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

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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.

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Notes

  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.

  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. TableI 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 101.1 ha, and the reported total was 100.7 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.

  6. TableII indicates which variables are from the household questionnaire, and which come from the lot questionnaire. The statistics in TableII 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.

  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 0.50. 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 0.48. The eigenvalue for this factor was 1.28, and the common variance was 42.8%.

  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 0.58. The eigenvalue for this factor was 2.12, and the common variance was 42.3%.

  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.

  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 withr2 values ranging from about 0.20 to 0.50 and significantF-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.

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Acknowledgments

This research was supported by a grant from the US National Science Foundation (SBR-9511965). We thank Charles Wood for support in the US, Adilson Serrão and Alfredo Homma for support in Brazil, and research team members André Caetano, Roberto Porro, Fabiano Toni, Célio Palheta, Rui Carvalho, and Luiz Guilherme Teixeira, as well as the people of Uruará, for insights about the study site. Errors are the responsibility of the authors.

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Authors and Affiliations

  1. Department of Sociology, University of Florida, 3219 Turlington Hall, PO Box 117330, Gainesville, FL, 32611-7330, USA

    Stephen G. Perz

  2. Department of Geography, Michigan State University, 116 Geography Building, East Lansing, MI, 48824-1117, USA

    Robert T. Walker & Marcellus M. Caldas

Authors
  1. Stephen G. Perz
  2. Robert T. Walker
  3. Marcellus M. Caldas

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Correspondence toStephen G. Perz.

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Perz, S.G., Walker, R.T. & Caldas, M.M. Beyond Population and Environment: Household Demographic Life Cycles and Land Use Allocation Among Small Farms in the Amazon.Hum Ecol34, 829–849 (2006). https://doi.org/10.1007/s10745-006-9039-8

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