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Investment climate and total factor productivity in manufacturing: Analysis of Indian states

01 Jan 2004-Research Papers in Economics (New Delhi: Indian Council for Research on International Economic Relations (ICRIER))-
TL;DR: In this paper, the influence of investment climate on the levels of total factor productivity (TFP) in the organised manufacturing sector across the major Indian states was investigated and data from Annual Survey of Induatries (ASI) was used and multilateral TFP indices for the total registered manufacturing sector in all the major states for the period 1980-2000 calculated.
Abstract: Liberalisation initiatives have been taken by India with a view to improve the efficiency of manufacturing industries and achieving faster GDP growth. The present paper investigates the influence of Investment Climate (IC) on the levels of total factor productivity (TFP) in the organised manufacturing sector across the major Indian states. Data from Annual Survey of Induatries (ASI) is used and multilateral TFP indices for the total registered manufacturing sector in all the major states for the period 1980-2000 calculated [WP 127].

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Citations
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BookDOI
TL;DR: In this article, the impact of infrastructure quality on the total factor productivity (TFP) of African manufacturing firms is evaluated based on 10 different productivity measures, and the results are robust once controlled for observable fixed effects (red tape, corruption and crime, finance, innovation and labor skills).
Abstract: This paper provides a systematic, empirical assessment of the impact of infrastructure quality on the total factor productivity (TFP) of African manufacturing firms. This measure is understood to include quality in the provision of customs clearance, energy, water, sanitation, transportation, telecommunications, and information and communications technology (ICT). Microeconometric techniques to investment climate surveys (ICSs) of 26 African countries are carried out in different years during the period 2002–6, making country-specific evaluations of the impact of investment climate (IC) quality on aggregate TFP, average TFP, and allocative efficiency. For each country the impact is evaluated based on 10 different productivity measures. Results are robust once controlled for observable fixed effects (red tape, corruption and crime, finance, innovation and labor skills, etc.) obtained from the ICSs. African countries are ranked according to several indices: per capita income, ease of doing business, firm perceptions of growth bottlenecks, and the concept of demeaned productivity (Olley and Pakes 1996). The countries are divided into two blocks: high-income-growth and low-income-growth. Infrastructure quality has a low impact on TFP in countries of the first block and a high (negative) impact in countries of the second. There is significant heterogeneity in the individual infrastructure elements affecting countries from both blocks. Poor-quality electricity provision affects mainly poor countries, whereas problems dealing with customs while importing or exporting affects mainly faster-growing countries. Losses from transport interruptions affect mainly slower-growing countries. Water outages affect mainly slower-growing countries. There is also some heterogeneity among countries in the infrastructure determinants of the allocative efficiency of African firms.

131 citations

01 Jan 2009
TL;DR: In this article, the impact of infrastructure quality on the total factor productivity (TFP) of African manufacturing firms was evaluated using 10 different productivity measures and found that infrastructure quality has a low impact on TFP in countries of the first block and a high (negative) impact in the second block.
Abstract: This paper provides a systematic, empirical assessment of the impact of infrastructure quality on the total factor productivity (TFP) of African manufacturing firms. This measure is understood to include quality in the provision of customs clearance, energy, water, sanitation, transportation, telecommunications, and information and communications technology (ICT). We apply microeconometric techniques to investment climate surveys (ICSs) of 26 African countries carried out in different years during the period 2002–6, making country‐specific evaluations of the impact of investment climate (IC) quality on aggregate TFP, average TFP, and allocative efficiency. For each country we evaluated this impact based on 10 different productivity measures. Results are robust once we control for observable fixed effects (red tape, corruption and crime, finance, innovation and labor skills, etc.) obtained from the ICSs. We ranked African countries according to several indices: per * This study is part of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world’s knowledge of physical infrastructure in Africa. Financing for AICD is provided by a multi-donor trust fund to which the main contributors are the Department for International Development (United Kingdom), the Public Private Infrastructure Advisory Facility, Agence Francaise de Developpement, and the European Commission. Inquiries concerning the availability of datasets should be directed to vfoster@worldbank.org. The authors are indebted to Vivien Foster and her group of researchers at the World Bank for providing data sets and further insight into the African region. All errors in this report are the sole responsibility of the authors. A version of this paper is available in the Policy Research Working Paper Series of the World Bank. † Telefonica Chair of Economics of Telecommunications, Department of Economics, Universidad Carlos III de Madrid; alvaroe@eco.uc3m.es The World Bank and the University of California, San Diego; jguasch@worldbank.org. § Department of Economics, Universidad Carlos III de Madrid; jpizquie@eco.uc3m.es. CHAPTER II ‐ ASSESSING THE IMPACT OF INFRASTRUCTURE QUALITY ON FIRM PRODUCTIVITY IN AFRICA 2 capita income, ease of doing business, firm perceptions of growth bottlenecks, and the concept of demeaned productivity (Olley and Pakes 1996). We divided countries into two blocks: high‐income‐ growth and low‐income‐growth. Infrastructure quality has a low impact on TFP in countries of the first block and a high (negative) impact in countries of the second. We found heterogeneity in the individual infrastructure elements affecting countries from both blocks. Poor‐quality electricity provision affects mainly poor countries, whereas problems dealing with customs while importing or exporting affects mainly faster‐growing countries. Losses from transport interruptions affect mainly slower‐growing countries. Water outages affect mainly slower‐growing countries. There is also some heterogeneity among countries in the infrastructure determinants of the allocative efficiency of African firms.

