Household production and forest clearing: the role of farming in the development of the Amazon

Transcription

1 Environment and Development Economics 9: Cambridge University Press DOr: /S X Printed in the United Kingdom Household production and forest clearing: the role of farming in the development of the Amazon JILL L. CAVIGLIA-HARRIS* Department of Economics and Finance, Salisbury University, Salisbury, Maryland Telphone: (410) Fax: (410) I ( ABSTRACT. Global tropical deforestation continues to be a critical environmental issue and nowhere in the world is the issue more pronounced than in Brazil. This paper examines the land-use choices of small-scale farmers in Ouro Preto do Oeste, Rondonia, Brazil and investigates how agricultural production impacts deforestation levels. The data used to explore these issues consist of a panel collected from 152 households in 1996 and Overall, the empirical models indicate that access to credit, wealth, lot size, product markets, and off-farm labor opportunities, largely influence deforestation and production decisions. Among other things, the results suggest that more sustalnable production methods are unlikely to be adopted by a majority of households under current conditions because the production of milk has rapidly advanced due to its moderate labor requirements and existing market infrastructure. Households with greater levels of wealth have focused on milk while those with access to credit have focused on crops. Since the production of crops is largely influenced by access to credit, similar incentives may be proposed to support more sustainable production activities to help reduce deforestation.!.1 I j 1. Introduction Global tropical deforestation continues to be a critical environmental issue. An average of 15.2 million hectares, 1 of tropical rainforest are cleared every year, and nowhere in the world is the issue more pronounced than in Brazil (Abramovitz, 1998). Currently, over 19 per cent of the region has been deforested (Alves, 1999) and this amount is expected to increase with the implementation of the development plan, Avan<;a Brasil (Forward Brazil) (Laurance et ai., 2001b). * I would like to thank Galvanda Quciroz Galvao, Walmir de Jesus, and Marcos Pedlowski for help with the administration of the survey in Their help was invaluable. I would also like to acknowledge The National Science Foundation for financial support under grant SES And would like to thank the three anonymous reviewers who provided useful comments and guidance with earlier versions of this paper. 1 FAOpress release 01/ 61: and 'The State of the World's Forests 2001':

2 182 Jill L. Caviglia-Harris Under Avan<;a Brasil the Brazilian government is expected to make largescale investments amounting to nearly $45 billion in development projects in the Amazon region by These plans include the expansion of paved roads into central Amazonia, and the construction of ports, waterways, railways, and hydroelectric power plants. It is expected that the paved roads alone will increase current forest clearing levels by 120,000 to 270,000 km 2 over the duration of the plan, because they facilitate migration to the region (Carvalho et ai., 2001, Instituto de Pesquisa Ambiental da Amazonia 2000). In particular, an increase in the colonization of Amazonian towns and cities by subsistence farmers can be expected as the road system is developed (for example Fearnside, 2001). Since the original settlement programs of the 1970s, these small-scale farmers and ranchers have greatly impacted the rate of forest clearing in the Amazon (Mahar, 1989). Household farming practices are the topic of a rich body of literature on deforestation in Latin American since the primary agents of deforestation include these farmers (Giest and Lambin, 2001). Although not the primary source of deforestation in Amazonia (Fearnside, 1997,2001), households continue to make agricultural decisions that impact regional and global deforestation levels. This paper examines the current practices of smallscale farmers in Ouro Preto do Oeste, Rondonia, Brazil and investigates how agricultural production impacts deforestation (forest-clearing) levels. The data used to explore these issues consist of a panel series collected from 152 households in 1996 and The paper draws conclusions on how land-use is influenced by different markets and household constraints to determine which policies can best induce farmers to reduce forest-clearing levels. These results are used to provide policy options for the sustainable development of this region. Cattle and milk production play an increasingly important role in farmer livelihoods as well as deforestation levels. This is the case in Brazil (Walker and Homma, 1996; Faminow, 1998; Faminow et ai., 1999) and other regions of the Amazon including Peru (Locker, 1993), Ecuador (Murphy, 2001) and Honduras (Humphries, 1998). However, the situation in Brazil is contrastingly different than other tropical regions because the 'small' landholder commonly owns up to 100 hectares of land, permitting a larger amount of clearing and less restrictive limitations on land-use. Previous studies of deforestation have included production-based activities and market indicators such as farmer productivity, input and output prices, and off-farm labor opportunities (Walker et ai., 2000; Angelsen, 1999; Holden, 1997; Pichon, 1997; Godoy et ai., 1997; Jones et ai., 1995). This study includes all of these market indicators in the context of household decisions and traces changes that have occurred Ouro Preto do Oeste in relation to forest clearing levels and agricultural activities. Research in Rondonia has shown that households slash and burn forest to plant annual crops for two to three years, then convert this land to pasture and utilize it for up to ten years or until soil can no longer support grasses (Vosti and Whitcover, 1996; Tomich et ai., 1998; Fujisaka and White, 1998; Fujisaka et ai., 1996; Caviglia and Kahn, 2001). This paper advances this

