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Poverty and Inequality in the Rural Brazilian Amazon: A Multidimensional Approach

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Abstract

This paper analyses poverty and inequality dynamics among smallholders along the Transamazon Highway. We measure changes in poverty and inequality for original settlers and new owners, contrasting income-based with multidimensional indices of well-being. Our results show an overall reduction in both poverty and inequality among smallholders, although poverty decline was more pronounced among new owners, while inequality reduction was larger among original settlers. This trend suggests that families have an initial improvement in livelihood and well-being which tends to reach a limit later—a sign of structural limitations common to rural areas and maybe a replication of boom and bust trends in local economies among Amazonian municipalities. In addition, our multidimensional estimates of well-being reveal that some economically viable land use strategies of smallholders (e.g., pasture) may have important ecological implications for the regional landscape. These findings highlight the public policy challenges for fostering sustainable development among rural populations.

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Notes

  1. According to the 2010 Demographic Census, there are 186,882 people living in the region (including the municipalities of Altamira, Brasil Novo, Medicilândia, and Uruará), 33% in the rural area. The municipality of Altamira alone comprises 53% of the total population, and has the highest level of urbanization among the municipalities in the region (85%). In terms of basic infrastructure, all four municipalities rank far below the national average, with a very low proportion of households (both urban and rural) with access to water and sewage systems, and human development indices ranking below the national average (UNDP 2000).

  2. In order to protect the identity of the sampled families and properties, all the geographical coordinates, roads and grids were excluded.

  3. It was not possible to estimate poverty and inequality indices for rural Pará as a whole because these areas were not fully represented in the National Household Survey until 2003 (IBGE 2005).

  4. Absolute poverty line is set as a fixed reference value to compare per capita household income or any other wellbeing proxy. Relative poverty line represents a fixed percentage of the cumulated wellbeing function (e.g., income function). Some authors suggest predefined values for these lines, but the reference values vary according to the purpose of the study and the context (Hoffmann 1998; Iceland and Bauman 2007).

  5. The headcount ratio, for instance, is insensitive to poverty intensity while poverty gap is insensitive to inequality among the poor. The squared poverty gap, however, accounts for both poverty intensity and severity, although is difficult to interpret (Hoffmann 1998).

  6. We could have used both sets of Altamira longitudinal data to analyze the dynamics of multidimensional poverty. However, high levels of missing information on relevant dimensions made some distributions unstable for the second dataset. Thus, we opted for splitting the baseline sample between new owners and original setters, simulating the dynamics across different cohorts, assuming fixed returns of each livelihood across cohorts. This synthetic cohort approach to frontier regions is widely applied in rural demography with limited data (McCracken et al. 1999).

  7. The poverty line estimated by IPEA (2008b) is based on the amount of money required to buy a basket of essential products in order to supply adequate caloric intake. The poverty line is regionalized and estimated separately for rural, urban and metropolitan area. By 2001, for instance, the estimated poverty line in the metropolitan area of Belém (Pará state capital) was R$115.92 (U$47.70), for the urban area, R$119.86 (U$49.32), and for the rural area, R$104.88 (U$43.16).

  8. Poverty measures for Pará in 1997 were estimated based on per capita household income from PNAD (IBGE 1997). As PNAD was not representative of the rural population for the Northern states of Brazil until 2003, the measures are basically based on urban population. This is why from 1997 to 2005 (Tables 2 and 3) poverty levels seemed to have increased, although this has not continued. Poverty series from IPEA (2008a) only provide information on HC.

  9. We transformed production by crop and animal type into kilogram-equivalent. Then, we took price per kilo effectively received by Altamira smallholders and multiplied it by total production for self-consumption. This way, we monetized the production not sold by making two assumptions: a) perfect market clearing; b) supply is price inelastic.

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Authors and Affiliations

  1. Brown University, Providence, RI, USA

    Gilvan R. Guedes

  2. Indiana University, Bloomington, IN, USA

    Eduardo S. Brondízio & Rodrigo Penna-Firme

  3. Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

    Alisson F. Barbieri & Resende Anne

  4. Universidade Estadual de Campinas, Limeira, SP, Brazil

    Álvaro O. D’Antona

  5. GIT/Vale do Rio Doce University, Governador Valadares, MG, Brazil

    Gilvan R. Guedes

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  1. Gilvan R. Guedes
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  2. Eduardo S. Brondízio
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  3. Alisson F. Barbieri
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  4. Resende Anne
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  5. Rodrigo Penna-Firme
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  6. Álvaro O. D’Antona
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Correspondence to Gilvan R. Guedes.

