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EPTD DISCUSSION PAPER NO. 99 PUBLIC SPENDING IN DEVELOPING COUNTRIES TRENDS, DETERMINATION,

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EPTD DISCUSSION PAPER NO. 99

PUBLIC SPENDING IN DEVELOPING COUNTRIES: TRENDS, DETERMINATION, AND IMPACT Shenggen Fan and Neetha Rao

Environment and Production Technology Division

International Food Policy Research Institute

2033 K Street, N.W.

Washington, D.C. 20006 U.S.A.

February 2003

EPTD Discussion Papers contain preliminary material and research results, and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers will eventually be published in some other form, and that their content may also be revised.

ABSTRACT

The objective of this paper is to review trends in government expenditures in the developing world, to analyze the causes of change, and to develop an analytical framework for determining the differential impacts of various government expenditures on economic growth.

Contrary to common belief, it is found that structural adjustment programs increased the size of government spending, but not all sectors received equal treatment. As a share of total government spending, expenditures on agriculture, education, and infrastructure in Africa; on agricultural and health in Asia; and on education and infrastructure in Latin America, all declined as a result of the structural adjustment programs.

The impact of various types of government spending on economic growth is mixed. In Africa, government spending on agriculture and health was particularly strong in promoting economic growth. Asia’s investments in agriculture, education, and defense had positive growth-promoting effects. However, all types of government spending except health were statistically insignificant in Latin America. Structural adjustment programs promoted growth in Asia and Latin America, but not in Africa.

Growth in agricultural production is most crucial for poverty alleviation in rural areas. Agricultural spending, irrigation, education, and roads all contributed strongly to this growth. Disaggregating total agricultural expenditures into research and non-research spending reveals that research had a much larger impact on productivity than non-research spending.

Table of Contents

1. Introduction...........................................................................................................................1

2. Government Spending: Trends, Size, and Composition.......................................................3

3. Determination of Government Expenditures......................................................................13

4. Impact of Government Spending on Growth......................................................................20

5. Major Findings and Recommendations..............................................................................28

References................................................................................................................................30

Appendix 1: Data Sources and Measurement Issues...............................................................33

PUBLIC SPENDING IN DEVELOPING COUNTRIES:

TRENDS, DETERMINATION, AND IMPACT1

Shenggen Fan and Neetha Rao2

1. INTRODUCTION

Many developing countries are currently undergoing substantial macroeconomic adjustments. It is not clear how such programs are affecting government expenditure and hence longer-term economic growth and poverty reduction. Thus, it is important to monitor trends in the levels and composition of government expenditures, and to assess the causes of change over time. It is even more important to analyze the relative contribution of various expenditures to production growth and poverty reduction, as this will provide important information for more efficient targeting of these limited and often declining financial resources in the future.

There have been numerous studies on the role of government spending in the long-term growth of national economies (Aschauer 1989; Barro 1990; Tazi and Zee 1997). These studies found conflicting results about the effects of government spending on economic growth. Barro was among the first to formally endogenize government spending in a growth model and to analyze the relationship between size of government and rates of growth and saving. He concluded that an increase in resources devoted to non-productive (but possibly utility-enhancing) government services is associated with lower per capita growth. Tazi and Zee also found no relationship between government size and economic growth. On the other hand, Aschauer’s empirical results indicate that non-military public capital stock is substantially more

Partial funding from USAID and the World Bank is acknowledged.

Shenggen Fan is a Senior Research Fellow and Neetha Rao is a Senior Research Assistant in the Environment and Production Technology Division, International Food Policy Research Institute.

important in determining productivity than is the flow of non-military or military spending, that military capital bears little relation to productivity, and that the basic stock of infrastructure of streets, highways, airports, mass transit, sewers, and water systems has most explanatory power for productivity. Many studies also attempted to link government spending to agricultural growth and poverty reduction (Elias 1985; Fan, Hazell, and Thorat 2000; Fan, Zhang, and Zhang 2000; and Fan and Pardey 1998). Most of these studies found that government spending contributed to agricultural production growth and poverty reduction.

The purpose of this study is to review and analyze the trends and causes of change in government expenditures and their compositions in the developing world, and to develop an analytical framework for determining differential impacts of various government expenditures on economic growth. We first review trends in and the composition of government expenditures across developing regions of Africa, Asia, and Latin America. We then model determinants of composition of government expenditures. Next, we model effects of government expenditures on gross domestic product (GDP) growth by estimating a GDP function and estimate the impact of various public capitals on agricultural GDP growth. We conclude with the study’s major findings and recommendations.

2. GOVERNMENT SPENDING: TRENDS, SIZE, AND COMPOSITION

For the purpose of cross-country comparisons, we converted all government expenditures into 1995 constant international dollars. We collected data from 1980 to 1998 for 43 developing countries across Asia, Africa, and Latin America.3

TOTAL GOVERNMENT SPENDING AND COMPOSITION

Over the past two decades, government expenditures in 43 developing countries considered in this study experienced an erratic pattern. During the 1980s, expenditures increased from $776 billion in 1980 to $1,148 billion in 1990, with an annual growth rate of 4 percent (Table 1). In the 1990s, governments increased their spending power. By 1998, total expenditures reached $1,790 billion, with an annual increase of 5.7 percent. There appears to be no obvious adverse impact of macroeconomic adjustments on government spending for these developing countries as a whole.

For detailed explanation of data sources and country coverage, please refer to Appendix 1.

Table 1—Government expenditures 1995 international dollars, billions

Percentage of GDP

AFRICA Botswana Burkina Faso Cameroon Cote d’Ivoire Egypt Ethiopia Ghana Kenya Malawi Mali Morocco Nigeria Togo Tunisia Uganda Zambia Zimbabwe

ASIA

Bangladesh China India Indonesia Korea, Rep. of Malaysia Myanmar Nepal

Philippines Sri Lanka Thailand

LAC Argentina Belize Bolivia Chile Colombia Costa Rica

Dominican Rep. Ecuador El Salvador Guatemala Mexico Panama

1980 1990

108.30 138.38 0.78 2.32 0.61 1.03 2.33 4.34 5.42 4.50 41.78 39.36 4.50 7.50 2.05 3.09 4.25 6.89 1.16 1.11 1.01 1.38 17.43 22.16 9.43 20.05 1.55 0.93 8.02 12.48 0.90 2.11 2.22 1.81 4.85 7.30

454.70 789.30 5.63 13.37 196.65 289.63 93.45 215.02 45.55 70.12 30.80 68.80 17.73 33.41 5.97 6.86 1.68 3.20 25.10 43.54 10.50 10.84 21.63 34.49

212.57 219.97 57.78 28.77 0.12 0.24 2.11 2.17 13.68 14.41 15.64 18.90 3.12 4.05 3.35 2.97 3.54 4.44 3.02 1.85 3.65 2.79 78.67 106.82 2.73

2.43

1998

190.01 3.49 2.19 3.50 5.71 58.9 9.10 6.36 8.23 1.29 1.69 29.45 20.16 1.33 16.29 3.70 1.96 16.67

1273.3 24.02 538.01 299.43 97.55 129.81 39.53 5.34 4.75 55.81 14.36 64.68

326.55 68.29 0.32 4.05 27.63 40.05 6.30 6.34 8.69 2.30 4.75 112.81 4.27

1980 1990

28.46 26.25 29.82 33.80 12.20 14.98 15.74 21.17 31.68 24.48 50.28 27.81 18.75 27.17 10.89 13.25 25.26 27.46 34.59 26.55 19.44 25.00 33.09 28.82 12.80 24.49 30.80 16.70 31.56 34.60 9.47 15.60 37.05 27.26 27.92 27.32

19.06 16.82 7.41 11.06 27.20 16.63 12.25 15.96 22.13 18.36 17.28 16.22 28.49 30.12 15.85 16.03 14.30 17.22 13.36 19.60 41.36 28.37 18.80 14.08

16.84 15.47 18.23 10.57 22.87 28.40 16.09 16.38 28.01 20.38 11.48 9.94 25.04 25.61 16.92 11.66 14.22 14.50 17.14 10.90 14.32 10.04 15.75 17.88 30.53

23.70

1998

27.64 35.94 22.89 16.18 23.99 30.12 25.20 19.40 28.03 22.90 22.72 31.31 19.79 21.05 31.51 16.15 27.51 52.23

15.23 13.77 13.60 14.37 17.88 20.24 21.76 7.71 17.52 20.38 25.02 18.55

16.60 15.41 28.50 21.90 21.57 16.00 29.06 16.29 22.62 9.18 12.24 14.88 28.51

Table 1—Government expenditures (continued) 1995 international dollars, billions

Percentage of GDP

1980 1990 1998 1980 1990 1998 Paraguay 1.42 1.78 3.89 9.85 9.40 16.96 Uruguay 4.63 5.45 9.69 21.84 25.95 33.31 Venezuela 19.10 22.92 27.17 18.74 20.73 19.76

775.56 1,147.65 1,789.86 19.25 17.28 16.25 TOTAL

Source: Calculated using data from International Monetary Fund’s (IMF) Government Financial Statistics Yearbook (various issues).

