Factors effecting farmers’ risk attitude and risk perceptions: The case of Khyber Pakhtunkhwa, Pakistan

Author’s Accepted Manuscript Factors Effecting farmers’ risk attitude and risk Perceptions: The case of Khyber Pakhtunkhwa Pakistan Raza Ullah, Ganesh P. Shivakoti, Ghaffar Ali www.elsevier.com/locate/ijdr

PII: DOI: Reference:

S2212-4209(15)30012-1 http://dx.doi.org/10.1016/j.ijdrr.2015.05.005 IJDRR220

To appear in: International Journal of Disaster Risk Reduction Received date: 3 April 2015 Revised date: 16 May 2015 Accepted date: 18 May 2015 Cite this article as: Raza Ullah, Ganesh P. Shivakoti and Ghaffar Ali, Factors Effecting farmers’ risk attitude and risk Perceptions: The case of Khyber Pakhtunkhwa Pakistan, International Journal of Disaster Risk Reduction, http://dx.doi.org/10.1016/j.ijdrr.2015.05.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Factors Effecting Farmers’ Risk Attitude and Risk Perceptions: The Case of Khyber Pakhtunkhwa Pakistan

Raza Ullah1 Assistant Professor, Department of Agricultural and Applied Economics, The University of Agriculture Peshawar-Pakistan Email: [email protected]

Ganesh P. Shivakoti Professor, Agricultural Systems and Engineering, School of Environment, Resources and Development, Asian Institute of Technology, Thailand Email: [email protected]

Ghaffar Ali Associate Professor, Department of Agricultural and Applied Economics, The University of Agriculture Peshawar-Pakistan [email protected]

Abstract Farmers’ risk attitude and risk perceptions are crucial factors that affect their farm production, investment and management decisions. Risk averse farmers are less willing to take on activities and investments that have higher expected outcomes, but carry with them risks of failure. This research is an attempt to quantify farmers’ perceptions of catastrophic risks, their risk attitude and to assess the effect of farm and farm household characteristics, farmers’ access to information and credit sources on their risk perceptions and risk attitude. Equally Likely Certainty Equivalent approach is used to elicit farmers’ attitude towards risk and risk matrix is used to rank farmers’ perception of four calamitous risk sources including floods, heavy rains, pest and diseases and droughts. The results revealed that majority of the farmers are risk averse in nature and consider floods, heavy rains and pest and diseases to be potential threats to their 1

Corresponding Author

1

farms enterprise. Age and education of the household head, off-farm monthly income of the household, land ownership status and farmer’ access to informal credit sources significantly affect farmers’ attitude towards risk. The effects of socio-economic and demographic factors on farmers’ risk perceptions are insignificant while access to formal information and informal credit sources adds to the risk perceptions of farmers. The study provides useful insights for farmers, agricultural policy makers, extension services, researchers and agricultural insurance sector. Understanding farmers’ risk attitude and risk perceptions have implications for policy makers and research institutions in providing farmers with accurate information, formulating sophisticated risk management tools and providing agricultural credit and extension services. Key words: Catastrophic Risk, Risk Attitude, Risk Perceptions, Probit Model, Utility Function 1. Introduction Agriculture is a risky activity by nature and agricultural enterprises, particularly in developing countries, operate under a risky and uncertain situation [2]. Risks cause farmers to be less willing to take on activities and investments that have higher expected outcomes, but carry with them risks of failure [3]. Agricultural risks rises mainly due to the climate variability and change, the complexity of biological processes, the seasonality of production, the geographical separation of production region and end users of agricultural commodities [6], frequent natural disasters, the yield and prices variability of farm products, imperfect input/output markets and the absence of financial facilities along with limited extent and design of risk mitigation tools such as credit and insurance [21]. The climate variability and increase in extreme weather events affect crop and livestock production, impose biotic, and abiotic stresses on the crops, alter soil nutrient cycling [20, 39 and 10] insect-pests and disease incidence, soil metabolic process, and soil water content [27 and 37]. Moreover, the natural hazards, arising from climate change and variability, affect income distribution and ultimately the livelihood security of farming communities [37]. Among the major risks farmers face is production risk [16]. Farmers have little to do against extreme natural hazards including flood, drought, cyclone and storm surges, hails storm etc. as they are mostly uncertain. 1.1

