Beyond Population and Environment: Household Demographic Life Cycles and Land Use Allocation Among Small Farms in the Amazon

Transcription

1 Hum Ecol (2006) 34: DOI /s Beyond Population and Environment: Household Demographic Life Cycles and Land Use Allocation Among Small Farms in the Amazon Stephen G. Perz & Robert T. Walker & Marcellus M. Caldas Published online: 6 July 2006 # Springer Science+Business Media, Inc Abstract Most research featuring demographic factors in environmental change has focused on processes operating at the level of national or global populations. This paper focuses on household-level demographic life cycles among colonists in the Amazon, and evaluates the impacts on land use allocation. The analysis goes beyond prior research by including a broader suite of demographic variables, and by simultaneously assessing their impacts on multiple land uses with different economic and ecological implications. We estimate a system of structural equations that accounts for endogeneity among land uses, and the findings indicate stronger demographic effects than previous work. These findings bear implications for modeling land use, and the place of demography in environmental research. Key words Population. environment. land use. land cover change. Amazon. Introduction Concern about demographic factors and environmental damage has generated a large literature featuring a population and environment discourse (e.g., Arizpe et al., 1994; Lutz et al., 2002; Mazur, 1994; Ness et al., 1993; Pebley, 1998). Much of this literature draws on Malthusian and Boserupian perspectives and focuses on demographic phenomena in large-scale aggregates, particularly nation-states or the planet. One often encounters the use of demographic techniques such as population projections (e.g., MacKellar et al., 1998) or decompositions (e.g., Bongaarts, 1992), often involving the IPAT identity (e.g., York et al., 2003). The population and environment discourse in other disciplines also tends to S. G. Perz (*) Department of Sociology, University of Florida, 3219 Turlington Hall, PO Box , Gainesville, FL , USA sperz@soc.ufl.edu R. T. Walker : M. M. Caldas Department of Geography, Michigan State University, 116 Geography Building, East Lansing, MI , USA

2 830 Hum Ecol (2006) 34: focus on aggregate level processes, such as research by geographers on population-induced agricultural intensification (e.g., Turner et al., 1993). At the same time, it is widely acknowledged that human impacts on environments involve scale-dependent processes. This is in part due to recognition by biophysical scientists that distinct processes are at work as one moves from the cellular to the landscape level (e.g., O Neill et al., 1986). Attention to scale is also becoming more evident in environmental social science frameworks, which seek to incorporate factors ranging from individual agency to international politics (e.g., Gibson et al., 2000; Wood, 2002). Given the growing literature on scale dependency in human-environment interactions, demographic environmental research needs to move beyond the population and environment discourse to consider demographic processes operating on other scales and their impacts on environmental change. This paper presents an analysis of household-level demographic processes and their environmental consequences. We take up the case of frontier colonist households in the Brazilian Amazon and assess how the demographic location of a household affects its land use allocation. By demographic location we refer to a constellation of factors duration of residence, age structure, and generational transitions which together delineate the position of a household along its life cycle. We draw on economic anthropology and household economics and present a household-level framework based on Chayanovian theory fused with household production theory. The analysis models the effects of household demographic life cycles on land use allocation, and advances beyond previous efforts in three respects. First, we consider a broader suite of demographic variables than has generally been the case in household land use models. Second, the analysis evaluates land allocation among several land uses, rather than deforestation and other single-outcome approaches. In particular, we distinguish different types of crops, which is theoretically important but rarely done empirically. Third, the modeling approach explicitly recognizes that allocation of land to one use constitutes an opportunity cost for allocation to other uses, making land use decisions mutually endogenous. We therefore use three-stage least squares (3SLS) estimation and specify a system of equations which accounts for endogeneity among land uses and yields unbiased estimates of the effects of household demographic factors. The findings show strong effects of the household life cycle variables, which bears implications for understanding demographic impacts on environmental processes at different scales. Theoretical Background Household Demographic Life Cycles and Land Use The link between household demographic life cycles and land use was first featured by Chayanov (1986[1966]), who observed that peasant households in post-revolutionary Russia contained families with different age structures, and that those households also farmed different quantities of land. He reasoned that age structures are older in households with larger numbers of economically active adults and/or smaller numbers of dependent children. Both allow for greater allocation of labor to agriculture, which in turn enables cultivation of larger land areas. As a result, Chayanov argued that demographic differentiation among farm households explained differences in their land use. Chayanov thus characterized the peasant economy via its emphasis on family labor availability (e.g., Harrison, 1975; Hunt, 1979).

