What Factors Promote Peatland Fire Prevention?
Evidence from Central Kalimantan, Indonesia
Yuki Yamamoto
Graduate School of Economics, Kobe University
Rokko, Kobe 657-8501, Japan
Kenji Takeuchi1
Graduate School of Economics, Kobe University
Rokko, Kobe 657-8501, Japan

Gunnar Köhlin
Department of Economics, the University of Gothenburg

1

Corresponding author. takeuchi@econ.kobe-u.ac.jp1Abstract
This study investigates the factors that promote peatland ﬁre prevention in Central Kalimantan, Indonesia. We focus on Kalimantan
Forests and Climate Partnership (KFCP) as one of the earliest pilot projects to reduce emissions from deforestation and degradation(REDD). We estimate an agricultural labor allocation model by combining household survey data and satellite information. The results
suggest that the impact of KFCP on household decision making concerning ﬁre prevention at agricultural plots is statistically insigniﬁcant.
This can be attributed to the fact that the KFCP has not changed the
incentives for household ﬁre prevention at individual plots. Economic
factors such as the value of labor allocation for rubber productionand exogenous income as well as non-economic factors such as traditional mutual assistance, called Goton-royong, are statistically signiﬁcant. These results suggest that an appropriate design for intervention
would be a combination of economic and non-economic incentives toachieve eﬀective REDD policy.

2

1

Introduction

Reducing Emissions from Deforestation and Degradation (REDD) has beena focus of international discussion as an eﬃcient climate change policy. The
basic function of REDD is to oﬀer developing countries an opportunity forﬁnancial support from developed countries in response to avoiding carbon
emissions through forest conservation thereby reducing the conﬂict between
developed and developing nations regarding global warming negotiations.
Deveny et al. (2009) assess the capacity for implementing REDD in eachcountry using the Forest Carbon Index (FCI). They give Indonesia a highevaluation along with Brazil, Russia, Peru, Bolivia, and Colombia.
One of the key issues for implementing REDD in Indonesia is the high riskof peatland ﬁre. Forestland in Central Kalimantan, Indonesia is composed ofthick peat swamp and 2,000-6,000 metric tons of carbon per hectare (t C/ha),compared to the average 225 t C/ha in moist tropical forests in southeast Asia
(Page et al., 1999; IPCC, 2006). Once peat has been ignited, the burning of
its organic-rich materials is hard to put out and it releases its carbon into theatmosphere. For example, during a large-scale peatland ﬁre in 1997, the total
carbon dioxide emission from Indonesia was estimated to be between 0.81
and 2.57 gigatons, which is equal to 13–40 percent of the mean annual global
carbon emissions from fossil fuels (Page et al., 2002). The combination of3

ﬁre and the vast amount of carbon released made Indonesia the third largest
Greenhouse Gas (GHG) emitter in the world (Myers Madeira, 2008).
In 2008, the governments of Indonesia and Australia jointly announced
the Kalimantan Forest Carbon Partnership (KFCP), one of the world’s ﬁrst
REDD pilot projects. The project aimed to reduce GHG emissions fromdeforestation and peatland ﬁres by involving farmers in ﬁre prevention andreducing the use of ﬁre at individual plots. With a signiﬁcant budget, KFCP
was expected to be eﬀective REDD. However, the impact of the project iscurrently unclear, and has yet to be thoroughly evaluated. The results obtained by our analysis suggest that KFCP might not have oﬀered appropriate
incentives for farmers to promote ﬁre prevention behavior at individual plots.
While ﬁre prevention is an important aspect of forest and climate policy, it has not been seriously addressed in economic literature. An exception
is Bowman et al. (2008) who investigated household decision making concerning ﬁre prevention in the Brazilian Amazon. They explored the factorsthat aﬀected household decisions on ﬁre prevention and found that economic
factors (e.g., the value of labor allocation for agriculture) plays a signiﬁcantrole in promoting individual ﬁre prevention. As the value of labor allocation to agriculture increases, households become more productive in crop
production; hence, there is a greater expected beneﬁt of ﬁre prevention.
This study extends prior research by estimating a household labor allo4

cation model. The main contributions of this study are as follows. Firstly,
we evaluated the impact of the KFCP project and found that this program
was insuﬃcient to change household decisions on ﬁre prevention. KFCP was
one of the earliest of the 15 REDD pilot projects undertaken throughout
the world prior to 2009 (Carlson, 2009). While some studies have addressed
the consequences of REDD pilot projects using descriptive approach (Resosudarmo et al., 2012; Olbrei and Howes, 2012), there are no studies that
evaluate the impact of REDD pilot projects at the household level using
an econometric analysis. Secondly, we estimated the household labor allocation model by combining data from the household survey with global
NASA/MODIS satellite data. Satellite information is useful for considering
ﬁre risk objectively and investigating ﬁre prevention comprehensively. In our
analysis, the number of hot spots observed by satellite is used as a variable
explaining the degree of ﬁre risk. Thirdly, we hypothesized that social factors, not only economic factors, play an important role in ﬁre prevention. We
focused on the mutual assistance custom called Gotong-royong as being animportant factor in the decision making regarding household labor allocation
for ﬁre prevention. Gotong-royong includes a wide range of group activities
in the community, such as providing labor to clear agricultural plots, theconstruction and repair of public infrastructure, guarding the village, andﬁnancial support. This behavior provides the community with social norms
5and serves as a determinant of social capital based on the norms that are
generally observable in urban and rural Indonesia (Bowen, 1986).
Recent studies on development recognize the importance of social capitalin the self-governance of common property resources (Ostrom, 1990). Indonesia is a suitable place to evaluate the impact of social capital on REDD
schemes because households have traditionally been involved in the community through Gotong-royong. Policy makers in Indonesia have noted theimportance of Gotong-royong on social improvement, and have used this involvement to improve health care, education, sanitation and ﬁnancial support(Wibisana et al., 1999). Since the eﬀect of ﬁre prevention behavior depends
in part on the behavior of neighbors, it is important to take into account therelationships within the community. While many studies have considered therole of social capital in development and environmental policy (Feigenberg,
2010; Sujarwoto, 2013), no studies have examined its eﬀect in the context of
REDD.

