1 ASIA-PACIFIC RESEARCH AND TRAINING NETWORK ON TRADE ARTNeT CONFERENCE ARTNeT Trade Economists Conference Trade in the Asian century - delivering on the promise of economic prosperity rd September 2014 Parallel Session 8: Trade inclusiveness and inequality
2 Trade, Technology, and Institutions: How Do they Affect Wage Inequality? Evidence from Indian Manufacturing Amit Sadhukhan Institute of Development Studies Kolkata India The Asia-Pacific Trade Economists' Conference: "Trade in the Asian century delivering on the promise of economic prosperity 23 rd September 2014.
3 A. Trade Liberalization and Changes in Wage Inequality: Initial Evidence Since late 1970s, most of the developed countries started experiencing a rise in the wage inequality between skilled and unskilled labour (Wood, 1998). Some Continental European countries experienced an increase in unemployment of the unskilled labour instead of rising wage inequality (Freeman and Katz, 1996). Many developing countries have experienced similar rise in wage inequality since late 1980s the period when they initiated major economic liberalization processes (Goldberg and Pavcnik, 2007).
4 Trade Liberalization and Changes in Wage Inequality: Theories The Heckscher-Ohlin (HO) and Stolper-Samuelson (SS) theories. The specific factor models of trade and production outsourcing across the developed and developing world (Harrison et al., 2010; Feenstra and Hanson, 2001; Krugman, 2008; Choi, 2006). Developing country specific issues such as existence of informal labour market, imperfect capital mobility, production outsourcing, and labour migration explain wage inequality in developing countries (Marjit, 2008). Growing South-South trade and developing countries trade share in total world trade affect wage inequality in the developing countries than the conventional North-South trade (Davis, 1996). Trade liberalization in many countries comes along with the capital account liberalization such as FDI liberalization (Goldbarg and Pavcnic op. cit.; Choi, op. cit; Ghose, 2004)
5 Trade Liberalization and Changes in Wage Inequality: Empirical Evidence from Developing Countries Hanson and Harrison (1999) studied the effect of trade liberalization on wage inequality in the Mexican manufacturing sector for the period of Their findings do not support the SS effect. Sen (2008) observed an increasing wage inequality between skilled and unskilled-labour in Indian manufacturing because of India s trade liberalization. Chamarbagwala (2006) observes that India s external sector reform has benefited skilled men but has hurt skilled women. Mishra and Kumar (2005) show that trade liberalization had a strong, negative, and robust effect in reducing industry wage premium. Julien (2007) observed that increasing wage inequality in developing countries is more due to South-South trade liberalization than the typical trade liberalization with the North.
6 B. Skill-biased Technological Change (SBTC) and Wage Inequality: Some Theories The microeconomics of the SBTC and corresponding rise in wage inequality stands on the assumption of complementarity between capital-stock/technology and skilled labour. The rise in capital-stock demands more skilled labour relative to unskilled labour, because rising capital stock increases the marginal productivity of skilled labour, while it reduces the marginal productivity of unskilled labour (Krusell, et al., 2000; Autor et al., 1998 and Johnson, 1997).
7 Skill-biased Technological Change (SBTC) and Wage Inequality: Theories continued Increasing international trade might lead to trade-induced SBTC through increasing imports of advanced intermediate goods, inflow of foreign capital, and foreign competition (Wood, 1994 & 1997; Acemoglu, 2003; Robbins, 1995). A country s trade-induced SBTC takes place through two major ways: (a) defensive innovation in developed countries (b) technology augmentation in developing countries.
8 Skill-biased Technological Change (SBTC) and Wage Inequality: Empirical Evidence from Developing Countries Caselli (2010) measured effect of (a) the trade-induced SBTC and (b) the exogenous SBTC (based on price change in the US) on wage inequality for 129 Mexican manufacturing industries using the data for 1984 and The wage inequality (measured by the skilledunskilled wage ratio) increased by 25 per cent because of SBTC. Attanasio et al. (2004) found that the SBTC was larger in sectors that experienced larger tariff cuts, suggesting the existence of trade-induced SBTC. Sen (2008) studied the role of SBTC on wage inequality in Indian manufacturing for the period of A reduction in trade barriers (tariff and non-tariff barriers measured by ERP and ICR) raised the ratio of skilled labour to unskilled labour.
