# 3 The Data

## 3.1 Poverty and Environment Network (PEN)

The income data used in this report draw on the global PEN database. PEN is a large data collection effort, which has been led by the Center for International Forestry Research (CIFOR) (http://www1.cifor.org/pen/). The effort was originally inspired by the influential case study done by Cavendish (2000), which documented a high household income share from environmental sources, i.e. extraction of natural resources from wildlands – that is, natural forests, grasslands, rivers, fallows, or other non-cultivated environments. Notably, Cavendish discovered this sizeable ‘subsidy from nature’ by applying detailed quarterly household surveys with shorter recall periods (1-3 months) and capturing seasonal variations. Furthermore, much of this ‘hidden harvest’ came in the form of subsistence income, making it necessary to impute prices that could measure this income contribution in value terms.

The starting point of PEN was the idea to answer that question by replicating an adapted and extended version of the Cavendish survey across developing countries in various continents, covering different contextual preconditions. This would then allow for a much more consolidated view about the role of environmental incomes in the tropics and subtropics. PEN thus used standard household and village survey instruments, translated into different languages, to provide new data, so as to analyze the two-way linkages between poverty and environmental incomes. 33 PhD students and other junior scholars were involved in data collection between 2005 and 2008, and supervised by a dozen of senior researchers. The data gathered eventually covered 24 countries, spread over Sub-Saharan Africa, South and East Asia, and Latin America. We have data for a total of 58 sites, 333 villages, and 7978 households (Angelsen et al. 2014).<sup>7</sup> The intention was thus to make PEN the world's largest global-comparative quantitative review of the role of tropical forests in poverty alleviation.

We use different sector incomes and total household income in our analysis. Incomes include cash and non-cash components, as calculated by Angelsen et al. (2014). The agricultural sector includes crop and livestock income, while the environmental sector includes incomes from natural forests, as well as other non-cultivated sources. Among non-resource sector incomes, we separate out wage income from other sources, dominated by business and transfer incomes (remittances, pensions, etc.). Total income aggregates agriculture, environment and non-resource incomes. Finally, we compute households’ adult equivalent units (aeu) to enable adequate welfare comparisons across households with different compositions of productive earners vs. non-earners, and size-dependent economies of scale in the per-capita provision of intra-household services.<sup>8</sup>

## 3.2 Climate data and contextual variables

To relate the household data to climatic conditions, we use the gridded climate data of the Climate Research Unit of the University of East Anglia (CRU TS3.21). The CRU data contain monthly time series of temperature, precipitation and other climate variables spanning the period from 1901 to 2012 and covering the whole globe with 0.5x0.5 degree resolution. It is based on the analysis of over 4000 individual

> 7 The initial sample of 8 305 households was reduced by an attrition of 3.9%. In addition, the sample in multivariate analyses is further reduced by missing (or erratic) information on specific contextual variables (see below).

> 8 OECD discusses different options for using equivalence scales (http://www.oecd.org/eco/growth/OECD-Note- <u>EquivalenceScales.pdf).</u>

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