3 Hum Ecol (2006) 34: Chayanov also distinguished among stages in household life cycles on the basis of the timing of demographic events, particularly fertility and the onset of labor contributions by older children. Initially, land use by young parents is limited due to low labor availability. With the onset of childbearing, households exhibit increased dependency without added labor. The rise in consumption therefore impels expanded land use via the increased drudgery of the young parents. As the children grow up, they add to the household labor pool, facilitating further expansion of family land use. In late stages of the life cycle, children reach adulthood and either leave the household or remain to inherit the farm, forming multigenerational households. At this generational transition, consumption and labor availability may decline, resulting in a decrease in land use. However, this trend may be reversed when the next generation begins to move through its life cycle. Chayanov s insights have been generalized from post-revolutionary Russia to other contexts by economic anthropologists and other scholars (e.g., Chibnik, 1984). In developing regions, households also rely on agricultural production via family farming, land is abundant, and markets and access to capital are limited. Markets and Household Production in Developing Regions Nonetheless, contemporary developing countries exhibit changes left untreated by Chayanov, such as the formation of markets for land, labor, and capital. These alter the peasant economic calculus and potentially undermine the importance of household demography as an explanation for land use. As a result, household production theory emerged to account for markets via linked production and consumption decisions (e.g., Ellis, 1993; Singh et al., 1986; Walker, 2003). Household production theory features the role of markets in stimulating agricultural commercialization, proletarianization, and the shift of livelihoods toward non-farm activities. First, developing regions have markets for credit, agricultural inputs and agricultural products. Such markets open possibilities for households to acquire capital and substitute it for labor in land use, such as using a chainsaw to cut trees. Markets also make it possible to produce cash crops for commercial sales rather than household consumption, which can expand household land use beyond subsistence demand. Second, developing regions also have labor markets. The presence of labor markets implies that farm households cannot only hire labor, which changes the effective labor pool available for agriculture, but also sell labor, which provides an income stream for investment. Study Region: The Brazilian Amazon We take up the case of the Brazilian Amazon as a developing region in which to assess the impact of household demography on land use. The Amazon is an important study case because it is the world s largest contiguous rainforest biome and is experiencing rapid demographic expansion and land cover change. The population of the Legal Amazon rose from 5.3 million in 1960 to 19.6 million in 2000 (IBGE, 1962, 2000). Similarly, forest clearing in the Amazon has risen from 152,200 km 2 in 1978 to 587,700 km 2 in 2000 (INPE, 2002). The Amazon is also a good choice for the study of household farming. In 1996, the Legal Amazon had some 900,000 rural establishments (IBGE, 1998a). Of these, over 800,000 establishments were under 200 ha in size, and in most cases these are familyrun operations. There are aspects of family demography and land use specific to the Amazon. First, households on the Amazon frontier contain colonists who migrated from other regions of

4 832 Hum Ecol (2006) 34: Brazil. This requires attention to a household s duration of residence. The length of time since arrival has been viewed as important for allowing adaptation of land use strategies to those suitable in the Amazonian environment (Moran, 1989). Second, colonist agricultural systems in the Amazon vary substantially. Some households emphasize subsistence crops, others feature cash crops, yet others focus on cattle ranching, and some engage in diversified systems (e.g., Serrão and Homma, 1993; Walker et al., 2002). These farm system components require different quantities of labor inputs and have distinct economic and ecological ramifications. Consequently, it is not adequate to assess land use in terms of the extent of land in use as in Chayanovian theory, or in terms of agricultural and nonagricultural activities as in household production theory. And third, Amazonian agriculture involves agricultural fallows and degraded land. Chayanovian and household production theories both focus on productive activities, neglecting fallowing practices wherein land is temporarily allowed to rest, or land degradation where poor soils are taken out of production (Perz and Walker, 2002; Walker, 2003). It is therefore crucial to account for secondary vegetation, which appears in fallows and on degraded land. Household Demographic Life Cycles and Land Use Allocation in the Amazon This section presents a theoretical framework that fuses Chayanovian thought with household production theory, and adapts the two to the case of the Amazon. We draw on previous articulations of demographic processes involved in household life cycles in the Amazon (Brondizio et al., 2002; Marquette, 1998; McCracken et al., 2002; Perz, 2002; Perz and Walker, 2002; Walker, 2003; Walker and Homma, 1996; Walker et al., 2002). Our central argument is that even given markets and factors specific to frontier areas of the Amazon, land use allocation should still vary among households at different points in their life cycles because they have distinct demographic characteristics. We present our theoretical framework via the stylized case of a colonist household. The life cycle begins when migrants relocate to the frontier. They come as young families, whether as childless couples or parents with young children, and establish land claims by clearing primary forest. Having spent much of their savings on the move, and often with responsibility for young children, the parents begin by cultivating annual crops such as rice, beans, corn, and manioc. Annuals require considerable labor inputs for clearing, planting, weeding and harvesting, but land and capital requirements are limited. Because annuals produce soon after planting, they constitute a low-risk agricultural strategy. However, because Amazon soil fertility declines with repeated cultivation on a given plot, households must periodically fallow land and clear more forest to sustain production of annuals. Thus, early in the life cycle of a colonist household in the Amazon, primary forest area declines, and land allocated to annual crops and regrowth expands. As the seasons pass, farmers gain experience in Amazonian agriculture, the labor of growing children makes larger contributions to the household, and farms accumulate a stock of deforested land. These changes reduce the risk aversion of colonists, who seek to obtain credit to purchase capital or hire labor and engage in market-oriented farming activities, particularly perennial crops and pasture for cattle. Thus, later in the household demographic life cycle, primary forest declines further as colonists allocate more land to perennials and pasture. Older households with larger labor pools often plant perennial crops such as cocoa, coffee, coconuts, and black peppers. Perennials not only involve substantial labor inputs during harvesting and processing, they also require significant capital inputs for purchase and maintenance. Because perennials require several years of growth before the onset of