9 C. Labour Market Institutions Labour market institutions include institutions and policies that facilitate labour welfare. Because of the strong labour market institutional setup, most continental European countries had experienced rising unemployment instead of rising wage inequality observed in many other developed countries (Nickell, 1997; Freeman and Katz, 1996). An integrated European Union, a convergence in labour market institutions across European members, promotes firms to relocate their production to low-taxed and less regulated member countries, and undermine the bargaining power of trade unions and their ability to protect labour rights (Choi, 2006; Offe, 2006).
10 Labour Market Institutions: Evidence from Developing Countries In Latin America, improvements in labour market institutions played a major role in reducing wage inequality in many countries, especially in Argentina, Brazil, and Chile in 2000s (Keifman and Maurizio, 2012). India s labour market institutions can also be judged by the strength of trade unions. The number of trade unions and their membership has declined in India after 1991 (Zagha, 1999). Kijima (2006) found that as trade union membership in India is concentrated mostly among skilled labour in the organized sector. Therefore, the fall in the number of trade unions and its membership cannot be a reason for the overall rising wage inequality between skilled and unskilled labour.
11 D. Economic Growth The growth and inequality relation proposed by (Kuznets, 1955) is one of the well-known phenomena called the Inverted U Hypothesis. An increase in wage inequality between skilled and unskilled labour is possible when the growth takes place in high value-added or skilled labour-intensive sector, because of an increase in demand for skilled labour relative to unskilled labour (Pieters, 2010). As developing countries have surplus labour engaged in the lowgrowing primary sector, if this surplus labour is provided employment in the sector with high growth, it would reducem economic inequality (Ghose, 2004).
12 Economic Growth and Wage Inequality: Evidence from Developing Countries To test the inverted U hypothesis, Ravallion (1995) estimated a equation for 52 developing countries in 1985, where the Gini index is taken as a function of the per-capita consumption and the reciprocal of the per-capita consumption; the results rejected the inverted U hypothesis. Majid (2011) used a sample of 57 developing countries and divided them into two groups one the fast globalizers and the second the slow globalizer. His estimates suggest that for fast globalizers, the per-capita GDP growth has impacted positively on the Gini coefficient; for the slow globalizers he does not see any significant effect of per-capita GDP growth on the Gini.
13 E. Methodological Issues A. Three outcome-based measures of trade liberalization: 1. Export-orientation (X/Y), 2. Import-penetration (M/Y), 3. The ratio of South-South trade in total trade (SS/TT). Three outcomebased measures of trade liberalization: B. Technological change: Capital- Labour ratio. C. Labour market institutions: Share of contract labour in total labour. D. Economic Growth: 1. Initial period of liberalization ( ) with 4.3 % GDP growth, 2. Moderate growth period ( ) with 6.3 %, 3. High growth period ( ) with 8 % annually. B. Wage inequality is measured by five indicators: 1. Skilled-unskilled wage ratio (W S /W U ), 2. Gini coefficient (G), 3. Ratio of 90 percentile to median wage (W 90 /W 50 ), 4. Ratio of median to 10 percentile wage (W 50 /W 10 ), 5. Ratio of 90 percentile to 10 percentile wage (W 90 /W 10 ).
14 A Model of Wage Inequality Wage inequality function for organized manufacturing is presented as follow W S X M SS K L C = α + β W 1 + β U it Y 2 + β it Y 3 + β it TT 4 + β it L 5 + β it L 6 D Growth U it + β 7 (D Tech) + uit Here, i = 1, 2, 3,, 55 ; and t = 1989, 1990, 1991,, The u it is the remainder stochastic disturbance term.
15 Econometric Methodology Panel data econometric techniques have been used. The econometric results for the wage inequality function are based on robust standard error estimates, which control for possible heteroscedasticity and serial-correlation of the disturbances (Angrist and Pischke, 2009). To choose between the Fixed-Effect model and Random-Effect models, the Hausman-test statistic has been used.
16 F. Data Requirements and Sources, Concordance between Industry and Trade Classifications A. Three data sources: 1. Annual Survey of Industries (ASI) of the Central Statistical Office (CSO) for industry and wage for organized manufacturing 2. Employment and Unemployment Surveys of the National Sample Survey Organization (NSSO) for wage data for total manufacturing 3. The United Nations Commodity Trade Statistics Database (UN- COMTRADE) for trade data. B. Concordance between industry and trade databases: A concordance scheme between NIC-98 and SITC Rev.3 is derived from an available concordance from the UN that has matched the HS-96 with ISIC Rev. 3 and SITC Rev.3.
