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Agricultural Investment, Production Capacity and Productivity
Lydia Zepeda

This chapter provides an overview of current economic thinking on some aspects of agricultural investment and productivity, especially in the context of developing countries. While the importance of physical capital has long been recognized, economic research has identified human capital formation as a crucial, productive element of investment, both in its own right and as a complementary input to physical capital and other inputs. Human capital may be embedded in the inputs that go into production or may enhance the way inputs are utilized and combined. Current economic research also highlights the importance of taking into account the sustainability of agricultural production systems. Resource economists have identified the need to calibrate agricultural production for negative environmental externalities and resource depletion to represent the true value of agricultural output. The upshot of current economic thinking is that the analysis of investment in agriculture should encompass more than just physical capital formation. In order to examine the linkages between agricultural investment and agricultural production capacity and productivity, agricultural investment must include both human capital formation and environmental degradation.

1.1 Introduction

Improving the production capacity of agriculture in developing countries through productivity increases is an important policy goal where agriculture represents an important sector in the economy. The agricultural sector provides livelihood directly and indirectly to a significant portion of the population of all developing countries, especially in rural areas, where poverty is more pronounced. Thus, a growing agricultural sector contributes to both overall growth and poverty alleviation.

Within the context of growth in food and agriculture, emphasis is placed on productivity because expansion of arable land is very limited in most countries due to physical lack of suitable land and/or because of environmental priorities. In addition, the difference between actual and technically feasible yields for most crops implies great potential for increasing food and agriculture production through improvements in productivity, even without further advances in technology.

Investment is of special interest as a limiting factor to agricultural production capacity and production because an alarming trend is being observed: public and private investment in agriculture has been declining (World Food Summit (WFS), 1996a). The decline in public investment is of particular concern because public investment in basic infrastructure, human capital formation and research and development (R&D) are necessary conditions for private investment. (Antholt, 1994; Evenson and McKinsey, 1991; Pray and Evenson, 1991; Pardey Roseboom and Craig, 1992). Public investments also promote technology adoption, stimulate complementary on-farm investment and input use and are needed for marketing the agricultural goods produced (Feder, Just and Zilberman, 1985; Nelson, 1964 and 1981; Nelson and Phelps, 1966; Rahm and Huffman, 1984; Rogers and Stanfield, 1968; Wozniak, 1989).

This paper provides an overview of the economic terminology, research findings, modelling techniques and their limitations that are used to link agricultural investment to output and productivity. After an introduction to the basic concepts and terminology of agricultural productivity and investment in the next section, the paper introduces three methodological approaches used to measure production growth, along with their advantages and disadvantages. In Section 1.4 findings from various agricultural growth studies are presented, focusing on the linkage between different types of agricultural investments and growth. Section 1.5 addresses the data needs and limitations in measuring the linkages between investments and growth in agriculture. These are of particular importance because data availability can limit the type of analysis done and consequently the questions that can be answered regarding agricultural growth and productivity. Concluding remarks are presented in the final section.

1.2 Terminology

This section describes economic terminology used throughout the book and commonly used in studies of agricultural growth.

Agricultural Productivity

Agricultural products are usually measured by weight or volume. An immediate question arises as to how to best combine different agricultural products since summing over weights or volumes is not very meaningful. One approach when dealing with crops is to convert them to a common physical unit, such as wheat units (Hayami and Ruttan, 1985; Block 1994). More commonly, aggregate output in agriculture is measured in monetary units as the sum of the value of all production in the agricultural sector minus the value of intermediate inputs originating within the agricultural sector. Both cash and non-cash (barter, trade and self-consumption) transactions of final products should be included. This is referred to as "final output" and differs from agricultural GDP by not subtracting out the value of non-agricultural inputs (Rao, 1993). In other words, final output is the amount of agricultural output available for the rest of the economy, while agricultural GDP measures the net contribution of agriculture to the GDP of a country.

Productivity measures are subdivided into partial or total measures. Partial measures are the amount of output per unit of a particular input. Commonly used partial measures are yield (output per unit of land), labour productivity (output per economically active person (EAP) or per agricultural person-hour). Yield is commonly used to assess the success of new production practices or technology. Labour productivity is often used as a means of comparing the productivity of sectors within or across economies. It is also used as an indicator of rural welfare or living standards since it reflects the ability to acquire income through sale of agricultural goods or agricultural production (Block, 1995).

Partial measures of productivity can be misleading, as there is no clear indicator of why they change. For example, land and labour productivity may rise due to increased use of tractors, fertilizer or output mix (move to high value crops). To account for at least some of those problems a total measure of productivity, the Total Factor Productivity (TFP) was devised. TFP is the ratio of an index of agricultural output to an index of agricultural inputs. The index of agricultural output is a value-weighted sum of all agricultural production components. The index of agricultural inputs is the value-weighted sum of conventional agricultural inputs. These generally include land, labour, physical capital, livestock and chemical fertilizers and pesticides. Growth in TFP is referred to as the Solow residual. It is generally considered a measure of technological progress that can be attributed to changes in agricultural research and development (R&D), extension services, human capital development such as education and physical, commercial infrastructure, as well as government policies and environmental degradation (Ahearn et al., 1998). Change in TFP can also be due to unmeasured inputs or imperfectly measured inputs.


Investment is the change in fixed inputs used in a production process. In the most narrow definition, investment is the change in the physical capital stock, that is, physical inputs that have a useful life of one year or longer (land, equipment, machinery, storage facilities, livestock). However, Eisner (1985) estimated that less than 20 percent of total growth in the United States comes from physical capital formation, while Denison's (1967) estimates were 10 to 15 percent.

Economists recognize that, though difficult to measure, a comprehensive agricultural investment measure should include improvements in land, development of natural resources and development of human and social capital in addition to physical capital formation. Human capital is the stock of knowledge, expertise or management ability. Since it is directly influenced by educational, training and extension institutions, variables such as education level or extension contacts are often used as proxy measures. Public and private expenditures on R &D are often used to proxy the level of human capital as well. Coen and Eisner (1987) specifically include R&D, education and training as forms of human capital investment.

Social capital is the stock of personal relationships and knowledge of institutions that an individual or household has. This affects the individual's access to risk minimizing inputs like credit, insurance and land title. In other words, social capital measures the ability to utilize social networks and institutions. Status, gender and group affiliations are often used as proxies for social capital in economic studies. However, education and transportation, as well as the range of social institutions available, can also influence social capital.