Regional deviations from these averages among developing countries were quite marked. Across all regions, Asia experienced the most rapid growth, while Africa and Latin America increased at a much slower pace. In fact, most of the increase in total government expenditures came from Asia, accounting for 71 percent of total expenditures in 1998, up from 59 percent in 1980. This is due to the fact that most Asian countries experienced rapid growth in per capita GDP. With the exception of Sri Lanka and Myanmar, all countries in the region at least doubled their total expenditures for the period 1980–98. Republic of Korea and Bangladesh had the most rapid growth over 1980–98, followed by India and Thailand. Myanmar is the only Asian country to reduce its total government expenditures (by 11 percent) for the same period.

For African countries, expenditures grew at 3.26 percent over 1980–98. Growth was much slower in the 1980s, at 2.74 percent per annum. In fact, there was a brief contraction after 1982, and it was not until 1986 that total government expenditures recovered to 1982 levels, when many African countries implemented macroeconomic structural adjustments. However, during the 1990s African countries gained momentum in expanding government expenditures, growing at 4.3 percent per annum. Botswana had the most rapid growth, mainly due to the

outstanding performance of its national economy: more than 10 percent growth per annum during 1980–98.

Latin American countries had the slowest growth in spending between 1980 and 1998. There was virtually no growth in the 1980s, and rapid growth in the 1990s was primarily due to recovery from the decline in the 1980s. There were two contractions over the whole period. The first occurred between 1982 and 1984, with 18 percent reduction in spending. The second contraction was between 1987 and the early 1990s. Most of growth in the region in the 1990s was due to recovery from these two contractions.

Total government expenditure as a percentage of GDP measures the amount a country

spends relative to the size of its economy. For countries in this study, the percentage declined from 19 percent in 1980 to 16 percent in 1998. On average, developing countries spend much less than developed countries. For example, total government outlays as a percentage of GDP in Organisation for Economic Cooperation and Development (OECD) countries range from 27 percent in 1960 to 48 percent in 1996 (Gwartney, Holcombe, and Lawson 1998), compared to 13–35 percent in most developing countries.

For Asia, the percentage declined from 19 percent in 1980 to 15 percent in 1998. There is

a strong correlation between the level of economic development and government spending power in this region, with the exception of Sri Lanka. In 1998, Myanmar spent the least, only 8 percent of its GDP, while the rest of the Asian countries spent 13–25 percent of their GDP. The two largest economies in the region, China and India, spent the same amount relative to their GDP, about 13–14 percent.

Surprisingly, among the three regions, Africa spends the most as a percentage of GDP. Government spending as a percentage of GDP has been around 26–28 percent over the last two

decades, almost 10 percentage points higher than Asia and Latin America. Among all countries in the region, Botswana, Egypt, Tunisia, Morocco, Kenya, and Zimbabwe are among the largest spenders, often spending more than 30 percent of their GDP. Uganda and Cameroon spend only half as much, about 15–20 percent, the least among African countries in our study.

Latin America experienced an even more erratic spending pattern. The percentage increased at a rate of 2–3 percent per year until 1986, then declined thereafter at a rate of 1–2 percent per year from 1987 to 1991. After 1992, the percentage began another upward trend. For the region, the percentage averaged 16.6 percent in 1998, slightly higher than Asian countries. Costa Rica and Panama spend almost 30 percent, while El Salvador and Guatemala spend only 12 percent of their respective GDPs.

Equally important is the composition of government expenditures, which reflects government spending priorities. The composition across regions reveals many differences (Table 2).4

Comparison is made across six sectors, namely agriculture, education, health, defense, social security, and

transportation and communication. Other sectors, such as mining, manufacturing and construction, fuel and energy, and general administration, are not included in our analysis and are collectively termed “other” expenditures.

Table 2—Composition of total expenditure, 1980 and 1998 (percent)

Total

Agriculturea Education Health T & C

Social Security Defense Otherb

Africa 1980 100 6.0 12.0 3.0 6.0 5.0 12.0 55.0

1998 100 5.0 16.0 5.0 4.0 3.0 10.0 57.0

Asia 1980 100 15.0 14.0 5.0 12.0 4.0 18.0 33.0

1998 100 10.0 20.0 4.0 5.0 3.0 11.0 47.0

Latin America 1980 100 8.0 16.0 4.0 11.0 19.0 7.0 35.0

1998 100 3.0 19.0 7.0 6.0 26.0 7.0 32.0

Notes: T & C stands for transportation and communication. a

Includes agriculture, forestry, fishing, and hunting. b

Includes fuel and energy; mining, manufacturing, and construction; general administration.

Sources: Calculated using data from International Monetary Fund’s Government Finance Statistics (various issues).

The top three expenditures for Africa in 1998 are education, defense, and health. Although education expenditure is the largest (15.9 percent), the percentage is smaller than in Asia and Latin America. Defense accounts for 10 percent of total government expenditures in the region, similar to Asia but more than Latin America in 1998. On average, African countries spend only 5 percent of total government expenditures on health. This is particularly disturbing considering that HIV/AIDS is widespread among its general population. Another discouraging trend is that African countries spend very little on transportation and telecommunication compared to other regions, and their share in total government expenditures declined over time from 5.9 percent in 1980 to 3.9 percent in 1998.

Education spending is the largest among all government expenditures in Asia, accounting for 20 percent. It is not surprising that Asia has the highest quality of human capital among regions. Defense and agriculture spending rank second and third, accounting for 10 percent and 11 percent, respectively, of total government expenditures in 1998, reduced from 17 percent and 15 percent, respectively, in 1980. This indicates that as the economy continues to recover from the 1997 Asian Crisis, governments in the region may be spending less on health and social security, which are much needed to protect disadvantaged groups. Although defense spending declined from 17 percent in 1980 to 11 percent in 1998, the percentage is still high compared to Latin America, which spends 7 percent on defense, and is substantially higher than the region’s spending on infrastructure, social security, and health.

For Latin America, social security spending ranks at the top of all government expenditure items, indicating that higher income inequality among population groups in the region may call for government intervention. In addition, Latin America spent 15–18 percent of total expenditure on education between 1980 and 1998. This region also spends more on transportation and infrastructure than any other region, accounting for 6.3 percent of total government expenditures in 1998. Agricultural expenditure accounts for a small fraction of total government expenditures (3.3 percent), mainly due to the small share of agriculture in national GDP.

Other expenditures (which include government spending in fuel and energy, mining, manufacturing and construction, and general administration) account for more than 50 percent of total government spending in Africa over 1980–1998. For Asia, the share of this type of expenditures increased from 33 percent in 1980 to 47 percent in 1998. For Latin America, it also accounts for more than 30 percent of total government spending. Most of these are either

government subsidies or expenses relating to general administration. The large and increasing share of these expenditures may have competed with more productive spending items such as agriculture, education, and infrastructure. AGRICULTURAL SPENDING

Agriculture is the largest sector in many developing countries. More importantly, the majority of the world’s poor live in rural areas and are primarily engaged in agriculture. Therefore, agricultural expenditure is one of the most important government instruments for promoting economic growth and alleviating poverty in rural areas of developing countries. Agriculture expenditures increased at an annual growth rate of 3 percent between 1980 and 1998 (Table 3). During the same period of time, rural population grew at approximately 1 percent per year, and agricultural GDP by 4.2 percent. Therefore, these saw a slight increase in agricultural expenditures per capita of rural population, and a decrease of agricultural expenditures per unit of agricultural GDP.

