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The Role of Productivity and Labor Market Conditions in Wage

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The Role of Productivity and Labor Market Conditions in Wage

    Wage-Productivity Relationship in Organized

    Manufacturing in India: State-wise Analysis

    Bishwanath Goldar* and Rashmi Banga**

    December 2004

    [Paper to be presented at International Conference on „Wages and Income in

    India: Emerging Pattern and Policy Issues‟, Indira Gandhi Institute of Development Research, Mumbai, December 12-14, 2004, organized by the Indira Gandhi Institute of Development Research, Mumbai, the Institute for Human Development, Delhi, and the Indian Society of Labor Economics]

* Institute of Economic Growth, University of Delhi Enclave, North Campus, Delhi

    110007, India, Fax: +91-11-2766-7410, email: bng@ieg.ernet.in th** Indian Council for Research on International Economic Relations, Core-6A, 4 Floor,

    India Habitat Center, Lodi Road, New Delhi 110 003, India, Fax: +91-11-2462-0180,

    +91-11-2461-8941, e-mail: rashmi@icrier.res.in

    Wage-Productivity Relationship in Organized Manufacturing in India:

    State-wise Analysis

    Bishwanath Goldar and Rashmi Banga

1. Introduction

    The object of this paper is to analyze the wage-productivity relationship in organized manufacturing industry in India. While labor productivity is expected to play an important role in determination of industrial wages, there are several other factors including the labor market conditions that are expected to influence the wage setting process. Therefore, the paper also investigates these factors while studying the wage-productivity relationship.

    One part of the paper is devoted to an analysis of growth in labor productivity and real wage rate using time-series data. The purpose of this analysis is to assess how far gains in labor productivity get translated into higher wages. The analysis is carried out using time-series data on real wage rate and labor productivity for the organized manufacturing sectors of different states as well as such time-series data at the All-India level.

    Another part of the paper is devoted to a detailed cross-sectional econometric analysis of the relationship between wage rate and productivity. This analysis is carried out for the year 1998-99. Variations in labor productivity and wage rate across three-digit industries and major states are examined. How state-specific and industry-specific factors influence wage determination is investigated.

    The theoretical framework underlying the empirical analysis is one of bargaining between workers union and the firm. This is discussed briefly in Section 3 of the paper. The starting point of the cross-sectional analysis is a Variable Elasticity of Substitution (VES) production function. We assume that the VES production function would be the basis of wage setting in a perfectly competitive condition. However, the observed wages in a firm would differ from that given by the production function because, in reality, the labor markets of different regions and firms are not integrated and wages are set in a process of bargaining between workers union and the firm. Thus, the difference between the observed wage rate and that given by the production function reflects mostly the imperfections in labor market. Accordingly, an econometric analysis of the gap between the wage rate predicted by the production function and the actual wage rate is undertaken to assess the influence of labor market conditions and other such factors on the wage-productivity relationship.

The basic source of data for the study is the Annual Survey of Industries (Central

    Statistical Organization, Government of India). For the econometric analysis of

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    determinants of wage rate, a number of explanatory variables are used for which data have been drawn from other sources. This is discussed later in the paper.

    The paper is organized as follows. Section 2 briefly reviews some recent econometric studies on wages in Indian industry. Section 3 describes the theoretical framework underlying the empirical analysis presented in the paper. The analysis of growth in labor productivity and real wages based on time-series data is presented in Section 4. This is followed in Section 5 by inter-state comparison of labor productivity and wages for the year 1998-99. The cross-sectional econometric analysis of the determinants of industrial wages is presented in Section 6. The main findings of the study are summarized in Section 7.

    2. Review of Some Recent Studies on Wage-Productivity Relationship in Indian Manufacturing

    The relationship between wage and productivity in Indian manufacturing has as yet not been examined, or not adequately examined. However, there do exist some studies that have examined the issue of wage setting in the Indian context. Bhalotra (1998), for instance, examines in the Indian context the issue of rationality and near rationality in term of wage deviation from its efficient level. Deviation of the wage from efficient level is regarded, as near rationality if profit losses incurred is small. Firm also pay more than efficient wage in order to avoid cost incurred due to trade unions. According to the study, when effort depends on the wage, firm can increase output in short run either by increasing employment or by increasing the wage. If wages are at optimal level, the wage elasticity of output should be equal to employment elasticity of output.

    The study performs empirical tests of this hypothesis for the Indian labor market. The main reason for undertaking the study is that Indian labor market does not seem to be competitive. Bhalotra uses a panel of 18 two-digit industries desegregated by their location across the 15 major Indian states during 1979-87. Explanatory variables used are mainly capital stock, employment and actual hours worked per worker. She uses the GMM estimator, propose by Arellano Bond. The results reject the hypothesis that efficiency wages are paid in Indian factories. The other two conclusions of this paper are, first on an average Indian factories do pay more than efficiency wage, possibly on account of union intervention in wage setting. Second, this deviation from optimum does not impose a cost on them. A possible reason for this can be that positive productivity gains from the increasing wage beyond its efficient level compensates for the profit loss to some degree.