5 Hum Ecol (2006) 34: production, and because they are subject to insect and fungal attacks, they pose greater economic risks to households than annuals. However, produce from perennials often commands high prices. Perennials also offer environmental advantages because they can be planted on land formerly under annuals, and they protect soils by providing more permanent land cover. Households with less labor often allocate land to pasture for cattle. Pasture is valuable because it indicates investment in agriculture, which raises land values, and ownership of cattle constitutes a capital reserve that acts as an insurance substitute to cover unforeseen expenses, such as for illness (e.g., Tourrand and Veiga, 2003). But smallholders cannot afford to buy many cattle given the high initial investment involved, and ranching has often been vilified environmentally due to the large land tracts required, and because many pastures have not been managed sustainably, leading to land degradation (e.g., Serrão and Toledo, 1990). However, cattle are an attractive land use option due emerging urban markets for beef in the Amazon (Faminow, 1998). Late in the household life cycle, different trajectories may occur. One involves outmigration of young adults as they leave to establish their own farms or find urban employment (McCracken et al., 2002). Labor availability and subsistence demand declines, leading to a reduction in the land area under crops and pasture, and further expansion of secondary growth. However, another trajectory is possible if grown children stay in the parental household (Perz and Walker, 2002). This reflects a generational transition as one generation passes control of the property to the next. This is particularly likely if the young adults are parents with young children, for the farm provides the security of an established enterprise. In this scenario, forest clearing may expand to make way for agriculture as young children expand demand for subsistence. Household Life Cycles and Land Use in Previous Research Following the foregoing discussion, we find it necessary to account for a suite of household demographic factors: duration of residence, age structure, and generational transitions. Length of residence captures the effects of agricultural experience on land allocation. In addition, the age structure of a household, measured in terms of the number of children, working age adults, and elderly, is necessary to evaluate dependency and labor availability. Because colonists do not arrive on the frontier at exactly the same ages, and because the temporal distribution of fertility events varies among households, age structure effects are distinguishable from residence duration effects. We view generational transitions as occurring in multigenerational households with grandparents and grandchildren, for this implies a passing of responsibility of the farm from grandparents to new parents. 1 Few previous studies on household land use have considered duration of residence, age structure, and generational transitions. Most prior models of household land use in Latin America consider only one of these factors, if any (Walker et al., 2002). This oversight is 1 We considered other approaches to measuring household demographics, but found no satisfactory alternatives. One reviewer argued to aggregate cohorts instead of using duration of residence in single years, but this presents problems because there are many factors to consider for defining cohorts, which could result in many possible cohorts, and greatly affect the findings. Many analysts employ the age of the household head as a life cycle indicator, but this says little about past fertility events or overall household age structure. Others have employed Chayanovian dependency ratios, calculated as the units of labor divided by units of consumption, but these fail to distinguish between youth and elderly dependency. We also avoid true dependency ratios because they are unstable at the household level.

6 834 Hum Ecol (2006) 34: intriguing because studies that have incorporated most or all of these demographic factors have found strong effects on land use (e.g., Coomes et al., 2000; Perz, 2002; Pichón, 1997). Our theoretical framework also features five land uses: primary forest, annual crops, perennial crops, cattle pasture, and secondary vegetation. Previous work on household land use in the Amazon rarely involves analyses with more than one or two of these uses. Such work fails to distinguish among land uses with different economic and ecological consequences, and which become important at different moments in a household s life cycle. Data and Methods Study Site Our study case in the Amazon is Uruará, a colonist community situated on the Transamazon highway with a township located at Lat S, Long W in the Brazilian state of Pará (IDESP, 1990). Uruará was founded in the 1970s as a colonization project to resettle rural families from the Brazilian Northeast. The state land-titling agency, INCRA, surveyed and distributed lots of roughly 100 hectares (ha) to a first wave of colonists. In the mid-1980s, perennials such as cocoa and black pepper commanded high prices, which prompted households to expand their clearings for cash crops. This stimulated a second wave of in-migration, raising the municipality s population to 25,000 by 1991 (IBGE, 1996). This dynamism gave way to difficulties in the 1990s, as pest attacks reduced cash crop production and price declines reduced agricultural incomes. This crisis led to a shift in land use toward pasture for cattle (Toni, 2003), and catalyzed the emergence of social movements seeking to improve colonist living standards (Nascimento and Drummond, 2003). Local organizations served as conduits for new credit programs aimed at small producers, and used the financing for pasture formation and livestock purchases (Toni, 2003). By 2000, 23% of the forest cover in the municipality had been cleared (Nepstad et al., 2000, cited in Nascimento and Drummond, 2003, p. 126). Uruará is an appropriate site for an assessment of how household demographic life cycle factors affect land use allocation. First, this community consists almost entirely of small farms that rely primarily on family labor. Second, the Transamazon highway corridor around Uruará exhibits substantial deforestation for various land uses, but also substantial secondary growth. Data Collection In June and July 1996, a nine-member research team consisting of North American and Brazilian social and agricultural scientists administered a survey questionnaire to farm households in Uruará. The questionnaire was divided into two components, where the first addressed household characteristics and the second concerned the lot(s) held by households. The household component included items such as family age composition, sources of income, and material wealth. The lot component included items such as land use, access to credit, use of agricultural technologies, and distance to market. Systematic sampling of farm lots proved intractable because not all lots had houses. Moreover, systematic sampling of houses encountered was problematic because residents were sometimes absent. We therefore sampled by first opportunity of residents encountered on their lot. We employed a cadastral map of Uruará from the Pará state office of Brazil s