Asymmetry of Investment and Risk

A key characteristic of investment is its irreversibility, often referred to as asymmetry (Nelson, Braden and Roh, 1989). Once investments are made, there are few other productive activities for which they can be used. Dixit and Pindyck (1994) formulate the problem of the irreversibility of investment under uncertainty as the decision to pay a sunk cost and in return receive an asset with a value that can fluctuate. They demonstrate that under uncertainty actual investment will always be less than the expected present value of investment, the difference being attributable to the irreversibility of industry specific investments.

Agro-climatic factors may exacerbate the asymmetry of agricultural investment, as is the case when the land is suitable only for a particular crop. Other forms of investment, such as tractors and farm machinery have few other alternative uses besides agriculture, while human and social capital particular to agriculture may not adapt well to other sectors. Contrast this with investments made in capital markets or even factories. The former can be moved around to the most profitable enterprise, while, in general, the latter can be modified to produce more profitable products. Due to this fixity of agricultural assets and the uncertainty it entails, farmers are often reluctant to invest in equipment, land improvements or human capital. Uncertainty may cause the level of investment to be "sub-optimal", resulting in deteriorating physical and human capital and mining of soil nutrients.

Drawing on fixed asset theory, Nelson, Braden and Roh (1989) hypothesize that it is more difficult to dispose of capital specific to agricultural production than to add to the stock of specialized capital. This implies that periods of disinvestment (through depreciation) will be greater than those of investment in agriculture. Thus, in any given year net agricultural investment is likely to be negative (depreciation is higher than gross investment). Because investment is irreversible, farmers only invest during years when profits are high and/or borrowing costs are low.

Rosenzweig and Binswanger (1993) find that agricultural investment behaviour of farmers reflects their risk aversion, with poorer farmers accepting lower returns in exchange for lower risk to smooth their consumption. The wealthy are less risk averse; they can afford to accept higher risk in seeking higher returns. Hence, they find that wealthier farmers, particularly those with larger farms and diversified incomes, have higher rates of farm investment on a per hectare basis. They suggest that consumption credit and/or crop insurance would increase the overall profitability of agricultural investments.

Public Expenditures and Investment in Agriculture

Public expenditures on agriculture include short-term costs as well as long-term investments. Investment in agriculture and forestry includes government expenditures directed to agricultural infrastructure, research and development and education and training. Data on the proportion of all central government expenditures spent on agriculture and forestry are incomplete, particularly for African countries. Comparisons between developed and developing countries reveal that there is greater variation among developing countries than industrial countries. In industrial countries in 1992, the range of expenditures was between 0.4 to 9.1 percent, with most countries clustered around 1.5 percent. For those developing countries reporting, agricultural expenditures were between 1.5 to 7.9 percent in Africa, 1.7 to 23 percent in Latin America and 0.20 to 19 percent in Asia (IMF, 1995). As a percentage of expenditures, agricultural expenditures generally declined from 1988 to 1993 in Africa, Eastern Europe and industrialized nations, declined for some Asian countries, increased for China and were mixed for Latin America.

Human capital development is a key component of public agricultural investment. Judd, Boyce and Evenson (1991) examined the role of public expenditures in agricultural research and extension on agricultural output. They show that between 1959 and 1980, real spending on research and extension programs increased by factors of four to seven and that research intensities more than tripled for the lowest income developing countries. They show a decrease in the disparity between countries over time. They estimate world agricultural research public-sector expenditures at US$7.4 thousand million and world public sector agricultural extension expenditures at US$3.4 thousand million (both in 1980 dollars). Africa had the smallest share of world research expenditures (5.7 percent) and human resources (5.5 percent), yet a larger share of world extension expenditures (14.8 percent) than Asia and the second largest world share of extension human resources (20.7 percent). Calculating public sector expenditures as a percent of agricultural product, Africa's expenditures are higher than those of South and Southeast Asia.

The composition as well as the amount of public expenditure on agriculture is also of concern. As early as 1978, an FAO study identified a lack of investment in education and training in developing countries as an impediment to agricultural growth (Beal, 1978). In absolute and relative terms, expenditures on education and training by developing countries were less than those of developed countries. Beal proposed a target for education expenditures of at least 4.6 percent of GNP (the developed country average) and at least one field level extension worker per 1000 farm families.

1.3 Measuring Agricultural Productivity

Models of production growth have been used to measure the change in output, to identify the relative contribution of different inputs to output growth and to identify the Solow residual or output growth not due to increases in inputs.

Three different types of economic models have been used to investigate production growth:

  1. index numbers or growth accounting techniques,
  2. econometric estimation of production relationships and
  3. nonparametric approaches.

Each approach can be used to measure aggregate agricultural output or TFP. Each approach has different data requirements, is suitable for addressing different questions and has strengths and weaknesses.

Growth accounting involves compiling detailed accounts of inputs and outputs, aggregating them into input and output indices to calculate a TFP index (Diewert, 1976, 1980, 1981). The initial focus of growth accounting studies in the 1950s and 1960s was on partial measures of growth; only capital and labour were examined. However, growth accounting methods were unable to demonstrate much of a link between the amount of physical capital formation and output growth (Denison, 1987). Denison's (1967) growth accounting study of the 1950s and 1960s determined only 10 to 15 percent of growth could be accounted for by capital formation in non-residential plant and equipment (Cornwall, 1987). Nor did Bosworth (1982) find much of a role of reduced capital formation in the economic stagnation of the 1970s. Work by Abramovitz (1956), Solow (1957) and Kendrick (1973) "showed beyond reasonable doubt that the modern growth of the United States economy was in proportionate terms at least three-quarters due to increased efficiency in the use of productive inputs and not to the growth in the quantity of resource inputs per se " (Metcalfe, 1987). This implied that quality of inputs matters more than quantity.

The failure of economics in the 1950s, 1960s and 1970s to find strong relationships between capital formation and economic growth was due in part to a narrow definition of capital formation and partly due to failure to control for other inputs. The unexplained growth was of the order of half the change in real output. Subsequent studies have tried to close this gap by including more inputs (fertilizer, pesticides, etc.), or finding ways to quantify inputs (human capital) for the analysis. The Solow residual has been referred to as efficiency, technological progress, economies of scale, or a "measure of our ignorance" (Cornwall, 1987).