    Pal (2004) studies the relationship between wage inequality and technological change. In particular, the study examines the effect of technological change on wage differential in the context of the Indian manufacturing sector using the NSSO data for the years 1983-84, 1987-88 and 1993-94. The equation estimated takes the wage differential between skilled and unskilled labor (based on secondary education) the as the dependent variable, which is explained in terms of growth in capital-labor ratio, growth in total factor productivity and growth in net fixed capital formation along with time dummies for the years 1988

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    and 1994. The results show that among the technology variables, only growth in capital-labour ratio has any significant impact on secondary education premium. However, it has a negative coefficient implying that introduction of newer technology actually worked against the educated workers.

    Also relevant to the issue under investigation is the study by Bhalotra (2002), which discusses the impact of economic liberalisation on employment and wages in Indian industry. Interestingly, the study finds important inter-state differentials in wages. In the 1980s, nominal earnings in Andhra Pradesh were almost 50 per cent below the India average, and those in Maharashtra almost 50 per cent above. And, these wage differentials were remarkably stable, showing no tendency to narrow between 1979 and 1989. The state-wise variation in earnings was re-computed after controlling for differences in industrial composition. The pure state effects thus identified were still

    found to be very large. This indicates large dispersion of earnings within each industry

    across states. Thus, despite considerable migration across states, there appear to be state-specific labour markets

3. Theoretical Framework of this Study

    The theoretical framework that underlies our analysis of wage setting mechanism is a simple wage bargaining model. The starting point in this model is the real wage bargain between workers and firms, in which not only the real wage is set, but also the employment level at the firm (or at the industry). The procedure followed here is derived from the efficient contract model of McDonald and Solow (1981) and is sketched in Blanchflower and Oswald (1994). A discussion of the model is available in Bande, Fernandez and Monuenga (2000).

    Let us consider a bargaining model with supernormal rents. These have to be split, somehow, between workers and the employer. We assume that real wages are determined by a Nash bargaining problem, in which );is the bargaining power of employees. The

    problem is to maximize:

     a(1) V(w,n) = )!;ln{U(w) U(w) } + (1-)?!ln;??

     awhere U(?) is the union‟s utility function from wages, w is the wage available in the

    event of breakdown in the bargaining (alternative wage) and ?;is the firm?s profit. This

    formulation relies on the assumption that in the event of bargaining failure the firm aearns zero profits and the workers w. Define profits as f(n)-w?n where f(?) is a concave

    revenue function (p=1) and n is the firm‟s employment. Bande, Fernandez and

    Monuenga (2000) show that by solving the maximization problem, we get a first-order Taylor approximated expression:

    )?a(2)w(w 1n)

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    From the above equation, we can derive the following relationship (see Bande, Fernandez and Monuenga, 2000):

    a(3)w(1))w)x

where x stands for labor productivity.

    As is usually assumed, the alternative wage can be explained by some determinants. It seems reasonable to assume that the alternative wages is a function of w*, the going wage

    in other sectors of the economy and u, the unemployment rate.

    Given the relationship in (3) above, increases in labor productivity, x, over time need not lead to an equal or proportionate increase in wage rate, w. Whether or not that happens will depend on the changes in the alternate wage (determined by the wage in the agricultural sector or the wages in other industrial unit of the region or other regions) and changes in the bargaining power of laborers and the firm, reflected in ).

    In a cross-sectional context, similarly, variations in wages need not be associated with equal or proportionate variations in the wage rate. Thus, two firms producing the same products in two regions and having the same level of labor productivity need not have the same wage rate. Differences in wage rate may arise due to differences in the alternate wage and in the bargaining strength of employees.

    4. Growth in labor productivity and real wages in organized manufacturing

    During the period 1975-76 to 1999-00, labor productivity (gross value added per employee deflated by manufacturing price index) in organized manufacturing grew at the trend rate of 5.8 per cent per annum. The trend growth rate in real product wage (emoluments per employee deflated by manufacturing price index) in this period was much lower at about 3.1 per cent per annum.

    In the period mid-1970s to mid-1980s growth rate in real wages by and large maintained parity with the growth rate in labor productivity. However, since the mid-1980s, wage growth has been lagging behind productivity growth (see Figure 1). The gap between productivity growth and real wages growth was more than 3 percentage point per annum in the period 1985 to 1999. This may be attributed to weakening of the bargaining strength of labor. The decline of the public sector may have been a contributing factor since the wage setting in public sector plays an important on the wage setting in the private sector.

