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90 Chapter Four INTEGRATED FOOD CROP PEST MANAGEMENT IN INDONESIA: A COMPUTABLE GENERAL EQUILIBRIUM MODEL Abstract The excessive use of pesticides in Indonesia during the 1970s and 1980s caused serious environmental problems such as acute and chronic human pesticide poisoning, animal poisoning and contaminated agricultural products, destruction of both beneficial natural parasites and pest predators, and pesticide resistance in pests. To overcome these environmental problems, since 1989 the Indonesian government has actively adopted a strategy of integrated pest management (IPM).1 The first goal of this essay is to build a Computable General Equilibrium model that includes various links from pesticide use in agricultural sectors to human health problems, and the links from human health problems to societal health costs and the effectiveness of production activities. The second goal of this essay is to analyze the impact of the IPM program on Indonesian economic growth and household incomes for different socioeconomic groups. 4.1 Introduction The chronic food shortage during the first two decades of Indonesian independence (1945-1965) stimulated the Indonesian government to establish a comprehensive food intensification program as a national priority. Achieving and maintaining self-sufficiency in food, increasing farmers’ income, and providing strong support for the rapidly expanding industrial and service sectors were the main goals of this food intensification program (Oka, 1995). The food intensification program included large-scale adoption 1 This essay limits its analysis to the implementation of the IPM program in the food crop sector. The reason for this limitation is that, first, until now the implementation of the IPM program in Indonesia has been limited to food crops. Second, the government has not yet planned to implement the IPM program in the non-food crop sector. 91 of high-yielding modern seed varieties, development of irrigation systems, expansion of food crop producing areas, increased use of chemical fertilizers and pesticides, expansion of agricultural extension services, establishment of farmer cooperatives and input subsidies, and stabilization of national food crop prices (Oka, 1991). During the 1970s and 1980s, this food intensification program caused food crop production to grow at an annual rate of approximately 3.74 percent2 (CBS, 1973-1991). A major miracle occurred in rice production. Pushing the average annual growth rate of rice production to approximately 4.67 percent, the rice intensification program transformed Indonesia from the world’s largest importer of rice, importing approximately two million tons per year by the end of the 1970s, to self-sufficiency in 1983 and thereafter (Oka, 1991 and 1995). Despite the remarkable success of the food intensification program, the excessive use of pesticides caused serious environmental problems3 such as acute and chronic human pesticide poisoning, animal poisoning and contaminated agricultural products, destruction of beneficial natural parasites and pest predators, and pesticide resistance in pests (Achmadi, 1992; Oka, 1995; and Pimentel et al., 1992). To overcome these environmental problems 2 The average annual population growth was approximately 2.3 percent in the 1970s and 1980s. 3 In 1988, Achmadi found 1267 cases of acute pesticide poisoning in 182 general hospitals throughout the islands of Java and Bali. He also observed that approximately 20 to 50 percent of the farmers who utilized pesticides contracted chronic pesticide-related illnesses. These illnesses included headaches, weakness, insomnia, and difficulties in concentrating (Achmadi, 1992). In the case of pesticide resistance in pests, brown planthoppers and green leafhoppers became resistant to pesticides and damaged more than 450,000 hectares of rice fields in 1976/1977. The estimated yield loss was 364,500 tons of milled rice, which could have fed three million people for an entire year. In 1980 and 1986, the same pest problem broke out again, causing damage to at least 12,000 and 75,000 hectares of rice fields, respectively (Oka, 1995). 92 caused by the overuse of pesticide, the Indonesian government adopted a strategy of integrated pest management (IPM). The program altered the predominant government policy of pest control from a unilateral method, depending solely on pesticide, to a combination of various control tactics to manage pests, including synchronized planting, crop rotation, natural predator, and pesticides. During the first two years of the IPM program (1989- 1991), the government trained approximately 100,000 food crop farmers, mostly rice farmers, to implement the IPM. With the IPM, these farmers have been able to reduce the use of pesticides by approximately 56 percent, and increase yields by approximately 10 percent4 (Oka, 1995). With this positive result, the Indonesian government is eager to train more food crop farmers to implement the IPM. Economic literature which analyzes the impact of the IPM program on household incomes and national economic performance is very limited. The Indonesian IPM National Program Monitoring and Evaluation Team in 1993 argued that IPM farmers would increase their incomes by approximately 50 percent. The team also estimated that, if the IPM were to become a common practice throughout Indonesia, the reduction in pesticide use would result in total savings for farmers of approximately 212 billion rupiahs. This study by the IPM National Program Monitoring and Evaluation Team, however, only observed the partial impact of the IPM program on farmer incomes, i.e. the team did not take into account the multiplier impact of an IPM program on incomes of both farmers and other household groups. The team also did not mention the impact of the IPM program on national economic growth. 4 The increasing yields are caused by the elimination of serious or large-scale pest outbreaks. 