During the 1990s, there was a revival of interest in "new growth accounting" approaches, including endogenous growth models. The resurgence of interest in growth models has come in part from researchers incorporating omitted variables in their analysis, particularly measures of human capital, and new developments in the theory of growth. Hsieh (1998) developed a dual approach to computing the Solow residual using the growth in input prices rather than input quantities. Endogenous growth theory incorporates R&D as an intermediate input in the production process (Romer's [1990] varieties model) or views technological progress as improvements in the quality or cost of intermediate inputs (Grossman and Helpman's [1991] quality ladder model). Obsolescence in technology differentiates the quality ladder model from the varieties model (Barro, 1999). Both models contain endogenously driven technological change and exogenous technological change.

The econometric approach is based on econometric estimation of the production technology, either the production function (primal approach) or a cost function (dual approach) (Antle and Capalbo, 1988). The econometric approach started in the 1970s in response to the weak findings of the growth accounting approach. The idea was that one might find stronger relationships if one estimated production relations directly while employing less restrictive assumptions regarding aggregation and production technology (Capalbo and Vo, 1988). This approach also permits quantifying the marginal contribution of each category of inputs to aggregate production. For example, one can determine the impact of a one-percent increase in fertilizer use on overall agricultural output, holding all other inputs constant. Additionally, with the flexible functional form one does not impose as restrictive assumptions about technology as the accounting approaches. The general form of a flexible production function is:

where Q is an aggregate output index, T is a time trend representing technological change and Xi is a quantity index of input category i. For theoretical consistency, symmetry and hometheticity are imposed. However, to maintain sufficient degrees of freedom and to mitigate multicollinearity problems, it may be necessary to aggregate input data into a small number of categories. To avoid this, many researchers use the Cobb-Douglas function, despite the fact that it imposes some assumptions about technology, such as unitary elasticity of substitution. Capalbo (1988) compares econometric models that impose different assumptions about technology to estimate technological change, returns to scale and TFP in United States agriculture 1948-1982.

The application of endogenous growth theory using econometric approaches has focused on cross-country comparisons of the entire economy. These new growth models have been able to explain growth better than the old growth models. Using data from 1960 to 1985 for 98 countries, Mankiw, Romer and Weil (1992) augment a Solow model with a human capital variable to examine international variation in per capital GDP in three categories of countries (non-oil, intermediate and OECD). Even with a restrictive Cobb-Douglas functional form, they are able to capture about 80 percent of the variation in GDP among non-OECD countries. Using cross-section data from 98 countries on growth between 1960 and 1985, Barro (1991) incorporates both a human capital measure and population growth (arguing that raising the cost of children reduces fertility rates and increases investment in both physical and human capital). Barro finds that the returns to physical capital investment are positive but inelastic; a one-percent increase in the ratio of investment to GDP increases real growth in GDP by less than one percent. Levine and Renelt (1992) examine the average annual growth between 1960 and 1989 of 119 countries using an augmented Solow model to explore institutional and regional factors affecting growth. Using a simple linear regression model, their findings concur with Barro's that both human capital and fertility are important.

Nonparametric methods use linear programming techniques to calculate TFP (Chavas and Cox, 1992). This approach was also proposed as an alternative to growth accounting during its hiatus, prior to the development of endogenous growth theory. It shares the advantage of the flexible econometric approach by not imposing assumptions about the technology that generates agricultural output (Capalbo and Vo, 1988). The methodology is discussed at length in chapter 2. Essentially, linear programming techniques are used to identify the input-output combinations that define the production frontier (technological efficiency) either over time and/or across countries. The method can be utilized with detailed micro-level data (Chavas and Aliber, 1993) or time series (Chavas and Cox. 1992). In the former, efficiency is the portion of output not explained by the inputs and is measured relative to the other operations in the data set. One can use it to calculate an index of technological (as well as economic) efficiency. The index can be used by itself for comparative purposes or as a dependent variable to examine what factors might affect technological efficiency.

All three methods have strengths and weaknesses. The use of index numbers imposes several strong assumptions about technology (Hicks-neutral technical change, constant returns to scale and long-run competitive equilibrium). Another disadvantage is that since index numbers are not statistically derived, statistical methods cannot be used to evaluate their reliability. Additionally, they have not been particularly informative in identifying sources of growth. Their advantage of course is that they can be derived regardless of the number of observations and hence they are relatively easy to calculate.

The econometric approach has the advantage of being statistical, hence permitting hypothesis testing and calculation of confidence intervals to test the reliability of the model estimated. This approach explicitly measures the marginal contribution of each category of inputs to aggregate agricultural output. If a flexible functional form is chosen, a further advantage is that fewer restrictive assumptions about technology are imposed; the flexible functional form provides a second order approximation to a general function (Antle and Capalbo, 1988). The major disadvantage of the econometric approach is that it requires more data than the other approaches. In many cases, the number of observations may not exist to permit this approach. Barro (1999) notes that directly estimating the change in TFP through econometric techniques involves problems of simultaneity, measurement error and time variation or dynamics of factor shares.

An advantage of the nonparametric approach is that it does not impose restrictive assumptions on production technology. Nor is it data intensive, hence it can be widely applied. The major disadvantage is that since the models are not statistical, they cannot be statistically tested or validated.

1.4 Factors Influencing Growth in Agriculture

Economists originally limited themselves to examining the roles of labour and physical capital in economic growth. The failure to adequately explain growth led them to examine the roles of other factors and to develop endogenous growth theory. Investment in infrastructure has been cited as an important source of growth in agriculture (Jayne et al., 1994). However, Ferreira and Khatami (1996) claim that economic literature has not reached a consensus on the direction of causality between infrastructure and development. Nor can investment be viewed in isolation of policy reform which has been shown to be a vital stimulus of production (Auraujo Bonjean, Chambas and Foirry, 1997; Lachaal, 1994; Lin, 1992; McMillan, Whalley and Zhu, 1989; Wiens, 1983); as have institutions (North, 1994). Public investment in forms of human capital: education, extension, training and technology research have also been shown to increase productivity (Antholt, 1994; Beal, 1978; Evenson and McKinsey, 1991; Pray and Evenson, 1991; Pardey, Roseboom and Craig, 1992; Rosegrant and Evenson, 1992).

