The total factor productivity in China and India: new measures and approaches
The Authors
Alejandro Nin Pratt, International Food Policy Research Institute, Washington, DC, USA
Bingxin Yu, International Food Policy Research Institute, Washington, DC, USA
Shenggen Fan, International Food Policy Research Institute, Washington, DC, USA
Acknowledgements
JEL classification – D24, Q32, O47
Abstract
Purpose – This paper aims to measure and compare agricultural total factor productivity (TFP) growth in China and India and relates TFP growth in each country to policy milestones and investment in agricultural research.
Design/methodology/approach – TFP is measured using a non-parametric Malmquist index which allows the decomposition of TFP growth into its components: efficiency and technical change.
Findings – Comparing TFP growth in China and India it is found that efficiency improvement played a dominant role in promoting TFP growth in China, while technical change has also contributed positively. In India, the major source of productivity improvement came from technical change, as efficiency barely changed over the last three decades, which explains lower TFP growth than in China. Agricultural research has significantly contributed to improve agricultural productivity in both China and India. Even today, returns to agricultural R&D investments are very high, with benefit/cost ratios ranging from 20.7 to 9.6 in China and from 29.6 to 14.8 in India.
Originality/value – The applied methodology and the comparison between TFP growth patterns contribute to a better understanding of the consequences that the different approaches to agricultural reform followed by China and India had on the performance of agriculture in both countries.
Article Type:
Research paper
Keyword(s):
China; Productivity rate; India; Agriculture.
Journal:
China Agricultural Economic Review
Volume:
1
Number:
1
Year:
2009
pp:
9-22
Copyright ©
Emerald Group Publishing Limited
ISSN:
1756-137X
1 Introduction
China and India both have experienced rapid transformation in their agricultural sector for the last several decades. Following India's independence in 1947, and the foundation of People's Republic of China in 1949, they designed and implemented various agricultural policies and strategies for achieving multiple goals such as national food security, industrialization/urbanization, and overall economic growth. Both countries introduced land reforms in the early 1950s. They also began to use improved seeds and fertilizer in the late 1960s and invested heavily in irrigation in the 1960s and 1970s.
China began to reform its centrally planned agricultural sector in the late 1970s by decentralizing its production units from collective to individual households and by allowing development of markets to commercialize agricultural production. By 1993, Chinese agriculture had become a largely market driven economic sector and both production and productivity grew at an extraordinary pace. During most post-independence era, India implemented an agricultural production system, which included private ownership of major production assets including land, and state intervention in agriculture through input and output subsidies and through restrictions on domestic and international trade. The Green Revolution was India's main agricultural strategy between 1966 and 1991. Combining economic incentives with the introduction of new high-yielding seed varieties (HYV) of wheat and rice, India was able to substantially increase food grain supply. Following the Green Revolution period, India began to implement economic reforms in the early 1990s, starting with the macro economic policies. These reforms affected agriculture only indirectly through improved terms of trade and increased demand for agricultural products due to accelerated overall economic growth. How have these different institutions, policies and strategies affected the performance of agricultural production in both countries? Has productivity reached a ceiling? Do investments in agricultural R&D still have high payoffs? These questions have been hotly debated among scholars and policy circles.
The objective of this paper is to measure and compare agricultural total factor productivity (TFP) growth in China and India looking at structural differences in growth patterns and relating TFP growth to policy milestones and investment in agricultural research in both countries. The TFP measure is used here for comparison because this measure reflects a major potential source of future growth in the agricultural sector, as both countries will be further constrained by land, labor and other resources available for agriculture. The paper is organized as follows. We first define the concept and the methodology used to measure TFP growth and its decomposition into efficiency and technical change. We then present and discuss the results of our TFP measures comparing growth in TFP, technical change and efficiency improvement. In Section 3, we link TFP to major policy milestones and to investments in agricultural research, and assess returns to agricultural R&D investment. We finally conclude the paper by offering policy insights from the study.