2

Household model

We develop a subsistence household model to determine the factors that encourage households to allocate their time for ﬁre prevention. Households
are assumed to allocate family labor for rice cultivation, rubber cultivation,6

oﬀ-farm work, ﬁre prevention, and leisure (Beukering, 2008; Yamamoto and
Takeuchi, 2012). In our model, a household is both a producer and a consumer of agricultural goods, and thus, labor allocation depends mainly on
consumption needs (Fisher et al., 2005). Households have a quasi-concave
utility function:
max U = U (Cr , Cm , l; Ω),Cr ,Cm ,l(1)where Cr , Cm , l, and Ω are the consumption of rice, that of composite purchased goods, that of leisure time, and the household’s characteristics, respectively. The utility is maximized subject to the production functions for
rice, rubber and oﬀ-farm work, a full income constraint, a time constraint,
and non-negativity constraints:
Qr = Qr (Lr , Kr ; Ω),(2)

Qg = Qg (Lg , Kg , Lp ; Ω),(3)

Qo = Qo (Lo ; Ω),(4)

Y= Pr (Qr − Cr ) + Pgj Qg + Po Qo − Pm Cm ,(5)l

= T − Lr − Lg − Lo − Lp ,(6)

Cr , Cm , l, Qr , Qg , Qo , Lr , Lg , Lp , Lo ≥ 0.

(7)

Equations (2) and (3) describe, respectively, the production of rice and rubber, which are assumed to be functions of labor (Lr and Lg ), capital (Kr and7Kg ), household characteristics (Ω), and, for rubber production, labor input
for ﬁre prevention (Lp ). Households can reduce their risk of ﬁre damage andincrease the associated beneﬁt of rubber production by allocating labor forﬁre prevention. Fire prevention (Lp ) does not enter into the production function for rice because households in this area have adapted to the risk of ﬁre,changing their period of cultivation to avoid ﬁre damage. They have less riskof ﬁre in the rainy season (from October to March) and typically cultivate
rice in this period.
Equation (4) describes the oﬀ-farm work function requiring labor input(Lo ).2 We assume that the production functions for rice Qr (·), rubber Qg (·),
and oﬀ-farm work Qo (·) have decreasing returns to scale. Equation (5) describes a household’s full income constraint. A household can be either a
net seller or buyer of rice (Qr > Cr or Qr < Cr ). Households sell theirrubber crops at the local price Pgj , but do not buy any rubber crops. They
can also earn Po by allocating oﬀ-farm work Qo . Households buy composite
goods (Cm ) at market price (Pm ). Equation (6) describes a household’s time
constraint.
The Lagrangian of the household’s maximization problem is represented
2

We deﬁne oﬀ-farm work here as work hours spent outside of ones’ own agriculturalplot. This includes working on the another’s plot, hunting, logging, driving boats or
motorcycles, construction and working for the KFCP project.

8as
L = U (Cr , Cm , l; Ω)− λ{Y − Pr (Qr (Lr , Kr ; Ω) − Cr ) − Pgj Qg (Lg , Kg , Lp ; Ω)
− Po Qo (Lo ; Ω) + Pm Cm } + γ(T − Lr − Lg − Lo − Lp ).

(8)

The ﬁrst-order conditions can be derived as
∂L
∂Lp
∂L
∂Lo
∂L
∂Cr
∂L
∂Cm

∂Qg
− γ = 0,
∂Lp
∂Qo
= λPo
− γ = 0,∂Lo
= λPgj(9)(10)= λPr ,

(11)

= λPm .

(12)Equations (9) and (10) show that at the optimum, households allocate laborfor each activity until the expected marginal beneﬁt is equal to the marginal
opportunity cost of household time (γ). This suggests that marginal beneﬁt
and cost are prime candidates to be determinants of ﬁre prevention.
Slutsky equations help us understand the implications of the models. It
is possible to divide the total eﬀect of a slight change in marginal beneﬁt of
ﬁre prevention into substitution and income eﬀects:
∂Lp
∂Lp
∂Lp
=
|U =Ū +
Qg ,
∂Wp
∂Wp
∂Y

(13)

where Wp stands for the marginal beneﬁt of labor allocation for ﬁre preveng
tion (Pgj ∂Q). The ﬁrst term on the right-hand side of Equation (13) is a∂Lp9

substitution eﬀect; the second term is an income eﬀect. The substitution
eﬀect must be positive because a higher marginal value of prevention would
increase net beneﬁt of rubber production. The sign of the income eﬀect isambiguous; it depends on the relationship between income and leisure time.
Findings from empirical studies on agricultural production suggest that thesubstitution eﬀect outweighs the income eﬀect in developing countries; therefore, the total eﬀect might be positive (Fisher, 2005; Shively, 2004). The
eﬀect of a slight change in the marginal beneﬁt of oﬀ-farm work on the labor
allocation for ﬁre prevention can be described as follows:
∂Lp
∂Lp
∂Lp
=
|U =Ū +(Qo ),
∂Wo
∂Wo
∂Y