17 G. Trade Performance of Indian Manufacturing Sector Export-orientation and import-penetration measures for technology categories in Indian manufacturing Year High-tech Export-orientation or Export share in total output (%) Medium-high tech Medium-low tech Low-tech Total Manufacturing Year High-tech Import-penetration or Import share in total output (%) Medium-high tech Medium-low tech Low-tech Total Manufacturing
18 Share of Export in Gross Output (%) Export-orientation for different manufacturing groups Export Orientation for Hightech Manufacturing Export Orientation for Medium-high-tech Manufacturing Export Orientation for Medium-low-tech Manufacturing Export Orientation for low-tech Manufacturing Export Orientation for Total Manufacturing
19 Share of Import in Gross Output (%) Import-penetration for different manufacturing groups Import Panetration for Hightech Manufacturing Import Panetration for Medium-high-tech Manufacturing Import Panetration for Medium-low-tech Manufacturing Import Panetration for Lowtech Manufacturing Import Penetration for Total Manufacturing
20 South-South Trade Share of Exports to South in Total Exports (%) Share of Imports from South in Total Imports (%) Share of Trade with South in Total Trade (%) Manufacturing Groups High-tech Medium-high tech Medium-low tech Low-tech Total Manufacturing
21 Trade Liberalization in Indian Manufacturing: Main Observations The rise in import-penetration was more than that of export-orientation over the period 1989 to Moreover, the export-orientation has decreased in the period between 2000 and 2007, whereas the importpenetration has increased in the same period. Among the four technology-based manufacturing industry groups, the import penetration for the high-tech and medium-high tech manufacturing groups was higher than that for the medium-low-tech and low tech manufacturing; the export-orientation of the low-tech manufacturing group was higher than for the other three. The South s share in India s total manufacturing trade (exports and imports) has increased in the period ; however, the South s share in India s total manufacturing imports has increased more than South s share in India s total manufacturing exports.
22 H. Wage Inequality in Indian Manufacturing: New Evidence The Skilled-unskilled wage ratio for organized manufacturing The absolute wage-gap was INR in 1989 and it increased to INR in Manufacturing Group High Tech Medium-high Tech Medium-low Tech Low Tech Total Organized Manufacturing
23 Trend in the ratios of 90 percentile to median wage for 36 product groups in
24 90-10 Ratio Trend in the ratios of 90 percentile to 10 percentile wage Year
25 50-10 Ratio Trend in the ratios of median to 10 percentile wage Year
26 Estimates of the Growth of wages The 90 percentile wage : log W 90 = t (0.079) (0.008) R 2 = 0.03; No. of Obs. = 143 The median wage: log W 50 = t (0.06) (0.006) R 2 = 0.01; No. of Obs. = 143 The 10 percentile wage: log W 10 = t (0.06) (0.006) R 2 = 0.05; No. of Obs. = 143
27 Trend in the Gini coefficient in Year
28 Gini coefficient for different technology groups in Indian manufacturing: 1993, 1999, 2004, and 2009 Year Manufacturing Group Number of Observations in 2009 High Tech Manufacturing Medium-high Tech Manufacturing Medium-low Tech Manufacturing Low Tech Manufacturing Total Manufacturing
29 Wage Inequality in Indian Manufacturing: Main Observations The skilled-unskilled wage ratio for total organized manufacturing increased from 1.99 in 1989 to 4.06 in 2007; similar results prevailed across all four groups. The rise in skilled-unskilled wage ratio was due to high growth of wage for skilled labour relative to almost stagnant wage for unskilled labour. The wage ratio, and the Gini coefficient increased over the period However, there was a decline in wage inequality measured by the wage ratio, and no significant change was observed in the wage ratio over the same period stated above. The high growth of the 10 percentile wage relative to the median wage seems to be a possible explanation for the declining trend in wage ratio observed in this study.