66 citations


Cites background from "Investment climate and total factor..."

  • ...(see Veeramani and Goldar, 2004, for other use of industry-region averages with IC variables)....

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Journal Article
TL;DR: In this paper, an attempt at addressing this issue by analyzing an unpublished data set on the investment in computers and software at the industry level made available by the CSO is made, and the study finds that low level of IT investment intensity in the manufacturing sector notwithstanding, IT investment does have a positive and significant impact on both partial and total factor productivity.
Abstract: While India's remarkable performance in IT software and service exports may be inspirational for other Indian industries and more so for other countries in the south, the moot question is how has India fared in terms of harnessing this technology for enhancing manufacturing productivity. This paper is an attempt at addressing this issue by analyzing an unpublished data set on the investment in computers and software at the industry level made available by the CSO. The study finds that low level of IT investment intensity in the manufacturing sector notwithstanding, IT investment does have a positive and significant impact on both partial and total factor productivity. The findings of the paper suggest that in a context wherein the policy makers are concerned with low levels of growth in manufacturing output and productivity, policy measures and institutional interventions towards promoting IT diffusion in the manufacturing sector is likely to give rich dividends.

54 citations


Cites background from "Investment climate and total factor..."

  • ...This data, though 5 See Veeramani and Goldar (2004) and Banga and Goldar (2004) for application of the same methodology in Indian context....

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  • ...8 See Banga and Goldar (2004) 29 Total Persons engaged (L), that includes both workers and supervisory and managerial staff in the sector as reported by ASI is taken to be the measure for labour use in the manufacturing sector....

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  • ...5 See Veeramani and Goldar (2004) and Banga and Goldar (2004) for application of the same methodology in Indian context....

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  • ...Based on the above hypotheses the following model is specified: it SIZE it KLINT it ITINTa it LPROD ++++= βββ )ln( 3 )ln( 2 )ln( 1 )ln( (1a) Where; LPROD is the measure for labour productivity; (See Appendix for method of variable construction) ITINT is IT Investment Intensity; KLINT is capital intensity in production; SIZE is the average size of a factory; SKILLINT is the measure of skill intensity in the industry....

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  • ...See Indjikian and Siegel (2005) for a recent and detailed survey....

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Posted Content
TL;DR: In this article, an attempt to interpret inter-state differences in productivity movements in organized manufacturing sector, in a larger perspective of employment and output trends, was made, where the time-span of the study is 1980-81 to 2000-01 and it encompasses 10 major states of India.
Abstract: Industrial performance of various states needs to be viewed in totality, i.e, with respect to growth of output, employment and productivity. Moreover, productivity levels are as important as productivity growth trends, as both are pertinent in the convergence process. This study is an attempt to interpret inter-state differences in productivity movements in organized manufacturing sector, in a larger perspective of employment and output trends. The time-span of the study is 1980-81 to 2000-01 and it encompasses 10 major states of India. The study empirically confirms the existence of inter-state differences in productivity levels and growth rates. It points out that states, such as, Bihar and West Bengal are diverging away from rather than converging to the growth rates of output of organized manufacturing sector at the national level. Though productivity growth in Bihar appears to be high, it has been mainly achieved by joblessness. Madhya Pradesh and Rajasthan, which have been considered as BIMARU states, seem to be good performers from a wider perspective and show the promise to get themselves rid of their economically backward status.