(14)

o
where Wo is the marginal beneﬁt of labor allocation for oﬀ-farm work (Po ∂Q).
∂Lo

The substitution eﬀect of the ﬁrst term is non-positive, while the income
eﬀect of the second term is indeterminate. This is because the term ∂LP /∂Y
can be either positive or negative depending on the demand for oﬀ-farmwork and leisure time. Findings from previous empirical studies suggestthat the total eﬀect might be negative, and that increasing oﬀ-farm wages
decreases engagement in agricultural work in developing countries (Jolliﬀe,
2004; Shively and Fisher, 2004).3 With this point in mind, we empiricallyinvestigate the relationship between the marginal beneﬁt of labor allocation
3

Previous studies ﬁnd that increasing oﬀ-farm wages decrease the deforestation ra-

tio because they assume that oﬀ-farm work and agriculture are substitutional work and10

for rubber production or oﬀ-farm work and labor allocation to ﬁre prevention.
Economic incentives oﬀered by policy intervention can play a similar role
as a marginal increase in the value of rubber production. A certain economic
incentive for ﬁre prevention (ρ) oﬀered by a policy (KFCP, for example) increases the a household’s expected marginal beneﬁt of allocating labor forgﬁre prevention activities (λ(Pgj ) ∂Q
+ ρ(Lp )). The KFCP project announced∂Lpthat 30 million US dollars had been funded to mitigate deforestation in 14
communities in Blocks A and E of Kapuas Regency, this comprises 120,000
hectares of heavily degraded peatland and forests. KFCP was designed topromote household forest conservation and ﬁre prevention activities by providing economic incentives. The project design document of KFCP describes
three categories for incentives: (1) input-based incentives, such as building
dams, planting trees, growing nursery plants, and eliminating ﬁre use on
peat soils; (2) performance-based incentives, such as maintaining dams tokeep water levels high, protecting forests from encroachment, and reducing
the incidence and extent of ﬁres; and (3) outcome-based incentives, such as
payment for actual reduced GHG emissions (KFCP, 2009, p.24). When insufﬁcient economic incentives have been provided, the impact of KFCP is zero:ρ(Lp ) = 0. In our interview conducted from October to December in 2012,that additional agriculture work increases the ratio of deforestation. This factor is notconsidered in this paper.

11

all households in Blocks A and E reported that they had not received any
economic incentives from KFCP for ﬁre prevention at their plots, while someof them reported that they had received incentives for preparing nurseries
for reforestation or for planting on project plot. This suggests that inputbased incentives is oﬀered rather than performance-based or outcome-based
incentives.
Non-economic factors might also aﬀect household ﬁre prevention decisions. The role of social capital has received a great deal of attention in
the context of eﬀective management of common property resources (Ostrom,1990). Fire prevention by individual farmers has the characteristics of theprivate provision of public goods because it might reduce the risk of peatland
ﬁre on surrounding farmland. The existence of the mutual assistance custom Gotong-royong allows us to evaluate the impact of social capital on ﬁreprevention. Gotong-royong is a traditional community involvement and cooperative agricultural activities. For example, farmers jointly clear grass andharvest rice with nearby family as labor exchange or cooperation (Bowen,1986). It also involves a wide range of reciprocal activity, from providing
labor for infrastructure construction to ﬁnancial support. This tradition affects individuals to exhibit a normative behavior for community. We examine
the eﬀect of Gotong-royong on ﬁre prevention by using a dummy variable ofparticipation in this activity.
12

3

Data

We collected household data via face-to-face interviews in twenty-nine communities in Kapuas Regency, Central Kalimantan, Indonesia (Figure 1). The
communities were selected to obtain a balance of ethnicities and degree of
involvement in the KFCP project. The communities consisted of 11 transmigrant and 18 Dayak tribe communities. Of the 18 Dayak communities, six
were involved with the KFCP project. Table 1 summarizes the compositionof the communities.4

In the interviews, respondents were asked about their economic status,
type of agriculture, demographic characteristics, experience of ﬁre damage,and ﬁre prevention activities. Although we collected information from a totalof 288 randomly selected households, in order to focus on the decisions ofhousehold heads, we excluded 73 responses by non-household heads and 35
4

Two types of people have settled in Kapuas Regency: indigenous Dayak tribes thathave traditionally lived there, and transmigrants that moved from other islands of Indonesia or other parts of Kalimantan (Fearnside, 1997). Transmigration to this area waspromoted by government policy, especially during the Mega-Rice Project (MRP) in the
1990s. Although the government abandoned the MRP, a number of transmigrants continued to live in the area as rice farmers. Both communities rely on agricultural produce as
their main source of income (De Jong et al., 2001).

13

responses with incomplete data.5 Thus, the ﬁnal number of observations forthe analysis was 182. Table 2 summarizes the descriptive statistics of thesample, which was divided into households that engage in ﬁre prevention
more than 60 days a year and those that do not, as well as those that wereinvolved in the KFCP project and those that were not.

5

For example, we left out respondents who do not engage in agriculture or who do not

own any agricultural plots.

14

No.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.

