دانلود مقاله ISI انگلیسی شماره 9954
عنوان فارسی مقاله

رشد و نابرابری در هند: تجزیه و تحلیل از ماتریس گسترده حسابداری اجتماعی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
9954 2010 12 صفحه PDF سفارش دهید 9070 کلمه
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عنوان انگلیسی
Growth and Inequality in India: Analysis of an Extended Social Accounting Matrix
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : World Development, 38, No. 3, pp. 270–281, 2010

کلمات کلیدی
- رشد بخشینا برابریآموزش و پرورش ماتریس حسابداری اجتماعیآسیاهندوستان
پیش نمایش مقاله
پیش نمایش مقاله رشد و نابرابری در هند: تجزیه و تحلیل از ماتریس گسترده حسابداری اجتماعی

چکیده انگلیسی

Based on an extended Social Accounting Matrix (SAM) for 2002–03, this study shows how sectoral growth in India affects inequality. A breakdown of the wage account into three educational levels and 10 sectors of employment improves the link between sectoral expansion and household income in the SAM. The results show that only agricultural growth reduces inequality, while growth in heavy manufacturing and services sectors raises inequality. Given India’s current growth pattern, inequality is likely to increase further. In an analysis of the standard SAM growth in any sector would appear to reduce inequality, which underlines the importance of our extension.

مقدمه انگلیسی

In India, growth in the 1990s was accompanied by increasing inequality across and within states, between rural and urban areas, and within urban areas (Deaton & Dre`ze, 2002; Dhongde, 2007). Though growth led to considerable poverty reduction, increasing inequality offset part of its effect. The slowdown of poverty reduction is one reason to care about inequality, but even in itself it is a key characteristic of the development process and of actual concern to policy makers (Kanbur, 2000, 2007). Rising inequality puts stress on popular support for growth strategies and threatens social and political stability. As such, it may be detrimental to future growth and poverty reduction (Nissanke & Thorbecke, 2006). Reducing inequality and achieving more inclusive growth are in fact prime objectives in India’s current Five Year Plan (Government of India, 2008). Inequality is related to the sectoral structure of growth because different industries use different production factors and different households differ in their supply of various production factors. Ravallion and Datt (1996) show that during 1950–90 growth in the primary and tertiary sector reduced poverty in India, while growth in the secondary sector did not. They relate this to growth of the capital-intensive production in manufacturing in this period, which was not beneficial to the poor. Similar conclusions are drawn in Khan and Thorbecke (1989) and James and Khan (1997). Their study of the Indonesian Social Accounting Matrix confirms that traditional labor-intensive technologies are more egalitarian than modern capital-intensive technologies. The reason is that production under traditional technology creates more employment, directly and indirectly, and more income for rural households. These studies focus on the distribution of value added between capital and labor, but do not address inequality among workers. Inequality of earnings is an important source of total income inequality (Gottschalk & Smeeding, 1997). Many studies have shown that the wage rate of skilled relative to unskilled workers, the skill premium, has risen in developing countries (Anderson, 2005; Goldberg & Pavcnik, 2007). Kijima (2006) finds that earnings inequality in India in the 1990s increased due to a rising skill premium. Especially the returns to tertiary education increased much, because relative demand outgrew relative supply. Furthermore, and related to this, the service sector has been the leading sector in terms of output and employment growth and is the most skill-intensive sector. During 1980–2000, labor moved out of agriculture into services, while the employment share of manufacturing hardly changed (Mazumdar & Sarkar, 2008, p. 225). Likewise, Chamarbagwala (2006) finds that in the period 1983–2000 employment in India shifted from low-skilled into high-skilled and medium-skilled occupations due to service sector expansion and agricultural sector contraction. It seems clear that inequality of earnings, and especially the skill premium, is an important factor in the relationship between sectoral growth and household income inequality. Therefore, the distribution of skills across households should be taken into account when analyzing this relationship. The aim of this paper is to find out how the sectoral structure of growth contributes to household income inequality in India, and to show how one can—and should—account for inequality among workers. The analysis is based on an extended Social Accounting Matrix (SAM) for the years 2002– 03. The SAM is a suitable tool to analyze the distributive effects of sectoral growth, as it captures the flow of income and interdependence between industries, production factors, and households, among others. The SAM has been widely used for development planning, reflecting the view that aggregate economic growth is an inadequate policy objective unless attention is paid to distributional changes (see Defourny & Thorbecke, 1984; Hayden & Round, 1982; Pyatt & Round, 1977). Due to its underlying assumptions of constant technology and excess capacity, SAM-based multiplier analysis is * I would like to thank Marcel Timmer and three anonymous referees for helpful comments and suggestions. This paper also benefitted greatly from participants’ comments at the IE&B Staff Seminar, University of Groningen; at the IARIW 30th General Conference in Portoroz, Slovenia; at the EUNIOS Workshop on Agricultural Policies and Social Accounting Matrices in Developing Countries in Groningen, The Netherlands; and at the 2nd Sino-Netherlands Workshop on Input–Output Techniques in Beijing, China. Any remaining errors are my own. Final revision accepted: July 22, 2009. sometimes regarded as rather restrictive. However, it offers a transparent framework of data with macroeconomic consistency. Compared to regression-based studies of sectoral growth and inequality or poverty (e.g., Loayza & Raddatz, 2009; Ravallion & Datt, 1996) the SAM analysis offers more insight by taking into account sectoral interdependencies and uncovering the channels through which income flows. Standard SAMs for India are available and have been used, for example, in Ten Raa and Sahoo (2007). A methodological contribution is made by extending the standard SAM through accounting for the skill-intensity and the skill premium by sector and the education and sector of employment of households. This is done using satellite accounts for earnings and employment by sector and household survey data for education and employment characteristics. The extension of the SAM consists of dividing the single wage account into 30 sub-accounts: three levels of educational attainment and 10 sectors of employment. The main innovation is the fact that each sector has its own wage account in the extended SAM. It shows the distribution of wage income between 30 different worker subgroups within the SAM’s representative household groups. Distribution analysis based on the standard SAM shows that growth of any sector will slightly reduce inequality, suggesting that the sectoral structure of growth does not matter for inequality. After extending the SAM, however, we find that growth in several sectors increases inequality between and within household groups. The effects are largest for community, social, and personal services; followed by heavy manufacturing and the other services sectors. Growth in these sectors increases inequality because they pay relatively high wages (the sector premium), they are skill-intensive, and pay a high skill premium. Only agricultural growth reduces inequality. The results confirm the importance of our extension for an analysis of income distribution, and emphasize that employment creation is not sufficient to secure equitable growth. They strengthen the call for the development of unskilled- labor-intensive manufacturing, as India’s current pattern of growth offers too little opportunities for low-skilled workers. The rest of the paper is organized as follows. In Section 2 the structure of the SAM, multiplier analysis, and the method for extension of the SAM are discussed. In Section 3 the data are presented, and in Section 4 the results are discussed. In Section 5 the results are related to India’s policies and pattern of growth. Finally, Section 6 concludes.

