Abstract In comparable random samples of manufacturing businesses drawn from the two countries, Chinese establishments are found to have higher total factor productivity on the average than their Indian counterparts. Controlling for initial size, age, and line of industry, the average employment growth rate is higher for Indian establishments. Chinese plants grow faster in value added terms, nonetheless. This is mainly because the average net investment rate in fixed assets is higher in Chinese businesses. To a lesser extent, it is also because productivity grows faster on the average in Chinese plants. Partly because of this, the aggregate productivity growth rate that we compute industry by industry is larger for the Chinese sample. A second reason why the aggregate productivity growth rate is higher for the China sample is that allocative efficiency gains are larger in Chinese industry. By this we mean that market shares are reallocated from less productive plants to the more productive more rapidly (or steeply) in the Chinese sample. This is consistent with another finding: catch up effects and life cycle effects in productivity and growth, are stronger in the Chinese sample than in the Indian sample. Lastly, such key elements of the business climate as labor market flexibility and access to finance are major sources of the productivity and growth gaps between Chinese and Indian plants. If nothing else mattered, the average Chinese businesses would be more productive and would grow faster than its Indian counterparts on account of business climate differences between the two countries. This is not so much because business climate indicators are better in China than in India as because the marginal return to improvements in indicators is higher in China.
If [by the time China’s saving rates start to fall] India has completed the second generation reforms, built up its infrastructure and fully integrated itself into the world economy, we might find that the tortoise overtakes the hare… This race between the two Asian giants is set to be the most dramatic event of this century. (Deepak Lal in Business Standard, March 15, 2005)
1. Introduction China’s and India’s are among the largest economies in the world today. They have also been among the fastest growing over the last two decades and a half. They both entered the 1980s at comparable levels of per capita income following three decades of growth-China at an average rate of 4.4 percent per annum, and India at a rate of 3.75 percent (Srinivasan, 2003).1 Since then China’s economy has taken off to a state of unprecedented growth that averaged 10.1 percent per annum in the 1980s, 10.3 per cent per annum in the 1990s, and has yet to show any sign of slowing down. India’s GDP growth has also picked up to an averaged 5.6 per cent a year in the 1980s, 6 percent per annum in the 1990s’, and even higher since. Although India’s growth rate has been remarkably high by any standard, the sustained growth gap between the two countries has intrigued observers, especially given what seemed to be significant similarities in their initial conditions. According to Srinivasan (2003), India’s GDP per capita stood at 853 in 1990 international dollars in 1973 as compared to China’ 839. The divergence in growth rates since then has created a widening income gap in China’s favor, which stood at 3,117 dollars versus 1746 dollars by 1998 (Srinivansan, 2003). Figure 1 shows the evolution of the gap in Purchasing Power Parity terms computed from data in the World Bank’s World Development Indicators.
In this paper we analyze data from comparable samples of manufacturing businesses drawn from the two countries in order to help shade light on two complementary questions: Why is per capita income so much higher today in China than in India? And why is China’s GDP growing so much faster? One hypothesis is that China’s better performance on both scores reflects differences in the quality of institutions or in the immediate policy environment in which businesses operate. Another is that the contrast is partly a consequence of China’s earlier investments in superior physical infrastructure paying off. These are no doubt macro economic issues in the investigation of which the analysis of available national aggregate data has yet to be brought to bear. At the same time a key limitation of aggregate analysis has to be recognized in this particular context. This is that, at this stage, available time series are bound to be too short on key variables for problems of econometric identification to be resolved satisfactorily based solely on the observation of cross-country differences in national or sector aggregates. To this should be added what seems to be widespread skepticism about the comparability of China’s national account aggregates with India’s.2
Part of the remedy to this should be the exploitation of sub national variation in economic performance and its determinants as an additional means of identification. An obvious instance or component of such a strategy of is the analysis of firm level data, which are regularly generated in both countries by a variety of agencies. The data on the analysis of which we report in this paper come from business surveys that the World Bank sponsored in the two countries in 2003. The India survey covered 1860 manufacturing establishments sampled from the country’s top 40 industrial cities and its major exporting industries. The China survey covered 2400 enterprises sampled from 18 cities and 5 regions, and a wider set of industries including most of those covered by the India survey. Both surveys include production, employment and investment data on each business on annual basis for the three years leading to the year of survey. This is in addition to data on the local business and policy environment of each establishment as of the survey year, including indicators of the quality of the financial, regulatory, infrastructural, and labor market settings in which it operated at the time of the survey.