Table 1: Settlement composition
Ethnicity
Name of Village
Dayak Transmigrant KFCP site√√
Sei Ahas√√
Katimpun√
Sei Kayu√
Mampai√Sei Tatas√
Sei Pitung√
Sei Kayu√
Mandomai√
Mambulau√
Dahirang√
Sei Pasah√
Penda Katapi√
Lamunti A2√
Lamunti B1√
Lamunti B2√Dadahup G3√
Dadahup G1√
Dadahup G2√
Dadahup A1√
Dadahup A6√
Dadahup A5√
Palingkau SP1√
Palingkau SP2√√
Jangkang√√
Kalumpang√√
Tumbang Mangkup√
Tarantang√
Pulau Kaladan√√
Mantangai Hulu

15

16

3.45
0.79

Total size of agricultural plot (hectare)Households with plots for rice production (0,1)

0.38
2.89
13.82
30.32

Experienced ﬁre damage in last ﬁve years (0,1)

Number of hot spots observed in 2012

Number of hot spots observed from 2008 to 2012

Number of hot spots observed from 2001 to 2007

Fire-related variables

2291.71

1.218

Size of rubber plantation (hectare)

Cultivated rubber product last year (kg)0.58

928.11

Household with plots for rubber production (0,1)

Cultivated rice product last year (kg)

1.00

8.68

Shadow wage of oﬀ-farm work (IDR/day/person)

Size of rice plantation (hectare)

10.65

Shadow wage of rubber production (IDR/day/person)

Economic parameters

38.91

21.19

6.91

0.49

5387.24

1.69

0.50

1328.70

0.85

0.41

4.81

6.45

7.09

24.92

7.34

1.23

0.32

876.440.73

0.45

876.44

0.88

0.81

2.63

10.48

7.60

Mean

38.63

8.98

1.91

0.47

897.711.09

0.50

897.71

0.64

0.40

3.23

5.67

7.76

SD

n = 180SD

n = 73Full sample

MeanHouseholds practicing
ﬁre prevention
less than 60 days

34.00

18.24

4.03

0.43

3284.67

1.55

0.66

963.36

1.09

0.78

4.01

7.45

12.72

Mean

38.84

25.60

8.66

0.50

6584.77

1.93

0.47

1559.10

0.96

0.42

5.58

6.69

5.76

SD

n = 107

Households practicing
ﬁre prevention
more than 60 days

Table 2: Descriptive statistics for sample households

26.35

11.45

3.21

0.32

1280.76

0.94

0.50

1015.25

1.01

0.81

2.90

8.95

9.41

Mean

40.83

22.60

7.60

0.47

2907.73

1.41

0.50

1439.96

0.85

0.39

3.39

6.32

7.55

SD

n = 141

Households
not involved with KFCP

44.67

22.38

1.74

0.62

5946.67

2.23

0.87

613.08

0.98

0.72

5.46

7.70

15.12

Mean

SD

26.83

11.78

3.23

0.49

9383.95

2.19

0.34

739.62

0.85

0.46

7.83

6.90

0.35

n = 39Households
involved with KFCP

17

4.38
0.35
59.46
46.76
0.46
31.97
0.88

Number of household members

Number of children (<6 years)

Access time to market (minutes)

Age of household headTransmigrant (0,1)

Years on lot (year)

Engage in ﬁre prevention in last year (0,1)

0.78
51.17
0.81
78.25
0.37
0.77
0.65

Patrolling for ﬁre prevention in last year (0,1)

Days spent for patrolling

Clearing grass for ﬁre prevention in last year (0,1)

Days spend for clearing grasses

Engage in swidden agriculture in last year (0,1)

Size of burned plot for agriculture (hectare)

Allocate labor for oﬀ-farm work in last year (0,1)

129.42

0.64

Participate in Gotong-royong activities in last year (0,1)

Days spent on ﬁre prevention

0.22

Involved with KFCP site (0,1)