93 This essay utilizes a Computable General Equilibrium (CGE) model to analyze the total impact, including the multiplier impact, of the Indonesian IPM program on national economic growth and household incomes for various socioeconomic groups. A CGE model is a system of equations that represent all agents' behaviors and market clearing conditions in a national economy. The main goal of this essay is to build a CGE model that is appropriate to analyze the impact of an IPM program on a national economy. Implementation of an IPM program has three immediate impacts. A government has to reallocate its budget to finance the implementation. The implementation of an IPM program causes a reduction in the use of pesticides, and an increase in agricultural output. The CGE should be able to model how these immediate changes impact production and consumption behaviors in the economy, and in the end, affect national economic growth and household incomes. To capture these impacts, the CGE should include various relationships among pesticide use in agriculture, human health problems, societal health cost, and production activities. 4.2 Computable General Equilibrium Model This section explains some important features of the CGE utilized in this essay. To become familiar with other features of this CGE, one should review the Indonesian CGEs developed by Lewis (1991) and Thorbecke (1992).5 This essay combines the Indonesian CGEs just mentioned to create a new CGE model. The model describes various relationships to represent the 5 See also Appendix I. 94 impact of an IPM program on the economy. Finally, several dynamic equations are added to the model to transform the new CGE into a multi- period CGE. Facts and relationships modeled in the CGE to represent the impact of an IPM program on the economy are as follows: • Government needs to spend a certain amount of money to implement the IPM program. In this essay, government is assumed to take this IPM budget from government savings, resulting in a smaller government capital investment. • Most of the government IPM budget is allocated to the education or public service sectors, since the main activity of the IPM program is to educate food crop farmers in IPM. • The first direct impact of the IPM program is a reduction in the use of pesticides by food crop farmers. • The second direct impact of the IPM program is a more efficient food crop production sector, i.e. with a lesser amount of pesticides and the same amount of other inputs, IPM farmers are able to increase their output.6 This increased output is due to the fact that the IPM program can better control pest problems than a program that solely depends on pesticides. • Since the use of pesticides causes pesticide poisoning cases among farmers, the reduction in the amount of pesticide use in the food crop sector decreases the number of these pesticide poisoning cases.7 6 Dr. Oka, former chairman of the Indonesian Working Group on IPM (1991-1994), argues that the IPM program does not require more factor inputs than a program that solely depends on pesticides. 7 Non-farmers also may be poisoned by pesticides used in agriculture. Available data, however, concern the number of farmers poisoned by these pesticides. This essay thus limits its analysis to pesticide poisoning cases which affect farmers. 95 • This reduction in the number of pesticide-related illnesses lowers food crop farmer households’ spending on necessary treatment to recover from pesticide-related illnesses. These lower health costs enable households to spend money on other goods and services, mostly food. • The occurrence of pesticide-related illnesses negatively affects the productivity of agricultural labor input. This negative effect might reduce the productivity of other agricultural factor inputs, i.e. land and capital. The reduction in the number of pesticide-related illnesses among food crop farmers hence improves the productivity of all factor inputs in the food crop production sector. The detailed modeling of the impact of the IPM program now follows. Figure 4.1 shows a diagram of sectoral production activities. The CGE in this essay has 20 different sectoral production activities. The important features of these sectoral production activities are the value-added function, sectoral production function, and the input-output coefficient of the quantity of pesticide used in the food crop sector. Let us first observe the value added function. Value added is a function of human pesticide-related illnesses and factor inputs. The factor inputs are expressed in the Constant Elasticity of Substitution (CES) function. 96 Output X Intermediate Input Value Added X1 X2 AgLab CES CESFixed Prop. Xn ManCler Prof Lab Capi- tal Land Note: CES is the Constant Elasticity of Substitution production function Fixed Prop. is the Fixed Proportion (Leontief production function) Figure 4.1 Structure of the Sectoral Production Function VA HE FACDEMi i i i f v i f f i v i v = ⋅ ⋅ ⋅ − − α β ρ ρ , , 1 (4.1) where: i is the index for production sectors VAi is the value-added input for sector i HEi is the impact of human pesticide-related illnesses on the value-added production activity FACDEMi,f is the demand for factor input f in sector i. 97 The factors represented by f are agricultural workers, manual-clerical personnel, professional laborers, land, and capital. Land and capital are fixed. The market for professional workers is assumed to be in a full-employment condition. Both the agricultural and manual-clerical labor markets experience unemployment. In this essay the impact of human pesticide-related illnesses on production activity, i.e. HEi, is simply a function of restricted activity days caused by pesticide-related illnesses. Furthermore, since data on the number of restricted activity days are limited to farmers only, the HEi function is: HE RAD DAi i i = − 1 ∀ i ∈ crop sectors (4.2) and HEi = 1 ∀ i ∉ crop sectors (4.3) where: RADi is the number of restricted activity days caused by pesticide- related illnesses DAi is the number of man-days that should be available if no pesticide-related illness occur. The second important point about sectoral production activities is the production of sectoral output. The form of the sectoral production function is: ( )X IN VAi ix ix i i x iix ix ix= ⋅ ⋅ + − ⋅− − −α β βρ ρ ρ( )1 1 (4.4) where: Xi is gross domestic sectoral outputs 98 INi is composite intermediate inputs. In food crop sector, farmers who