Willingness to Pay for Critical Land

Sulistya Rini Pratiwi

, Erry Purnomo

, Said Usman

Universitas Borneo Tarakan

Universitas Tidar

Keywords: Agroeconomiec, Border Area, Contingent Valuation Method, Externalities.

Abstract: The purpose of this research is to analyze the farmers', and to identify the factors influencing the farmers'

Willingness to Pay (WTP) in reducing the impact of critical land. The research method used to calculate the

farmers' willingness to pay the land restoration is the Willingness to Pay (WTP) Method, and The Ordinal

Logistic Regression Method was used to analyze WTP's influencing factors. The result showed that the

farmers' Average of Maximum Willingness to Pay is Rp. 21.196.-. This means that the farmers’ Average of

Maximum Willingness to Pay is lower than the average cost incurred by the farmers for the land restoration

activity, which is Rp. 58.000.-. This indicated that the farmers' awareness of the efforts to do critical land

restoration is low. The independent variable with the significant influence is the OWN (the Status of the Land

Ownership) variable. The other variables that the logit coefficient is positive are income, age, education, long

stay, family numbers, and status of land ownership. Then the variables that the logit coefficient is negative

are marital status, occupation, and land restoration activity.

1 INTRODUCTION

Changes in land use and management reflect the

dynamic activities of the society so that the more

rapidly the dynamics take place, the faster the

changes in land use and management (Sandy, 1992).

The higher the economic activities of society will

increase land use. Unfortunately, this is not followed

by the land cultivation as the provider of

environmental services. Thus, the increased use of the

environmental service is not proportional to the

maintenance of the environmental quality, and the

benefits got from the environmental goods and

services are limited because there is some limitation

in environmental goods and services value (Bonnieux

and Goffe, 1997). The decrease of productivities

perceived because of the productive field narrower as

the effect of the overland function i.e., rice field,

moreover global issues about the increasing of the

degraded land that potentially turns into a critical

land. One of the causal factors of the process of the

critical land is the increase of population that using

the land as farm cultivation by giving no interest for

the principal of the critical land management for land

and water (Mulyani and Las, 2008).

The increasing of the degraded land can occur

because of the characteristic of the land, which is

susceptible to any harm, whether due to wildfire,

pests, shifting cultivation, encroachment,

overgrazing, or mistakes in cultivating. The critical

land is occurred due to the change in the land use in

Indonesia from farm or forest areas to be the non-farm

or built-up areas, so the water-absorbing areas are

reduced that causing degraded land, drought, or

critical clean water in the dry season, landslide, and

flood in the rainy season (Haryanto et al., 2007;

Acharya A and Kafle N 2009). The combination of

the market failure, the policy and management, such

as the ambiguous of the ownership rights, the market

price distorted, non-competition, and the adverse

incentive that affect the farmers’ perception of the

cost and the benefits of the controlling degraded land,

cause the critical land to be more severe (Coxhead,

1996).

Some researches show the increasing of the

overland function that causing the degraded land

(Ramayanti et al., 2015; Mirzabaev et al. 2016;

Tadesse et al., 2017 ). The population increase and the

economic activities cause an increase in the overland

function. Then the inefficiency cost of the degraded

land as the provider of the environmental services

Pratiwi, S., Purnomo, E. and Usman, S.

Willingness to Pay for Critical Land.

DOI: 10.5220/0009443101570162

In Proceedings of the 1st International Conference on Industrial Technology (ICONIT 2019), pages 157-162

ISBN: 978-989-758-434-3

157

(Yesuf et al. 2007; Deng & Li 2016). Taking for

example, estimating the annual cost of the degraded

land in the Mid Asia’s villages, due to the land use

and the change of the field in between 2001 and 2009

is about 6 billion USD, largely due to the desert

degradation (4.6 billion USD), followed by the

desertification (0.8 billion USD), deforestation (0.3

billion USD) and the abandon farm field (0.1 billion

USD) (Mirzabaev et al 2016).

There have been several attempts to measure the

cost of soil degradation, and several other studies

have undertaken the valuation of the environmental

services, by measuring direct and indirect use values

(Cho et al., 2005; Cho et al., 2008; Prasmatiwi et al,

2011; Suwarto et al, 2012). Each research showed the

society’s participation in reducing the impact of the

critical land or the degraded land.

