Microsoft Word - Risk Indviduality in Crisis Planning.doc





Risk Indviduality in Crisis Planning: the Case of Gender in Amercan Agriculture 

DANA L.K. HOAG 
Colorado State University 

Professor, Agricultural and Resource Economics 
Fort Collins, CO  80523-1172 

dhoag@colostate.edu 
 

CATHERINE M H KESKE 
Colorado State University 

Assistant Professor, Soil and Crop Sciences 
Associate Director, Institute for Livestock and Environment 

Fort Collins, CO  80523-1170  USA 
Catherine.Keske@colostate.edu 

 
REBECCA GOLDBACH 

Research Assistant, Agricultural and Resource Economics,  
Fort Collins, CO  80523-1172 

 

 

 

Crisis response can be improved by accounting for differences in risk perception.  We confirm that gender 
individuality can be an important divider by using a case study of agricultural producers.  Women were more risk 
averse and less confident.  However, we found less difference than studies have found in other sectors.   Our 
research shows that context matters.  Results will vary by geographical location.  Gender is one division that should 
be considered in crisis planning; however, it will not always be important. 

Keywords: Agriculture, Gender, Risk Management, Crisis Response. 

 

1. Introduction 

 As related to crisis response, "risk management … 
encompasses a broad range of activities to increase 
capacities and reduce vulnerabilities to prepare for, cope 
with, respond to and recover from disaster events.1”  
But exactly whose risk is being addressed?   No single 
response plan can be universally effective since 
everyone perceives and reacts differently to risk.  The 
effectiveness of a plan, however, can be improved by 
targeting groups when there are clear and systematic 
divisions or subcultures.  For example, a plan for 

responding to extreme weather might group livestock 
pastoralists in Kenya differently than crop producers.    
It is difficult and sometimes impossible, however, to 
accurately aggregate, homogenize or stereotype social 
systems.  For example, pastoralists from differing tribes 
might respond to risk as differently from each other as 
they do from crop producers.  Nevertheless, it is 
important to develop crises response plans that properly 
account for the social contexts in which risks occur, 
especially when the differences are large and/or 
important1.     

Journal of Risk Analysis and Crisis Response, Vol.1, No. 1 (July, 2011).

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A growing body of research shows that there might 
be a systematic division between men and women when 
it comes to risk.  According to the Hyogo Framework 
for Action 2000-2015: on Building Resilience of 
Nations and Communities to Disaster, “a gender 
perspective should be integrated into all disaster risk 
management policies, plans and decision making 
processes, including those related to risk assessment, 
early warning, information management, and education 
and training.2" However, an unsettled question in the 
literature is whether men and women perceive and react 
to risk in the same way.  For example, some studies 
show that women are more risk averse than men3 or that 
women are less confident.4 Others have found no 
differences.5   Whether differences are found likely 
depends on the context.    If, when and where 
differences are found, they should be integrated into risk 
analysis and crises response.  That is, women and men 
are a good example of two sub-cultures that may need 
very different plans as directed by the Hydro 
Framework for Action.  However, as we will show, 
every case could be unique.  Therefore,  no category, 
even gender, is automatically different enough to justify 
unique risk planning. 

Risk analysis in economics and other financial 
fields can be a useful guide where there can be wide 
differences in risk attitudes.6 The goal of this study is to 
apply that knowledge to examine the differences in risk 
perception and management between men and women 
in one setting that can confirm whether risk gender 
individuality is important to account for in crisis 
planning.  Specifically, we examine farm or ranch 
opperators/producers in the United States.  There is no 
information about risk management differences across 
gender in agriculture, despite the fact that U.S. Census 
data shows that female-operated farms are a rapidly 
growing component of the sector.7  

We base our analysis on two surveys conducted at 
risk management programs in 2008.8  Data were 
collected in a self assessment survey and a revealed 
preference survey connected to a computerized risk 
management simulation program called Ag Survivor.9 
We examine differences between men and women in the 
areas of  risk preference, confidence, motivation,  and 

influence .  The purpose of this particular examination is 
to demonstrate that differences do occur, but that gender 
differences in agriculture are different than gender 
differences in other business sectors.  Due to these 
differences, results might not necessarily transfer to 
females in Kenya.  However we can learn some lessons 
about why women and other sub-cultures might need 
individual attention.   

