1. University of Goettingen
Land property rights and agricultural intensification at forest margins in Indonesia
Christoph Kubitza
University of Goettingen
“ANNUAL WORLD BANK CONFERENCE ON LAND AND POVERTY” The World Bank - Washington DC, March , 2017

2. WB Conference on Land and Poverty 21.03.2017
Motivation
How to preserve ecosystem functions of tropical
forest in the face of a rising land demand?
One possible solution: Agricultural intensification to
spare forest from conversion (Green et. al. 2005; Phalan et. al. 2011)
However, with which polices can agricultural intensification be increased while simultaneously decreasing deforestation?
We highlight the role of farmers’ land property rights for a land sparing agricultural intensification using the example of smallholders in Indonesia
Source: FAO (2016)
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3. Land tenure in Indonesia
Customary tenure shaped historically land access
Only partial recognition by the Indonesian government
Government declared exclusive property rights over most forest land
In 1982, 74% of Indonesia was classified as state forest
Deforestation by smallholders is however common
Formal private land tenure is still low in rural areas
High costs of titling and overlaps of customary claims and state forest
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4. WB Conference on Land and Poverty 21.03.2017
Hypotheses
Do land titles increase agricultural intensification and if so, does the legislation impede the land titling and thus intensification at the forest margins, leading to more extensive, potentially forest threatening, farming systems?
Hypothesis 1: Possession of land titles increases agricultural intensification.
Hypothesis 2.1: Plots close to the forest margin are less likely to have a government land title.
Hypothesis 2.2: Farm sizes of households close to the forest margin are on average bigger.
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5. WB Conference on Land and Poverty 21.03.2017
Research location
Data used originates from Jambi Province, Sumatra
43% of 2.7 million ha of primary forest lost between 1990 and 2010
Rubber is the major crop, although oil palm gains importance
High involvement of independent smallholders (~40% of plantation area)
Multi-stage random sampling of 33 autochthonous villages with 473 households and 902 plots
Two rounds of survey in 2012 and 2015
12% of plots with government land title
21% of plots with local land title
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6. WB Conference on Land and Poverty 21.03.2017