Theoretical Approach

The assessment of farmers’ perceptions and their response to risk are crucial because this can describe the decision making behavior of farmers when facing uncertain situations [17]. Farmers’ decisions under risky and uncertain situations can be best analyzed by taking into consideration their risk perceptions and attitude towards risk [28]. Climate extremes are rare and interact with more physiographic factors indicating a unique character of each single event (hazard). Therefore, it is important to anticipate the nature of expected changes and to understand how climate change and its relate hazards are perceived, experienced and interpreted by local people [36 and 30]. These local perceptions of natural hazards stem from routine interaction with environment [24] therefore, reflecting local issues [15] including the actual impacts of climate change and its related hazards on lives of local people [30]. Literature on the effect of socioeconomic and demographic factors on farmers’ risk perceptions and risk attitude revealed mix results. Farm and farm household characteristics effect risk perceptions and risk attitude of the farmers. Literacy and farming experience develop farmers understanding of the risk sources; their incidence and severity, and consequently effect their perceptions and enhance their capabilities to manage farm risk more efficiently. Some studies found that risk preferences differ 2

significantly based on education [19 and 28], age [42, 29 and 14], income [12], farming experience [8], farm size [28 and 29] and ownership status [28]. Climate information is of great importance to manage production risks in agriculture arising from climate variability [10]. Access of farmers to information sources enables them to understand and manage farm risks through adoption of efficient risk management strategies [22]. The insufficient knowledge on farmers’ risk perceptions and their attitude towards risk present a challenge for policy makers and researcher in creating a proper risk management system for the farmers [17 and 34]. Better understanding of farmers’ risk perceptions and their risk attitude are important factors in designing improved and sophisticated risk management tools and strategies for farmers to overcome losses due to various sources of risks. Policies to expand not only farm production but also farmers’ risk management ability essentially consider farmers’ risk perceptions and risk attitude [28]. Despite the importance of evaluating farmers’ perceptions of risk sources and their attitude towards risk to better understand their risk management decisions, no studies have been carried out in Pakistan to quantify the risk attitude and risk perceptions of farmers. Therefore, this research is an attempt to fill the gap by examining farmers’ perceptions of production risk sources, their risk attitude and the effect of farm and farm household characteristics, farmers’ access to information and credit sources on their risk perceptions and risk attitude. 2. Methodology 2.1 Study Area and Sampling

For the present study, a multistage sampling technique is used where Khyber Pakhtunkhwa (KP) province of Pakistan is purposively selected in the first stage. Majority of the people in the province live in rural areas and depend mainly on agriculture for their livelihoods. Agricultural sector engages 48 percent of the total labor force and contributes 40 percent to the GDP of the province [25]. KP province is a risk prone area for various weather related risks including floods, heavy rains and cyclone etc. the province faced 8 major flood incidences in the last 25 years of which the flood that hit Pakistan in July 2010 was the worst affecting 24 out of 25 districts of the province [33]. The flood hit just before the rice harvest, which is one of the two principal crops grown in KP, destroying 71% of the crop. The water also caused significant damage to vegetable crops and fruit orchards, both of which offer essential income sources and food. Due to the floods, the perception of farmers in this province regarding the catastrophic risk to agriculture is expected to be high and farmers are expected to have high tendency to adopt various weather related risk management measures to safeguard their income from farm enterprise.

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Figure 1: Map of Pakistan

In the second stage, four districts in KP province viz Peshawar, Charsadda, Swat and Shangla were selected purposively. The main reason behind the selection of these four districts is that two districts, Peshawar and Charsadda, are located in Peshawar valley and the farmers have better access to input output market, extension and other publicly provided services such as credit and information services. While Swat and Shangla are far from the provincial capital and the farmers have limited access to agricultural markets, information, agricultural extension and other government services and are less adoptive of modern technologies [1 and 40].

Figure 2: Study Area In the third stage two villages from each District are randomly selected for the purpose of data collection and Yamane’s formula [45] was used to select the sampled households in each village. The formula is given as under:

4

N / (1  Ne2 )

n

=

n

=

Sample size in each Village

N

=

Total number of farming households in a village

e

=

Precision which is set at 15% (0.15)

(1)

Where,

Based on the above equation, 330 sampled respondents were randomly selected for the present study and data was obtained from the sampled households through face to face meetings using a comprehensive interview schedule (questionnaire) from November, 2012 to April 2013.