implement the IPM can increase their yields. To represent these increasing yields, this essay defines the share parameter of the food crop production function (αFOODCROP x) as a function of the number of farmers who adopt the IPM. The more farmers who implement the IPM, the higher this share parameter will be: α αFOODCROP x t FOODCROP x t FOODCROP AGLAB t IPMFARM FACDEM , , = ⋅ − 1 + ⋅ ⋅α FOODCROP x t FOODCROP AGLAB t IPMFARM FACDEM , .110 (4.5) where: 1.10 is due to the fact that IPM farmers are ten percent more efficient than non-IPM farmers α FOODCROP x is the initial/benchmark shift parameter of food crop sectoral production α FOODCROP x t, is the shift parameter of food crop sectoral production in year t IPMFARMt is the number of food crop farmers implementing the IPM in year t FACDEMtFOODCROP,AGLAB is the number of total food crop farmers in year t. The third important feature of the sectoral production activities is the iomiPEST,FOODCROP, which is the input-output coefficient of the amount of pesticide used in the food crop sector. Farmers who implement the IPM can reduce the amount of pesticide used. The pesticide input-output coefficient in the food 99 crop sector is a function of the number of IPM farmers. The more farmers who adopt the IPM, the smaller this pesticide coefficient will be: iomi iomi IPMFARM FACDEMPEST FOODCROP t PEST FOODCROP t FOODCROP AGLAB t, , , = ⋅ − 1 + ⋅ ⋅iomi IPMFARM FACDEMPEST FOODCROP t FOODCROP AGLAB t, , .0 44 (4.6) where: 0.44 is due to the fact that IPM farmers are able to reduce the use of pesticides by 56 percent iomi PEST FOODCROP, is the initial/benchmark input-output coefficient of pesticide use in the food crop sector iomiPEST FOODCROP t , is the input-output coefficient of pesticide use in the food crop sector in year t. This essay considers ten different types of household groups. Each household group maximizes its utility as a Cobb-Douglas function of all goods and services, except for the necessary health treatments related to pesticide- related illnesses, subject to its budget constraint: ( )U HCDh h i h chs i aph i h = ⋅ ≠ ∏α , , ; chsi h i aph , ≠ = 1 (4.7) subject to: PQ HCD YH HTAX HSAV CDHE HHTRi i h i aph h h h h h⋅ ≤ − − − − ≠ , (4.8) where: h is the index for household groups 100 aph is the index for health services consumed by households which experience pesticide-related illnesses YHh is the income of household h HCDi,h is household consumption PQi is the price of commodity i HTAXh is income taxes HSAVh is household savings HHTRh is net household transfers CDHEh is necessary health costs to recover from pesticide-related illnesses. Note that this essay limits its analysis to the case of pesticide-related illnesses among farmers. The health costs associated with pesticide-related illnesses (CDHEh) in the relationship (4.8) hence only appear in agricultural household groups’ budget constraint, i.e. for non-agricultural households, CDHEh always equals zero. From the relationship (4.8), one can see that a reduction in health costs associated with pesticide-related illnesses creates “extra income” for agricultural households to spend on goods and services. In developing countries, agricultural households mostly spend this extra income on food. The amount of health spending by households depends on the number of pesticide-related illnesses which occur. The quantity of pesticide-related illnesses is a function of the quantity of pesticide used in agricultural sectors: PESHLT apesht iomi IN R AGLABag ph ag ph PEST ag ag, , , ( )= ⋅ ⋅ ⋅ (4.9) where: 101 ag is the index for agricultural sectors ph is the index for the pesticide-related illnesses PESHLTag,ph is the number of pesticide-related illnesses apeshtag,ph is the pesticide-health coefficient iomiPEST,ag⋅INag is the amount of pesticide used in agricultural sector ag R(AGLAB) is the ratio between agricultural labor in any simulation scenario and in the benchmark situation. The pesticide-related illnesses are chronic and acute pesticide poisoning. Farmers who contract chronic or acute pesticide poisoning usually cannot work for at least one day. In this CGE, the capital accumulation equation is the important dynamic equation related to the implementation of the IPM program. Capital accumulates as new capital is invested; the amount of capital next year is a function of the existing capital plus new capital, minus depreciated capital. ( )FACDEM FACDEM depr DKi CAPITALt i CAPITALt i it, , .+ = − +1 1 (4.10) where: depri is the depreciation rate DKi t is the new capital invested in year t. Government and private savings fund new capital investments. Government savings also must provide the budget for IPM program implementation. In the absence of this program, the government would use the funds allocated for the IPM budget for new capital investment. Implementation of the IPM program, hence, reduces the amount of new capital invested, and, in the end, decreases the rate of capital accumulation. 102 Other important dynamic equations, although not related to the implementation of the IPM program, are the equations which determine the wages for manual-clerical and agricultural workers. For manual-clerical laborers, wages increase as a function of the growth in both total value added and employment.8 Growth in total value added this year increases the wage next year. In contrast, growth in total employment this year decreases the wage next year. WA WA WA GDVA GDVA GDVA MANCLER t MANCLER t MANCLER t mn mn t t t + − − − = + ⋅ − 1 0 1 1 1α α − ⋅ − − − α 2 1 1 mn t t t TOTLAB TOTLAB TOTLAB (4.11) where: GDVAt is the gross domestic value added in year t TOTLABt is the total employment in year t. For agricultural workers, wage growth is a function of growth in both agricultural value added and agricultural employment: WA WA WA AGVA AGVA AGVA AGLAB t AGLAB t AGLAB t ag ag t t t + − − − = + ⋅ − 1 0 1 1 1α α − ⋅ − − − α 2 1 1 ag t t t AGLAB AGLAB AGLAB (4.12) where: AGVAt is the total value added in the agricultural sector in year t 8 Indonesia exhibited wage relationships (4.11) and (4.12) during the 1970s and 1980s. See Thorbecke (1992). In Thorbecke’s work, wages are a function of the price deflator, value added, and total employment. This essay omits the price deflator variable from the wage function since in any year of the simulation wages (also other prices) are in real terms. See also footnote 17. 