Household characteristics

0.48

3.78

0.48

114.25

0.40

79.97

0.42

153.18

0.33

17.82

0.50

10.95

70.75

0.58

1.69

0.48

0.41

0.78

0.40

0.33

12.29

0.58

11.03

0.52

23.32

0.70

30.82

0.51

46.16

61.95

0.44

4.19

0.49

0.18

0.42

0.65

0.47

15.48

0.50

15.20

0.50

21.95

0.46

17.40

0.50

11.20

75.92

0.65

1.33

0.50

0.39

0.56

1.03

0.40

123.25

0.96

78.56

0.95

201.81

-

32.75

0.42

47.16

57.77

0.29

4.50

0.74

0.24

0.50

4.87

0.49

129.76

0.19

93.68

0.21

162.02

-

18.15

0.50

10.80

67.31

0.53

1.89

0.44

0.43

0.67

0.43

0.31

84.74

0.79

41.80

0.75

126.55

0.85

29.48

0.55

47.48

38.35

0.33

4.19

0.66

-

0.47

0.83

0.46

124.46

0.41

58.62

0.43

152.28

0.36

18.11

0.50

10.91

31.01

0.57

1.46

0.48

-

0.56

1.99

0.59

54.77

0.87

85.05

0.87

139.82

0.97

40.97

0.13

44.13

135.77

0.41

5.05

0.56

-

0.50

7.92

0.50

60.80

0.34

126.350.34

157.95

0.16

13.47

0.34

10.80

111.38

0.64

2.22

0.50

-

Table 2 suggests that households in the area are faced with a high riskof ﬁre damage. 38% of the households have experienced accidental ﬁre damage on their agricultural plots. To reduce the risk of ﬁre damage, 88% of
the households have been engaging in ﬁre prevention activities, either bypatrolling (78%) or clearing grass (81%). We deﬁne a household as a ﬁreprevention participator if it has engaged in patrolling or clearing grass at
individual plots for ﬁre prevention within the past 12 months prior to theinterview.6 Households engaging in ﬁre prevention more than 60 days a year
tend to be exposed to a higher risk of ﬁre damage. Experience of ﬁre damage
and the number of total hot spots nearby are higher for them than for others.
Households living in areas where the KFCP project has been implemented
seem more likely to engage in ﬁre prevention than others. This may containa reverse causality since the KFCP project has been implemented in areas
where the risk of ﬁre is higher. As a result, the number of hot spots in the
KFCP project area is typically higher than places not involved with KFCP.
To take this bias to our empirical estimation, we use instrumental variable
methods. The dummy variable for the KFCP site is controlled by the number of hot spots from 2001 to 2007. Since KFCP began in 2008, the risk ofﬁre from 2001 to 2007 might be suitable to control the endogeneity of policy
6

The exact wording of the questionnaire is, “Do you clear grass on your land to prevent

ﬁres?” and “Do you look around your land carefully to prevent ﬁres?”.

18

site choice.
The estimated mean shadow wage of rubber cultivation is IDR10.65 forthe full sample.7 There are substantial diﬀerences in this ﬁgure between
households that engage in ﬁre prevention and those that do not. The estimated mean shadow wage of rubber cultivation for households that engage
in ﬁre prevention is IDR12.72, while that for households that do not engage
in ﬁre prevention is IDR7.60. The mean shadow wage of exogenous income is
IDR7.45 for households engaging in ﬁre prevention, and that for households
not engaging in ﬁre prevention is IDR10.48.
We deﬁne a household as a Gotong-royong participators if they haveparticipated in any mutual activities, such as agricultural cooperation, constructing infrastructure, or maintaining public facilities for the community,in the 12 months prior to the interview. Table 2 suggests that Gotong-royong
7

To deﬁne the opportunity cost of rubber production time, we estimated the produc-tion function. The F-statistic of the estimation is 38.17 with 127 degrees of freedom. The
annual amount of rubber produced by households was used as the dependent variable. The
independent variables that were statistically signiﬁcant (P > 0.01) were household labortime (+), capital input (+), hired labor (+), chemical fertilizer input (+), years since therubber was planted (+), and square of years since the rubber was planted (−). Chemical
input and experience of ﬁre damage were statistically insigniﬁcant. The estimated coefﬁcient for the household labor time variable was used as the opportunity cost of rubber
production time in the analysis of this section.

19

participators are more likely to engage in ﬁre prevention than those not involved in Gotong-royong (74% and 49%).

4

Empirical Results

As Equation (9) suggests, households will not engage in ﬁre prevention if thegmarginal beneﬁt of ﬁre prevention (λPgj ∂Q) is less than the value of time
∂Lp(γ). This can be described as the corner solution of zero ﬁre prevention,gwhere λPg ∂Q< γ ⇔ Lp = 0. Since our sample contains households whose∂Lp

level of ﬁre prevention is zero, we use a Poisson regression model and a Tobit
model to consider the selection bias for estimation.

ln(Lp ) = β0 + βe Xe + βs Xs + βf Xf ,(15)

where Xe is a vector of explanatory variables of economic factors, Xs is avector of explanatory variables of non-economic factors, such as household
characteristics, decisions and KFCP intervention, and Xf is a vector of explanatory variables of the risk of ﬁre damage such as experience of ﬁre damage
and the number of hot spots in 2012.8
8

Hot spot data in our analysis cover the entire Kapuas Regency, from 114.000 to 114.450

degrees east longitude and from 2.05 to 3.40 degrees south latitude in the period from 2001
to 2012. We counted the number of hot spots on basis of 532 meshes that divided the20

To evaluate the impact of KFCP, we must consider policy endogeneity
since there is a possibility that households in the KFCP site are more likely toengage in ﬁre prevention–not because of the project, but because of the higher
risk of ﬁre damage. To take this bias into account, we used the instrumental
variable method. The instrument used in our analysis is the total number ofhot spots observed by MODIS from 2001 to 2007. Since the project started in
2008, the hot spots from 2001 to 2007 can be considered as having an impact
on KFCP site selection, but not on households ﬁre prevention decision in
2012. Thus, we can mitigate the endogeneity caused by KFCP site selection.
This is estimated with a IV-Tobit model.

Kapuas Regency with 0.05 degrees of longitude and 0.025 of latitude.

21

22

Poisson0.029(0.012)∗∗

Shadow wage of rubber products∗∗∗−0.005Tobit-252.502

569.187626.859
182

Akaike Information Criterion (AIC)

Bayes Information Criterion (BIC)

Number of observations∗ ∗ ∗, ∗∗, and ∗=statistically signiﬁcant at 1, 5, and 10%, respectively.
Cluster standard errors are given in parentheses estimated coeﬃcients.
Variables except for dummies are in log form.

182

603.881543.0050.125-266.593

1.657(1.752)

Log-likelihood

0.997

Fitted test Prob > χ2 (164)0.174(0.159)
0.086(0.185)

Pseudo R2

0.411(0.759)

Constant

0.011

0.142

0.078(0.072)
0.006(0.083)

Size of agricultural plot

The size of burned plot for agriculture−0.567(0.372)

0.574∗

0.318(0.171)∗

Agriculture as primary source of income

0.593(0.326)∗−0.472∗−0.261(0.162)

Number of years on lot−0.000(0.001)−0.421(0.437)−0.000−0.394−0.000(0.000)−0.218(0.191)

Square age of household head

0.027(0.052)−0.017(0.134)−0.426(0.317)

0.311(0.332)

0.028(0.217)

0.083(0.135)−0.032(0.028)−0.034(0.014)∗∗

0.052(0.023)∗∗0.660(0.263)∗∗

0.061(0.360)

Coeﬃcient(Cluster S.E.)