This research is conducted to find out how much

the farmers' interest, and to identify the influencing

factors of how much the farmers' willingness to pay

(WTP) in reducing the impact of the critical land.

2 RESEARCH METHODS

The research method used was CVM (contingent

valuation method). Contingent Valuation Method

(CVM) is a direct survey method to the samples that

are suitable with a willingness to pay (WTP) and

willingness to accept (WTA). CVM has two benefits

comparing to the indirect method. First, CVM can

take two values at once. Use value as a non-use value.

Second, the answers from CVM's questions related to

WTP or WTA can be directly corrected by the theory

with the monetary measure on it is level changes

(Lee, 1999).

CVM was used to measure the total values of

individual consumer willingness to pay public goods

under several market hypothesis scenarios (Miller et

al., 2011). This method was used because it can (1)

estimating individual WTP on the changes of

hypothesis related to the quality of economic

activities; (2) evaluating a trip with many

destinations; (3) judging the convenience of using the

environmental resources by the direct or indirect

users; (4) estimating goods valued too low.

The WTP measurement is usually related to the

environmental quality and degradation by calculating

the cost that an individual spent to reduce the negative

impact on the environment due to restoration

activities (Hoevenagel, 1996; Tanrivermis, 1998;

Veisten et al. 2004). The synergy between society and

stakeholders is needed to elevate the environmental

quality. Besides, improving knowledge of

environmental effects is also necessary. Thus, it is a

need to know the factors affecting farmers'

willingness to pay in order to restore the quality of the

environment due to the critical land.

This method assisted when doing economic

valuation analysis of critical land in the dried farm

field in Sempayang Village Malinau Barat District,

Malinau Regency, North Kalimantan Province. The

economic valuation was conducted through WTP

(Willingness to Pay) approach. The value amount of

WTP was obtained from the bidding game method.

Selecting the samples was done when collecting

data through interviews and questionnaires. The

sample target in this research is farmers who have

farming activities, whether as the landowners, as both

the landowners and the workers, or as the tenants.

This research location to take the samples was

decided based on the landowners that have the largest

critical land with width land 541,08ha, 77% of the

width total of the critical land in North Kalimantan

Province.

Analyzing the factors affecting how big the

farmers’ willingness to pay was done using ordinal

logistic analysis, with the following formula:

  







  



  



 





  



  



  

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



  



   (1)

Where: WTP is the respondent’s WTP value (Rp);

β0 isIntersep; β1,..., β6 is regression coefficient;

D1....D4 is Dummy; INC is Income (Rupiah);

AGE is age (year); EDUC is Education length (year);

LONG is long stay (year); FAM is number of family

members (people); WORK is kind of occupation

(activity); MAR is Marital status (D= married and

D=0 others); OWN is Landowner status (D=1 is own

and D=0 other); PROCS is land restoration activities

(D=1 yes and D=0 no); ε is error term.

3 RESULTS AND DISCUSSION

3.1 Farmers’ Willingness To Pay

(WTP) as the Environmental

Restoration Efforts

The identified WTP data can be analyzed to get a

maximum average of WTP and total economic value.

The maximum average of WTP that can be used as a

new price for the environmental restoration efforts,

due to the critical land. The new price is at least higher

than the current set price because the respondents

ICONIT 2019 - International Conference on Industrial Technology

158

have understood the importance of economy and

environmental value. The average of maximum

farmers’ WTP (Average of Maximum Willingness to

Pay) was Rp. 21.196,- . This meant that the price was

lower than the average price the farmers spent on the

land restoration activities, that was Rp.58.000,-. This

could be concluded that the farmers' interest in the

restoration efforts due to the critical land still lacks.

3.2 The Farmers’ WTP and The

Influencing Factors

To estimate the WTP function, the researcher used the

ordinal logistic regression model because the

dependent variables had the ordinal scale. This

method was used to examine how far the log odds'

change from some cases when the change of the

independent variables happened. The dependent

variables used in this regression were WTP

(Willingness to Pay), which had seven levels where

each with 1, 2, … and 7. The use of the logistic

regression method was meant to examine the

influence of the independent variable on the

probability of farmers' willingness to pay the WTP on

a certain relative scale compared to another scale.

