Chapter 4__________________________________________________________

External Openness and Firm Productivity in China and India:
Evidence from Business Enterprises Surveys
Takahiro Sato1

Introduction
China and India have experienced high economic growth over the last two decades after along period of stagnation. During the period 1980-2007 India's annual growth rate of per capita
income was 6.4 percent compared with that of China at 10.9 percent.2 While India's growth performance has been considerable, China's has been nothing short of a miracle. The main propose ofthis paper is to provide a comparative analysis of China's and India's economic growth pattern.
The growth process in China and India is different in the sense that the Indian growth has
been driven by the service sector, whereas in China it has been led by the industrial sector. For
example, for the period 2005-2007, while the manufacturing sector contributed 49 percent ofChina's GDP, the figure for India is only 29 percent (Table 1). According to Bosworth and Collins(2008, p. 54), the annual growth rate of the total factor productivity (TFP) in the Indian industrial
sector during the periods 1978-1993 and 1993-2004 was 0.3 and 1.1 percent compared with 3.0
and 6.1 for China's TFP during the same periods. The gap between China's and India's productivity
performance is also significant.
Table 1 Macroeconomic Performance, 1980-2007

Source: World Bank, World Development Indicators.
Note: Figure means the average value in each period.

China's performance of the manufacturing sector has been far superior to that of India's. In
other words, India has not succeeded in enhancing the manufacturing industries that have contrib1
I would like to thank Shoji Nishijima, Tomokazu Nomura, Yoshifumi Usami, Atsushi Kato, Kai Kajii, Masashi
Hoshino and the seminar participants at Kobe University for their helpful comments. This paper was funded by agrant-in-aid for scientific research on innovative areas (20101004). All remaining errors are my own.
2
We estimate the growth rate by using the purchasing power parity measure of gross national income per capita interms of the current international dollar drawn from the World Bank's World Development Indicators.

- 83 -

Takahiro Satouted to China's sustainable economic growth. Therefore, growth in the manufacturing sector explains the divergent growth experiences in China and India.
Looking at the external openness of China and India, there is remarkable expansion in the
trade ratio of GDP since 1980 (Table 1). But India has lagged behind China in external openness.
For example in 2005-2007, while the trade ratio contributed 72 percent of China's GDP, the figure
for India was 45 percent (Table 1). Therefore, it is assumed that external openness also influences
the growth pattern of China and India.
A large body of empirical evidence indicates that exporting firms are more efficient thannon-exporting firms in developing countries (Aw, Chung and Roberts, 2000; Delgado, Farinas andRuano, 2002; Pavcnik, 2002; Van Biesebroeck, 2007; Clerides, Lach and Tybout, 1998; Tapalova,
2004; Isgut, 2001; Aw and Hwang, 1995; Loecker, 2007; Bigsten et al., 2004; Hiep and Ohta,
2009; Amitt and Konings, 2007; Blalock and Veloso, 2007; Krisha and Mitra, 1998; Fernandes,
2007; Tybout and Westbrook, 1995). Therefore, to understand the cause and nature of the differences in comparative economic performance between China and India, it is interesting to examine
the positive nexus between the external openness and the productivity in manufacturing industries.
One limitation of the existing literature on comparative studies on the economic performance in China and India is that it is based on aggregate data or micro data which are not necessarily
consistent across the two countries (Bosworth and Collins, 2008; Felipe, Lavina and Fan, 2008;
Srinivasan, 2004; Bardhan, 2006; Bardhan, 2007; Bardhan, 2008; Bardhan, 2009; Hsieh andKlenow, 2007; Das, 2006; Chai and Roy, 2006; Lall, 1995; Winters and Yusuf, 2007).3 Therefore,
findings in existing works might be limited due to the data problem. The World Bank's BusinessEnterprises Survey provides detailed information on sample firms in the many developing countries surveyed based on a questionnaire with a common framework. In this paper, we use thefirm-level data set of both China and India drawn from the World Bank's Business Enterprises Surveys.
This paper contributes to the literature on comparative studies on China and India in two
ways. First, most of the existing literature is based on aggregate data. We focus on the determinantof firm-level productivity, which has been omitted by most of the existing studies.4 Second, thepositive nexus between external openness and firm productivity is tested by using the comparable
data set in China and India. We expect these two ways to allow us to examine the firm-level heterogeneity, which affects the divergent growth pattern in China and India
The rest of this paper is organised as follows. In Section 2 we discuss the model and estimation strategy for analysing the relationship between external openness and productivity. In Section
3 we describe our data and main variables. In Section 4 we report our empirical evidence and in
Section 5 we provide a summary of the main findings with some remarks.
3

The study by Gregory, Nollen and Tenev (2009) is exceptional. This is based on the International Finance Corporation (IFC) survey, which collected firm-level data of software services and hardware manufacturing companies in
China and India. But the number of sample manufacturing firms is only 91 for China and 49 for India.
4
Hsieh and Klenow (2007) use firm-level data in order to investigate the comparative performance of the productivity in manufacturing firms in China and India. This is an exceptional study but there is the problem of the datacomparability.