3 Environment and Development Economics 183 literature by investigating how these decisions have changed over time in the context of market activities and trends. 2. Conceptual framework for analyzing household land-use decisions Household land-use decisions in agricultural societies reflect the management of factors of production, land, labor, and technology based on current market conditions and household characteristics (Pichon, 1997). Household production of agricultural goods influences deforestation, or forest clearing, levels because cleared land is an input to farm production. Small-scale farm management strategies in the Amazon involve a wide variety of activities including the production of annual and/ or perennial crops alone or in integrated systems, cattle ranching, and the husbandry of other farm animals. A majority of these activities require land clearing because many households own land that was originally forest. However, the integration of annuals and native perennial crops into agroforestry plots can be done according to sustainable standards and reduce, or eliminate, forest-clearing needs. Much research has been done on the use of agroforestry in Ouro Preto do Oeste, other regions of the Amazon, and around the world; however, the rate of adoption of agroforestry techniques has been low and as yet it has not been shown to reduce deforestation levels in the region (Browder, 1994; Ozorio de Almeida and Campari, 1995; Caviglia and Kahn, 2001). The focus of this paper is to determine the impact that production activities have on forest clearing. Employing previous theory used in the rnicroeconomicmodeling of agricultural expansion and forest clearing, this paper presents a household model representing decisions on landuse, farming techniques, and labor allocation. The conceptual model presented here and utilized as the foundation for the empirical models, is based on imperfect markets where households are both producers and consumers of goods. (For a more detailed discussion of household models developed for imperfect markets, see Singh et al., 1986; de Janvry et al., 1991; and Behrman and Oliver, 2000). Labor opportunities exist in Ouro Preto do Oeste. However, agricultural employment is often sporadic and seasonal. And, since farmers' production and consumption decisions are often inseparable, a profit or production maximization model would not adequately describe the household-level decision-making process (de Janvry et a/., 1991). Non-separability implies that consumption needs and asset distribution may have significant impacts on production decisions and the management of land. The production decisions of households are modeled in the expected utility maximization framework to account for market imperfections and leisure choices that may involve the minimization of labor time. According to the model developed here, households maximize expected utility, based on the consumption of goods (C), and leisure (LLl through the choice variables: labor (L) and land (D) allocation. Labor time is divided between agriculture (LA), off-farm or wage employment (Lw) and leisure (LLl. And, as shown in equation (1), household consumption is a function of agricultural revenue (PAQ), input costs (PNN) and off-farm income

4 184 Jill L. Caviglia-Harris (LwW) where: Max E{U(C,LL;H)} L,D S.T. c.:::: ((PAQ - PNN) + LwW;'l!) L = LA+Lw+LL D= DA+DF +Dc E = expectations operator U = utility function C = household consumption of goods D = household land endowment, divided between agriculture (pasture and crops) (D Al, forest (DF), and agroforestry (Dc) L = household labor endowment, divided between agricultural production (LA)' wage labor (Lw), and leisure (LLl N = agricultural inputs P N = matrix of input prices W = wage for off-farm labor H = household characteristics P A = matrix of agricultural good prices Q=matrix of quantities of agricultural goods harvested (agricultural production) 'l! = degree of risk impacting agricultural production. and labor opportunities Households choose a combination of labor, leisure, and land-use to maximize utility. Risk, 'l!, arises from unexpected market conditions (related to market imperfections) as well as weather and/or unexpected environmental occurrences. Assuming that households are risk averse, they may reduce risk by diversifying crops and producing a greater variety and/ or by diversifying income sources and entering into different markets by producing goods other than crops (such as milk) and participating in off-farm labor markets. The current level of forest clearing enters into the agricultural production decision in any given year, because cleared land is used as an input to production. In addition, off-farm labor serves as a substitute for agricultural production through the household labor constraint. The household production function can be represented by m Q=Q(N,H,'l!) and Q= :LQi (2) where Q represents total household production, which is the sum of production across different agricultural activities. A household participates in m production markets, such as milk, annual crops, and perennial crops. Inputs, N, and exogenous household characteristics, H, largely determine production. Unexpected environmental occurrences, 'l!, can also influence production levels. i=l (1)

