wage rate in high and low

union presence industries increased from approximately Rs. 7,539 in 1989 to Rs. 13,397 in 2004.6

All monetary values reported in this paper are in 1993 Rupees.

Next, the data on output tariffs are from the Asian Development Bank (ADB) and are an

extension of the series used by Hasan et al. (2007). The original data are at the sector level and

were converted to 1987 National Industrial Classification (NIC) industries.7 These data suggest

6These figures are in 1993 Indian rupees. In 1993 US dollars, these amounts are approximately $243 and $432

respectively. This conversion uses the 1993 exchange rate of Rs. 31 per US dollar.

7We thank Rana Hasan at the ADB for providing us the tariff data.

10

that tariffs fell from an average of 149.4% in 1989 to 23.9% in 2004. Note that these tariffs vary

by industry and year, but not by state. Lastly, we use the labor market flexibility classification

constructed by Gupta, Hasan, and Kumar (2009). This time-invariant, state-level measure classifies

states into either flexible, neutral, or rigid labor law categories. It is based on information from

Besley and Burgess (2004), Bhattacharjea (2006), and OECD (2007). Summary statistics for all

variables reported in the regression tables are listed in Table 4.

4 Econometric Methodology

4.1 Trade Liberalization and Unionization

The model in section 2 makes two predictions: (a) that trade liberalization will lead to

deunionization and (b) that trade liberalization will have ambiguous effects on union wages. In

addition, we argue in section 2 that the effect of trade liberalization on deunionization and union

wages will be stronger for net importer industries. We test these predictions below. We begin by

examining the relationship between trade liberalization and deunionization. We do so by estimating

the following regression:

Uist = αu + β1Tariffit−1 + β2NMi × Tariffit−1 + β3Zit−1

+β4Xist + θi + θs + θt + εist (13)

where Uist is the degree of unionization in a 3-digit industry i, state s, and year t. We use two

alternative measures of unionization. Our first measure is the fraction of individuals in a given

industry and state that work in unionized activities. We refer to this as union presence. Our

second measure is the fraction of individuals in a given industry and state that are members of a

union. We refer to this as union membership.

Tariffit−1 is the one-year lagged output tariff in 3-digit industry i. NMi is a time-invariant

dummy variable that is one for industries with positive net imports in 1993 and zero otherwise.

Zit−1 includes controls for the skill intensity and the degree of competition in an industry. Both

of these factors are likely to affect the degree of unionization. We proxy skill intensity using the

one-year lagged ratio of non-production to production workers in an industry. We proxy the degree

of competition using the natural logarithm of one-year lagged output per plant in an industry. Both

11

of these industry-level measures are constructed using the ASI data.

In Eq. (13), we also include a vector of control variables, Xist, that includes the fraction

of casual workers in total employment, the fraction of household employees in total employment,

the fraction of workers employed in rural areas, the fraction of old (age > 40) and young (age <

30) workers in total employment, and the fraction of educated workers (secondary education and

above) in total employment. All variables included in Xist are aggregated from the NSSO data and

vary by industry, state, and time. Lastly, θi, θs, and θt are industry, state, and year fixed effects

respectively while εist is an error term. Based on Hypothesis 1, we expect β1 and especially β1 +β2

to be positive.

We also estimate a version of Eq. (13) where we interact Tariffit−1 and NMi×Tariffit−1

with a time-invariant categorical variable that classifies states into either flexible, neutral, or rigid

labor law categories. Given that greater labor market flexibility implies lower levels of unionization

throughout the sample period, there is less scope for deunionization in these states.8 This implies

that the effect of trade liberalization on deunionization will be weaker in states with greater labor

market flexibility.

Lastly, we estimate Eq. (13) separately for three samples: (a) all workers, (b) workers

with at least a secondary education (high-skilled), and (c) workers with below secondary education

(low-skilled).

4.2 Trade Liberalization and Union Wages

Our second hypothesis in section 2 is that trade liberalization will have ambiguous effects on

union wages. We examine this issue using two approaches. In our first approach, we use the NSSO

data and estimate the following regression:

Ln(WUjst) = δ0 + δ1Tariffjt−1 + δ2NMj × Tariffjt−1 + δ3Zjt−1

+δ4Xjst + θj + θs + θt + εjst (14)

8Over the entire sample period, 29% of workers in flexible labor market states work in unionized activities. In

rigid states, this number is 36.1%. Similarly, over the entire sample period, 21.5% of workers in flexible labor market

states are members of a union while 30.5% of workers in rigid labor market states are members of a union.

12

where WUjst is the median wage earned by a unionized worker in 2-digit industry j, state s, and year

t and εjst is an error term. All other control variables in Eq. (14) are as defined in the previous

subsection, but are now at the 2-digit industry level. As described in detail in section 3, there are

many 3-digit industry and state pairs in the data without any unionized wage workers. Thus, we

were unable to construct union wages at the 3-digit industry level.

While Eq. (14) provides a direct way to test the effect of trade liberalization on union

wages, this approach has several important limitations. First, as also described in section 3, the

wage data from round 55 (1999) are not comparable to the wage data from the other two rounds.

This is due to changes in the way wages were recorded. In particular, in rounds 50 (1993) and 61

(2004), the NSSO’s definition of wages excluded ‘overtime’ wages. However, in round 55 (1999),

the wage data included these ‘overtime’ wages. Because no information was provided on ‘overtime’

hours worked, we were unable to adjust the round 55 wage data to make it comparable to the

other rounds. As a result, we were forced to estimate Eq. (14) using only data from rounds 50

(1993) and 61 (2004). Second, the nationally representative NSSO data includes individuals in

both the formal and informal sectors. Given that our model in section 2 is mainly applicable to

wage setting in the formal sector, the NSSO data may not be the most appropriate one to use to

test our model’s predictions. To address these concerns, we also estimate the relationship between

trade liberalization and union wages using an industry-level manufacturing panel. These data are

from the Annual Survey of Industries (ASI) and are representative of formal sector manufacturing

firms. They also span the period 1989 to 2004. Thus, by using the ASI data, we are not only able

to focus on formal sector union wages but are also able to examine the impact of output tariffs on

union wages before and after the trade reforms of 1991.

