trade, technology and wage inequality in developing ... · moved slowly towards developing...

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TRADE, TECHNOLOGY AND WAGE INEQUALITY IN DEVELOPING COUNTRIES: EVIDENCE FROM INDIAN MANUFACTURING

Chris Milner1 Dev Vencappa Peter Wright

Abstract

This paper explores the roles of trade and technological change behind the rising wage

inequality observed in Indian manufacturing following the 1991 trade policy reforms.

We use the mandated wage methodology and explore the effects of technological

change on wage inequality within a unified general equilibrium framework that

accounts for the wage effects of both price and technological change. Two versions of

the model are estimated, depending on whether the maintained assumption is that

price and TFP growth are exogenous or endogenous. Under the assumption of

exogeneity, we find that post-reforms, price changes and technological change pull in

opposite and expected directions on wage inequality, but the forces of technological

change are of a greater magnitude. Assuming endogeneity of price and technological

change, we find that the rise in inequality post-reform is due only to technological

change, and not price changes, although the magnitude of wage changes mandated by

the model are quite high compared to actual wage changes. Our results confirm the

findings of Berman, Somanathan and Tan (2005), who argue that part of the increase

in the relative demand for skilled workers is due to skill-biased technological change.

While our technology variable captures all types of technological change, our results

seem to point out that a large component of this technical change may actually be

skill-biased in nature.

Work-in-progress: comments welcome

April 2005

Preliminary draft: please do not quote.

1 The authors are all at the School of Economics, University of Nottingham.

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1.0 Introduction

Virtually all analysts of labour markets now accept as fact the deterioration in

employment prospects and relative returns to labour market participation for unskilled

workers in developed countries in the 1980s. Many studies have documented these

trends and changes (Levy and Murnane, 1992; Bloom & Brender, 1993; OECD, 1993;

Goldin and Katz, 1995; Mincer, 1996; OECD, 1997; Gottschalk & Smeeding, 1997;

Murphy & Topel, 1997). What is controversial, however, is the relationship between

these trends and patterns and any (or all) of the elements of globalisation. Just about a

decade ago, these trends were generally ignored by trade economists, and were mostly

the concern of labour economists; for instance, analyses of causes of growing earnings

inequality, like Levy and Murnane (1992), gave little weight to global factors, such as

trade. However, the last two decades witnessed an unprecedented number of research

papers on the possible links between globalisation and the labour market changes of

the types witnessed in the 1980s. The specific focus of such research was on the

possible impact of globalisation on wages and employment and more specifically, on

increasing wage inequality between skilled and unskilled workers in developed

countries. Many have tried to research the causes behind such rising inequalities, and

have come up with several competing explanations. Of the many candidates explored,

trade and technological change have come out as the most plausible explanations.

Trade economists have turned to the Hecksher-Ohlin-Samuelson (HOS) model as a

potential explanation to the worsening income positions of unskilled workers, because

of its simple postulate relating greater trade to factor rewards. According to this

model, international trade will induce a decline in the reward to the production

factor(s) a country is relatively badly endowed with. Viewed from a global

perspective, the implications of the model are that developed countries will witness an

increase in wage differentials between skilled and unskilled workers, while the

converse is true for developing countries due to their endowments of these two

categories of labour. The main competing alternative explanation to the observed skill

wage differentials is skill-biased technological change, going back to the observation

of Lawrence and Slaughter (1993) that despite the increase in the skill premium in the

U.S since the 1970s, the skilled workers’ share in employment has been increasing as

well. The extensive research into the outcomes of the HOS model led to the

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conclusion that the evidence has weighed more in favour of skill-biased technological

change.

While initially such research was focused on developed countries, over time it has

moved slowly towards developing countries (mostly East Asian and Latin American

countries). An interesting facet of these developing countries’ labour markets has

been the coincidental increase or decrease in skill-wage differentials with their trade

liberalisation episodes. The East-Asian evidence, has witnessed reduced wage

inequality and this has been attributed to greater trade: in a critical review of these

East-Asian studies, Wood (1994) point out gaps and deficiencies in the relative wages

data used in these studies, but nevertheless claims that:

‘most of the evidence supports the conventional view that the adoption of more

outward-oriented polices increases the demand for workers with only a basic general

education relative to the demand for workers with more education and skills. The

evidence is also consistent with the theoretical prediction that a switch in trade regime

causes a step (or once-and-for-all) change in the composition of demand, whose

effects on wage differentials appear to be spread over a period of about ten years.’

Wood (1997, p.42)

While this statement is close to concluding that trade is responsible for the narrowing

wage differentials in these countries, Wood (1997) however emphasises that more

research into other developing countries, African and South Asian, is needed as the

available empirical evidence is limited to East Asia and Latin America only. The

Latin American experience has been different, with increasing skill premiums

coinciding with their trade liberalisation episodes (e.g. Cragg and Epelbaum, 1996;

Hanson and Harrison, 1999 and Feliciano, 2001 for Mexico; Beyer et al, 1999 for

Chile). An in-depth exploration of the causes of such rising wage inequality has

however produced mixed explanations, of which trade and technological change have

been isolated as mutually exclusive forces.2 Evidence for technological change is

2 As Cragg and Epelbaum (1994) point out, there are at least two other competing explanations to this

shift in relative demand in favour of skilled workers: (a) the reallocation or dissipation of rents which

affect unskilled workers disproportionately; (b) entrepreneurs or managers who use the new

opportunities created by institutional changes to catalyse change, and who are paid a premium since

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produced by Cragg and Epelbaum (1996), Feenstra and Hanson (1997b), Lopez-

Acevedo (2002a, 2002b) for Mexico, Tan and Batra (1997) for Colombia, Mexico and

Taiwan and Pavcnik (2000) for Chile. Pro-trade explanations, on the other hand,

include Hanson and Harrison (1999),3 Robertson (2000a, 2000b) for Mexico and

Beyer et al (1999) for Chile. While the above studies view trade and technological

change as two mutually exclusive forces, other studies have explored the possibility

that trade brings about technological change and interact to affect the wage structure.

Robertson (2000c) uses logit and multinomial logit models for Mexico and finds that

exposure to foreign markets increases the likelihood of introducing new and more

advanced technologies. Alvarez and Robertson (2002) find a positive relationship

between exposure to foreign market and firm-level innovation for Chile and Mexico,

with the role of exporting being more pronounced. These studies suggest that such

innovative technology, via the trade liberalisation channel, if skill-biased in nature,

will drive up wage differentials. On balance, the evidence for the Latin American

studies quoted above seems to be inclined towards skill-biased technological change.

Over time, a few studies from other parts of the developing world have emerged

although not all of them have focused explicitly on wage inequality. In the African

context, Teal (2000) reports a rise in the relative wages of skilled workers in Ghana,

but observes that this has not resulted in a rise in the share of skilled labour in total

wages, as this rise in relative wages has led to the substitution to unskilled workers.

Other studies have attempted to test the impact of trade openness on labour demand

elasticities. For instance, for the Middle East, Krishna, Mitra and Chinoy (2001) do

not find any evidence of any relationship between trade openness and labour demand

elasticities using Turkish plant-level data.

India’s experience with trade reforms in 1991 has not been very different. Wage

differentials in manufacturing have been increasing at exactly the same time as the

reforms were implemented (see figure 1), suggesting a possible link between the two.

While the rising wage inequality has not been as drastic as in the developed

economies, there is nonetheless cause to worry, as the clear pattern emerging from

their supply is relatively limited. Empirical studies have however discarded these two explanations as

insignificant. 3 While Hanson and Harrison find that tariff reductions were larger for less skill-intensive industries,

they do not find any significant evidence between changes in output prices and relative wages.

