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Risk, Ambiguity and the Adoption of New

Technologies: Experimental Evidence from a

developing economy*

Nicholas Ross, Paulo Santos and Tim Capon

October 2010

Despite the expected benefits, the slow adoption of innovations in less developed countries

has long been a puzzle. Aside from market constraints, risk-aversion dominates the

discussion on the behavioural determinants of technology adoption. This paper investigates

the role of ambiguity-aversion as another possible explanation, given farmers may have less

information about the outcomes of new technologies compared with traditional technologies.

Using primary data from behavioural experiments used to measure risk and ambiguity

preferences in the field we find that farmers’ aversion to ambiguity and not risk constrains the

adoption of new technologies.

Keywords: risk preferences, ambiguity preferences, technology adoption.

*Acknowledgements: I thank the assistance of Vilas Gobin, Kenekeo Sayarath at SNV Khammouane and my field surveyors Somsy Xayalath and Bouathong Khounxieng. I gratefully acknowledge the University of Sydney and my father, David Ross, for financial support.

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1. Introduction

Despite the potential rewards, the adoption of new technology is often slow and

incomplete. Such delays help explain differences in productivity across countries and why

poverty can persist. Given the prevalence of poverty in rural areas, agricultural

transformation is widely seen as a precondition for overall growth. This explains the great

focus of attention on farmers’ decisions to adopt new technologies in developing countries by

agricultural and development economists. Feder, Just and Zilberman (1985) is the classical

review of much of the earlier research that followed the Green Revolution. More recent

developments are reviewed in Zilberman and Sunding (2004).

Amongst the explanations, these studies have identified the role played by uncertainty,

in particular, the importance of aversion to risk (Feder 1980; Feder et al. 1985; Ghadim et al.

2005, Binswanger, 1980; Knight et al. 2003; Liu 2007; Gong et al. 2010), implicitly

assuming that the Subjective Expected Utility (SEU) approach of Savage (1954) is valid and

that decision makers do not distinguish between known and unknown probabilities.

This last assumption is the focus of Ellsberg (1961) criticism of SEU, where it is

suggested that individuals prefer to bet on risky prospects for which they can clearly assess

the probability of outcomes over ambiguous prospects for which the probabilities of its

outcomes are unknown and the individuals feel less competent. The importance of ambiguity-

aversion, has been the focus of work by Heath and Tversky (1991), Fox and Tversky (1995)

and Chow and Sarin (2002) and has been empirically found to exist in the market (e.g. Sarin

and Weber 1993; Halevy 2007; Capon 2009) and among subsistence farmers in developing

countries (Akay et al. 2009).

The adoption of new technology provides a natural setting to test the importance of

ambiguity-aversion for decision-making, given that the probability distribution of outcomes

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associated with a new technology are rarely known (Liu 2007). Yet the literature on adoption

of innovations has systematically failed to differentiate between risk-aversion and ambiguity-

aversion in explaining these decisions, with few exceptions (e.g. Engel-Warnick et al., 2008).

In this paper, we address the importance of ambiguity in explaining the adoption

intensity of a new crop, non-glutinous rice, in the context of a developing country, Lao PDR,

which is simultaneously very poor and experiencing fast growth. After a brief review of the

literature on the role of risk and ambiguity in the adoption process in section 2, we introduce

this new technology in section 3. To understand the importance of ambiguity, we collected a

unique data set from rice farmers in one village in the province of Khammouane, in the

central part of Lao PDR. We use behavioural field experiments to elicit farmers’ risk and

ambiguity preferences in conjunction with a household survey. We present our data in section

4 after which we ask how these preferences affect the intensity of adoption of this new

technology. We show that ambiguity, but not risk, matter for the adoption decision. We

conclude in Section 5 with some policy implications and suggestions for future research.

2. Risk and Ambiguity

A farmer’s choice to implement a new technology over an existing one depends on

numerous complex factors, some of which are not directly observable in a standard household

survey. Examination of the adoption decision began with Grilliches’ (1957) pioneering

conclusion that economic variables were significant determinants of the adoption and

diffusion of hybrid corn in the US Midwest. The introduction of High Yielding Varieties

(HYV) of rice in developing countries during the Green Revolution of the 1960s spurred

increased efforts to understand what determines the adoption of new technologies, both

within economics and other social sciences (e.g. Rogers 1995). Over time, the focus of

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research effort has shifted away from market imperfections and market constraints towards

the roles played by information, education and social learning.

Much of the earlier work focused on the functioning of input markets and their

differentiated impact on smallholders (Weil 1970; Binswanger 1978; Gafsi and Roe 1979;

Feder et al. 1985, Wills 1972; Khan 1975; Bhalla 1979).The focus soon shifted to the

importance of information (Feder 1979) and decision makers capacity to process information,

with the analysis of the role of education (Lin 1991; Duraisamy 2002), access to extension

(Nkonya et al. 1997) and the role of social learning (Besley and Case 1993; Foster and

Rosenzweig 1995; Munshi 2004, Udry and Conley 2010). This change in emphasis has

highlighted the importance of the behavioural and social factors that affect farmers’

technology adoption choices under conditions of uncertainty.

Theories of decision making under risk in economics are primarily built upon the

foundations of Expected Utility Theory (EUT) (von Neumann and Morgenstern1947): A

decision maker who is faced with prospects with a probability distribution known a priori

(𝑝1, … ,𝑝𝑛) over a number of outcomes (𝑥1, … , 𝑥𝑛), is assumed to maximise the probability

weighted sum of the utility of the outcomes, given by ∑ 𝑝𝑖.𝑢(𝑥𝑖)𝑖 . A decision maker’s

attitude to risk determines the shape of the utility function, with concavity representing risk-

aversion. The Subjective Expected Utility (SEU) Hypothesis (Savage 1954) relaxed the

assumption that 𝑝𝑖 is objectively known a priori, instead assuming that decisions will be

governed by a set of subjectively formed probabilities.