Table 3—Agriculture expenditure 1995 international dollars, billions

Percentage of agricultural GDP

AFRICA Botswana Burkina Faso Cameroon Cote d’Ivoire Egypt Ethiopia Ghana Kenya Malawi Mali Morocco Nigeria Togo Tunisia Uganda Zambia Zimbabwe

ASIA

Bangladesh China India Indonesia

Korea, Rep. of Malaysia Myanmar Nepal

Philippines Sri Lanka Thailand

LAC Argentina Belize Bolivia Chile Colombia Costa Rica

Dominican Rep. Ecuador El Salvador

1980 1990 6.79 7.52 0.08 0.15 0.03 0.06 0.05 0.18 0.18 0.13 1.82 1.86 0.30 0.52 0.25 0.13 0.36 0.42 0.12 0.12 0.09 0.02 1.13 1.10 0.26 0.58 0.11 0.35 1.16 1.00 n.a. 0.03 0.51 0.05 0.34 0.82

67.22 97.7 0.73 1.60 24.00 28.91 26.01 44.51 4.91 5.82 1.72 6.51 1.55 2.25 1.41 0.64 0.27 0.27 1.52 2.95 3.00 0.62 2.09 3.60

16.84 6.89 4.54 0.23 0.02 0.03 0.72 0.05 0.24 0.29 0.06 1.18 0.11 0.17 0.48 0.43 0.26 0.18 0.18

0.10

1998 9.27 0.16 0.05 0.10 0.07 3.32 1.16 0.21 0.33 0.09 0.01 0.94 0.25 1.08 1.25 0.02 0.02 0.22

132.60 2.87 57.53 43.52 6.98 10.57 1.33 0.77 0.29 3.22 0.69 4.83

10.71 0.64 0.02 0.08 0.80 0.52 0.15 0.59 0.40 0.06

1980 7.51 26.37 2.08 1.22 4.17 12.56 2.25 2.30 7.65 8.97 3.77 11.59 1.80 7.87 32.42 n.a. 59.89 13.01 9.58 2.53 11.03 9.95 9.94 6.70 11.38 8.02 4.05 3.22 45.82 7.82

12.67 22.54 12.98 29.59 6.87 0.21 4.77 11.99 8.51

2.62

1990 1998 5.65 6.00 47.79 45.15 2.79 1.52 3.58 1.16 2.24 1.19 7.13 10.38 4.05 6.96 1.21 6.07 6.64 4.94 7.34 4.73 0.93 0.19 8.11 6.02 2.20 0.79 18.56 40.91 17.61 19.38 0.38 0.23 4.36 1.42 20.60 4.13 8.62 8.18 4.67 7.41 6.14 7.91 11.94 7.81 7.85 6.55 18.05 33.59 10.81 5.56 2.34 2.70 2.99 2.82 6.07 6.96 6.87 6.33 11.77 12.38 4.81 7.22 1.04 2.69 19.96 10.58 2.35 2.86 4.97 8.37 3.32 1.53 6.60 4.49 12.55 12.92 4.36 8.07 3.45

1.95

Table 3—Agriculture expenditure 1995 international dollars, billions

Guatemala Mexico Panama Paraguay Uruguay Venezuela

TOTAL

1980 0.16 9.13 0.14 0.05 0.06 0.71

90.85

1990 0.12 3.26 0.06 0.02 0.08 0.69

112.1

1998 0.12 6.11 0.09 0.21 0.12 0.82

152.59

Percentage of agricultural GDP

1980 2.48 22.01 18.56 1.20 2.20 14.48 9.82

1990 1.64 7.59 6.29 0.44 3.50 11.6 7.95

1998 1.38 16.29 8.18 3.67 4.83 12.01 7.93

N. a. means not available.

Source: Calculated using data from International Monetary Fund’s Government Financial Statistics Yearbook (various issues).

In Africa, government expenditure on agriculture increased gradually at an annual rate of 3.5 percent. Agricultural expenditures in Asia more than doubled in the past two decades, with an annual growth rate of 3.8 percent, the highest growth among the three regions. Latin America is the only region that reduced its spending in agriculture, with an annual reduction of 8.4 percent, and eight out of 15 countries included in this study reduced their government expenditures in agriculture.

Agriculture expenditure as a percentage of agriculture GDP measures government spending on agriculture relative to the size of the sector. Compared to developed countries, agricultural spending as a percentage of agricultural GDP is extremely low in developing countries. The former usually have more than 20 percent, while the latter average less than 10 percent. In Africa, agriculture expenditure as a percentage of agricultural GDP remained at relatively similar levels (7–8 percent) throughout the study period. About two-thirds of African countries decreased agriculture expenditure relative to agricultural GDP. Asia’s performance was similar to that of Africa, as its percentage remained constant at 7.5–9 percent. For Latin

America, agricultural spending as a percentage of agricultural GDP hovered around 4–13 percent during 1980–1998.

The share of total government expenditures on agriculture provides important information on whether the agriculture sector received biased treatment under macroeconomic adjustment programs. For all countries in the study, the share gradually declined from 12 percent in 1980 to 9 percent in 1998. The share has been constant for Africa, indicating no effects of macroeconomic adjustment programs on agricultural spending. In Asia, the share declined from 15 percent to 10 percent for the study period. Latin America experienced the most rapid decline in its share, from 8 percent to a mere a 3 percent, during the same period.

Among all types of agricultural expenditures, agricultural research and development is the most crucial to growth in agricultural and food production. Pardey and Beintema (2001) show that agricultural research and development (R&D) expenditures as a percentage of agricultural GDP saw a relatively stable increase in the last three decades. For example, in 1995, the share of agricultural R&D expenditure in agricultural GDP in Africa and Asia was between 0.53–0.85 percent, and Latin America’s share was 0.98 percent. These rates are relatively low compared to 2–3 percent in developed countries.

3. DETERMINATION OF GOVERNMENT EXPENDITURES

In this section, we attempt to gain insights about government spending behavior with the aid of a model. Determination of total government spending and its patterns is complex and may include many factors, such as fiscal conditions and political, cultural and economic factors. In recent years, macroeconomic structural adjustment programs heavily influenced spending in many developing countries.

TOTAL GOVERNMENT SPENDING

How much a government can spend depends on its revenues and its ability to borrow from international and domestic sources. For many small developing countries, international aid also has become a significant source of government expenditures. The relative importance of these factors changes over time. In particular, when a government introduces budget cuts under the aegis of macroeconomic reforms and adjustments, spending patterns are likely to be affected. We use the following specification to model changes in government expenditures.

GEPGDPt = f(RGDPt-1, SAt,, Xt)

(1)

where GEPGDPt is government expenditure as a percentage of GDP at year t and RGDPt-1 is government revenue5 as a percentage of GDP at year t-1. The one-year lag of the government revenue variable reflects the fact that in many developing countries, the amount the government can spend depends on revenues generated from the previous year. The variable SAt is a dummy variable that is equal to 1 when macroeconomic adjustments are implemented and equal to 0 otherwise.6 Apart from revenue and structural adjustment variables, Xt captures the effect of other factors on government spending. Since it is difficult to quantify them, we use both year and country dummies to proxy these factors. To avoid the potential endogeniety of the independent variables of government revenue and structural adjustment programs, these two variables are also estimated as dependent variables in a system equation. The one-year lag of GEPGDPt and the two-year lag of RGDPt are used as independent variables in these two equations.

Regression results are presented in Table 4. We have four different specifications. Regression 1 includes only revenue and structural adjustment program variables. In regression 2, we added GDP per capita (GDPPt), and urbanization (URBANPt) variables. These two variables 56

Government revenue includes current (tax and non-tax revenue), capital revenue, and grants, including foreign aid. For the initiation years of structural programs by country, refer to Appendix 2.

illustrate how economic development levels affect government spending. Regressions 3 and 4 are results from variable coefficient models in which all parameters in the regressions vary by region. This is because determination of government expenditures may differ by region even after controlling for all variables in the equations.

Table 4—Determinants of total government expenditures R2 R1 R3 R4

RGDPt-1

Africa Asia

Latin America

0.185 (8.530)*

0.179 (8.050)*

-0.032

0.331 (5.830)* 0.150 (5.500)* 0.604 (6.420)*

3.760 (3.880)* 0.152 (6.790)* 0.589 (6.070)*

(-0.490) Africa 0.343 (2.700)* Asia -0.800 (-9.010)* Latin America -0.169 (-0.800)

-0.406 URBANPt-1

(-1.840)* (3.500)* Africa -1.403 (-6.470)* Asia 2.970 (6.980)* Latin America -0.104 (-0.130)

0.419 0.452 SAt

(4.500)* (4.650)* Africa 0.370 0.669 (3.250)* (3.880)* Asia 0.150 0.281 (0.880) (2.120)* Latin America 0.539 0.552 (4.280)* (4.280)* 20.713 0.710 0.720 0.870 R Notes: The dependent variable is the percentage of government expenditures in total GDP. Figures in parentheses are t-values. Asterisk (*) indicates significance at the 10 percent level. All regressions included country dummies to capture country-fixed effects.

GDPPt-1

Results in regression 1 indicate that government expenditure is largely determined by revenue and structural adjustment. However, contrary to common belief, the latter was found to increase government expenditure (the coefficient of the structural adjustment variables is positive and statistically significant). Regression 2 shows that after controlling for GDP per capita and for urbanization, the structural adjustment program variable is still statistically significant and positive. When we break our analysis into regions, we find that for all regions, structural adjustments increased government spending. The only exception is Asia, when economic development variable is not controlled for. COMPOSITION OF SPENDING

Some studies have analyzed the impact of composition of government spending on economic growth (Devarajan, Swaroop, and Zou 1996), but few have modeled the determination of composition. Understanding why certain countries spend more on one sector than others will help developing countries reallocate government resources to the most productive sector by focusing on major forces behind existing patterns. The composition of government spending is modeled in the following specification:

Si,t = g(GEPGDPt-1, GDPPt-1, SAt, Zi,t)

(2)

where Si,t is the share of ith sector7 in total government expenditure, GEPGDPt-1 is a one-year lag of government expenditure as a percentage of GDP, GDPPt-1 is a one-year lag of per capita GDP, and Zi,t comprises other factors that may affect government spending in the sector. Again, we use year and country dummies to proxy for Z and to control for other factors excluded from the equation. Similar to equation 1, we also endogenize the independent variables of where S1 = agriculture, S2 = education, S3 = health, S4 = social security, S5 = transportation and communication, and S6 = defense.