With the ASI data in hand, we estimate the following regression:

Ln(Wjst) = γ0 + γ1Tariffjt−1 + γ2U1993j × Tariffjt−1 + γ3Zjt−1

+ϕj + ϕs × ϕt + νjst (15)

where Wjst is the annual wage per worker in 2-digit industry j, state s, and year t. Tariffjt−1

is the one-year lagged 2-digit output tariff and Zjt−1 is the 2-digit aggregate of the skill-biased

technological change and level of competition variables described above. U1993j is either the union

presence in industry j in 1993 or the union membership in industry j in 1993. Note that both

13

measures of unionization are now at the 2-digit industry level. An alternate approach would be

to interact tariffs with the contemporaneous level of unionization. The drawback of this approach

is that the contemporaneous level of unionization is likely to be jointly determined with industry

wages. Thus, to avoid this simultaneity problem, we interact tariffs with the initial level of union-

ization in 1993. Lastly, ϕj , ϕs, and ϕt are 2-digit industry, state, and year fixed effects respectively

while νjst is an error term.

Our choice of dependent variable in Eq. (15) highlights a shortcoming of the ASI data.

In particular, the ASI does not provide data on wages separately for union and nonunion workers.

Instead, it only provides data on total wages paid in an industry. As a result, we are unable to

use union wages as the dependent variable in Eq. (15). We account for this shortcoming by using

the following strategy. We begin with the fact that industry wages are a combination of union and

nonunion wages. As a result, changes in industry wages can be due to either changes in union wages

or changes in nonunion wages. Thus, for the dependent variable in Eq. (15) to reflect changes in

union wages, we must control for changes in nonunion wages. We do this by including state and year

interaction fixed effects in Eq. (15). This relies on the assumption that a state is the appropriate

labor market in India and therefore the nonunion (or outside) wage is determined at the state level.9

As a result, by including state and year interaction fixed effects, we are controlling for time-varying

changes in the state-level nonunion wage. Conditional on including these interaction fixed effects,

changes in the industry wage in Eq. (15) will reflect changes in union wages. It therefore follows

that, if trade liberalization does raise (lower) the union wage, then we should observe that industry

wages in highly unionized industries will increase (decrease) after trade liberalization relative to

less unionized industries. In other words, if trade liberalization does raise (lower) union wages, we

would expect γ1 + γ2 to be negative (positive).

A third way of estimating the impact of trade liberalization on union wages would be to

use the wage data provided by the NSSO and employ the inter-industry wage differential (IWD)

approach. This approach was pioneered by Krueger and Summers (1988) and has been used in the

9This assumption will not hold if there is significant cross-state migration. Fortunately, Dyson, Cassen, and Visaria

(2004), using decennial population census data, argue that the bulk of migration in India occurs among women on

account of marriage. Mobility across states for economic reasons is limited. The migration that occurs does so mostly

within and across districts. The low levels of migration in India is also confirmed by Topalova (2010).

14

union wage context by Gaston and Trefler (1995) and Shendy (2010), among others. The IWD

approach proceeds in two stages. In the first stage, a Mincerian wage equation with industry fixed

effects is estimated. The coefficients of the industry fixed effects are then normalized so that they

represent the additional wage a worker in a certain industry receives relative to the average worker

in the sample. These normalized industry fixed-effect coefficients are then used as the dependent

variable in the second stage that includes various measures of trade policy as independent variables.

The IWD approach has two important shortcomings in our context. First, in the second

stage of this approach, the unit of observation is an industry. This leads to very small sample

sizes.10 Second, because the second stage unit of observation is an industry, the IWD approach

prevents us from incorporating cross-state variation in union wages as well as cross-state variation

in labor market policies. Given that labor market flexibility varies markedly across Indian states

and given the cross-state variation in unionization highlighted in Table 2, this is likely to be an

important shortcoming. As a result, we do not pursue the IWD approach in this paper.

Lastly, a general concern with our econometric approach is the potential endogeneity of

output tariffs. Endogeneity may arise if both unionization/union wages and output tariffs are

correlated with unobservable political economy factors such as industry size, lobbying power and so

forth. Such concerns are less relevant in our context due to the exogenous nature of the Indian trade

reforms of 1991. As mentioned earlier in the paper, the reforms were undertaken as a precondition

for obtaining emergency loans from the IMF. Given earlier attempts to avoid IMF loans and the

associated conditionalities, the adoption of these reforms came as a surprise (Hasan et al., 2007).

Thus, not only were these reforms due to external pressure, the timing of it was such that Indian

industries were likely unable to anticipate it. Thus, the changes in tariffs associated with these

reforms is likely to be exogenous to political economy factors. In addition, all regressions in this

paper include industry fixed effects, which will capture the effect of any time-invariant political

economy factors.

Nonetheless, to further address endogenenity concerns, we use an instrumental variable

(IV) strategy adapted from Goldberg and Pavcnik (2005). We begin by converting our econometric

specifications to first differences. This removes all time-invariant variables. We then use five-year

lagged tariffs and five-year lagged tariffs squared to instrument current first-differenced tariffs. For

10With 15 two-digit industries and 2 survey rounds, the second stage will have 30 observations.

15

the interaction between current first-differenced tariffs and a second variable, we use the interaction

between this second variable and five-year lagged tariffs and five-year lagged tariffs squared respec-

tively as instruments.11 This IV strategy relies on two key assumptions: (a) that there is a strong

correlation between five-year lagged tariffs and current changes in tariffs and (b) that five-year

lagged tariffs are uncorrelated with current changes in the error term. These assumptions are likely

to be satisfied for the following reasons. First, in addition to lowering tariffs, another objective

of the Indian trade reforms of 1991 was to harmonize tariffs across industries. This meant that

industries that had high tariffs in a given year received larger tariff changes in subsequent years.

This ensures a strong correlation between five-year lagged tariffs and current first-differenced tar-

iffs. Second, given the five-year gap between the endogenous variable and the instrument, it is

reasonable to assume that current changes in the error term are far removed from five-year lagged

tariffs.

5 Results

5.1 Unionization

Table 5 lists the results from estimating a version of Eq. (13) without the NMi×Tariffit−1

interaction. The dependent variable in column (1) is the fraction of workers in a given 3-digit

industry and state that work in unionized activities (union presence). Note that all regressions

reported in Tables 5 − 7 include 3-digit industry, state, and year fixed effects. The coefficient

of output tariffs in column (1) suggests that lower output tariffs, on average, did not have a

statistically significant effect on union presence. The coefficient of skill intensity suggests that

union presence was higher in industries with higher ratio of non-production to production workers.