2 TFP measures
TFP growth shows the relationship between growth of output and growth of input, calculated as a ratio of output to input. In other words, productivity is raised when growth in output outpaces growth in input. Productivity growth without an increase in inputs is the best kind of growth to aim for rather than attaining a certain level of output by increasing inputs, since these inputs are subject to diminishing marginal returns. However, how to measure the total input and total output is both conceptually and empirically difficult. Methods to estimate TFP can be classified in four major groups:
- least-squares econometric production models;
- growth accounting TFP indices;
- data envelopment analysis (DEA); and
- stochastic frontiers (Coelli et al., 2005).
The first two methods are normally used with times series data and assume that all production units are technically efficient. Methods (3) and (4) can be applied to a cross-section of firms, farms, regions or countries to compare their relative productivity. In this study, we use both a Törnqvist-Theil index (growth accounting framework) and a non-parametric Malmquist index (DEA approach) to measure agricultural TFP growth in China and India.
The Malmquist index, pioneered by Caves et al. (1982) and based on distance functions, has become extensively used in the measure and analysis of productivity after Färe et al. (1994) showed that the index can be estimated using a non-parametric approach. The non-parametric Malmquist index has been especially popular since it does not entail assumptions about economic behavior (profit maximization or cost minimization) and therefore does not require prices for its estimation, which in many cases are not available for international comparisons. Most important for this study is its ability to decompose productivity growth into two mutually exclusive and exhaustive components: changes in technical efficiency over time (catching-up) and shifts in technology over time (technical change).
To define the output-based Malmquist index we assume, as in Färe et al. (1998), that for each time period t=1, … , T the production technology describes the possibilities for the transformation of inputs x t into outputs y t . This is the set of output vectors that can be produced with input vector x. For the technology in period t and with y t ∈R + m outputs and x t ∈R + n inputs: Equation 1 The frontier of the output possibilities for a given input vector is defined as the output vector that cannot be increased by a uniform factor without leaving the set. In our analysis, we will refer to these production units as countries. The output distance function is defined at t as the reciprocal of the maximum proportional expansion of output vector y t given input x t : Equation 2 where is the coefficient dividing y t to get a frontier production vector at period t given x t . The distance measure equals 1 when the production point in period t is on the frontier for period t.
The Malmquist index measures the TFP change between two data points (e.g. those of a country in two different time periods) by calculating the ratio of the distance of each data point relative to a common technological frontier. Following Färe et al. (1994), the Malmquist output-oriented index between period t and t+1 is given by: Equation 3 which is a geometric mean of two Malmquist indices: one using the technology frontier in t as the reference, and a second index that uses frontier in t+1 as the reference.
Färe et al. (1994) showed that the Malmquist index could be decomposed into an efficiency change component and a technical change component, and that these results applied to the different period-based Malmquist indices: Equation 4 The ratio outside the square brackets measures the change in technical efficiency between period t and t+1. The expression inside brackets measures technical change as the geometric mean of the shift in the technological frontier between t and t+1 evaluated using frontier at t and at t+1, respectively, as the reference. The efficiency change component of the Malmquist indices measures the change in how far observed production is from maximum potential production between period t and at t+1, and the technical change component captures the shift of technology between the two periods. A value of the efficiency change component of the Malmquist index greater than one means that the production unit is closer to the frontier in period t+1 than it was in period t: the production unit is catching-up to the frontier. A value less than one indicates efficiency regress. The same range of values is valid for the technical change component of total productivity growth, meaning technical progress when the value is greater than one and technical regress when the index is less than one.
3 Measures of agricultural TFP in China and India
Fan and Zhang (2002) constructed total output, total input and TFP indices by using the Chinese official data over 1952-1997 and a Törnqvist-Theil index approach. The gross value of agricultural production (GVAO) is used as output. As defined by National Statistical Bureau, GVAO refers to total volume of output of farming, forestry, animal husbandry, and fishery in value terms, reflecting the scale of and the achievements made in agricultural production during a given period of time. Labor, land, machinery, chemical fertilizer, pesticide, seed, animal stock, animal feed, and irrigation are used as inputs.