Nelson (1964 and 1981) recognized that there are important interactions between capital formation, labour allocation, technical progress and productivity. This calls into question whether the growth due to physical capital can be separated from growth attributed to other inputs. Unless a production technology is a fixed Leontief process, there is always some degree of substitutability among categories of inputs. However, since inputs are not perfect substitutes, the lack of adequate investment can slow down production growth. Estimates of the elasticity of substitution in agriculture between hired labour and capital equipment vary from 0.32 in the short run (Brown and Christensen, 1981) to 1.78 percent (Lopez, 1980) in the long run.

Most measures of TFP incorporate inputs and physical capital, leaving human and social capital, technology, institutions, infrastructure and policy to "explain" growth in TFP. Social and human capital are the on-farm human elements that mediate how policy, technology, institutions and infrastructure affect input and physical capital use. Human capital directly affects whether and how technology will be adopted. Technology choice in turn, affects the inputs and physical capital used. That is, technology is embodied in the types of inputs and how they are used. Social capital affects access to physical capital (e.g. land directly or through land titling and loans) and variable inputs (e.g. through credit or cooperatives).

In general, researchers have estimated TFP and then focused on how one or several of these factors might be driving its growth (Antle, 1983; Nehru and Dhareshwar, 1994; Evenson and McKinsey, 1991; Rosegrant and Evenson, 1992). Usually, they have done so using the change in TFP as a dependent variable in a regression with explanatory variables that represent measures of technology, human capital and policy (which are not easily quantifiable or assignable in constructing the production indices). In the following sections, policy is divided between budgetary policies that affect investment in R&D and infrastructure, political and economic policies and political stability.

Human Capital

Human capital directly influences agricultural productivity by affecting the way in which inputs are used and combined by farmers. Improvements in human capital affect acquisition, assimilation and implementation of information and technology. Human capital also affects one's ability to adapt technology to a particular situation or to changing needs.

Schultz's (1963) classic work attributed between 21 to 23 percent of the growth in U.S. income, between 1929 and 1957, to education of the labour force. Contemporaneously, Griliches (1963) focused on minimizing the unexplained portion of growth in U.S. agriculture by adjusting labour for quality, using education. When he included research and extension expenditure as an input to production, he found that virtually all the "unexplained" growth could be explained by economies of scale, R&D and labour quality changes. Romer (1986) and Lucas (1988) provide theoretical grounds for human capital being the driving force behind economic growth.

Jamison and Lau (1982) explored the role of farmer education and extension on farm efficiency. They found that farmer education and extension were not only important to enhancing production on Thai, Korean and Malaysian farms, but that there was an interaction effect between education and extension. In contrast, they found physical capital had an insignificant impact on production and profits. On the other hand, some researchers are finding evidence that returns to education are low, especially for those who stay in agriculture. In their summary of the findings on the determinants of rural poverty for six country studies based on econometrically estimated income equations, Lopez and Valdes (2000) conclude that the return to education in farming is surprisingly small in most cases. An increase in one year in the average level of schooling raises per capita annual income of the family by less than US$ 20 per person in most cases. The main contribution of education in rural areas appears to be to prepare young people to emigrate to urban areas and towns.

Using an econometric approach, Nehru and Dhareshwar (1994) examined sources of TFP growth in 83 industrial and developing countries for the period 1960-1990. They found that human capital formation was three to four times more important than raw labour in explaining output growth. Using human capital as a separate variable, they found that the countries with the fastest growing economies have based their growth on factor accumulation (human capital, labour and physical capital), not growth in efficiency or technology.

Research and Technology Transfer

Research increases the set of available technologies, hence agricultural R&D expenditures are used as a proxy for agricultural technological change. However, the development of technology does not always result in its adoption. In some cases this may be because the technology being developed is not appropriate, that is, it does not meet the needs of agricultural producers. Hence, researchers focus on public expenditure as an explanatory variable in TFP growth. Additionally public research has been shown to lead private research (Chavas and Cox, 1992).

Several caveats arise in focusing on public R&D to explain growth in agricultural TFP. Public R&D expenditures are used as proxy for R&D results, yet there is not an exact correspondence between expenditures and technology. Even when technology is produced, researchers may have different goals than farmers, e.g. yield maximization rather than profit maximization or risk minimization or improvement in commercial crops rather than staple crops. Additionally, when an appropriate technology does result, the process of technology adoption in agriculture is widely recognized as one that occurs over many years in which some adopt quickly and others wait for extension or the results of their neighbours to convince them to adopt.

Bearing this in mind, researchers have found that public investment in developing and extending agricultural technology is justified by the high rates of return to such investment. In a survey of studies on Asia, Pray and Evenson (1991) found rates of return to national research investment from 19 to 218 percent, returns to national extension investment from 15 to 215 percent and returns to international research investment of 68 to 108 percent. A report of the Taskforce on Research Innovations for Productivity and Sustainability indicated that the returns to research, though variable, were always high, from 22 to 191 percent. Using an index number approach to calculate TFP for several crops in India, Rosegrant and Evenson (1992) and Evenson and McKinsey (1991) used econometric analysis to identify sources of growth in TFP. Rosegrant and Evenson (1992) found that public research accounted for 30 percent of growth and extension for about 25 percent, with rates of return for each respectively of 63 percent and 52 percent. Evenson and McKinsey (1991) found that public investment in India in research accounts for over half of growth, while extension contributes about one-third and infrastructure accounted for very little growth. They calculated internal rates of return of 218 percent for public research, 177 percent for public extension and 95 percent for private research expenditures in India.

Block (1994) compares econometric estimates of TFP for Sub-Saharan Africa between 1963 and 1988. He uses three different methods of aggregating agricultural output: official exchange rates, purchasing power parity and wheat units. He finds that one-third of the growth in agricultural TFP in Sub-Saharan Africa is due to research expenditures. In India, Rao and Hanumantha (1994) attribute continued growth in agriculture, despite a sharp decline in physical capital formation, to better utilization of existing infrastructure, fertilizer and high yielding varieties.

