Deforestation in Indonesia: A Household Level Analysis of the Role of Forest Income Dependence and Poverty
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1 Deforestation in Indonesia: A Household Level Analysis of the Role of Forest Income Dependence and Poverty Ririn S. Purnamasari Department of Economics The University of Melbourne
2 Organization of the Presentation Introduction Research Objectives Data and Study Sites Model Results Conclusion 2
3 Introduction Forest dependent people: 20% (12 million) rural people living on state forest land are poor Forest loss in Indonesia: Rate: 1 million ha/year (1980s) to 2 million ha/year (since 1996) Forest cover lost: 162 million ha (1950) to 88 million ha (2005) Agent: logging industrial timber and agricultural plantations transmigration small scale farmers - shifting cultivation 3
4 Research Objectives Motivation: profound impact of the current alarming deforestation rate on large numbers of Indonesian rural poor who are dependent on forests Aims: Investigate the factors associated with deforestation by small-scale farmers Investigate the factors associated with the Indonesian household s decision-making process with respect to forest clearing Role of poverty? Impact of forest income dependence? Effect of agricultural practices? 4
5 Household level data Survey of 214 households in five villages in East Kalimantan Survey design: Data Data: socio-economic, income, land clearing practices (livelihoods, forest environments, institutional forms, market contexts) Village level data Village survey Village Potential Statistics Statistics Indonesia Land use/forest maps Ministry of Forestry 5
6 Study Sites East Kalimantan 6
7 Study Sites East Kalimantan High forest, low poverty High forest, high poverty Low forest, high poverty Low forest, low poverty No data (World Bank, 2006) 7
8 Semi-commercial farms Study Sites 90% households have cash agricultural income 86% households have non-cash agricultural income Forest income dependence 85% households have forest income (share to total income: 30%) 68% households have non-cash forest income (share to total income:20%) Forest clearing practices (for agricultural expansion) Sedentary agriculture (103 households) Shifting cultivation (77 households) 8
9 Labor allocation decision Model Activities: agriculture (L G ), forestry (L F ), off-farm (L W ), land clearing (L C ) SUR estimation The relationship is captured by the correlation of the error terms A = b + b H + b V + b W + e ij 1 2 ij 3 j 4 ij ij FI = p + p H + p V + p W + u ij 1 2 ij 3 j 4 ij ij WI = h + h H + hv + v ij 1 2 ij 3 j ij A FI WI : Area cleared : Forest income : Wage income H V W : Vector of household variables : Vector of village variables : Vector of wealth measures 9
10 Q1:Initial Dependent Variable: Cleared area (Ha) Poverty Indicators Agricultural land own 2000 (ha) (3.70) *** Agricultural land own 2000 squared (-2.64) *** Wealth index 1999 Household (HH) Level Variable HH Size (persons) (1.65) * HH member 15 yrs old (2.22) ** HH head age (years) (-2.34) ** HH head age squared (2.26) ** HH member average educ. tertiary school (-2.67) *** Dependent variable: Forest Income Agricultural land own 2000 (ha) (-2.27) ** HH member average educ. tertiary school (-2.35) ** HH member avrg. educ.= secondary school (-2.16) ** HH head ethnicity (dummy indigenous) (2.09) ** Number of Observations 214 R z-values in brackets; *:significant at 10%; ** :significant at 5%; *** :significant at 1% 10
11 Q1:Initial Q2:Poverty Dependent Variable: Cleared area (Ha) Poverty Indicators Agricultural land own 2000 (ha) (3.70) *** (4.53) *** Agricultural land own 2000 squared (-2.64) *** (-3.65) *** Wealth index (1.77) * Household (HH) Level Variable HH Size (persons) (1.65) * (0.59) HH member 15 yrs old (2.22) ** (1.74) * HH head age (years) (-2.34) ** (-2.79) *** HH head age squared (2.26) ** (2.67) *** HH member average educ. tertiary school (-2.67) *** (-1.89) * Dependent variable: Forest Income Agricultural land own 2000 (ha) (-2.27) ** (-0.67) HH member average educ. tertiary school (-2.35) ** (-1.26) HH member avrg. educ.= secondary school (-2.16) ** (-1.11) HH head ethnicity (dummy indigenous) (2.09) ** (0.85) Number of Observations R z-values in brackets; *:significant at 10%; ** :significant at 5%; *** :significant at 1% 11
12 Sedentary Agriculture Shifting Cultivation coefficient z-val coefficient z-val Dependent Variable: Cleared area (Ha) Poverty Indicators Agricultural land own 2000 (ha) (2.48) ** (-0.61) Agricultural land own 2000 squared (-1.65) * (1.73) * Household (HH) Level Variable HH Size (persons) (0.10) (0.24) HH member 15 yrs old (1.86) * (3.52) *** HH head age (years) (-1.30) (1.72) * HH head age squared (1.18) (-1.86) * HH member average educ. tertiary school (-0.55) (-2.31) ** HH member average educ.= secondary school (-0.43) (-3.08) *** HH head duration of occupancy (years) (0.20) (-1.66) * HH head ethnicity (dummy indigenous) (-0.95) (2.43) ** Use chainsaw (dummy) (-1.01) (4.14) *** Number of Observations R *:significant at 10%; ** :significant at 5%; *** :significant at 1% 12
13 Correlation of Residuals Initial Poverty Sedentary Agriculture Shifting Cultivation ρlf (cleared area - forest income) ρlw(cleared area - wage income) ρfw(forest income - wage income) Breusch-Pagan test of independence (P-value) Number of Observations
14 Conclusion 1. Poorer farmers are more dependent on forest based income 2. Poorer farmers are less likely to clear forests 3. No significant interdependencies between forest income and clearing 4. Higher off-farm employment opportunities are significantly correlated with lower forest pressure 5. Shifting cultivators drive the relationships: - Determinants of forest clearing - Interdependencies between activities 14
15 Thank you 15
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