2.2

Risk Attitude

The method most commonly used to elicit utility from an economic agent is the Equally Likely Certainty Equivalent (ELCE) model [18] where certainty equivalents (CE) are derived for a sequence of risky outcomes and matches them with utility values [9]. Following Binici et al. [9] household income is used in the utility function to represent wealth. For instance, the respondent was asked to specify the monetary value of a sure outcome that make him indifferent between the two risky outcomes of PKR2 (Total Annual Household Income, say PKR 50,000) and PKR 0 with equal probability (the utility associated with PKR 50,000, in this example, is 1 and the utility of PKR 0 is 0). Suppose the response is PKR 26,000, this is the CE of the farmers for the payouts of PKR 50,000 and PKR 0 with equal probabilities. the respondent was again asked to specify the monetary value of a sure outcome that make him indifferent between the two risky outcomes of PKR 26,000 and PKR 0 with equal probability. In this way, several certainty equivalents were derived and were matched with utility values. The same process is repeated to obtain CE for the other half of the income distribution and match them with utility values. After deriving several certainty equivalents and match them with utility values, a cubic utility function was used to estimate the utility of each individual respondents. The cubic utility function can be written as; ( )

(2)

Cubic utility function is consistent with risk aversion, risk preferring and risk indifferent attitudes [9]. Utility is generally measured on an ordinal scale; however, the shape of the utility function on an ordinal scale can be transformed into a quantitative measure of risk aversion called absolute risk aversion [7 and 38]. The absolute risk aversion is mathematically defined as; ( )

( ) ( )

( )

ra(W) is coefficient of absolute risk aversion, Uʹ and Uʹʹ are first and second order derivatives of wealth (W) respectively. The coefficient of absolute risk aversion is positive if individual is risk averse, negative if individual prefers risk and zero if individual is indifferent to risk. There were no farmers reflecting risk indifferent attitude in the area and the coefficient of absolute risk aversion is categorized as 1, if farmer reflect risk averse nature and 0, otherwise.

2

PKR is abbreviation for Pakistan Currency (in 2013, 1 PKR approximately equals 0.009 USD)

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2.3

Risk Perception

Assessing risk provides an insight concerning how likely something is to go wrong (likelihood) and what the related impact (consequences) will be [44]. Ranking the risks based on product of likelihood (P) and consequence (c) gives a risk factor (RF) [13]. In the present study weather related risks to agriculture are categorized into i) risk of floods ii) risk of heavy rains iii) risk of pest and diseases and iv) risk of drought. Farmers were asked to score the incidence and severity of each source of risk, on a likert scale, from 1 (very low) to 5 (very high) to express how significant they consider each source of risk in terms of its potential impact on their farm enterprise. These scores were combined in a risk matrix as suggested by Lansdowne [26] and Cooper et al, [13] and were categorized as low if it is between 2 and 5 and high if it is from 6 to 10. Table 1: Likert scale rating for incidence and severity of weather related risks Calamitous Risk Sources Ranking Very Low Low Normal High Very High Risk of Floods Incidence 1 2 3 4 5 Severity 1 2 3 4 5 Risk of Heavy Rains Incidence 1 2 3 4 5 Severity 1 2 3 4 5 Risk of Pest and Diseases Incidence 1 2 3 4 5 Severity 1 2 3 4 5 Risk of Droughts Incidence 1 2 3 4 5 Severity 1 2 3 4 5

2.4 Socio-economic and Demographic Factors The socio-economic and demographic factors included in the study are age, education and farming experience of the household head, monthly household off-farm income, family size, and farm size of the farming household along with land ownership status. Age, education and farming experience of the household are continuous variables representing number of years, offfarm income is the total monthly income of the household from off-farm sources in PKR, family size is measured as head count of family members in the household, farm size is measured as number of hectares household operates and land ownership status is represented by 1, if the farming household is owner of the land and 0, otherwise. 2.2.4 Access to Information and Credit Sources A composite index was used to measure the access of sampled households to information sources. The farmers were asked to report the number of contacts they made with each information source in one month period. The values for each information source were first transformed using the following equation. (

)

(4)

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The composite index for each sampled household was calculated by taking the sum of TV’s for all formal and informal sources of information separately. The value of composite index indicates access of the sampled household to each information source relative to other sampled households. Access to credit of the sampled households was analyzed using the following equation as used by several other researchers [4]. (5) Where: ACi = ci = C = li = L =

Access to credit of ith household Amount of credit received by the ith household Total amount of credit received by all sampled households in the study area land holding belonging to ith household Total landholding belonging to all sampled households in the study area