30 I. Trade Liberalization and Wage Inequality: Econometric Results Panel Regressions for 55 industries in organized manufacturing and years 1989 to 2007 Independent Variables Model Specification Dependent variable: Skilled-unskilled wage ratio (1) (2) (3) (4) Fixed industry- effects Export Orientation ( X ) Y (0.03) Import penetration ( M ) ** Y (2.09) South-South Trade ( SS ) ** TT (2.42) Capital-labour Ratio ( K ) (1.33) L Labour Market Institutions ( L C L U ) D Growth Moderate ( ) D Growth High ( ) K L K L D Tech Medium-low D Tech Medium-high D Tech High *** (3.31) Random industry- effects (0.61) (-0.61) * (1.81) (0.69) ** (1.96) *** (4.41) *** (9.30) Random industry- effects (0.41) ** (-2.38) ** (1.82) (0.69) ** (2.09) *** (4.31) *** (9.15) (-1.23) (-0.83) ** (2.1) Fixed industry- effects (0.86) *** (-3.34) (0.82) *** (3.33) # (1.49) * (1.91) *** (5.76) *D Tech Medium-low *** (-2.7) *D Tech Medium-high *** (-3.28) K L Constant *** (6.66) *** (7.48) *** (6.76) (0.27) *** (8.53)
31 Panel Regressions for 36 industries in total manufacturing and years 1994, 1999, 2004, and 2009 Model Specification Independent Variables Dependent Variable: Gini Coefficient (1) (2) (3) (4) Random industry- effects Export Orientation ( X Y ) (0.26) Import penetration ( M ) *** Y (3.38) South-South Trade ( SS ) TT (0.9) Capital-labour Ratio *** ( K ) (3.33) L Labour Market Institutions ( L C L U ) D Growth High (2004 & 2009) D Tech Med-low D Tech Med-high D Tech High (1.01) Random industry- effects (0.18) *** (3.37) (0.78) *** (3.44) (0.06) * (1.81) Random industry- effects (0.73) (1.24) (0.95) *** (3.57) (0.19) * (1.87) (0.73) *** (4.25) * (1.74) Random industry- effects (0.2) * (1.88) (0.62) (0.52) (0.01) * (1.67) K *D Tech Med-low L (-0.29) K *D Tech Med-high L (0.31) K *D Tech High * L Constant *** (25.98) *** (24.31) *** (26.37) (1.69) *** (23.98)
32 Model Specification Independent Variables Dependent Variables: Wage Ratio (1) (2) (3) (4) Fixed industry- effects Export Orientation ( X ) ** Y (2.18) Import penetration ( M ) Y (0.46) South-South Trade ( SS ) TT (0.01) Capital-labour Ratio *** ( K ) (3.52) L Labour Market Institutions ( L C L U ) D Growth High (2004 & 2009) K L D Tech Med-low D Tech Med-high D Tech High *D Tech Med-low (0.56) Random industry- effects * (-1.86) (2.09) (-0.66) *** (4.81) (-0.09) * (1.61) Random industry- effects (-1.33) (0.34) (-0.5) *** (6.09) (-0.11) * (1.75) (0.75) *** (5.78) * (1.58) Random industry- effects * (-1.71) (0.49) (-0.85) (0.59) (-0.2) # (1.38) (-0.02) K *D Tech Med-high # L (1.38) K *D Tech High *** L Constant *** (6.67) *** (14.31) *** (13.81) (2.64) *** (15.18)
33 Model Specification Independent Variables Dependent Variables: Wage Ratio (1) (2) (3) (4) Fixed industry- effects Export Orientation ( X Y ) ** (2.44) Import penetration ( M Y ) (0.1) South-South Trade ( SS TT ) (1.26) Capital-labour Ratio ( K ) (-1.44) L Labour Market Institutions ( L C L U ) ** (-2.02) Fixed industry- effects *** (2.58) (-0.77) (0.23) # (-1.61) ** (-2.17) Random industry- effects *** (2.72) (-1.01) (0.68) ** (2.03) ** (-2.08) Fixed industry- effects *** (2.62) (-0.31) 0.23 (0.28) * (-1.86) ** (-1.96) D Growth High (2004 & 2009) # (1.5) (0.31) ** (2.08) D Tech Med-low (0.62) D Tech Med-high # (1.48) D Tech High *** (6.6) K * L (1.74) K L (0.89) K *D Tech High L Constant *** (7.35) *** (5.34) *** (9.38) (-1.87) *** (5.46)
34 Independent Variables Model Specification Dependent Variable: Wage Ratio (1) (2) (3) (4) Fixed industry- effects Export Orientation ( X ) *** Y (2.8) Import penetration ( M ) Y (-0.61) South-South Trade ( SS ) * TT (1.82) Capital-labour Ratio *** ( K ) (3.68) L Labour Market Institutions ( L C L U ) D Growth High (2004 & 2009) D Tech Med-low D Tech Med-high D Tech High (-0.83) Fixed industry- effects *** (3.24) (-1.18) (0.38) *** (4.41) * (-1.74) ** (2.17) Random industry- effects (0.68) (0.29) (0.29) *** (4.83) * (-1.87) * (1.9) (1.08) *** (3.47) *** (4.11) Fixed industry- effects *** (3.08) (-1.11) (0.24) (-0.33) * (-1.68) ** (2.29) K *D Tech Med-low L (0.52) K *D Tech Med-high L (0.63) K *D Tech High L Constant *** (4.83) *** (4.59) *** (8.74) (-0.26) *** (4.41)