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figure 1 is that wage inequality looks set to have adopted an increasing path.4 There

have been very few studies that have attempted to analyse the link between

globalisation and wage inequality for India. A few studies have however attempted to

link the Indian trade reforms to changes in it’s labour market. For instance, in an

attempt to provide quantitative estimates of the effects of trade on manufacturing

output and employment, Nambiar and Tadas (1994) find that trade had squeezed

manufacturing output and employment except in resource-intensive industries. They

conclude that this observation was pointing to a gradual increased demand for

unskilled labour. Nambiar, Mangekar and Tadas (1999), using an extended data set

found similar results: trade had actually shrunk manufacturing base both in terms of

value added and employment and was shifting manufacturing from high skilled to

low-skilled and labour-intensive production. They also find that the existing wage

disparity between skilled and unskilled labour was worsening but merely concluded

that these patterns were at odds with the simple Hecksher-Ohlin-Samuelson model,

without attempting to explain this controversy.5 Using survey level data,

Kambhampati and Howell (1998) find that trade liberalisation had led to a decline in

formal sector employment in the cotton mills in India through closure, downsizing

and a shift towards more capital-intensive technologies. Wage levels have however

been maintained for those still employed, due to protective legislations and formal

agreements between unions and employers. Where firms have begun to modernise,

demand for skilled labour had increased, leading to higher wages.

More rigorous methods of testing for the impact of trade reforms on labour markets

have also been used in the context of Indian studies. For instance, Khambhampati,

Krishna and Mitra (1997) utilise firm level data in five different import competing

industries to assess the impact of trade reforms on the Indian labour market. First,

they find only a small and insignificant effect of the reforms overall and in each of the

five import competing industries. By investigating the relationship between labour

demand and mark-ups in imperfectly competitive settings, they find that there is a

significant negative relationship between the two,6 overall and in four of the

4 Our data covers the period 1984-97 only, and this observation may not necessarily be true after 1997. 5 They went further to argue that this finding was at odds with their finding that trade had increased the

weight of value added in low skill-intensive segments of manufacturing. 6 This reduction in industry markups following trade reforms is documented by Krishna and Mitra

(1995) for India. Levinsohn (1991) and Harrison (1994) provide similar evidence for other countries.

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industries. Their results provide evidentiary support for the theoretically suggested

“pro-competitive” effects of trade reforms: trade liberalisation increases the demand

elasticity perceived by firms and induces them to reduce mark-ups and increase their

output.7 This induces an increase in the demand for labour, which may at least

partially offset the reductions in the demand for labour caused by other factors. Hasan,

Mitra and Ramaswamy (2003) find that lower protection raises labour demand

elasticities, but these responses are conditioned by the nature of labour institutions:

states with more flexible labour markets experience larger increases in labour demand

elasticities as protection levels are reduced. Dutt (2005) does not find any significant

relationship between net exports and real wages, but finds that across sectors, the

post-reform period witnessed a significant improvement in wages and employment,

but argues that this should not be attributed to trade liberalisation, but rather to the

rigidities in labour markets that hamper the labour mobility to expanding sectors. In a

study of Indian manufacturing, Berman, Somanathan and Tan (2005) attribute the

rising demand for skilled workers to three factors: increased output, an increase in

capital-skill complimentarity, and skill-biased technological change.

Beyond the above papers, we are not aware of any studies that directly attempt to link

the wage inequality patterns to trade and technological change within the commonly

invoked Hecksher-Ohlin-Samuelson framework. This study contributes to the

developing country literature by exploring the causes behind the rising wage

inequality in Indian manufacturing using the HOS framework. In particular, we look

at trade and technological change as the two most commonly invoked causes behind

changing patterns in wage inequality. We use a mandated wage approach along the

lines of the model by Haskel and Slaughter (2001). Rather than looking at the factor

bias of technological change, they argue that in a multi-sector framework, it is the

sector bias of technological change that matters most. Using this model, we find

evidence that the sector bias of price changes mandated a fall in wage inequality pre-

reforms while technological change mandated a rise in wage inequality post-reforms.

The plan of the paper is as follows. In section 2, we present some stylised facts on

wage inequality in Indian manufacturing. Section 3 describes the theoretical

7 This is in direct contradiction to the predictions of competitive models of trade.

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framework guiding our empirical analysis while section 4 describes the data set used

and discusses the results. A concluding section follows.

2.0 Stylised facts on wage inequality in India

Like many other countries, the phenomenon of increasing wage differentials in India

coincided with a period of extensive trade reforms. Table 1 shows the picture for

overall Indian registered manufacturing. We use the production/non-production

dichotomy to reflect the unskilled/skilled labour distinction.8 The annual average

nominal wages are presented overall and for the two categories of labour. This is

based on the average of wages across all manufacturing industries. We compute wage

inequality as the ratio of skilled wages to unskilled wages. In 1984, skilled workers

were receiving about twice the salary of unskilled workers, and by 1991, they were

receiving only about 70 % more. By 1997 however, wage differentials had increased

again to almost reach the level in 1984. These trends are clearer from figure 1, which

tracks the movement in wage inequality for each of the years 1984-97.

Table 1: Annual nominal earnings and wage inequality in Indian manufacturing

Year Avg. wages

(Rs) Avg. unskilled

wages (Rs) Avg. skilled wages (Rs)

Wage Inequality

1984 12408 10121 19641 1.94

1987 16438 13561 25120 1.85

1991 25955 22055 37261 1.69

1994 35043 29415 51125 1.74

1997 48404 39928 73235 1.83 Source: Author’s computations from Annual Survey of Industries data.

Note: All wages are annual nominal earnings per worker, averaged across the industries for

each year.

India’s drastic reforms were implemented in 1991, and from figure 1, the coincidental

increase in wage inequality from around 1991 onwards makes it tempting to attribute

the rising wage inequality to these reforms. In the following sub-section, we analyse

the relationship between skilled workers employment and their wages.

8 We recognise that there is a huge literature on the skilled/unskilled distinction that may question this

classification, but data unavailability, meant we could not produce more suitable proxies for skill over a

long period of time. Bhalotra (1996), Nambiar and Tadas (1994) use a similar classification.

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2.1 Trends in employment and wages

Figure 2 plots the share of skilled labour in total employment and their share of wages

in total wage bill for overall manufacturing.9 The share of skilled labour in total

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9 These are averaged across all industries for each year.

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manufacturing employment has been increasing slowly from around the late 1980s

onwards while their share of wage bills has been increasing more rapidly. Since wage

inequality has been increasing from around 1991 onwards, one explanation as to why

skilled workers share of employment has increased (albeit slowly) over this period

could be skill-biased technological change, going back to the observation of Lawrence

and Slaughter (1993) that despite the increase in the skill premium in the U.S since

the 1970s, the skilled workers’ share in employment has been increasing as well.

It may be more insightful to see whether a similar pattern emerges across broadly

defined skill-intensive and unskill-intensive industries. We define any industry above

the median of the share of skilled workers in total employment as skill-intensive and

the rest as unskill-intensive. This classification is intended to be illustrative only, and

shows that the bulk of the industries are unskill-intensive industries (altogether, they

represent about 68 % of total employment). Nambiar et al (1994) also provide a

similar classification for Indian industries, but using different cut-off points. In their

study, industries that are defined as skill- and medium-intensive fall under our list of

skill-intensive industries. Figure 3 graphs the evolution of the share of skilled

employment and relative wages in skill- and unskill-intensive industries.

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On average, there seems to be a clear increase in skilled labour’s share of employment

and total wage bill in both skill- and unskill-intensive industries, suggesting that skill-

biased technical change must have taken place across all industries.