2. Literature Review 

Female business ownership has increased steadily 
in the United States.  According to a 2006 study10, 
women now own 41% of all privately held firms in the 
United States.  Census data found that the number of 
women who owned businesses grew 43% from 1990-
1994.11 According to a 2001 article, “the relative surge 
of women entrepreneurs in the last decade has been 
nothing short of revolutionary.11” Census data did not 
start differentiating male and female farm operators 
until 1978, but it is assumed that females have been 
involved in agriculture for much longer. Commercial 
agriculture in the United States is widely dominated by 
males; as of 2002, only 5% of commercial farms in the 
US were operated by women. The numbers for 
commercial farms show greater differences in farm 
operation across gender than do private farms, as this is 
where females have been employed for decades, often 
as co-operators with their husbands.  The number of 
women operated private farms is increasing.  Women 
were principle operators of 209,784 farms.12 This 
number increased to 237,819 in 2002, an increase of 
13.4%. 13   The National Agricultural Statistics Service 
created a publication entitled Women in Agriculture: 
Quick Facts from the 2002 Census of Agriculture which 
highlight women’s impact on agriculture.14 The data 
show that female-operated farms are smaller than male-
operated farms both in acres and sales; women’s farms 
also have a smaller average income than male farms, 
indicating a less favorable financial position.  

Finance theory suggests that the higher the risk, the 
higher the expected rate of return on investment. As will 
be shown later, females have been found to be more risk 
averse than males.  This would suggest that males, who 
have a higher risk preference, will have higher returns 
on their investments; thus, differences in risk taking 

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propensity may help explain differences in the financial 
performance of male- and female-owned businesses.  A 
2003 study5 found that although profits are significantly 
higher for males, so are the variations in profits.  After 
adjusting for the variability associated with risk, this 
study found no significant differences between the 
performances of male- and female-owned businesses.  
The lower risk-taking propensity in females can also 
help explain the differing level of growth of male- and 
female-owned firms, where male-owned firms show a 
faster rate of growth.  Based on their greater risk 
aversion, female entrepreneurs seem to be more 
concerned than male entrepreneurs with the risks 
associated with fast-paced growth and tend to 
deliberately adopt a slow and steady rate of expansion.15   

It has also been found that women accumulate less 
savings for retirement and that this may, at least in part, 
be connected to their risk preferences.16  A 1998 study16 
displays how risk aversion can affect wealth 
accumulation later in life, and that women’s lower risk 
preference may result in their having less wealth in 
retirement then men.  Their review of the literature finds 
that when wealth equals $20,000, single women are 
predicted to hold 43% of that wealth in risky assets and 
single men are predicted to hold 51% of their wealth in 
risky assets.  When wealth increases to $100,000, both 
sexes will allocate a greater percentage into risky assets 
but for males this increase is greater than for women (an 
increase for women of 19% and an increase for men of 
28%). 

One study looked at differences across gender 
related to the optimal storage rule.17 The results from 
this analysis were as follows: women sell less often than 
men, men sell earlier than women, and the longer a 
producer stores the lower the price the producer is 
expected to receive. The authors state that the most 
likely explanation for women having fewer sales than 
men is that men gain more utility from trading than 
women.  In other words, the authors attribute this 
difference to men’s higher propensity to enjoy 
competition.  Overall, the authors found that because 
women store longer, they tend to make 1.4 cents less per 
bushel than men.   