On the other hand, the coefficient of the natural logarithm of concentration indicates that there was

no statistically significant relationship between the degree of concentration in an industry and the

level of unionization. Lastly, the coefficients of the other variables included in the regression but

not reported in Table 5 suggest that union presence was lower in industry-state pairs where there

are more low-educated workers, younger workers (age < 30), casual workers, household employees,

11For example, for the interaction between first-differenced output tariffs and NMi in Eq. (13) we use the interaction

between NMi and five-year lagged tariffs and five-year lagged tariffs squared respectively as instruments.

16

and rural workers.

In column (2) of Table 5 we restrict the sample to high-skilled workers, i.e. workers with

at least a secondary education. The dependent variable in this case is the fraction of high-skilled

workers in a given 3-digit industry and state that work in unionized activities. The coefficient

of output tariffs suggests that lower tariffs did not have a statistically significant effect on union

presence. In column (3) of Table 5 we restrict the sample to workers without a secondary education

(low-skilled). The dependent variable in this case is the fraction of low-skilled workers in a given 3-

digit industry and state that work in unionized activities. The coefficient of output tariffs suggests

that lower output tariffs, on average, did not have a statistically significant effect on union presence.

Next, in columns (4) to (6) we use union membership as the dependent variable. Union membership

is defined as the fraction of workers in a given 3-digit industry and state that are members of a

union. The results from using this alternate dependent variable are similar to the earlier findings.

The only exception is column (5) where the coefficient of output tariffs is positive and statistically

significant. In particular, the coefficient estimate suggests that a 10 percentage point decline in

output tariffs lowered union membership among high-skilled workers by 1.9 percentage points.

In Table 6 we report the results from estimating Eq. (13). In particular, we examine

whether the impact of trade liberalization on unionization depends on the trade orientation of an

industry. To do so, we create an indicator variable (net importer) that is one for industries with

positive net imports in 1993. All other industries have a net importer dummy equal to zero. We

then interact this net importer variable with output tariffs and add it to our specification. The

estimates in column (1) suggest that lower output tariffs led to lower union presence in net importer

industries. In particular, given a 10 percentage point decline in output tariffs, union presence in

net importer industries declined by an additional 1.1 percentage point relative to net exporter

industries.

In column (2) of Table 6 we once again restrict the sample to high-skilled workers. The

results suggest that lower output tariffs did not have an additional effect on union presence for

high-skilled workers in net importer industries. Next, in column (3) we restrict the sample to

low-skilled workers. The results suggest that lower output tariffs led to lower union presence for

low-skilled workers in net importer industries. Lastly, in columns (4) to (6) we repeat the above

using union membership as the dependent variable. The results in column (4) suggest that a 10

17

percentage point decline in output tariffs lowered union membership in net importer industries by

an additional 1.2 percentage points relative to net exporter industries. The results in columns (5)

and (6) are similar to our findings in columns (2) and (3) respectively.

In Table 7 we examine whether labor market flexibility affects the relationship between

trade liberalization and unionization. Given that greater labor market flexibility implies lower

levels of unionization throughout the sample period, there is less scope for deunionization in these

states. This implies that the effect of trade liberalization on deunionization will be weaker in states

with greater labor market flexibility. We test this hypothesis by adding two new interaction terms

to Eq. (13). The first is an interaction between output tariffs and labor market flexibility while the

second is an interaction between output tariffs, net importer indicator, and labor market flexibility.

We measure the labor market flexibility of a state by using the classification constructed by Gupta,

Hasan, and Kumar (2009). This time-invariant measure classifies states into either flexible, neutral,

or rigid labor law categories.

Column (1) of Table 7 indicates that the coefficient of the triple interaction term is

negative and significant. This implies that the impact of trade liberalization on deunionization in

net importer industries was attenuated in states with flexible labor markets. This result holds for

both the high-skilled and low-skilled unionization measures in columns (2) and (3) respectively. In

addition, these results are generally robust to using union membership as the dependent variable.

The only exception is the high-skill union membership regression in column (6) where the triple

interaction term is negative, but not statistically significant.

The results in Table 5 − 7 allow us to draw several broad conclusions. First, there is

evidence that trade liberalization, on average, led to greater deunionization for high-skilled workers.

Second, for low-skilled workers, the deunionization effects of trade liberalization were concentrated

in net importer industries. Third, the deunionization effects of trade liberalization in net importer

industries were generally attenuated in flexible labor market states.

5.2 Union Wages

Next, we turn to the relationship between trade liberalization and union wages. In Table 8

we report the results from estimating Eq. (14). These regressions use NSSO-based wage data to

examine the effect of trade liberalization on union wages. The dependent variable in column (1)

18

is the natural logarithm of the median wage among all unionized workers in a particular 2-digit

industry, state, and year cell. Recall that a unionized worker in this instance is a worker that is

a member of a union. Note that all regressions in Table 8 include 2-digit industry, state, and

year fixed effects. In column (1) the point estimate for the interaction between output tariffs and

the net importer indicator is negative and highly statistically significant. It suggests that, given a

10 percentage point decline in output tariffs, union wages for high-skilled workers in net importer

industries increased by an additional 8.3 percent relative to net exporter industries.

In columns (2) and (3) we restrict the sample to high-skilled and low-skilled workers

respectively. The dependent variable in column (2) is the natural logarithm of the median wage

among all high-skilled unionized workers in a particular 2-digit industry, state, and year cell. The

dependent variable in column (3) is constructed in an equivalent manner. In column (2) the

point estimate suggests that, given a 10 percentage point decline in output tariffs, union wages

for high-skilled workers in net importer industries increased by an additional 7.2 percent relative

to net exporter industries. Next, in column (3) the point estimate suggests that, given a 10

percentage point decline in output tariffs, union wages for low-skilled workers in net importer

industries increased by an additional 8.6 percent relative to net exporter industries. While the

results is Table 8 may be somewhat counterintuitive, they support some of the findings from the

previous literature. For example, Gaston and Trefler (1995) examine the impact of trade on union

wages using U.S. data. Interestingly, they find that lower tariffs in the U.S. are associated with

higher union wages. However, they also find that other measures of trade (i.e. import and export

volumes) do not support this conclusion. In addition, and Bastos, Kreickemeier and Wright (2010)

use U.K. data to examine the relationship between product market competition and union wages.