GEPGDPt-1, GDPPt-1, SAt as functions of lagged revenue and GDP variables. Regression results are presented in Table 5.

Table 5--Determinants of sector share in total government expenditures S2 S3 S4 S5 S1

-0.003 (-0.230) -0.023 (-1.430) -0.397 (-3.930)*

-0.032 (-1.300) -0.063 (-2.970)* -0.280 (-1.560) -0.016 (-1.720) -0.010 (-0.830) -0.061 (-0.960)

0.220

GEPGDPt-1

Africa Asia

Latin America

-0.098 (-3.750)* -0.004 (-0.300) 0.042 (3.330)*

-0.028 (-1.790)* -0.020 (-1.680) 0.003 (0.410)

0.026 (1.170) -0.411 (-3.060)* -0.004 (-0.340)

0.570

-0.025 (-2.300)* -0.021 (-2.700)* -0.001 (-0.060)

0.070 (3.940)* 0.021 (2.070)* -0.052 (-1.600)

-0.013 (-1.950)* -0.001 (-0.040) -0.057 (-5.440)*

0.720

-0.003 (-0.450) -0.001 (-0.280) 0.018 (1.860)*

0.003 (0.030) 0.026 (3.450)* 0.027 (1.270)

0.006 (1.300) -0.010 (-2.450)* -0.010 (-1.700)

0.840

-0.020 (2.620)* 1.104 (9.140)* -0.020 (-1.030)

-0.014 (-1.150) 0.365 (2.290)* -0.104 (-2.500)* -0.005 (-1.050) -0.031 (-0.360) -0.020 (-1.600)

0.520

-0.028 (-0.680) -0.098 (-0.980) -0.005 (-0.440)

0.074 (1.070) -0.013 (-7.290)* -0.014 (-0.550)

-0.076 (-2.870)* -0.008 (-0.800) -0.029 (-3.870)*

0.530

GDPPt-1

Africa Asia

Latin America

SAt

Africa Asia

Latin America

GDPS1t

Africa Asia

Latin America

Notes: S1 = agriculture, S2 = education, S3 = health, S4 = social security, S5 = transportation and communication, and S6 = defense.Figures in parentheses are t-values. Asterisk (*) indicates significance at the 10 percent level. All regressions include country dummies to capture country-fixed effects.

For all regressions, we disaggregated our analysis into regions. As total government expenditures increase, the share of agriculture expenditure (S1) declines in Africa and increases in Latin America. For Asia, the relationship is statistically insignificant. The share of the agriculture sector in total GDP (GDPS1) is not statistically correlated with government expenditure shares in agriculture in Africa and Latin America, but in Asia as the share of agriculture in total GDP declines, the share of expenditures on agriculture increases, implying that these countries may have started to protect their agriculture. The most important finding is that structural adjustments reduced government expenditure shares in the agriculture sector in Africa. But such a biased treatment from structural adjustment is not obvious in Asia and Latin America.

Results for S2 (education sector) indicate that as a country becomes richer, the share of

education expenditures becomes larger in Asia and Africa, evidenced by positive and statistically significant coefficients of GDPPt-1 variables in the education shares equation. In Latin America, however, this relationship is not significant. Structural adjustments had no impact on education spending in Asia. However, education has suffered from structural adjustment programs in Africa and Latin America—the coefficient of the adjustment program variable is negative and statistically significant in these two regions.

The relationship of health expenditure share to government revenue and per capita GDP variables differs sharply among regions, as shown in regression S3 of Table 5. In Africa and Asia, the relationship is negative and statistically insignificant. In Latin America, as the economy grows and revenues increase, governments increasingly spend more on health care. Structural adjustment programs had little impact on health shares in total expenditures in Africa and Latin

America. However, Asian governments reduced their spending shares on health as a result of structural adjustment programs.

Results from S4 show that the shares of social security in total government expenditures

in Africa and Latin America are generally negatively correlated with their economic development level (per capita GDP) or spending power (government expenditures as a percentage of GDP). By contrast, as economy and spending power expand, governments tend to spend more on social security in Asia. In all regions, the structural adjustment programs showed no impact on social security spending.

Structural adjustments had an adverse impact on government spending on infrastructure across all regions, although they are statistically insignificant in Asia (regression S5 in Table 5). This implies that governments may have reduced infrastructure investment during macroeconomic structural adjustment programs, particularly in Africa and Latin America.

Defense expenditures as a share of total government expenditures had a negative relationship with the level of economic development in Asia and Latin America. In other words, poorer countries spent large shares of total government expenditures on military defense than less poor countries in the study. This inverse relationship is particularly strong for Asia. Structural adjustment programs reduced defense spending in all regions. However, this reduction is not statistically significant.

4. IMPACT OF GOVERNMENT SPENDING ON GROWTH

Many studies have analyzed how government expenditures contribute to economic

growth (Barro 1990; Kelly 1997). However, they focused on the impact of total government expenditures and overall GDP growth. Very few studies attempted to link different types of

government spending to growth, and even fewer attempted to analyze the impact of government spending at the sector level. In this section, we first model the impact of different types of government spending on overall GDP growth, then analyze the effect of agricultural spending on agricultural GDP.

SPENDING AND OVERALL GDP GROWTH

We estimate a production function with national GDP as the dependent variable, and labor, capital investment, and various government expenditures as independent variables.

GDPt = h(LABORt, Kt, KGE i,t, SAt, Wt)

(3)

where GDPt is GDP at year t, LABORt and Kt are labor and private capital inputs at year t, and KGEi,t is capital stock constructed from current and past government spending in the ith sector with KAGEXPt representing government stock in the agricultural sector, KEDEXPt representing the education sector, KHEXPt representing the health sector, KTCEXPt representing the transportation and telecommunication sector, KSSEXPt representing the social security sector, and KDEXPt representing the defense sector. Usually this stock cannot be observed directly, so it serves more as a part of the conceptual apparatus than an empirical tool. To construct a capital stock series from data on capital formation, we used the following procedure:

Kt=It+(1−δ)Kt-1

(4)

where Kt is the capital stock in year t, It is gross capital formation in year t, and δ is the

depreciation rate. Since the depreciate rate varies by country, we simply assume a 10 percent depreciation rate for all the countries. To obtain initial values for the capital stock, we used a similar procedure to Kohli (1982):

K1980=

I1980

(δ+r)

(5)

Equation 5 implies that the initial capital stock in 1980 (K1980) is capital investment in 1980 (I1980) divided by the sum of real interest rate (r) and depreciation rate.

Impact of structural adjustment programs on economic growth is captured by variable

SAt, and other factors not included in the equations are captured through the year and country dummies of Wt.

Results are shown in Table 6. Regression 1 (R1) reports results by region when structural adjustment variables SA,t are excluded, while regression 2 (R2) reports those with SA,t included. The labor and capital coefficients are positive and statistically significant for all regions. For government expenditures on agriculture, coefficients are positive and statistically significant in Africa and Asia. For Latin America, the coefficient is insignificant although positive. For education expenditure, the coefficients are positive and statistically significant only in Asia. This indicates that continued education investment in Asia will contribute greatly to GDP growth. Coefficients for Africa and Latin America are negative.

Table 6—Estimates of GDP function

Africa 0.766 0.812 (15.790)*(16.990)* LABORt

Asia 0.922 (6.210)* Latin America (26.830)*Kt

Africa 0.325 (10.190)*Asia 1.165 (11.230)*Latin America (7.780)* KAGEXPt

Africa 0.052 (2.160)* Asia 0.076 (1.870)* Latin America (0.800) KEDEXPt

Africa -0.099 (-2.230)*Asia 0.283 (2.650)* Latin America (-1.800)* KHEXPt

Africa 0.211 (6.170)* Asia -0.081 (-1.390) Latin America (6.720)* KTCEXPt

Africa 0.021 (1.000)

0.871 (5.890)* 1.092 1.000

(17.260)*

0.312 (9.690)* 1.171 (11.610)* 0.784 0.836

(8.190)*

0.051 (2.150)* 0.087 (2.160)* 0.0198 0.007

(0.290)

-0.107 (-2.420)* 0.257 (2.410)* -0.083 -0.066

(-0.960)

0.219 (4.350)* -0.089 (-1.530) 0.176 0.178

(6.900)*

0.021 (1.070)

Table 6—Estimates of GDP function (continued) R1 R2

Asia -0.228 -0.225 (-6.210)*(-6.180)* Latin America 0.023 0.022

(0.930) (1.070)

Africa -0.182 -0.173 (-5.300)*(-5.070)* Asia 0.122 0.127 (3.580)* (3.790)* Latin America -0.085 -0.083

(-3.810)*(-3.730)*

KDEXPt

Africa 0.007 0.016 (0.300) (0.620) Asia -0.017 -0.016 (-0.990) (-0.920) Latin America -0.016 -0.011

(-0.960) (-0.690)

KSSEXPt

Africa -0.031 (-1.810)* Asia 0.065 (2.990)* Latin America 0.046

(2.370)* 0.997 0.998

Notes: The dependent variable is total GDP. Figures in parentheses are t-values. Asterisk (*) indicates significance at the 10 percent level. All regressions included country and year dummies to capture country- and year-fixed effects.