7 Hum Ecol (2006) 34: agricultural research agency, EMBRAPA/CPATU, as our sampling frame, to ensure that sampling was not clustered spatially or selective of households by socioeconomic status. 2 The sample includes 261 households, or 12% of all rural establishments in Uruará in 1996 (IBGE, 1998a). The sample also includes 347 lots, as 25% of households held more than one lot, and the same questions were asked about each lot. The sample consists of households with one family (71%) and two or more families (29%), indicating some multifamily households working the same land. 3 In addition, 12% of households had one or more elderly members, indicating multigenerational families. Outcome Variables: Land Use Allocation The outcome variables are measures of land allocation among primary forest, annual crops, perennial crops, cattle pasture, and secondary vegetation. 4 Separation of annual and perennial crops is an advance beyond previous work and difficult to do. Annuals are often interplanted, and data for perennials refers to trees (and vines) rather than area planted. But because annuals differ from perennials in many respects, it is crucial to separate the two. We therefore used other data from the 1996 survey, information for Uruará from the 1995/ 1996 agricultural census (IBGE, 1998a) and our field notes (from yearly visits beginning in 1996) to separately estimate land areas for annuals and perennials. 5 Table I presents descriptive statistics and correlations for the land use variables. The farms in the survey had most of their land in primary forest (65 ha), with substantial pasture (22 ha), some annuals and perennials (3.9 ha each), and secondary growth (6.8 ha). Standard deviations indicate substantial variation in land allocation among the lots in the sample. Skew values for all five raw measures were large, so we transformed them into natural logarithms (ln) with smaller skew values, indicating more normal distributions that are more appropriate for regression analysis. 2 The 1996 Brazilian population count (IBGE, 1998b) and 1995/1996 Brazilian agricultural census (IBGE, 1998a) allow for comparisons to assess sampling bias. The Uruará sample had a mean household size of 7.5, while the 1996 population count figure for the municipality of Uruará was only 5.6, but it is not clear from census documentation whether families beyond the first were counted. If we exclude people outside the first family, household size in the Uruará sample is also 5.6. The 1995/1996 agricultural census indicated the following land use allocation in Uruará: 65% in primary forest, 5.6% under cropland, 23% under pasture, and 5.9% under secondary growth. Table I indicates a very similar distribution. We conclude that sampling bias is limited. 3 We recognize that different families in a given household may be at different life cycle locations. However, Chayanov left open the possibility of multifamily households. For purposes here, it is crucial to recognize the labor contributions and dependency of families rather than exclude them from the analysis, for their presence affects land use. Nonetheless, we ran models keeping only the lots held by one family, and the results are similar to those presented. 4 These measures refer to land use reported by households, which may or may not correspond to physical land cover. Land use is still analytically important because use categories reflect distinctions and decisions made by households. 5 We assumed that beans and corn are interplanted, so if both were planted, we divided their combined area in half, and added the result to other annual crops to estimate the total land area under annuals. For perennials, we assumed that the tree crops (cocoa, coffee, oranges, cupuaçu, etc.) were planted with 3m by 3m spaces, yielding 1,111 trees per hectare, while vine crops (i.e., black peppers) were planted with 2m by 2m spaces, yielding 2,500 vines per hectare. This allowed conversion from plantings to areas, which we then summed for all perennials reported. We validated the accuracy of our estimates by adding the areas under annuals and perennials to the reported areas under primary forest, cattle pasture and regrowth, and comparing the sum to the reported total land area. The summed total was ha, and the reported total was ha, a difference of 0.4 ha; the correlation between the two figures was very high (r > 0.99). We conclude that the estimates are valid.