They find that, for low levels of unionization, greater product market competition increases union

wages. However, this effect is reversed for unionization levels above a certain threshold.

In columns (4)–(6) of Table 8 we examine the effect of trade liberalization on the union

wage premium. We define union wage premium as the ratio of union wages in natural logarithm to

nonunion wages in natural logarithm. The dependent variable in column (4) is constructed using

the sample of all workers while the dependent variables in columns (5) and (6) are constructed using

the sample of high-skilled and low-skilled workers respectively. In column (4) the point estimate

for the interaction between output tariffs and the net importer indicator is negative and highly

19

statistically significant. On the other hand, in column (5), we find that trade liberalization did not

have a statistically significant effect on the union wage premium for high-skilled workers. Lastly,

in column (6), we find that trade liberalization significantly increased the union wage premium

for low-skilled workers in net importer industries. Thus, these results suggest that the increase in

union wages for low-skilled workers due to trade has outpaced the increase in nonunion wages.

In Table 9 we report the results from estimating Eq. (15). These regressions use wage

data from the ASI, which is representative of formal-sector manufacturing plants in India. The

dependent variable in this case is the natural logarithm of the annual industry wage rate, where

wage rate is defined as industry wage bill divided by the number of workers. This measure varies

by 2-digit industry, state, and year. Note that all regressions reported in Table 9 include 2-digit

industry fixed effects along with state and year interaction fixed effects. As described in further

detail in section 4.2, these interaction fixed effects control for time-varying changes in nonunion

wages. As a result, after including these interaction effects, changes in the dependent variable

should reflect changes in union wages.

In column (1) we interact output tariffs with the union presence in an industry in 1993.

Given that wages and unionization are likely to be jointly determined, we refrain from interacting

output tariffs with contemporaneous levels of union presence. The results in column (1) suggest

that lower output tariffs led to relatively higher wage rates in industries with high union presence.

We interpret this as an indication that trade liberalization raised union wages. In addition, our

estimates suggest that, for a 10 percentage point drop in output tariffs, the industry with the

highest union presence experienced an additional 1.41% increase in its wage rate relative to the

industry with the median union presence.12

In column (2) we restrict the sample to industries that were net importers in 1993. The sign

of the coefficients match those from column (1). These estimates suggest that, for a 10 percentage

point drop in output tariffs, the net importer industry with the highest union presence experienced

an additional 1.36% increase in its wage rate relative to the net importer industry with the median

union presence. Thus, the magnitude of the impact of trade liberalization on wage rates is very

12The coefficients also suggest that only industries where more than 40% of workers were in unionized activities in

1993 experienced an increase in wage rates after trade liberalization. Of the 15 two digit industries in our sample, 7

industries fall into this category.

20

similar in both columns (1) and (2). Next, in column (3) we restrict the sample to net exporter

industries. In this case, the sign of the coefficients are the opposite of column (1). They indicate

that net exporter industries with high union presence in 1993 experienced a relatively larger decline

in wage rates due to trade liberalization. In particular, for a 10 percentage point drop in output

tariffs, the net exporter industry with the highest union presence experienced an additional 1.4%

decrease in its wage rate relative to the net exporter industry with the median union presence.13

Lastly, in column (4) we add the interaction between our measure of labor market flexibility and

both output tariffs as well as its interaction with the net importer indicator. The results, which

are for the entire sample of industries, suggest that lower tariffs led to even higher relative wage

rates in industries with high union presence that were in flexible labor market states.

In columns (4) to (6) we interact output tariffs with union membership in 1993 instead

of union presence. The estimates in column (4) suggest that, for a 10 percentage point drop in

output tariffs, the industry with the highest union membership experienced an additional 1.89%

increase in its wage rate relative to the industry with the median union membership. Next, in

columns (6) and (7) we restrict the sample to net importer and net exporter industries respectively.

We find that, for a 10 percentage point drop in output tariffs, the industry with the highest union

membership experienced an additional 1.19% increase and a 1.12% decrease in its relative wage

rate respectively. Lastly, in column (8) we include the two additional interactions with our labor

market flexibility measure. As before, we find that lower tariffs led to even higher wage rates in

industries with high union membership that were in flexible labor market states.

To summarize, the results in Table 8 suggests that, relative to net exporter industries,

trade liberalization raised union wages in net importer industries for both high-skilled and low-

skilled workers. These results use the NSSO wage data. Similarly, the results in Table 9 suggest

that, in net importer industries, trade liberalization led to higher wage rates in highly unionized

industries relative to less unionized ones. We interpret this as an indication that, in net importer

industries, trade liberalization raised union wages relative to nonunion wages. These results use

13The coefficients suggest that only net exporter industries with more than 60.5% of workers in unionized activities

experienced a decline in wages due to trade liberalization. However, as no net exporter industry in the sample had

union presence above this cutoff in 1993, it follows that trade liberalization raised wage rates for all net exporter

industries in the sample.

21

the ASI wage data. Thus, given that they use different data and estimating equations, it is encour-

aging that both sets of results yield similar conclusions regarding the relationship between trade

liberalization and union wages.

5.3 Robustness Checks

As mentioned previously in section 4.2, a concern with our econometric approach is the

potential endogeneity of output tariffs. This may arise if both unionization and output tariffs

are correlated with unobservable political economy factors such as industry size, lobbying power

etc. Such concerns are mitigated in our context due to the exogenous nature of the Indian trade

reforms of 1991. As mentioned earlier in the paper, the reforms were undertaken as a precondition

for obtaining emergency loans from the IMF. In addition, there were uncertainty regarding the

implementation of the IMF directives. As a result, the changes in tariffs associated with these

reforms is likely to be exogenous to political economy factors. In addition, all regressions reported

thus far included industry fixed effects, which will capture the effect of any time-invariant political

economy factors.

