Conceptual framework

Angelsen and Kaimowitz (1999) propose five components for the analyses of deforestation (see also Kaimowitz and Angelsen 1998, Sunderlin and Resosudarmo 1996):

- The magnitude and location of deforestation - The agents of deforestation

- The choice of variables (the decision about land allocation that determines the level of deforestation by the agent)

- The agents decision making parameters (parameters which directly influence the decision of the agents, but are external to them)

- Macroeconomic economic variables

They argue for grouping these components on three major levels and they suggest starting with the identification of the agents of deforestation and their relative importance for deforestation (in terms of their contribution). The actions of the identified agents Angelsen and Kaimowitz (1999) name “sources of deforestation”. The decision parameters, which are based on the characteristics of the agents such as background, preferences and resources, but as well as on prices, technologies, institutions, information, access to services, infrastructure etc. are seen as immediate causes of deforestation. Broader forces that determine the agent’s characteristics and decision parameters are described as underlying causes. Examples for underlying causes are the market, infrastructure development, institutions (especially the property regime), etc.

As our analysis refers to the household level, we mainly analyze the “sources” and the “immediate causes” of deforestation. Building on the framework of Angelsen and Kaimowitz (1999), we further define the internal and external parameters which are influencing the land allocation decision made by households. Doing so, we differentiate a set of conditional factors which influence the land allocation of farm-households and therefore, the decision to clear natural forest. Regarding the internal factors, the land allocation decision is influenced by the household resource endowment with physical, human and social capital, as well as by the overall household objectives (Figure 11).

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Conditioning Factors The Farm Household

Population

Institutions

Agricultural Input and Output Markets

Prices and Wages

Infrastructure Agricultural Technologies

Household Objectives

Natural Forest Cleared Land Allocation Household Resources Physical Capital Social Capital Human Capital Natural Capital Fiancial Capital

Source: own construction

Figure 11: Conceptual framework of land allocation decision by rural households

To capture the effects discussed above, we have included the variables described in Table 24 as independent variables in our models. In the research area, many farmers cultivate wet rice on irrigated rice fields. We incorporated the percentage “share of irrigated rice fields owned” into the analysis, as a proxy for technology adoption, which can play an important role in reducing the pressure on forests (Nuryartono 2005, Maertens 2006). The age of household head is considered an important demographic variable (proxy experience). The number of adult household members ranges from 1 to 8, and can be used as a proxy for the availability of labor force within a household, which is important in this analysis. For example, land clearing in rural Peru is highly depended on the available labor force (Zwane 2005).

To represent education, we used an ordinal variable ranging from 1 (never attended school) to 8 (attended academy or university). We assumed that higher education might decrease the probability of deforestation by rural households, because especially higher education might address environmental problems. Furthermore, we wanted to test for the influence of social capital on the households land allocation decision. Social capital is represented by the mean number of organizations to which all adult household members belong. We hypothesize that presence of social capital increases trust and

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empowerment and therefore enhances a sustainable treatment of forest resources (cf. Meyer et al 2003, Rodruíguez and Pascual 2004).

In addition, migrant households may foster deforestation by buying plots cleared by local people in order to grow cocoa on the former forest plots (Weber et al. 2007). Therefore, we included a dummy variable indicating whether the household head is migrant or indigenous. Non-agriculture income is widely assumed to reduce deforestation. To proxy a household’s market access we used a variable indicating how far the household lives from the closest road in walking hours. We also included a dummy variable for “credit availability” in our analysis (measured as whether the household received a formal credit within five years prior to the survey in 2000) because it is likely that available credit finance deforestation (Angelsen and Kaimowitz 1999), as they for example increase the household’s ability to invest in an extension of perennial crop production which was found to be a major cause of deforestation in the research area (Schwarze et al. 2006). However, Godoy et al. (1996; 1997) found that credits reduced deforestation conducted by forest dwellers in Bolivia and Honduras. In addition to the factors mentioned above, property rights may have effects on deforestation, but the role is not clear (Geist and Lambin 2002). However, there is some evidence that tenure insecurity increases deforestation (Godoy et al. 1998). Therefore, we controlled for land tenure (represented as share of titled land owned) in the vicinity of LLNP. As the agricultural land use in the research area is very location specific depending on local rainfall, topography and soil conditions (Keil et al. 2008), we also controlled for agro-ecological and other regional differences by including sub district dummy variables. All variables included in the analyses were lagged variables. Thus always the values from 2000 were taken to explain deforestation in the subsequent years.

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Table 24: Variable description and summary statistics (independent variables)

Variable Mean Std.

Dev.

Min Max

Poverty index -0.014 0.97 -1.84 2.87

Percent of irrigated rice fields owned 27.16 35.13 0 100

Age of household head 43.97 14.33 20 83

Number of adult household members 3.46 1.63 1 8

Maximum level of schooling 4.91 1.77 1 8

Mean number of memberships in organizations per adult

0.93 0.74 0 3.5

Ethnicity of household head (1=non- indigenous)

0.19 0 1

Household gained non-agricultural income(1=yes)

0.15 0 1

Walking distance house - road (in hours) 0.92 2.7 0 13 Household has credit available (1=yes) 0.15 0 1 Share of land with title owned (%) 26.3 42.5 0 100 Sub district id Lore Utara (1=yes) 0.28 0 1 Sub District is Sigibiromaru (1=yes) 0.31 0 1

Sub District is Kulawi (1=yes) 0.27 0 1

Source: own calculation with STORMA data from 2000; N=266