103 AGLABt is the number of agricultural workers in year t. 4.3 Integrated Food Crop Pest Management in Indonesia This section reviews the history of the implementation of the integrated food crop pest management (IPM) program in Indonesia. The description in this section serves as the base to develop the simulation scenarios in later sections. The Plant Protection Directorate under the Ministry of Agriculture introduced the IPM program at the end of the 1970s. Until the mid-1980s, the implementation of this program was very limited. No systematic effort to educate extension workers and farmers existed. Government subsidies for pesticides continued to reach 80 percent of total prices for pesticides. Farmers really had no incentive to implement the IPM. The massive outbreak of brown planthoppers (caused by pesticide resistance) and human pesticide poisoning problems in the mid-1980s motivated high-ranking government officers to consult Indonesian agricultural scientists. The scientists convinced the government that the solution to the two problems associated with the overuse of pesticide was to implement actively the IPM program. Indonesian agricultural scientists believed that, with implementing the IPM program, farmers would be able to reduce the use of pesticides without decreasing yields. The government then issued Presidential Decree No. 3 of 1986. This presidential decree established the IPM program as a national policy that all government agencies would support. The decree had the following objectives (Oka, 1995): 104 • develop manpower, both farmers and field personnel, at the grassroots level to implement the IPM • increase efficiency of input use, in particular pesticides • improve the quality of the environment and its influence on human health. Along with this decree, the government decreased subsidies for pesticides from 75-80 percent of total prices for pesticides in 1986 to 40-45 percent in 1987. Finally in January 1989 these subsidies were totally abolished.9 The government also banned 57 broad-spectrum insecticides, and only allowed the use of a few relatively narrow-spectrum insecticides. To actively implement the IPM, in 1989 the National Development Planning Agency (BAPPENAS) established an Advisory Board which consisted of high-ranking officers from BAPPENAS, the Ministry of Agriculture, and the Ministry of Home Affairs. The Board was the supreme policy-making body, and responsible for the success of the IPM program. Under the Board, a Steering Committee was formed to direct the project activities, and to ascertain the need for policy improvement. The Committee consisted of IPM experts from various government agencies, universities, and international institutions. Certain members of the Committee formed the Working Group which conducted the day-to-day tasks of the Committee. The central activity of this national IPM program was to educate farmers in IPM using the “learning by doing” method. The Working Group first trained extension workers and field pest observers to teach farmers. By the end of 1991, 2,000 extension workers and 1,000 field pest observers had been able to train approximately 100,000 farmers. After 1991, approximately 9 With the abolishment of pesticide subsidies, the Indonesian government saved approximately 200-300 billion rupiahs (100-150 million dollars) per year. 105 200,000 farmers were trained each year.10 Approximately ten percent of these 200,000 farmers became one-on-one trainers. Each of these farmer trainers was required to train one farmer twice per year, and repeat this training with a new farmer in the following year. The cost of all IPM training activities is approximately 11.25 billion rupiahs (5.36 million dollars) each year.11 The central government provides approximately 80 percent of this total cost; the various regional governments provide the rest.12 4.4 Data Sources and Initial Situation This section discusses data sources utilized in this essay. The main sources of data are the 1990 Indonesian Social Accounting Matrix (SAM) and Input-Output (I-O) Table which are available from the Indonesian Central Bureau of Statistics (CBS). This essay modifies the 1990 SAM in two ways. First, it reduces the classification of factor inputs to five categories: agricultural labor, manual-clerical labor, professional labor, capital, and land. Second, by disaggregating certain production sectors (using the I-O Table) and combining others, the number of production sectors decreases from 22 to 20. Important to note in the new classification is that three agricultural sectors exist: Food Crop, Non-food Crop, and Other Agriculture. Pesticide production also is removed from the Chemical and Basic Metal sector to become a separate Pesticide sector. In addition, the health activities related to pesticide poisoning illnesses are separated from the Public Service sector to become the Pesticide-Health sector. 10 During the beginning years of this IPM training program, the training was mostly for rice farmers. Later, training for other food crop farmers was also provided. 11 For a national program, this IPM budget is relatively small. The budget is approximately 0.08 percent of total government spending on goods and services in 1990. 106 The SAM in this essay uses the same categories for household classes that the CBS SAM does. The categories are as follows: • Agricultural Employee : Agricultural workers who do not own land • Small Farmer : Agricultural land owners with land between 0.0 and 0.5 ha • Medium Farmer : Agricultural land owners with land between 0.5 and 1.0 ha • Large Farmer : Agricultural land owners with land larger than 1.0 ha • Rural Low : Non-agricultural households, consisting of small13 retail store owners, small entrepreneurs, small personal service providers, and clerical and manual workers in rural areas • Rural Non-labor : Non-agricultural households, consisting of non- labor force and unclassified households in rural areas • Rural High : Non-agricultural households, consisting of managers, technicians, professionals, military officers, teachers, big14 entrepreneurs, big retail store owners, big personal service providers, and skilled clerical workers in rural areas • Urban Low : Non-agricultural households, consisting of small retail store owners, small entrepreneurs, small 12 Based on an interview with Dr. Oka. 13 Small refers to a low income level. 