Nonetheless, we further address concerns about the endogeneity of tariffs by employing

an instrumental variable (IV) approach adapted from Goldberg and Pavcnik (2005). In particular,

we convert our baseline specification to first-differences and then use five-year lagged output tariffs

and five-year lagged output tariffs squared to instrument the first-differenced current output tariff

term. For the first-differenced interaction between current output tariffs and a second variable,

we use an interaction between the second variable and five-year lagged output tariffs and five-year

lagged output tariffs squared respectively. See section 4.2 for further details on the instrument as

well a discussion of the assumptions that need to be satisfied for this IV strategy to be valid. These

IV results are reported in Table 10. In columns (1) and (2) we run an IV regression on the first-

differenced version of Eq. (13). In column (1) the dependent variable is the union presence while

in column (2) it is union membership. The coefficient of the interaction term of interest retains

the correct sign in both columns, but is only statistically significant in column (2). The Shea’s

partial R2 in columns (1) and (2) ranges from 0.05 to 0.65. In columns (3) and (4) we run an IV

regression on the first-differenced version of Eq. (14). The dependent variable in column (3) is the

union wage in natural logarithm while in column (4) it is the union wage premium. Once again the

22

interaction term of interest retains the correct sign in both columns. In addition, both coefficient

estimates are statistically significant. The Shea’s partial R2 in columns (3) and (4) ranges from

0.54 to 0.99. Lastly, in columns (5) and (6) we run an IV regression on the first-differenced version

of Eq. (15). The dependent variable in both columns is the industry wage rate in natural logarithm.

The interaction term of interest in both columns is negative and statistically significant. The Shea’s

partial R2 in columns (5) and (6) ranges from 0.28 to 0.69. Thus, overall, the IV estimates are very

similar to the OLS baseline.

In Table 11 we subject the primary results in this paper to a series of robustness checks.

In columns (1) to (2) we estimate Eq. (13). The dependent variable here is the union presence. In

column (1) we add a measure of delicensing to our baseline specification. The delicensing measure is

an indicator variable that is one for 3-digit industries that have been delicensed and zero otherwise.

This is constructed using data from Aghion, Burgess, Redding, and Zilibotti (2008). Our intention

here is to capture the fact that the economic reforms initiated in 1991 included more than trade

liberalization. Thus, it is possible that these alternate aspects of the reforms are the primary cause

of the subsequent changes in unionization and union wages in India. However, the results in column

(1) suggest that even after controlling for delicensing, the coefficient of the interaction between

output tariffs and the net importer indicator remains negative and significant. The coefficient of

the delicensing variable suggests that industries that were delicensed experienced a relative increase

in union presence.

In column (2) we replace one-year lagged output tariffs with one-year lagged non-tariff

barriers (NTBs). The coefficient of interest remains highly robust. We’ve also estimated a different

version of columns (1) to (2) with union membership as the dependent variable. These results are

very similar to the ones presented in Table 11. In columns (3) to (4) we repeat the robustness

checks above on Eq. (14). In column (3) we add a measure of delicensing to our baseline union wage

specification. Given that these regressions are the 2-digit industry and state level, the delicensing

measure now captures the fraction of 3-digit industries within a 2-digit industry that has been

delicensed. Once again, after adding the delicensing measure, the coefficient of the interaction

between output tariffs and the net importer indicator remains robust. In column (4) we replace

one-year lagged output tariffs with one-year lagged non-tariff barriers (NTBs). The coefficient of

interest again remains highly robust. Finally, in columns (5) to (6) we run our robustness checks

23

on Eq. (15). In column (5) we again add our measure of delicensing at the 2-digit level to our

baseline union wage specification. As above, we find that, after adding the delicensing measure,

the coefficient of the interaction between output tariffs and the net importer indicator remains

robust. In column (6) we replace one-year lagged output tariffs with one-year lagged non-tariff

barriers (NTBs). The coefficient of interest again remains highly robust.

6 Conclusion

We first present our theory which extends existing theory due to Brock and Dobbelaere

(2006) and McDonald and Solow (1981) by endogenizing union formation. We show that trade

liberalization (tariff cut) leads to a lower likelihood of union formation and a smaller proportion of

firms in an industry penetrated by unions in equilibrium. This means there are fewer firms that

pay the union wage. The impact of trade reforms on the union wage itself and the union-nonunion

wage inequality is ambiguous.

We test the predictions of our theory using a combination of National Sample Survey

Organization (NSSO) and Annual Survey of Industries (ASI) data from India. Our analysis is

done at the level of 3-digit industry by state (over time). We find that unionization indeed does

go down with trade liberalization for high-skilled workers. For low-skilled workers, this effect is

more pronounced for industry categories in which imports exceed exports. We also find that the

deunionization effects of trade liberalization in net importer industries were generally attenuated

in flexible labor market states. Using both NSSO wage data and the ASI wage data, the evidence

suggests that, in net importer industries, trade liberalization raised union wages relative to nonunion

wages.

References

[1] Aghion, P., Burgess R., Redding S., Zilibotti, F., 2008. “The Unequal Effects of Liberalization:

Evidence from Dismantling the License Raj in India.” American Economic Review 98(4), 1397–

1412.

24

[2] Baldwin, R.E., 2003. The Decline of US Labor Unions and the Role of Trade. Peterson Institute

of International Economics, Washington DC.

[3] Bastos, P., Kreickemeier, U., Wright, P.W., 2010. “Open-Shop Unions and Product Market

Competition.” Canadian Journal of Economics 43(2), 640–662.

[4] Besley, T., Burgess, R., 2004. “Can labor regulation hinder economic performance? Evidence

from India.” Quarterly Journal of Economics 119(1), 91–134.

[5] Bhattacharjea, A., 2006. “Labour market regulation and industrial performance in India: a

critical review of the empirical evidence.” The Indian Journal of Labour Economics 49(2),

211–232.

[6] Brock, E., Dobbelaere, S., 2006. “Has International Trade Affected Workers’ Bargaining

Power?” Review of World Economics, 142(2): 233–266.

[7] Dreher, A., Gaston, N., 2007. “Has Globalization Really Had No Effect On Unions?” Kyklos

2, 165-186.

[8] Dyson, T., Cassen, R., Visaria, L., 2004. Twenty-first Century India: Population, Economy,

Human Development, and the Environment. Delhi: Oxford University Press.

[9] Gaston, N., Trefler, D., 1995. “Union wage sensitivity to trade and protection: theory and

evidence.” Journal of International Economics 39(1), 1–25.

[10] Goldberg, P., Pavcnik, N., 2005. “Trade, Wages, and the Political Economy of Trade Pro-

tection: Evidence from the Colombian Trade Reforms.” Journal of International Economics

66(1): 75–105.

[11] Gupta, P., Hasan, R. Kumar, U., 2009. “Big Reforms but Small Payoffs: Explaining the Weak

Record of Growth in Indian Manufacturing,” India Policy Forum 5.