Transmigrant

0.021−0.003(0.060)
0.012(0.022)

Access time to market

Age of household head

0.236
−0.3110.131(0.146)−0.172(0.145)

Number of household members

Number of children age < 6

0.011

0.086

0.048(0.062)
0.006(0.097)

Number of hot spots observed in 2012

Experienced ﬁre damage in last ﬁve years−0.027−0.015(0.012)

Amount of rice product cultivated last year

−0.028−0.016(0.007)∗∗

0.052∗∗

0.584

0.045

Marginal
eﬀect

Shadow wage of oﬀ-farm work∗∗

0.323(0.132)∗∗

0.025(0.163)

Coeﬃcient(Cluster S.E.)

Participate in Gotong-royong

Household involved with KFCP site

Independent variable

∗∗

0.083

0.168

0.573∗−0.548−0.407−0.0000.026−0.017−0.412

0.300

0.027

0.080

−0.031−0.033∗∗0.050∗∗

0.638

0.058

Marginal
eﬀect

182

630.847

541.135-242.568

1.656(1.758)

0.082(0.208)

0.172(0.159)

0.595(0.330)∗−0.563(0.375)−0.402(0.522)−0.000(0.001)

0.028(0.052)−0.026(0.184)−0.422(0.335)

0.308(0.323)

0.020(0.260)

0.086(0.159)−0.032(0.029)−0.034(0.014)∗∗

0.052(0.024)∗∗

0.661(0.261)∗∗

0.104(0.759)

Coeﬃcient(Cluster S.E.)

IV-Tobit

0.079

0.166

0.575∗−0.544−0.388−0.0000.027−0.025−0.407

0.297

0.019

0.083

−0.031−0.033∗∗0.050∗∗

0.638∗∗

0.100

Marginaleﬀect

Table 3: Estimation models for either patrolling or clearing grass

23

0.039(0.017)∗∗

Shadow wage of rubber products

0.000(0.000)
0.005(0.244)

Square age of household headTransmigrantTobit-252.368

518.782576.454
182

Akaike Information Criterion (AIC)

Bayes Information Criterion (BIC)

Number of observations

0.003

0.153

0.543∗−0.006

0.204

0.000

−0.0561

0.017−0.325

0.104

0.175

0.172

0.017

−0.029∗∗0.051∗∗

0.504∗∗−0.148Marginaleﬀect∗ ∗ ∗, ∗∗, and ∗ = statistically signiﬁcant at 1, 5, and 10%, respectively.
Cluster standard errors are given in parentheses with the estimated coeﬃcients.
Variables except for dummies are in log form.

182

603.612542.735

0.112

0.951(1.866)-241.391

0.897

Fitted test Prob > χ2 (164)Log-likelihood

0.036(0.899)

Constant

0.004(0.199)

Pseudo R2

0.103−0.0920.082(0.080)−0.073(0.125)

Size of agricultural plot

The size of burned plot for agriculture

0.178(0.150)−0.007(0.445)
0.629(0.344)∗−0.0700.516∗−0.055(0.224)
0.410(0.207)∗∗

Number of years on lot

Agriculture as primary source of income

0.236(0.490)

0.001(0.001)−0.065(0.049)

0.020(0.134)−0.376(0.342)

0.121(0.316)

0.202(0.241)

0.199(0.149)

0.019(0.036)−0.034(0.015)∗∗

0.059(0.026)∗∗

0.583(0.244)∗∗−0.171(0.427)

Coeﬃcient(Cluster S.E.)

0.006

0.000

0.025
−0.0410.020(0.073)−0.033(0.023)

Access time to market

Age of household head

0.088
−0.2300.070(0.180)−0.183(0.192)

Number of household members

Number of children age < 6

0.123

0.158

0.126(0.083)
0.098(0.131)

Number of hot spots observed in 2012

Experienced ﬁre damage in last ﬁve years

0.005

0.004(0.018)

Amount of rice product cultivated last year

−0.024−0.019(0.009)∗∗

0.049∗∗

0.497

Shadow wage of oﬀ-farm work∗∗

0.395(0.163)∗∗∗−0.141−0.112(0.232)∗∗

Marginaleﬀect

PoissonCoeﬃcient(Cluster S.E.)

Participate in Gotong-royong

Household involved with KFCP site

Independent variable

IV-Tobit

0.001(0.001)

182

628.354538.641-241.321

0.898(2.005)−0.071(0.212)

0.147(0.139)

0.670(0.355)∗

0.077(0.544)

0.624(0.733)−0.0610126

0.575∗

0.066

0.536

0.000

−0.052−0.126−0.060(0.049)−0.147(0.201)

0.050

0.049

0.223

0.023

−0.027∗∗0.044∗−0.250∗∗0.506∗∗∗

0.565

Marginal
eﬀect−0.292(0.373)

0.058(0.307)

0.058(0.276)

0.259(0.181)

0.027(0.036)−0.031(0.015)

0.051(0.028)∗

0.590(0.227)∗∗∗

0.659(0.930)

Coeﬃcient(Cluster S.E.)