45 citations

Journal ArticleDOI
TL;DR: In this article, the authors employ a "wedge" methodology based on the first order conditions of a multi-sector neoclassical growth model to ascertain the output and factor market sources of the divergent economic performances.

25 citations

References
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Journal ArticleDOI
TL;DR: This paper showed that differences in physical capital and educational attainment can only partially explain the variation in output per worker, and that a large amount of variation in the level of the Solow residual across countries is driven by differences in institutions and government policies.
Abstract: Output per worker varies enormously across countries. Why? On an accounting basis, our analysis shows that differences in physical capital and educational attainment can only partially explain the variation in output per worker--we find a large amount of variation in the level of the Solow residual across countries. At a deeper level, we document that the differences in capital accumulation, productivity, and therefore output per worker are driven by differences in institutions and government policies, which we call social infrastructure. We treat social infrastructure as endogenous, determined historically by location and other factors captured in part by language.

7,208 citations

Journal ArticleDOI
TL;DR: This article showed that the differences in capital accumulation, productivity, and therefore output per worker are driven by differences in institutions and government policies, which are referred to as social infrastructure and called social infrastructure as endogenous, determined historically by location and other factors captured by language.
Abstract: Output per worker varies enormously across countries. Why? On an accounting basis our analysis shows that differences in physical capital and educational attainment can only partially explain the variation in output per worker—we find a large amount of variation in the level of the Solow residual across countries. At a deeper level, we document that the differences in capital accumulation, productivity, and therefore output per worker are driven by differences in institutions and government policies, which we call social infrastructure. We treat social infrastructure as endogenous, determined historically by location and other factors captured in part by language. In 1988 output per worker in the United States was more than 35 times higher than output per worker in Niger. In just over ten days the average worker in the United States produced as much as an average worker in Niger produced in an entire year. Explaining such vast differences in economic performance is one of the fundamental challenges of economics. Analysis based on an aggregate production function provides some insight into these differences, an approach taken by Mankiw, Romer, and Weil [1992] and Dougherty and Jorgenson [1996], among others. Differences among countries can be attributed to differences in human capital, physical capital, and productivity. Building on their analysis, our results suggest that differences in each element of the production function are important. In particular, however, our results emphasize the key role played by productivity. For example, consider the 35-fold difference in output per worker between the United States and Niger. Different capital intensities in the two countries contributed a factor of 1.5 to the income differences, while different levels of educational attainment contributed a factor of 3.1. The remaining difference—a factor of 7.7—remains as the productivity residual. * A previous version of this paper was circulated under the title ‘‘The Productivity of Nations.’’ This research was supported by the Center for Economic Policy Research at Stanford and by the National Science Foundation under grants SBR-9410039 (Hall) and SBR-9510916 (Jones) and is part of the National Bureau of Economic Research’s program on Economic Fluctuations and Growth. We thank Bobby Sinclair for excellent research assistance and colleagues too numerous to list for an outpouring of helpful commentary. Data used in the paper are available online from http://www.stanford.edu/,chadj.

6,454 citations

Posted Content
TL;DR: In this article, the authors investigate whether the industrial relations climate in Indian States has affected the pattern of manufacturing growth in the period 1958-92 and show that pro-worker amendments to the Industrial Disputes Act are associated with lowered investment, employment, productivity and output in registered manufacturing.
Abstract: This paper investigates whether the industrial relations climate in Indian States has affected the pattern of manufacturing growth in the period 1958-92. We show that pro-worker amendments to the Industrial Disputes Act are associated with lowered investment, employment, productivity and output in registered manufacturing. Regulating in a pro-worker direction is also associated with increases in urban poverty. This suggests that attempts to redress the balance of power between capital and labour can end up hurting the poor.

1,110 citations


"Investment climate and total factor..." refers background or methods in this paper

  • ...…Nadu Karnataka Kerala Madhya Pradesh Rajasthan -2 -2 -1 -1 -1 -1 Assam Bihar Haryana Jammu and Kashmir Punjab Uttar Pradesh 0 0 0 0 0 0 Source: Besley and Burgess (2002) Making use of the variation in labor regulation across states and overtime, for the period 1958-1992, the regression…...