[12] Hasan, R., Mitra, D. Ramaswamy, K.V., 2007. “Trade Reforms, Labor Regulations and Labor

Demand Elasticities: Empirical Evidence from India.” The Review of Economics and Statistics

89(3), 466–481.

25

[13] Krishna, P., Mitra, D. and Chinoy, S., 2001. “Trade Liberalization and Labor Demand Elas-

ticities: Evidence from Turkey.” Journal of International Economics 55(2): 391–409.

[14] Krueger, A.B., Summers, L.H., 1988. “Efficiency wages and the inter-industry wage structure.”

Econometrica 56(2), 259–293.

[15] Lazear, E.P., 1983. “A Competitive Theory of Monopoly Unionism.” American Economic

Review 73(4), 631-643.

[16] Magnani, E., Prentice, D., 2003. “Did Globalization Reduce Unionization? Evidence from US

Manufacturing.” Labor Economics 10, 705-726.

[17] OECD, 2007. OECD Economic Surveys: India. Volume 2007, Issue no. 14.

[18] Rodrik, D., 1997. Has Globalization Gone Too Far? Peterson Institute of International Eco-

nomics, Washington DC.

[19] Shendy, R., 2010. “Do Unions Matter? Trade Reform and Manufacturing Wages in South

Africa.” Journal of African Economies 19(2), 163–204.

[20] Topalova, P., 2010. “Factor Immobility and Regional Effects of Trade Liberalization: Evidence

on Poverty from India.” American Economic Journal: Applied Economics 2(4): 1-41.

26

Figure 1: Trends in the ratio of median weekly union wages to median weekly nonunion wages. The wage data are from the NSSO "employment-unemployment" surveys.

Figure 2: Trends in average annual industry wage rates. High union presence refers to industries with above median union presence in 1993. Low union presence refers to industries with below median union presence in 1993. The reported wages are in thousands of 1993 Rupees and are from the Annual Survey of Industries.

.51

1.5

22.5

Uni

on W

ages

/ N

onu

nion

Wag

es

1990 1995 2000 2005Year

All High skilledLow skilled

20

25

30

35

40

45

Rea

l Wage

Rat

e (R

s. T

hous

and

s)

1990 1995 2000 2005Year

High Union Presence Low Union Presence

Code Description Unionization Code Description Unionization

372 Manufacture of Railway Wagons 0.816 386Manufacture of Musical Instruments 0.056

301Manufacture of Fertilizers and Pesticides 0.710 273

Manufacture of Wooden and Cane Boxes 0.063

314Manufacture of Refined Petroleum Products 0.705 269

Manufacture of Textile Products n.e.c. 0.083

317 Processing of Nuclear Fuels 0.684 279Manufacture of Wood, Cane Products n.e.c. 0.083

324Manufacture of Cement, Lime, and Plaster 0.645 293

Manufacture of Leather Consumer Goods 0.092

Code Description Unionization Code Description Unionization

372 Manufacture of Railway Wagons 0.798 273Manufacture of Wooden and Cane Boxes 0.030

314Manufacture of Refined Petroleum Products 0.644 272

Manufacture of Structural Wooden Goods 0.035

301Manufacture of Fertilizers and Pesticides 0.590 269 Manufacture of Textile Products 0.036

324Manufacture of Cement, Lime, and Plaster 0.544 279

Manufacture of Wood, Cane Products n.e.c. 0.050

361Manufacture of Insulated Wires and Cables 0.524 277

Manufacture of Bamboo and Cane Furniture 0.055

Notes: Union presence represents the fraction of workers in an industry working in activities where unions are present. Union membership represents the fraction of workers in an industry that are members of an union. Both measures are averaged over the period 1993 to 2004.

Top Five Industries by Union Presence Bottom Five Industries by Union PresenceTable 1: Unionization By Industry

Top Five Industries by Union Membership Bottom Five Industries by Union Membership

State Union Presence Union Membership

Andhra Pradesh 0.288 0.211Assam 0.300 0.245Bihar 0.270 0.220Gujarat 0.231 0.184Haryana 0.269 0.206Karnataka 0.354 0.283Kerala 0.474 0.343Madhya Pradesh 0.249 0.194Maharashtra 0.390 0.321Orissa 0.268 0.246Punjab 0.235 0.179Rajasthan 0.250 0.202Tamil Nadu 0.343 0.230Uttar Pradesh 0.205 0.146West Bengal 0.385 0.322

Mean 0.305 0.237Standard Deviation 0.347 0.321

Table 2: Unionization by State

Notes: The unionization rates reported here are averaged over the three NSSO sample years.

All High-Skilled Low-Skilled

Panel A: Union Presence

1993 0.349 0.462 0.2861999 0.289 0.376 0.2182004 0.282 0.356 0.212

Percentage Change (1993-2004) -19.00 -22.99 -25.82

Panel B: Union Membership

1993 0.283 0.383 0.2221999 0.214 0.291 0.1522004 0.219 0.294 0.150

Percentage Change (1993-2004) -22.63 -23.35 -32.69

Table 3: Trends in Unionization by Skill

Notes: High-skilled refers to individuals with a secondary education or above. The remaining individuals are classified as low-skilled.

Sample YearsLevel of Industrial Aggregation

Total Obs. Mean Standard Deviation Total Obs. Mean

Standard Deviation

Union Presence 3361 0.305 0.347 - - -

Union Membership 3361 0.238 0.321 - - -

Output Tariffs (One-Year 3361 0.567 0.362 3511 0.754 0.493Lagged)Output Tariffs (Five-Year 3361 0.840 0.520 2625 0.900 0.487Lagged)Output NTBs (One-Year 3361 0.339 0.307 3511 0.484 0.294Lagged)Non Production / Production 3361 0.344 0.125 3287 0.246 0.080Worker RatioLn(Concentration) 3361 4.897 0.987 3287 10.270 1.126 mean

Net Importer 3361 0.493 0.500 3511 0.474 0.499

Labor Market Flexibility 3361 0.153 0.747 3511 0.139 0.723count

Delicensing 3361 0.804 0.397 3511 0.690 0.264

Ln(Industry Wage Rate) - - - 3511 3.267 0.251

Notes: The NSSO data cover three rounds and were collected in the years 1993, 1999, and 2004. The ASI data are annual data that covers the period 1989-2004. The unionization measures used in the ASI-based regressions were constructed using only NSSO data from 1993. This explains why the sample means differ in the two cases. The industry wage rate above is in thousands of 1993 Rupees. 1 US dollar in 1993 was approximately equal to Rs. 31.