While the returns to research are high, the technology is not always adopted. For example, high yield varieties (HYV) of wheat and rice have been introduced on less than one-third of the 423 million hectares planted to cereal grains in the Third World. Specifically, in Asia and the Middle East 36 percent of the grain area was HYV, 22 percent in Latin America and one percent in Africa (Wolf, 1987). This implies there is much potential for increasing agricultural productivity using existing technology. However, the use of HYV requires increased use of fertilizer, but external debt in Latin America and poverty and inadequate water supply in Africa have made fertilizer use and hence HYV unprofitable. Jahnke, Kirschke and Lagemann (n.d.) also attributed low adoption of HYV in Africa to lack of appropriate technology development and few extension services directed to women. Additionally, nontraditional crops have rarely been the focus of improved varieties or technology and potential exists to develop them to increase agricultural production.

Public Investment and Policy

Public policy and budgetary decisions regarding infrastructure also have a profound effect on agricultural production. The financing aspects of public R&D and human capital development were discussed above, but both physical and institutional infrastructure affect the development and transfer of technology. For example, irrigation systems and roads may be required to make a technology profitable to implement. Reforms in pricing policy or the marketing system may be needed to provide incentives.

A serious conflict arises with structural adjustment reforms. Budget cuts in public services often accompany market reforms. While fiscal restraint may be required to stabilize the economy in the short run, cuts in human capital development, public R&D, and infrastructure have a detrimental long-term effect on productivity growth. Policy makers need to choose carefully to mitigate the deleterious impacts of budget cuts on future growth.

Using an econometric approach, Jayne et al. (1994) demonstrated the complementarity of public policies and public investments in facilitating the use of new technology. They point to the sharp decline in public investments and growth in Zimbabwe during the 1980s. Pal (1985) underscores the complementarity of public policy towards investment in irrigation technology and private variable input use.

The importance of policy reform is increasingly viewed as fundamental for agricultural productivity gains. Liberalizing markets so prices can send proper signals to producers is the fundamental objective of structural adjustment programs in developing countries and policy reform in economies in transition. Assigning property rights is viewed as a means of promoting development through the efficient and responsible use of resources (North, 1994) and therefore underlies the distribution of capital in economies in transition, land reform and most land policy. Block (1994) discusses the complementarity of economic reform and technical change, but cautions that policy reform offers a one-time effect.

An example of the relation between policy reform and productivity is the implementation of China's "responsibility system" (RS) in 1980-81, which linked productivity to material reward, resulted in increased crop yields "for every major crop" (Wiens, 1983). McMillan, Whalley and Zhu (1989) calculated that in response to the RS and price reforms, output in the Chinese agricultural sector increased by over 61 percent between 1978 and 1984. They attribute 78 percent of the increase to the RS and 22 percent to higher prices for crops. They calculate the RS increased productivity in agriculture by 32 percent. Lin (1992) calculated that 42 to 47 percent of the growth in agricultural output was attributable to the RS during the same period.

In another example, price reforms in Egypt implemented in 1986 resulted in increased wheat and maize yields from 1987 to 1993. Rice production increased by 62 percent, while yields increased by 42 percent (Khedr, Ehrich and Fletcher, 1996). Bevan, Collier and Gunning (1993) contrast the performance of agriculture in Kenya and Tanzania. In Kenya where there was little intervention production of food and cash crops increased by 4.6 and 5.5 percent per annum, respectively. In Tanzania, where policies controlled prices and taxed export crops, agricultural production stagnated until policy reforms were instituted in 1984.

Using an econometric approach to estimate TFP for the United States dairy industry 1972-1992, Lachaal (1994) examined how protectionist policies in the form of direct subsidies to agriculture reduced productivity growth in the United States dairy industry. Lachaal showed that government subsidies encouraged using materials at the expense of feed and raised the cost of production by 1.8 percent for each 10 percent increase in subsidy. The subsidy policy was the source of technical inefficiency, creating biases that distorted factor usage.

Political Stability and Conflict

Another aspect of policy that can influence or hinder agricultural production is the political situation. In a study of the productivity growth of 83 industrial and developing countries between 1960 and 1990, Nehru and Dhareshwar (1994) found that the economies that perform the worst are those involved in wars (particularly civil wars) and those that have the most price distorting policies. They explore a variety of policy variables and find that apart from political stability and the initial endowments of a county, virtually no other policy variable is associated with growth.

The World Food Summit Plan of Action items 2 and 3 (1996b) recognize the role of government in providing an environment conducive to investment, through guarantee of rights and law as well as policies encouraging investment. Corruption is the extreme case where law enforcement breaks down and incentives are lacking. While long-standing institutionalized bribery can be seen as simply an added cost of doing business, pervasive corruption and violence increase risk and result in capital flight, disinvestment and jeopardize assistance.

1.5 Data Needs for Growth Models

In order to estimate any type of growth model, data are needed on agricultural output and inputs, including capital and labour. Comparable and consistent data are needed to make cross-country comparisons over time and space. The FAOSTAT under the World Agricultural Information Centre (WAICENT) is one of the most comprehensive agricultural databases created by FAO. FAOSTAT data are available by country and by year on agricultural production (crop and livestock), trade, land, economically active population in agricultural activity and means of production. The data on means of production include details of agricultural tractors, harvesters and threshers and milking machines in use, trade (export and import) portion of other agricultural machinery, fertilizers and pesticides. These data are generally expressed in quantity except for data on agricultural production and trade, which are in value.

FAOSTAT database does not include any data on structures, hand tools and value of improvements to land. The System of Economic Accounts for Food and Agriculture (SEAFA) (FAO, 1996) represents guidelines for the creation of a comprehensive database of physical capital for use of FAO member countries. SEAFA is a specific application of the United Nations 1993 System of National Accounts (SNA). The UN Statistics Division, jointly with the OECD, will be collecting data on national accounts aggregates based on 1993 SNA from member countries, starting in 2000. These data will include fixed gross capital formation in agriculture.

Gross fixed capital formation under SEAFA will include:

  1. acquisitions less disposal of farm buildings and other structures, machinery and equipment, plantations, trees and livestock that can be used repeatedly or continually to produce fruit, rubber or milk;
  2. improvements to tangible assets including land; and
  3. costs associated with the transfer of ownership of non-produced assets.

