1. Technology, Gender and Food
Security Interface:
Cramer’s V & phi-test
Source: Babu and Sanyal (2009) 1

2. Technology, Gender and Food Security
Interface
• Gender empowerment: A major policy option considered for
reducing income deprivation and food insecurity.
• Reasons:
 Women produce more than half the food grown in the
developing countries.
 Women farmers in sub-Saharan Africa produce more
than three-quarters of the region’s basic food, manage
about two-thirds of the marketing and at least one-half
of the activities for storing food and raising animals.
 In Asia, women account for more than two-thirds of food
production
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3. Gender & its Importance
• Reasons:
 They contribute to about 45 per cent of production in
Latin America and the Caribbean.
• Disadvantage:
 Women are risk-averse and hence, differential gender
profile with respect to technology adoption or
commercialization.
 Differential commercialization profile also; women prefer
to grow food for home consumption; women have
limited access to land labor, credit & extension services.
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4. Differential gender profile of
technology
• Implications:
Differential distribution of gains of growth.
Adverse implications for family welfare and
nutrition.
Resource allocation depends on women’s share of
resources (crop ownership) and household head’s
gender, education and age.
 Household characteristics, such as time spent in
household activities by men and women, access to
protected water and health and sanitation
conditions impact on children’s nutritional status.
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5. Issues
1.Gender profile of technology adoption
2. Gender profile of food security among
technology adopters
• Empirical evidence has policy implications on
food security
Malawi: Male-headed households have a higher
likelihood of adoption of hybrid maize than female-
headed households after controlling for other
important observable factors.
Nigeria, Kenya, Ghana.
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6. Empirical Verification
• Issues:
– (i)Gender profile of technology adoption;
– (ii) Gender profile of commercialization;
– (iii) Implications for food security.
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7. Empirical Verification
• Method:
– ‘cross-tabulation procedure’ ; pertains to relationship
between two or more categorical variables: if male- or
female-headed households are more likely to be
technology adopters, whether the different households
(male or female) are more food secure and finally we
want to determine if male- or female-headed households
are more likely to commercialize crops and thereby
receive greater income from the proceeds.
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8. Empirical Verification
Variables:
1. HYBRID: Dummy variable; whether a household grows hybrid maize
(HYBRID =1) or not (HYBRID = 0).
2. FEMHHH: Dummy variable; whether the household head is male
(FEMHHH=0) or female (FEMHHH = 1).
3. CASHCROP: tobacco, groundnuts, cotton and plantain are the major cash
crops in Malawi; Dummy variable for commercialization; CASHCROP = 1
if the household grows at least one of these four major cash crops and 0
otherwise.
4. CALREQ: Measure of food security ; if per adult equivalent calorie intake
for households meets at least 80 per cent of the calorie requirement
(2200 kcal); Dummy variable; CALREQ =1 if the household is food secure
and CALREQ = 0 otherwise.
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9. Table 4.1 Cross-tabulation results of technology
adopters and gender of household head
FEMHHH
Female Male Total
No 128 305 433
HYBRID
Yes 31 140 171
Total 159 445 604=n
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10. Table 4.2 Cross-tabulation results of food
security and gender of household head
FEMHHH
Female Male Total
INSECURE 39 247 286
CALREQ
SECURE 22 140 162
Total 61 387 448 =n
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11. Statistical Test: phi coefficient
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12. Phi coefficient (Ф) & Cramer’s V
• Chi-square test:
 test of association between categorical variables;
 it does not tell us the strength of the relationship.
• Phi coefficient and Cramer’s V:
 Quantify this relationship
 Based on the chi-square statistic that controls for the sample
size.
 Designed for use with nominal data and with chi-square they
jointly indicate the strength and the significance of a
relationship.
• Limitation: Provide some sense of the strength of the
association, they do not, in general, have an intuitive
interpretation.
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13. Phi coefficient (Ф)
• Ф coefficient:
 A measure of the degree of association between two
binary variables.
 Ratio of the chi-square statistic to the total number of
observations, i.e. Ф=√χ2/N.
 Range: (-1, +1) for 2*2 tables.
 Sampling distribution is known; possible to compute its
standard error and significance.
 SPSS and other major packages report the significance
level of the computed phi value.
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14. Phi coefficient (Ф)
General rule of thumb for Ф coefficient of correlation
is:
 -1.0 to 0.7 strong negative association
 -0.7 to -0.3 weak negative association
 -0.3 to +0.3 little or no association
 +0.3 to +0.7 weak positive association
 +0.7 to +1.0 strong positive association.
• It does not have a theoretical upper bound when
either of the variables has more than two
categories.
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15. Cramer’s V
• Cramer’s V:
Appropriate for tables that are larger than 2 *2
It is Ф rescaled so that it varies between 0 and 1.
 Cramer’s V is V= √χ2/(N-1), where N is the total
number of observations and k is the smaller of
the number of rows and columns.
 For 2*2 tables, Cramer’s V is equal to the
absolute value of the phi coefficient. This is
because since k =2, the (k -1) term becomes 1.
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16. Test: Gender & Technology adoption
• H0 : No relationship between technology
adoption and gender of the household head,
i.e. incidences of hybrid maize adoption are
not statistically different between the male-
and female-headed households.
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17. Table 4.3 Tests between technology adopters (HYBRID)
and gender of household head (FEMHHH)
Value p value
Phi -0.117 0.004
Cramer’s V 0.117 0.004
Number of valid cases 604
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18. Test: Gender & Technology adoption
• (p value) < 0.01 => , the null hypothesis is
rejected at the 1 per cent level of
significance.
• Inference: incidences of hybrid maize
adoption are statistically different between
the male- and female-headed households.
Although the value of the phi coefficient is
low (0.117), it is statistically significant at the
1 per cent level.
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19. Test: Gender & Food Security
• H0 : No relationship between food security and gender of the
household head for hybrid maize growers, i.e. both male-
and female-headed households are not statistically different
with regard to food security.
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20. Table 4.4 Tests between food security (CALREQ) and
gender of household head (FEMHHH)
Value p value
Phi -0.001 0.987
Cramer’s V 0.001 0.987
Number of valid cases 448
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21. Test: Gender & Food Security
• Significance level ( Cramer’s V & phi statistic) >
0.01 =>, the null hypothesis cannot be rejected
even at the 10 per cent level.
• Inference: For both groups of households (male-
and female-headed), incidences of food security
are not statistically different among hybrid
maize growers; no pattern of relationship
between gender of the household head & food
security for the technology adopters for this
sample.
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22. Gender & Commercialization
• H0 : No relationship exists between cash crop
growing and gender of the household head,
i.e. incidences of cash crop commercialization
or adoption are not statistically different
between male- and female-headed
households.
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23. Table 4.5 Tests between cash crop commercialization
(CASHCROP) and gender of household head (FEMHHH)
Value p value
Phi -0.097 0.017
Cramer’s V 0.097 0.017
Number of valid cases 604
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24. Gender & Commercialization
• Significance level (Cramer’s V & phi statistic) = 0.017,
the null hypothesis can be rejected at the 5 per cent
level.
• Incidences of cash crop commercialization are
statistically different for both the groups of
households (male- and female-headed).
• Inference: Incidence of cash crop commercialization is
statistically different between male- and female-
headed households. Although the value of the phi
coefficient is low (0.097), it is statistically significant
at the 5 per cent level.
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