- 84 -

External Openness and Firm Productivity in China and India

Model and Estimation Strategy
We examine the relationship between external openness and productivity at the firm-level in
two steps. In the first step, we calculate firm-level TFP, and in the second step we specify how
external openness affects TFP.
Following Aw, Chung and Roberts (2000), Caves, Christensen and Diewert (1982) and Good,
Nadiri and Sickles (1997), the multilateral TFP index for firm i in year t is defined as:

By using this TFP index, each firm in year t is compared to a hypothetical firm in year 0,
――
――
――
――
with an average log of value added (ln Y ), capital (ln K), labour (ln L), capital income share (SK)――and labour income share (SL). According to the experiments conducted by Van Biesebroeck (2007),
this type of TFP index is superior to the other four widely used techniques for estimating the productivity, i.e. data envelopment analysis, stochastic frontier, GMM and semi-parametric estimation.
Employing the firm-level measures of TFP from Equation (1), we estimate the equation:

I use OLS with region fixed effects, r, industry fixed effects, c and year fixed effects, t,for controlling unobserved regional and industrial heterogeneity and shocks over time that affectproductivity across all firms. Explanatory variables are time-invariant so we cannot use fixed effect models for the estimation.
We hypothesize that export activity will increase productivity (γ1 > 0), as exposure to theexport market is likely to force firms to become more efficient via the learning-by-exporting
channel. A large body of literature examines the relationship between export and productivity (Aw,
Chung and Roberts, 2000; Delgado, Farinas and Ruano, 2002; Pavcnik, 2002; Van Biesebroeck,2007; Clerides, Lach and Tybout, 1998; Tapalova, 2004; Isgut, 2001; Aw and Hwang, 1995;
Loecker, 2007; Bigsten et al., 2004; Hiep and Ohta, 2009).
The importing firms will be expected to obtain the productivity gain due to the foreign technology embodied in importing goods (γ2 > 0). Many studies show the empirical evidence on therelationship between importing intermediate goods and productivity (Amitt and Konings, 2007;- 85 -

Takahiro Sato

Blalock and Veloso, 2007; Krisha and Mitra, 1998; Fernandes, 2007; Tybout and Westbrook, 1995).
Increasing import competition raises productivity via new technology embodied in import goods.
It also forces the domestic competing firms to become more competitive and more efficient, resulting in increasing productivity.
To see whether the other factor has an effect on TFP, we use several business environment
variables such as infrastructure deficiency, credit availability and so forth.

Data
The World Bank has conducted an intensive survey on the business environments in many
developing countries, including China and India. We use the World Bank Investment Climate Survey of China, which was undertaken in 2003 in collaboration with the National Bureau of Statisticsof China and the Firm Analysis and Competitiveness Survey of India 2002, which was conducted
in 2002 in collaboration with the Confederation of Indian Industry. These two surveys are available
on the website of the Enterprises Surveys of the World Bank Group at [https://www.enterprisesurveys.
org/Portal/].5
Chinese firms were randomly sampled from the following eighteen cities: Benxi, Changchun, Changsha, Chongqing, Dalian, Guiyang, Haerbin, Hangzhou, Jiangmen, Kunming, Lanzhou,
Nanchang, Nanning, Shenzhen, Wenzhou, Wuhan, Xian, and Zhengzhou. The Indian sample firms
were randomly drawn from the following forty cities: Ahmedabad, Bangalore, Calcutta, Chandigarh, Chennai, Cochin, Delhi, Hyderabad, Kanpur, Mumbai, Pune, Mysore, Vijayawada, Lucknow,
Guntur, Surat, Vadodara, Gurgaon, Faridabad, Panipat, Hubli-Dharwad, Calicut, Palakkad, Bhopal,
Gwalior, Indore, Nagpur, Nashik, Thane, Jalandhar, Ludhiana, Coimbatore, Hosur, Madurai,
Ghaziabad, NOIDA, Shahjahanpur-Lakimpur, Howrah, and Mangalore. The industries sampled inboth China and India include not only manufacturing, but also services. In this paper, we ignore
the observations on the service industry.
The Enterprises Surveys offer micro data in two formats: One is standardised data and theother is complete data. The standardised data is constructed by matching to a standard set of survey questions. This format enables us to compare the economic performances of China's and India's firms. But those country-specific questions that cannot be matched are unavailable in thisformat. The complete data offers the full information for a country survey. However, in this format,
the question coding is different in each country.
By using commonly available survey questions in China's and India's standardised data sets,
we could conduct solid comparative analysis on the economic performance of manufacturing firms
in these two countries. But the standardisation was incomplete in the standardised data format.
5