The SEU approach forms the basis of much of the work on the effects of risk on

technology adoption. For example, Feder (1980) uses differences in risk-aversion to explain

differences in the allocation of land to new crops, under the assumption that the new crop

exhibits a higher variability in yield or returns than the existing crop. This result was later

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expanded by Just and Zilberman (1988) who show that the intensity of adoption depends on

whether the new technology is risk increasing or decreasing and whether risk-aversion is

increasing or decreasing with wealth. The development of the conceptual frameworks linking

risk with the adoption of new technologies was not accompanied by similar advances in

empirical work (Feder et.al. 1985). Empirical research was limited by the difficulties

encountered in measuring risk preferences, and the corresponding lack of consensus on the

best way to recover parameters such as the coefficient of risk-aversion. In practice, two

approaches have dominated: the elicitation of subjective probabilities of outcomes and the

direct elicitation of risk preferences. 1

The more common method of directly measuring risk in adoption literature is through

the elicitation of subjective probabilities. For example, subjective estimates of crop riskiness

influence the adoption of grain among Mexican farmers (O’Mara 1980), whilst the risk

perceptions of new seed varieties among maize growers in Malawi influenced both their

probability and intensity of adoption (Smale et al. 1994). Ghadim (2000) elicits subjective

yield and price probabilities from traditional crops and chickpeas from farmers in Western

Australia and finds that relative riskiness has a negative effect on adoption.

Studies of technology adoption that directly elicit risk preferences are scarce.

Binswanger (1980), who conducted experiments characterising risk attitudes in rural India,

was the first to directly measure risk-aversion in a low-income environment. He provided

respondents with choices between lotteries involving 50:50 chances of higher and lower

payoffs (and, intermittently, certainties). Knight, Weir and Woldehanner (2003) use

hypothetical questions to divide farmers into risk and non risk-averse groups in Ethiopia, 1 More abundant throughout earlier adoption literature, econometric methods have frequently estimated the probability distribution of output given input, measuring farmers risk attitudes’ through deviations in input choice from profit-maximising input choices (Moscardi and de Janvry 1977; Antle 1989; Pope and Just 1991; Chavas and Holt 1996). As misallocation of inputs can be related to credit and information factors, using this method to measure risk preferences can be highly dubious without a structural model with strong assumptions (Liu 2007; Alpizar et al. 2009).

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finding risk-aversion is related to lower levels of technology adoption. Liu (2007) conducts

lotteries with pair-wise choices to elicit preferences, finding Chinese farmers with higher

levels of risk-aversion are slower to adopt new cotton varieties. Such studies commonly

ignore the role of ambiguity in decision-making. In the context of the adoption of a new

technology, where decisions are made with limited prior knowledge, this is an assumption

that can be questioned. We do not make this assumption in our own study of decisions

regarding the adoption of non-glutinous rice. Instead, we examine the potential for

ambiguity-aversion, in addition to risk-aversion, as a possible explanation for some farmers’

reluctance to adopt new agricultural technologies in developing countries. To understand the

potential effects of ambiguity on decision-making, we first review some of the earlier work

on ambiguity.

Knight (1921) was one of the first to address the role of ambiguity in decision-making,

distinguishing between measurable known probabilities and unmeasurable unknown

probabilities. However, the concept of ambiguity was scarcely explored until Ellsberg (1961)

demonstrated the effect of ambiguity on decision-making with a thought experiment.

Essentially, Ellsberg (1961) demonstrated that many people would prefer to bet on a lottery

with an objectively known probability rather than a lottery with an unknown yet subjectively

equivalent probability and thereby exhibit ambiguity-aversion.

Various descriptive theories have attempted to explain attitudes to ambiguity (for a

review, see Camerer and Weber 1992). Einhorn and Hogarth (1985) proposed an anchoring

and adjustment model, where individuals use an initial estimate of a probability as an anchor

and then adjust it according to the ambiguity perceived in the situation and their ambiguity

preferences. Kahn and Sarin (1988) generalised the axioms underlying SEU to construct a

model accounting for decisions under both risk and ambiguity, with the anchor as the

normative expected probability. The individual’s adjustment process is determined by the

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anticipated amount of disappointment they feel when the ambiguous probability is below or

above their normative estimate.

Confirming Ellsberg’s conjecture, evidence of ambiguity-aversion has been found in

empirical studies (e.g. Becker and Brownson 1964; Slovic and Tversky 1974; Sarin and

Weber 1993; Capon 2009). The vast majority of experiments, however, are conducted in

laboratory environments with university students, and evidence for ambiguity-aversion

amongst populations in developing regions is more limited. For example, Henrich and

McElreath (2002) found no evidence for ambiguity-aversion among Chilean farmers, arguing

that ambiguity-aversion may be driven by cultural factors and that it does not generalize to a

developing agriculture context. Conversely, Akay et al. (2009) found evidence of strong

ambiguity-aversion among Ethiopian farmers.

Two papers investigate the role of ambiguity in technology choice among farmers in

developing regions and are similar, in spirit, to our study. Engle-Warnick et al. (2008) are the

first to empirically distinguish the effects of risk and ambiguity-aversion in technology choice

among subsistence farmers in rural Peru. Their findings suggest risk and ambiguity-aversion

are highly correlated, but were not significantly related to the adoption of new technology.2

Alpizar et al. (2009) conducts experiments with Costa-Rican farmers in the aftermath

of extreme climatic conditions, measuring the effects of risk and ambiguity in farmer

adaptation

Their approach differs from ours in that it doesn’t focus on a specific technology, instead it

defines a farmer as an innovator if she plants any modern crop in a twelve month period,

regardless of previous knowledge, either direct or indirect..

