Problem setting

2.1.1 The need for poverty reduction in economical and ecological terms

Although the first millennium development goal of the United Nations is to reduce extreme poverty and hunger by half until 2015 (United Nations 2008), that goal has yet to be achieved and poverty remains a pervasive problem in many countries. In general, poverty reduction is one of the main goals of development policies, programs and projects (e.g. Zeller et al. 2001, Collier and Dollar 2002).In Central Sulawesi we found that almost 20% of the households are very poor, i.e. the household members live on less than $1 US purchasing power parities (PPP) per capita and day. This poverty headcount is quite high in comparison to the Indonesian average of 7.5% (HDR 2007/2008).

In Indonesia, the annual deforestation rate rose from 1.2% in the 1990s to 2.0% from 2000 through to 2005 (FAO 2009). Erasmi et al. (2004) give the research area an annual deforestation rate of 0.6% on data from 1972-2001. Schwarze et al. (2005) find a positive correlation between the relative poverty status of a household and forest encroachment. The Nature Conservancy (2005) state that the natural forest in Central Sulawesi is threatened by smallholder conversion of forest into farmland. At the rainforest margin of the Lore Lindu National Park, land use change is mainly driven by a change of strategy from “food crops first” to “cash crops first” (Weber et al. 2007). This is in part from the Bugi migrants who brought knowledge of cocoa cultivation into the region and the desire by the indigenous population to also gain the economic benefits of cacao-production. The local ethnic groups often sell their land to the Bugi

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migrants and clear new plots in the forest for themselves (Weber et al. 2007). This is further evidence that forest degradation is often fostered by poverty combined with internal and external change factors such as population pressure as described by Wunder (2001). Poor people often clear forest areas for short term gains, even if they are aware of the long term negative effects this could have (Eckholm et al. 1984). Hence, poverty reduction in Central Sulawesi could not only improve peoples’ livelihoods but also contribute to the achievement of conservation goals.

Beside their important ecological role forests are often crucial for people’s livelihoods. They can support subsistence, generate income or act as safety networks. Therefore, forests are often extensively used by rural households. Thus, clearing forest areas to increase arable land can also have a negative impact as it diminishes the availability of the forest as a resource (PEN 2009). In the research area 76% of the households collect forest products, mostly firewood. However for the poorest people in the region the sale of rattan is an important income source (Schwarze et al. 2007).

Moreover, forests provide environmental services - such as coffee pollination (see for example Olschewski et al. 2007).

Furthermore, forests can fill gaps as part of the response to ex ante risks, e.g. to overcome seasonal fluctuations in the availability and affordability of goods. Moreover, they can act as a form of insurance for larger ex post shocks such as droughts (Wunder 2001). Such shocks can get more frequent and severe due to climate change. It is assumed that climate change, which deforestation contributes to, affects poor people more severely than wealthy people (IPCC 2001, OECD 2009). Therefore, forests are not only important for the global eco- and climatic systems but also important for the livelihood of rural people.

The picture drawn above clearly shows the need for poverty alleviation for both economical and ecological reasons. To better target poor households with poverty reduction programs, organizations aiming to provide these projects need good instruments to detect poor households.

2.1.2 The need for poverty assessment tools (PATs)

Several attempts in poverty assessment try to meet poverty as a multidimensional phenomenon in contrast to a pure measure of inadequate income or expenditures (Osmani 2003). In his book “Development as freedom” (1999) A. Sen argues that

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poverty rather is a deprivation of basic capabilities and not only a matter of the lowness of income. Nevertheless, he also admits that low income is one of the major causes of poverty in the sense that it is often a reason for capability deprivation. Hence, it is necessary to use approaches which account for low incomes and other forms of deprivation.

To better target absolute poor households easy applicable tools for poverty assessment are needed. For non-governmental organizations (NGOs) and other stakeholders concerned with poverty reduction, it is particularly important that tools which enable the detection of absolute poor households are low in costs and contain indicators which are robust over time and space.

Most poverty assessments done in the last 25 years referred to relative poverty (Zeller 2004). The concept of relative poverty defines the situation of an individual or the situation of a group of persons in relation to the average living standard of their society (see for example Foster 1998, Witt 1998). For example, Grootaert and Braithwaite (1998) conducted research on correlates of poverty and indicator based targeting in Eastern Europe using a relative poverty line for their analysis. In this study they used multivariate regression analysis to detect determinants of poverty. They found that a strong relationship between poverty incidence and the number of children in a household exists and that poverty in Eastern Europe has, to some extent, an age and gender dimension. Another example of an approach to assess relative poverty is “the construction of a poverty index using a range of qualitative and quantitative indicators” (Zeller et al. 2001, p. 3) as done by Zeller et al. (2001) using Principal Component Analysis (PCA) to derive the indicators.

Until now few attempts have been made to assess absolute poverty. One approach used is to assess a household’s poverty status via food security scales. Three different scales – ‘non food insecure’, ‘moderately food insecure’ and ‘severely food insecure’ – are used to predict daily per capita expenditures. This tool faces the problem that food insecurity is not always identical with (income) poverty (Alcaraz V. and Zeller 2008).

2.1.3 Objectives of the chapter

The aim of the survey in Central Sulawesi was to test new tools for the assessment of absolute poverty. The methodology used is based on poverty assessment tools (PATs) developed by The IRIS Centre on behalf of USAID. Out of the nine regression models

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tested by IRIS, two very promising types of regression models were tested in Central Sulawesi (see section 2). The study used two sets of household data. In 2005, we conducted research to identify two sets of 15 indicators each for poverty assessment in Central Sulawesi, Indonesia. We wanted to compare the capability of the models in predicting very poor households with the observed poverty headcounts.

In 2007, we conducted the same survey again to test the identified sets of indicators regarding their capability in poverty prediction and robustness over time. Furthermore, we tested a national calibrated poverty assessment tool developed and provided by The IRIS Centre and USAID with the 2007 household data from Central Sulawesi. Such poverty assessment tools are provided for over 20 different countries at URL: http://www.povertytools.org. They are approved by USAID and can be used by anyone. USAID requires its implementation partners, including organizations that deal with micro-enterprises with the aim of poverty reduction and receiving funds from USAID, to assess their target population with these tools.