Table 4: Summary StatisticsNational Sample Surveys

(NSSO)Annual Survey of Industries

(ASI)1993, 1999, 2004 1989-2004

3-Digit NIC 1987 2-Digit NIC 1987

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

Sample AllHigh-

SkilledLow-

Skilled AllHigh-

SkilledLow-

SkilledDependent Variable

Output Tariffs -0.01 0.13 -0.03 0.04 0.19** 0.00(0.052) (0.081) (0.065) (0.039) (0.087) (0.043)

Skill Intensity 0.33** 0.42** 0.16 0.20** 0.43** -0.05(0.151) (0.167) (0.152) (0.096) (0.186) (0.107)

Ln(Concentration) -0.04 0.00 -0.04 0.03 0.06 0.04**(0.031) (0.037) (0.037) (0.019) (0.038) (0.017)

Constant 0.61*** 0.38 0.67*** 0.21* -0.01 0.27**(0.170) (0.238) (0.211) (0.109) (0.246) (0.106)

Observations 3,361 2,678 2,871 3,361 2,678 2,871R-squared 0.564 0.387 0.481 0.562 0.374 0.466

Table 5: Trade Liberalization and Unionization

Union Presence Union Membership

Notes: The dependent variable in columns (1)-(3) is the fraction of workers in firms where unions exist in various subsamples of the data. The dependent variable in columns (4)-(6) is the fraction of workers that are union members in various subsamples of the data. Output tariffs are at the 3-digit industry level and are lagged by one year. Skill intensity is the one-year lagged ratio of non-production to production workers. Concentration is the one-year lagged ratio of output per plant. All regressions include industry-state-level controls for the fraction of casual, household, rural, highly educated, old, and young workers. All regressions are weighted by the total number of workers in each industry and state pair and include state, industry, and year effects. Robust standard errors in parentheses are clustered at the 3-digit industry level, *** p<0.01, ** p<0.05, * p<0.1.

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

Sample AllHigh-

SkilledLow-

Skilled AllHigh-

SkilledLow-

SkilledDependent Variable

Output Tariffs -0.06 0.14 -0.08 -0.02 0.20** -0.05(0.055) (0.093) (0.071) (0.037) (0.098) (0.039)

Output Tariffs * Net 0.11*** -0.03 0.11*** 0.12*** -0.02 0.11***Importer (0.034) (0.059) (0.038) (0.033) (0.069) (0.030)

Constant 0.75*** 0.35 0.80*** 0.37*** -0.03 0.41***(0.175) (0.280) (0.225) (0.101) (0.279) (0.094)

Observations 3,361 2,678 2,871 3,361 2,678 2,871R-squared 0.568 0.388 0.484 0.567 0.375 0.471

Table 6: Trade Liberalization, Trade Orientation, and Unionization

Union Presence Union Membership

Notes: The dependent variable in columns (1)-(3) is the fraction of workers in firms where unions exist in various subsamples of the data. The dependent variable in columns (4)-(6) is the fraction of workers that are union members in various subsamples of the data. Output tariffs are at the 3-digit industry level and are lagged by one year. All regressions include industry-level controls for skill intensity and concentration and industry-state-level controls for the fraction of casual, household, rural, highly educated, old, and young workers. All regressions are weighted by the total number of workers in each industry and state pair and include state, industry, and year effects. Robust standard errors in parentheses are clustered at the 3-digit industry level, *** p<0.01, ** p<0.05, * p<0.1.

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

Sample AllHigh-

SkilledLow-

Skilled AllHigh-

SkilledLow-

Skilled

Dependent Variable

Output Tariffs -0.06 0.13 -0.08 -0.01 0.20** -0.04(0.055) (0.091) (0.070) (0.037) (0.098) (0.038)

Output Tariffs * Net 0.11*** -0.02 0.10*** 0.12*** -0.02 0.11***Importer (0.035) (0.057) (0.039) (0.033) (0.067) (0.030)Output Tariffs * Labor 0.01 0.04 0.01 -0.01 0.00 -0.01Market Flexibility (LMF) (0.014) (0.036) (0.017) (0.010) (0.033) (0.012)Output Tariffs * Net -0.09*** -0.08*** -0.10*** -0.08*** -0.04 -0.08***Importer * LMF (0.027) (0.024) (0.031) (0.023) (0.028) (0.027)

Constant 0.76*** 0.35 0.80*** 0.37*** -0.03 0.41***(0.174) (0.275) (0.224) (0.101) (0.279) (0.092)

Observations 3,361 2,678 2,871 3,361 2,678 2,871R-squared 0.574 0.391 0.492 0.576 0.376 0.479

Table 7: Trade Liberalization, Labor Market Flexibility, and Unionization

Union Presence Union Membership

Notes: The dependent variable in columns (1)-(3) is the fraction of workers in firms where unions exist in various subsamples of the data. The dependent variable in columns (4)-(6) is the fraction of workers that are union members in various subsamples of the data. Output tariffs are at the 3-digit industry level and are lagged by one year. The labor market flexibility measure is from Gupta, Hasan, and Kumar (2009). All regressions include include industry-level controls for skill intensity and concentration and industry-state-level controls for the fraction of casual, household, rural, highly educated, old, and young workers. All regressions are weighted by the total number of workers in each industry and state pair and include state, industry, and year effects. Robust standard errors in parentheses are clustered at the 3-digit industry level, *** p<0.01, ** p<0.05, * p<0.1.