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    Fig.1: Three-yearly moving average of growth rates in labor productivity and real

    wage rate, Indian manufacturing, 1975-76 to 1999-0014.0%

    12.0%

    10.0%

    8.0%

    6.0%Gr_LP4.0%Gr_wage2.0%

    0.0%

    -2.0%1976-4.0%

    1978-6.0%

    1980

     1982

     1984

    1986The observed gap between growth rates in labor productivity and real wages at the All-

    India level is found also for different states, as may be seen from Table 1. 1988

     1990 Table 1: Growth rates in labor productivity and real wages,

    1992 1980-81 to 1999-00,organized manufacturing, by state

     1994Growth rate in Growth rate in real 1996labor productivity wages (% per

    State (% per annum) annum) Difference 1998Assam 5.0% 4.3% 0.7% Andhra Pradesh 6.6% 3.5% 3.2% Bihar 6.3% 3.4% 2.9% Gujarat 8.8% 4.7% 4.2% Haryana 5.9% 4.7% 1.2% Himachal Pradesh 8.5% 3.7% 4.8% Karnataka 6.3% 4.1% 2.2% Kerala 4.5% 3.6% 0.9% Madhya Pradesh 5.9% 3.3% 2.5% Maharashtra 7.2% 4.1% 3.1% Orissa 5.8% 2.0% 3.7% Punjab 6.4% 4.6% 1.8% Rajasthan 6.8% 3.6% 3.2% Tamil Nadu 5.0% 2.9% 2.1% Uttar Pradesh 9.1% 4.6% 4.5% West Bengal 3.9% 2.8% 1.1%

    All India 6.6% 3.3% 3.3%

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    In Gujarat, Himachal Pradesh, Maharashtra and Uttar Pradesh, the growth rate of labor productivity during 1980-81 to 1999-00 was relatively higher. In all these cases, the growth rate of real wages lagged well behind the growth rate in labor productivity. On the other hand, in Assam, Kerala, Tamil Nadu and West Bengal, the growth rate of labor productivity was relatively low. In all these cases, the gap between labor productivity growth and real wages growth was relatively small. Among the rest, there was a significant gap between growth rates in labor productivity and wage rate in Andhra Pradesh, Bihar, Orissa, and Rajasthan. By contrast, the gap was relatively small in Haryana and Punjab, which might have something to do with the agricultural development in these states.

    Across states, there is a significant positive correlation (r= 0.5) between growth rates of labor productivity and real wages (see Figure 2), indicating that labor productivity exerts an important influence in wage setting. But, the regression coefficient is found to be 0.25, significantly lower than one. The implication is that a hike in labor productivity would lead to much less than proportionate hike in real wages.

    Fig.2: Growth rate in labor productivity and real wages, across

    states, 1980-81 to 1999-00

    5.0%

    4.5%

    4.0%

    3.5%

    3.0%

    2.5%

    2.0%

    1.5%

    1.0%growth rate in real wages0.5%

    0.0%

    0.0%1.0%2.0%3.0%4.0%5.0%6.0%7.0%8.0%9.0%10.0%

    growth rate in labor productivity

     6 r =0.5

    To analyze the wage-productivity relationship further, an attempt has been made to estimate equation (3) given in the previous section. The equation has been estimated from panel data 16 states and 20 years, 1980-81 to 1999-00. The equation has been specified as:

    a(4)wxx~it01it2itit

    In this equation, subscript i denotes state and t denotes year (0 for 1980-80 increasing to 20 for 1999-00). ~ is the random error term. As in equation 3, w denotes wage rate and x alabor productivity. x is the alternate wage. For each state (each year), the alternate wage has been measured by taking the average of wage rate prevailing in the other 15 states (in that year).

    It has been noted above that the growth rate in real wages by and large maintained parity with the growth rate in labor productivity in the period from the mid-1970s to mid-1980s. But, since the mid-1980s, wage growth has been lagging behind productivity growth (Figure 1). To incorporate this aspect in the econometric model, the coefficient of x has been allowed to change over time. Thus, is written as ?+?t. Accordingly, the equation 2

    to be estimated becomes:

    a(5)w?x?xtx~it0itit2itit

    The above equation has been modified further to incorporate the possibility of lagged response of wage rate to changes in labor productivity. The equation with lagged response has been specified as

    a(6)w?x?xtx(w~it0itit2iti,t1it

In the above equation, ( represent the lag parameter. The higher the value of (, the

    greater is the lag in response of wage rate to a change in labor productivity.

    Estimates of the wage equations given in (4), (5) and (6) are presented in Table 2. As mentioned above, these estimates have been made from panel data for 16 states for 20 years, 1980-81 to1999-00. Estimation of parameters has been done by the Ordinary Least Squares (OLS) technique.

