Technology, Gender and Food Security Interface: Cramer’s V & phi-test

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Technology, Gender and Food Security Interface:

Cramer’s V & phi-test

Source: Babu and Sanyal (2009)

Technology, Gender and Food Security Interface

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• 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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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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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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Issues

1.Gender profile of technology adoption2. Gender profile of food security among

technology adopters• Empirical evidence has policy implications on

food securityMalawi: 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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Empirical Verification

• Issues:– (i)Gender profile of technology adoption; – (ii) Gender profile of commercialization;– (iii) Implications for food security.


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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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Empirical VerificationVariables: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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Table 4.1 Cross-tabulation results of technology adopters and gender of household head

FEMHHH

HYBRID

Female Male Total

No 128 305 433

Yes 31 140 171

Total 159 445 604=n


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Table 4.2 Cross-tabulation results of food security and gender of household head

FEMHHH

CALREQ

Female Male Total

INSECURE 39 247 286

SECURE 22 140 162

Total 61 387 448 =n


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Statistical Test: phi coefficient


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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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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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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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Cramer’s V

• Cramer’s V: Appropriate for tables that are larger than 2 *2It 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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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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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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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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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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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



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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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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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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



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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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Technology, Gender and Food Security Interface: Cramer’s V & phi-test