8 836 Hum Ecol (2006) 34: Table I Descriptive Statistics and Correlations for Land Use Outcomes, Farm Lots, Uruará, Pará, Brazil, 1996 (n = 347) Land use Mean Std. dev. Skewness Correlation with: primary forest Annual crops Perennial crops Pasture Secondary growth Primary forest Hectares (ha) Natural log (ln) ha Annual crops Ha Ln ha Perennial crops Ha *** 1.00 Ln ha *** 1.00 Pasture Ha *** Ln ha *** 0.27*** 0.18** 1.00 Secondary growth Ha * Ln ha ** 0.14* p < 0.15, *p < 0.05, **p < 0.01, ***p < Table I indicates that the land use outcomes are interrelated. Primary forest exhibits negative correlations with the other land uses, which is expected given that forest is cleared for agriculture. Primary forest area shows the strongest inverse relationship with pasture, which is not surprising given the large extent of pasture relative to crops and regrowth. Annuals, perennials and pasture are positively correlated, a reflection of their expansion during the process of farm establishment. Secondary growth also shows positive associations with the agricultural land uses, a reflection of the need to fallow or abandon land. That said, the correlations are moderate, which may reflect the different points in the household life cycle at which specific land uses become important. Explanatory Variables: Household Life Cycle Location and Other Factors Table II presents seven groups of explanatory variables: socioeconomic background, initial land cover, context of lot, institutional context, remittances and hired labor, land management practices, and household life cycle location. We feature the role of household demographic life cycle variables from Chayanovian theory, and include the other variables as controls following household production theory and specificities of the Amazon frontier. 6 6 Table II indicates which variables are from the household questionnaire, and which come from the lot questionnaire. The statistics in Table II are calculated for lots, including for the household variables, so the figures are weighted toward households with more than one lot. However, the values do not change much if calculated for households, since 75% held one lot.

9 Hum Ecol (2006) 34: Table II Descriptive Statistics for Explanatory Variables and Correlations with Land Use Outcomes, Farm Lots, Uruará, Pará, Brazil, 1996 (n = 347) Explanatory variable Unit 1 Mean Std. dev. Correlation with primary forest Annual crops Perennial crops Pasture Secondary growth Socioeconomic background Previous job of household head (0 = non-agricultural, 1 = agriculture) H *** Initial wealth (factor index) H * 0.19*** 0.17** 0.11* 0.08 Initial agricultural H * * capital (factor index) Initial land cover Ln ha cleared upon L * acquisition Context of lot Ordinal lot number (1 = 1st, 2 = 2nd...6th) L *** 0.33*** 0.34*** 0.23*** Kilometers to Uruará town L *** 0.18** 0.28*** 0.35*** 0.13* Neighborhood organization L * (0 = No, 1 = Yes) Damage by fire set L ** 0.15** 0.11* 0.22*** 0.19*** by neighbor (0 = No, 1 = Yes) Institutional context Use of credit (0 = No, L * 0.25*** 0.30*** 0.42*** = Yes) Extension agency assistance L ** *** 0.15** 0.07 (0 = No, 1 = Yes) Commercial business H ** 0.13* 0.13* 0.12* (0 = No, 1 = Yes) Remittances and hired labor Remittance income H * 0.07 (0 = No, 1 = Yes) Ln days of labor hired H * 0.11* Land management practices Agricultural inputs L * 0.11* 0.24*** 0.22*** 0.06 (factor index) Pasture rotation L * 0.28*** 0.27*** 0.61*** 0.04 (0 = No, 1 = Yes) Life cycle location Years on lot L * 0.17** 0.30*** 0.23*** 0.27*** Number of adults H *** 0.22*** 0.11 (ages 15 65) Number of adults squared H Number of children H * (under age 15) Number of elderly H (ages 66+) Generational transition (elderly children) H + p < 0.15, *p < 0.05, **p < 0.01, ***p < H Household-level characteristic, L lot-level characteristic. All statistics are calculated for lots.