7. WB Conference on Land and Poverty 21.03.2017

8. Methods: Land titles and intensification (Part I)
To test if land titles affect intensification, we regress on…
Log of yield per hectare per year as outcome variable
We use random and fixed effect models on household and plot-level
To address the endogeneity of land titles and robustness, we use…
Hausman test for both plot and household-level
Broad set of control variables
Sample splitting if heterogeneous impacts are expected – Migrants vs. Locals
Regressions on labor input and expenditures on fertilizers and pesticides
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9. Results (Part I) 0.352 0.351*** 0.586*** 0.328*** (0.298) (0.085)
Log of yield
per hectare
(1)
(2)
(3)
(4)
Fixed
effects
Random effects:
Full Model
Random effects: Migrants
Random effects: Non-migrants
Share of land with gov. land title (%)
0.352
0.351***
0.586***
0.328***
(0.298)
(0.085)
(0.107)
(0.098)
Share of land with local land title (%)
-0.098
0.019
0.111
-0.038
(0.199)
(0.071)
(0.090)
(0.123)
Further control variables
Yes
F & chi2
1.986***
***
***
***
Obs.
564
665
174
491
Notes: Marginal effects with robust standard errors clustered at village level in parentheses. * p ≤ 0.10, ** p ≤ 0.05, *** p ≤ 0.01
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10. Methods: Forest margins and land titles (Part II)
To test if settling at the forest margins affects land titling, we…
Use LandSat satellite imageries to obtain historical maps
Identify land-use based on automatic classification
Use households’ residence as reference point
Take the radii over 2 km, 5 km and 10 km around household’s residence
Calculate share of forest around household’s residence in 1990
We regress this metric on a dummy if a plot is titled
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11. WB Conference on Land and Poverty 21.03.2017
Methods (Part II)
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12. WB Conference on Land and Poverty 21.03.2017
Results (Part II)
Government land title (=1)
(1)
(2)
(3)
(4)
Probit model
(2km radius)
(5km radius)
(10km radius)
Share of forested area in 1990
-0.162**
-0.129**
-0.184***
(0.076)
(0.050)
(0.064)
Direct forest encorachment (=1)
-0.059**
(0.029)
Further control variables
Yes
Wald chi2
91.814***
74.366***
90.630***
72.424***
Observations
328
628
719
594
Notes: Average marginal effects with robust standard errors clustered at village level in parentheses.
* p ≤ 0.10, ** p ≤ 0.05, *** p ≤ 0.01
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13. WB Conference on Land and Poverty 21.03.2017
Discussion
Gov. land titles increase agricultural intensity and productivity
At forest margins farms are bigger and gov. titles are less likely
Impeded land titling could possibly slow down agricultural intensification at forest margins
Yet, forest margins are crucial in this respect, since to increase output, land expansion by encroaching surrounding forest is an available option versus a further intensification
Current land and forest governance, letting farmer encroach forest while impeding their land titling, might be the worst option both for economic development and forest protection.
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14. WB Conference on Land and Poverty 21.03.2017
Policy implications
Forest sparing intensification could be possible by improving access to land titling, however this process seems to be impeded by current dualism of the post-independence tenure system
 Increase in farmers’ access to land titles
 Better recognition of farmers’ customary land rights
 Simultaneous protection of forestland without recognized claims
Thank you!
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15. References
Green, R. E.; Cornell, S. J.; Scharlemann, J. P. W.; & Balmford, A. (2005). Farming and the Fate of Wild Nature. Science 307 (5709), 550–555. Phalan, B.; Onial, M.; Balmford, A.; & Green, R. E. (2011). Reconciling food production and biodiversity conservation: land sharing and land sparing compared. Science 333 (6047), 1289–1291.
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16. Supplementary material
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17. Conceptual framework Land Title Intensification Deforestation
Land price
Assurance effect
Collateral effect
Realizability effect
Intensification
In-migration
Additional free labour
Additional free capital
Less land to satisfy demand
Less land for subs. income
Rural development
Land and
forest
legislation
Deforestation
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18. Possible caveats
In-migration incentivized by intensification could be problematic
Not the case in the specific research location, locals are mostly involved in deforestation
Labor-saving technologies can set free further labor
Fertilizer application is labor intensive
Intensification and forest protection can be the best conservation option
Production differences of land-uses
Unique ecosystem functions of forest
However, intensification should be reached in a sustainable manner
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19. Land titles and deforestation
Land titling took of after most deforestation activities
Small overlap to test directly the effect of land titles on deforestation
Fewer land titles at the forest margins
Source: Household survey
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20. Random effect: Migrants
Results: Productivity on plot-level
Log of output
per hectare
(1)
(2)
(3)
(4)
(5)
Fixed
effect
Random
effect:
Full Model
Random effect: Migrants
Random effect:
Non-Migrants
Government land title (=1)
0.028
0.148**
0.150**
0.343***
0.096
(0.268)
(0.074)
(0.064)
(0.096)
(0.071)
Local land title (=1)
0.066
-0.013
-0.017
0.040
-0.068
(0.202)
(0.057)
(0.072)
(0.107)
Total farm size (ha)
0.015
0.014**
-0.021*
0.014
-0.023*
(0.029)
(0.006)
(0.011)
(0.021)
(0.013)
Plot size (ha)
-0.140***
-0.094***
-0.085***
-0.121***
-0.081***
(0.048)
(0.014)
(0.017)
(0.033)
(0.018)
Wealth index (Quintiles)
0.016
0.040**
0.031**
0.018
0.043**
(0.038)
(0.015)
F and chi2
3.046***
75.172***
***
***
***
Observations
516
851
231
620
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21. Results: Intensity on plot-level
(1)
(2)
(3)
(4)
(5)
(6)
Random effects tobit:
Material input (‘000 IDR)
Full sample
Material input (‘000 IDR):
Migrants
Random effects: 
Log of labor input (hours)
Random effects:
Log of labor input (hours):
Log of yield per hectare (kg):
Full Sample
Systematic land title (=1) ** **
0.125*
0.122
0.141**
0.145**
(48.649)
(97.340)
(0.070)
(0.104)
(0.062)
Sporadic land title (=1)
-9.365
26.157
0.055
0.198*
-0.015
-0.026
(36.395)
(61.016)
(0.056)
(0.105)
(0.073)
chi2
***
82.550***
***
***
***
***
Number of observations
1101
286
1015
269
850
846
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22. Results: Farm-size Log of farm size (ha) (1) (2) (3) (4) (5)
Share of forested area in 1990 (5km radius)
0.396***
0.282**
(0.135)
(0.128)
Share of forested area in 1990 (10km radius)
0.514***
0.348**
(0.175)
(0.165)
Direct forest encorachment (=1)
0.149*
(0.082)
Years since household establishment
0.011***
0.013***
0.009***
(0.004)
(0.003)
chi2
4.89***
4.78***
14.40***
14.45***
13.38***
Observations
440
449
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24. Results: Credit access
(1)
(2)
Formal Credit
Informal credit
Share of land with systematic land title (Ratio)
0.699***
-0.290
(0.246)
(0.322)
Share of land with sporadic land title (Ratio)
0.016
-0.144
(0.179)
(0.207)
Total farm size (ha)
0.028**
0.026
(0.014)
(0.018)
Wealth index (Quintiles)
0.087*
-0.213***
(0.048)
(0.060)
Spontaneous Migrant (=1)
0.252*
0.338**
(0.147)
(0.163)
Non-random village (=1)
-0.136
-0.201
(0.169)
(0.193)
Constant
-0.793**
0.226
(0.375)
(0.411)
chi2
33.342
27.390
Observations
473
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25. Doctoral Seminar