(1) (2) (3) (4) (5) (6)Dependent Variable

Sample AllHigh-

SkilledLow-

Skilled AllHigh-

SkilledLow-

Skilled

Output Tariffs -0.20 -0.59 -1.07*** 0.90** 0.54 -0.15(0.376) (0.684) (0.319) (0.382) (0.582) (0.320)

Output Tariffs * Net -0.83*** -0.72*** -0.86*** -0.64*** -0.20 -0.90***Importer (0.179) (0.243) (0.120) (0.168) (0.166) (0.132)

Constant 1.61 1.38 5.01*** -2.72** -3.48*** 1.62**(1.114) (1.489) (0.660) (1.179) (1.220) (0.721)

Observations 339 277 284 331 247 276R-squared 0.703 0.726 0.678 0.442 0.492 0.357

Table 8: Trade Liberalization and Union Wages - NSSO

Ln(Union Wage) Union Wage Premium

Notes: The dependent variable in columns (1)-(3) is the median wage earned by union members in a 2-digit industry and state. In columns (4)-(6), the dependent variable is the ratio of median union wage and median nonunion wage (both in natural logarithm) in a 2-digit industry and state. Sample sizes in columns (4)-(6) decline because not all 2-digit industry and state pairs had nonunion workers. Output tariffs are at the 2-digit industry level and are lagged by one year. All regressions include industry-level controls for skill intensity and concentration and industry-state-level controls for the fraction of casual, household, rural, highly educated, old, and young workers. All regressions are weighted by the total number of workers in each industry and state pair and include state, industry, and year effects. Robust standard errors in parentheses are clustered at the 2-digit industry-year level, *** p<0.01, ** p<0.05, * p<0.1.

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

Sample AllNet

ImportNet

ExportNet

ImportNet

Export AllDependent Variable

Output Tariffs 0.19** 0.52*** -0.49*** 0.18** 0.18** 0.44*** -0.67*** 0.18**(0.085) (0.103) (0.095) (0.086) (0.079) (0.089) (0.093) (0.080)

Output Tariffs * Union -0.52*** -1.01*** 0.84*** -0.51***Presence (1993) (0.112) (0.203) (0.219) (0.111)Output Tariffs * Labor 0.19** 0.17**Market Flexibility (LMF) (0.079) (0.071)Output Tariffs * Union -0.30***Presence (1993) * LMF (0.066)Output Tariffs * Union -0.58*** -1.01*** 2.04*** -0.57***Membership (1993) (0.101) (0.197) (0.396) (0.100)Output Tariffs * Union -0.34***Membership (1993) * LMF (0.061)Observations 3,287 1,558 1,729 3,287 3,287 1,558 1,729 3,287R-squared 0.857 0.802 0.845 0.859 0.858 0.802 0.846 0.860

Table 9: Trade Liberalization, Unionization, and Industry Wages

Ln(Industry Wage Rate)All

Notes: The dependent variable is the natural logarithm of total industry wages divided by total workers. Output tariffs are at the 3-digit industry level and are lagged by one year. The labor market flexibility measure is from Gupta, Hasan, and Kumar (2009). All regressions include one-year lagged controls for skill intensity and concentration and are weighted by the total output in each industry and state pair. All regressions also include a constant, industry fixed effects, and state-year interaction fixed effects. Robust standard errors in parentheses are clustered at the 2-digit industry-year level, *** p<0.01, ** p<0.05, * p<0.1.

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

Dependent VariableUnion

Presence

Union Member-

shipLn(Union

Wage)

Union Wage

Premium

Output Tariffs -0.00 0.22 -0.68 0.53 0.39** 0.39***(0.354) (0.226) (0.836) (0.763) (0.154) (0.138)

Output Tariffs * Net 0.06 0.07* -0.77*** -0.55**Importer (0.044) (0.038) (0.235) (0.219)Output Tarrifs * Union -0.64*Presence (1993) (0.359)Output Tarrifs * Union -0.72**Membership (1993) (0.343)Observations 2,050 2,050 143 137 2,622 2,622R-squared 0.111 0.087 0.210 0.140 0.062 0.062

Ln(Industry Wage Rate)

Table 10: IV Regressions

Notes: The dependent variables in columns (1) and (2) are as defined in Table 5. The dependent variables in column (3) and (4) are as defined in Table 8. Lastly, the dependent variable in columns (5)-(6) is as defined in Table 9. In all six columns, the instruments are five-year lagged tariffs, five-year lagged tariff squared, and the interaction between both of these variables and the interacted term. Output tariffs are lagged by either four or five years. Columns (1)-(4) include all of the control variables listed in the body and notes of Table 5. These regressions are weighted by the total number of workers in each industry and state pair and include state, industry, and year effects. Columns (5)-(6) include all of the control variables listed in the body and notes of Table 8. These regressions are weighted by the total output in each industry and state pair and include industry fixed effects and state-year interaction fixed effects. All regressions include a constant that is not reported. Robust standard errors in parentheses are clustered at the 3-digit industry level in columns (1)-(2) and 2-digit industry-year level in columns (3)-(6), *** p<0.01, ** p<0.05, * p<0.1.

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

Dependent Variable

Output Tariffs -0.06 -0.40 0.19**(0.055) (0.388) (0.085)

Output Tariffs * Net 0.11*** -0.85***Importer (0.034) (0.179)Delicensing 0.05*** -0.84* 0.04

(0.017) (0.425) (0.105)Output NTBs -0.03 0.08 0.36***

(0.030) (0.161) (0.080)Output NTBs * Net 0.14*** -1.45***Importer (0.037) (0.234)Output Tarrifs * Union -0.53***Presence (1993) (0.115)Output NTBs * Union -0.70***Presence (1993) (0.173)Observations 3,361 3,361 339 339 3,287 3,287R-squared 0.568 0.568 0.704 0.705 0.857 0.856

Ln(Industry Wage Rate)Union Presence Ln(Union Wage)

Table 11: Robustness Checks

Notes: The dependent variable in columns (1)-(2) is the fraction of workers in activities where unions exist. The dependent variable in column (3)-(4) is the median wage earned by unionized workers. The dependent variable in columns (5)-(6) is the natural logarithm of total industry wages divided by total workers. One-year lagged output tariffs are at the 3-digit industry level in columns (1)-(2) and at the 2-digit industry level in columns (3)-(6). Industry-level output NTBs are also lagged by one year. The delicensing measure in columns (1)-(2) is an indicator variable that is one for 3-digit industries that have been delicensed and zero otherwise. The delicensing variable in columns (3)-(6) is the fraction of 3-digit industries within a 2-digit industry that has been delicensed. Columns (1)-(4) include all of the control variables listed in the body and notes of Table 5. These regressions are weighted by the total number of workers in each industry and state pair and include state, industry, and year effects. Columns (5)-(6) include all of the control variables listed in the body and notes of Table 8. These regressions are weighted by the total output in each industry and state pair and include industry fixed effects and state-year interaction fixed effects. Robust standard errors in parentheses are clustered at the 3-digit industry level in columns (1)-(2) and 2-digit industry-year level in columns (3)-(6), *** p<0.01, ** p<0.05, * p<0.1.