This method was considered relevant enough to

describe the valuation pattern of farmer economic

value in the Malinau Regency.

In the estimation processing of the logistic

method, the iterative-reweight least square algorithm

process was used to get the parameter estimation

through maximum likelihood estimation. If the

dependent variable of the WTP method is an ordinal

variable, then the independent variables consisted of

covariant (continuous variable) and factor (nominal

variable). The estimation result of this method can be

seen in table 3, and the descriptive statistic value for

the method variables can be seen in table 1.

Tabel 1. The Description of Reseach Variable Statistic

Variable Mean Std. Dev Minimum Maximum

WTP 5.431373 1.688252 0 50000

Income (INC) 15.13195 0.7345967 2500000 8000000

Age (AGE) 37.31373 10.51568 19 60

Education (EDU) 8.705882 3.015255 0 12

Marital Status (MAR) 0.8627451 0.3475404 0 1

Long Stay (LONG) 6.705882 6.435197 1 30

Land Ownership Status (OWN) 0.5294118 0.5041008 0 1

Number of Family Member (FM) 3.647059 1.764353 1 8

Occupation (Activity) 1.019608 0.140028 1 2

Land Restoration Activity (PROC) 0.7058824 0.460179 0 1

Source: Processed Data, 2018.

Table 1 explains that the standard deviation value

of each variable was fewer than the average value of

each variable. This indicates that the spread data

about respondent answers for each variable was good.

Tabel 2. The Estimated Result of the Ordinal Logistic Regression Method on WTP Method

No. Independent Variable Coefficient Z P > | z | Odds Ratio (OR)

1 Income (INC) 0.2355 0.63 0.528 1.26

2 Age (AGE) 0.0050 0.14 0.890 1.00

3 Education (EDU) 0.0883 0.93 0.351 1.09

4 Marital Status (MAR) -1.7105 -1.48 0.139 0.18

5 Long Stay (LONG) 0.1085 1.40 0.162 1.11

6 Land Ownership Status (OWN) 2.1164 2.85 0.004 8.30

7 Number of Family Member (FAM) 0.1327 0.55 0.585 1.14

8 Occupation (WORK) -61.0827 -0.00 1.000 1.97

9 Land Restoration Activity (PROC) -0.57886 -0.94 0.346 0.56

Source: Processed Data, 2018.

Based on the estimated result, the value of the

prob<chi2 was 0,0000. This indicated that the

independent variables of the WTP method overall

significance influencing the dependent variables. On

Willingness to Pay for Critical Land

159

table 3, there can be seen that only one independent

variable significantly influencing the WTP method,

which is the land ownership status (OWN). That is

shown by the p-value, which was under α (5%).

Meanwhile, the other variable p-value shows that

almost all of the variables have P >|z| value above α

(0,05). It means that the variables had no significant

effect on the WTP method.

By resulting in the variant value of the samples,

the OWN variable was also the most influential

variable of farmers' WTP value because it had the

biggest z characteristic i.e., 2,85. The pseudo value of

R2 was 0.1760. That means the influence of the

independent variable on the WTP method is 17,6%.

The influencing variables were shown on the OR

value that equal to 1. The OR value greater than 1

indicated that the independent variable had a negative

effect.

The regression coefficient value for the INC

variable of 0.2355 with the p-value = 0.528 indicated

that the INC variable had no significant effect

because the p-value was greater than α (0,05). The

regression coefficient value was 0.2355, and this

means that any change of the INC variable unit will

increase the logit value or the WTP log odds as much

0.2355 units. The OR value of the INC variable was

1.26. This can be concluded that any change of 1 INC

variable unit, ceteris paribus, will result in an increase

of 26%WTP odds.

The coefficient of the AGE variable of 0.0050

with the p-value = 0.890 indicated that the AGE

variable had no significant effect on the WTP. The

0.0050 coefficient value with the OR value equal to 1

indicated that the AGE variable had no effect on the

WTP. This means that both older and younger

farmers did not differ in paying WTP.