2.2 Trade policy changes and relative prices.

The underlying notion behind the HO model is that trade policy changes influence

relative prices, which in turn affects relative wages by affecting intersectoral

profitability. Therefore, the explicit assumption is that trade liberalisation changes

relative prices. One of the main aspects of India’s trade policy reforms was drastic

cuts in tariff rates in 1991 followed by reductions in NTBs afterwards. Tables 2 & 3

detail these changes for two-digit manufacturing industries. Between 1989 and 1997,

nominal tariff protection has gone down drastically in all manufacturing industries,

although these rates are still considered high by developed countries standards.10

India

has also made some effort on the non-tariff barriers front, although these barriers are

still high in many industries. The changes in these trade policy variables reflect the

trade liberalisation process that India is going through and we use these to assess their

impact on wage inequality via price and technological changes in our empirical

application.11

Figure 4 graphs nominal protection against the relative employment of skilled to

unskilled workers. Note that the tariffs data are available from 1991 only on a

consistent year-to-year basis.12

In the left panel, we graph tariff levels in 1991 against

relative employment levels (factor intensity) in that year. There seems to be no

indication that trade policy was biased towards a particular category of labour prior to

the reforms. In the right panel of figure 4, we graph changes in the tariffs between

1991 and 1997 against average relative employment over that period. Again, there

seems to be no indication that India was protecting a particular category of labour at

10 Note however that the reforms in India are ongoing, and in the latest Trade Policy Review (2002)

India pledged to reduce trade restrictions even further. 11 We have trade policy data at three-digits industry level. We wish to thank Rajesh Chadha, from the

National Council of Applied Economic Research for kindly providing us with these data via Hasan

Rana. 12 Earlier data is available for 1988, but there are no data for 1989 and 1990, and hence we preferred to

construct the graph using data from 1991 onwards. There was no change in tariffs between 1988 and

1991 anyway.

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Table 2- Structure of nominal rates of protection in India

1988-89 1991-92 1997-98 1998-99 Average all sectors N/A 105 34.4 40.2

Activity based

Primary 92.25 91.86 20.61 23.85

Secondary 152.46 153.40 37.67 42.46

Industry based

1. Food, beverages and tobacco 144.70 147.45 40.81 46.27

2. Textiles and leather 145.36 142.38 42.57 46.06

3. Wood, cork and products 127.76 127.76 45 46.93

4. Paper and printing 149.56 131.57 35.11 40.40

5. Chemicals, petrol and coal 177.71 175.79 37.01 41.64

6. Non-metallic minerals 145.94 145.66 42.62 47.94

7. Basic metal industries 197.51 218.93 32.82 39.10

8. Metal products and machinery 138.17 139.44 35.06 39.28

9. Other manufacturing 151.48 151.25 37.76 42.04

10. Agriculture 91.09 90.05 22.08 23.98

11. Mining 106.46 114.04 18.80 22.26

Use based 1. Consumer non-durables 99.41 98.04 24.72 42.52

2.Consumer durables 153.21 153.97 38.12 44.77

3. Intermediate goods 128.87 131.96 29.08 38.88

4. Basic goods 188.77 201.32 33.73 40.19

5. Capital goods 124.70 125.99 32.32 38.79 Source: National Council of Applied Economic Research (NCAER), Delhi, India.

Table 3- Import Coverage ratio for NTBs on Indian imports

1988-89 1997-98 1998-99 1999-00 Average all sectors 95.21 64.03 62.16 24.24 Activity based

Primary 99.96 76.22 75.01 57.41

Secondary 87.43 39.42 36.3 27.71

Industry based

1. Food, beverages and tobacco 100 66.92 63.98 47.95

2. Textiles and leather 100 54.88 53.37 45.07

3. Wood, cork and products 100 34.48 26.41 5.74

4. Paper and printing 100 30.9 26.93 22.54

5. Chemicals, petrol and coal 97.54 30.74 26.24 15.45

6. Non-metallic minerals 98.25 50.52 47.04 36.28

7. Basic metal industries 53.37 15.85 11.41

8. Metal products and machinery 80.11 34.55 31.57 25.03

9. Other manufacturing 78.48 37.28 29.76 21.53

10. Agriculture 100 80.23 78.87 59.88

Use based 1. Consumer non-durables 100 75.65 74.07 56.19

2.Consumer durables 84.34 46.77 41.56 32.80

3. Intermediate goods 98.45 42.02 39.71 33.53

4. Basic goods 70.34 22.72 23.23 16.09

5. Capital goods 74.12 20.29 18.26 13.81 Source: Pandey (1999): National Council of Applied Economic Research (NCAER), Delhi, India.

Note: all forms of NTBs observed assigned equal weight of 100 percent to arrive at these figures.

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Figure 4

the expense of the other, post-reforms. Unfortunately, there are no objective and

detailed analyses of the changes in trade policy in India (e.g. the political economy

behind the tariff changes) to inform us. In figure 5, we replicate the above exercise

using NTB data (available from 1994 only) and although it is hard to establish the

scenario pre-reforms, since import coverage of NTBs was 100 % in almost all

industries during that period, post-reforms, there is some evidence that India was

protecting its unskilled workers. NTB changes were lower in industries with low skill-

intensity.

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These tariff changes would have affected wages if they affected relative prices first.

First, if these changes affected relative prices across industries, and if industry output

responded to these relative price changes, then resources would have shifted towards

the unskill-intensive industries. This shift would raise the demand for unskilled

workers, and thus their wages. In figure 6, we plot relative wages along with relative

prices. Both series are normalised so that 1984=1. The relative price series represents

the ratio of prices in skill-intensive to unskill-intensive industries, using our earlier

skill-intensity classification. Figure 6 shows that relative prices increased until around

1992, and starting falling afterwards. Relative wages on the other hand, were moving

in an exactly opposite path to relative prices.

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year��Relative prices Relative wages

Relative Prices and Relative wages

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What does the above picture tell us in terms of the Stolper-Samuelson mechanics?

Post-reforms, relative prices of skill-intensive industries seem to have gone down.

Since these skill-intensive industries have become less profitable, one would expect

an overall increased demand for unskilled labour, a rise in their wages and hence a fall

in overall wage inequality. Nambiar and Tadas (1994) and Nambiar, Mangekar and

Tadas (1999) note a gradual increase in demand towards unskilled labour. However,

as we have seen from figure 4, exactly the opposite happened. Industries (whether

skill-intensive or unskill-intensive) have been employing more skilled labour and this

has given rise to correspondingly higher wages for this category of labour. These

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findings rebel against the neat geometry of the simple HOS model, which predicts that

wage inequality in developing countries should fall with reductions in protection

rates. This suggests that other factors are at play, such as technological change.

These findings are preliminary, and are only indicative of the overall picture in total

manufacturing. In the following sections, we analyse the causes of wage inequality in

Indian manufacturing using the more rigorous methods of assessing the impact of

trade reforms and technological change on relative wages that have been used in the

literature. First, we describe the theoretical and empirical framework guiding our

empirical analysis.

3.0 The mandated wage methodology: a theoretical framework

While the HOS model has clear predictions, they are not met in real world. However,

testing the implications of the model is not in itself a straightforward task. Empirical

studies have attempted to provide checks on whether changes in labour markets are

consistent with the predictions of trade theory instead of looking for proof that the

changes in labour markets are the results of trade liberalisation. Yet, one cannot

discard the model as irrelevant, because there are a number of qualifications that

apply to any empirical application of the HOS model. First, Stolper-Samuelson effects

are explicitly long run effects. Given that real world processes rarely reflect pure

equilibrium states, it is important that any empirical application of the HOS model

takes account of both the long run nature of the equilibrium relationship predicted by

the theory, as well as the fact that adjustment to equilibrium and dynamics may be an

important feature of the modelling of the impact of globalisation on labour markets.