5 Environment and Development Economics 185 Municipalities Surveyed in the State ofrond6nia, Brazil Figure 1. Map of the survey site in Ouro Preto do Oeste, Rondonia, Brazil Deforestation is a production input and considered the household's derived demand for cleared land (D A). Note that while typically the derived demand for land would be related to price, in this case, cleared land is not bought and sold in the land market but produced by households through deforestation. The use of land therefore depends upon current market conditions, and its shadow value. The reduced-form demand is therefore a function of all exogenous household and land characteristics The conceptual framework developed in equations (1) through (3) is presented here as the foundation for the empirical analysis in which agricultural production (equation (2)) and forest clearing (equation (3)) are estimated in a simultaneous system. Since DAis a choice variable for the household, this enters into the production decision as an endogenous variable and must be corrected for in the empirical analysis. 3. Study site, survey design and data description The Ouro Preto do Oeste region of Rondonia, established in the 1970s, is located along BR-364, the Cuiaba-Porto Velho interstate highway (figure 1). This region is comprised of six municipalities: Ouro Preto do Oeste, Vale do Paraiso, Urupa, Mirante da Serra, Nova Uniao, and Teixeiropolis, which have a combined population of over 92,000, of which 58 per cent is rural (Instituto Brasileiro de Geografia e Estatistica, 2001). Most of the urban population (68 per cent) is concentrated in the municipality of Ouro Preto do Oeste, the only municipality in the sample located along BR-364. The main agricultural activities of the rural population include the harvesting of maize, rice, beans, coco, manioc, coconut, and coffee, and the production of meat and milk from cattle (Caviglia, 1999). Well-developed markets exist (3)

6 186 Jill L. Caviglia-Harris for all of these products with the exception of maize and manioc, which primarily serve as feed for farm animals. The data collected for this study include a stratified random sample of 152 households surveyed in 1996 and In the initial data collection, a random sample of 171 households, stratified by the rural population of each of the six municipalities, were interviewed over a five-month period (September 1996 through January 1997). The author, assisted by a local farmer, conducted each of the interviews. The local farmer identified each of the lots, and provided assistance to participants that could not recall the harvest of subsistence crops. It was often difficult for farmers to remember the yearly harvest of mangoes, papaya, and other crops grown for subsistence purposes only as well as the level of natural fertilizers (manure) used in agricultural production. Since the local farmer influenced these reported levels, they were used for informational purposes only and not included in the empirical analysis. The distance between interviews, in terms of lots, was designated in each municipality to represent the areas equally and to obtain spatial variation in topography, soil type, and distance to market. If the house was unoccupied at the time of the interview or the owner of the lot was not available, the next house on the same side of the road was interviewed, and if the same occurred at the next house, the following house was interviewed on the same side of the road. This occurrence was generally uncommon since most of the farmers stay close to, or on, their lots during the time of year the surveys were conducted (September through December), which ranges from the end of the burning period to the beginning of the planting season. 3 There was only one refusal to participate in the survey. This was in the municipality Mirante da Serra. The high response rate is attributed to two factors: first, culturally; Brazilians are generally very hospitable to strangers, and, second, because of the lack of contact with people, the farmers were generally pleased to communicate with outsiders. Many of the farmers do not leave their farms on a daily basis (especially during the rainy season) due to a lack of transportation, long distances to city centers, and poor road conditions. Data were collected from the same farm lots in the August to November 2000 interview period. 4 Some of the original 171 lots were dropped from 2 The 2000 data collection was supported by the National Science Foundation Grant: SES The data collection was supported by grants from the National Security Education Program, the Organization of American States, the Institute for the Study of World Politics, and the McClure Fund Foundation. 3 There is no expected bias resulting from the skippjng of unoccupied homes at the time of interview since the occurrence was low and the interviews did not coincide with seasonal events or opportunities. 4 The panel data are consistent across lots. Therefore if land was sold and another family occupied the lot in 2000, this new household was interviewed. The primary purpose of the continuing data collection is to investigate how land-use changes over time. An important factor to the analysis with such data is the number of years a family occupies a lot, and whether ownership changes or not.