Fan et al. (1999) used a similar approach in constructing a TFP index for Indian agriculture by using the state level data over 1970-1994. Five major crops (rice, wheat, sorghum, pearl millet, and maize), 14 minor crops (barley, cotton, groundnut, other grain, other pulses, potato, rapeseed, mustard, sesame, sugar, tobacco, soybeans, jute, and sunflower), and three major livestock products (milk, meat, and chicken) are included in their measure of total production. Five inputs (labor, land, fertilizer, tractors and buffalos) are included. Labor input is measured as the total number of male and female workers employed in agriculture at the end of each year; land is measured as gross cropped area; fertilizer input is measured as the total amount of nitrogen, phosphate, and potassium used; tractor input is measured as the number of four-wheel tractors; and bullock input is measured as the number of adult bullocks. The wage rate for agricultural labor is used as the price of labor to aggregate total cost for labor. The costs of draft animals and machinery are taken directly from the production cost surveys; and the fertilizer cost is the product of total fertilizer use and fertilizer price calculated as a weighted average of the prices of nitrogen, phosphate, and potassium. The land cost is measured as the residual of total revenue net of measured costs for labor, fertilizer, tractors, and bullocks. Therefore, the cost share of each input is calculated by their respective cost divided by total production value.
Since the above TFP measures cannot identify the separate effects of technical change and efficiency improvement on TFP and total output growth, in this study we estimate the non-parametric Malmquist index and its efficiency and technical change components for Chinese and Indian agriculture. The data used are from FAOSTAT 2007, the statistical database of the Food and Agriculture Organization of the United Nations (FAO, 2007). Two aggregate outputs (crops and livestock production) and seven inputs (land, labor, tractors, fertilizers, area under irrigation, feed and animal stock) covering the period 1967-2003 for 59 countries
Figure 1 shows growth in total production, productivity, technical change and efficiency improvement in Chinese agriculture. Our TFP estimates are in line with previous estimates using a Törnqvist-Theil Index by Fan and Zhang. There is a high correlation between the Törnqvist-Theil and the non-parametric Malmquist estimates. The correlation coefficient between the two different indices is 0.87 for the whole period. If calculated for the 1978-1997, the correlation coefficient increases to 0.92.
TFP growth was high during the studied period, with an average annual growth rate of 2.11 percent. Growth is relatively low during 1974-1983 (1 percent) and accelerates during the 1980s and 1990s (5.6 and 4.4 percent, respectively). Poor growth performance coincides with the Cultural-revolution period (1966-1976), while TFP growth accelerates after the 1978 reforms.
The period of poor performance during the Cultural Revolution is explained by the low-technical change and increasing technical inefficiency. During 1974-1984, the government increases incentives to raise the level of agricultural output through a new production management system (household responsibility system) which marked the beginning of decentralization of agricultural production to the household level. With these changes, the country starts a period of increasing technical efficiency in agricultural production, transforming the poor performance of the past into sustained TFP growth and catching-up to the frontier. These changes were followed by the domestic marketing reform and the rise of the rural nonfarm (RNF) sector during 1985-1993, and changes in trade regime and accession to WTO in 2001. By the end of the period, China reaches the frontier but continues to increase agricultural TFP by increasing growth in technical change.
Figure 2 shows various measures of TFP, technical change and efficiency improvement for Indian agriculture. Malmquist estimates for India also reflect a similar pattern of productivity growth as that estimated using the Törnqvist-Theil index (the correlation coefficient for the period 1970-1994 is 0.71).
During the second face of the Green Revolution in India (1973-1980), the extension of HYV technology from wheat to rice and an expansion of the area under irrigation, spread the Green Revolution to new areas (Fan et al., 2007). However, this did not translated in an improved performance of the agricultural sector in terms of productivity growth. Agriculture's performance during this period is poor, with TFP growth rate close to zero (−0.3 percent). According to Fan et al. (2007) this poor performance is related to the extended role of the state in key areas of economic management. In the case of agriculture, the government took over the wholesale trade in wheat in 1973-1974, resulting in a negative impact on agriculture where procurement dropped, prices increased and India slid back into grain imports.
This situation changed after the early 1980s, where India consolidated its status as a food self-sufficient country during the third phase of the Green Revolution (1981-1990), which was followed by the macroeconomic and nonagricultural sector reforms of 1991-1994. In the mid-1980s, TFP starts a sustained growth that lasted until the end of the period considered in this study (0.8 percent in 1984-1993 and 1 percent in 1994-2003).