What does information of this kind have to do with the (macro economic) questions of growth and development we just raised? Per capita income is probably used far more often as indicator of wellbeing than anything else, but one obvious interpretation of it is also as an index aggregate labor productivity, as is the case in, for example, Hall and Jones (1999). Thus the fact that it is presently twice in China of what it is India means that China’s labor productivity is at least higher than India’s. In general this should mean that output per worker is greater in the average Chinese firm than it is in its Indian counterpart, either because production is more capital intensive in the Chinese firm, or because total factor productivity is higher, or both. Likewise, China’s higher GDP growth rates should be reflected in faster growth of the average Chinese firm or faster allocative productivity gains in China’s industries.3 Like its aggregate analogue, growth at the firm level can only originate in one of two sources, namely, growth in factor inputs, and growth in their productivity. If the average Chinese firm is indeed growing faster than the average Indian firm, then it must be investing at a higher rate in physical or human capital, or its net job creation rate must be higher, or it must have greater total factor productivity growth.
Our analysis is focused on two issues. The first concerns whether or not the performances of Chinese and Indian firms differ significantly in terms of productivity and growth, as should be expected from the macro-economic performance contrast between the two countries. Secondly, assuming that such differences do exist, how far can they be attributed to differences in “business environment”? The first issue can be broken down into a series of subsidiary questions the answers to which describe the linkages between business climate and firm level determinants of aggregate productivity and growth. These include, first, whether or not there is significant productivity gap between Chinese and Indian firms as the per capita income gap between the two countries suggests. Secondly, do Chinese firms grow faster as should be expected from China’s better GDP growth performance? Third, assuming they do, what are the proximate sources of their faster growth: is it that they invest at a higher rate, or that they are getting more efficient in factor use more quickly, or some combination of both?
Since the data we analyze here are entirely on manufacturing firms, our answers to these questions are most pertinent to the comparative performance of the manufacturing sectors of the two economies. However, given the weight of manufacturing in each economy, and given that China has done particularly well in this sector compared to India, knowledge of the factors behind the contrast between the performance of Chinese manufacturers and their Indian counterparts should help us better understand of the relative performance of the broader national economies. In the context of manufacturing, the projection of the performance indicators of the average firm to its aggregate analogues would be strictly valid only on two assumptions. One is that the structure of manufacturing production is the same between the two countries. The second is that the equilibrium size distribution of plants within individual industries is the same for both countries. Since we are working with samples of observations from the selected industries rather than census data on all sectors, we have no way of testing either of these assumptions. We have nonetheless sought to make our conclusion robust to the possible failure of the first assumption by confining our comparison of businesses to industries that are common to both countries.
The actual size distribution of businesses could vary between the two countries in any of the industries from which our data are drawn as result of policy induced distortions, or as a consequence of differences in the stages of industry evolution observed at the time of the surveys. This in turn should drive a wedge between the (sample) average firm’s performance we observe and the aggregate performance we ultimately care about-that is, between (sample) average firm level productivity and (aggregate) industry productivity, on the one hand, and between the average firm growth rate and the industry growth rate, on the other. In order to eliminate this distortion we compute mean firm level performance indicators conditional on firm size and firm age. By helping us to control for differences in catch up and life cycle effects stemming from differences in the stages of industry evolution between the two countries, this should help us get at true industry effects in performance gaps. In addition we computed market share weighted (sample) mean levels and growth rates of productivity in order to separate the dynamics of firm level productivity from intra-industry reallocation effects on aggregate productivity, which, together with the relative strength of catch up and life cycle effects, provide a picture of the comparative dynamism of industry in the two countries.