7 Environment and Development Economics 187 the panel data because they were sold to land speculators who did not take up residence on the land purchased some time during the four-year time period. This reduced the stratified random sample to 152 households; a combined set of 304 observations across both years. A research assistant, and the local farmer that participated in the first survey in 1996, conducted these interviews. The response rate was 100 per cent. Of the 152 farmers included in this analysis, only two remembered being interviewed four years prior reducing any attrition bias, and none reported remembering the specific survey questions provided. It was not required that both household heads participate in the survey, so in many cases different members of the family were interviewed in 1996 and The survey questions consisted of inquiries about the household (including age, education level, farming experience, and number of farm animals owned), lot characteristics (including size and division between pasture, forest, agriculture and, agroforestry), harvest of market and subsistence crops, agricultural and other forms of income, the use of agroforestry, and major influences determining farming techniques. Questions about income derived from agriculture provided information about the harvest of all perennial and annual crops, milk harvest and meat harvest, the amount of each item that was sold, and at what price Household and lot characteristics Over 84 per cent of the Ouro Preto do Oeste settlers in the sample migrated from the southern region of the nation (including the states of Rio do Janeiro, Siio Paulo, Rio Grande do SuI, Parana, Mato Grosso do SuI, Gioas, Minas Gerais, and Espirito do Santo). Only 13 per cent migrated from the northern states (including Bahia, Maranhiio, Piau!, Ceara, Rio Grande do Norte, Pernambucu, Parailia, Alagaos, and Sergipe) while the remaining 3 per cent moved from other states within the Amazon region. Almost all of the household heads, 99 per cent, had secure property rights with legal documentation by Ninety-four per cent of the lots are inhabited by the legal owners, while the remaining 6 per cent are sharecropped. The owners of these sharecropped lots generally live within the urban borders of Ouro Preto do Oeste. The following tables provide variable definitions (table 1) and descriptive statistics for the households surveyed (table 2). In addition, t-statistics are provided to test the null hypothesis of equal means over the two time periods. Information collected in the surveys can be grouped into household demographics, income sources, land-use designations, and household wealth measurements. According to these data the average number of family members living on the lot is eight. Of the eight family members, there are approximately three adult males, three adult females, one male child, and one female child. 5 The average age is 48 years and the average education level is two years for the adult household heads. 5 Adults are defined as those family members 10 years and older since respondents indicated that they began farming at this age. This corresponds to the age when children end primary school.

8 188 Jill L. Caviglia-Harris Variable FAMILY FEMAL09 FEMALE9 MALES09 MALES9 AVEDUHH AVEAGEHH YEARSLOT DISTCITY COOP RESIDE SOUTH INCTOTAL INCANN INCPER INCMILK INCHON INCOFF PRODTOT PRODANN PRODPER PRODMILK PRODHON USEAGRO FERT HERB PEST VEHICLE MOTOR CATTLE BANKACCO LOANS LOTSIZE DEF Definition Table 1. Variable definitions number of family members residing on the lot number of female household members between the ages of 0 and 9 number of female household members over the age of 9 number of male household members between the ages of 0 and 9 number of male household members over the age of 9 average education of the household heads, in years average age of the household heads, in years number of years the household has resided on the lot distance from the city center (or highway BR-364), kilometers number of cooperatives in which the household participates dummy variable indicating whether the owner resides on the lot, otherwise the lot is rented; = 1 if owner resides on the lot, = 0 if rented dummy variable indicating whether the household migrated from the southern states of Gioas, Mato Grosso du SuI, Parana, Rio de Janeiro, Rio Grande do SuI, S;mta Catarina, or Sao Paulo; = 1 if household migrated from the south, = 0 if migrated from another region total yearly household income, 2000 reais 1 total yearly household income from annual crops, 2000 reais total yearly household income from perennial crops, 2000 reais total yearly household income from milk, 2000 reais total yearly household income from honey, 2000 reais total yearly household income from off-farm labor (including pensions), 2000 reais total yearly household agricultural production, 2000 reais total yearly household production of annual crops, 2000 reais total yearly household production of perennial crops, 2000 reais total yearly household production of milk, 2000 reais total yearly household production of honey, 2000 reais dummy variable indicating use of agroforestry; = 1 if used, = 0 otherwise dummy variable indicating use of fertilizer; = 1 if used, = 0 otherwise dummy variable indicating use of herbicides; = 1 if used, = 0 otherwise dummy variable indicating use of pesticides; = 1 if used, = 0 otherwise number of cars and trucks owned by the household, number of motorcycles owned by the household number of cattle owned by the household number of bank accounts held by household members number of bank loans held by household members lot size, in hectares total number of hectares deforested on the lot (includes agriculture, pasture, and fallow)