In contrast with China, India's TFP growth was affected by lack of improvement in technical efficiency. Efficiency in India's agriculture shows a declining trend from the beginning of the period to the late 1980s. Efficiency improves after the 1980s, but growth is slow, with TFP still 20 percent lower than TFP at the frontier and only slightly higher than TFP at the beginning of the period. This recovery in efficiency together with faster growth in technical change explains India's improved performance in the last 15-20 years. Differences in efficiency growth between China and India reflect country heterogeneity in terms of efficiency changing variables. These differences could be related to the specific policies and the implementation of agricultural reform in both countries as discussed in the literature (Fan and Gulati, 2007).
There were original differences in the general approach to reform in both countries. In China, reform of incentives resulted in greater returns to the farmers and in more efficient resource allocation, which in turn strengthened the domestic production base and made it more competitive. Agricultural growth and increased efficiency favored the development of a dynamic RNF in China, which encouraged the government to expand the scope of policy changes and put pressure on the urban economy to reform as well, because nonfarm enterprises in rural areas had become more competitive than the state-owned enterprises (Fan et al., 2007). Reforms of the state enterprises in turn triggered macroeconomic reforms, opening up the economy further. As the level of food supply achieved through the reforms ensured a critical level of grain production Chinese policy makers were able to abandon the old agricultural policy framework geared toward self-sufficiency in food grains (Vyas and Ke, 2004). The procurement system was dismantled everywhere except for the main grain-producing regions, and the food rationing system was abolished in the early 1990s. As a consequence, China also pursued more aggressive open door policies in investments and trade. In 2001, the year that China joined WTO, the share of agriculture trade in agricultural GDP was 17 percent in China and 9 percent in India (FAOSTAT, FAO, 2007), while India's weighted average tariff (29 percent), is double that of China, (16 percent, Ahluwalia, 2002), implying that agricultural liberalization was deeper in China and that the goal of self-sufficiency as a prerequisite for food security was abandoned.
Finally, the particular approach to reform in China achieving food self-sufficiency by the late 1970s immensely helped diversification of agriculture because it allowed the government to feed the increasing population and relax controls on the food grain sector (Vyas and Ke, 2004). This diversification is probably one of the factors behind China's fast growth in agricultural efficiency. Also, the Chinese experience has amply demonstrated that the evolution of a dynamic RNF sector offers great potential for rural diversification, with rapid growth of rural enterprises in China being one of the most striking differentiators between reform processes in China and India (Fan et al., 2007).
By contrast, and following Fan et al. (2007), India was reluctant to change the self-sufficiency goal and reform policies that have helped ensure its food security in the past three decades, including price supports and input subsidies. Reforms started in the 1990s and the focus was on macroeconomic and nonagricultural reforms. Policy changes related to agriculture were carried out much later, and even then were only partial. India continues state food procurement and distribution, mainly because these are seen as forms of affirmative action for over two thirds of the population, including the poorest, show are dependent on agriculture and the rural economy for their livelihood. In part as a consequence of these policies, farm output growth rates decelerated, dampening demand as well as farm and nonfarm employment. These developments together with India's nonfarm economy producing low-profit services of the informal sector primarily for the rural markets has made difficult the possibility of diversification of agricultural production as happened in China.
We conclude that differences in the approach and implementation of the agricultural reforms in China and India resulted in contrasting performances in terms of TFP growth, which are explained mainly by a significant difference in the rate of efficiency growth favoring China.
4 TFP growth, technical change and R&D investment
In this section, we analyze the effect of policy change and agricultural R&D investment on TFP growth in Chinese and Indian agriculture. Although there have been many studies on this, we want to analyze returns to agricultural R&D investments in recent years and compare them with payoffs in the 1970s and 1980s, which were found to be high by previous studies.
