Common Pool Resources, Rural Poverty and Inequality: A multi-country Study

Description

Our study draws on data from about 6500 households across fifteen regions in nine low and medium-income countries covering Africa, Asia, and Latin America to estimate the contribution of CPR-based income to poverty alleviation and inequality, based on an understanding of how CPR-based income is influenced by household assets, the abundance of CPRs and institutions of resource access. Our analysis confirms that CPR-based income makes up a significant portion of the income of the rural poor. However, elite households, generally derive more monetary benefit from CPRs not only in absolute terms but also in terms of share in household income, not because of differential access to the resource but because of their differential access to complementary productive assets and the capital-intensive nature of most technologies required for increasing returns from CPRs through markets. CPRs reduce inequality only when their use is for subsistence purposes using labour-intensive methods. For this study we created a multi-country household-level dataset on household demography, income, resource tenure by pooling data from a number of existing livelihood-environment studies under Nature4SDGs project (https://www.nature4sdgs.org/). We also collated several global datasets to generate information on the social-ecological context of each settlement as close to the time period covering original data collection as possible (i.e., 2011–2015). We have used secondary data for variables such as market access, population density and resource abundance. Using these data , we created a data file named Processed_data_for_analysis.dta in STATA for analysing our research questions. Please refer to the README file for better understanding of the data.

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We created a multi-country data set on household demography, income, resource tenure, by pooling data from a number of existing livelihood-environment studies under Nature4SDGs project (https://www.nature4sdgs.org/). Using these data, we created a data file named Processed_data_for_analysis.dta in STATA for analysing our research questions. We conducted our analysis using STATA 12 software. In order to estimate how much is the CPR income per adult equivalent across of rural households and how does it compare with the global income-based poverty line, we prepared a Staked-Bar chart considering daily cpr and non-cpr income per adult equivalent across income deciles. We then compare this with World bank's $1.9 poverty line. To test which variables are correlated with household CPR income, we estimated a two-level model with varying country level intercepts for the dependent variable- absolute CPR income per adult equivalent derived by households. The model was estimated as generalised linear and latent mixed models (used gllamm command) in STATA 12 . To account for heteroscedasticity, we clustered the standard error at the settlement level. We used a Gaussian conditional distribution and identity link function. CPR income was log-transformed to account for the non-normal distribution of the income data. To tackle 0 values, we added 0.01 to the CPR income column before log-transformation . To analyse distributional aspects of CPR income, we used Kuznets ratio analysis (Kuznets, 1955) and Gini-decomposition method . We calculated the Absolute and relative Kuznets ratios of CPR income of each settlement and estimated the mean for two categories of settlements based on CPR harvest techniques. We decomposed household income inequality at the settlement by sources of household income, categorised into CPR and non-CPR. This enabled us to estimate the percentage change in Gini-coefficient due to a 1% increase in CPR income across households. We calculated the mean of the percentage changes in Gini-coefficients for the two types of settlements (capital-intensive vs labour-intensive) to explore how the influence of CPR income on inequality varies across these two types of settlements.

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