9 Variable DEFPREV AGRI PAS FOR INT YEARDUM INCANNL INCpERL INCMKL INCOFFL VEHICLEL MOTORL CATTLEL Definition Environment and Development Economics 189 Table 1. Continued number of hectares deforested in year prior to the survey year number of hectares on the lot in agriculture number of hectares on the lot in pasture and fallow number of hectares on the lot in primary forest number of hectares on the lot in intercropping dummy variable; YEARDUM = 0 for 1996; YEARDUM = 1 for total yearly household income from annual crops, 2000 reais, lagged for the 2000 sample 1996 total yearly household income from perennial crops, 2000 reais, lagged for the 2000 sample 1996 total yearly household income from milk, 2000 reais, lagged for the 2000 sample 1996 total yearly household income from off-farm labor (including pensions), 2000 reais, lagged for the 2000 sample number of cars and trucks owned in 1996, lagged for the 2000 sample number of motorcycles owned in 1996, lagged for the 2000 sample number of cattle owned in 1996, lagged for the 2000 sample Note: 1 Inflation rates are based on the annual average consumer price index, found in: World Economic Outlook May 1999 and World Economic Outlook May 2000, International Monetary Fund, Washington DC. In 2000 US$l was approximately equal to R$1.83 according to the National Trade Data Bank, US Department of Commerce, found at Income statistics for the stratified sample reveal some significant changes (also in table 2). After adjusting for inflation, total annual household income is shown to have increased significantly: 63 percent. 6 The almost R$ increase primarily resulted from additional milk sales and an increase in off-farm income sources (including wage labor and pensions). Over the same time period, income from perennials remained fairly constant, while income from annual crops fell. Total agricultural production activities, including all products harvested on the lot (those sold in markets and consumed by the household), increased significantly as well, primarily due to an increase in the production of milk. 8 The production of annual crops fell and was unchanged for perennial crops and honey. 6 Inflation rates are based on the annual average consumer price index, found in: World Economic Outlook May 1999 and World Economic Outlook May 2000, International Monetary Fund, Washington DC: 7 In 2000 US$l was approximately equal to R$1.83 according to the National Trade Data Bank, US. Department of Commerce, found at July 200l. 8 Agricultural production is calculated as the sum of all goods harvested multiplied by the household at market prices. The average market price was used for households that did not sell the goods.

10 Table 2. Selected descriptive statistics for households in Ouro Preto do Oeste, Rondonia 2000 households (n = 152) 1996 households (n = 152) Mean Std. Dev. Min. Max. Mean Std. Dev. Min. Max. t-stat ~ FAMILY FEMALE * Q FEMALE ~ '" MALES ;S., MALES ff AVEAGEHH **... ::I. AVEEDUHH '" INCANN D *** INCPER INCMILK *** INCHON INCOFF '** INCTOTAL *** PRODANN *** PRODPER PRODMILK *** PRODHON PRODTOT ** COOP *** VEHICLE MOTOR *** CATTLE *** LOTSIZE AGRI PAS FOR '* INT Note: *, **, *** indicate significance at the 10%, 5%, and 1 % levels respectively. >-' \0 0 - t-< '~"'-.".,,?' J',.,

11 Environment and Development Economics 191 Data on land-use designation indicate that the average lot size decreased because some farmers subdivided their lots, however this was not a significant reduction. Although there was a slight increase in hectares devoted to intercropping, this rise was also insignificant. At the same time these data show that there was a significant reduction in the number of hectares left in primary forest. Lastly, household wealth increased by This is evident because the number of motorcycles and heads of cattle owned by the household increased significantly. The increases in wealth and large increase in income are likely due to the development of the relatively new settlements. Since the 1970s, resources, infrastructure, and means of communication have slowly become more complex, supporting the development of a more sophisticated market system. The ownership of motorcycles, cars, trucks, and cattle are used to represent wealth since these, in addition to farm equipment, are major household purchases. 9 (Houses are generally built by family members in these rural areas.) The motorcycle market, in particular, has recently expanded as disposable income increased, making the purchase of this means of travel within reach for many households. The impacts of the developing milk market (which is highly organized including the pick up of milk by processing companies at household fronts), changes in the markets for perennial and annual crops, and the increase of off-farm labor opportunities in forest clearing are tested in the empirical analysis. It is obvious from these data that significant changes occurred over the four-year time period, however the simultaneous impact of these changes on household land-use cannot be tested with t-statistics alone. A system of equations is set up to estimate these complex household decisions in the empirical section of the paper. 4. Empirical models Empirical models are used to analyze household land-use decisions pertaining to total land cleared, or deforested, percentage of the lot deforested, and agricultural production. Forest clearing and the percentage of the lot cleared are estimated first using OLS. The stock of cleared land is defined as the sum of deforested land on the lot (table 1). Based on the theoretical model presented earlier, household forest clearing is determined by the full set of exogenous variables (D, P A, W, N, H). Available explanatory variables include the household land endowment, D, and income from the sale of crops, milk, and off-farm labor (where income levels are based on exogenous prices for labor and goods, P A and W, 9 The ownership of cars and trucks (VEHICLE) and motorcycles (MOTOR) are presented as dummy variables in the empirical analyses since the purchase of such items requires a threshold income criterion that only a small percentage of households have obtained (after first settling in the region with minimum wealth and! or savings). Motorcycles are separated from other vehicles in the analysis because they cost significantly less and have seen recent growth: 22 per cent of farmers owned them in 1996, while this increased to 64 per cent by 2000.