It is common practice to include agricultural R&D investment in the TFP function together with other factors also contributing to productivity growth such as institutional reforms and improved quality of inputs. For instance, Evenson and Pray (1991) and Evenson et al. (1998) discuss possible methods for explaining agricultural productivity growth, demonstrating that changes in productivity can be attributed to “determining” variables, such as R&D, extension, and farmers' education. They also proposed a “two-stage decomposition” procedure, where the productivity indexes are first constructed and then regressed against the productivity-changing factors. This two-stage procedure allows us to impose fewer restrictions on the production technology. To examine how quality improvements in input used and overall economic growth contribute to productivity improvement in the agricultural sector, we use the share of agricultural GDP in total GDP as a proxy to capture this effect.
Dummy variables are introduced to capture economic reforms in China and the Green Revolution in India. In China, 1979-1984 is the first phase of agricultural reform, and 1985-2003 is the post agricultural reform. In India, 1981-1990 is the period of the third phase of the Green Revolution, and 1991-2003 the macroeconomic and non-agriculture reform. Dummy variables capturing accession of China (2001) and India (1995) to WTO are also included.
The data for this analysis come from several sources. TFP indexes are calculated in the previous section. Agricultural R&D expenditures in China, measured in 2000 constant millions Yuan, consist of R&D expenditure from both research institutes and universities (Fan et al., 2006). Agricultural R&D expenditures in India are obtained from the State Planning Commission, Government of India, measured in 1995 constant millions rupees (Fan, 2003). Share of agriculture sector in national GDP is obtained from World Development Indicator (World Bank, 2006). Both agricultural spending and TFP or technical change are measured in logarithm transformation. Figure 3 shows the evolution of the Agricultural R&D and shares of agriculture in GDP in China and India, used in this analysis.
Estimating the effect of agricultural research investment on production and productivity functions could be problematic given that agricultural research takes long time lags to affect agricultural production and once the effect begins to occur, it will also affect production for the following several periods. Many ways of tackling this problem have been proposed in the literature. Here, we use the polynomial distributed lags (PDL) estimation technique, that forces the coefficients of each lagged variables to lie on a polynomial curve, avoiding the use of lagged independent variables that are often highly correlated with each other or follow a common trend. The length of time during which R&D investment in a particular year affects production and productivity (the so-called lag structure) was determined using the Akaike Information Criterion (AIC). In the case of China, the minimal AIC value in the lag 16 specification suggests an optimal lag of 16 for TFP regression. The same test suggests a lag length of 10 for India.
We choose a quadratic or second-degree polynomial for our lag structure given that empirical evidence suggests that the effects of agricultural R&D are usually negligible at the initial years, but build up over time and eventually trail off. If this is the case, the lag scheme is an inverted U. In this case, only three instead of a dozen or more parameters need to be estimated and we expect the coefficient associated with quadratic term as negative. The final model to be estimated is: Equation 5 where TFP is the productivity index calculated from previous section, RDE is lagged agricultural R&D investment, and agshare is the share of agriculture in the year, L is the maximal lag length. And coefficients of PDL are calculated as: Equation 6
The PDL estimates of TFP for China are summarized in Table I where results for four different models are presented including different policy dummy variables. The coefficient of the aggregated polynomial terms is consistently significant in the different models, indicating that this approach greatly reduces multicollinearity problems. Share of agriculture in the economy is introduced into the model to capture the agriculture-industry linkage. This coefficient is significant and negative, indicating that agriculture productivity grows faster as the country becomes more industrialized. In other words, there is a positive reinforcement from the nonagricultural sector to the agricultural sector. Estimated coefficients of dummy variables reflecting different policy regimes (agricultural reform and post-reform) are negative and non-significant and they are not able to capture impact of policy changes on TFP growth. One possible explanation for this is the strong and positive association verified between the policy variables and agshare, which appears to be capturing the same phenomenon.
Looking at the effect of R&D on TFP growth, the results show that agricultural research has contributed significantly to TFP growth in China. The coefficient of the quadratic term is −0.004 and significant, indicating that the lag structure has an inverted U shape as expected. The productivity elasticity of agricultural R&D, is 0.374 when policy variables are included (model “Reform”), and is the sum of the coefficients over the past 16 years, derived from the PDL estimation.