The EDU variable had no significant effect

indicated by the p-value of 0.351. It had a positive

effect on education, only 9%, shown by the OR value

of 1.09. This means that high education will improve

insight and knowledge about the importance of

environmental restoration. Then a good environment

will be created.

The coefficient of marital status variable (dummy)

of -1.7105 indicated that the average WTP to the

married farmers was fewer than to the unmarried

farmers of 1.7105. However, the variable had no

significant effect on the WTP value. If it is based on

the OR value of 0.18, this figure indicated that the

odds of WTP was paid by the married farmers of

(0.18-1) 100% = -82% compared with the unmarried

farmers. The negative sign on the odds difference

indicated that WTP paid by the married farmers tend

to be lower than that of the unmarried farmers.

The LONG variable coefficient of 0.1085 with p-

value = 0.162 indicated that the LONG variable had

no significant effect on the WTP. The OR value of

1.11 means that the LONG variable had a positive

effect and increased the WTP by 11%. The positive

effect means that the longer the peasant farmers

occupy their occupancy, the higher the value of the

farmers' WTP. The length of stay in the dwelling

place caused the farmers to be more familiar with the

condition of the surrounding land. It is because the

farmers commonly lived near their farm fields. The

length of stay variable had a significant effect on the

amount of WTP.

The OR value of positive OWN variable (dummy)

showed the OR value greater than 1 and had a

significant effect (p-value > α 5%). This means that

the average of farmers’ WTP with the landowner

status was greater than those of the farmers with the

tenant status. The farmers who own the land tend to

pay more attention to the land. They will seek to

restore and prevent the damage of the land.

The coefficient of FAM variable of 0.1327 with

p-value = 0.585 indicated that the FAM variable had

no significant effect on the WTP. The OR value of

1.14 indicated that the FAM variable had a positive

effect. This means that the addition of family

members can lower the WTP value. The greater

number of the family member, it will reduce the

willingness to pay the effort of the environmental

restoration due to the finance allocation of the family

income.

The coefficient of the WORK variable of -

61.0827 with the p-value > α (0,05) indicated that the

WORK variable had no significant effect. The

coefficient of the WORK variable had a negative logit

coefficient. This means that if a farmer had various

occupations, then his WTP will reduce. The various

occupations the farmers did besides doing farming

will distract the farmers' focus from the land

restoration efforts.

The coefficient of the negative land restoration

activity variable (dummy) indicated that the average

WTP of the farmers’ that doing the land restoration

activities was greater than that of the farmers’ doing

no land restoration activity. The farmers doing the

land restoration activities were more aware of the

impact felt, so they tend to make the prevention

efforts.

4 CONCLUSION

The survey with contingent valuation (CV) method as

one of the economic valuation methods of

ICONIT 2019 - International Conference on Industrial Technology

160

environmental effect, in general, can be applied well

in estimating farmers' WTP in the role reducing the

critical land with the land restoration activities. The

farmers' Average of Maximum Willingness to Pay of

Rp. 21.196,-, was lower than the average cost the

farmers spent in the land restoration activities i.e.,

Rp.58.000,-. This indicated that the farmers’

awareness of the restoration efforts to the critical land

was lack.

The independent variable having some significant

effect is the OWN variable (the land ownership

status). Meanwhile, the variables with positive logit

coefficients are the income, the age, the education, the

length of stay, the number of family members, and the

land ownership status variables. The marital status,

occupation, and land restoration activity variable

have negative effects.

The Average of Maximum Willingness to Pay is

shown lower than the cost the farmers spent. This

indicates the low participation of the farmers in doing

the environment restoration to the critical land. Thus,

improving knowledge and insight into the critical

land impact is needed so the farmers' WTP can

increase. Besides the knowledge of the impact, the

information about land restoration aspects is also

important. As a result, the farmers are expected to do

the restoration of the environmental quality

independently.

ACKNOWLEDGMENTS

We thank the Ministry of Research, Technology, and

Higher Education for the funding for this research.

And many thanks to the community of Sempayang

Village, especially to Mr. Alfius and Mr. Nurgianto

as the coordinator of the farmer. Also, to my team

survey for collecting the data.