12 192 Jill L. Caviglia-Harris respectively). Inputs (N) are included as adult males (labor used in the clearing and burning of land) and dummy variables for fertilizer, herbicide, and pesticide usage, as the respondents' recall of prices for the chemical inputs was unreliable. Household characteristics, H, are included as the average education and age of the households heads (since adult males and females often jointly make production decisions) family size, cooperative participation, origin, and various wealth measurements. Agricultural production is estimated second using two-stage least squares (since the stock of cleared land enters into this decision). According to the theoretical model, production is determined largely by the inputs used in production, N, household attributes, H, and risk factors, w. Data available on inputs and household attributes are the same as used in the estimation of forest clearing. Risk and other random occurrences related to imperfect market and crop disease and weather related events are difficult to observe and therefore captured in the error term, and in the case of timespecific events they are captured by the dummy variable for interview year as well. Both models (forest clearing and agricultural production) are estimated first using a panel data set of 304 observations representing a stratified random sample of household lots, and are based on the theoretical model presented in section 2. The data are combined to increase degrees of freedom and capture changes occurring over the four-year time period. Deforestation and agricultural production are estimated again using data collected in These 136 observations represent households remaining on the same lots in 1996 and 2000 and also include variables not collected in In particular, access to credit, the holding of bank accounts, and an indication of whether the lot owner lives on the farm or rents/sharecrops the land are included in these estimations. In addition, variables collected in 1996 for these same households are used to predict behavior in Estimation of deforestation using panel data Discussion of the empirical results begins with the estimation of the stock, and percentage, of deforestation, since both can be important to policy makers. Random and fixed effects models were first estimated since the data consist of a panel, however Hausman and the Lagrange Multiplier 10 The data represent a panel collected keeping lots constant across years. Lots remain constant rather than household members because changes in ownership can be important determinants of land-use and land-use changes are the focus of the study. When using household-level information from 1996 to predict behavior in 2000, only lots that did not change ownership are included in the analysis so that 1996 values are correctly utilized as predictors of same household activities. The likelihood that a household was sold between 1996 and 2000 was estimated. It was determined thtough a Heckman selection that no bias results from dropping households.

13 Environment and Development Economics 193 tests reject both models. Instead a dummy variable is included to indicate changes in intercept over time. n. 12. It is clear from the regression results that the total stock and percentage of deforestation on the lot are impacted by the land constraint, lot size (table 3). Those households with larger lots have burned a greater number of hectares, but a smaller percentage of their lots. Therefore, it appears that lot size is a limiting factor for those households that own smaller lots. In 1996 households had burned an average of 77 per cent of their lots. This increased to 82 per cent by Pinchon (1997) also investigates the role lot size plays in land-use decisions and finds that those farmers with larger lots in the Ecuadorian Amazon have greater percentages in forests and pasture and those with smaller lots use the land more intensively for agriculture. And, in the same region as this study, Jones et al. (1995) find that lot size does not significantly impact the stock of cleared land. This result most likely reflects the younger age of the settlement. Most lots in the Jones et al. (1995) study were settled for only about ten years and the total stock of deforestation (total number of hectares cleared on the lot) was Significantly lower. Also influencing the stock and percentage of deforestation are wealth (represented by the ownership of cattle), distance to the town center, forest clearing the year prior to the interview, milk income, and the use of agroforestry. From the results it appears that households using agroforestry (USEAGRO) have substituted forest clearing for the intercropping of perennial and annual crops and therefore deforest less hectares and smaller percentages of their lots because agroforestry provides natural fertilizer and soil protection through the leaf litter created by the standing trees. Households that cleared more land the year prior to the interview (DEFPREV) also had higher levels of total, and the percentage of, deforestation on the lot. This suggests that more recent burning and clearing may impact the stock and overall percentage of cleared land at greater levels. And, households with greater levels of milk income (INCMILK) also had greater levels of deforestation, but this did not significantly impact the percentage of deforestation on the lot. The total stock and percentage of deforestation is lower for those households located farther distances from the city center (DISTCITY) most likely because these areas were settled later than those located on or near the major highway BR-364. And lastly it is found that those farmers with greater numbers of cattle have deforested more of their lots. Cattle are linked to deforestation levels through direct and indirect effects. It has been suggested that cattle serve multi-purposes for households in the Amazon, including 11 A Chow test was also used to test if the inclusion of interactive dummy variables that capture changes in slope over time was appropriate. The interactive model was rejected. 12 As Godoy and Contreras (2001) point out, some explanatory variables, such as income and education may be endogenous, biasing the coefficients. A Hauseman test indicates that income is not endogenous in the models. However, unobserved information that may influence both education and income jointly cannot be controlled for.