A second consideration is that the theory explicitly presumes a uniform impact of

trade liberalisation across all sectors in an economy. This assumption of homogeneity

across sectors is clearly questionable. One needs to understand the political economy

behind trade liberalisation, which often explains why sectors differ substantially, from

the degree or even the direction of liberalisation. The presence of non-tariff barriers

and changes in them, developments within labour market institutions, composition of

trade between developed and developing trading partners, and many other factors can

materially affect the extent to which the HOS predictions of the impacts of

15

globalisation are met in real world.13

In the Indian context, for instance, the trade

policy data shows varying degrees of liberalisation across sectors, whether in terms of

tariffs reductions, or in terms of eliminating NTBs on imports.

A third consideration is that trade liberalisation is not the only possible reason for

price and demand changes in labour markets. Changes in the pattern of wages and

employment may also reflect the impact of skill-biased technological change (see

Leamer, 1997 and Feenstra and Hanson, 1999). As we mentioned earlier, evidence to

date has weighed in favour of such technological change as more plausible

explanations of labour market adjustments in terms of wage differentials.

In a survey of nine well-known product-price studies, Slaughter (2000) reviews the

emergence of three distinct approaches to testing the HOS theorem. The first relies on

a simple factor proportions regression, controlling for technology in an ad-hoc

manner. The second takes care to disentangle the impact of globalisation effects and

the impact of technology on factor usage.14

This approach assumes that price changes

and technological change are exogenous. A third approach, started by Feenstra and

Hanson (1997, 1999), accounts for the endogeneity of price and technological

change.15

The second and third approaches are more commonly known as the

mandated wage methodology, which incorporates the one- and the two-stage

methodologies, corresponding to the assumptions of exogeneity and endogeneity of

price and technological change respectively.

Besides the above difficulties, a further difficulty of the HO trade theory lies in the

dimensionality of the model. The predictions of the HOS model rest on the simplistic

assumption of a two-factor-two product world that does not extend unambiguously to

multi-factor and multi-product contexts. For this reason, the most prominent test of

the impact of trade on labour markets has adopted what Deardoff calls the ‘correlation

version’ of the Stolper-Samuelson theorem. This relates any vector of relative product

price changes to relative factor price changes, and predicts that on average, factors

13 In an empirical application to South African manufacturing, Federke, Shin and Vaze (2002) address

these two concerns using dynamic heterogenous panel estimation techniques. 14 See Leamer (1997) and Baldwin and Cain (1997) for this approach. 15 See Feenstra and Hanson (1999) and Haskel and Slaughter (2001) for an application to the US and

the UK respectively.

16

used intensively in industries with rising (falling) prices will experience relative price

increases (decreases). Yet, the correlation version of the Stolper-Samuelson theorem

is nothing more than a consistency check of the trade theory: it is difficult to

unambiguously associate product price changes with trade effects. Furthermore,

empirical application has frequently linked product price changes to factor

proportions rather than relative factor price changes. Thus, for developed countries, a

common check is whether observed price changes of unskilled labour-intensive goods

after liberalisation are consistent with factor scarcity, i.e. whether unskilled labour-

intensive product prices fell. A typical regression specification estimated across i

industries (see, e.g., Lawrence and Slaughter, 1993; Sachs and Satz, 1994; Desjonques

et al, 1997) is given by (1).

ln sit it

u

NpN

� � ��

� � ��

(1)

where, ln itp� represents price changes and N, with the relevant subscript, is

employment of skilled (s) and unskilled (u) workers respectively and it� is a random

error term. The ratio of the two represents relative employment. If the coefficient �

is positive, then this is taken as supportive of the predictions of the Stolper-Samuelson

theorem for industrialised countries.16

This simple consistency check is restrictive. In order to be interpreted as a test of the

impact of international trade, it must assume that all domestic prices are exogenously

set internationally. To argue that all domestic price changes are caused by trade-

induced changes would have to rely on the assumption that for a small economy,

domestic industries are international price takers. This assumption is valid as long as

tariff changes are not changing the differential between domestic prices and

international prices and only as long as we ignore the industry and factor specific

impacts of technological progress. Yet, there is no reason to suppose that

technological progress will be factor-neutral, in which case one should expect that the

relative factor productivity and hence factor prices would reflect its shifts. One

solution to the ambiguity introduced by technological change has been to control

directly for some identifiable technological changes (see Sachs and Shatz, 1994). The

16 Lawrence and Slaughter (1993) find a non-positive value for� and interpret this as evidence

against the Stolper-Samuelson theorem.

17

most widely used measure of technological change is TFP growth. Applying this

argument to (1), the TFP-augmented consistency check can be specified as follows:

,

,

lns it

it it itu it

Np TFP

N� � �

� ��

� (2)

Even then, equations (1) and (2) still suffer from many shortcomings. First, it

considers only the intensity of two factors. Also, the Stolper-Samuelson price effects

arise from the assumption that each sector in the economy makes zero profits, so that,

when price changes, relative wages have to change to restore zero-profit equilibrium.

The zero-profit relation links the level of prices and levels of factor inputs. Yet

equation (1) regresses the change in prices on the level of factor inputs. Since the HO

model is based on zero profit conditions, a more informative approach would be to

allow for the impact of technological change on sounder theoretical foundations.

Leamer (1997) proposes to estimate the zero-profit conditions. In what follows, we

describe the model as initially proposed by Leamer (1997), and later extended by

Feenstra and Hanson (1997a) and used by Haskel and Slaughter (2001) amongst

others.

Suppose an economy produces I different tradable goods, each of which requires

some combination of J primary factors and I intermediate inputs. Sectors in the

economy differ in their factor intensity at the same relative factor prices. Then for

each sector i, we can write the zero profit condition as:

�� IiGiiiJj jji

Gi pbwap i=1….I (3)

where Gip is the domestic gross-output price in sector i; jw is the unit cost of the jth

input; ija is the employment of input j per unit of output in sector i; and iib is the

amount of intermediate input required to produce a unit of good i. There are I

equations in (3), one for each sector where production occurs. Totally differentiating

(1) with respect to time gives:17

17 See appendix for full derivation.

18

log log logit it jit jtj Jp TFP v w

�� (4)

where log ( log log )g git it jit itj J

p p v p�

� � � � �� is the change in value added prices,

jitv is the share of factor j in total costs in sector i at time t; and itTFPlog� is the

growth in total factor productivity for sector i. The final term in (2), jtwlog� , is the

change in the wage of factor j, which again is economy-wide since all factors are

mobile across sectors.

Equation (4) shows how changes in product prices )plog( it� or technology

)TFPlog( it� cause shifts in relative demand and lead to adjustments in economy-

wide factor prices )wlog( jt� to restore zero profits in all sectors. Two interesting

points from equation (4) are worth noting. First, we note that log itTFP� captures

technical change of any form, including skill-biased technological change (SBTC).18

A second point is that the sector bias of price or technology shocks will determine

their qualitative wage effects. Thus, a larger price or TFP increase in the skill-

intensive (unskill-intensive) sectors will cause skill premiums to rise (fall).

Equation (4) can be used in two ways depending on the assumption made about

technology and prices changes. If they are assumed to be exogenous, for example if

India were a small open economy, and technical change were exogenous,19

then the

effects of technology and trade (via prices) on wages are respectively estimated via

the following equations:20

log it jit jt itj JTFP v � �

�� (5)

log it jit jt itj Jp v � �

�� (6)

18 Note that as Jonhson and Stafford (1998) point out, different types of SBTC have different impacts

on TFP. Thus it is not necessarily the case that SBTC raises TFP in a sector (quoted in Haskel and

Slaughter, 2001) 19 The assumption of open economy may not be particularly appropriate for India before 1991, but

there were modest attempts by India to open up in the 1980s. (see, e.g. Srivastava , 1996) 20 Note that TFP growth does not affect prices with a small economy assumption, i.e. there is zero pass-

through from TFP growth to price changes.