SAt

The coefficient for health expenditures is positive and statistically significant in Africa and Latin America. In Asia, the coefficient is not statistically significant. The coefficient for social security spending in all regions is statistically insignificant. Similar to social security, transportation and communication expenditures did not have a positive and statistically

significant impact on economic growth. Defense expenditure had a very strong negative impact on economic growth in Africa and Latin America. Finally, structural adjustment programs increased GDP growth in Asia and Latin America but not in Africa. AGRICULTURAL SPENDING AND GROWTH IN AGRICULTURE

Since agricultural growth has been one of the most effective ways for poverty reduction through the so-called “trickle-down” process, we estimate the determinants of agricultural growth in developing countries. We pay special attention to how government spending can promote growth in the agricultural sector. We include an explanatory variable in the agricultural production function that measures government expenditures on agriculture to identify output-enhancing effects of public expenditures. The production function to be estimated is specified as: AGOUTt = h(AGLANDt, LABORt, FERTt, TRACTt, ANIMALSt, IRRIPt, ROADSt, LITEt, KAGEXPt, SAt, Ut)

(6)

where AGOUTt is agricultural output, the dependent variable; the independent variables are labor (LABORt), land (AGLANDt), fertilizer (FERTt), number of tractors (TRACTt), number of draft animals (ANIMALSt), and public input variables such as percentage of crop areas under irrigation (IRRIPt), road density (ROADSt), literacy rate (LITEt), and an agricultural expenditure capital variable (KAGEXPt). Impact of structural adjustment programs on economic growth is captured by variable SAt. The variable Ut is used to capture the other factors not included in the equation, and is proxied by year and country dummies.

We further disaggregate government expenditures into research (KAGREXPt) and non-research expenditure capitals (NKAGREXPt) to capture separate effects of these two types of

expenditures. These capital variables are converted from government expenditures using procedures similar to those described in equations 4 and 5.

Output is measured as the agricultural output index reported by Food and

Agriculture Organization (FAO), where agriculture is broadly defined to include crop, livestock, forestry, and fishery production. All these variables were incorporated into the estimating equation as indices and in logarithm forms to minimize bias that may arise from using different scales or units of input and output for each country.

Two different specifications were estimated, and the results are presented in Table 7. The first specification includes conventional inputs such as labor, land, fertilizer, machinery, and draft animals; physical public inputs such as irrigation, road density, and literacy rate; and a stock variable of total government expenditure on agriculture. The second specification disaggregates total agricultural expenditures into agricultural and non-agricultural research expenditures (total agricultural expenditures net of agricultural research expenditures). Due to the limited number of observations (21), we were unable to conduct this analysis at the regional level.

Table 7—Estimates of agriculture production function

KAGEXPt KAGREXPt

KNAGREXPt AGLANDt IRRIPt LABORt FERTt TRACTSt ANIMALSt ROADSt

0.0370

(3.1100)*

0.0430

(1.8700)*

0.0170 0.4430 0.6480 0.2540 0.2450 -0.0590 0.1660 0.0560 0.0480 0.0007 0.0660 0.1780 -0.0840 0.1840 0.1770

(1.0300) (3.1500)* (3.0500)* (7.1700)* (5.3300)* (-0.5400) (1.0400) (3.7000)* (1.4400) (0.0300) (1.7500)* (3.0500)* (-0.8900) (3.0900)* (2.5600)* LITERACYt

0.0200 0.0170 (8.1400)* (2.6300)*

R2 0.9970 0.9980

Notes: The dependent variable is agricultural production index. Figures in parentheses are t-values. Asterisk (*) indicates significance at the 10 percent

level.. All regressions included country dummies to capture country-fixed effects.

Similar to the results in Table 6, total agricultural expenditures had a significant effect on agricultural GDP, as shown in the first regression of Table 7. The coefficients for all conventional inputs except labor and machinery are statistically significant. Insignificant coefficients of labor and machinery inputs imply that there may be a large surplus of labor in

rural areas. Physical public capital inputs, including roads, irrigation, and literacy rate, are all positive and statistically significant. This strongly suggests that broader rural investments in infrastructure and education contributed to agricultural production growth.

Disaggregating total agricultural expenditure into research and non-research expenditures reveals an interesting finding: although both their coefficients are positive, the coefficient for agricultural research is larger in magnitude and more significant in statistical level than non-research expenditures. This is prima facie evidence that productivity-enhancing expenditures, such as agricultural research investments have much larger output-promoting effects than other forms of public spending (including subsidies).

5. MAJOR FINDINGS AND RECOMMENDATIONS

In this study, we compiled government expenditures by types across 43 developing countries between 1980 and 1998. We then analyzed trends, determination, and impact of various forms of government spending. The following are the major findings of this study.

Total government expenditures for 43 countries included in the study increased over time. Macroeconomic adjustments do not seem to adversely affect total government spending. However, when we control for other variables and disaggregate the analysis into different regions, structural adjustment programs increased total government spending in almost all regions.

Structural adjustment programs had different consequences for different sectors. In Africa, governments reduced shares for agriculture, education, and infrastructure, while Asian governments reduced shares for agriculture and health. Education and infrastructure suffered from reduction in government expenditures in Latin America.

The performance of government spending in economic growth is mixed. In Africa, government spending in agriculture and health were particularly strong in promoting economic growth. Among all types of government expenditures, agriculture, education, and defense contributed positively to economic growth in Asia. In Latin America, health spending had a positive growth-promoting effect. Structural adjustment programs had a positive growth-promoting effect in Asia and Latin America, but not in Africa. In fact, structural adjustment programs hurt economic development in the region.

Agricultural spending, irrigation, education, and roads contributed strongly to growth. Disaggregating total agricultural expenditures into research and non-research spending reveals that research had a larger productivity enhancing impact than non-research spending.

Several lessons can be drawn from this study. First, various types of government spending have differential impacts on economic growth, implying greater potential to improve efficiency of government spending by reallocation among sectors. Second, governments should reduce their spending in unproductive sectors such as defense, and curtail excessive subsidies in fertilizer, irrigation, power, and pesticides. Third, all regions should increase spending in agriculture, particularly on production-enhancing investments such as agricultural R&D. This type of spending not only yields high returns to agricultural production, but also has a large impact on poverty reduction since most of the poor still reside in rural areas and their main source of livelihood is agriculture.

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APPENDIX 1: DATA SOURCES AND MEASUREMENT ISSUES

DEFINITION AND MEASUREMENT OF GOVERNMENT EXPENDITURES9 Total expenditure is broken down into various sectors following the International Monetary Fund’s Government Finance Statistics Yearbook sectors. This study concentrates on six sectors, namely agriculture, defense, education, health, social security, and transportation and communication. Please see Appendix Table 1 for definitions.

To convert expenditures denominated in current local currencies into international dollar aggregates expressed in base year (1995), prices were first deflated from current local currency expenditures to a set of base year prices using each country’s implicit GDP deflator. We then used 1995 exchange rates measured in 1995 purchasing power parity reported by the World Bank (2000) to convert local currency expenditures measured in terms of 1995 prices into a value aggregate expressed in terms of 1995 international dollars.

Data Sources

We included 43 developing countries from three regions in our analysis, partly reflecting

availability of data and partly because these countries are important in their own right while representing broader rural development throughout all developing countries. The 17 countries included for Africa are Botswana, Burkina Faso, Cameroon, Côte D’Ivoire, Egypt, Ethiopia, Ghana, Kenya, Malawi, Mali, Morocco, Nigeria, Togo, Tunisia, Uganda, Zambia, and Zimbabwe. We included 11 countries from Asia: Bangladesh, China, India, Indonesia, Korea, Malaysia, Myanmar, Nepal, Philippines, Sri Lanka, and Thailand. For Latin America, we included 15 countries: Argentina, Belize, Bolivia, Chile, Colombia, Costa Rica, Dominican

Republic, Ecuador, El Salvador, Guatemala, Mexico, Panama, Paraguay, Uruguay, and Venezuela.