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2 Although they are able to show that ambiguity-averse farmers exhibit a less diversified crop portfolio.

to climate change, through the adoption of soil conservation practices. They show

3 Adaptation can be seen as the adoption of new technology with the anticipation of new shocks.

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that both risk and ambiguity preferences significantly influence adoption decisions,

concluding that ambiguity-aversion is an important factor in technology choice. Similarly to

Engle-Warnick et al. (2008), and contrary to our own work, they define the adoption variable

as simply the presence of the new technology, ignoring the possibility that a farmer might

only partially adopt a new innovation.

3. Non-Glutinous Rice in Central Laos

Lao PDR is a very poor country in SE Asia, heavily dependent on the agricultural

sector which in 200X accounts for nearly half of its GDP and employs 77% of its labour

force (Bountavy 2006). Poverty remains ubiquitous among farming households, with 87% of

the country’s poor living in farmer-headed households (NSC 1999). Rice production, mostly

produced on small family farms, dominates the agricultural sector, accounting for 50% of

agricultural output. Despite the introduction in 1986 of the New Economic Mechanism (NEC)

with the intention of liberalising the economy and broadening the nation’s exposure to

international markets, the largest proportion of the country’s agricultural output remains

focused on glutinous-rice farming. It consisted of 85% of rice production in 2004 (Schiller

2006). Laos has the highest per capita consumption and production of the variety in the world

and this crop represents roughly 20% of its GDP. For the purposes of the present study,

glutinous rice is the “existing” technology, to be compared with the introduction of “non-

glutinous rice” as a new technology.4

4 We focus on the two varieties most commonly adopted in the region, specifically the varieties VND 95-20 and CR203.

. Unlike glutinous-rice, which is typically grown for

direct consumption, non-glutinous varieties are cultivated as a base ingredient in the

production of noodles and beer. Widely produced in neighbouring Thailand and Vietnam, the

varieties offer farmers greater yields, shorter growth maturity and higher, more stable prices

(SNV 2009).

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This new crop has been promoted in Central Laos by the Dutch Development Agency,

in collaboration with local mills, since 200X . Typically, farmers adopting the crop enter a

contract with a miller, who agrees to purchase a specified amount of paddy rice at an agreed

upon price and time, whilst providing production inputs and credit, whilst the farmer agrees

to supply the contracted quantities at specified quality standards (moisture level). The price

paid is considerably higher than the price that farmers may receive for glutinous rice (in

2010). The additional revenue seems to translate into additional profits, as the two crops

require similar amounts of inputs. Since the adoption of non-glutinous rice requires little

initial capital and, although it creates linkages to domestic and foreign markets, it also seems

to be less risky with regard to market conditions than glutinous rice (given the guaranteed

prices), although there is certainly an element of trust needed, as millers may be personally

unknown to farmers so the possibility that contracts may not be honoured exists. That said the

adoption of the new crop variety has been slow.

In order to explain the adoption of non-glutinous rice, we conducted a household

survey and behavioural field experiments in Natai, a village in the central province of

Khammouane, Lao PDR during July 2010. The village was selected with the assistance of the

Netherlands Development Organisation (SNV), who has conducted research on the adoption

for non-glutinous rice in the province (SNV 2009). Natai was chosen because whilst non-

glutinous rice was introduced to this region during the 2009 dry season it has so far only been

partially adopted. This provides an ideal case study for examining the factors that affect the

adoption decision. The size of Natai also means that it was possible to interview 66 of the

total number of 69 household heads for this study.

Household heads were interviewed prior to the field experiments, leading to a rich

data set that, in addition to the standard information collected in these type of surveys

(household demographic and socio-economic characteristics, land use and crops produced in

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the last two seasons) also collected detailed information on the cultivation of glutinous and

non-glutinous rice varieties, including yield, price received, capital equipment, labour and

exposure to shocks. Table 1 presents summary statistics for the key variables of interest from

the household survey.

The average interviewee in the sample was 48 years old and had completed 5.2 years

of education. Nearly all farmers had received support from an extension service, with an

average of around 2 visits from extension services. Glutinous-rice was the predominant crop

cultivated in Natai village, with an average of 1.27 hectares per household. Non-glutinous

varieties were grown on an average of 0.92 hectares per household, with 54 (82%) of the

households growing some area of the crop, although nearly all of them have relatively limited

experience with non-glutinous relative to glutinous rice since its introduction in 2009.

To understand the degree of agreement between the farmers’ opinion of non-glutinous

rice and the opinion of its advocates, we asked farmers for their responses to a set of

statements which compared the characteristics of non-glutinous to glutinous varieties.

Farmers were asked to rate their agreement on a Likert scale of 1 to 5, where 1 indicated

strong disagreement, 3 indicated neutrality, and 5 indicated strong agreement. The responses

displayed in Figure 1, illustrate farmers’ perceptions of the new variety’s characteristics as

being largely positive. This implies that other behavioural factors lie behind their limited

adoption of non-glutinous rice rather than a simple preference for glutinous rice.

All survey respondents participated in decision-making experiments designedto

measure risk and ambiguity preferences. Given relatively low levels of formal education in

Natai, the experiments were implemented with the help of visual aids to assist the

respondents in developing a clear understanding of the probabilities of the alternative payoffs.

Participants in the experiment were paid for their participation and, depending on outcomes,

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could receive up to 40000 Lao Kip (LAK)5

4. Methods for eliciting risk and ambiguity preferences

.This amount is higher than the average daily

household income in the district and close to 2 days of rural wage. Although the number of

studies that conducts such experiments has increased, this type of data is still infrequently

used and for that reason we describe our procedure in detail regarding the elicitation of risk

preferences and ambiguity in more detail.