10 838 Hum Ecol (2006) 34: Control Variables Socioeconomic background refers to three variables that characterize assets held by households upon their arrival in Uruará. Previous job indicates whether the household head had worked in agriculture. 7 Previous agricultural experience should be particularly important for cultivating crops, given their labor intensity. Most household heads did have previous agricultural experience, which shows a positive correlation with perennials. Initial wealth refers to financial assets. Households who brought wealth were better able to liquidate assets, facilitating farm implementation and altering land allocation. We measure initial wealth using indicators of durable goods possession and housing quality. These were converted to z-scores, weighted by factor loadings from principle components analysis, and summed to form an index with a mean of zero. 8 Initial agricultural capital is also a factorweighted index, constructed using three measures of whether a household owned specific agricultural implements at the time of their arrival. 9 Agricultural technologies such as chainsaws may afford more rapid implementation of farming systems. The standard deviations for both wealth indexes indicate asset inequality in the sample, and both exhibit significant correlations with land use. Initial land cover is operationalized in terms of the ln ha deforested when the household acquired a lot. The antilog of the ln mean was only 1.3 ha, though the standard deviation indicates considerable variation. More initial deforestation facilitates farm implementation. This reduces the labor inputs necessary for agricultural land use, but also makes more extensive regrowth possible. The correlations, though weak, confirm these expectations. Context of lot comprises four indicators that situate a lot in a household s farming system and among neighboring lots. First, we consider the order in which a household acquired a lot. The first lot acquired is generally the most heavily used, so second and later lots (25% of all lots in the survey) should have more forest and less cropland, pasture and regrowth, expectations confirmed by the correlations. Second, we account for distance to market, especially important for commercial land use decisions because transport costs are high on unpaved roads in the study area, reducing the profitability of more distant lots. Lots averaged about 30 km from Uruará town, though this varied substantially. Larger distances should correspond to more primary forest and less land under the other use types, expectations confirmed by the correlations. Third, the presence of neighborhood organizations indicates whether neighboring households were mobilized for cooperative labor arrangements, against land invasions, and/or to secure agricultural credit, all of which should allow for greater agricultural land use. About 34% of lots were in organized neighborhoods, but weak correlations suggest ambiguous effects on land allocation. And fourth, we consider damage to vegetation from fires set by neighbors. Fire damage may reduce primary forest, facilitating the expansion of agricultural land, but the damage may exceed a household s ability to use the burned land productively, leading to substantial 7 We also considered the household head s region of birth and years of schooling. However, neither of these variables exhibited significant effects. 8 Variables and factor weights from principle components analysis for the initial wealth index are: house in town 0.80, brick walls 0.50, electricity, 0.64, generator 0.57, gas stove 0.67, sewing machine 0.54, refrigerator 0.79, radio 0.53, television 0.81, satellite dish 0.70, bicycle 0.66, and car The eigenvalue for this factor was 5.08, and the common variance was 42.4%. 9 Variables and factor weights from principle components analysis for the initial agricultural capital index are: chainsaw 0.81, cocoa dryer 0.63, and tractor The eigenvalue for this factor was 1.28, and the common variance was 42.8%.

11 Hum Ecol (2006) 34: secondary growth. About 20% of lots had incurred fire damage, and it shows significant land use correlations in the expected directions. Institutional context comprises three variables that tie a lot to public and private agencies and the urban economy. First, the use of credit indicates the importance of lending institutions. Because credit can offset capital scarcity, it facilitates commercialization. Consequently, use of credit should lead households to allocate less land to forest and more to perennials and cattle. 10 Nearly half of the lots surveyed were owned by households with credit, and credit exhibits the anticipated associations with land use. Second, extension assistance indicates whether government agricultural agents had ever visited a given lot. Extension agents in Uruará focus on commercial activities, so assistance should correspond to less forest and more of the other uses, especially perennials and cattle. Only 16% of lots had been visited by extension agents, but it shows the expected correlations. And third, some farm households ran local businesses in Uruará town. Investment in commercial enterprises initially diverts resources from agriculture, though earnings may help finance expanded land use. About 9% of lots were owned by households with businesses, and the correlations suggest that those lots had less agricultural land, perhaps due to diverted investment. Remittances and hired labor are included to assess the effects of labor markets. The remittances variable refers to whether a household had absent family members sending money, and this occurred among households who owned 11% of the lots surveyed. Like credit, remittances can offset capital scarcity and facilitate greater land use. However, the correlations are weak and run in the other direction, which implies that remittances are put to uses other than agriculture. Hired labor, measured as the ln days of labor paid by a household in the previous year, can offset family labor scarcity and encourage forest clearing, especially for commercial agriculture. On average, households paid for 9.5 days of hired labor. The positive association with perennials is consistent with the use of hired labor to expand cash crops. Land management practices refers to two strategies households may employ to sustain production on their lots, namely the use of agricultural inputs and pasture rotation. The agricultural inputs measure is a factor-weighted index calculated using indicators of use of pesticides and fertilizers to sustain crop productivity. 11 While some households may employ inputs to reduce the land area in use, others may do so to sustain production in larger areas. The correlations suggest that the latter interpretation is correct, via the negative association with forest and positive associations with crops and pasture. Pasture rotation requires more grazing land for a given number of cattle. Rotation, used on 69% of the lots surveyed, shows a positive correlation with pasture and a negative association with forest as expected, but also positive associations with crops. Demographic Life Cycle Variables Demographic variables that define a household s life cycle location should also influence land allocation. Table II measures life cycle location using six variables: time on lot, 10 We considered using measures of tenure status, but land titles are usually necessary to obtain credit, and titles have a high correlation with credit (r > 0.60). Because credit is more proximate to land use, and because credit exerted stronger effects, we exclude tenure status. 11 Variables and factor weights from principle components analysis for the agricultural inputs index are: insecticides 0.74, fungicides 0.54, herbicides 0.53, chemical fertilizers 0.81, and organic fertilizers The eigenvalue for this factor was 2.12, and the common variance was 42.3%.