19

where it� is an additive error term in each equation. We interpret each estimate jt�

and jt� as each factor j’s wage change mandated to restore zero profits in all sectors

in response to the sector bias of TFP and trade-induced product prices, respectively,

ceteris paribus. Adding together the s� and s� from (3) and (4) for each factor j gives

the net wage change mandated by trade plus technology.

If on the other hand, changes in technology and prices are assumed to be endogenous,

then estimation warrants a two-stage approach. In the first stage, TFP and price

changes are each regressed on a set of underlying regressors, Z, which are assumed to

drive TFP and price changes, respectively over some period t, as shown by equations

(5) and (6):

itStec

t,tecit,tecittec

ZTFPlog ��

(7)

itSpri

t,priit,priitpri

Zplog ��

(8)

The tecZ vector includes many factors. Studies on the determinants of TFP growth

have identified many forces, such as trade policy, industrial structures and domestic

competition. For instance, for the UK, Haskel and Slaughter (2001) identify these

forces as computerisation, innovations, industry concentration, union density and

foreign prices based on the large literature explaining TFP in this country. In the

context of developing countries, many similar forces have been identified although

quantifying them in econometric studies depends very much on data availability (see

Das, 2002 for a critical review of studies on the impact of trade liberalisation on

productivity growth in developing countries). In the Indian context, we identify

several forces. Trade policy measures represent trade-related influences. Industrial

policy reforms will be represented by capital intensity,21

while a measure of industrial

concentration will highlight the role of domestic competition in enhancing

productivity improvements. Trade union density will also be used as a force that

might impede improvements in productivity growth. The change in Indian prices

relative to foreign prices (proxied by US prices) will test whether international

21 Das (2002) uses capital intensity as one of the three non-trade determinants in his study of industrial

productivity in India.

20

competition induces technical change. As in Haskel and Slaughter, our priZ vector

includes a measure of foreign prices (proxied by U.S. prices), tariffs, and industry

concentration. The first two forces represent the effects of trade-related influences on

prices, while concentration represents a domestic influence on prices.

In the second stage, each underlying variable in the Z vector that contributes to price

and technology changes are regressed on cost shares:

, ,ˆtec tec it jit jt tec itj J

Z v� � ��

� � (9)

, ,ˆ

pri pri it jit jt pri itj JZ v� � �

�� (10)

The share coefficients ( tec,jt� and pri,jt� ) give the wage changes mandated by the

sector bias of each structural force working through either itTFPlog� or itplog� .

For instance, a measure of trade policy in (6), such as tariffs will explain the quantity

of observed product-price variations accounted for by tariff changes. Using this

quantity in turn as the regressor in (8) then determines the wage changes mandated by

the sector bias of tariff changes working through product prices.

4.0 Empirical estimation and results.

In this section, we discuss the estimation results for the model described in section

3.0. We first describe the data set used in estimation, explain how we computed TFP

and price changes, and then move on to estimation.

4.1 Data description

The main data set used in this study is the Annual Survey of Industries (ASI), which

we arranged into a balanced panel of 158 three-digits manufacturing industries at the

Indian level of classification. The data set is augmented with other crucial variables

that are not present in ASI, and that are not reported under the same classification. We

21

therefore matched the different classifications to create a combined data set.22

However, because these additional variables were not reported for all the industries

under ASI, our sample size varied according to each of the additional variables used

in this analysis. Table 1 shows the number of three digits industries that have data for

each of the relevant non-ASI variables, as well as the share of these industries in total

ASI employment, value-added and output. Because these shares are computed on a

year-to-year basis, we report the range of values for the shares over the 14 year

period. Both value-added and output proxy industry size very closely. To a lesser

extent, employment also proxies industry size reasonably closely and it can be seen

that in general and as expected, industries with higher output share also tend to be

industries with higher employment share. Clearly, the sample size used in each part of

the analysis will largely depend on the number of industries that have data for the

relevant variable. While this is not a problem for the one-stage mandated wage

regressions, it represents a potential problem for the two-stage analysis. For instance,

in the one-stage mandated wage methodology, we will be using price data as the only

non-ASI variable. Therefore, the results will be based on a sample of 98 industries for

which the price data was originally computed. In the two-stage analysis, we are likely

to combine the simultaneous use of all these variables, which do not have common

industries in terms of data availability, and therefore represents an additional source of

sample size constraint.23

Table 4: Data availability of non-ASI variables24

Range of industries’ share (%) in total ASI’s:

Non-ASI variables Matched ASI

industries Employ. Val. Add. Output 1. Wholesale Price Index 98 77-79 % 82-85% 83-87%

2. Trade policy � 59 50-54% 63-67% 62-67%

3. Herfindahl index 53 42-46% 50-54% 51-55%

5. U.S Wholesale Price Index25

158 27-33 % 33-43% 33-43 %

� available only from year 1991 onwards.

22 Concordance codes used are available from authors on request. 23 Where possible, we have attempted to make use of as many observations as analysis permits. This

was not always possible though, as is clear from the sample sizes of our results. 24 We are grateful to Rajesh Chadha, Rana Hasan and Uma Kambhampati for providing us with the

price data, trade policy data, and Herfindalh indices, respectively. 25 The U.S. Wholesale Price Index data is given at the ISIC revision 2 level. The matching with the ASI

data was carried out via a matching of ASI industries with ISIC revision 2 industries.

22

4.2 TFP growth, price growth and factor shares.

Our measure of TFP growth is the more commonly used primal Tornqvist index,

which equals the log change of output minus the share-weighted growth of inputs:

, , , , , ,ln ln ( ln ) ( ln ) ( ln ) ( ln )it it s it s it u it u it it k it it m itTFP Y L v L v M v K v� � � � � � � � � � � � � � �

Where Y, M and K are output, intermediate inputs and capital stock respectively. The

L’s with the relevant subscript denote skilled (s) and unskilled (u) labour respectively,

while the v’s are the shares of each factor in gross output and the subscripts s and u as

before, denote the two categories of labour, skilled and unskilled. The shares of

skilled labour, unskilled labour and materials are the shares of skilled wages,

unskilled wages and expenditure on materials as a proportion of output. This is

computed for each industry for each year. Capital share is one less the sum of all other

shares. We compute the growth in value-added prices in a similar way:

, , ,ln ln ( ln ) ( ln ) ( ln )it it it s it it u it it k itVADPRI PRI PRI v PRI v PRI v� � � � � � � � � � � �

where ln itPRI and ln itVADPRI are output and value-added prices respectively, with the

v’s representing the factor shares as before.

The main approach of interest is the mandated wage methodology. Since almost all

studies of the mandated wage methodology are cross-sectional in nature, we will

adopt a similar approach using standard cross-sectional regression analyses. We

define two periods: the pre-reform periods (1984-1991) and the post-reforms period

(1991-97). We compute price changes and TFP changes as changes over each of these

periods. For factor shares, we use each industry’s average of beginning of period and

end of period factor shares26

in the regressions, in view of the fact that factor shares in

26 Our data is split into two distinct periods, 1984-91 and 1991-97, corresponding to the pre- and post-

reforms period respectively. If we use long period changes, such as seven-year changes, then 1984 and

1991 are the beginning points of each period while 1991 and 1997 are the endpoints of each period.

23

Indian manufacturing seem to have evolved over these 14 years under study.27

This is

clear from figure 8, where there appears to have been a gradual fall in both categories

of labour in favour of an increasing share of capital in production.