The vast majority of risk elicitation procedures are based on type of instruments

developed by Binswanger (1980) or Holt and Laury (2002). The Holt and Laury (2002)

procedures uses choices from a list of binary lotteries that differ in expected payoffs and

variance to infer parameters for risk-aversion from the choices made. Our instruments,

presented in Table 2, differs from the one employed by these authors, and instead is similar to

the approach of Akay et al. (2009) and Capon (2009) in asking respondents to directly

compare certain amounts and lotteries, in order to more directly elicit certainty equivalents

(CE) for the lotteries.. Obtaining a decision maker’s CE allows a comparison of risk

preferences across respondents6

A CE represents a certain amount that is equally preferred to a risky alternative. Once

the CE is elicited, the Risk Premium (RP), defined as the difference between the expected

value and the certainty equivalent, allows for inference regarding a decision maker’s attitude

towards risk. A positive risk premium implies risk-aversion, a negative risk premium implies

risk seeking and no risk premium implies risk neutrality.

.

For each participant, CEs were elicited using two different risky prospects, a coin toss

and an urn. The coin toss offered the participant equal probabilities of winning 20,000 Kip

and nothing. The urn, containing exactly 5 red and 5 yellow balls, offered the participant the 5 At the time of the experiment, the exchange rate was LAK 7,200=AUD $1.00 6 For prospect 𝑎𝑖, 𝑈(𝐶𝐸𝑖) = 𝐸[𝑈(𝑎𝑖)]

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possibility of winning 20,000 Kip if a red ball was drawn and nothing for a yellow ball,.

Although both have the same probability distributions and payoffs, the use of different

mechanisms allows us to account for potential bias towards a particular way of eliciting

preferences, leading to a more balanced assessment of risk preference

Participants were offered 11 choices between a certain payoff (option one) and the

risky prospect (option two), with the certain payoff increasing in 2000 Kip increments from 0

Kip to 20000 Kip. For small certain payments, most participants would prefer to play the

risky prospect, while for very large certain payments, most participants would prefer a sure

thing. Given this, at some point most participants will reveal their risk preferences by

switching from option 2 to option 1. We calculate the CE in similar fashion to Eggert and

Lokina (2007), as the midpoint between the lowest certain payment for which the participant

chooses option 1 and the highest certain payment for which they choose option 2. 7

The participants were informed they would receive real payment from the

experimenter, depending on the choices they made for each of the 11 options: participants

would draw a ticket, numbered from 1 to 11, and would play the prospect corresponding to

their selection for the respective choice. One was selected at random for payement, with the

participant either receiving a certain payment or playing the prospect depending on the

specific choice made when facing the selected option.

The distribution of the outcomes for both procedures is presented in Table 3. The

results suggest that participants were more risk-preferring for the urn prospect than coin toss.

The urn prospect displayed risk-preferring average CE of 11 000 compared to a marginally

risk-averse CE of 9939.4 for the coin toss, with 63.3% and 52.7% of participants risk-

preferring for the urn and coin toss respectively.

7 All subjects made consistent choices when switching over from option two to option one; that is they switched from gamble 2 to gamble 1 only once.

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In addition to these games, we followed Anderson et al.’s (1977) suggestion and also

asked farmers how they feel about risks. Participants were visually presented with a

numbered scale ranged from 1 to 10, where 1 represented the statement “I never like take

risks” and 10 represented “I always like take risks”, and asked to rank themselves in that

scale.8

Numerous empirical studies of ambiguity have tested Ellsberg’s (1961) thought

experiment, mostly in laboratory settings (Becker and Brownson 1964; MacCrimmon and

Larsson 1979; Kahn and Sarin 1988; Bowen et al. 1994). Previous field experiments

measuring ambiguity preferences in developing countries are scarce, limited to those

conducted by Engle-Warnick et al. (2008), Alpizar et al. (2009), and Akay et al. (2009).

Figure 3 presents the results from participants’ self-assessed risk. The majority (66.6%)

of the participants provided Likert scores between 6 and 10, with an average score of 6.48

(and standard deviation of 2.13). The results follow a distribution that is similar to the one

already described, suggesting that most participants are willing to take risks and think of

themselves as often willing to take risks. We delay the discussion of a formal test of the

concurrence between the three procedures for after the discussion of the results regarding the

elicitation of ambiguity preferences.

Our measure of ambiguity preference depicted in Table 4, is a variation of Ellsberg’s

2-colour urn experiment, and is similar to that used by Lauriola and Levin (2001) and Capon

(2009) in a laboratory setting. It involves a choice set between an unambiguous urn (option

one) and an ambiguous urn (option two), manipulating the objective probability of success in

the unambiguous urn whilst leaving the ambiguous urn unchanged.

8 Eliciting individual attitudes to risk using a singular self-assessment question has been frequently used as a proxy for risk aversion by agricultural economists (Kastens and Featherstone 1996; Patrick and Ullerich 1996; Bard and Barry 2000).

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Prior to the experiment, participants were informed that analogous to the risk

procedures, yellow and red balls rewarded them with 20,000 Kip and nothing, respectively.

Participants were presented with a set of 11 choices, where each choice asked them to select

between playing the unambiguous urn or the ambiguous one. In each choice, the

unambiguous urn held a known proportion of ten coloured balls, with the proportion of

yellow balls (and hence the p of winning) decreasing in increments of 0.1 for each successive

choice. This was reinforced visually, where for each choice the participant was shown one

yellow ball removed from the urn and replaced by a red ball. Participants were advised the

ambiguous urn contained 10 balls, although the number of each type of balls was not revealed.

Payment was determined in the same fashion of the risk experiments.

We build on Lauriola and Levin (2001) and Capon (2009), who value ambiguity

preferences as the objective probability of winning the unambiguous urn prior to crossing

over to the ambiguous urn. Our analysis follows similar lines to Lauriola and Levin (2001),

and Kahn and Sarin (1985) and set a normative anchor at p=0.5 for ambiguity-neutrality, we

correspondingly set our EMV equivalent at 10000 Kip. This implies that ambiguity-

preferring participants display an EMV greater than10000 whilst ambiguity-averse

participants will display an EMV smaller than 10000. To illustrate, and using the values

presented in table 4, a participant who crosses from the unambiguous urn over to the

ambiguous one on the third choice prefers the unknown ambiguous prospect rather than the

objective p=0.8 of the unambiguous prospect. Their midpoint-calculated EMV of 17000

reveals they are ambiguity-preferring. Conversely, crossing over at the eight choice would

they are averse to ambiguity, with an EMV of 7000.