 

2.1. Risk Preference 

 Past research, based on finance theory, found that 
greater risk-taking preferences are positively correlated 
to performance in small firms; however, this past 
research has defined performance in terms of sales and 
profit.  When results are controlled for risk and the 
variations in financial profit caused by risk, differences 
in performance between genders disappear. Women 
appear to be more risk averse than men.3,18  Studies 
found that females stay in the market less across all 
levels of cost than males, indicating that females have a 
lower risk preference.18 

2.2. Confidence 

 Studies have also found that women are less 
confident and estimate a higher likelihood of the 
negative outcome, making them less likely to take the 
risk.4 Psychological studies show that most people are 
overconfident about their own relative abilities, and this 
overconfidence affects business performance.  Research 
on confidence and business performance has found that 
overconfidence is not necessarily a good thing,19 finding 
that overconfidence can lead individuals to undertake 
activities that are not in their best interest. Research has 
also shown that men are more overconfident than 
women.20,21 One study that finds such results is Grain 
Price Expectations of Illinois Farmers and Grain 
Merchandisers.22  These researchers found that the 
phenomenon of overconfidence holds in their sample 

2.3. Motivation   

Individual motivations can highly influence 
business success.  Studies found that women indicate 
their most important entrepreneurial motivations are: 
desire for challenge, self-determination and desire to 
balance family and work responsibilities.  For women, 
profits and growth were important, but not as important 
as the above-stated motivators.  In contrast, male 
entrepreneurs were most motivated by the need to 
improve their positions in society for themselves and 
their families.23 ” If females’ main motivations are not 
sales and growth, it could explain some of the 
performance gap between the genders. However, 

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research on women entrepreneurs has found differences 
in motivations across women.24-27 

2.4. Influence 

According to one study, “for decades, researchers 
in various domains of the bio-behavioral sciences have 
had an enduring affinity for the idea of the dominant 
male….Females, by contrast, are often portrayed as 
being far less aggressive, more communal, and less 
interested in social politics.28" If this statement is true, it 
might affect how individuals influence the decision-
making process in their business.  An individual’s 
influence over business decisions will affect their ability 
to impact the performance of their operation.  Research 
indicates that males exert more influence over the 
decision making process.29 The literature confirmed 
many common gender findings, such as men describing 
themselves as having a higher ability to influence 
others, and peers viewing males as having higher 
influencing ability.28   

3. Research Methods 

The data in this study was collected from 
agricultural producers that live and work in Colorado.   
Two survey instruments were developed and 
administered to measure gender differences in 
agricultural producers.  The first instrument, a self-
assessment survey, asked questions about how 
individuals perceive themselves with regard to risk 
preferences and decision-making variables. The second 
survey was a revealed behavior instrument developed to 
accompany a computerized simulation game called Ag 
Survivor,9 which simulates realistic farm management 
decisions.    Using experimental risk preferences based 
on a computer simulation to compare risk seeking by 
females and males has been done before.18 We agreed 
with these authors that a computerized approach was the 
best option because it “reduces the likelihood of gender 
effects from peer group pressure, public performance 
and perception of others relative to self.18"  

 
Four hypotheses were developed that correspond to 

the four areas where we found differences between men 
and women in the literature: 

1. H1: RISK AVERSION: Women show more 
risk aversion than men. 

2. H2: CONFIDENCE: Women will display less 
confidence about their decisions than men 

3. H3: MOTIVATION: Women are less 
motivated by profit than men  

4. H4: INFLUENCE: Women will have less 
influence than men. 

Each survey addressed these four hypotheses.  The 
self-assessment survey assigned a numerical scoring 
system using a scale of one to five for 10 questions.   
The Ag Survivor survey ranked individual risk 
preference on a scale of zero to three, and confidence on 
a Likert scale of one to five based on the way each 
survey respondent played the Ag Survivor scenario.  
The averages of scores for both surveys were compared 
across gender and analyzed using a one- and/or two-
sample t-test assuming unequal variances in Microsoft 
Excel. 