Similar results are shown in Table II for India. The share of agriculture in GDP shows a negative and significant coefficient as in the case of China. There is no significant impact of the third phase of the Green Revolution on agricultural TFP and no effect on TFP from accession to WTO is captured in our model. On the other hand, the fourth model (reform and WTO) shows a significant and positive impact on TFP of the implementation of macroeconomic and structural reforms on agriculture. On the other hand, WTO membership did not significantly improve productivity since 1995. When using PDL the aggregate impact of agricultural R&D on productivity is reported as 0.136 (marginally significant) in the first model, and only 0.032 in the model including reform and WTO variables.
Using the estimated coefficients of the TFP and technical change functions (first model), we can calculate benefit/cost ratios of agricultural R&D investment (Table III). The effect of agricultural research in year t is reflected in increased agricultural production in the following t+1, t+2,… t+L years, where L is the maximal lag length. Benefit is calculated as the sum of additional agricultural output (discounted to the year of investment) over year t to t+L. Discount rate is assumed to be 10 percent per year. Under these assumptions, the estimated benefit/cost ratios for China range from 20.7 in the 1960s to 9.6 in the 2000s. There is no clear sign of diminishing marginal returns over time. The benefit/cost analysis implies that internal rates of return are higher than our assumption of 10 percent. For example, the internal rates of return of agricultural research range 21-26 percent in China. These results are largely consistent with Fan et al. (2002) estimates. In India, benefit/cost ratios for agricultural R&D range from 29.6 around the time of the Green Revolution in 1960s to 14.8 in the 1990s, when TFP is used as the dependent variable.
5 Conclusions
Rapid growth in Chinese and Indian agriculture can be attributed to many factors. In the 1960s, agricultural growth can be explained mainly by increased use of inputs and only a small contribution of productivity gains, particularly in the case of India. But as both countries began to use modern technologies, their production and productivity growth accelerated. In China, efficiency improvement played a dominant role in promoting TFP growth after the 1978 reforms, while technical change has also been an important factor. In India, the major source of productivity improvement came from technical change as efficiency barely changed over the last several decades. Contrasting performances in efficiency growth between China and India resulted from differences in the approach and implementation of the agricultural reforms in the two countries.
We also verified that returns to agricultural R&D investment in China and India are still very high, with benefit/cost ratios above 20 in both countries. Indian and Chinese governments have recently increased their investments in agricultural research, and this effort needs to be maintained given the high returns of investment and the time lag that take these investments to affect production.
Equation 1
Equation 2
Equation 3
Equation 4
Equation 5
Equation 6
Figure 1Production, productivity, technical change and efficiency in Chinese agriculture
Figure 2Production, productivity, technical change and efficiency in Indian agriculture
Figure 3Agricultural R&D investment and shares of agriculture in GDP in China and India
Table IEstimation results of the productivity impact of agricultural R&D using PDL, China
Table IIEstimation results of the productivity impact of agricultural R&D using PDL, India
Table III
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Further Reading
Fan, S. (2000), "Research investment and the economic return to Chinese agricultural research", Journal of Productivity Analysis, Vol. 14 No.2, pp.163-82.
About the authors
Alejandro Nin Pratt is a Research Fellow in the Development Strategy and Governance Division (DSGD) at IFPRI. He joined IFPRI as a Research Fellow in 2005. Prior to IFPRI he worked as a Post-Doctoral Fellow in the Agricultural Economics Department at Purdue University, and as a Scientist for the International Livestock Research Institute in Ethiopia and Kenya. He received his PhD in Agricultural Economics from Purdue University; a MS in International Economics and a BS in Agronomy both from the Universidad de la República in Uruguay. Alejandro Nin Pratt is the corresponding author and can be contacted at: a.ninpratt@cgiar.org
Bingxin Yu is a Post-Doctoral Fellow at the DSGD at IFPRI. Bingxin joined IFPRI in 2004 as a research analyst. She has a PhD and an MS in Biometry/Statistics from University of Nebraska-Lincoln and a BS in Management Science from University of Science and Technology of China.
Shenggen Fan is the Director of IFPRI's the DSGD. He joined IFPRI in 1995. He led IFPRI's public investment program before the current position. Prior to IFPRI, he worked for the International Service for National Agricultural Research, and the University of Arkansas. He received his PhD in applied economics from the University of Minnesota. Both of his BS and MS are from Nanjing Agricultural University, China.