35 Wage Inequality Measures Comparison of the effects of different explanatory variables on different wage inequality measures Organized Total Manufacturing Sector Manufacturing (1) (2) (3) (4) (5) Skilled-unskilled wage ratio Gini coefficient (G) The wage ratio ( W 90 W 50 ) The wage ratio ( W 50 W 10 ) The wage ratio ( W 90 W 10 ) Independent ( W S ) W U Variables Export Orientation ( X ) Not significant Not significant Negative Positive Positive Y Import penetration ( M ) Negative Positive Not significant Not significant Not significant Y South-South Trade ( SS TT ) Positive Not significant Not significant Not significant Not significant Capital-labour Ratio ( K L ) Positive Not significant Not significant Negative Not significant Labour Market Institutions (( L C L U ) D Growth Moderate ( ) D Growth High ( ) D Growth High (2004 & 2007) Positive Not significant Not significant Negative Negative Positive NA NA NA NA Positive NA NA NA NA NA Positive Positive Positive Positive D Tech Med-low Not significant Not significant Not significant Not significant Not significant D Tech Med-high Not significant Positive Positive Not significant Positive D Tech High Positive Positive Positive Positive Positive
36 Trade factors and wage inequality The skilled-unskilled wage ratio is affected negatively by the import-penetration, positively by the South-South trade, however, not affected by the exportorientation. The export-orientation has shown an insignificant impact on the skilled-unskilled wage ratio, and this result is consistent with the results obtained by Sen (2008). The effects of three trade liberalization variables on the four wage inequality measures for total manufacturing are the following: Export orientation has a negative effect on wage ratio, but its effect is positive on the wage ratio and the wage ratio, and it has no significant effect on the Gini coefficient. This result is quite plausible because of India s high export-orientation in low-tech manufacturing goods, which are unskilled labour intensive. Given the wage rigidity at the bottom deciles, an increase in demand for unskilled labour due to an increase in export-orientation leads to an increase in the median wage relative to the 90 percentile wage and 10 percentile wage. Therefore, the negative effect of export-orientation on wage ratio, and positive effect on the wage ratio and wage ratio seems to be consistent with our predicted outcomes. A similar kind of result is observed in a study on Mexican industries by Frias et al. (2012).
37 Non-trade factors and wage inequality The capital-labour ratio ( K ), a measure of capital intensity, has a positive L effect on the skilled-unskilled wage ratio. This result shows that a rise in capital intensity leads to an increase in skilled wage relative to unskilled wage. Moreover, the negative effect of capital-labour ratio on the wage ratio indicates that a rise in capital intensity has led to an increase in the 10 percentile wage more than the median wage in the wage distribution. Labour market institution, measured by the ratio of contract unskilled labour in total unskilled labour ( L C ), has a positive effect on the skilledunskilled wage ratio. L U However, the negative effects of the contractualization on wage ratio and the wage ratio suggest that rising contractualization has reduced the median wage and the 90 percentile wage relative to the 10 percentile wage.
38 Non-trade factors and wage inequality continued For all five wage inequality measures, the period of high growth of GDP ( ) has shifted wage inequality function upward, which indicates a significant rise in wage inequality in the period of high growth in the post-2000 period compared to the earlier period of moderate growth. The evidence shows inter-industry heterogeneity in the wage inequality outcomes. Controlling for other factors, wage inequality by any wage inequality measure is high within the high-tech industry compared to other categories of industries.
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