Gross fixed capital formation specifically includes breeding stock, dairy cattle, sheep reared for wool and draught animals. Land improvements include reclamation of land by construction of dams, dikes or walls, drainage of marshes and flood control. Thus, SEAFA represents a vast improvement in the database on physical capital.

However, time series on gross fixed capital formation are not based on primary data collected in the field. Benchmark estimates of farm assets are extrapolated using quantity and price indices. Consumption of fixed capital is based on estimated useful life and estimated current value of the stock of fixed assets.

Data Issues

Regardless of the source of data, there are several data issues that are widely recognized in estimating growth models. Each is discussed below to highlight the importance and difficulty of developing consistent international data standards. However, developing data standards that will be in place a long time facilitate future researchers' ability to analyse and explain trends.


Griliches (1987) definition of productivity, "a ratio of some measure of output to some index of input use," highlights the vagaries of aggregating outputs and inputs. The physical units are simply not interchangeable unless converted to some common physical equivalent (Block, 1994). However, monetary values are the most widely used method of aggregating both inputs and outputs, since monetary values can be summed together in a meaningful way and prices reflect the relative value of the items being aggregated.

Exchange Rates

In order to do cross country comparisons over time, data must be converted to a common real unit (i.e. adjusted for currency differences and inflation). However, an extensive literature outlines the problems associated with using official exchange rates to convert values to a common unit (Antle, 1983; Block, 1994; Nehru and Dhareshwar, 1994; and Pardey, Roseboom and Craig, 1992). The argument is that official exchange rates that do not reflect the actual currency values distort relative price relations. Purchasing power parity (PPP) has been used as an alternative (Pardey, Roseboom and Craig, 1992). However, Antle (1983) argues that PPP has little relevance to agricultural output because it is based largely on non-agricultural goods and services and their use overstates agricultural production in developing countries. Another technique to avoid exchange rate distortions is to convert production to a common physical unit, such as "wheat units" (Block, 1994). Summers and Heston (1988) provide recommendations and develop a System of Real National Accounts that permits cross country comparisons.


Insufficient disaggregation of inputs implies the inability to assign inputs to particular outputs. For example, the total amount of fertilizer or labour may be known, but how they are allocated among agricultural products may not. This is of particular importance when allocation of inputs is skewed to a minority of producers or crops such that reallocation could greatly improve total agricultural output.

Perhaps a greater problem exists with public expenditures and how to allocate them to agriculture. Rural development projects, for example, may have an agricultural component, but may not have an exclusively agricultural focus. Public education and training is rarely exclusively for agriculture, creating problems of how to allocate the expenditures to agriculture. Private education and training investments also are difficult to separate out an agricultural component.

Measuring Inputs and Outputs

A well-recognized problem is simply in measuring output. Kelly et al. (1995) estimate that data collection methods underestimate African agricultural production by up to 50 percent. This is because mixed cropping is common, crop by-products are not enumerated, crops are consumed at home or as inputs to other household production activities, or farmers have diversified into new products that are poorly enumerated in national surveys. On the input side, little data is available on small capital investments such as implements and land improvements, especially the value of family labour in land improvements.

Valuing Natural and Human Resources

Neither technology nor human capital can be quantified directly. Expenditures on research and extension have been successfully used as proxies for technology. Proxies for human capital are more problematic. Education level is generally available only at the national or regional level, not for the agricultural sector, thus is only a rough estimate of the level of agricultural human capital.

Measuring social capital generally requires a micro-data collection to develop a proxy. In village level studies, group affiliation or status has been used. At the country level, an aggregate proxy may be difficult to implement. One possibility is to use the percent of farms that are headed by males. Rural household income could be used as a proxy for relative wealth or status.

In official statistics, neither the value of natural resources nor the cost of environmental degradation are recognized in valuing land (FAO, 1996, p. 49). Wolman (1985) estimates that ignoring these costs can be high. He reports agricultural productivity losses due to soil erosion up to 40 percent in the former USSR, 25 percent in the US, 30 percent in Haiti and 25 percent in Nigeria.

Lag Length and Dynamics of Investments

Another issue that affects data requirements, is exploring the time lag over which investments affect productivity. Capital investments by definition affect production in more than one year. The contribution of capital items to production diminishes or depreciates over time. In some cases the process may be linear, but in others the trajectory may be quite nonlinear or even discontinuous. Additionally, the process may be quite long. Chavas and Cox (1992) found that 30 years are required to fully capture the effects of public research expenditures in US agricultural productivity. This implies the need for extensive time series data to measure the effects of investments on productivity.

1.6 Summary and Conclusions

This paper has surveyed a number of issues relating to different aspects of agricultural investment, agricultural productivity and its determinants. Economic research indicates that the investigation of the relationship between agricultural investment and productivity requires updating the working definition of investment and extending it beyond physical capital. Researchers have found a relatively weak relationship between physical capital and growth, as compared to investment in technology and human capital. Nonetheless, physical capital investments may be the precursor that stimulates private investment and it is complementary to public and private investments in human capital. Other factors that are important stimulants or inhibitors to growth include: the policy environment, political stability and natural resource degradation. Evaluating the importance of the latter runs into problems of lack of data on the value of natural resources and the cost of their depletion and degradation.

Furthermore, this paper provides background on methodologies used in the rest of the book. It presents the advantages and the drawbacks of the different approaches that have been used to measure agricultural productivity. Some of the main data issues related to estimating growth models were identified and the importance and difficulty of developing consistent international data has been highlighted. In fact, the existence of consistent data over time will facilitate future researchers' ability to analyze and explain trends. FAOSTAT, under the World Agricultural Information Centre (WAICENT), and the UN Statistics Division, jointly with OECD, are operating along these lines to develop a comprehensive and consistent dataset of fixed capital formation in 170 countries.


Abramovitz, M. 1956. Resource and output trends in the United States since 1870. American Economic Review, Papers and Proceedings, 46 May: 5-23.

Ahearn, M., Yee, J., Ball, E. & Nehring, R. 1998. Agricultural productivity in the United States. Resource Economics Division, Economic Research Service, U.S. Department of Agriculture. Agriculture Information Bulletin no. 740.

Antholt, C.H. 1994. Getting ready for the twenty-first century: technical change and institutional modernization in agriculture. World Bank Technical Paper #217. February.

Antle, J. 1983. Infrastructure and aggregate agricultural productivity: international evidence. Economic Development and Cultural Change, 31 April: 609-619.