12 840 Hum Ecol (2006) 34: number of adults, adults squared, number of children, number of elderly, and a child elderly interaction term. Time on lot captures a household s duration of residence with reference to their land. This indicates a household s experience with a property via exploration of its resources and experimentation with agricultural techniques. Long-term ownership should yield less land allocated to forest and more to the other uses. The survey data indicate a mean duration of 10 years with substantial variation, and significant correlations with the land use outcomes in the expected directions. The next four variables assess age structure effects on land use allocation. Theoretically, these four variables change in tandem with time on lot. But as shown in Table III, they are independent to the extent that children are born over time and households acquire lots at different moments in their life cycles. The number of working-age adults (persons age 15 65) measures household labor availability. 12 More adults should lead to larger production systems with less primary forest and more agriculture. Because crops require heavy labor inputs, the effect of adults should be especially important for annuals and perennials. We also consider the square of adults because households with especially large labor pools may increasingly allocate labor to off-farm activities such as wage work in town. The effect of the adults squared term should be the opposite of the adults effect. Hence, the overall impact of adults should be non-linear, with declining marginal effects, such that forest decline and agricultural expansion attenuate in especially large households due to increasing off-farm labor allocation. Table II shows an average above four adults for the sample, with substantial variation, and correlations with land use largely as expected. The number of children (persons under age 15) measures the impact of young household members on land use. 13 Children constitute pressure to plant annual crops to meet subsistence demand, but older children expand the household labor pool, allowing for larger areas of commercial crops. Table II shows a mean of nearly three children for the sample, with a large standard deviation. Correlations with land use outcomes are somewhat weak, which may reflect the countervailing effects of children, though there is a significant positive effect on perennials, consistent with an interpretation emphasizing child labor contributions. The number of elderly (persons age 66+) measures the extent of aging among colonist households. Elderly household members imply that children are grown and some have left to start their own farms or other enterprises. This suggests a decline in household size, reducing agricultural land areas and increasing secondary growth. Table II indicates few elderly on average but substantial variation, and weak correlations with land use. We operationalize generational transition using a child elderly interaction term. Conceptually, this term defines multigenerational households as those where the farm is being handed from one generation to the next, which often happens when the grandchildren arrive. The interaction term allows for evaluation of the generational transition effect on land use net of the distinct influences of elderly members and children by themselves. Households with elderly members as well as children are taken to exhibit transitions from one generation s life cycle to another, implying a rise in subsistence demand, which should prompt greater agricultural land use and a decline in secondary growth. 12 One might object that men and women should have separate variables to assess their distinct effects on land use. However, correlation analysis indicated a strong association between the number of men and women (r > 0.60), and models with a single variable for adults were stronger. 13 One might object that aggregating children ages 0 15 mixes true dependents and those contributing labor. We recognize other possible age cutoffs but use the 0 15 due to limitations in the survey data. This still provides an indication of the net effect of young household members on land use.

13 Hum Ecol (2006) 34: Table III Correlations Among Life Cycle Demography Variables, Farm Households and Lots, Uruará, Pará, Brazil, 1996 Life cycle demography variable Time in Uruará/on lot Number of adults Number of children Number of elderly Households (n = 261) Duration of residence in Uruará 1.00 Number of adults (ages 15 65) 0.22** 1.00 Number of children (under age 15) *** 1.00 Number of elderly (ages 66+) 0.19** 0.16** 0.18** 1.00 Lots (n = 347) Time on lot 1.00 Number of adults (ages 15 65) 0.12* 1.00 Number of children (under age 15) *** 1.00 Number of elderly (ages 66+) 0.12* 0.15** 0.15** p < 0.15, *p < 0.05, **p < 0.01, ***p < Modeling Land Use Allocation Land allocation must be viewed in terms of joint decisions among competing land uses. This makes land allocation decisions mutually endogenous (i.e., simultaneous), for the decision to allocate more land to one use on a lot of a given finite size constitutes an opportunity cost and limitation on the quantity of land left to allocate to other uses. Planting of annuals comes at the expense of forest; later, perennials and pasture replace forest and annuals; and eventually, secondary growth replaces cropland and pasture. But household models of land use rarely account for endogeneity in land allocation decisions (e.g., Jones et al., 1995). Most common are models that assume independence among the various outcomes (e.g., Perz, 2001; Pichón, 1997). Such efforts overlook endogeneity and the consequent problems of estimation bias and inconsistency, with the result that conclusions about factors affecting land use may be incorrect. As a result, analysts have used other approaches, such as seemingly unrelated regression (SURE), which accounts for correlated error terms (Pan et al., 2001; Perz, 2002). The limitation of SURE is that it only indirectly accounts for the effect of one outcome variable on another, and does not allow direct observation of whether, for example, more pasture or something else is planted at the expense of perennials. We therefore employ three-stage least squares (3SLS) estimation. This involves creation of a system of structural equations where the error terms are correlated and one or more dependent variables are endogenous explanatory variables in other equations. Like 2SLS, 3SLS uses instrumental variables to produce consistent estimates of the endogenous variables. And like SURE, 3SLS uses 2SLS estimation for each equation to adjust for correlated errors and obtain a consistent error covariance matrix. But 3SLS then uses GLS estimation, which adjusts for correlated errors and incorporates the instrumented variables to simultaneously estimate the entire system of equations. 3SLS thus goes beyond SURE by creating instrumented variables; 3SLS also goes beyond 2SLS by generating results for the entire system. Both advances are necessary to adequately account for the endogeneity of land allocation decisions and simultaneously evaluate the effects of household demographic variables for multiple land uses.