��

��

.03

.04

.05

.06

.07

skill

ed

& u

nskill

ed

la

bo

ur

sh

are

.26

.28

.3.3

2.3

4.3

6

ca

pita

l sh

are

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

year��

capital share skilled labour share

unskilled labour share

Average factor shares

Figure 8

4.3 A simple consistency check: only trade determines product prices.

Although mandated wages is the main approach here, we perform some simple

consistency checks, as described in section 3.0. This is done by estimating equation

(1). While Lawrence and Slaughter (1993) use relative employment as the skill-

intensity variable, Sachs and Shatz (1994) use the share of unskilled workers in total

employment. We adopt both definitions and report the results in table 5. Note that

since we have domestic price data only, we could only go as far as replicating the

exercise on such data only, as compared to these two studies that also use U.S. import

and export prices.

ln stit it

ut

NpN

� � ��

� � ��

(1)

Lawrence and Slaughter (1993) estimate � as zero or negative, and thus interpret this

as evidence against the hypothesis that international trade contributed to rising U.S. 27 We also note that some of the most influential mandated wages studies have also used average of

beginning and end of period factor shares, e.g. Feenstra and Hanson (1997a, 1999) and Haskel and

Slaughter (2001).

24

wage inequality by raising the relative price of skilled-labor-intensive products. Sachs

and Shatz (1994) find � to be negative: industries employing a larger share of

production workers had lower relative price increases over the 1980s. Sachs and Shatz

conclude that this supports the hypothesis that international trade contributed to rising

U.S. wage inequality via the rise in relative price of skilled-labour-intensive products.

Table 5: Simple consistency checks

Relative employment of skilled workers

Production workers share of employment

Pre-reforms

Post-reforms

Pre-reforms

Post-reforms

Factor proportions -0.02 -0.31** 0.07 0.64**

(0.21) (0.10) (0.37) (0.20)

Constant 0.68*** 0.65*** 0.62** 0.06

(0.08) (0.04) (0.27) (0.15)

No. of observations 98 99 99 99

Adjusted R-squared 0.00 0.06 0.00 0.07 Note: robust standard errors in parentheses

What is of interest in table 5 is the coefficient for the post-reforms period, because

this is the period when rising wage inequality is witnessed, and which we seek to

explain. Interpreted along the lines of the findings of Lawrence and Slaughter (1993),

since � is non-positive, it suggests that India’s greater engagement in the process of

international trade that would have accompanied the trade reforms did not contribute

to the rising wage inequality: industries with higher relative employment of skilled

workers did not have larger price increases post-reforms. Along the lines of Sachs and

Shatz (1994), the positive coefficient for� also suggests that trade was not

responsible for the rising wage inequality in Indian manufacturing, since the results

imply that industries with a higher share of unskilled workers witnessed large price

increases post-reforms. These confirm our earlier findings from figure 6, that relative

prices fell post-reforms.

4.4 Extending the simple consistency check: allowing for technological progress.

The above simple consistency check is restrictive. It does not account for the possible

influence of technological progress on factor prices, especially if such technological

progress is not factor-neutral. Therefore, there is the need to control for technological

25

change. This is evidenced by the relatively low R-squared values reported in table 5.

One way to control for some readily identifiable technological progress is to specify

the TFP-augmented consistency check equation as in (2). The results for this are

reported in table 6.

,

,

lns it

it it itu it

Np TFP

N� � �

� ��

�

Table 6: Consistency checks augmented with technological change.

Relative employment of skilled workers

Share of production workers

Pre-reforms Post-reforms


Factor proportions 0.01 -0.04 0.02 0.08

(0.13) (0.10) (0.26) (0.22)

Technological progress -0.77*** -0.68*** -0.77*** -0.68***

(0.10) (0.10) (0.10) (0.10)

Constant 0.63*** 0.64*** 0.62** 0.57***

(0.05) (0.04) (0.19) (0.16)


R-squared 0.43 0.48 0.43 0.48 Robust standard errors in parentheses

Our results suggest that technological change was not factor-neutral either pre- or

post-reforms. Using either definition of factor proportions suggests that, controlling

for factor proportions (which are insignificant in all estimated regressions) industries

with higher TFP growth (or higher technological progress) experienced lower price

changes both pre- and post-reforms. Even then, the above formulations suffer from

many shortcomings. First of all, it considers only the intensity of two factors. Also,

the Stolper-Samuelson price effects arise from the assumption that each sector in the

economy makes zero profits, so that, when prices change, relative wages have to

change to restore zero-profit equilibrium. The results based on equation (1) regress the

change in prices on the level of factor inputs, but the zero-profit relation links the

level of prices and levels of factor inputs. Also, technological progress is introduced

on an ad-hoc basis. A more informative approach would be to allow for the impact of

technological change on sounder theoretical foundations. This was described in our

model earlier. We present the results of this approach in the following section.

26

4.5 One-stage mandated wage regressions

We recall that the first-stage regression involves running equations (5) and (6), which

we rewrite with our three factors of production as:

, , , , ,log it s it s it u it u it k it it itTFP v L v L v K �� (5a)

, , , , ,log it s it s it u it u it k it it itp v L v L v K �� (6a)

where L and K are labour and capital and the v’s are the corresponding shares of

skilled (s) and unskilled (u) labour and capital (k) respectively. TFP growth and

growth in prices have been computed along the lines of section 3. The results are

reported in table 7.

Table 7: Wage inequality regressions

Pre-reforms Post-reforms log itp� log itTFP� log itp� log itTFP�

Skilled share -0.77 -0.82 -4.18** 3.24***

(2.25) (1.44) (1.72) (1.15)

Unskilled share 0.67 0.18 1.52 -1.16**

(1.11) (0.54) (0.97) (0.57)

Capital share 0.29 -0.10 0.65** -0.42**

(0.29) (0.22) (0.25) (0.19)

Mandated % change in inequality

0.00 0.00 -4.18 4.40

p-value 0.64 0.59 0.01 0.00


R-squared 0.03 0.01 0.16 0.09 *, ** and *** are 10 %, 5 % and 1 % levels of significance respectively. Robust standard errors in parentheses

Looking at the pre-reforms regression results, the coefficient of -0.77 on skilled shares

indicates that the sector bias of the pre-reforms price changes mandated an

insignificant 77 % fall in the skilled wage to maintain zero profits in all sectors.

Similarly, the mandated wage was an insignificant increase of 67 % for unskilled

wages and a significant increase of 29 % for capital. The corresponding mandated

changes for the sector bias of technological change are a fall of 82 %, an increase of

18 % and a fall of 10 % respectively, with all values being insignificant. Post-reforms,

price changes mandated a significant 418 % fall in skilled wages while technological

change mandated a significant 324 % increase in skilled wages. The price and

27

technological changes mandated an insignificant 152 % increase and a significant 116

% reduction in unskilled wages respectively.

Below the coefficient estimates, we report the mandated change in skill premium. Pre-

reforms, it is 0 % for both price and technological change. This is simply the

difference between the coefficient of skilled and unskilled shares. Reported below this

coefficient is the probability of accepting the hypothesis that the coefficient on skilled

share equals the coefficient on unskilled share. Put another way, it is the hypothesis

that the difference between the two coefficients is zero. The probability values for

these pre-reform coefficients are non-significant. Note that our interpretation of the

mandated wage coefficients is different from Haskel and Slaughter (2001) who add up

coefficients, irrespective of whether they are statistically significant or not. We

consider only statistically significant coefficients and any insignificant value is

interpreted as mandating a zero percent change.