Antle, J. & Capalbo, S. 1988. An introduction to recent developments in production theory and productivity measurement. In S. Capalbo & J. Antle, eds. Agricultural productivity measurement and explanation. Washington, DC, Resources for the Future, pp17-95.

Auraujo Bonjean, C., Chambas, G. & Foirry, J.P. 1997. Consequences de l'ajustement des finances publiques sur l'agriculture marocaine et tunisienne. Unpublished FAO study, March.

Barro, R. 1991. Economic growth in a cross-section of countries. Quarterly Journal of Economics, 106 (2): 407-444.

Barro, R. 1999. Notes on growth accounting. Journal of Economic Growth, 4 June: 119-137.

Beal, D.W. 1978. Agricultural education and training in developing countries. In FAO Studies in Agricultural Economics and Statistics, 1952-1977. Rome, FAO, pp. 282-291.

Bevan, D., Collier, P. & Gunning, W. 1993. Government policies and agricultural performance: Tanzania and Kenya. In I. Goldin, ed. Economic reform, trade and agricultural development. Paris, OECD.

Block, S. 1994. A new view of agricultural productivity in Sub-Saharan Africa. American Journal of Agricultural Economics, 76 August: 619-624.

Bosworth, B. 1982. Capital formation and economic policy. Brookings Papers on Economic Activity, 2: 273-317.

Brown, R.S. & Christensen, L.R. 1981. Estimating elasticities of substitution in a model of partial static equilibrium: an application to US agriculture, 1947-74. In E. Berndt & B. Fields, eds. Modeling and measuring natural resource substitution. Cambridge, USA, MIT Press.

Capalbo, S. 1988. A comparison of econometric models of US agricultural productivity and aggregate technology. In S. Capalbo & J. Antle, eds. Agricultural productivity measurement and explanation. Washington, DC, Resources for the Future, pp. 159-188.

Capalbo, S. & Vo, T. 1988. A review of evidence on agricultural productivity and aggregate technology. In S. Capalbo & J. Antle, eds. Agricultural productivity measurement and explanation. Washington, DC, Resources for the Future, pp. 96-137.

Chavas, J.P. & Aliber, M. 1993. An analysis of economic efficiency in agriculture: a nonparametric approach. Journal of Agricultural and Resource Economic, 18(1): 1-16.

Chavas, J.P. & Cox, T. 1992. A nonparametric analysis of the influence of research on agricultural productivity. American Journal of Agricultural Economics, 74: 583-591.

Coen, R.M. & Eisner, R. 1987. Investment. In J. Eatwell, M. Milgate & P. Newman, eds. The new Palgrave dictionary of economics, vol 2. New York, USA, Stockson Press.

Cornwall, J. 1987. Total factor productivity. In J. Eatwell, M. Milgate & P. Newman, eds. The new Palgrave dictionary of economics, vol 4. New York, USA, Stockson Press.

Denison, E. 1967. Why growth rates differ: postwar experience in nine western countries. Washington,DC, Brookings.

Denison, E. 1987. Growth accounting. In J. Eatwell, M. Milgate & P. Newman, eds. The new Palgrave dictionary of economics, vol 2. New York, USA, Stockson Press.

Diewert, W.E. 1976. Exact and superlative index numbers. Journal of Econometrics, 4: 115-145.

Diewert, W.E. 1980. Capital and the theory of productivity measurement. American Economic Review, 70: 260-267.

Diewert, W.E. 1981. The economic theory of index numbers: a survey. In A. Deaton, ed. Essays in the theory and measurement of consumer behaviour in honour of Sir Richard Stone. London, Cambridge University Press.

Dixit, A.K. & Pindyck, R.S. 1994. Investment under uncertainty. Princeton, USA, Princeton University Press.

Eisner, R. 1985. The total incomes system of accounts. Survey of Current Business, 56 January: 24-48.

Evenson, R.E. & McKinsey Jr., J.W. 1991. Research, extension, infrastructure and productivity change in Indian agriculture. In R.E. Evenson & C.E. Pray, eds. Research and productivity in Asian agriculture. Ithaca, USA, Cornell University Press.

Feder, G., Just, R.E. & Zilberman, D. 1985. Adoption of agricultural innovations in developing countries: a survey. Economic Development and Cultural Change, 33: 255-298.

Ferreira, D. & Khatami, K. 1996. Financing private infrastructure in developing countries. World Bank Discussion Paper # 343 December. Washington, DC, World Bank.

Food and Agriculture Organization. 1996. A system of economic accounts for food and agriculture. FAO Statistical Development Series, 8. Rome, FAO.

Griliches, Z. 1963. The sources of measured productivity growth: United States agriculture, 1940-1960. Journal of Political Economic, 71(4): 331-346.

Griliches, Z. 1987. Productivity: measurement problems. In J. Eatwell, M. Milgate & P. Newman, eds. The new Palgrave dictionary of economics, vol 3. New York, USA, Stockson Press.

Hayami, Y. & Ruttan, V.W. 1985. Agricultural development: an international perspective. Baltimore, USA, Johns Hopkins University Press.

Hsieh, C.T. 1998. What explains the industrial revolution in East Asia? Evidence from factor markets. Unpublished paper, University of California, Berkeley, USA, January.

International Monetary Fund. 1995. Government finance statistics yearbook. Washington, DC, IMF.

Jahnke, H. E., Kirschke, D. & Lagemann, J. n.d. The impact of agricultural research in tropical Africa: a study of the collaboration between international and national research systems. CGIAR Study Paper Number 21. Washington, DC, World Bank.

Jamison D. & Lau, L. 1982. Farmer education and farm efficiency. Washington, DC, World Bank.

Jayne, T.S., Khatri, Y., Thirtle, C. & Reardon, T. 1994. Determinants of productivity change using a profit function: smallholder agriculture in Zimbabwe. American Journal of Agricultural Economics, 76 August: 613-618.

Judd, M.A., Boyce, J.K. & Evenson, R.E. 1991. Investment in agricultural research and extension programs: a quantitative assessment. In R.E. Evenson & C.E. Pray, eds. Research and productivity in Asian agriculture. Ithaca, USA, Cornell University Press.