To highlight our main results, we find that output per worker is higher for the China sample than for India sample. This is in part because the average Chinese plant is more capital intensive. It is partly because total factor productivity is higher in for the China sample. The average Chinese establishment is also about the same age as its Indian counterpart, but much larger by all three measures of scale, that is , sales revenue, fixed assets and employment. This is consistent with a second set of results, namely, that output and fixed assets growth rates are higher for the Chinese sample than for the Indian sample, while employment growth rates are higher for the Indian sample.4 Third, of the two sources of the growth advantage of Chinese sample, higher rate of investment is by far the more important. It accounts for more than four times the growth advantage explained by faster TFP growth. China’s faster productivity growth at the firm level has meant that the growth rate of aggregate (or industry level) productivity has been higher for Chinese sample. This effect of on the growth rate of aggregate productivity has been reinforced by allocative efficiency being higher in the China sample. Consistent with this catch up and life cycle effects are found to be stronger in the Chinese sample.
To investigate how far differences in business environment could explain the first three sets of results we estimate various firm performance equations. The main business climate influences in the TFP gap between Chinese and Indian firms are differences labor market flexibility, in access to finance, and in levels of skill and technology. Differences in access to finance and in skills and technology are also powerful influences in the growth performance gap between the two groups. This finding is consistent with results of other cross-country firm level studies based on the World Bank’s investment climate surveys including Dollar et al. (2005), Eifert et al. (2005)…. A novelty of our estimation strategy compared to existing work is that we allow for the possibility that the marginal effects of individual elements of business climate could vary between the two countries even if all business climate indicators had assumed the same values in both countries. . It turns out that while the better performance of Chinese firms in our sample is partly on account of “better business environment”, this less because China’s business climate indicators are better than India’s than because the marginal effect of a better business climate on firm productivity or on firm growth is higher in China.
The rest of the paper is organized as follows. We lay out the empirical framework of our analysis in the next section. We discuss our data in Section 3 along with the econometric issues arising from them. Our findings are reported in detail in Section 4. We conclude in Section 5.
2. Empirical Framework
In order to address the questions we have posed we have carried out three distinct analytic tasks. The first of these concerns the measurement of relative performance of Chinese and Indian firms. The second involves accounting for observed performance gaps between the two groups of firms, in terms of their proximate causes (or components). The third task is one of explaining the gaps in the sense of identifying their ultimate causes, of which one set, we hypothesize, is business environment.
Measuring and accounting for performance gaps Our basic measures of performance are plant level productivity and the plant level rate of output growth. The reason we have chosen these particular indicators is that they are the micro-economic analogues of the two main indicators of aggregate performance, namely, per capita income and the rate of GDP growth. Knowledge o the factors behind the relative productivity and growth performance Chinese and Indian producers should help us understand as to why per capita income and the GDP growth rate are higher in China than in India.
Our approach to accounting for performance gaps is also strictly analogues to established practices in aggregate growth accounting and accounting for cross-country aggregate productivity gaps. Although the latter is relatively uncommon, it is levels analogue of growth accounting and, as shown in Hall and Jones (1999), no less illuminating a tool of analysis of international inequality. In adapting it to our setting, a natural plant level analogue for per capita income is output per worker. The problem with this particular productivity measure is that it is a function of factor proportions, which, depending on relative factor prices could vary between Chinese firms and Indian firms even when they produce an identical product. As a rule output per worker should be higher where capital input per worker is also higher. Since there is nothing inherently good or bad about greater capital intensity from the point of view of efficiency in resource use at the firm level, however, a meaningful comparison of labor productivity between groups of firms should make allowances for possible differences in factor proportions. For this reason we use as our productivity measure total factor productivity (TFP), rather than output per worker.