Annex

Annex

Table 7

Table 7 Variables used in the multidimensional well-being scalar for Altamira study area (1997/98)
Full size table

Details on construction of weighted indices

Wealth upon arrival

Wealth status upon arrival was created based on a regression approach (as suggested by ABEP 2008). We regressed selected household assets and holdings upon arrival on the log of household total income and then used the estimated coefficients as weights to create a final index of possession of assets and holdings upon arrival. This weighted factor was, then, cumulated and the classification of well-off smallholder was based on being at two-thirds or above the median of the cumulated distribution. For the initial wealth index we included the following dummy variables (with weights in parenthesis): possession of refrigerator (1), radio (−1), sewing machine (−1), color TV (3), dish antenna (4), chainsaw (−2), tractor (3), commerce (−2), urban house (−2), urban land (7), rural house (−3), rural land (4), other assets (3). Index ranged from −6 to 13. Model statistics: R2 = 60.70%; ρ (income; index) > 0 significant at 1%. The model also controlled for current education of household head, current possession of the same referred assets, and if the house currently has bathroom. Cronbach’s alpha (scale) = 0.6955.

Agricultural Technology

The weighted factor for agricultural technology was created using the same regression strategy as applied to the initial wealth factor. The agricultural technology factor combines information on manual/animal-based and motor-based technology and on type of fertilizer applied to farming, regressed on the log of total agricultural production. The index was cumulated and categorized into below or above the median, suggesting high and low production technology. For production technology we included the following dummy variables (with weights in parenthesis): manual (0), draft animal (9), motor (10), chemical (−3), non-chemical (4). Manual technology was constructed from use of grader/harrow, plough, or trailer/wagon. Animal-based technology was created from use of draft animal grader/harrow, plough, or trailer/wagon. Motor-based technology was created from use of chainsaw, grinder for manioc flour, or generator. Chemical inputs are the categorization of use of insecticide, fungicide, herbicide, chemical fertilizer or medicines. Non-chemical inputs are derived from use of organic fertilizer, mineral salt or irrigation. Model statistics: R2 = 19.02%; ρ (production; index) > 0 significant at 1%. Cronbach’s alpha (scale) = 0.5644.

Assets Factor

For the assets factor, we gathered information on possession of selected household assets, and then regressed on the log of total household income. The index was cumulated and categorized in quintiles of the cumulated distribution (0 – 20%, 21–40%, 41–60%, 61–80%, 81–100%). The advantage of the regression-based weighted factors is that the weights are derived empirically from the sample instead of arbitrarily assigned, and produces a closer description of sample heterogeneity along distributions (ABEP 2008). Selected household assets with corresponding weights (in parenthesis): refrigerator (4), radio (−1), sewing machine (−1), color TV (3), dish antenna (1), chainsaw (4), tractor (2) and small truck (6). Model controlled for current holdings, education of household head and if the house has bathroom. Index ranged from −2 to 19. Model statistics: R2 = 55.23%; ρ (production; index) > 0 significant at 1%. Cronbach’s alpha (scale) = 0.5367.

Additional Information of Transformations of Variables Used

The land use/cover variables were transformed into proportion of lot size under specific classes (annuals, perennials, pasture and forest), and then cumulated and categorized into quartiles (0–25%, 26–50%, 51–75%, 76–100%). Other variables defined in terms of quantiles of cumulated distribution were: monetized value of agro-pastoral production for self-consumption,Footnote 9 total household income, and cattle herd size. Dummy variables include: family members living on the lot, upward financial transfers, other relatives living in the region, family members living in urban areas, household members with off-farm activities, lot accessibility during the rainy season, and membership to agricultural association. Number of properties belonged to household head was defined as count variable. The additional variables used in our multidimensional poverty index have categories rearranged according to the absolute frequency in each category.

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Guedes, G.R., Brondízio, E.S., Barbieri, A.F. et al. Poverty and Inequality in the Rural Brazilian Amazon: A Multidimensional Approach. Hum Ecol 40, 41–57 (2012). https://doi.org/10.1007/s10745-011-9444-5

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