Kelly, V., Hopkins, J., Reardon, T. & Crawford, E. 1995. Improving the measurement and analysis of African agricultural productivity: promoting complementarities between micro and macro data. Michigan State University International Development Paper No. 16. East Lansing, USA, Department of Agricultural Economics.

Kendrick, J. 1973. Postwar productivity trends in the United States, 1948-1969. New York, USA, National Bureau of Economic Research.

Kerf, M. & Smith, W. 1996. Privatizing Africa's infrastructure. World Bank Technical Paper no. 337, September. Washington, DC, World Bank.

Khedr, H., Ehrich, R. & Fletcher, L.B. 1996. Nature, rationale and accomplishments of the agricultural policy reforms, 1987-1994. In L.B. Fletcher, ed. Egypt's agriculture in a reform era. Ames, USA, Iowa State University Press.

Lachaal, L. 1994. Subsidies, endogenous technical efficiency and the measurement of production growth. Journal of Agriculture and Applied Economics, 26(1): 299-310.

Levine, R. & Renelt, D. 1992. A sensitivity analysis of cross-country growth regressions. American Economic Review, 82(4) : 942-963.

Lin, J.Y. 1992. Rural reforms and agricultural growth in China. American Economic Review, 82 March: 34-51.

Lopez, R. & Valdes, A. 2000. Rural poverty in Latin America: Analytics, new empirical evidence, and policy. London: Macmillan Press. Forthcoming.

Lopez, R. 1980. The structure of production and the derived demand for inputs in Canadian agriculture. American Journal of Agricultural Economics, 62(1): 38-45.

Lucas, R. 1988. On the mechanics of economic development. Journal of Monetary Economics 22: 3-42.

Mankiw, N.G., Romer, D. & Weil, D.N. 1992. A contribution to the empirics of economic growth. Quarterly Journal of Economics, 107(2): 407-437.

McMillan, J., Whalley, J. & Zhu, L. 1989. The impact of China's economic reforms on agricultural productivity growth. Journal of Political Economy, 97 August: 781-807.

Metcalfe, S. 1987. Technical change. In J. Eatwell, M. Milgate & P. Newman, eds. The new Palgrave dictionary of economics, vol 4. New York, USA, Stockson Press.

Nehru, V. & Dhareshwar, A. 1994. New estimates of total factor productivity growth for developing and industrial countries. World Bank Policy Research Working Paper #1313, June. Washington, DC, World Bank.

Nelson, C. H., Braden, J.B. & Roh, J.S. 1989. Asset fixity and investment asymmetry in agriculture. American Journal of Agricultural Economics, 71November: 970-979.

Nelson, R. 1964. Aggregate production functions and medium-range growth projections. American Economic Review, 54(5): 575-606.

Nelson, R. 1981. Research on productivity growth and productivity differences: dead ends and new departures. Journal of Economic Literature, 19(3): 1029-1064.

North, D. 1994. Economic performance through time. American Economic Review, 84(3): 359-368.

Pal, S.P., ed. 1985. Contribution of irrigation to agricultural production and productivity. New Dehli, National Council of Applied Economic Research.

Pardey, P.G., Roseboom, J. & Craig, B.J. 1992. A yardstick for international comparisons: an application to national agricultural research expenditures. Economic Development and Cultural Change, 40: 333-349.

Pray, C.E. & Evenson, R.E. 1991. Research effectiveness and the support base for national and international agricultural research and extension programs. In R.E. Evenson & C.E. Pray, eds. Research and productivity in Asian agriculture. Ithaca, USA, Cornell University Press.

Rahm, M.R. & Huffman, W.E. 1984. The adoption of reduced tillage: the role of human capital and other variables. American Journal of Agricultural Economics, 66: 405-413.

Rao, C. & Hanumantha, H. 1994. Agricultural growth, rural poverty and environmental degradation in India. New Dehli: Oxford University Press.

Rao, D.S.P. 1993. Intercountry comparisons of agricultural output and productivity. FAO Economic and Social Development Paper 112. Rome, FAO.

Rogers, E. & Stanfield, J.D. 1968. Adoption and diffusion of new products: emerging generalizations and hypotheses. In F.M. Bass, C.W. King & E.A. Pessemier eds. Application of the sciences in marketing management. New York, USA, John Wiley & Sons.

Romer, P. 1986. Increasing returns and long run growth. Journal of Political Economic, 94: 1002-1037.

Romer, P. 1990. Endogenous technological change. Journal of Political Economic, 98 part II: S71-S102.

Rosegrant, M.W. & Evenson, R.E. 1992. Agricultural productivity and sources of growth in South Asia. American Journal of Agricultural Economics, 74 August: 757-761.

Rosenzweig, M.R. & Binswanger, H.P. 1993. Wealth, weather risk and the composition and profitability of agricultural investment. The Economic Journal, 103 January: 56-78.

Schultz, T.W. 1963. The economic value of education. New York, USA, Columbia University Press.

Solow, R. 1957. Technical change and the aggregate production function. Review of Economics and Statistics, 39 August: 312-320.

Summers, R. & Heston, A. 1988. A new set of international comparisons of real product and price levels estimates for 130 countries 1950-1985. The Review of Income and Wealth, 34(1): 1-25.

Taskforce on Research Innovation for Productivity and Sustainability. n.d. Global research on the environmental and agricultural nexus for the 21st century: a proposal for collaborative research among US universities, CGIAR centers and developing country institutions. Gainesville, USA, Office of International Studies and Programs at the University of Florida.

Wiens, T.B. 1983. Price adjustment, the responsibility system and agricultural productivity. American Economic Review, papers and proceedings, 73 May: 319-324.

Wolf, E.C. 1987. Raising agricultural productivity. In State of the world: a Worldwatch Institute report on progress toward a sustainable society. Worldwatch, pp. 139-156.

Wolman, M.G. 1985. Soil erosion and crop productivity: a worldwide perspective. In R.F. Follett & B.A. Stewart, eds. Soil Erosion and Crop Productivity. Madison, USA, American Society of Agronomy.

World Food Summit. 1996a. Technical background documents 1-15. Rome, FAO.

World Food Summit. 1996b. Plan of Action. Rome, FAO, 13-17 November.

Wozniak, G.D. 1989. The adoption of interrelated innovations: a human capital approach. Review of Economics and Statistics, 66: 70-79.

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