What is holding back productivity growth in India
13 Jan 2010-Oecd Journal: Economic Studies (Organisation for Economic Cooperation and Development (OECD))-Vol. 2009, Iss: 1, pp 1-22
TL;DR: The authors examines recent micro-evidence on the productivity of Indian firms, helping to explain why India's manufacturing sector has not performed as well as many observers expected, and suggests that much remains to be done to improve the strength and sustainability of India's development path.
Abstract: This article examines recent micro-evidence on the productivity of Indian firms, helping to explain why India’s manufacturing sector has not performed as well as many observers expected. A series of structural distortions are documented, all of which may depress the performance of manufacturing, and thus the economy as a whole. These distortions exist at multiple levels, and reflect long-standing problems with the reallocation of labour across sectors, the excessively small scale of firms, low firm turnover, poor product market integration, high industry concentration and persistent state ownership. Combined, these phenomena represent severe restraints on the level and growth of productivity in manufacturing, and suggest that much remains to be done to improve the strength and sustainability of India’s development path.
01 Jan 2000
TL;DR: In this paper, the authors compare the periods of rapid economic growth in China since 1978 and India since 1992 and show different patterns of development and structural change, showing that both countries experienced some of the advantages of relative economic backwardness and some aspects of the "fordist model of growth".
Abstract: The comparison of the periods of rapid economic growth in China since 1978 and India since 1992 markedly show different patterns of development and structural change. However, both countries experienced some of the advantages of “relative economic backwardness” and some aspects of the “fordist model of growth”. China had an anticipated and deeper structural change, spurred mainly by economic reforms and the growth of the internal market in the 1980s and since the mid-1990s by a very rapid penetration of its industrial products in the world market. However, a substantial part of its exports in medium and high tech sectors are due to joint- ventures with foreign multinationals. India had a more balanced structural change and a slower insertion in the world market, although some sectors, such as software, steel, automotive and pharmaceuticals are recently increasing their share in the world markets.
TL;DR: In this article, the authors consider the longer-term viability of the internationalization and success of Indian multinational enterprises and highlight the vulnerabilities of a growth-by-acquisition approach.
Abstract: This paper considers the longer-term viability of the internationalization and success of Indian multinational enterprises (MNEs). We apply the ‘dual economy’ concept (Lewis, Manch Sch 22(2):139–191, 1954) to reconcile the contradictions of the typical emerging economy, where a ‘modern’ knowledge-intensive economy exists alongside a ‘traditional’ resource-intensive economy. Each type of economy generates firms with different types of ownership advantages, and hence different types of MNEs and internationalisation patterns. We also highlight the vulnerabilities of a growth-by-acquisitions approach. The potential for Indian MNEs to grow requires an understanding of India’s dual economy and the constraints from the home country’s location advantages, particularly those in its knowledge infrastructure.
TL;DR: In this article, the authors collected establishment-based-freight-survey data from 432 establishments in Kerala, India and used these data to classify the establishments into 13 homogenous industry sectors; several freight generation models are developed for these sectors.
Abstract: Freight generation (FG) models are important to transportation authorities and planning agencies as they can be used to forecast local/regional/state/national freight movements for facility planning and evaluation of freight-specific investments. Compared to the modelling efforts in passenger transportation, freight transportation remains largely unexplored in developing economies like India mainly because of the absence of national wide commodity flow survey unlike in developed economies. In recognition of this, we collected establishment-based-freight-survey data from 432 establishments in Kerala, India. These data are used to classify the establishments into 13 homogenous industry sectors; several FG models are developed for these sectors. The relationships of annual freight production (FP) and attraction (FA) with establishment size variables (employment and area) were investigated with three modelling approaches. Firstly, a set of 52 practice-ready FG models are estimated using linear regression technique for establishments in each industry sector. Modelling results revealed that employment is a better representative of FP, whereas area represents FA better. The employment-based FP rates in Kerala are found to be lower than that in New York, much like what is observed in passenger transportation; passenger trip rates in developing economies are lower than that in developed countries. Secondly, FG rate tables and Nomograms are developed using Multiple Classification Analysis technique for all industry sectors considering employment and floor area as categories. These nomograms and FG rate tables may be used as planning tools for city developing agencies, while incorporating freight transportation in the overall planning process. Lastly, ANCOVA analyses is provided to assess the geographical disparities on FG and, thereby the model transferability. Study findings will be useful in developing policy guidelines for freight-specific investments, operational strategies, freight movement regulations, and taxation policies, etc. for Indian cities.