14 842 Hum Ecol (2006) 34: Model specification worked from results from SURE models (Perz, 2002), rerun for all five outcomes, in order to identify instrumental variables. We then evaluated the performance of the SURE models, and constructed a 3SLS system by using significant variables from the SURE equation for a given land use outcome in that outcome s equation in the 3SLS model. We then iteratively tested the 3SLS system, dropping variables that were insignificant in a given equation if used in more than one, and excluded variables that never reached significance and whose removal did not significantly change or weaken the system. We constrained the process of specification by including all of the household demographic life cycle variables in each equation, and using the control variables to identify the system. This reflects our focus on life cycles and facilitates comparisons of their effects among the land use outcomes. It also reflects our expectation that the life cycle variables do not have the same effects on each land use outcome. 14 Findings Table IV presents results from our modeling effort: a system of five equations, each with coefficients for instrumented land use variables, selected control variables, and the household demographic life cycle variables. All five equations have significant chi-square values. Primary Forest The weakest equation is the first, for primary forest. None of instrumented land use variables exerted independent effects on forest area. However, lots had more primary forest if they 1) were farther from Uruará town, 2) they had not been damaged by fire, and 3) they had not been visited by extension agents. Household demographic variables exhibit limited impacts on primary forest. The number of children reduces forest area, likely a reflection of subsistence demand early in the household life cycle. Annual Crops The story for annual crops is considerably different, in large part due to significant effects of household demographic variables. Among the instrumented land use variables, pasture area has a positive effect on annuals. This suggests that households manage risk not only by planting annuals but also by running cattle as a form of rural insurance. It is likely also a period effect, for in the years just before the 1996 survey, problems with perennials led many households to focus on annuals for food security and cattle for their marketability. With respect to the control variables, lots had larger areas planted under annuals if they 1) belonged to households who arrived with less initial wealth, and 2) were the first lot acquired. While the initial wealth effect is weak, both of these findings are consistent with the interpretation that annuals provide a subsistence. Households generally live on the first 14 One potential problem with 3SLS is that misspecification of one equation yields inconsistent and biased estimates of coefficients in the other equations. We worked from a SURE system with equations with r 2 values ranging from about 0.20 to 0.50 and significant F-ratios (p < 0.001). This suggests that there were effective instruments for the land use outcomes. By systematically changing model specification and evaluating the results, we were able to evaluate specifications by iterating toward equations such that further alterations produced similar but weaker models. Through this process, we distinguished the most effective instruments, which allowed us to identify the system and satisfy the order condition.

15 Hum Ecol (2006) 34: Table IV Three-stage Least Squares Model of Land Use Allocation with Life Cycle Location and Other Variables, Farm Lots, Uruará, Pará, Brazil, Explanatory variable Primary forest Annual crops Perennial crops Pasture Secondary growth Equation parameters Model chi-square 60.62*** *** *** *** 78.18*** Intercept 3.81*** * 0.01 Endogenous land use variables Ln ha under primary forest Ln ha under annual crops * 1.01** 0.41 Ln ha under perennial crops Ln ha under pasture * 0.38* 0.66*** Ln ha under secondary growth Socioeconomic background Previous job (0 = non-agricultural, 0.39* 1 = agricultural) Initial wealth (factor index) ** Initial agricultural capital (factor index) 0.16* 0.24* Initial land cover Ln ha cleared upon acquisition *** Context of lot Ordinal lot number (1 = 1st, 2 = 2nd...6th) 1.43*** 2.27** Kilometers to Uruará town 0.01*** Neighborhood organization (0 = No, 1 = Yes) Damage by fire set by neighbor (0 = No, * 1 = Yes) Institutional context Use of credit (0 = No, 1 = Yes) 0.41* Extension agency assistance (0 = No, 1 = Yes) Commercial business (0 = No, 1 = Yes) 1.01* Remittances and hired labor Remittance income (0 = No, 1 = Yes) Ln days of labor hired 0.13** Land management practices Agricultural inputs (factor index) 0.11* Pasture rotation (0 = No, 1 = Yes) 1.45*** Life cycle location Years on lot *** Number of adults (ages 15 65) ** 0.84*** Number of adults squared *** 0.07** Number of children (under age 15) 0.05** 0.09* 0.13* 0.18** 0.09 Number of elderly (ages 66+) * * 0.42 Generational transition (elderly children) * p < 0.15, *p < 0.05, **p < 0.01, ***p < Valid cases after listwise deletion of cases with missing values: n = 310. lot acquired, which is also the lot where food is grown, and poor households are especially concerned to minimize risks by planting annuals. That said, the most important explanation for land allocation to annual crops involves the demographic life cycle variables, especially age structure. The number of adults has a strong and positive but non-linear effect on annuals, a reflection of the importance of