There were 82 usable Self-Assessment surveys and 
81 useable Ag Survivor surveys.  The Self-Assessment 
survey included 61 females (74%) and 21 males (26%).  
The Ag Survivor survey had a more equal distribution 
with 44 females (54%) and 37 males (46%).   A 
summary of the participant demographics is presented 
in Table 1.   Three demographic variables were 
collected: age, number of children, and marital status. 
Marital status was the only statistically significant 
variable that was different between the genders. Sixty-
five men and 45 women reported their age on the 
survey, with the male average age being 45 years and 
the female average 48 years.  The male age range was 
23-77 years, while the female age range was 21-84 
years. Sixty-one men and 45 women reported their 
number of offspring.  The average number of children 
for males was 1.8 while the average number for females 
was 1.4.  The range of children was also similar across 
gender, with males ranging from 0-6 children and 
females ranging from 0-5 children.   

 

 

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 Male Female 

Average Age 45 48 

Percent Married 81 57 

Average Number of 
Children 

1.8 1.4 

Table 1: Demographic Features of Survey Respondents 

3.1. Self-Assessment Survey 

The Self-Assessment survey was administered to 
individuals at risk conferences around Northern 
Colorado.  To conceal their identity, but link the two 
surveys, individuals were assigned a common number 
for the self-assessment surveys and the Ag Survivor 
survey.  A linked number also saved subjects the trouble 
of filling out demographic information twice.  

The Self-Assessment survey consisted of ten 
questions, some of which had multiple parts, leading to 
16 individual parts. Two of the parts aimed to assess 
whether the individual had a business partner and, if so, 
what relationship the partner had with them (spouse, 
friend, etc.).  Five of the parts asked individuals 
questions about their investment portfolio or about 
hypothetical investments they could make. There was 
also an investment question asking about the business 
partner’s investment portfolio.  Two questions asked 
about the respondent’s  perceived capability of running 
the business alone, one about the individuals perceived 
capability and another about their business partner’s 
capability.  There was also a part addressing with 
overall business satisfaction, influence, correctness of 
decisions made, positive motivations, and negative 
motivations for the respondent’s actual business.  The 
survey ended with a question asking individuals who 
they seek input from when making a business decision. 

3.2. Ag Survivor Survey 

The revealed behavior, Ag Survivor survey was 
administered at the same conferences as the Self-
Assessment survey.  At each of these conferences, 
individuals participated in a farm computer-simulation 
program called Ag Survivor.9  Participants were asked 

to track both their group decisions and the decisions 
they would have made had they been acting on their 
own.  The survey also asked participants to document 
how confident they were in their decisions.   Surveys 
were administered at conferences that used different 
versions of the Ag Survivor game, so the decisions had 
to be normalized across versions to allow comparison 
across conferences.  The conferences had different 
versions since the Ag Survivor program is targeted to 
provide realistic risk management decisions to different 
groups.  Participants played the Lazy U Ranch scenario 
(about sheep) at four conferences, the King Family 
Ranch (about cattle ranching) at one conference, and 
The Wheatfields (wheat farming) at two conferences.  

The Ag Survivor program9 is an innovative risk 
research and education effort to help farm managers 
understand and explore risk management decisions and 
evaluate the financial consequences of those decisions 
with the presence of risk.  Ag Survivor uses real world 
farm/ranch settings, probabilities, and impacts to depict 
the financial effects of risk.  Participants must make 
management decisions for the operation through several 
decision making periods.  After each period, random 
outcomes are generated based on probability and the 
management teams receive updated prices, yield 
information, inventories, etc.   Risk management 
decisions included buying and storing hay for the 
winter, buying crop insurance, forward pricing, and 
hiring a shepherd to guard sheep from predators.  A 
brief description of each program is provided below. 