Goswami et al. (2002) and the World Bank (2004) provide a comprehensive business profile of sample firms andinvestment climate information in India by using the Firm Analysis and Competitiveness Survey of India 2002. Using firm-level data of this survey, Lall and Mengistae (2005) examine the impact of the business environment on
firm-level productivity in India. We cannot find any similar comprehensive report on the World Bank Investment
Climate Survey of China in 2003. But Ayyagari, Demirguc-Kunt and Maksimovic (2008) show the sampling method
and the characteristics of sample firms in this survey.

- 86 -

External Openness and Firm Productivity in China and India

Therefore, several crucially important variables for this paper such as the productivity measures
are drawn from the complete data. Thus, we use the standardised data adjusted by complementing
with complete data.
Most of the qualitative questions are available in the year 2002 for the Chinese survey and in
the year 2001 for the Indian survey. In the case of quantitative information on the productivity
measures, while short panel data from 1999 to 2001 are available for India, it is available from1999 to 2002 for China. We limit attention to the sample firms of manufacturing industries withthe productivity measures for the period 1999 to 2001 and commonly available survey questions.
The data is cleaned by dropping some observations with missing values or unreliable values. The
final dataset of China is a balanced panel for the period 1999 to 2001 of 1073 firms per year with a
total of 3219 observations. India's is a balanced panel for the period 1999 to 2001 of 633 firms per
year with a total of 1899 observations.6 Table 2 shows the sample firms' distribution in terms ofindustry.
Table 2 Number of Sample Firms by Industry GroupThe main variables are calculated as follows:
Real value added (Y ): Nominal values added can be obtained by subtracting materials cost
including fuel and power from the sales value. In calculating real value added, the double deflation
method, which has practically become a standard in this research field in recent years, is used.7
Through this method, the calculation of real value added is performed by using the corresponding
output prices to deflate the sales values, and total input prices to deflate total material cost.
Wholesale price indices in the Reserve Bank of India, Handbook of Monetary Statistics of India
for India and producer price indices, retail price indices and consumer price indices in the National
Bureau of Statistics of China, China Statistical Yearbook for China were used as output price indices. Total input prices for China were the “purchasing price index for raw material, fuel andpower” in the China Statistical Yearbook. Total input prices for India were calculated as theweighted average of the prices of intermediate goods, using input share from the Government ofIndia, Input-Output Transaction Table 1998 as weights.8
6

As Equation (1) indicates, we cannot calculate ln TFPi in base year 1999. Therefore, we use the panel data of2000 and 2001 for regression analysis. The number of observations is 2,146 for China and 1,266 for India.
7
The double deflation method is better than the single deflation method for calculating the estimates of real value
added. Balakrishnan and Pushpangadan (1994) provide a detailed discussion of the relationship between the double
deflation method and TFP in India's manufacturing sector.
8
We use wholesale price indices drawn from the Handbook of Monetary Statistics of India and the implicit deflatorof services obtained from the Government of India, National Account Statistics.

- 87 -

Takahiro Sato

Capital (K ): We use the net book value of fixed assets in order to construct capital stocks.
The perpetual inventory accumulation method is employed for estimating capital stocks. Real
fixed capital formation (I ) is defined using the equation:

Consequently, the sum of increases in fixed assets (i.e. Bt – Bt –1) equals the nominal fixed
capital formation, which, when deflated by the investment goods price, becomes real fixed capitalformation. As for the investment goods price (P I ), we use the “price index for investment in fixed
assets” in the China Statistical Yearbook for China and the implicit deflator for fixed capital formation in the National Account Statistics for India. Next, we calculate time series data for realcapital stock using the following formula:

For base year capital (K 0 ) , we use the net book value of fixed capital (B0 ) given in theoriginal data.
Labor (L ): This paper uses the number of workers as an indicator of labour inputs.
Capital income share (SK ) and labour income share (SL ): The SL is obtained as the ratio of
the nominal total labour cost paid to workers over nominal value added. The remaining share of
the nominal value added is defined as the SK.
Export (export ): Export variable is the percentage of sales sold by exporting.
Import (import ): Import variable is the percentage of material inputs and supplies purchased
by importing.
Table 3 shows the profile of the sample firms in terms of value added (ln Y ), capital (K ),labour (L ), capital income share (SK ), labour income share (SL ), productivity (ln TFP ) and theother variables.
Table 3 Summary Statistics- 88 -

External Openness and Firm Productivity in China and India

Results
The results from estimating Equation (2) are presented in Table 4 for China and Table 5 forIndia. First, we regress TFP only on the export ratio as the benchmark. Column 1 of Table 4 showsthat an increase in the export ratio raises TFP. This significant positive coefficient is consistent
with the existing studies. In Columns 2 to 10, we include the import ratio in addition to the export
ratio. The inclusion of the import ratio reduces the t-value on the export ratio while the estimatedcoefficients of the export ratio are still positive. In Column 3, the coefficient of the import ratio is
positive and statistically significant. However, in the other columns, it is still positive but insignificant. Before we conclude that external openness does not raise the productivity in China, weshould check the joint null hypothesis as to whether the coefficients of export and import are simultaneously zero or not. The last row of Table 4 shows the F-test on this joint null hypothesis.
These results show that in four cases out of nine, the null hypothesis is rejected at the 10 percent
significance level. Accordingly, we can argue that external openness is likely to raise the productivity in China as a whole.
Table 4 Results: China

Robust statistics parentheses# significant at 10%; * significant at 5%; ** significant at 1%

If a firm has monopoly power, the estimated TFP of the firm can be overestimated to the degree of monopoly power. To check this, we add the market share of main products sold by a firm.
Column 3 of Table 4 shows the coefficient of market share is negative and insignificant. Based onthese findings, market share does not affect TFP in China.
In Columns 4 to 5, we include variables relating to infrastructure, i.e. “days of inventory of main
input” (inventory) which indicate the deficiency of transportation, and “average cost of a kilowatthour of electricity from the public grid” (power cost) and “lost value per total sales due to power- 89 -

Takahiro Satooutages or surges from the public grid” (power loss) which mean the problem of public supply of
power. In Columns 3 and 4, inventory and power cost are insignificant, but in Column 5, we see thatpower loss is negative and significant. Therefore, we can confirm poor infrastructure will depress TFP.
Regarding the business information advantage, whether a firm is a “member of a businessassociation or chamber of commerce” (association) is seen as an important factor that influences
the productivity. We check this by including the association dummy variable. In Column 7, theassociation dummy is unexpected sign and significant. There are two possibilities accounting forthis. In the first, inefficient firms tend to be members of business associations for their survival,
resulting in a negative relationship between the association dummy and TFP. In the second, business associations provide the incentive to member firms not for improving their efficiency but for
demanding the government's support and protection.
For other business environments, we include the credit dummy (credit), which indicates
whether a firm has an ``overdraft facility or line of credit” in Column 8 and the “percentage ofsenior management's time that was spent in dealing with requirements imposed by government
regulations” (regulation) in Column 9. The former represents the credit availability, whereas thelatter means the burden of government regulations. In Column 8, we see that the coefficient ofcredit dummy is positive and significant while Column 9 shows the regulation variable is not significant. Based on these findings, credit availability improves TFP.
So far, we have seen the results for China. In Table 5, we address the case of India. First ofall, we see that in all specifications, the import ratio is positive and highly significant. But in any
column, the export ratio is not significant with a considerably low t-value. We conclude that it is
not the export but import channel of external openness that enhances the productivity in India.
Table 5 Results: India

Robust statistics parentheses# significant at 10%; * significant at 5%; ** significant at 1%- 90 -

External Openness and Firm Productivity in China and India

The other variables are statistically insignificant in all columns except for Column 6. In
Column 6 of Table 5, the coefficient of power cost is negative and significant. This suggests infrastructure deficiency constrains the firms' productivity in India. This is consistent with China's results.