There were several factors which made this procedure appealing in the field. The

unknown and known probability distributions were visually represented to the participants,

permitting a clearer understanding of the question. This was reinforced by the fact that the

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binary-choice list, payoffs and payment determination closely resembled the procedures used

to elicit risk preferences, allowing the participants to understand the procedure through prior

experience.

Table 5 presents the results of the probability equivalence procedure measuring

ambiguity preferences. The distribution of results is evenly spread and suggests the ambiguity

attitudes for farmers in Natai were fairly heterogeneous, with slight skewness towards

ambiguity-preference (53% of participants elicited EMVs of more than 10000 Kip).

Comparing these results to earlier papers using similar samples (Akay et al. 2009) and

procedures (Lauriola and Levin 2001), Lao farmers appear to be significantly more

ambiguity-preferring.

In addition to explore the role of preferences in the process of adoption of new

technologies, our data allows us to address two questions: the first, of methodological nature,

is whether the way risk preferences are measured matters for our conclusion regarding our

classification of respondents’ behaviour. The second, of more substantive nature, is whether

ambiguity and risk preferences are so similar that defy any meaningful distinction.

To address these questions, we estimate the correlation between the relative rankings

of participants’ results for each measurement using Spearman correlation coefficients. The

results are presented in Table 6, where values in bold are statistically significant at the 5%

level. In addressing the first question, it is important to notice that the correlations between

all risk measurements are statistically significant and relatively high: the positive correlation

between the coin toss and urn prospects (.5107, p < 0.01) largely suggests that the different

procedures lead to similar conclusions. Similarly, both prospects for the CE procedure are

also statistically correlated with the self-assessed risk measure.

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Finally, and contrary to previous work, we find that ambiguity measures exhibit no

statistical correlation with the risk measures at the usual 5% level of significance and only in

one of the cases, the coin-toss CE procedure, does it exhibit a correlation that is significant at

the 10% level: in our data, at least, ambiguity preferences are distinct from risk preferences.

5. Explaining adoption decisions

Our primary focus is to understand whether risk and/or ambiguity preferences have

any significant effect on the adoption of innovations. Because most of the households in the

village where we conducted our study have already adopted non-glutinous rice, we study

their decision in terms of intensity of adoption by specifying a model of the form:

𝑌𝑖∗ = 𝑋𝑖𝛽 +∈𝑖 (1)

Where 𝑌𝑖∗is the unobserved latent dependent variable that represents the proportion of

non-glutinous rice planted by farmer i; 𝑋𝑖 is the k x n matrix of observed explanatory

variables expected to influence adoption; 𝛽 is a vector of parameters to be estimated and ∈𝑖 is

a random error term, The observed proportion on non-glutinous rice grown by farmers, 𝑦𝑖, is

left censored at 0 (no adoption) if the unobserved latent variable 𝑌𝑖∗ does not exceed the

threshold level 0, after which it becomes a continuous function of the explanatory variables.

𝑦𝑖 = �𝑌𝑖∗, 𝑔𝑖𝑣𝑒𝑛 𝑌𝑖∗ > 00, 𝑌𝑖∗ ≤ 0

� (2)

Under the additional assumption that∈𝑖 ~𝑁 (0,𝜎2), we can estimate this relation as a

Tobit model (Tobin, 1958), an approach used previously in studies of agricultural technology

adoption, including studies of conservation adoption (Norris and Batie 1987; Gould et al.

1989) and the adoption of alternative crop varieties (Adesina and Zinnah 1993).

The explanatory variables include, in addition to risk and ambiguity preferences,

several other correlates of adoption identified in the literature and for which we have

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information, collected through the household survey that we conducted: farm size

(Binswanger 1978; Akinola 1987), visits from extension services (Polson and Spencer 1991;

Nkonya et al. 1997), age (Bultena and Hoiberg 1983; Gould et al. 1989) and years of

education (Duraisamy 1989; Liu 1991). Some summary statistics are presented in Table 7.

There are two concerns with our data. The first is that we only have cross-sectional

data collected after the adoption decision. Previous studies (for example, Besley and Case

(1993)) raised the concern that any ex-post measurement of explanatory variables could be

affected by the adoption decision, therefore being endogenous. Our selected covariates are

unlikely to suffer from this problem as they are unchanging over time, and as such unlikely to

be affected by initial adoption decisions that date as late as the 2009 dry season. The second

is that, given the correlation between the different measures of risk preferences,

multicollinearity may be a problem. To circumvent it, we will estimate a separate Tobit

model with each of the risk preference variables included separately.

Our estimates are presented in table 8. In columns (1) to (3), we estimate the Tobit

model with only one risk measurement, whereas the estimation in column (4) includes all risk

measurements. There appears to be little presence of multicollinearity caused by the risk

preference variables. Only negligible changes in standard errors are observed across all 4

models and thus with exception of the intercept, the significance of all estimates are

consistent across all 4 estimations. For that reason, we concentrate our discussion on the

estimates in column 4. Although we are mostly interested in the relative importance of risk

and ambiguity, it is important to notice that the sign of all other covariates are as expected,

given the results in the ex ante literature. However, the estimates are not precisely estimated

at the usual levels of significance of 5%. Focusing on our main concern, the importance of

individual preferences, an immediate first conclusion is that ambiguity preferences matter,

but risk preferences, irrespective of the specific measurement procedure, do not.