نتیجه گیری انگلیسی

Rapid growth in India in the past decades has led to the relative decline of agriculture and growing importance of services. This has fueled inequality, which is likely to increase further in the future. Based on an extended SAM, we find that growth of community, social, and personal services raises inequality between and within household groups most, followed by heavy manufacturing and other services sectors. Only agricultural growth reduces inequality, but the growth potential of agriculture is limited compared to manufacturing and services. Much of our findings are explained by the high skill-intensity and skill premium of some sectors, combined with a high sector premium. This stresses the importance of taking into account the educational level and sector of employment of households, as well as sector- and education-specific wages, when analyzing the effects of sectoral growth on inequality. We find that the sector premium is higher in sectors with a higher public share of GDP, which is related to the observations by Deaton and Dre`ze (2002) that public salaries grew twice as fast as the agricultural real wage in the 1990s. It is not at all clear that public wages are driving wage differentials between sectors or to what extent, but this may be an interesting question for further research. We do not conclude that India’s development strategy should focus solely on agricultural growth to reduce inequality. Of course, investing in agriculture to increase the sector’s labor productivity could benefit the agricultural workers. But demand growth for agricultural products will be lower than for other sectors, so there is limited scope for expansion. Therefore, our results confirm the importance of expansion in unskilled-labor-intensive manufacturing, as argued by Kochhar et al. (2006) and Krueger (2007). Inclusive growth requires employment opportunities for lowskilled workers outside agriculture, in both rural and urban India. As a more general conclusion, our results confirm that the sectoral composition of growth and the production technology of sectors matter for inequality, in line with Ravallion and Datt (1996), Datt and Ravallion (2002), and James and Khan (1997). However, we find that the skill-intensity of a sector is more important for inequality than the capital-labor ratio. This is in line with the increasing wage inequality that has accompanied globalization in many developing countries (Anderson, 2005; Goldberg & Pavcnik, 2007). Since countries can differ greatly in terms of the sectoral structure of growth and production technologies, one should question the relevance of cross-country studies that assume a common relationship between GDP growth and inequality (e.g., Barro, 2000). We also draw an important methodological lesson from this study, namely, that a social accounting matrix (SAM) with a single wage account will produce misleading results: the level of aggregation in the wage account matters for the distributive effects of growth that can be measured. The standard SAM for India has a single wage account and shows that between-group inequality is neutral to the sectoral structure of growth. Only once labor is divided according to educational level and sector of employment, it becomes clear that the sectoral structure of growth matters for inequality. Though SAMs for other countries usually have separate labor accounts for different educational levels and sometimes for agricultural versus non-agricultural labor (e.g., Khan & Thorbecke, 1989, for Indonesia; Jensen & Tarp, 2005, for Vietnam), the subdivision according to sector of employment is never made. Especially when households are grouped on the basis of geography or ethnicity, the link with their sector of employment is too indirect. Besides improving this link, another advantage of our extension is that it adds information on within-group inequality of earnings. For India, we find within-group inequality constitutes more than one-fourth of total earnings inequality. This study is a simple exercise, and the results are best considered as an indication of the relationships we are interested in. Despite the limitations of the SAM model, the results are intuitive and emphasize the importance of inequality among workers due to differences in educational attainment and sector of employment in India’s economic structure. The increasing capital-intensity of India’s industrial sector is a concern to the government (Government of India, 2008), but it should be clear that employment creation will not be enough to secure equitable growth. Rather, a focus on employment growth for low-skilled labor is essential for reducing inequality.

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