All three scenarios are set up over two years of 
decisions, with each year having four periods and each 
period involving one to four decisions. The Lazy U 
Ranch is a hypothetical sheep operation in the 
mountains of Colorado, which runs 1500 head of ewes 
in a range flock operation utilizing a mixture of public 
and private land.  Each year, participants may choose 
one of three options for marketing their lamb crop:  
forward contract weaned lambs to a feedlot for October 
delivery, sell weaned lambs to a feedlot on the October 
cash market, or retain ownership of the weaned lambs 
through the feedlot and sell them on the February cash 
market for slaughter.  The King Family Ranch is a 
hypothetical cow/calf/hay operation in the mountains of 

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Colorado.  They raise 350 acres of hay each year and 
use much of their production as winter feed for their 500 
cows.  They typically calve in March-April and sell 
weaned calves in October.  The Wheatfields is a 
hypothetical wheat/fallow farming operation in eastern 
Colorado. They farm 6,000 acres of farmland, typically 
harvesting 3,000 acres of winter wheat annually.  They 
also have 100 head of cows that typically calve in 
March-April with weaned calves sold in October.   

4. Results 

4.1. Risk Preferences 

Three questions in the self-assessment survey 
addressed risk preferences.  Two of three questions, 1 
and 5, showed no statistical difference between men and 
women.  Question 1 asked about the riskiness of the 
respondent's investment portfolio.  Both male and 
female agricultural producers report their investment 
portfolio to contain very little or little risk.  Self- 
assessment question 5 asked individuals to select 
between four investment choices that presented different 
risk levels.   

On the other hand, self-assessment question 4 asked 
individuals to list their financial investment portfolio by 
levels of risky assets.  When the averages of males and 
females were compared, the p-value was significant 
using a one-tailed test but not significant using a two-
tailed test.  The two-tailed test would be significant at 
0.10.  

When the male and female averages were compared 
in the Ag Survivor survey, using a two sample t-test 
assuming unequal variances, the p-value was significant 
and showed women are slightly more risk averse than 
men.  Considering all results from both surveys, women 
show a slightly higher aversion to risk than men. 

4.2.  Confidence 

There was no statistically significant difference in 
the questions directly aimed at differences in male and 
female confidence.  However, when asked about their 
capability of taking over the business if a partner 
became unable to, men scored significantly higher.  
Therefore, males stated that they feel more capable 

about taking over the business on their own.  It is 
believed that an individual’s confidence will affect their 
perceived personal capabilities; specifically, the higher 
an individuals’ confidence the more capable their 
perception of oneself.  The literature indicates that 
males are more confident than females, which would 
lead to males perceiving themselves as more capable 
than females.  Our combined results indicate that men 
show some signs of more confidence, but less so than 
implied in studies of other types of businesses. 

4.3 Influence 

We also analyzed whether there was a gender 
difference in perceived influence over the business 
decision making process. The influence score for males 
was 2.76, while the influence score for females was 
2.72, which presented no significant difference.  This 
contradicts previous literature;28,29 their findings 
indicate that males will exert more influence than 
females.  

4.4. Motivation 

 We examined a total of 12 potential places where 
we might find different motivations between men and 
women:   

Negative motivations: Receiving a poor price, 
Producing a low yield, Not having enough money for 
retirement, Having to add an additional employee to the 
operation, Facing a legal action against you, You or 
your business partner getting sick/injured and not able 
to work.  

Positive motivations: Growth of business, Savings for 
children’s future, Sense of accomplishment or 
recognition, Cushion to deal with illness or injury, To 
make ends meet, Independence 

Only three variables showed significant differences.  
Males were more motivated to avoid the risk of a poor 
price and poor yield than females.  Therefore, men place 
more value on avoiding low profits than women.  
Female operated farms have been found to have a lower 
net income than male farms.  If females are less 
motivated to reduce the risk of a poor price or yield, 
instead valuing other motivators as greater influences, it 

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could help explain this difference in net income.    The 
other significant variable was that men are more 
concerned saving for their children's future. 