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As it is known, we cannot directly interpret the Tobit estimates given that, although

they allow us to observe both the significance and direction of the relationship between the

dependent and explanatory variables, the coefficients only represent the marginal effects of

changes on the unobserved latent variable or 𝛽 = 𝜕𝐸( 𝑌𝑖∗)/𝜕𝑋𝑖 . In order to understand

whether ambiguity also matters in an economic sense, we follow the Tobit decomposition

framework suggested in McDonald and Moffit (1980) to obtain the marginal effects of the

explanatory variables on the adoption probability and use intensity.

If we let the expected value of the dependant variable PROPORTION across all

observations be represented by 𝐸(𝑦𝑖) , the expected value of the dependant variable

conditional of a farmer growing non-glutinous rice be given as 𝐸(𝑦𝑖 ∣∣ 𝑦𝑖 > 0 ) and the

probability of the farmer being uncensored (i.e. the probability of adoption) be represented by

𝐹(𝑧), the cumulative normal distribution of z where 𝑧 = 𝑋𝑖𝛽𝜎

. The relationship between these

variables can be shown as:

𝐸(𝑦𝑖) = 𝐹(𝑧)𝐸(𝑦𝑖 ∣∣ 𝑦𝑖 > 0 ) (3)

Differentiating equation (3), the marginal effects of a change in variable 𝑋𝑖 on 𝐸(𝑦𝑖)

is expressed as:

𝜕𝐸(𝑦𝑖)𝜕𝑋𝑖

= 𝐹(𝑧)𝜕𝐸(𝑦𝑖 ∣∣ 𝑦𝑖 > 0 )/𝜕𝑋𝑖 + 𝐸(𝑦𝑖 ∣∣ 𝑦𝑖 > 0 )𝜕𝐹(𝑧)/𝜕𝑋𝑖

(4)

Equation (4) reveals that the marginal change in the observed dependant variable 𝑦𝑖

can be decomposed into our two parts of interest, represented in equations (5) and (6). The

marginal effect of variable 𝑋𝑖 on the conditional expected value 𝐸(𝑦𝑖 ∣∣ 𝑦𝑖 > 0 ), which we

can interpret as the change in adoption intensity, is:

19

𝜕𝐸(𝑦𝑖 ∣∣ 𝑦𝑖 > 0 )

𝜕𝑋𝑖= 𝛽𝑖 �1 −

𝑧𝑓(𝑧)𝐹(𝑧) −

𝑓(𝑧)2

𝐹(𝑧)2�

(5)

where 𝑓(𝑧) represents the standard normal density and 𝛽𝑖 represents the vector of Tobit

estimates for variables 𝑋𝑖. The change in the probability of adoption as variable 𝑋𝑖 changes is:

𝜕𝐹(𝑧)𝜕𝑋𝑖

= 𝑓(𝑧)𝛽𝑖/𝜎 (6)

The results of this decomposition are shown in table 9, distinguishing between the

marginal effects of changes on the probability and intensity of adoption. The estimates

suggest participants who were more averse to ambiguity had a greater likelihood of either

adopting less non-glutinous rice on their land or not adopting it at all. A farmer who has an

EMV of 5000 Kip less than another farmer (i.e. they were more ambiguity-averse)

subsequently has a 7.3% lower expected probability of adopting the new variety and is

expected to grow 10% less of the new variety on their plots if they have decided to adopt.

Our finding extends beyond the results of previous attempts to measure this association, by

demonstrating that the probability and intensity of technology adoption decreases with

ambiguity-aversion (Engle-Warnick et al. 2008; Alpizar et al. 2009).

Conversely, risk preferences appear to have no significant relationship with adoption

decisions. This contrasts with the long-held notion that risk-aversion prevents the adoption of

new technology (Feder 1980; Just and Zilberman 1988; Knight et al. 2003; Liu 2007). Given

there is low or no correlation with our ambiguity measure, this suggests the decision making

process under ambiguity is different from the decision-making process under risk.

Visits from extension services show the strongest significant relationship to the

adoption of non-glutinous rice; an additional visit from extension services increases the

20

probability of adoption by 7.2% and the expected intensity by 9.8%. Although this is

consistent with the findings of Nkonya et al. (1997), care must be taken in determining the

impact of this variable due to possibility of reverse causality; where extension visits increased

if non-glutinous adoption increased. Farm size significantly and positively influenced

adoption. A farmer with 1 Hectare more in farm size is 2.4% more probable to adopt and will

grow 3.3% more of the new variety if adopted. Larger farmers are likely to have more

opportunities to learn about the new technology, have more incentive to adopt it, and are able

to bear risks associated with early technology adoption (Feder et al. 1985). The variables of

farmer age , household size, education and gender did not significantly influence non-

glutinous rice production.

6. Conclusions

Given the importance of innovation, the phenomenon of incomplete adoption of new

technologies has appropriately received much attention in agricultural and development

economics. In addition to a number of market constraints, risk-aversion dominates the

discussion on the behavioural determinants to technology adoption. Somewhat paradoxically,

given that the outcomes of innovations are, almost by definition, unknown to adopters (at

least the earlier ones), not much attention was paid to preferences towards scenarios with

unknown probabilities, that Ellsberg (1961) called ambiguity.

In this paper we have investigated whether a farmer’s aversion to ambiguity is

important in explaining adoption decisions. To answer this, a unique dataset was collected,

combining field experiments intended to measure the behavioural parameters of risk and

ambiguity preferences with a household survey, collecting information on the technology

choices and socioeconomic characteristics of farmers in one developing country, Lao PDR.

Given the way we measure innovation, we are able to extend beyond the simple binary

21

adoption variables employed by Engle-Warnick et al. (2008) and Alpizar et al. (2009).Also,

we are able to avoid the problems with the definition of innovation that limit earlier studies

(for example, Engle-Warnick et al. (2008)).