The analysis based on the above formula has two advantages; firstly, it represents relative access of farmer to credit as compared to the whole sampled households in the study area. Secondly, it gives credit access per unit of land for each farming household. The credit access ratio will be calculated for both formal and informal sources separately. 2.2.5 Probit Model Probit regression, also called a probit model, is a type of regression where the dependent variable can only take two values. The purpose of the model is to estimate the probability that an observation with particular characteristics will fall into a specific one of the categories. In the present study we use probit model as the dependent variables (risk perceptions of flood, heavy rains, pest and diseases and drought and risk attitude) are dichotomous. The probit model is given as: Y = α + Σxiβ + ε (6) Where Yi is the dichotomous dependent variable, in our case Yi represent the high risk perceptions and risk averse nature. xi is a vector of independent variables used in the analysis (such as socio-economic characteristics of the farming households, access to information and credit sources), βi is the vector of unknown parameter (to be estimated) and εi is the error term. 3

Results and Discussion

The descriptive statistics of the variables used in the analysis are presented in table 2. Majority of the farmers consider risk of flood (70 percent), risk of heavy rains (70 percent) and risk of pest and diseases (74 percent) to be the major risk sources and 80 percent of the respondents reflects risk averse nature. Table 2: Descriptive Statistics of the Variables Variables Description

Mean

SD

0.79

.40

Risk Attitude Risk Aversion

1, if the individual reflects risk averse attitude and 0, otherwise

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Risk Perceptions Risk of flood

1, if risk score is above 5 and 0, otherwise

0.70

0.46

Risk of Heavy Rains

1, if risk score is above 5 and 0, otherwise

0.70

0.46

Risk of Pest and Diseases

1, if risk score is above 5 and 0, otherwise

0.74

0.44

Risk of Drought

1, if risk score is above 5 and 0, otherwise

0.26

0.44

Farm and Farm Household Characteristics Age

Age of the Household Head in Years

48

13.16

Education

Number of Years of Schooling

3.88

5.27

Farming Experience Number of Years of Farming Experience

28

14.99

Off-farm Income

Household Monthly Of-Farm Income

25434.55

Family Size

No. of Family Members in the Household

9.42

3.59

Farm Size

Total Farm Size in Hectares

2.38

1.84

Land Ownership

1, if the Household is Owner of the Land and 0, otherwise

.50

.50

15002.36

Access to Information and Credit Sources Information Sources Formal Sources

Composite Index score

0.27

0.39

Informal Sources

Composite Index score

0.66

0.40

Credit Access Ratio

0.83

3.77

Informal Sources Credit Access Ratio Source: Own Survey Data, 2012-2013

1.27

2.42

Credit Sources Formal Sources

The farming households have relatively higher access to informal information and credit sources compared to formal sources. Ahmad et al, [1] and Shahbaz et al, [40] also reported a weak access of farmers to formal information sources (particularly to agricultural extension services). The largest formal source of agricultural credit as reported in the study is bank. However; the access of farmers to institutional lendings is restricted by a number of factors in the area. During the field survey farmers reported the lack of collateral, lack of awareness, high markup rate and complicated procedure of loan sanctioning imposed by financial institutions to be the main reasons that hinder farmers’ access to institutional credit sources. Due to their lower access to formal information and credit sources, farmers mostly rely on informal sources for information and credit to meet their needs. Among the informal credit sources, relatives and friends are reported to be the most dominant sources of informal lendings in the area (33.6 percent). Radio is reported to be the most frequently used formal information sources (24.2 percent) followed by the use of TV (16.4 percent). Among the informal information sources, fellow farmers are reported to be the most dominant source of information (73 percent) followed by input dealers (49.1 percent). 8