REFERENCES

Acharya A and Kafle N 2009 Land degradation issues in

Nepal and is management through agroforestry The

journal of agriculture and environment 10 pp115-123

Bonnieux F and Goffe P L 1997 Valuing the benefit of

landscape restoration: A case study of the cotentin in

Lower-Normandy France Journal of environmental

management 50

Cho S-H, Newman D H and Bowker J M 2005 Measuring

rural homeowners' willingness to pay for land

conservation easements Forest Economics and Policy 7

pp757-770

Cho S-H, Yen S, Bowker J M and Newman D H 2008

Modeling willingness to pay for land conservation

easements: treatment of zero and protest bids and

application and policy implications, Journal of

agricultural and applied economics 40 pp267-285

Coxhead I 1996 Economic modeling of land degradation in

developing countries Agricultural and applied

economics staff paper series (USA: University of

Wisconsin-Madison)

Deng X and Li Z 2016 Economics of land degradation in

China Economics of land degradation and improvement

– a global assessment for sustainable development

Springer International Publishing pp385-399

Haryanto E T Herwanto T dan Kendarto D R 2007

Perubahan bentuk penggunaan tanah dan implikasinya

terhadap koefisien air larian DAS Citarum Hulu Jawa-

Barat Jurnal Bionatura 9 pp1-15

Hoevenagel R 1996 The validity of the contingent valuation

method: perfect and regular embedding Environ.

Resour. Econ. 7 pp57-78

Lee J A 1999 Natural resources and environmental

economic 2nd Edition (London: Pearson Education

Limited)

Miller K M, Hofstetter R, Krohmer H and Zhang Z J 2011

How should consumers' willingness to pay be

measured? an empirical comparison of state-of-the-art

approaches Journal of Marketing Research 48 pp172-

184

Mirzabaev A et al 2016 Economics of land degradation in

Central Asia Economics of land degradation and

improvement – a global assessment for sustainable

development Springer International Publishing pp261-

290

Mirzabaev A et al 2016 Economics of land degradation in

Central Asia Economics of land degradation and

improvement – a global assessment for sustainable

development Springer International Publishing pp261-

290.

Mulyani A dan Las I 2008 Potensi sumber daya lahan dan

optimalisasi pengembangan komoditas penghasil

bioenergi di Indonesia. Jurnal Litbang Pertanian 27

pp31-41

Prasmatiwi F E, Irham, Suryantini A and Jamhari 2011

Ketersediaan membayar petani kopi untuk perbaikan

lingkungan Jurnal Ekonomi Pembangunan 12 pp187-

199

Ramayanti L A, Yuwono B D, Awaluddin M 2015

Pemetaan tingkat lahan kritis dengan menggunakan

penginderaan jauh dan sistem informasi geografi (studi

kasus: Kabupaten Blora) Jurnal geodesi 4 pp200-208

Suwarto, Suwarto, Anantanyu S 2012 Model partisipasi

petani lahan kering dalam konservasi lahan Jurnal

Ekonomi Pembangunan 13 pp218-234.

Tadesse L, Suryabhagavan K V, Sridhar G, Legesse G

2017 Land use and land cover changes and soil erosion

in Yezat Watershed, North Western Ethiopia

International soil and water conservation research 5

pp85-94.

Tanrivermis H 1998 The willingness to pay (WTP) and

willingness to accept (WTA) measures in Turkey: may

the WTP and WTA indicators to share the

environmental damage burdens: case Sandy I M 1992

Willingness to Pay for Critical Land

161

Watersheds, ecosystems and land use Integrated

watershed management workshop in Indonesia (Bogor;

IPB)

Veisten K, Hoen H F, Navrud S and Strand J 2004 Scope

insensitivity in contingent valuation of complex

environmental amenities J. Environ Manage 73 pp317-

331

Yesuf M, Mekonnen A, Kassie M and Pender J 2007 Cost

of land degradation in Ethiopia: a critical review of past

studies (Ethiopia: Environmental economics policy

forum for Ehtiopia/ EEPFE) tudy Journal of Economic

Coorperation Among Islamic Countries 19 pp67-93

ICONIT 2019 - International Conference on Industrial Technology

162