We present two main conclusions. First, farmers have separate risk and ambiguity

preferences. Second, and perhaps more importantly, we find that ambiguity-aversion, but not

risk-aversion, significantly reduces both the probability and intensity of adoption. These

findings are important for two reasons.

Firstly, these findings have potential policy implications. The vast majority of the

literature that proposes risk-aversion as a possible explanation for hindered adoption in

developing countries then prescribes crop insurance (Liu 2007) and money-back guarantees

(Sunding and Zilberman 2001) as a means to potentially hedge against production risk and

reduce the fear of loss associated with new technology. Our finding that ambiguity, not risk,

is important in explaining adoption decisions, implies that policy should be directed at

ensuring farmers have access to greater information about the performance of new

innovations, allowing them to make more accurate subjective probability evaluations on new

innovations. Our additional finding, that adoption responds positively to extension,

complements this. The best way of providing extension is a topic of intense debate in the

literature, and we do not add to it. We simply argue that, in this case, they seem to be useful.

Finally, this study connects the findings of field experiments to tangible decisions in

the real world. The external validity of game experiments has been the subject of long

standing debate (Samuelson 2005). Unlike experiments conducted in laboratory environments

which hypothesise how risk and ambiguity dictate decision-making, our subjects are the

decision-makers. The results of this study suggest that game experiments can predict real

decisions, hence strengthening their validity.

22

We conclude our paper with suggestions for further contributions to the understanding

of the role of ambiguity in technology adoption. Previous studies have demonstrated that

farmers update their subjective beliefs of new technology over time through learning from

their own experience (e.g. Ghadim 2000) and their social network (e.g. Foster and

Rosenzweig 1995), increasing their likelihood of adoption. Future investigations could

potentially link this with our findings, determining how learning impacts the adoption

decisions of farmers constrained by ambiguity-aversion.

Our experimental procedures elicited participants’ risk and ambiguity preferences

across the domain of gains. Further comprehension of the importance of risk and ambiguity

on a farmers adoption decisions could be achieved by measuring preferences over gains and

losses. Prospect theory (Kahneman and Tversky 1979) describes a “reflection effect” where a

decision-maker exhibits risk-aversion in the domain of gains and is relatively risk-seeking in

the domain of losses, perhaps more accurately predicting the behaviour of inexperienced

individuals (List 2003). This reflection effect has also been observed under ambiguity, with

differing attitudes over gains and losses by Chakravaty and Roy (2009). There lies potential

for future research to identify whether this exists among farmers in the developing world and

what bearing it has on their preferences to risk, ambiguity and adoption.

23

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Figures and Tables

Figure 1: Likert Comparison between rice varieties with means denoted

2.27

2.66

2.09

2.96

3.10

3.18

3.03

3.00

2.75

2.60

3.08

2.34

1.13

2.60

Needs More Labour for Preparing

Needs More Labour for Planting

Needs More Labour at Harvest

Needs More Fertilizer

Needs more Pesticide

More subsceptible to Rats

More subsceptible to Pests

More subsceptible to Disease

More subsceptible to Lack of Water

More subsceptible to Excess Water

Needs more Seeds

Costs more to maintain

Harder to Sell

Harder to Eat

I think non-glutinous rice is..

33

Figure 2: Results Self-Assessed Risk Attitude

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7 8 9 10

0 0

5

13

4

109

12

8

5

Freq

uen

cy

Self-Assessed Risk Scale

34

Table 1: Descriptive statistics of variables used

Variable Mean (S.D.)

Age 48.02 (10.85)

Years of Education 5.20 (3.02)

Female 0.18 (0.36)

Household Size 4.14(1.31)

Visits from Extension 2.20 (1.01)

Total Land Size (Ha) 2.43 (1.50)

Non-Glutinous Rice Planted (Ha) 0.92 (0.84)

Glutinous Rice Planted (Ha) 1.27 (1.13)

Observations 66

Table 2: Certainty Equivalent Procedure

Turn Option One:

Certain Payments

Option Two: Urn (P(Payoffs))

Switchpoint from 1 to 2

CE at Switchpoint

Implied Risk Preference

1 0 0.5(0),0.5(20000) - 0

Risk Averse

Risk Neutral

Risk Preferring

2 2000 0.5(0),0.5(20000) 1 to 2 1000 3 4000 0.5(0),0.5(20000) 2 to 3 3000 4 6000 0.5(0),0.5(20000) 3 to 4 5000 5 8000 0.5(0),0.5(20000) 4 to 5 7000 6 10000 0.5(0),0.5(20000) 5 to 6 9000 7 12000 0.5(0),0.5(20000) 6 to 7 11000 8 14000 0.5(0),0.5(20000) 7 to 8 13000 9 16000 0.5(0),0.5(20000) 8 to 9 15000

10 18000 0.5(0),0.5(20000) 9 to 10 17000 11 20000 0.5(0),0.5(20000) 10 to 11 19000

35

Table 3: Ambiguity Preferences: Ellsberg Urns

Turn Option One: Urn

(P(Payoffs)) EMV 𝒂

Option Two

Crossover from One to

Two

EMV at Crossover

Implied Ambiguity Preference

1 1(20000) 20000 ?,? - 20000 Ambiguity Preferring Ambiguity Neutral Ambiguity Averse

2 0.1(0),0.9(20000) 18000 ?,? 1 to 2 19000 3 0.2(0),0.8(20000) 16000 ?,? 2 to 3 17000 4 0.3(0),0.7(20000) 14000 ?,? 3 to 4 15000 5 0.4(0),0.6(20000) 12000 ?,? 4 to 5 13000 6 0.5(0),0.5(20000) 10000 ?,? 5 to 6 11000 7 0.6(0),0.4(20000) 8000 ?,? 6 to 7 9000 8 0.7(0),0.3(20000) 6000 ?,? 7 to 8 7000 9 0.8(0),0.2(20000) 4000 ?,? 8 to 9 5000