Table 4: Estimation models for patrolling24

Poisson0.027(0.012)∗∗

Shadow wage of rubber products∗−0.932∗∗∗−0.001(0.000)∗∗−0.838(0.264)∗∗∗∗∗∗

Square age of household headTransmigrant

1.952(1.628)

182

Number of observations

495.456182

556.332∗ ∗ ∗, ∗∗, and ∗ = statistically signiﬁcant at 1, 5, and 10%, respectively.
Cluster standard errors are given in parentheses with the estimated coeﬃcients.
Variables except for dummies are in log form.

484.681542.353−0.054−0.9420.257∗∗∗∗∗−1.079∗∗∗−0.001∗∗

0.094∗∗−0.015-228.728

Akaike Information Criterion (AIC)

0.350−0.530∗∗−0.017(0.181)

0.121

Bayes Information Criterion (BIC)−0.0600.004

0.292(0.147)∗∗-224.340

0.990

Fitted test Prob > χ2 (164)∗∗∗−0.052∗∗−0.0290.003

Log-likelihood

0.507(1.033)

Constant∗

0.030∗∗

0.388

0.217

0.172∗−1.069(0.388)∗∗∗−1.225(0.400)∗∗∗−0.001(0.000)∗∗

0.106(0.045)∗∗−0.061(0.108)−0.601(0.296)∗∗

0.397(0.311)

0.004(0.189)−0.069(0.098)−0.059(0.026)∗∗∗∗∗−0.033(0.012)

Pseudo R2

0.002(0.115)
0.155(0.097)

Total size of agricultural plot

Size of burned plot for agriculture∗

0.034(0.014)∗∗0.440(0.228)

0.246(0.292)

Marginal
eﬀect

0.206

0.226(0.228)

Agriculture as primary source of income

Tobit
Coeﬃcient(Cluster S.E.)

0.233(0.297)

0.251−0.725(0.234)

Number of years on lot−0.805−0.001∗∗∗∗∗−0.0420.083∗∗−0.038(0.067)
0.074(0.030)∗∗

Access time to market

Age of household head

0.266
−0.395∗0.240(0.193)−0.355(0.210)∗

Number of household members

Number of children age < 6−0.041−0.032−0.037(0.068)−0.028(0.128)

Number of hot spots observed in 2012

Experienced ﬁre damage in last ﬁve years−0.045∗∗−0.040(0.016)∗∗

Amount of rice product cultivated last year

−0.023−0.021(0.009)∗∗

0.030∗∗

0.303

0.190

Marginal
eﬀect

Shadow wage of oﬀ-farm work∗∗

0.272(0.155)∗

0.171(0.161)

Coeﬃcient(Cluster S.E.)

Participate in Gotong-royong

Household involved with KFCP site

Independent variable

IV-Tobit∗

182

583.127493.415-218.707

1.955(1.582)

0.309(0.155)∗∗−0.009(0.183)

0.226(0.301)−1.086(0.356)∗∗∗−1.312(0.348)∗∗∗−0.001(0.000)∗∗

0.105(0.047)∗∗−0.023(0.165)−0.621(0.292)∗∗

0.412(0.310)

0.038(0.219)−0.083(0.094)−0.060(0.026)∗∗−0.033(0.012)∗∗∗

0.036(0.015)∗∗0.438(0.226)

0.053(0.490)

Coeﬃcient(Cluster S.E.)

Table 5: Estimation models for clearing grasses

0.272∗∗−0.0080.199−0.957∗∗∗−1.156∗∗∗−0.001∗∗

0.093∗∗−0.020−0.547∗∗

0.363

0.033

−0.073−0.053∗∗−0.029∗∗∗0.031∗∗

0.386∗

0.047

MarginaleﬀectThe estimation results for each category of prevention activity, patrollingand clearing grass, and both, are summarized in Table 3, Table 4 and Table
5, which show the respective speciﬁcations. The ﬁrst speciﬁcation (Column
1) uses a Poisson estimation and the second speciﬁcation (Column 2) usesa Tobit model to take the sample selection biases into account. The third
speciﬁcation (Column 3) uses the instrumental variable method for the Tobit
model. Table 6 shows the results of the ﬁrst-stage regression for IV-Tobit
models.
We found that the coeﬃcients of KFCP are statistically insigniﬁcant inall estimations, indicating that the impact of KFCP is not enough to causehouseholds to allocate additional time for ﬁre prevention. This might bebecause KFCP has not oﬀered payments for ﬁre prevention on individual
plots. Instead of oﬀering performance-based or outcome-based incentives,
most of KFCP project have focused on input-based incentives. For example,
households reported that KFCP has rewarded them for preparing nurseries
for reforestation or for planting on project plots. Such rewards can constitute an increase in households’ exogenous income, which may create negative
incentive for ﬁre prevention activity. By increasing households’ exogenous income, KFCP can deprive households of incentives to allocate labor for ﬁreprevention at their own plots. As the coeﬃcient of the shadow wage ofoﬀ-farm work is negative and statistically signiﬁcant, this might be plausi25

Table 6: KFCP site selection, ﬁrst stage regression
Independent variable

ProbitCoeﬃcient(Robust S.E.)
2.426∗∗∗(0.354)
Number of hot spots observed from 2001 to 2007
−0.398
Participate in Gotong-royong(0.813)0.287∗∗∗
Shadow wage of rubber products(0.069)0.071∗
Shadow wage of oﬀ-farm work
(0.041)−0.047
Amount of rice product cultivated last year
(0.078)−1.007∗∗
Number of hot spots observed in 2012
(0.469)2.504∗∗∗
Experienced ﬁre damage in last ﬁve year
(0.443)
1.221∗∗
Number of household members(0.488)−1.678∗∗∗
Number of children age < 6(0.346)2.479∗∗∗
Access time to market(0.372)−0.220∗∗∗Age of household head
(0.079)0.002∗∗∗
Square age of household head
(0.001)−4.502∗∗∗
Transmigrant(0.612)−1.139∗∗Number of years on lot(0.473)−0.695Agriculture as primary source of income(0.734)−0.406
Total size of agricultural plot
(0.361)1.880∗∗∗(0.646)Size of burned plot for agriculture−12.566∗∗∗(3.703)
Constant
Number of observations