    The estimates of the wage equation clearly show that labor productivity is an important factor determining industrial wages. However, the marginal effect is found be about 0.16 to 0.22, much less than one, implying thereby that the increase in wage rate arising from productivity gains is only a small fraction of the increase in labor productivity achieved. The coefficient of alternate wage is found to be positive (as it should be) and statistically significant. The coefficient of the product term involving labor productivity and time is

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    negative and statistically significant. It may be inferred accordingly that the marginal effect of productivity increase on real wages has been declining over time.

    Table 2: Estimates of the Wage Equation, Panel data, 1980-81 to 1999-00 Dependent variable: w (real product wage rate)

Explanatory Model I Model II Model III

    variables (equation 4) (equation 5) (equation 6)

    x (labor 0.1615(15.1) 0.2218(8.3) 0.1586(7.7)

    productivity)

    xt (labor -0.0037(-2.5) -0.0059 (-5.0)

    productivity

    multiplied by time) aw (alternate wage) 01430(1.9) 0.3333(3.1) 0.4325 (5.1)

w (lagged wage 0.6346(17.3) t-1

    rate)

    Constant 0.0404(5.5) 0.0102(0.7) -0.0423 (-3.6)

R-squared 0.601 0.609 0.793

F-ratio {degrees of 239.1 {2,317} 164.0 {3,316} 287.1 {4, 299}

    freedom}

Number of 320 320 304

    observations

    Note: Figure in parentheses are t-ratios. For estimating Model III, one observation is lost of each state as lagged wage rate is introduced as an explanatory variable.

    The coefficient of the lagged wage rate variable is positive and less than one. The coefficient is statistically significant and about 0.6 in numerical value. It seems therefore that there is a significant lag in the adjustment of wage rate to increases in labor productivity. Going by the estimated coefficients of Model III presented in the table, it seems that in the second half of the 1990s, the short-run effect of labor productivity increase on wage rate was less than 0.1, and the long-run effect was about 0.2.

    5. Variations in labor productivity and wage rate: inter-state and inter-industry

    We consider next the inter-state and inter-industry variations in labor productivity and wage rate. This is a cross-sectional analysis undertaken for 1998-99.

    Indices of labor productivity (gross value added per employee), capital intensity (fixed capital per employee) and wage rate (emoluments per employee) for different states are shown in Table 3. The index for Maharashtra, the most industrially developed state, is

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taken as one. Each state is compared to Maharashtra. Ratio of labor productivity, capital

    intensity and wage rate is computed for each three-digit industry for which data are available for both Maharashtra and the state in question. Then, an average is taken across

    three-digit industries (after excluding the top three and bottom three observations). This

    yields the indices for the state. Repeating this procedure for each state, the indices for

    different states have been computed, which are shown in Table 3 (ordered by labor productivity). A graphic presentation of the indices is made in Figure 3.

    Table 3: Indices of labor productivity, capital intensity and wage rate, Cross-state comparison, 1998-99

    state Labor Productivity Wage rate index Capital-labor index

    index

     Maharashtra 1.00 1.00 1.00

     Haryana 1.00 0.82 1.46

     Himachal Pradesh 0.92 0.65 1.22

     Pondicherry 0.91 0.48 0.80

     Gujarat 0.88 0.71 1.13

     Rajasthan 0.82 0.73 1.25

     Goa 0.81 0.69 1.59

     Delhi 0.77 0.74 0.68

     Uttar Pradesh 0.76 0.75 1.30

     Madhya Pradesh 0.73 0.68 1.11

     Tamil Nadu 0.71 0.70 1.13

     Punjub 0.65 0.69 0.91

     Kerala 0.64 0.77 0.84

     Chattisgarh 0.64 0.43 0.77

     Karnataka 0.63 0.82 1.32

     Meghalaya 0.62 0.45 0.26

     West Bengal 0.62 0.84 0.85

     Chandigarh(U.T.) 0.57 0.69 0.57

     Andhra Pradesh 0.56 0.64 0.98

     Orissa 0.48 0.55 0.86

     Daman & Diu 0.48 0.37 0.47

     Assam 0.46 0.47 0.37

     Dadra & Nagar Haveli 0.46 0.54 0.51

     Jharkhand 0.45 0.67 0.51

     Uttaranchal 0.41 0.56 0.72

     Bihar 0.38 0.41 0.55

     Jammu & Kashmir 0.33 0.53 0.35

     Andaman & N. Island 0.22 0.76 0.43

     Manipur 0.19 0.59 0.23

     Tripura 0.13 0.27 0.28

     Nagaland 0.06 0.41 0.28

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