10 0.9(0),0.1(20000) 2000 ?,? 9 to 10 3000 11 1(0) 0 ?,? 10 to 11 1000

𝑎 Expected Monetary Value

36

Table 4: Outcomes of CE Procedures

Implied Risk Preference

Elicited CE Urn

Frequency (%)

Coin Toss

Frequency (%)

Risk Averse

Risk Neutral

Risk Preferring

1000 2 (3%) 1 (1.5%)

3000 3 (4.5%) 6 (9.1%)

5000 3 (4.5%) 8 (12.1%)

7000 8 (12.1%) 8 (12.1%)

9000 8 (12.1%) 8 (12.1%)

11000 13 (19.6%) 11 (16.7%)

13000 7 (10.6%) 10 (15%)

15000 19(28.9%) 10 (15%)

17000 2 (3%) 4 (6%)

19000 1 (1.5%) 0

Mean (S.D.) 11000 (4165.8) 9939.4 (4299.8)

Median 11000 11000

*Where a CE of 10000 indicated risk-neutrality, less than 10000 indicated risk-preferring and greater than 10000 indicated risk aversion.

37

Table 5: Outcomes of Ambiguity Procedure

Implied Ambiguity Preference

Elicited EMV Frequency (%)

Ambiguity Averse

Ambiguity Neutral

Ambiguity Preferring

1000 0

3000 3 (1.5%)

5000 5 (7.6%)

7000 10 (15.15%)

9000 13 (19.7%)

11000 9 (13.6%)

13000 10 (15.2%)

15000 8(12.1%)

17000 4(6.1%)

19000 4(6.1%)

Mean (S.D.) 10845.29 (4202.785)

Median 11000

Table 6: Correlation of Procedure Results

Spearman rank correlations 𝒂

Risk: Coin Toss

Risk: Container

Risk: Self-Assessed

Ambiguity

Risk: Coin Toss 1 Risk: Urn 0.511 (0.00) 1 Risk: Self-Assessed 0.247 (0.045) .2820 (.022) 1

Ambiguity 0.216 (0.082) .097 (.440) -0.032 (0.798) 1

𝑎 P-values are in parentheses

38

Table 7: Definitions of Variables in Empirical Model

Dependent Variable

PROPORTION The proportion of total plot area of which non-glutinous rice varieties are planted.

Independent Variables

AGE Age of the household head, measured in years.

GENDER Gender of the household head, where male = 1.

HOUSEHOLDSIZE Number of people in the household

FARMSIZE Total plot area, measured in hectares.

VISITFROMEXT. Visits from extension agents; measured as number of visits

EDUCATION Education of farmer, measured in years of education.

AMBIGUITY Ambiguity preferences, measured as the elicited Expected Monetary Value.

COINTOSS Elicited risk preferences using urn prospect, measured as the elicited Certainty Equivalent.

URNGAIN Elicited risk preferences using urn prospect, measured as the elicited Certainty Equivalent.

PSYCHOMETRIC Self-assessed risk, measured on scale of 1 to 10.

39

Table 8: Tobit Left-Censored Regression Estimates

Independent Variablea (1) (2) (3) (4)

INTERCEPT -.45764* (.24493)

-.37021 (.25304)

-.39585 (.24469)

-.39895 (.25106)

AGE -.00023 (.00298)

-.00048 (.00300)

-.00052 (.00302)

-.00022 (.00299)

HOUSEHOLDSIZE -.00834 (.02712)

0.0008 (.02689)

-.00061 (.02660)

-.00741 (.02714)

GENDER .00226 (.09363)

.01648 (.09439)

.01393 (.09419)

.00519 (.09302)

FARMSIZE .04466* (.02421)

.04143* (.02426)

.04103* (.02444)

.04564* (.02439)

VISITFROMEXT. .13290*** (.03896)

. 12472*** (.03902)

.12471*** (.03925)

.13109*** (.03972)

EDUCATION .00799 (.01079)

. 00590 (.01110)

.00629 (.01084)

.00579 (.01113)

AMBIGUITY .00003*** (7.50e-06)

.00003*** (7.45e-06)

.00003*** (7.45e-06)

.00003*** (7.47e-06)

RISK CE: COINTOSS 8.84e-06 (7.46e-06) - - .00001

(8.73e-06)

RISK CE: URN - -1.57e-06 (7.54e-06) - -8.34e-06

(8.90e-06)

PSYCHOMETRIC - - .00225 (.01433)

.00032 (7.48e-06)

Estimated 𝑌𝑖∗ at means .34830 .34900 .34880 .37490

Variance of Error Term .23430 .23633 .02353 .23249

Pseudo 𝑅2 .564 .533 .532 .5856

Observations = 66

Note: standard errors within parentheses. ***: significant at 1%, *: significant at 10%

40

Table 9: McDonald and Moffit Tobit Decomposition

Marginal Effect

of:

Adoption

Probability Standard

Error Expected Use

Intensity Standard Error

AGE -.0001239 .00166 -.0001704 .00229

HOUSEHOLDSIZE -.0041168 .01509 -.0056603 .02073

GENDER .0029232 .05308 .0039528 .07061

FARMSIZE .0253438* .01509 .0348459* .01864

VISITFROMEXT. .0727838*** .02876 .1000726*** .03068

EDUCATION .0032156 .00624 .0044212 .0085

AMBIGUITY .0000145*** .00001 .00002*** .00001

COINTOSS 7.23e-06 .00001 9.95e-06 .00001

URNGAIN -4.63e-06 .00001 -6.37e-06 .00001

PSYCHOMETRIC .0001789 .00846 .0002459 .01163

***, significant at 1% *, significant at 10% Expected value of 𝐸( 𝑦𝑖 ∣∣ 𝑦𝑖 > 0 ) at means of X =0.375 Expected probability of adoption at means of X= 0.9260