3.1

Factors Affecting Risk Attitude and Risk Perceptions

Probit model is used in the study to assess the impact of socio-economic factors, farmers’ access to information and credit sources on their risk attitude and risk perceptions. The results of the probit models for risk attitude and perceptions of weather related risk sources (risk of flood, risk of heavy rains, risk of pest and diseases and risk of drought) are presented in table 3. The results for risk attitude equation points to the importance of age, education, off-farm income, land ownership status and access to informal credit sources. The results suggest that younger farmers are more willing to take risk compared to older farmers. The result is in agreement with Dadzie Acquah [14] and Manandhar et al. [30] who also found an inverse relationship of age with risk attitude of the farmers. Our results suggest that with an increase in the education level of the farmer the risk aversion also increases. Education of the household head (decision maker) expands his/her knowledge on various sources of risk, its impacts at farm level and possible strategies which can be used to protect their earnings from natural hazards. Lucas and Pabuayon [28] also found similar results for rain-fed lowland rice farmers in Ilocos Norte, Philippines. Farmers with lower off-farm incomes are found to be more risk averse compared to farmers with higher off-farm incomes. Higher off-farm incomes may indicate a greater risk bearing capacity and represents a form of diversification that would have an impact on farmers’ risk attitude [39]. The result is in agreement with findings of Mosley and Verschoor [32] and Lamb [25] who reported that farmers with lower incomes are more risk averse and avoid situations where the probability of failure is high. Similarly, tenant farmers are found to be more risk averse in nature compared to owners. Landowners can decide on their own, hence, they are more determined to take more risks than tenants. The result is in line with findings of Dadzie and Acquah [14] who also reported a negative effect of tenure status on farmers risk aversion. Access to informal sources of credit also significantly affects the risk aversion of the farmers. Access to agricultural information can enhance productivity [33], affect the risk attitude of farmers [8] and guide them to adopt sophisticated risk management tools to overcome risks and uncertainties at farm level. Farmers with lower access to credit sources are more risk averse in nature compared to farmers with higher access to credit sources. Credit reserves are one way farmers manage risk [41] by providing liquidity to guide a business through hard times [5]. However, this result is in contrast with Lucas and Pabuayon [28] who found a positive effect of availability of credit on individual’s risk attitude in 4 out of 5 cropping patterns.

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Table 3: Parameter Estimates of the Probit Model Independent Variables Risk Attitude

Diseases Drought Socio-economics Characteristics Age -.0068

High Risk Perceptions

Risk Aversion

Flood

Heavy Rains

Pest and

-.0262**

-.0046

.0021

.0109

(.0121)

(.0103)

(.0108)

(.0116)

.0479**

-.0116

-.0130

.0091

(.0216)

(.0172)

(.0171)

(.0187)

.0097

-.0009

.0002

.0028

(.0104)

(.0092)

(.0096)

(.0105)

-.00004***

-0.000009

-.0000004

-.00001

(.000006)

(.000006)

.0156

.0323

.0020

(.0270)

(.0226)

(.0230)

(.0241)

.0188

.0821*

.0876*

.0050

(.0429)

(.0425)

(.0464)

(.0423)

-.3383*

-.1035

-.3549**

.2995

(.1954)

(.1706)

(.1698)

(.1829)

-.2115

.5428**

.4049*

.3438

(.2199)

(.2155)

(.2132)

(.2291)

-.0418

-.3607*

-.1650

(.2399)

(.2087)

(.2116)

(.2357)

.0063

-.0218

-.0001

-.0261

(.0248)

(.0207)

(.0215)

(.0206)

-.0313

.0305

(.0333)

(.0391)

(.0615)

-194.0742

-192.9081

-

(.0113) Education -.0204 (.0184) Farming Experience .0023 (.0103) Off-Farm Income .00001* (.000007) (.000006) .0275

(.000006) Household Size .0056 (.0238) Farm Size -.0210 (.0400) Land Ownership Status .1563 (.1813) Access to Information Sources Formal Sources 1.0130*** (.2272) Informal Sources .7721***

-.2481

(.2186) Access to Credit Sources Formal sources -.0814** (.0375) Informal Sources .2388***

-.0612* .0970*** (.0352)

(.0351) Log likelihood 169.1503

-138.5126 -171.0991

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LR chi2 (11) 58.71*** 15.02 19.03* 38.23*** 36.44*** Pseudo R2 0.1749 0.0373 0.0470 0.1015 0.0962 Number of observations 330 330 330 330 330 Standard Errors are in Parenthesis. *, ** and *** represent significant at 10%, 5% and 1% probability level respectively.