182

Pseudo R2

0.853
26

IV
Coeﬃcient(Robust S.E.)
0.139∗∗∗(0.038)−0.040(0.065)0.008∗(0.004)0.004(0.003)−0.013(0.008)−0.110∗∗(0.048)0.160∗∗∗(0.051)−0.016(0.052)−0.052(0.044)0.189∗∗∗(0.042)−0.009(0.010)0.000(0.000)−0.429∗∗(0.167)−0.019(0.142)−0.099∗(0.060)0.017(0.042)0.104∗(0.057)−0.306(0.518)∗ ∗ ∗, ∗∗, and ∗ = statistically signiﬁcant at 1, 5, and 10%, respectively. Cluster standard
errors are given in parentheses under the estimated coeﬃcients.
Independent variables except for dummies are in log form.

182

ble. Furthermore, KFCP might be insuﬃcient in providing information andknowledge on its strategies. Resosudarmo et al. (2012) compared the knowledge of villagers between several REDD pilot projects and found that KFCP
is relatively less acknowledged by local residents.
The estimation results suggest that in general, economic factors have asigniﬁcant eﬀect on decision making for ﬁre prevention. The coeﬃcient of the
shadow wage of rubber production is positive and statistically signiﬁcant at
the 5% level at least in most of the estimations, indicating that householdswith higher values of rubber work are more likely to allocate labor for ﬁreprevention. A higher rubber product value creates an incentive for households to protect themselves from ﬁre damage. This result is consistent with
Bowman et al. (2008), who found that the shadow wage of agricultural work
has positive and statistically signiﬁcant eﬀect on household decision making
on ﬁre prevention. The coeﬃcients of the shadow wage of oﬀ-farm work arenegative and statistically signiﬁcant, indicating that an increasing oﬀ-farmwage increases the opportunity cost of time (γ), and a high opportunity costleads households not to allocate their time for ﬁre prevention. These resultsmean that agricultural work and oﬀ-farm work are substitutes for households
in this area and the substitution eﬀect outweighs the income eﬀect. The total
eﬀect of Equation (14) is positive and that of Equation (15) is negative.
The coeﬃcients of Gotong-royong are positive and statistically signiﬁcant27

in most of the estimation, indicating that the households that participate in
Gotong-royong are more likely to engage in ﬁre prevention. Since Gotongroyong reinforces community’s norms and ethics, it leads households to cooperate with each other. Another possibility is that Gotong-royong plays therole of a mutual surveillance system, allowing farmers to know what theirneighbor are doing at their agricultural plots. This can function as peerpressure for more extensive ﬁre prevention.
The coeﬃcients of the experience of ﬁre damage and the number of hot
spots in 2012 are positive but statistically insigniﬁcant in the estimations.
These results suggest that prevention activities by households might be basedon subjective risk perception. Empirical ﬁndings show that households indeveloping countries are typically deﬁned as risk-averse (Rosenzweigh andBinswanger, 1993; Morduch, 1995; Barrett 1999; Shively, 2001).

5

Conclusions

This study estimated household labor allocation models by combining household survey data and satellite information for Kapuas Regency, Central Kalimantan, Indonesia. On the basis of the results, we can conclude that peatland
ﬁre prevention can be promoted by economic and non-economic incentives.
This suggests that the REDD intervention incentive scheme would be able28

to mitigate the risk of peatland ﬁre as long as the design of the incentives isappropriate.
The eﬀect of KFCP was not found to be statistically signiﬁcant in our
results. While KFCP was expected to be an ideal model to mitigate the riskof peatland ﬁre, we found that it has no impact on household ﬁre prevention
decisions. This can be attributed to the fact that KFCP failed to develop an
appropriate incentive scheme. For example, KFCP pays households planting
or preparing trees for reforestation projects, but not for ﬁre prevention. Increasing this kind of payment might decrease the amount of labor allocation
for ﬁre prevention since increasing the attractiveness of oﬀ-farm work would
deprive households of their willingness to allocate labor for ﬁre prevention.
Economic factors play a signiﬁcant role in policy implementation. Increasing the productivity of rubber (the shadow wage of rubber production) would
be an incentive for households to increase their labor share of ﬁre prevention
activity; however, increasing exogenous income may deprive households oftheir willingness to engage in ﬁre prevention.
Non-economic factors are also important for eﬀective policy implementation. Mutual assistance customs such as Gotong-royong might enhance therelationships between and the responsibility of households in the community.
Thus, households that participate in Gotong-royong activities tend to allocate more labor and time for ﬁre prevention at individual plots. Toghether,29

these ﬁndings provide policy implications for the mitigation of forest ﬁre anddeforestation in Central Kalimantan as well as in other tropical regions. The
high cost of implementing incentive schemes and the diﬃculty in monitoring individual actions are at the center of the international discussion on
REDD as measurement, reporting and veriﬁcation (MRV) problem. Policy
design should include mutual pressure from each household and appropriate
economic incentives to secure implementation.

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34