TL;DR: In this article, the authors evaluate and compare some aspects of the different growth patterns of the two economies analyzing in particular the relations between structural change and economic development, using three concepts: Gerschenkron's "relative economic backwardness", Valli's "Valli's 2002, 2005, 2009" and Syrquin's distinction between the productivity effect and reallocation effect.
Abstract: 1. Introduction China since 1978 and India since 1992 have passed through a phase of very rapid economic growth accompanied by very important structural changes in the productive systems and severe and largely unresolved social problems. The objective of this paper is to evaluate and compare some aspects of the different growth patterns of the two economies analyzing in particular the relations between structural change and economic development. In doing so, we will utilise three concepts: Gerschenkron's "relative economic backwardness" (Gerschenkron, 1962; Fua, 1980), "the fordist model of growth" (Valli, 2002, 2005, 2009) and Syrquin's distinction between the productivity effect and reallocation effect (Syrquin, 1986). The first concept is well known and stresses the fact that an emerging backward economy may benefit from some advantages, such as the adoption of modern technologies coming from more advanced countries and the possibility of transferring large masses of the labour force from low productivity sectors (agriculture and traditional tertiary activities) to sectors with higher productivity (industry and modern services). The second concept, which is not to be confused with the more general concept of "Fordism" of Gramsci or of the French regulation school, (3) is mainly associated to a phase of strong growth of some interlinked industrial and service sectors where scale economy and network economies are of crucial importance. The third concept is a useful device to decompose productivity growth and comes from a long and important tradition of studies on structural change and development carried on by authors such as Kuznets (1966), Chenery and Syrquin (Chenery et al., 1979; Chenery, Robinson, Syrquin, 1986; Syrquin, 1986; IMF, 2006). 2. The Third Wave of the Fordist Model of Growth The US experienced the first wave of the fordist model of growth for some decades following 1908. (4) West Europe, Japan and the four Asian tigers passed through their second wave in the 1950s and the 1960s. Since the late sixties, the US, Western Europe and Japan experienced a crisis of the fordist model and have entered a post-fordist phase. In contrast, China and India have entered the third wave of the fordist model of growth respectively in the 1980s and the 1990s, benefiting at the same time from some aspects of post-fordism and from several advantages of relative economic backwardness. In the US the crucial sectors of the fordist model of growth were the automobile industry with all its interlinked sectors (steel, oil, tyres, auto repair, construction of roads and motorways, etc.). When in 1908 the Ford motor corporation launched, as a mass production product, the new Model T, which was much less expensive than pre-existing cars, it greatly accelerated the demand and the diffusion of the automobiles in the US market, and stimulated a rapid expansion of the steel, tyre and oil industries, road building, etc. In the 1980s in China, in a very different economic and socio-political context, the crucial sectors of the fordist model of growth were instead the electrical domestic appliances and their interlinked sectors (steel, plastics, electricity, etc.). In the 1990s in China there was the addition of microelectronics, telecommunication and energy. Finally, since the 2000s, there also has been a rapid growth in the production of industrial vehicles, motorcycles and automobiles. In India, since 1992, machinery, household electric appliances, steel, pharmaceuticals, and, more recently, software services, telecommunication, motorcycles, automobiles and air communication have been the crucial dynamic sectors. 3. Structural Transformation Most empirical analyses about structural transformation have two severe shortcomings. They often only consider the changes between the three great productive branches: agriculture, industry, services. However, also the changes among the different sectors of industry and services have great importance. …
TL;DR: This article reviewed research that uses longitudinal microdata to document productivity movements and examine factors behind productivity growth, including the dispersion of productivity across firms and establishments, the persistence of productivity differentials, the consequences of entry and exit, and the contribution of resource reallocation across firms to aggregate productivity growth.