5.  Discussion 

There appears to be some differences in risk 
preferences between men and women, although they do 
not appear very strong. Some questions did not reveal 
any statistical difference between risk preferences, but 
differences were measurable in one question on the self 
assessment survey and in behavior as men and women 
played the Ag Survivor scenarios.  Men were more 
confident than women in only one way, their perceived 
capability of taking over the business on one’s own.   

In terms of motivation, men were found to be 
statistically different from women in three cases.   Men 
were much more concerned about poor prices or yields, 
which are highly related to profits, than women.  
Finally, there appears to be no difference in influence 
when it comes to agricultural producers. 

Our purpose was to take a close look at whether 
female and male agricultural producers in the United 
States had significant differences in the way they 
perceive and react to risk.  Based on the body of 
literature from other studies, we should have found 
bigger differences.  We can only speculate, but perhaps 
agricultural women have fewer differences because 
they, like most men, grew up on a farm or ranch and are 
therefore relatively comfortable with how the property 
is managed.  This shows, circumstantially, that context 
matters and that results will therefore be very different 
from one location to the next.  Returning to our example 
of Kenyan pastoralists, women have an extremely less 
say in management issues than American women,30 and 
face different risks if catastrophe strikes.  About 40 
percent of smallholder households are managed by 
women and women are the primary managers of small 
livestock such as poultry and goats.  Despite their role 
in managing livestock, they hold title to less than 1 
percent of the land and are often purposefully excluded 
from making management decisions.30  Female-headed 
households have lower income and are more vulnerable 
than other households.  Therefore, it is imperative that 
the unique risk needs of these women be individually 

investigated and addressed to assure that their welfare is 
equally addressed in any crisis response plan. 

6. References 

1. Center for Disaster Preparedness. Integrating 
gender into community based disaster risk 
management.  (Community Based Disaster 
Risk Management: Training and Learning 
Circle- Philippines, 2009). 

2. Hyogo Framework for Action 2005-2015: 
Building the Resilience of Nations and 
Communities to Disasters. (World Conference 
on Disaster Reduction, Kobe, Japan, 18-22 
January 2005) 

3. L. Karakowsky and A. Elangovan. Risky 
Decision Making in Mixed-Gender Teams: 
Whose Risk Tolerance Matters? Small Group 
Research 32(1) (2001) 94-111 

4. Gysler, M., J. Kruse, and R. Schubert. 
Ambiguity and Gender Differences in 
Financial Decision Making: An Experimental 
Examination of Competence and Confidence 
Effects.  Center for Economic Research, Swiss 
Federal Institute of Technology, Working 
Paper 02-23. 
(http://ideas.repec.org/p/eth/wpswif/02-
23.html#provider,2002). 

5. J. Watson and S. Robinson. Adjusting for Risk 
in Comparing the Performances of Male- and 
Female-Controlled SMEs. Journal of Business 
Venturing 18(6) (2003) 773-788 

6. J. Pennings, B. Wansink and M. Meulenberg. 
A note on modeling consumer reactions to a 
crisis: The case of the mad cow disease.  
Intern. J. of Research in Marketing 19(1) 
(2002) 91–100.   

7. U.S. Census.  National Agricultural Statistics 
Service, U.S. Department of Agriculture. 2007 

8. R. Goldbach.  Gender Influence Of 
Agricultural Performance.  Masters Thesis, 
Department of Agricultural and Resource 
Economics, Colorado State University, Fort 
Collins, (2008). 

9. D. Hoag.  Applied Risk Management in 
Agriculture.  CRC Press, Taylor & Francis 
Group, Boca Raton, Florida, USA (2010) 403 
p. 