Concluding Remarks
Existing comparative studies on China's and India's growth experiences are confined to themacro- or industry-level and use less comparable data. In this paper we use firm-level comparable
data of China and India. As many studies have shown a positive relationship between external
openness and firm productivity in developing countries, we find similar evidence for manufacturing firms in both China and India. But the channel which raises the firm-level productivity isslightly different between China and India. For India's manufacturing firms, it is not the export
channel but the import channel of the external openness that is significant, while for China's firms
both channels are likely to be important. Taken together with the finding that infrastructure constrains depress the productivity of manufacturing firms, export activity does not necessarily stimulate the productivity of manufacturing firms under poor business environments. Therefore, if India
tries to catch up with China, we conclude that the tentative statement “Ultimately, India will needto redress its inadequate infrastructure and to broaden its trade beyond the current emphasis onservice” (Boswarth and Collins, 2008, p. 64) may be corrected by emphasising India's need to improve its manufacturing-cum-export-oriented infrastructure.
We cannot address the issues of the potential endogeneity of the export and import ratio. As
Goldar and Kato (2008) and Hiep and Nishijima (2009) have investigated the determinant on a
firm's export activity, we can insert the model on export or import decision making into our Equation (2) by using 2SLS. This is one of the future subjects of our paper.

References
Amitt, Mary, and Jozef Konings, “Trade Liberalization, Intermediate Inputs, and Productivity: Evidence
from Indonesia,” The American Economic Review, 97, 5: 1611-1638, 2007.
Aw, B. Y., and A. R. Hwang, “Productivity and the Export Market: A Firm-Level Analysis,” Journal of Development Economics, 47: 313-332, 1995.
Aw, Bee Yan, Sukkyuan Chung, and Mark J. Roberts, “Productivity and Turnover in the Export Market:
Micro-Level Evidence from the Republic of Korea and Taiwan (China),” The World Bank Economic Review, 14, 1: 65-90, 2000.
Ayyagari, Meghana, Asli Demirguc-Kunt, and Vojislav Maksimovic, “Formal versus Informal Finance:
Evidence from China,” World Bank Research Working Paper, 4465, 2008.
Balakrishnan, Pulapre, and K. Pushpangadan, “Total Factor Productivity Growth in Manufacturing Industry,” Economic and Political Weekly, (June 30, 1994), 2028-2035, 1994.
Bardhan, Pranab, “Awakening Giants, Feet of Clay: A Comparative Assessment of the Rise of China andIndia,” Journal of South Asian Development, 1, 1: 1-17, 2006.
Bardhan, Pranab, “Poverty and Inequality in China and India: Elusive Link with Globaisation,” Economic- 91 -