14 Big refers to a high income level. 107 personal service providers, and clerical and manual workers in urban areas • Urban Non-labor : Non-agricultural households, consisting of non- labor force and unclassified households in urban areas • Urban High : Non-agricultural households, consisting of managers, technicians, professionals, military officers, teachers, big entrepreneurs, big retail store owners, big personal service providers, and skilled clerical workers in urban areas. Table 4.1 shows characteristics of each household group in 1990. Column 1 lists the proportions of the total population of Indonesia that belonged to certain household groups. From column 1, one can see that 39 percent of the total Indonesian population belonged to Small Farmer households. Since the total population in Indonesia in 1990 was approximately 180 million people, Small Farmer households equaled approximately 70.2 billion people. Column 2 shows the ratio between the income of each household group and total household income of all groups. In 1990, total household income was approximately 161,544.5 billion rupiahs.15 From columns 1 and 2, one can see that, although Urban High households formed only five percent of the total population in the country, the total income of Urban High households contributed 25 percent to total household income. In contrast, Agricultural Employee and Small Farmer households represented 10 and 39 percent of the 15 Gross of Domestic Product in Indonesia in 1990 was 210,866.5 billion rupiahs. 108 total population, respectively, but only received 4 and 20 percent of total household income. Table 4.1 Characteristics of Household Groups in 1990 Population Househol d Income Transfer from: Income Ag. Lab. Man-Cler. Prof. Lab. Land Capital (1) (2) (3) (4) (5) (6) (7) Ag. Employee 0.10 0.04 0.13 0.01 0.00 0.04 0.02 Small Farmer 0.39 0.20 0.45 0.09 0.03 0.44 0.07 Medium Farmer 0.07 0.05 0.12 0.02 0.01 0.11 0.02 Large Farmer 0.07 0.06 0.17 0.02 0.01 0.13 0.02 Rural Low 0.10 0.05 0.03 0.07 0.02 0.04 0.02 Rural Non-labor 0.01 0.02 0.00 0.01 0.01 0.01 0.02 Rural High 0.04 0.16 0.08 0.21 0.27 0.18 0.04 Urban Low 0.16 0.13 0.01 0.18 0.05 0.02 0.07 Urban Non-labor 0.01 0.04 0.00 0.02 0.01 0.03 0.03 Urban High 0.05 0.25 0.01 0.37 0.59 0.00 0.12 Total 1.00 1.00 1.00 1.00 1.00 1.00 0.43 Source: CBS, 1991. Columns 3 to 7 show income transfers from factors of production to households. For example, column 3 shows that 45 percent of payments received by agricultural laborers in 1990 were transferred to Small Farmer households, i.e. approximately 45 percent of agricultural workers were small farmers. Note that only 43 percent of capital revenues in 1990 were transferred to households. The remaining capital returns were distributed to companies, the government, and international institutions. Achmadi’s work provides the estimate of the number of acute and chronic pesticide poisoning cases. He estimated that in 1988 approximately 3000 cases of acute poisoning were associated with the use of pesticides in agricultural sectors. This essay assumes that the number of acute pesticide poisoning cases in 1990 is the same as in 1988. This essay uses a figure of 35 109 percent (based on Achmadi’s estimate, see the third footnote) as the proportion of farmers who utilized pesticides and contracted chronic pesticide-related illnesses. CBS (1991) estimated that approximately 40 million people worked in agricultural sectors in 1990 and approximately 28 million of them were farmers (and agricultural workers) who utilized pesticides. Thus, the estimate of chronic pesticide-related illness cases for 1990 is approximately 9.8 million. Achmadi also noticed that, on average, each time a farmer contracts acute pesticide poisoning, the farmer misses work approximately five days. Each time a farmer contracts chronic pesticide poisoning, the farmer, on average, misses work approximately one day. 4.5 Simulation Scenarios This section explains the five scenarios simulated in this essay. It is important to note that, in the simulation scenarios, the IPM program is only implemented in the food crop sector.16 Another point to note is that this essay considers the simulation period to be 10 years long.17 The five simulation scenarios are as follows: 1. Base Condition (No IPM Program):18 This scenario assumes that the Indonesian government does not implement the IPM program at all.19 The 16 See also footnote 1. 17 Throughout these 10 years, the Price Index is set at a constant level. Hence prices in any year of the simulation are in real terms. 18 In all scenarios, the CGE assumes that during the ten-year simulation horizon: • world prices of goods and services remain at a constant level • total annual government loans from international institutions decrease at a rate of three percent; this same reduction rate applies to total annual private sector loans from international institutions • government interest and amortization payments of international loans increase at annual rates of five and two percents, respectively • private sector interest and amortization payments of international loans increase at annual rates of seven and four percent, respectively 110 Indonesian government does not spend 11.25 billion rupiahs each year to educate farmers. Thus no farmers are aware of IPM practice. Pesticides continue to be overused. 2. Basic IPM Program: This scenario simulates the IPM program planned by the Indonesian government. From year one, the government spends 11.25 billion rupiahs annually throughout the ten year time horizon to educate food crop farmers. Each year, 200,000 farmers graduate from IPM training. Approximately ten percent of them are required to teach two other farmers IPM practices each year. It is assumed that farmers who implement the IPM are able to reduce their use of pesticides by 56 percent, and increase their yields by 10 percent. 