Total GDP, agricultural GDP, total population, agricultural population, employment and private investments by sector, road density, literacy rate, and information on structural change were taken from the World Bank database. Agricultural land, agricultural labor, irrigated areas, number of tractors, and number of draft animals were taken from the FAO database. The main sources for expenditure data reported here are International Monetary Fund’s (IMF) Government Financial Statistics Yearbook (various issues), Asian Development Bank’s (ADB) Key Indicators of Developing Member Countries of ADB (various issues), FAOStat Database (June 2000), the World Bank’s 2000 World Development Indicators, United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics for education data (http://unescostat.unesco.org/, December 1999), Inter-American Development Bank’s (IDB) Economic and Social Progress in Latin America (various issues), and Asian Productivity Organization’s Public Expenditures on Agriculture in Asia (1991). All data for agricultural research and development expenditures are taken from Pardey, Roseboom, and Beintema (1997).

For large countries such as India, Malaysia, Philippines, and Indonesia, both central and

local government expenditures were reported by IMF sources. For many of the remaining countries, only central government expenditures were reported, either by IMF and other sources. This may not cause a serious problem for the broad, cross-country comparisons reported here because many of these countries have minimal local government expenditures or lack sub-national government entities. In addition, we estimated arithmetic averages and geometrically

extrapolated data for countries whose values were missing to ensure continuity of data. Please see Appendix Table 1 for a summary of these extrapolations by country.

Appendix Table 1—Data source and extrapolation

Countries Expenditure data

Years extrapolateda SAPb

AFRICA Botswana Data for all sectors and years available 1991 Burkina Faso Agriculture 1994–95 1989 Cameroon Education 1998 1981 Cote d’Ivoire Total expenditure 1981–83, 91–92 1991 Agriculture 1981–84, 1986–98 Defense Education Egypt Ethiopia Ghana Kenya Malawi Defense Mali Agriculture Defense Morocco Total Transportation Nigeria Total Togo T&C Tunisia Uganda Total Zambia Defense Zimbabwe Health ASIA

Bangladesh Total Health T&C China Health India Social Indonesia Social Korea, Rep. of Malaysia

Social security, T&C Total revenue, total expenditure, Capital expenditure, agriculture, health, social security Data for all sectors and years available Data for all sectors and years available Data for all sectors and years available revenue revenue Total expenditure Data for all sectors and years available revenue T&C 1987–90 Agriculture, T&C Education, social security revenue security security Agriculture 1998

Data for all sectors and years available 1981–83, 1986–88 1981–84, 1986–89 1981–83 1998

1987 1980 1981

1990–95 1989–98 1989–90 1997–98 1988–90 1988–91 1980–83 1988–91 1987–88 1984–88 1990–92, 1998 1990–92 1998 1990–92 1986–88 1998 1998 1998 1980–1993 n. a. 1993 1990 1988 1986 1983 1988 1987 1985 1992 1984

1983 1991 1998 1981

Appendix Table 1—Data source and extrapolation (continued)

Countries Expenditure data

Data for all sectors and years available Myanmar

Data for all sectors and years available Nepal

Data for all sectors and years available Philippines

Data for all sectors and years available Sri Lanka

Data for all sectors and years available Thailand

Years extrapolateda SAPb

LATIN AMERICA

Argentina Education 1986–88 1980 Health 1980–88

Social security 1982–87

Belize Revenue, expenditure, agriculture, capital 1986–87 1985 Agriculture, T&C 1998 Bolivia Agriculture, T&C 1985–86 1985 Chile Agriculture 1989–90 Colombia Agriculture, T&C 1985–89 1985 Defense, health, social security 1985–88 Costa Rica Data for all sectors and years available 1994 Dominican Rep. T&C 1998 1991

Ecuador Agriculture 1991–98

Data for all sectors and years available El Salvador 1982 Data for all sectors and years available 1983 Guatemala

Mexico Agriculture, T&C, health, 1998 1987 education, social security Panama Data for all sectors and years available 1989 Paraguay Data for all sectors and years available Uruguay Education 1982–85 Venezuela Education 1995–98 Sources: IMF’s Government Finance Statistics Yearbook (various issues) unless otherwise noted.

Data for China are taken from the Chinese Statistical Yearbook (various years). N.a. means not available. Note: T&C is transportation and communication. a

Data were extrapolated using a five-year period. b

Year of first structural adjustment program.

38Appendix Table 2—Definitions of government and sectoral expenditures Includes Current revenue (tax and nontax revenue), capital revenue, and grants Central government (government departments, offices, establishments, and other bodies that are agencies or instruments); state, provincial, or regional government; local government; supranational authorities Administration, supervision, and operation of military defense affairs and forces: land, sea, air, and space defense forces; administration, operation, and support of civil defense forces; administration of military aid; research and experimental development of defense Pre-primary and primary education affairs and services: administration, management, inspection, operation, and support of schools and other institutions providing training at these levels; administration of secondary education affairs and services: general programs and vocational and technical; administration of tertiary education affairs and services: university and other institutions providing tertiary education services; subsidiary services to Type of expenditure Government revenue Government expenditure Defense Education Health education (other services for students regardless of level of education) Administration of general hospital affairs and services: management, operation, inspection, or support for hospitals that do not limit their services to a particular medical specialty; specialized hospital services (for a particular condition or disease); medical and maternity center services; nursing and convalescent homes; clinics, medical, dental, and paramedical practitioners; public health affairs and services (such as blood bank operations, disease detention centers, prevention services, and population control services); applied research and experimental development related to health and medical delivery system Transfer payments, including payments in kind (to compensate for reduction/loss of income or inadequate Social security and welfare earning capacity); administration, management, or operation of social security affairs involving chiefly provision of benefits for loss due to sickness, childbirth, or temporary disability resulting from industrial and other accidents— includes maternity benefits; administration, management, or operation of retirement, pensions, or disability plans for government employees, both civil and military and their survivors; administration, operation, and support of old age, disability, or survivor’s benefits; unemployment compensation benefits; family and child allowances; welfare affairs and services (children’s and old age residential institutions, handicapped persons, and other residential institutions) 39Appendix Table 2—Definitions of government and sectoral expenditures (continued) Includes Administration of agricultural land conservation affairs and services, including: land reclamation and land expansion, land clearance, installation of drainage systems, provision of irrigation systems, reduction of salinity, outlays for construction of dams, dikes and irrigation canals, installation of equipment, management and operation of all physical works (as mentioned above), research and development; administration of agrarian reform and land settlement affairs and services: design, field management, operation, and evaluation of land reform and resettlement activities, extension of credit in connection with such activities, outlays to landowners whose title to the land was changed, research of land reform and resettlement; administration of affairs and services designed to stabilize or improve farm prices and farmers’ incomes: subsidies or other forms of payments, research into design and efficacy of price support schemes; public information and statistics collected, administration of agricultural extension affairs and services, administration of veterinary affairs and services including research, administration of pest control affairs and other services; administration of forestry affairs and services including regulation of government forest operations and the issuance of tree- felling licenses; outlays in the form of loans, transfers, and subsidies; research into all aspects of forest management and exploitation; administration of commercial or sport fishing and hunting affairs and services; support for fish hatcheries or game preserves Road transport affairs and services includes highway construction affairs and services (including loans, transfers, and subsidies; research into road design and construction methods); road system operation affairs and services (other than construction); water transport affairs and services includes: water transport facility construction affairs and services (including loans, transfers, and subsidies; research into water transport design and construction methods); water transport operation affairs and services (other than construction); railway affairs and services includes: railway facility construction affairs and services (including loans, transfers,and subsidies; research into railway transport design and construction methods); railway transport operation affairs and services (other than construction); air transport affairs and services includes: air transport facility construction affairs and services (including loans, transfers, and subsidies; research into air transport design and construction methods); air transport operation affairs and services (other than construction); pipeline Type of expenditure Agriculture, forestry, fishing and hunting Transportation and communication )deunitnco( serutidnepxe alrotces dna tnemnrevog fo sn soeidtiunlcifnIeD—2 eerlbutaiTdn expidxen feo peppy AT hcar ;ess e eni rad ;his;st b.)ecierutdseih std,sbonu( aarss ,lu esccdierinnavfrsu ,enssa f ,serdtren ,lsfasbn naasaciro trall a,f fgisnanera iand oou,lils tcygananiwier(dp lsiuoeal crcti eniorv lprb (ses)assd o.cendh6 casitrnt8av ae9 e hernsm1 ,csiiru landsdefoni(tu npiafcFsap e nu ycs;otriisarvirs)tradantefocoesafhicn tn od eudnnmaoi mnMa l tsnm narcoiuiognatcioftrc sisufetafnoda n trtosnnrco nooertiipyottnstc aa Inildtrc,aicnsisinctraanuit fniertmmsihrgidmtatosopeaot ;cS dsd e nnatrn)ocaot troinnt ptiac oresuhnicpnnisl atrrF rneotsat aptneninirosetrpiacermnrevoG no launaM A :ecruo S 40