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  • ...…-2 -1 -1 -1 -1 Assam Bihar Haryana Jammu and Kashmir Punjab Uttar Pradesh 0 0 0 0 0 0 Source: Besley and Burgess (2002) Making use of the variation in labor regulation across states and overtime, for the period 1958-1992, the regression analysis of Besley and Burgess (2002) indicated the following....

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  • ...Aghion et al (2003), extending the regression analysis of Besley and Burgess (2002), observed the following: • States with more pro-worker regulation experienced less growth in output, employment, labor productivity and TFP for the 1980-1997 period....

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  • ...Besley and Burgess (2002) read the text of each amendment over the period of 1958-1992 and coded each amendment as pro-worker (+1), neutral (0) or pro-employer (-1)....

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  • ...Orissa 4 2 1 1 Andhra Pradesh Tamil Nadu Karnataka Kerala Madhya Pradesh Rajasthan -2 -2 -1 -1 -1 -1 Assam Bihar Haryana Jammu and Kashmir Punjab Uttar Pradesh 0 0 0 0 0 0 Source: Besley and Burgess (2002) Making use of the variation in labor regulation across states and overtime, for the period 1958-1992, the regression analysis of Besley and Burgess (2002) indicated the following....

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Posted Content
TL;DR: In this paper, the authors used data on the Indian rural branch expansion program to provide empirial evidence on the issue of lack of access to finance, which is often cited as a key reason why poor people remain poor.
Abstract: Lack of access to finance is often cited as a key reason why poor people remain poor. This paper uses data on the Indian rural branch expansion program to provide empirial evidence on this issue. Between 1977 and 1990, the Indian Central Bank mandated that a commercial bank can open a branch in a location with one or more bank branches only if it opens four in locations with no bank branches. We show that between 1977 and 1990 this rule caused banks to open relatively more rural branches in Indian states with lower initial financial development. The reverse is true outside this period. We exploit this fact to identify the impact of opening a rural bank on poverty and output. Our estimates suggest that the Indian rural branch expansion program significantly lowered rural poverty, and increased non-agricultural output.

1,006 citations


"Investment climate and total factor..." refers background in this paper

  • ...Burgess and Pande (2003) analyzed the impacts of rural branch expansion of banks in India, using panel data of the 16 main Indian states for the period 1961-2000....

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Posted Content
TL;DR: In this paper, the authors used panel data on sixteen main Indian states from 1958 to 1992 to consider whether the large volume of land reforms as have been legislated have had an appreciable impact on growth and poverty.
Abstract: In recent times there has been a renewed interest in relationships between redistribution, growth and welfare. Land reforms have been central to strategies to improve the asset base of the poor in developing countries though their effectiveness has been hindered by political constraints on implementation. In this paper we use panel data on the sixteen main Indian states from 1958 to 1992 to consider whether the large volume of land reforms as have been legislated have had an appreciable impact on growth and poverty. The evidence presented suggests that land reforms do appear to be associated with poverty reduction.

505 citations


"Investment climate and total factor..." refers background or methods in this paper

  • ...Aghion et al (2003), extending the regression analysis of Besley and Burgess (2002), observed the following:...

    [...]

  • ...Making use of the variation in labor regulation across states and overtime, for the period 1958-1992, the regression analysis of Besley and Burgess (2002) indicated the following....

    [...]

  • ...Besley and Burgess (2002) read the text of each amendment over the period of 1958-1992 and coded each amendment as pro-worker (+1), neutral (0) or pro-employer (-1)....

    [...]

  • ...Orissa 4 2 1 1 Andhra Pradesh Tamil Nadu Karnataka Kerala Madhya Pradesh Rajasthan -2 -2 -1 -1 -1 -1 Assam Bihar Haryana Jammu and Kashmir Punjab Uttar Pradesh 0 0 0 0 0 0 Source: Besley and Burgess (2002) Making use of the variation in labor regulation across states and overtime, for the period 1958-1992, the regression analysis of Besley and Burgess (2002) indicated the following....

    [...]

  • ...West Bengal Maharashtra Gujarat Orissa 4 2 1 1 Andhra Pradesh Tamil Nadu Karnataka Kerala Madhya Pradesh Rajasthan -2 -2 -1 -1 -1 -1 Assam Bihar Haryana Jammu and Kashmir Punjab Uttar Pradesh 0 0 0 0 0 0 Source: Besley and Burgess (2002)...

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