14 Table 3. Estimation of deforestation and agricultural production using panel data Estimation of stock of Estimation of percent Estimation of total Estimation of crop Estimation of milk "'" cleared land of land deforested agricultural production production production ';:;-! OLS (n=304) OLS (n = 304) 2SLS (n = 304) 2SLS (n = 304) 2SLS (n = 304) ::::: " CoefJ. Std.Err. CoefJ. Std.Err. CoefJ. Std.Err. CoefJ. Std. Err. CoefJ. Std. Err. Q CONSTANT *" oq. '" AVEAGEHH AVEEDUHH ' ' "., FAMILY " '" ~ ""< MALES tn YEARSLOT DISTCITY '" '" '" COOP " '" SOUTH lncann INCPER INCMILK 0.000' INCOFF ' " USEAGRO '" '" HERB FERT ' PEST " " VEHICLE ' '" " '" MOTOR " '" CATTLE 0.051'" '" LOTSIZE 0.692'" *" DEFPREV 0.392'*' '" " ' DEF '" '" YEARDUM *" R Adj-R F-stat ''* 4.77*** 5.45'" 3.23*** 12.89'" Note: " ", **' indicate significance at the 10%, 5%, and 1% levels respectively. >-' \0

15 Environment and Development Economics 195 insurance, bank accounts, and as a source of food for households (Hecht, 1992; Faminow, 1998). During years of poor harvest, cattle can be traded for money or other goods. In addition, since bank accounts can be difficult for peasant farmers to open (most have minimum balance requirements), cattle serve a similar purpose and are therefore one measurement of wealth. Cattle also require cleared land for pasture and directly impact deforestation levels in this manner Estimation of household production using panel data Household agricultural production consisting primarily of the harvest of annuals (manioc, rice, maize, and beans) perennials (cacao, coconut, coffee, bananas, and oranges), milk, and honey is estimated using 2SLS (table3). A Hausman test indicates that forest clearing is endogenous in the model. Endogeneity is corrected for with a 2SLS estimation, including the instrument lot size.b Family size (FAMILY), rather than adult males (MALES9), is included as the labor constraint in this model since the entire family may participate in crop and milk production while adult males generally contribute to the burning and clearing land (since it is relatively more labor intensive). In addition, income measurements (with the exception of off-farm labor (INCOFF)) are not included in the production estimations, because agricultural goods sold in markets (as well as those consumed at horne) are part of household production. The estimated model indicates that larger families, with greater wealth, that participate in more cooperatives, use pesticides, and have cleared greater amounts of their lots have larger agricultural production levels (table 3). Participation in farmer cooperatives likely increases production since many of the cooperatives focus on establishing prices for agricultural goods and increase access to local markets. In addition larger families are more likely to have a greater labor allocation and therefore the ability to increase harvest. Off-farm labor activities are shown to serve as a substitute for agricultural production, indicating that labor availability is restricted by leisure and offfarm work activities. Also negatively related to agricultural production is the amount of land cleared in the previous year. This effect reflects the negative impact that clearing in the previous year has on productivity in the current time period. Agricultural production can be broken down into the two major sources of farm income: the production of crops and milk. In the comparison of the income and production levels of these goods between 1996 and 2000 provided in table 2, it is found that significant changes occurred. In particular, the milk market increased in importance in the total production and income levels of households, while annual and perennial crops declined in importance. In the estimation of crop production, it is found that family size, the ownership of cars and trucks (VEHICLE), participation in cooperatives, and the use of pesticides are all significant and positively 13 Lot size is an instrument because it is a good identifier for forest clearing, is exogenous to the household (since lots were distributed by the government), and is uncorrelated with the disturbance term.

16 196 Jill L. Caviglia-Harris related to production levels (table 3). Total lot forest clearing is not a significant determinant of crop production. This is not unexpected since the total amount of cleared land on the lot is not capable of supporting crops. Instead smaller areas containing nutrient rich soils are used for agriculture for up to 3-5 years. The stock of land deforested is significantly related to milk production and requires large amounts of cleared pasture to support cattle (table 3). And since this is the fastest-growing agricultural market in the region, this suggests that forest clearing is likely to increase at current, if not accelerating, rates in the immediate future. Also significant and positively related to the production of milk is wealth (the ownership of cars, trucks and motorcycles), stock of deforestation (DEF), and education of the household heads. The impact of ed ucation is important since it suggests that more-educated farmers participate in the milk market. And, since the clearing of land is essential to the maintenance of cattle in this region, because a majority of households cannot afford to provide feed outside of the grasses that clearing provides, this may have implications for policy. The dummy variable indicating interview year is also positive and significant, indicating that other changes occurring over the time period, not captured by the included variables helped to increase production. Heavily influencing the production of milk is the number of hectares burned in the previous year, income from off-farm labor opportunities, and the distance to the central regional market and town center. The amount of land cleared in the year prior to the interview is found to reduce milk production in the current time period. Households may invest time and labor into pasture preparation for the future acquisition of cattle. From these results it is also evident that households are substituting off-farm labor for milk production and that households closer to the town center are producing more milk, most likely the results of the increased levels of forest clearing that are found closer to the town center and because of close proximity to milk processing and pasteurizing facilities and markets Estimation of deforestation using 2000 data The estimation of the stock and percentage of deforestation on the lot are also made using the data collected in 2000 that were not collected in 1996, including the number of bank accounts held in the household, loans, and Whether the owner and family resided on the lot (table 4). In addition, wealth and income measurements are lagged according to 1996 values to investigate whether previous values impact current decisions. The empirical results indicate that in addition to variables discussed earlier, the number of bank accounts held by the lot holder, origin, income from perennials (lagged), and vehicle ownership (lagged) are significant. Bank accounts represent another measurement of stored household wealth, and the relation is similar, in that those with greater levels of wealth have deforested greater amounts of land. Households that migrated from the southern part of the country have deforested their lots to a greater extent. This supports the result of Ozorio de Almeida and Campari (1995) who find that origin is a signifi<;ant determinant of total forest clearing in the state of Para where a majority