3. IPM Plus Tax Program: It is assumed that the Indonesian government implements the IPM program by spending 11.25 billion rupiahs each year to train farmers. To further reduce the use of pesticides, the government collects a five percent sales tax on pesticides. This five percent sales tax is implemented at the start of the ten year simulation period, and continues through year ten. 4. Ambitious IPM Program: In this scenario, the Indonesian government is assumed to double its training budget for farmers in the IPM program, compared with the budget in the Basic IPM Program. Starting with the first year of the simulation, the Indonesian government spends 22.50 billion rupiahs annually to train 400,000 food crop farmers. Government savings provides the money for this training. By implementing this • foreign capital investment grows at an annual rate of 15 percent. 19 The benchmark data set in this essay is the Indonesian economy in 1990. The IPM program in that year was in its preliminary stage. It is, hence, reasonable to assume that there was no IPM program yet in 1990. 111 Ambitious IPM strategy, the Indonesian government is assumed to expect a greater growth in the output of the food crop sector and a larger reduction in the number of pesticide-related illnesses. 5. Strategic IPM Program: The Indonesian government spends 11.25 billion rupiahs annually to train 200,000 food crop farmers each year in the IPM in the first five year simulation period. In the sixth and later years, the Indonesian government doubles its spending on this IPM training program, i.e. 400,000 food crop farmers receive IPM training each year from year six. 4.6 Results and Discussion This section presents and discusses the results of all simulation scenarios. The discussion focuses on how each scenario affects national economic growth and household incomes, and how the results from the various scenarios differ from those of the Base Condition scenario. Table 4.2 exhibits the Gross Domestic Product (GDP), household incomes for different socioeconomic classes, and the total health costs of pesticide-related illnesses in the benchmark/initial year (t0) and in the last year of simulation (t10) from the Base Condition scenario. 112 Table 4.2 GDP, Household Incomes, and Health Costs of Pesticide-Related Illnesses Under the Base Condition Scenario (in billions of rupiahs) Benchmark Base Condition Percentage Difference t0 t10 GDP 210866.5 329549.6 56.28% Ag. Employee 6337.09 10814.86 70.66% Small Farmer 31911.62 56179.98 76.05% Medium Farmer 7390.52 13194.53 78.53% Large Farmer 9839.94 17543.98 78.29% Rural Low 8894.81 14951.09 68.09% Rural Non-labor 2948.40 3896.09 32.14% Rural High 25765.43 44844.62 74.05% Urban Low 20698.48 32893.04 58.92% Urban Non-labor 6610.97 10253.11 55.09% Urban High 41147.27 67492.64 64.03% Pesticide-Health 9.01 15.20 68.71% Note: t0 = initial year t10 = year ten (the last year of the simulation period) Benchmark = the situation in the initial year of simulation Base Condition = the government does not implement the IPM program Table 4.3 shows the estimated impact of various IPM programs on GDP, average annual GDP growth rate, household incomes, and health costs associated with pesticide-related illnesses. These impacts are presented as percentage differences between the results from the Base Condition scenario and those from other scenarios.20 From Table 4.3 one can see that the impact 20 To calculate the percentage differences, first determine the differences between results from other scenarios and the Base Condition, i.e. results from other scenarios minus results from the Base Condition. Then, divide these differences by results from the Base Condition and multiply by 100 percent. 113 Table 4.3 Estimated Impact of Various IPM Program Scenarios on GDP, Average Annual GDP Growth Rate, Household Incomes, and Health Costs of Pesticide-Related Illnesses Basic IPM IPM+Tax Ambitious Strategic Program Program IPM IPM t10 t10 t10 t10 GDP 0.056% 0.021% 0.111% 0.145% Annual Growth 0.131% 0.050% 0.260% 0.331% Ag. Employee 0.046% 0.024% 0.090% 0.094% Small Farmer 0.009% -0.024% 0.017% 0.066% Medium Farmer 0.007% -0.025% 0.014% 0.061% Large Farmer 0.014% -0.016% 0.028% 0.067% Rural Low 0.087% 0.048% 0.172% 0.183% Rural Non-labor 0.046% 0.006% 0.092% 0.130% Rural High 0.075% 0.036% 0.149% 0.171% Urban Low 0.109% 0.069% 0.216% 0.216% Urban Non-labor 0.055% 0.010% 0.110% 0.153% Urban High 0.112% 0.071% 0.223% 0.225% Pesticide-Health -4.606% -4.814% -9.206% -6.093% Note: Basic IPM = the government spends 11.25 billion rupiahs to train farmers in IPM IPM+Tax = in addition to training farmers, the government imposes a tax on pesticides Ambitious IPM = the government doubles (compared with the Basic IPM Program) its IPM budget from the first year Strategic IPM = the government doubles (compared with the Basic IPM Program) its IPM budget after the first five years of the IPM programs on the average annual GDP growth rate are relatively small. The rates under the programs are 0.050 to 0.331 percent higher than the average annual GDP growth rate under the Base Condition. The impacts of the IPM programs on household incomes are relatively small. The largest impact on household incomes is 0.225 percent. This percentage represents the increase in income that Urban High households experience in year ten, as compared to the Urban High household situation under the Base Condition. 114 The IPM programs, however, effectively reduce health costs associated with pesticide-related illnesses. The range of health cost reduction is 4.606 to 9.206 percent. Table 4.4 summarizes the impact of various scenarios on annual GDP growth rate and health costs of pesticide-related illnesses. For the annual GDP growth rate, more stars mean a higher GDP growth rate. For health costs associated with pesticide-related illnesses, more stars mean lower health costs associated with pesticide-related illnesses. Table 4.4 Summary of Impact of Various Scenarios on Annual GDP Growth Rate and Health Costs of Pesticide-Related Illnesses Basic IPM IPM+Tax Ambitious Strategic Program Program IPM IPM t10 t10 t10 t10 Annual Growth ** * *** **** Pesticide-Health * ** **** *** Note that more stars mean a higher GDP growth rate or lower health costs associated with pesticide-related illnesses. 