Appendix Figure 1—Government spending intensities

Percentage of government expenditure in GDP

353025201510501980198219841986198819901992199419961998PercentageAfricaAsiaLAC

Percentage of agriculture expenditure in total AgGDP

141210864201980198219841986198819901992199419961998PercentageAfricaAsiaLAC

Share of agricultural research expenditure in agriculture expenditure

20Percentage151050198019821984198619881990LACAfricaAsia

Appendix Figure 2—Composition of expenditures by region, 1980–1998

AFRICAPPP US$ 1995, Bn2001501005001980198219841986198819901992199419961994AgricultureHealthSocial SecurityOtherEducationTransp. & Comm.Defense

ASIA600PPP US$ 1995, Bn50040030020010001980198219841986198819901992199619981998AgricultureHealthSocial SecurityOtherEducationTransp. & Comm.DefenseLATIN AMERICA400350300250200150100500198019821984198619881990199219941996PPP US$ 1995, BnAgricultureTransp. & Comm.OtherEducationSocial SecurityHealthDefense

1998

EPTD DISCUSSION PAPERS

LIST OF EPTD DISCUSSION PAPERS 01

Sustainable Agricultural Development Strategies in Fragile Lands, by Sara J. Scherr and Peter B.R. Hazell, June 1994.

Confronting the Environmental Consequences of the Green Revolution in Asia, by Prabhu L. Pingali and Mark W. Rosegrant, August 1994.

Infrastructure and Technology Constraints to Agricultural Development in the Humid and Subhumid Tropics of Africa, by Dunstan S.C. Spencer, August 1994. Water Markets in Pakistan: Participation and Productivity, by Ruth Meinzen-Dick and Martha Sullins, September 1994.

The Impact of Technical Change in Agriculture on Human Fertility: District-level Evidence From India, by Stephen A. Vosti, Julie Witcover, and Michael Lipton, October 1994.

Reforming Water Allocation Policy Through Markets in Tradable Water Rights: Lessons from Chile, Mexico, and California, by Mark W. Rosegrant and Renato Gazri S, October 1994.

Total Factor Productivity and Sources of Long-Term Growth in Indian Agriculture, by Mark W. Rosegrant and Robert E. Evenson, April 1995. Farm-Nonfarm Growth Linkages in Zambia, by Peter B.R. Hazell and Behjat Hoijati, April 1995.

Livestock and Deforestation in Central America in the 1980s and 1990s: A Policy Perspective, by David Kaimowitz (Interamerican Institute for Cooperation on Agriculture. June 1995.

Effects of the Structural Adjustment Program on Agricultural Production and Resource Use in Egypt, by Peter B.R. Hazell, Nicostrato Perez, Gamal Siam, and Ibrahim Soliman, August 1995.

Local Organizations for Natural Resource Management: Lessons from

Theoretical and Empirical Literature, by Lise Nordvig Rasmussen and Ruth Meinzen-Dick, August 1995.

EPTD DISCUSSION PAPERS

Quality-Equivalent and Cost-Adjusted Measurement of International

Competitiveness in Japanese Rice Markets, by Shoichi Ito, Mark W. Rosegrant, and Mercedita C. Agcaoili-Sombilla, August 1995.

Role of Inputs, Institutions, and Technical Innovations in Stimulating Growth in Chinese Agriculture, by Shenggen Fan and Philip G. Pardey, September 1995. Investments in African Agricultural Research, by Philip G. Pardey, Johannes Roseboom, and Nienke Beintema, October 1995.

Role of Terms of Trade in Indian Agricultural Growth: A National and State Level Analysis, by Peter B.R. Hazell, V.N. Misra, and Behjat Hoijati, December 1995.

Policies and Markets for Non-Timber Tree Products, by Peter A. Dewees and Sara J. Scherr, March 1996.

Determinants of Farmers’ Indigenous Soil and Water Conservation Investments in India’s Semi-Arid Tropics, by John Pender and John Kerr, August 1996. Summary of a Productive Partnership: The Benefits from U.S. Participation in the CGIAR, by Philip G. Pardey, Julian M. Alston, Jason E. Christian, and Shenggen Fan, October 1996.

Crop Genetic Resource Policy: Towards a Research Agenda, by Brian D. Wright, October 1996.

Sustainable Development of Rainfed Agriculture in India, by John M. Kerr, November 1996.

Impact of Market and Population Pressure on Production, Incomes and Natural Resources in the Dryland Savannas of West Africa: Bioeconomic Modeling at the Village Level, by Bruno Barbier, November 1996.

Why Do Projections on China’s Future Food Supply and Demand Differ? by Shenggen Fan and Mercedita Agcaoili-Sombilla, March 1997.

Agroecological Aspects of Evaluating Agricultural R&D, by Stanley Wood and Philip G. Pardey, March 1997.

Population Pressure, Land Tenure, and Tree Resource Management in Uganda, by Frank Place and Keijiro Otsuka, March 1997.

EPTD DISCUSSION PAPERS

Should India Invest More in Less-favored Areas? by Shenggen Fan and Peter Hazell, April 1997.

Population Pressure and the Microeconomy of Land Management in Hills and Mountains of Developing Countries, by Scott R. Templeton and Sara J. Scherr, April 1997.

Population Land Tenure and Natural Resource Management: The Case of Customary Land Area in Malawi, by Frank Place and Keijiro Otsuka, April 1997.

Water Resources Development in Africa: A Review and Synthesis of Issues,

Potentials, and Strategies for the Future, by Mark W. Rosegrant and Nicostrato D. Perez, September 1997.

Financing Agricultural R&D in Rich Countries: What’s Happening and Why? by Julian M. Alston, Philip G. Pardey, and Vincent H. Smith, September 1997. How Fast Have China’s Agricultural Production and Productivity Really Been Growing? by Shenggen Fan, September 1997.

Does Land Tenure Insecurity Discourage Tree Planting? Evolution of Customary Land Tenure and Agroforestry management in Sumatra, by Keijiro Otsuka, S. Suyanto, and Thomas P. Tomich, December 1997.

Natural Resource Management in the Hillsides of Honduras: Bioeconomic

Modeling at the Micro-Watershed Level, by Bruno Barbier and Gilles Bergeron, January 1998.

Government Spending, Growth, and Poverty: An Analysis of Interlinkages in

Rural India, by Shenggen Fan, Peter Hazell, and Sukhadeo Thorat, March 1998. Revised December 1998.

Coalitions and the Organization of Multiple-Stakeholder Action: A Case Study of Agricultural Research and Extension in Rajasthan, India, by Ruth Alsop, April 1998.

Dynamics in the Creation and Depreciation of Knowledge and the Returns to Research, by Julian Alston, Barbara Craig, and Philip Pardey, July, 1998.

EPTD DISCUSSION PAPERS

36 Educating Agricultural Researchers: A Review of the Role of African

Universities, by Nienke M. Beintema, Philip G. Pardey, and Johannes Roseboom, August 1998. 37

The Changing Organizational Basis of African Agricultural Research, by Johannes Roseboom, Philip G. Pardey, and Nienke M. Beintema, November 1998.

Research Returns Redux: A Meta-Analysis of the Returns to Agricultural R&D, by Julian M. Alston, Michele C. Marra, Philip G. Pardey, and T.J. Wyatt, November 1998.

Technological Change, Technical and Allocative Efficiency in Chinese

Agriculture: The Case of Rice Production in Jiangsu, by Shenggen Fan, January 1999.

The Substance of Interaction: Design and Policy Implications of NGO-Government Projects in India, by Ruth Alsop with Ved Arya, January 1999. Strategies for Sustainable Agricultural Development in the East African Highlands, by John Pender, Frank Place, and Simeon Ehui, April 1999. Cost Aspects of African Agricultural Research, by Philip G. Pardey, Johannes Roseboom, Nienke M. Beintema, and Connie Chan-Kang, April 1999. Are Returns to Public Investment Lower in Less-favored Rural Areas? An Empirical Analysis of India, by Shenggen Fan and Peter Hazell, May 1999. Spatial Aspects of the Design and Targeting of Agricultural Development Strategies, by Stanley Wood, Kate Sebastian, Freddy Nachtergaele, Daniel Nielsen, and Aiguo Dai, May 1999.

Pathways of Development in the Hillsides of Honduras: Causes and Implications for Agricultural Production, Poverty, and Sustainable Resource Use, by John Pender, Sara J. Scherr, and Guadalupe Durón, May 1999.