As already pointed out, the fact that Chinese firms are growing faster on the average than Indian firms in our dataset poses the question of whether this is because they are investing at a higher rate, or because they are getting ever more productive. This was the very question that Young (1994, 1995) raised in the context of the exceptional growth performance of what were then known as the “Newly Industrialized Countries” of Hong Kong, Singapore, South Korea, and Taiwan. The answer Young provided then was that, contrary to what then seemed to be the conventional wisdom, faster accumulation and “static neoclassical gains from sectoral reallocation”, rather than rapid TFP growth accounted for “the lion’s share of the East Asian growth miracle.” Young (2003) draws more or less the same conclusion about the growth performance of the Chinese economy over the period 1978-1998: in China too, TFP growth was a far less important source of GDP growth than accumulation and gains from the reallocation of manpower and capital between sectors. Our results basically confirm this latter finding based on firm level data in as far as investment in fixed assets turns out to be far more important than TFP growth as a source of the growth advantage of Chinese firms over Indian firms in our sample.5
Firm level productivity, allocative efficiency gains, and aggregate productivity growth That said higher productivity growth remains to be one source of the growth advantage of Chinese firms over their Indian counterparts. And while we lack the data to test Young’s other hypothesis that inter-sector reallocation of manpower and capital has been a significant source of China’s GDP growth, we do find that intra industry allocation of market share from less productive firms to the more productive has been a more important source of productivity growth in the China sample than in the India sample our dataset. We draw this conclusion based on an Olley-Pakes cross-sectional decomposition of industry level productivity growth for each sample.6 The decomposition expresses the average productivity, , of a given industry in year as the weighted mean of establishment level productivities, , with establishment market share, , as weights where indexes establishments. The decomposition can alternatively be written as
(1) , where
letters with upper bars represent unweighted industry means of variables. In other words the industry-level average productivity is the sum of the (unweighted) average of establishment level productivity and the sample covariance between establishment productivity and market share. A positive covariance term implies that more productive firms have higher market shares. Considering changes over time, this means that it is not necessary that increases for average industry productivity to grow. can increase even in the absence of significant changes in as a result of the reallocation of market share in favor of more productive firms, which measures of industry deregulation or market liberalization are often found to lead to. In practice actual change in industry productivity is often a result of a bit of both, and one objective of our analysis has been to assess the relative weight of the two elements as potential sources of the productivity growth gaps we observed between our China and India samples.
Explaining performance gaps: the role of business climate Output per worker is higher in the average Chinese firm than in its Indian counterpart for two reasons. One is because Chinese plants have more capital per worker. The other is because their total factor productivity is higher. Likewise Chinese firms grow faster on the average in terms of output partly because they invest at a higher rate and partly because of their faster TFP growth. But why are TFP and its rate of growth higher for the average Chinese plant? And why do Chinese firms invest at higher rates? A popular hypothesis is that part of the answer lies in China’s allegedly superior physical infrastructure. A second common hypothesis is that other aspects of the business environment significantly differ between the two countries, and have on balance influenced economic outcomes in China’s favor. Differences seem to be particularly pronounced between the two sets of firms in terms of access to finance, labor market flexibility, the predictability of the regulatory environment and the level of skills and technology. In order to test these hypotheses we have estimated performance equation on each country dataset whereby the performance, , of firm , is assumed to depend on a vector of business climate variables, , a vector of firm level controls and industry characteristics,, and an iid random error term, summing up a set of unobservable influences assumed to be orthogonal to firm characteristics and business environment. The performance equations we have estimated are of the form
where indexes China or India, are constants, and is random error terms assumed to be iid and orthogonal to and . It is possible, but not necessarily the case that the coefficients of the two versions of equation (2) are identical. In particular, it could be that, meaning that the various elements of business environment we just listed would have the same marginal effect in China as India, or improvements in business climate would have the same marginal “rate of return” in both countries. In that case the average performance gap between the two countries on account of differences in business climate would simply be the differences in business climate indicators scaled up by the common marginal rate of return. However, it is also possible that marginal rates of return are different for the two countries. We have therefore chosen to treat the equality of and as a testable proposition rather than an assumption of our analysis. Because we find that the two sets of coefficients are in fact different, we conclude that there would always be performance gaps between Chinese and Indian firms for business climate reasons even if even if all business climate indictors assumed identical values in the two countries. Let be the amount by which the mean performance of indicator of Chinese firms exceeds that of Indian firms on account of differences with respect of business environment indicator. Then we have
where is the coefficient of the kth business climate indicator in the performance equation and is the mean value of in the indicated country. This is an Oaxaca-Blinder decomposition of the performance gap into the “endowment effect” of the fact that has different values in China and India, and the “rate of return effect” of the fact that the marginal effect of would be different in China from what it is in India even when the indicator assumes the same values in the two counties.7
The effects of firm and industry characteristics on the performance gaps can likewise be decomposed into “endowment” and “rate of return” effects.8