4.6.1 Base Condition From Table 4.2 one can see that, under the Base Condition scenario, the GDP is growing throughout the ten year simulation period, from 210,886.5 billion rupiahs in the initial year (t0) to 329,549.6 in year ten (t10), i.e. the GDP in year ten is 56.28 percent higher than that in the initial year. Table 4.2 also shows that incomes for all household groups increase under the Base Condition scenario. Medium Farmer households experience the highest increase in income, while Rural Non-labor households experience 115 the least increase. All agricultural households increase their incomes more (in percentage terms) than the urban households do. Since the average income of agricultural households on a per capita basis is smaller than that of urban households (see Table 4.1), a greater increase in incomes of agricultural households could contribute to a more equal income distribution in the country. Under the Base Scenario, societal health costs caused by the use of pesticides grow from 9.01 billion rupiahs in the initial year to 15.20 billion rupiahs in year ten. 4.6.2 Basic IPM Program Table 4.3 shows that the average annual GDP growth rate under the Basic IPM Program scenario is 0.131 percent higher than that under the Base Condition. Table 4.3 also shows the GDP in year ten under the Basic IPM Program scenario is 0.056 percent higher than that under the Base Condition. All households receive higher incomes under the Basic IPM Program than under the Base Condition scenario. Urban High households receive the highest increase in income from the Basic IPM Program, while Medium Farmer households receive the least. Among the agricultural households, the Agricultural Employee households receive the highest income increase. From Table 4.3 one also can see that the Basic IPM Program affects the incomes of each household group differently. These differences, however, are relatively small, hence one might expect that the Basic IPM Program produces the same income distribution as under the Base Condition. 116 Societal health costs associated with the use of pesticides is 4.606 percent lower compared to that under the Base Condition, i.e. the Basic IPM Program successfully reduces the health cost associated with the use of pesticides. 4.6.3 IPM Plus Tax Program The average annual GDP growth rate under the IPM Plus Tax Program is 0.050 percent higher than the average annual GDP growth rate under the Base Condition. This average annual GDP growth rate under the IPM Plus Tax Program is certainly lower than that under the Basic IPM Program (see Table 4.3). One, then, can argue that implementing a tax on pesticides adversely affects national economic growth. Another negative impact of implementing a tax on pesticides is that the total incomes of Small, Medium, and Large Farmer households under the IPM Plus Tax Program scenario are lower than those under the Base Condition scenario. Medium Farmer households incur the highest negative impact of this tax policy. Note that this negative impact does not happen under the Basic IPM Program. The goal of combining a tax on pesticides and the IPM Program is to further reduce the quantity of pesticide-related illnesses than the reduction obtained under the Basic IPM Program scenario. From Tables 4.3, one can see that this goal is achieved. The societal health cost associated with pesticide- related illnesses under the Basic IPM Program is 4.606 percent lower than that under the Base Condition. The societal health cost associated with pesticide- 117 related illnesses under the IPM Plus Tax Program is 4.814 percent lower than that under the Base Condition. 4.6.4 Ambitious IPM Program In the Ambitious IPM Program, the government doubles its annual budget (compared to the Basic IPM Program) for the IPM Program to achieve faster economic growth and to decrease health costs associated with pesticide- related illnesses. Table 4.3 shows the achievement of this ambition. The average annual growth rate of GDP is 0.260 percent higher than that under the Base Condition, and is also higher than the annual GDP growth rates under the Basic IPM Program and the IPM Plus Tax Program. Under the Ambitious IPM Program, the total incomes of all household groups in year ten are higher than those under the Base Condition, the Basic IPM Program, and the IPM Plus Tax Program (see Table 4.3). Similar to the situation under the Basic IPM Program, urban household groups receive higher benefits than agricultural household groups under the Ambitious IPM Program. While the Urban High households receive the most benefits from the Ambitious IPM Program, the Medium Farmer households receive the least. One issue is that this higher income received by urban households might induce a more unequal income distribution in the country. However, comparing Tables 4.2 and 4.3, one can see the impact of the Ambitious IPM Program on household incomes is relatively small. The differences between the increase in incomes that all household groups receive under the Ambitious IPM Program and the Base Condition are trivial. One then may expect that the Ambitious IPM Program generates approximately 118 the same income distribution as that under the Base Condition. Since the Base Condition scenario mostly likely produces a more equal income distribution, a more equal income distribution also most likely occurs under the Ambitious IPM Program. Reviewing the performance of GDP growth and household incomes under the Ambitious IPM Program, one can conclude that most households would prefer the Ambitious IPM Program rather than the Base Condition or the Basic IPM Program (see also Table 4.4). Societal health costs associated with pesticide-related illnesses in year ten under this Ambitious IPM Program are 9.206 percent lower than those under the Base Condition, and are also lower than the health costs associated with pesticide-related illnesses under the Basic IPM Program and the IPM Plus Tax Program. 