Determinants of Land Use Change: Evidence from a Community Study in Honduras, by Gilles Bergeron and John Pender, July 1999.

Impact on Food Security and Rural Development of Reallocating Water from Agriculture, by Mark W. Rosegrant and Claudia Ringler, August 1999.

EPTD DISCUSSION PAPERS

Rural Population Growth, Agricultural Change and Natural Resource Management in Developing Countries: A Review of Hypotheses and Some Evidence from Honduras, by John Pender, August 1999.

Organizational Development and Natural Resource Management: Evidence from Central Honduras, by John Pender and Sara J. Scherr, November 1999. Estimating Crop-Specific Production Technologies in Chinese Agriculture: A Generalized Maximum Entropy Approach, by Xiaobo Zhang and Shenggen Fan, September 1999.

Dynamic Implications of Patenting for Crop Genetic Resources, by Bonwoo Koo and Brian D. Wright, October 1999.

Costing the Ex Situ Conservation of Genetic Resources: Maize and Wheat at CIMMYT, by Philip G. Pardey, Bonwoo Koo, Brian D. Wright, M. Eric van Dusen, Bent Skovmand, and Suketoshi Taba, October 1999.

Past and Future Sources of Growth for China, by Shenggen Fan, Xiaobo Zhang, and Sherman Robinson, October 1999.

The Timing of Evaluation of Genebank Accessions and the Effects of Biotechnology, by Bonwoo Koo and Brian D. Wright, October 1999. New Approaches to Crop Yield Insurance in Developing Countries, by Jerry Skees, Peter Hazell, and Mario Miranda, November 1999.

Impact of Agricultural Research on Poverty Alleviation: Conceptual Framework with Illustrations from the Literature, by John Kerr and Shashi Kolavalli, December 1999.

Could Futures Markets Help Growers Better Manage Coffee Price Risks in Costa Rica? by Peter Hazell, January 2000.

Industrialization, Urbanization, and Land Use in China, by Xiaobo Zhang, Tim Mount, and Richard Boisvert, January 2000.

Water Rights and Multiple Water Uses: Framework and Application to Kirindi Oya Irrigation System, Sri Lanka, by Ruth Meinzen-Dick and Margaretha Bakker, March 2000.

EPTD DISCUSSION PAPERS

Community natural Resource Management: The Case of Woodlots in Northern Ethiopia, by Berhanu Gebremedhin, John Pender and Girmay Tesfaye, April 2000.

What Affects Organization and Collective Action for Managing Resources? Evidence from Canal Irrigation Systems in India, by Ruth Meinzen-Dick, K.V. Raju, and Ashok Gulati, June 2000.

The Effects of the U.S. Plant Variety Protection Act on Wheat Genetic Improvement, by Julian M. Alston and Raymond J. Venner, May 2000. Integrated Economic-Hydrologic Water Modeling at the Basin Scale: The Maipo River Basin, by M. W. Rosegrant, C. Ringler, DC McKinney, X. Cai, A. Keller, and G. Donoso, May 2000.

Irrigation and Water Resources in Latin America and he Caribbean: Challenges and Strategies, by Claudia Ringler, Mark W. Rosegrant, and Michael S. Paisner, June 2000.

The Role of Trees for Sustainable Management of Less-favored Lands: The Case of Eucalyptus in Ethiopia, by Pamela Jagger & John Pender, June 2000. Growth and Poverty in Rural China: The Role of Public Investments, by Shenggen Fan, Linxiu Zhang, and Xiaobo Zhang, June 2000.

Small-Scale Farms in the Western Brazilian Amazon: Can They Benefit from Carbon Trade? by Chantal Carpentier, Steve Vosti, and Julie Witcover, September 2000.

An Evaluation of Dryland Watershed Development Projects in India, by John Kerr, Ganesh Pangare, Vasudha Lokur Pangare, and P.J. George, October 2000. Consumption Effects of Genetic Modification: What If Consumers Are Right? by Konstantinos Giannakas and Murray Fulton, November 2000.

South-North Trade, Intellectual Property Jurisdictions, and Freedom to Operate in Agricultural Research on Staple Crops, by Eran Binenbaum, Carol Nottenburg, Philip G. Pardey, Brian D. Wright, and Patricia Zambrano, December 2000.

Public Investment and Regional Inequality in Rural China, by Xiaobo Zhang and Shenggen Fan, December 2000.

EPTD DISCUSSION PAPERS

Does Efficient Water Management Matter? Physical and Economic Efficiency of Water Use in the River Basin, by Ximing Cai, Claudia Ringler, and Mark W. Rosegrant, March 2001.

Monitoring Systems for Managing Natural Resources: Economics, Indicators and Environmental Externalities in a Costa Rican Watershed, by Peter Hazell, Ujjayant Chakravorty, John Dixon, and Rafael Celis, March 2001.

Does Quanxi Matter to NonFarm Employment? by Xiaobo Zhang and Guo Li, June 2001.

The Effect of Environmental Variability on Livestock and Land-Use Management: The Borana Plateau, Southern Ethiopia, by Nancy McCarthy, Abdul Kamara, and Michael Kirk, June 2001.

Market Imperfections and Land Productivity in the Ethiopian Highlands, by Stein Holden, Bekele Shiferaw, and John Pender, August 2001.

Strategies for Sustainable Agricultural Development in the Ethiopian Highlands, by John Pender, Berhanu Gebremedhin, Samuel Benin, and Simeon Ehui, August 2001.

Managing Droughts in the Low-Rainfall Areas of the Middle East and North Africa: Policy Issues, by Peter Hazell, Peter Oram, Nabil Chaherli, September 2001.

Accessing Other People’s Technology: Do Non-Profit Agencies Need It? How To Obtain It, by Carol Nottenburg, Philip G. Pardey, and Brian D. Wright, September 2001.

The Economics of Intellectual Property Rights Under Imperfect Enforcement: Developing Countries, Biotechnology, and the TRIPS Agreement, by Konstantinos Giannakas, September 2001.

Land Lease Markets and Agricultural Efficiency: Theory and Evidence from Ethiopia, by John Pender and Marcel Fafchamps, October 2001.

The Demand for Crop Genetic Resources: International Use of the U.S. National Plant Germplasm System, by M. Smale, K. Day-Rubenstein, A. Zohrabian, and T. Hodgkin, October 2001.

EPTD DISCUSSION PAPERS

How Agricultural Research Affects Urban Poverty in Developing Countries: The Case of China, by Shenggen Fan, Cheng Fang, and Xiaobo Zhang, October 2001.

How Productive is Infrastructure? New Approach and Evidence From Rural India, by Xiaobo Zhang and Shenggen Fan, October 2001.

Development Pathways and Land Management in Uganda: Causes and Implications, by John Pender, Pamela Jagger, Ephraim Nkonya, and Dick Sserunkuuma, December 2001.

Sustainability Analysis for Irrigation Water Management: Concepts,

Methodology, and Application to the Aral Sea Region, by Ximing Cai, Daene C. McKinney, and Mark W. Rosegrant, December 2001.

The Payoffs to Agricultural Biotechnology: An Assessment of the Evidence, by Michele C. Marra, Philip G. Pardey, and Julian M. Alston, January 2002. Economics of Patenting a Research Tool, by Bonwoo Koo and Brian D. Wright, January 2002.

Assessing the Impact of Agricultural Research On Poverty Using the Sustainable Livelihoods Framework, by Michelle Adato and Ruth Meinzen-Dick, March 2002.

The Role of Rainfed Agriculture in the Future of Global Food Production, by Mark Rosegrant, Ximing Cai, Sarah Cline, and Naoko Nakagawa, March 2002. Why TVEs Have Contributed to Interregional Imbalances in China, by Junichi Ito, March 2002.

Strategies for Stimulating Poverty Alleviating Growth in the Rural Nonfarm Economy in Developing Countries, by Steven Haggblade, Peter Hazell, and Thomas Reardon, July 2002.

Local Governance and Public Goods Provisions in Rural China, by Xiaobo Zhang, Shenggen Fan, Linxiu Zhang, and Jikun Huang, July 2002.

Agricultural Research and Urban Poverty in India, by Shenggen Fan, September 2002.

EPTD DISCUSSION PAPERS

Assessing and Attributing the Benefits from Varietal Improvement Research: Evidence from Embrapa, Brazil, by Philip G. Pardey, Julian M. Alston, Connie Chan-Kang, Eduardo C. Magalhães, and Stephen A. Vosti, August 2002. India’s Plant Variety and Farmers’ Rights Legislation: Potential Impact on Stakeholders Access to Genetic Resources, by Anitha Ramanna, January 2003. Maize in Eastern and Southern Africa: Seeds of Success in Retrospect, by Melinda Smale and Thom Mayne, January 2003.

Alternative Growth Scenarios for Ugandan Coffee to 2020, by Liangzhi You and Simon Bolwig, February 2003.

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