Post-reforms, the mandated change in skill premium is 418 % fall and a 440 %

increase for price and technological change respectively. These are both statistically

significant. This suggests that technology, via the sector bias of TFP growth was a

major factor in explaining increasing wage inequality in the 1990s. The post-reform

results are strongest in terms of statistical significance. To assess how well the model

described above performs with respect to the results we have obtained, table 8

compares actual wage changes with net mandated wage changes, i.e. wages changes

mandated by both changes in prices and technology. Actual wage changes are

employment-weighted change in economy-wide log skilled and unskilled nominal

wages respectively.

The top-left cell shows the pre-reforms net mandated wage change of 0 % for skilled

labour (from table 7, this is the 0 % mandated by price changes plus the 0 %

mandated by technology. Remember we assume these are zero because of the

statistical insignificance of the coefficients). Similarly, the price and technological

changes mandated a 0 % wage change for unskilled labour. These mandated wage

changes compare with an actual rise of 66 % in wages for skilled labour and a 77 %

increase in unskilled labour average wages over the pre-reform periods. Overall, price

28

Table 8 Net mandated wage changes


sw� mandated 0 -0.94

sw� actual 0.66 0.66

uw� mandated 0 1.16

uw� actual 0.77 0.57

( )s uw w� � mandated 0 0.22

( )s uw w� � actual -0.11 0.09

*note: weighted means of the actual change in skilled and unskilled wages

are not very different from unweighted means.

changes and technological change mandated a 0 % change in wage inequality,

compared to an actual fall of 11 %. The model therefore under-predicts wage

inequality in the pre-reforms period.

Post-reforms, price and technological change mandated a 94 % fall in skilled wages, a

massive under-prediction, compared to a 66 % actual increase. These changes

mandated a 116 % increase in unskilled wages, compared to an actual increase of 57

%, now a massive over-prediction. Overall, the model predicts a 22 % increase in

wage inequality, whereas actual wage inequality is seen to have risen by an average of

9 % post-reforms. Therefore, post-reforms, the model over-predicts the rise in wage

inequality.

4.6 Two-stage mandated wage regressions

In the previous section, we established that our one-stage model underpredicts the

changes in wage inequality mandated by both price changes and technological change

pre-reforms, but over-predicts such changes post-reforms. A basic limitation of the

one-stage model described above is that it does not allow institutions to raise wages

above that consistent with the pressure of labour-market competition from workers in

other industries. To the extent that institutions like trade unions protect primarily the

less-skilled workers from these pressures, one would expect under-prediction of

unskilled wages from the model.28

This is clearly the case for the pre-reforms period

in table 8, where we see that the model under-predicts the change in unskilled wages.

28 In fact, our industry data relate to the organised sector of manufacturing, where trade unions are very

active, although their importance is seen to diminish over time.

29

This suggests the need for a two-stage regression model along the lines discussed in

section 3. In this section, we account for the endogeneity of prices and technological

change via our two-stage regressions. We recall that our two-stage regressions involve

running the following regressions in the first stage:

itStec

t,tecit,tecittec

ZTFPlog ��

itSpri

t,priit,priitpri

Zplog ��

In empirical analysis, the TFP growth equation, with its vector of structural

determinants, translates into:

1 2 3

4 5

log it it it it

it it it

TFP CAPINT CONC FORPRITP UNION

� � ��

� � �

(7a)

where:

CAPINT: capital intensity

CONC: a measure of competition (the Herfindalh index in this case)

FORPRI: a measure of Indian prices relative to foreign prices (proxied by US prices)

TP: a measure of trade policy (tariffs & non-tariff barriers.)

UNION: union density (two-digits level)

Note that equation (7a) shows the basic form of the regression only. The actual form

will involve levels and changes in the right-hand side variables.29

The results from

estimating various variants of equation (7a) are shown in table 9. We use weighted

least squares in all regressions that follow, using each industry’s share of total

employment as weights.

The first column reports a general form of equation (7a), with as many variables

thought to explain changes in TFP. Not all variables are significant, and in the other

two columns, we drop all the insignificant variables and re-estimate the model. All

variables are significant and have the correct sign apart from the union density

variable. In the third column, this variable is dropped and this time all the variables

29 Beginning of period values are used for levels of variables.

30

Table 9: First stage log itTFP� regressions (equation 7a)

(1) (2) (3)

�Capital Intensity -0.10**

(0.04)

Herfindalh -0.28 -2.44** -2.56**

(1.14) (1.08) (1.20)

� Herfindalh -0.14 -1.53** -1.43**

(0.72) (0.63) (0.65)

�FORPRI -0.82*** -0.75*** -0.71***

(0.09) (0.14) (0.12)

Nominal tariffs -0.01** -0.002** -0.003***

(0.00) (0.00) (0.00)

�Nominal Tariffs -0.01**

(0.00)

� Non-Tariff Barriers -0.003** -0.003*

(0.00) (0.00)

Union density -8.54 -9.17

(8.28) (7.92)

�Union density -4.74

(4.10)

Reformdum -0.01 0.01 0.01

(0.01) (0.01) (0.01)

Constant 0.07** 0.07** 0.07**

(0.02) (0.02) (0.02)

No. of observations 48 48 48

R-squared 0.85 0.84 0.83 *, ** and *** are 10 %, 5 % and 1 % levels of significance respectively.

are significant. Note that dropping these variables causes very insignificant changes to

the R-squared.

In the second stage regression, we regress the fitted values of each of the coefficients

of the structural determinants in equation 7a on the factor shares:

, ,ˆtec tec it jit jt tec itj J

Z v� � ��

� �

Thus, to start with, for each structural determinant of log itTFP� in each period, we

report the mandated change in skilled wages, unskilled wages, and inequality. The

results are presented in table 10. For the sake of brevity, we have not reported the

coefficient on capital share. Note that the second stage regression of the two-stage

procedure uses generated dependant variables from the first stage regressions.

31

Feenstra and Hanson (1997) propose a procedure to correct the standard errors of the

coefficients in the second stage regressions. Dumont, Rayp, Thas and Willeme (2004),

(henceforth DRTW) have however shown that this correction is negatively biased,

sometimes resulting in negative estimated variances, hence overestimating the

inferred significance of the variables of interest. They therefore propose an unbiased

correction procedure. Our corrected standard errors therefore follow the correction

procedure by DRTW.30

Our computations using the DRTW correction procedure does

not produce negative variances, as in Feenstra and Hanson (1997a) and Haskel and

Slaughter (2001, 2002).

Interpreting our coefficients along the lines of the DRTW correction procedure, the

main message of table 10 is that pre-reforms, none of the variables mandated any

significant change in wage inequality through TFP changes.

Table 10: Second-stage mandated wage regressions for log itTFP�

(a) Pre-reforms

Herfindalh � Herfind. �FORPRI Tariffs �NTB Skilled share 0.79 -0.61 0.85 -0.36 0.34

2.35 0.83 0.67 0.69 0.39

Unskilled share -0.29 -0.01 -0.05 0.07 0.29

1.29 0.43 0.36 0.38 0.24


0.00 0.00 0.00 0.00 0.00

Corrected T-statistic 0.11 -0.51 1.14 -0.52 0.21

R-squared 0.61 0.05 0.81 0.94 0.34

No. of observations 24 24 24 24 24

(b) Post-reforms

Herfindalh � Herfind. �FORPRI Tariffs �NTB Skilled share -1.26 0.12 -0.21 -0.29 0.65

(3.29) (0.58) (0.88) (0.25) (0.65)

Unskilled share -0.25 0.20 -0.82* -0.11 0.03

(1.60) (0.30) (0.46) (0.12) (0.28)


0.00 0.00 0.82 0.00 0.00

Corrected T-statistic -0.05 -0.13 0.43 -1.72 0.89

R-squared 0.56 0.07 0.39 0.99 0.78

No. of observations 24 24 24 24 24 * is the 10 % level of significance, based on the corrected standard errors shown in parentheses.