4.6.5 Strategic IPM Program In the Strategic IPM Program, the Indonesian government doubles its budget (compared to the Basic IPM Program) for the IPM Program from year six onward. The Strategic IPM Program is able to increase the average annual GDP growth rate 0.331 percent higher than the rate achieved under the Base Condition scenario; it is, in fact, higher than rates in all other scenarios (see Tables 4.3). Under the Strategic IPM Program, household incomes for all socioeconomic classes increase. The Strategic IPM Program increases the income of each household group more than other scenarios do. Although urban households receive higher benefits than agricultural households, the 119 distribution of household income group under the Strategic IPM Program is most likely the same as the distribution of household income under the Base Condition (for the same reason as in the Ambitious IPM Program). Under the Strategic IPM Program, societal health costs associated with pesticide-related illnesses in year ten are 6.093 percent lower than those under the Base Condition, the Basic IPM Program, and the IPM Plus Tax Program, but higher than those under the Ambitious IPM Program. Hence, while the Strategic IPM Program performs better in increasing the growth rates of GDP and household incomes as compared with other scenarios, the Strategic IPM Program does not reduce societal health costs lower than the Ambitious IPM Program (see Table 4.4). 4.7 Conclusion The CGE developed in this essay analyzes the total impacts of an IPM program on national economic growth and income distribution. This CGE includes various relationships including: • the relationship between the use of pesticides and human pesticide poisoning problems (pesticide-related illnesses) • the relationship between human health problems and both societal health costs and human productivity in production activities • the relationship between the implementation of an IPM program and a more efficient food crop production sector • the relationship between the government budget to support an IPM program and future investment. 120 This essay then implements the CGE to analyze the IPM program in Indonesia. To conduct the analysis, this essay constructs four different scenarios -- Basic IPM, IPM Plus Tax, Ambitious IPM, and Strategic IPM Programs -- and a scenario in which no IPM program is implemented, the Base Condition. Before discussion of the results, it is important to note that these results need to be qualified. Since data are limited, the CGE in this essay cannot capture perfectly all relationships within the economy, within the environment, and between the economy and the environment. The underlying assumptions for the CGE and the simulation scenarios also should be carefully examined. Five major findings result from the CGE.21 First, a more equal income distribution in Indonesia results from the Base Condition scenario; each year during the ten year horizon, this income distribution becomes increasingly more equal. Second, the implementation of the IPM program, either the Basic IPM, IPM Plus Tax, Ambitious IPM, or Strategic IPM Program, most probably produces an almost identical income distribution as that which occurs under the Base Condition scenario. In effect, implementation of any IPM program produces a more equal income distribution in Indonesia. Third, the implementation of only an IPM Program, i.e. the Basic IPM, Ambitious IPM, or Strategic IPM Program, successfully increases the average annual GDP growth rate and the incomes of all household groups, while decreasing the health costs associated with pesticide-related illnesses. The combination of an IPM Program with a tax on pesticides is capable of reducing health costs associated with pesticide-related illnesses to a level 121 lower than that produced with only an IPM Program, and is capable of increasing the average annual growth rate of GDP higher than that under the Base Condition. This combination program, however, reduces the incomes of farm owners. Fourth, the Ambitious IPM program is able to reduce the health costs associated with pesticide-related illnesses to a level lower than that of other program scenarios. Hence, if the goal of the government is to reduce the use of pesticides to as low a level as possible, i.e. reducing the environmental damage associated with pesticides, increasing the budget for the IPM Program from the first year of the IPM implementation might be the appropriate choice. Finally, the Strategic IPM Program is able to increase all household incomes and produce a higher annual GDP growth rate than the rates achieved under the Basic IPM, IPM Plus Tax, and Ambitious IPM Programs. Therefore, if the goal of the government, besides reducing the environmental damage caused by the use of pesticides, is to increase national economic growth and household incomes as high as possible, it might be wise for the government to periodically increase the budget for the IPM Program. 21 Please see footnote 17 for several assumptions imposed during the ten-year simulation. 122 4.8 References Achmadi, U.F. “Agricultural Injuries in Indonesia: Estimates and Surveys.” Department of Public Health Working Paper, University of Indonesia, Jakarta, 1991. Central Bureau of Statistics. Statistical Yearbook of Indonesia 1990. Jakarta: Central Bureau of Statistics, 1991. IPM National Program Monitoring and Evaluation Team. The Impact of IPM Training on Farmers’ Behavior: A Summary of Results from the Second Field School Cycle. Jakarta: BAPPENAS, 1993. Lewis, J.D. “A Computable General Equilibrium (CGE) Model of Indonesia.” Development Discussion Papers, Harvard Institute for International Development, Cambridge, 1991. Oka, I.N. “Success and Challenges of the Indonesian National Integrated Pest Management Program in the Rice-based Cropping System.” Crop Protection, 10 (3 1991): 163-65. ____. “Integrated Crop Pest Management With Farmer Participation in Indonesia.” Working Papers, Food Crop Research Center, Bogor, 1995. Pimentel, D., H. Acquay, M. Biltonen, P. Rice, M. Silva, J. Nelson, V. Lipner, S. Giordano, A. Horowitz, and M. D’AMore. “Environmental and Economic Costs of Pesticide Use.” BioScience, 42 (November 1992): 750- 60. Thorbecke, E. “A Computable General Equilibrium Model Integrating Real and Financial Transactions.” Adjustment and Equity in Indonesia. E. Thorbecke et al., pp. 85-102. Paris: OECD Publications, 1992.