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10. J. Cohoon and W. Aspray.  The State of Social 
Science Research on Gender and IT 
Entrepreneurship.  National Center for Women 
and Information Technology Entrepreneurial 
Report Series 5, (2006) 

11. S. Weiler and A. Bernasek. Dodging the Glass 
Ceiling? Networks and the New Wave of 
Women Entrepreneurs.  The Social Science 
Journal 38(1) (2001) 85-103 

12. US Census. National Agricultural Statistics 
Service, U.S. Department of Agriculture. 1997 

13. US Census. National Agricultural Statistics 
Service, U.S. Department of Agriculture.  2002 

14. National Agricultural Statistics Service.  
Women in Ag Quick Factos (2002 Census of 
Agriculture (Ref. 13)) 

15. G. Johnson, R. McMahon. Owner-Manager 
Gender, Financial Performance and Business 
Growth amongst SMEs from Australia’s 
Business Longitudinal Survey. International 
Small Business Journal 23(2) (2005) 115-142 

16. N. Jianakoplos, and A. Bernasek,  Are Women 
More Risk Averse? Economic Inquiry 36(4) 
(1998) 620-630. 

17. L. Cunningham, Brorsen, B., Anderson, K., 
and Tostao, E. (2007). Gender Differences in 
Marketing Styles, Agricultural Economics 
38(1) 1-7. 

18. M. Powell and D. Ansic. Gender Differences in 
Risk Behavior in Financial Decision Making: 
An Experimental Analysis. Journal of 
Economic Psychology 18(6) (1997) 605-628 

19. C. Camerer and Lovallo, D. Overconfidence 
and Excess Entry: An Experimental Approach.  
The American Economic Review 89(1) (1997) 
306-318 

20. B. Barber and Odean, T. 2001. Boys will be 
boys: Gender, Overconfidence, and Common 
Stock Investment. The Quarterly Journal of 
Economics 116 (1) (2001): 261-292 

21. C. Bengtsson, Persson, M., and Willenhag P. 
2004. Gender and Overconfidence.  Journal of 
Economic Letters. 86(2) 199-203. 

22. J. Eales, Engel, B., Hauser, R., Thompson, S.  
Grain Price Expectations of Illinois Farmers 
and Grain Merchandisers.  American J. of 
Agricultural Economics 72(3) (1990) 701-708 

23. H. Buttner and D. Moore. Women’s 
Organizational Exodus to Entrepreneurship: 
Self-Reported Motivations and Correlates with 
Success.  Journal of Small Business and 
Management 35(1) (1997) 34-46. 

24. J. Harwood, R. Heifner, K. Coble, J. Perry, and 
A. Somwaru. Managing Risk in Farming: 
Concepts, Research, and Analysis. U.S. 
Department of Agriculture, Agricultural 
economics Report # 774  (Washington DC. 
1999) 

25. J.E. Cliff.  Does One Size Fit All? Exploring 
the Relationship between Attitudes Towards 
Growth, Gender, and Business Size. Journal of 
Business Venturing. 13(6)(1998) 523-542. 

26. G. Powell and K. Eddleston, The Paradox of 
the Contented Female Business Owner. 
Journal of Vocational Behavior 73(1) (2008) 
24-36 

27. E. Doty, Tomkiewicz, J. Bass, K.. Sex 
Differences in Motivational Traits and Ethical 
Decision Making among Graduating 
Accounting Majors.  College Student Journal 
39(4) (2005) 817-826 

28. P. Hawley, T. Little, and N. Card. The Myth of 
the Alpha Male: A New Look at Dominance-
Related Beliefs and Behaviors among 
Adolescent Males and Females. International 
Journal of behavioral Development  32(1) 
(2008) 76-88. 

29. S. Colarelli, J. Spranger, and R. Hechanova. 
Women, Power, Sex Composition in Small 
Groups: an Evolutionary Perspective. Journal 
of Organizational Behavior  27(2) (2006) 163-
184 

30. United States Government: Feed the Future 
Initiative.  Kenya FY 2010 Implementation 
Plan.  www.feedthefutue.gov (accessed 
January 22, 2011). 

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