17 Table 4. Estimation of deforestation and agricultural production using 2000 sample Estimation of stock of Estimation of per cent of Estimation of total agricultural Estimation of crop Estimation of milk cleared land OLS land deforested OLS production 2SLS production 2SLS production 2SLS (n = 136) (n =136) (n= 136) (n = 136) (n= 136) Coeff. Std. Err. Coeff. Std. Err. Coeff. Std. Err. Coeff. Std. Err. Coeff. Std. Err. CONSTANT *** AVEAGEHH AVEEDUHH " ' FAMILY '" '" " MALES YEARSLOT ' '" DISTCITY " *** COOP SOUIB 4.705' ' USEAGRO -7.98I' ttl HERB ;; FERT ' " ~. PEST '" LOTSIZE 0.812*** ;;; DEF ;; '" ~ DEFPREV ;; LOANS " ;:,.. '" )3ANKACCO 4.686' ' ti RESIDE "'" INCANNL '" *** il<....g INCPERL "'** *** ' ;;; INCMKL *** *** ;; INCOFFL '" ~ VEHICLEL ' *** " ttl MOTORL ;; CATTLEL ' ;;; RZ ,,' Adj-RZ '" F-stat 43.91*** 6.17*** 3.65*** 6.44***... Note: " ", '*' indicate significance at the 10%,5%, and 1% levels respectively. '" "I

18 198 Jill L. Caviglia-Harris of the colonists came from the poorer northeastern part of the country. And, along similar lines, Perz (2001) finds that households of southern origin are less likely to have extensive amounts of land in crops, since households with higher income levels are found to focus on pasture. The relationship between perennial income and deforestation is complex. It is found that 1996 perennial income is negatively related to the 2000 stock of deforestation but positively related to the percentage of the lot deforested in Therefore, households with significant perennial incomes may deforest less total hectares because these crops are labor intensive (as is the clearing and burning of land) and do not require large amounts of cleared land (compared with cattle) but have deforested a greater percentage because the lots of these households are smaller. It is therefore possible that households with smaller lots focus on production of crops because they do not have the stock of land necessary to support large numbers of cattle Estimation o/household production using 2000 data Agricultural production is also estimated using the new data collected in 2000 (table 4). In all three cases, family size is found to positively influence production. However, like Perz (2001) who finds that diverse determinants can influence various land- uses, the impact of other determinants is found to differ between production types. Loans are positively related to the production of crops. Those households having higher income in crops in 1996 were more likely to have higher production levels in And those households with higher levels of wealth (VEHICLEL) in 1996 also had lower levels of crop production. And, finally, those households that lived longer on their lots (YEARSLOT) were less likely to have higher levels of crop production. This presents some support to the life-cycle hypothesis presented by Walker and Homma (1996) Perz (2001), and Walker et al. (2002). The life cycle hypothesis, as adapted to the Amazonian frontier family, supposes that households focus on annual crops when first arriving in the Amazon (that is early points in the family life cycle). As households save from the sale of these crops and the family labor force grows, discount rates fall, and investment into perennial crops and cattle becomes a possibility. However, to fully support this hypothesis one would expect the reverse to be true in terms of milk production, but the number of years that a household has lived on a lot is not statistically significant in this estimation. 5. Discussion and conclusions Further development of the Amazon through Advanca Brasil will impact existing settlements and likely foster new colonization as the plan calls for accelerated development of the agriculture, timber, and mining sectors of the economy (Laurance et al., 2001a). Although the extent of future environmental impact is debatable (Nepstad et a/., 2002a, 2002b; Laurance and Fearnside, 2002; Laurence et al., 2001a, 2001b; Cavalho et al., 2001), sustainable policy initiatives should be part of Amazonian development if the minimization of environmental costs is an objective. Towns such as Ouro Preto do Oeste are expected to be impacted by the government