The impact of farming experience, household size, farm size and access to information sources is insignificant on farmers’ attitude towards risk. Farmers with higher experience are more risk averse in nature compared to farmers with less farming experience. This result is in contrast with Ayinde [8] who reported a negative relationship of farming experience with their risk averseness. The larger household size reflects greater total consumption needs of the household and hence positively contribute to the risk averse behavior of the farmers. Dadzie and Acquah [14] also reported similar result for the effect of household size on farmers’ risk aversion for food crop farmers in Ghana. With increase in the farm size, aversion to risk increases. Other things being equal, larger landholding requires more inputs use and more time for crop management thus contributing positively to the risk averse attitude of the farmers. This result is in line with the findings of Lucas and Pabuayon [28] who reported a positive impact of farm size on risk averse attitude of farmers in Ilocos Norte, Philippines. Although the effect of access to information is insignificant on farmers’ risk averse attitude, the negative sign indicates that higher access to information reduces the risk aversion by developing farmers’ understanding of the risk and the strategies to cope with it. The effect of socio-economic and demographic characteristics on farmers’ risk perceptions are mix and mostly insignificant. Previous studies also found an insignificant effect of socioeconomic and demographic factors on their risk perceptions [see 28 and29]. For instance, age of the farmer negatively affect their perceptions of flood and drought but positively affect their risk perceptions of heavy rains and pest and diseases. Older farmers consider risk of heavy rains and risk of pest and diseases to be the major threats to their farm enterprise while younger farmers perceived that risk of flood and risk of drought are the major sources of production risks that can alter their farm earnings. Similarly, farmers with higher education levels are concern more about risk of pest and diseases while farmers with lower educational background recognize risk of flood, risk of heavy rains and risk of drought as major production risk sources. Farmers with more years of experience identify risk of heavy rains, risk of pest and diseases and risk of drought as main threats compared to farmers with lower farming experience. Manandhar et al. [30] also found that farmers with more than 30 years of farming experience have higher risk perception of natural hazards resulting from climate change. Higher off-farm income reduces farmers concern of risk of flood, risk of heavy rains and risk of pest and diseases however farmers with higher off-farm income see risk of drought to be a potential threat to their farming activities. The perceptions for all the production risk sources of households with more family members are high compared to households with fewer family members. Similarly, large farmers perceive risk of flood, risk of heavy rains and risk of pest and diseases to be major weather related risk sources while small farmers consider risk of drought as a potential source of risk to their farms. Tenant farmers are reported to be more anxious of flood and heavy rains while owner farmers, on the other hand, are more concerned about pest and diseases and droughts.

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It’s surprising to see farmers’ access to formal information sources adds to their risk perceptions. It was expected that access to farmers’ access to formal information sources may lead to develop their understandings of risk and enable them to cope with it, which will eventually reduce their perceptions of the risk sources. These findings questions the quality of information disseminated to farmers from formal sources. Mittal and Mehar [31] found several institutional and infrastructural issues related to information dissemination in India and argued that as long as these constraints exist, the farmers cannot fully utilize the benefits of information. The informal sources of information lead to a decline in the risk perceptions of flood, heavy rains and drought however; farmers with higher access to informal information sources perceive pest and diseases as a leading catastrophic risk that can adversely affect their earnings from farm sector. Access to formal credit sources provides an opportunity to the farmers to manage their farm risk as credit can be used as an ex-post risk management strategy thus reducing their concern of the risk sources. However, the effect of access to informal lending sources is positive related to their risk perceptions for all the risk except for risk of flood. 4

Conclusion

Farmers’ risk attitude and risk perceptions are important factors in their farm production, investment and risk management strategies/decisions and should be considered an important aspect while designing risk management strategies for the farm sector. Majority of agricultural producers are risk averse in nature and will avoid a risky situation even if the returns are higher. Farmers consider risk of flood, risk of heavy rains and risk of pest and diseases as potential threats than can alter their farm earnings. Age, education, off-farm income, land ownership status and farmer’ access to informal credit are the factors that significantly affect farmers’ risk attitude towards risk. The effects of socio-economic and demographic factors on farmers’ risk perceptions are insignificant while access to formal information and informal credit sources adds to the risk perceptions of farmers. Though the findings of the study are specific to KP province of Pakistan, they may have wider implications particularly for developing country conditions. The study provides useful insights for farmers, agricultural policy makers, extension services, researchers and agricultural insurance sector in a number of ways. Understanding farmers’ risk attitude and risk perceptions have implications for policy makers and research institutions in providing the farmers with accurate information, formulating sophisticated risk management tools and providing agricultural credit and extension services. The findings also provide useful insights for researchers to understand how farmers’ socio-economic and demographic factors, their access to information and credit sources effect their risk perceptions and risk attitude. These findings can be used in future studies investigating farmers’ adoption of risk management tools. References [1] Ahmad S., Jamal, M., Ikramullah, A., and Himayatullah. 2007. Role of Extension Services on the Farm Productivity of District Swat: A Case Study of Two Villages. Sarhad Journal of Agriculture, 23(4): 1265-1272. [2] Akcaoz, H., and Ozkan, B. (2005) Determining risk sources and strategies among farmers of contrasting risk awareness: A case study for Cukurova region of Turkey. Journal of Arid Environments. 62(4): 661–675. [3] Alderman, H. (2008). Managing risks to increase efficiency and reduce poverty. World Development Report 2008. Washington D.C: World Bank. 12

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