Takahiro Satoand Political Weekly, (September 22, 2007), 3849-3852, 2007.
Bardhan, Pranab, “What Makes a Miracle: Some Myths about the Rise of China and India,” Boston Review,
January/Februray, 2008.
Bardhan, Pranab, “Economic Reforms, Poverty and Inequality in China and India,” in K. Basu and R Kanvur, eds., Arguments for a Better World: Essays in Honor of Amartya Sen, Vol II. Oxford, UK: Oxford
University Press, 350-364, 2009.
Bigsten, Arne, Paul Collier, Stefan Dercon, Marcel Fafchamps, Bernard Gauthier, Jan Willem Gunning,
Abena Oduro, Remco Oostendorp, Catherine Pattillo, Mans Soderbom, Francis Teal, and Albert
Zeufack, “Do African Manufacturing Firms Learn from Exporting?” The Journal of Development Studies, 40, 3: 115-141, 2004.
Blalock, Garrick, and Francisco M. Veloso, “Imports, Productivity Growth, and Supply Chain Learning,”
World Development, 35, 7: 1134-1151, 2007.
Bosworth, Barry, and Susan M. Collins, “Accounting for Growth: Comparing China and India,” Journal of
Economic Perspectives, 22, 1: 45-66, 2008.
Chai, Joseph C. H., and Kartik C. Roy, Economic Reform in China and India: Development Experience ina Comparative Perspective. Cheltenham, UK: Edward Elgar, 2006.
Clerides, Sofronis K., Saul Lach, and James R. Tybout, “Is Learning by Exporting Important? Micro-Dynamic Evidence from Colombia, Mexico, and Morocco,” Quarterly Journal of Economics, 113,
3: 903-957, 1998.
Das, Dillip K, China and India: A Tale of Two Economies. Abingdon, Oxon, NY: Routledge, 2006.
Delgado, Miguel A., Jose C. Farinas, and Sonia Ruano, “Firm Productivity and Export Markets: A
Non-Parametric Approach,” Journal of International Economics, 57: 397-422, 2002.
Felipe, Jesus, Editha Lavina, and Emma Xianqin Fan, “The Diverging Patterns of Profitability, Investment and Growth of China and India during 1980-2003,” World Development, 36, 5: 741-774, 2008.
Fernandes, Ana M, “Trade Policy, Trade Volumes and Plant-Level Productivity in Colombian Manufacturing Industries,” Journal of International Economics, 71: 52-71, 2007.
Goldar, Bishwanath, and Atsushi Kato, “Export Intensity of Indian Industrial Firms in the Post-Reform
Period,” in S.R. Hashim, K. S. Chalapati Rao, K. V. K. Ranganathan and M. R. Murty, eds., Indian Industrial Development and Globalisation: Essays in Honour of Professor S. K. Goyal. New Delhi, India:
Academic Foundation, 2008.
Goswami, Omkar, A. K. Arun, Srivastava Gantakolla, Vishal More, Arindam Mookherjee, David Dallar, Taye Mengistae, Mary Hallward-Driemier, and Giuseppe Iarossi, Competitiveness of Indian
Manufacturing: Results from a Firm-Level Survey. Confederation of India Industry and the World Bank,
2002.
Gregory, Neil, Stanley Nollen, and Stoyan Tenev, New Industries from New Places. Stanford University
Press and The World Bank, 2009.
Hiep, Nguyen, and Nishijima Shoji, “Export Intensity and Impacts from Firm Characteristics, Domestic
Competition and Domestic Constraints in Vietnam: A Micro-Data Analysis,” RIEB (Research Institutefor Economics & Business Administration) Discussion Paper Series, 238, 2009.
Hiep, Nguyen, and Ohta Hiroshi, “Superiority of Exporters and the Causality Between Exporting and Firm
Characteristics in Vietnam,” RIEB (Research Institute for Economics & Business Administration) Discussion Paper Series, 239, 2009.
Hsieh, Chang-Tai, and Peter J. Klenow, “Misallocation and Manufacturing TFP in China and India,” NBER
Working Paper, No. 13290, 2007.
Isgut, A. E., “What's Difference about Export? Evidence from Colombia Manufacturing,” Journal of Development Studies, 37, 5: 57-82, 2001.
Kasahara, Hiroyuki, and Joel Rodrigue, “Does the Use of Imported Intermediates Increase Productivity?
Plant-Level Evidence,” Journal of Development Economics, 87: 106-118, 2008.
Krisha, Pravin, and Devashish Mitra, “Trade Liberalization, Market Discipline and Productivity Growth:- 92 -

External Openness and Firm Productivity in China and India

New Evidence from India,” Journal of Development Economics, 56, 2: 447-62, 1998.
Lall, Deepak, “India and China: Contrasts in Economic Liberalization?” World Development, 23, 9:1475-1494, 1995.
Lall, Somik V., and Taye Mengistae, “The Impact of Business Environment and Economic Geography on
Plant-Level Productivity: An Analysis of Indian Industry,” World Bank Policy Research Working Paper,
No. 3664, 2005.
Loecker, Jan De, “Do Exports Generate Higher Productivity? Evidence from Slovenia,” Journal of International Economics, 73: 69-98, 2007.
Pavcnik, Nina, “Trade Liberalization, Exit, and Productivity Improvements: Evidence from Chilean Plants,”
The Review of Economic Studies, 69, 1: 245-276, 2002.
Srinivasan, T. N., “China and India: Economic Performance, Competition and Cooperation: An Update,”
Journal of Asian Economics, 15: 613-636, 2004.
Tapalova, Petia, “Trade Liberalization and Firm Productivity: The Case of India,” IMF Working Paper,(April 28, 2004), 2004.
The World Bank, India: Investment Climate Assessment, 2004.
Tybout, James R., and M. Daniel Westbrook, “Trade Liberalization and the Dimensions of Efficiency
Change in Mexican Manufacturing Industries,” Journal of International Economics, 39: 53-78, 1995.
Van Biesebroeck, Johannes, “Exporting Raises Productivity in Sub-Saharan African Manufacturing Firms,”
Journal of International Economics, 67, 2: 373-391, 2005.
Van Biesebroeck, Johannes, “Robustness of Productivity Estimates,” The Journal of Industrial Economics,
LV, 3: 529-569, 2007.
Winters, L. Alan, and Shahid Yusuf, eds., Dancing with Giants: China, India and the Global Economy.
Washington, DC: The World Bank, 2007.

- 93 -