Abstract: This paper reviews research that uses longitudinal microdata to document productivity movements and to examine factors behind productivity growth. The research explores the dispersion of productivity across firms and establishments, the persistence of productivity differentials, the consequences of entry and exit, and the contribution of resource reallocation across firms to aggregate productivity growth. The research also reveals important factors correlated with productivity growth, such as managerial ability, technology use, human capital, and regulation. The more advanced literature in the field has begun to address the more difficult questions of the causality between these factors and productivity growth.
TL;DR: This article used micro data on manufacturing establishments to quantify the potential extent of misallocation in China and India compared to the U.S. They measured sizable gaps in marginal products of labor and capital across plants within narrowly-defined industries in both countries.
Abstract: Resource misallocation can lower aggregate total factor productivity (TFP). We use micro data on manufacturing establishments to quantify the potential extent of misallocation in China and India compared to the U.S. Compared to the U.S., we measure sizable gaps in marginal products of labor and capital across plants within narrowly-defined industries in China and India. When capital and labor are hypothetically reallocated to equalize marginal products to the extent observed in the U.S., we calculate manufacturing TFP gains of 30-50% in China and 40-60% in India.
TL;DR: Bosworth et al. as mentioned in this paper examined sources of economic growth in China and India, comparing and contrasting their experiences over the past 25 years, and found that China was about two-thirds of the estimate for India.
Abstract: Through most of the twentieth century, only those in the high-income industrial countries, less than one-fifth of the world’s population, have enjoyed the fruits of economic well-being. However, since 1980, China and India have achieved remarkable rates of economic growth and poverty reduction— and taken together, these countries comprise over a third of the world’s population. The emergence of China and India as major forces in the global economy has been one of the most significant economic developments of the past quarter century. This paper examines sources of economic growth in the two countries, comparing and contrasting their experiences over the past 25 years. In many respects, China and India seem similar. Both are large geographically and have enormous populations that remain very poor. In 1980, both had extremely low per capita incomes. The World Bank and the Penn World Tables show GDP per capita for India was roughly equal to the World Bank’s 1980 average for all low-income countries, while per capita GDP for China was about two-thirds of the estimate for y Barry Bosworth is Senior Fellow and holds the Robert V. Roosa Chair, Brookings Institu
TL;DR: In this paper, the authors show that the assumption of optimal resource allocation fails and that the heterogeneity of rates of return to the same factor within a single economy, a heterogeneity that dwarfs the cross-country heterogeneity in the economy-wide average return, poses problems for old and new growth theories alike.
Abstract: Growth theory has traditionally assumed the existence of an aggregate production function, whose existence and properties are closely tied to the assumption of optimal resource allocation within each economy. We show extensive evidence, culled from the micro-development literature, demonstrating that the assumption of optimal resource allocation fails radically. The key fact is the enormous heterogeneity of rates of return to the same factor within a single economy, a heterogeneity that dwarfs the cross-country heterogeneity in the economy-wide average return. Prima facie, we argue, this evidence poses problems for old and new growth theories alike. We then review the literature on various causes of this misallocation. We go on to calibrate a simple model which explicitly introduces the possibility of misallocation into an otherwise standard growth model. We show that, in order to match the data, it is enough to have misallocated factors: there also needs to be important fixed costs in production. We conclude by outlining the contour of a possible non-aggregate growth theory, and review the existing attempts to take such a model to the data.
TL;DR: The authors used data from the Census Bureau's Longitudinal Research Database (LRB) to describe the dynamics of geographic concentration in U.S. manufacturing industries, finding that the location choices of new firms play a deagglomerating role, whereas plant closures have tended to reinforce agglomeration.
Abstract: This paper uses data from the Census Bureau's Longitudinal Research Database to describe the dynamics of geographic concentration in U.S. manufacturing industries. Agglomeration results from a combination of the mean reversion and randomness in the growth of state-industry employment. Although industries' agglomeration levels have declined only slightly over the last quarter century, we find a great deal of movement for many geographically concentrated industries. We decompose aggregate concentration changes into portions attributable to plant births, expansions, contractions, and closures. We find that the location choices of new firms play a deagglomerating role, whereas plant closures have tended to reinforce agglomeration.