30 We wish to thank Gordon Hanson and Jonathan Heskel and Michel Dumont for sharing the core

programming codes used to implement these corrections.

32

Post-reforms, we find that changes in the Indian prices relative to foreign prices

mandated a signigicant increase in wage inequality through TFP changes. These

results are in line with the earlier findings from table 7, which suggested that overall,

log itTFP� significantly increased inequality post-reforms.

We now move on to the estimation of the price regressions, which in its basic form

translate to:

1 2 3log lnit FOR itp CONC p TP� � � �� (8a)

where ln FORp� represents a foreign price level, proxied by the price level in the U.S.

Other variables are as defined before. The one-stage and two-stage results for the

price regressions are reported in tables 11 and 12.

From column (1) in table 11, we find that although the concentration variable is

correctly signed, it has no significant effect on price changes. As with the TFPG

regressions, out approach is to drop as many of the insignificant variables as possible,

while retaining the key variables that represent domestic and international influences

on prices. Column (3) reports the results of this approach and it can be seen that price

changes are affected by several factors that are significant at the conventional levels.

Changes in foreign (U.S.) prices affect domestic prices, consistent with the idea that

world prices have a significant effect on Indian domestic prices. Trade policy, as

measured by levels of nominal tariffs and NTB as well as changes in NTB in this case

also affect price changes. Interestingly, only the nominal tariffs variable has the

correct sign, while the NTB variables have the wrong sign, suggesting that higher

NTBs reduce domestic prices. Overall, therefore, table 11 suggests that Indian prices

are influenced by international trade and domestic forces.

The wage effects of these structural determinants of log itPRI� can be gauged from

the second-stage regressions, which use the results from column (3) in table 11, and

regress them on factor shares. The results are reported in table 12. Here also, the

results for the capital share coefficients are not reported for brevity. The main

message of table 12 is that the sector bias of changes in foreign prices, working

through log itPRI� mandated a significant fall in wage inequality pre-reforms. None

33

Table 11: First stage log itPRI� regressions (equation 8a)

(1) (2) (3)

Herfindalh -0.29 -0.02

(2.38) (0.44)

�Herfindalh -0.17

(1.56)

�PRIUS 0.62 0.60 0.88**

(0.40) (0.38) (0.27)

Tariffs 0.00 0.00 0.001*

(0.00) (0.00) (0.00)

�Tariffs 0.00 0.00

(0.00) (0.00)

NTB -0.01* -0.01** -0.01**

(0.01) (0.00) (0.00)

�NTB -0.01* -0.01** -0.005***

(0.00) (0.00) (0.001)

Reformdum 0.00 0.00 -0.002

(0.01) (0.02) (0.01)

Constant 0.05 0.05 0.03

(0.07) (0.03) (0.02)

No. of observations 48 48 84

R-squared 0.87 0.87 0.85 *, ** and *** are 10 %, 5 % and 1 % levels of significance respectively.

of the other variables mandated a significant change in wage inequality. Looking at

the post-reforms period, none of the variables mandated any significant change in

wage inequality via their effects on log itPRI� .

Table 12: Second stage mandated wage regressions for log itPRI�

(a) Pre-reforms

�PRIUS Tariffs NTB �NTB

Skilled shared 0.76** 0.03 -0.96 0.78

(0.38) (0.27) (0.78) (0.53)

Unskilled share 0.79*** 0.08 -0.64 0.41

(0.30) (0.18) (0.50) (0.33)


-0.02 0.00 0.00 0.00

Corrected T-statistic -0.14 -0.41 -0.31 0.75

R-squared 0.99 0.90 0.95 0.33


34

(b) Post-reforms

�PRIUS Tariffs NTB �NTB

Skilled shared -0.10 0.11 0.39 0.82

(0.24) (0.09) (0.88) (0.81)

Unskilled share 0.21 0.06 -0.05 -0.03

(0.15) (0.05) (0.48) (0.43)


0.00 0.00 0.00 0.00

Corrected T-statistic -2.47 5.57 0.29 0.68

R-squared 0.99 0.99 0.94 0.69


Corrected standard errors in parentheses

*, ** and *** are 10 %, 5 % and 1 % levels of significance respectively

How do the mandated wage changes from our two-stage model compare with actual

wage changes? If we add the mandated wage inequality coefficients for price change

and technological change, the model mandates a 2 % fall in wage inequality compared

to an actual fall of 11 %, a clear under-prediction. Post-reforms, price and

technological change mandated an increase of about 83 % in wage inequality, a

massive overprediction compared to an actual increase of 9 %. What explains this

overprediction?

One possibility could be that the assumption of perfect mobility underlying the HOS

model may not be entirely appropriate in the Indian context, given the stringent labour

regulations that impose many restrictions on layoffs of workers. These restrictions on

layoffs reduce the ability of firms to adjust their skill mix to make it appropriate for

new technologies. We do not have sectoral data on labour regulations and could not

account for the impact of these labour regulations in our model. Alternatively, it could

be that our post-reform period of six years is too short for the expected adjustments to

have taken place in a HOS fashion, which are typically long run effects.

6.0 Conclusion

This paper contributes to the developing country literature on trade and wage

inequality by exploring the causes behind rising wage inequality in Indian

manufacturing using a mandated wage approach. The use of the mandated wages

methodology has been limited to a very few developing country studies only. The

available evidence on wage inequality in developing countries (mostly Latin

35

American) suggests that technological change is the main explanation to rising wage

inequality. Our findings for India also suggest that the main factor driving up relative

wages in India is technological change and not price changes. In this respect, India’s

experience with the trade reforms has not been different from the experience of these

Latin American countries.

Our technological variable captures all types of technical change, including skill-

biased technological change and our results may therefore reflect the presence of

skill-biased technological change, similar to Berman, Somanathan and Tan (2003)’s

findings for Indian manufacturing. Our model mandates wage changes arising from

technological change that are quite huge in magnitude compared to actual changes.

One possible reason could be that the model’s assumption of perfect factor mobility

may not be appropriate in the context of India, where the labour markets are highly

regulated. Another reason could be that the length of time we allowed for the expected

HOS adjustments to take place is too short. In future work, we will control for these

two potential problems. Thanks to Besley and Burgess (2002) data on labour

regulations is now available at the state level, and presents a natural extension to this

work. Also, it is now possible to add about 4 years worth of data from the ASI.

36

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41

Appendix

Deriving mandated wage regressions

Zero profit conditions are given by:

i ik kk

p A w�� (1)

ikik

i

vAQ

� (2)

(2) is the input intensity, i.e. number of inputs per unit of output

diff. (1) totally:

� �i ik k ik kk

dp A dw dA w� �

Divide by p throughout:

î ik k ik ki

ki i i

dp A dw dA wpp p p

� ��

� �

ˆ .i ik k k ik ki

ki i k i

dp A w dw dA wpp p w p

� ��

� �

îk k ik ik kk

k i ik i

A w dA A wwp A p

� ��

� �

ˆˆ î ik k ik kk k

p w A� ��

Differentiate (2) using quotient rule:

2

i ik ik iik

i

Q dv v dQdAQ�

�

2

ik ik i

i i i

dv v dQQ Q Q

� �

ˆ îk ik ikik i

ik ik i ik i

dA dv vA QA A Q A Q

� � �

since ik i ikA Q v� from (2):

ˆ ˆ ˆîk ik ikik i ik i

ik ik ik

dA dv vA Q v QA v v

� � � � �

42

From standard TFP growth measurement, we know that:

ˆ ˆˆ ˆi i ik ik ik ik

k kTFP Q v A� ��

which implies that:

ˆˆ ˆi ik k i

kp w TFP��

trade, technology and wage inequality in developing ... · moved slowly towards developing...

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