1. Policy Options on Technology:
Statistical t-test
Source: Babu and Sanyal (2009)

2. Technological Progress & Implications for
FNS
• Policy options for agricultural Growth:
Technological progress.
• Example: High yielding varieties of crops/
technology for post harvest operations.
• Beneficial outcomes: Increase in household food
consumption and nutritional adequacy.
• Process: Direct impact on food & nutrition
security due to increase in income + indirect
impact due to higher non-food expenditures on
health and sanitation, along with food
consumption.
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3. Technological Progress & Implications for
FNS
• Questions:
1. Identify the process and quantify the extent
of improvement in food consumption of the
household.
2.Identify the process of impact on nutrition
security.
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4. Evidence from Malawi
• Household level data from Malawi on the
impact of adoption of hybrid maize technology
on household food security and nutritional
situation.
• Maize: Major food crop and source of calories
(85%).
• Statistical approach: Estimate differential levels
of food security between technology adopters
and non-adopters; and test for its significance .
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5. Empirical evidence on Technological
Impact
• Zambia: Land is not a constraint; still scope for
growth by extensive cultivation is limited due to
diminishing returns to land.
• Adopted improved technology- HYVs (hybrid)
maize - to raise maize production.
• Constraints:
• Farmers in eastern Province of Zambia grow
traditional maize for self-consumption and
hybrid maize as a cash crop due to storage and
processing requirements.
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6. Empirical evidence on Technological
Impact
• Constraints:
• Low adoption rate due to limited availability and poor
distribution channels of hybrid seeds and fertilizers.
• Policy imperatives:
 Market infrastructure, storage facilities and improvement
of marketing channels.
 Government incentives and support to improve on-farm
storage capacity and village-level access to milling
facilities.
 Policies that offer innovative extension and credit
systems.
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7. Empirical evidence on Technological
Impact
• Impact:
Benefit for small farmers & their food consumption.
Adverse impact on women's share of income in large
farms.
• Evidence from other countries:
Guatemala, Rwanda, Bangladesh
• Bangladesh: Provision of credit and training to
women for the production of polyculture fish and
commercial vegetables increased incomes but not
micronutrient status of members of adopting
households.
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8. Empirical evidence on Technological
Impact
• International Food Policy Research Institute and
International Center for Tropical Agriculture finding:
biofortification an effective tool to end
malnutrition.
• Constraint: lack of infrastructure, inadequate
policies, lack of delivery systems for new varieties,
low level of investment in research and less
demand for such crops in the poorest regions.
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9. Empirical evidence on Technological
Impact
• Madagascar:
 Strong association between better agricultural
performance (higher rice yields) and real wages,
rice profitability and prices of staple food.
Net sellers, net buyers & wage labourers
benefited.
Technology diffusion is important; so are
improved rural transport infrastructure,
increased literacy rates, secure land tenure and
access to extension services.
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10. Post-harvest Technology & Food Security
• ‘Post-harvest crop loss’:
Crop losses occur during pre-processing, storage
(estimated loses 33 to 50%), packaging and
marketing.
Adversely affect household food security by
reducing output, and income due to poor quality
of crop.
Major constraint on food security in developing
countries.
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11. Empirical verification
• Data source:
 Socioeconomic household survey data of Malawi.
• Question:
 Does food security differ between technology adopters &
and non-adopters?
• Data requirement:
 Household characteristics, such as age and sex,
household income and expenditure patterns on food and
non-food items and food intakes by the members of the
family.
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12. Empirical verification
• Options:
(i) Panel data: Survey the same set of households before
and after technology adoption.
(ii) Cross section data households for a single time period
from technology adopters and non-adopters.
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13. Statistical Procedure
• Test for the statistical significance of the
observed differences in food security between
technology adopters versus non-adopters.
• Computes sample means for both subgroups
and test the null hypothesis that there is no
difference between their respective population
means.
• Two assumptions: (i) Same variance for the two
population groups (ii) unequal variances.
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14. Testing: Different Steps
1. Data description and analysis.
2. Descriptive statistics.
3. Threshold of food insecurity by each individual
component.
4. Tests for equality of variances.
5. t-test.
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15. Data Description & Analysis
• Sample size: 604 households from regions Mzuzu, Salima and
Ngabu out of 5069 households
• Criteria for selection:
 Household has at least one child as member below the age of
5.
 Regions chosen because detailed data on food consumption
patterns for the household and nutritional status of the
children are available; , they represent varied agro-ecological
zones, cropping and livestock rearing patterns, consumption
patterns and geographical (northern, central and lakeshore
and southern) locations within the country.
• Out of the 604 households, 197 had information on 304 children
(below the age of 5) related to nutritional status and general
health conditions.
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16. Data Description & Analysis
• Comprehensiveness of information:
 All households provided information on food intake, quantity
harvested for various crops and other socioeconomic information;
facilitated identification of households (who had at least one child
below the age of 5) which suffered from a nutrition insecurity
problem.
 All household data provided information on household
characteristics such as age, education, sex of the household head,
expenditure on and share of different food and non-food items
consumed, number of meals consumed by the household on a daily
basis (this variable in combination with other variables is used as an
indicator of food security) and the time after harvest when the
household stock of food runs out.
• Data can also be classified with respect to other characteristics like
region and technology adoption.
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17. Measures for Analysis: Technology
• Technology: HYBRID (Dummy variable)- adoption of
hybrid maize (a value of 1); non-adoption (a value of 0)
• Food Security:
• (i) INSECURE: f(Household dependency ratio, the
number of meals that a household consumes)
Categories:
 If Depratio ≥ 0.5 and NBR ≤ 2 then INSECURE = 3
 If Depratio < 0.5 and NBR ≤ 2 then INSECURE = 2
 If Depratio ≥ 0.5 and NBR > 2 then INSECURE = 1
 If Depratio < 0.5 and NBR > 2 then INSECURE = 0
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18. Measures for Analysis
• Food Security: Income and consumption components
(i) ‘Income component’ is determined by total livestock
ownership (LIVSTOCKSCALE) and measured in tropical
livestock units (TLUs) (equivalence scale based on an
animal’s average biomass consumption).
• LIVSTOCKSCALE – a proxy for income levels and ability to
withstand shocks (Table 2.1).
• Aggregation: Biophysical scale of TLU is used (a la HDI
normalization procedure) (Table 2.2).
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19. Table 2.1 Tropical livestock unit values for different
animals
Animal type TLU value
Cattle 0.8
Goat 0.1
Sheep 0.1
Pigs 0.2
Chicken, ducks, and doves 0.01
Source: International Livestock Research Institute (1999)

20. Table 2.2 Scaled values for livestock owned
Data value of livestock units (TLUs) Scaled value
6+ 1
4 0.67
3 0.5
2 0.33
1 0.17
0 0
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21. Food Security Index
(ii) Consumption components:
 Number of meals (NBR) that the household consumes
during a given day (Table 2.3) and the months when the
stock of food runs out (RUNDUM).
 RUBNDUM, a measure of adequate stock of food, is also
measured on a 0–3 scale, with the truncation being at the
minimum value of 0.
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22. Table 2.3 Scaled values for number of meals per day
Number of meals per day Scaled value
3 1
2 0.67
1 0.33
0 0
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23. Food Security Index
• Food Security Index: A weighted average of the
three components - (1) the number of livestock
owned (LIVSTOCKSCALE), (2) the number of meals
consumed per day (NBR), and (3) stocks of food
running out (RUNDUM).
• The weights are chosen in proportion to the
variance of each component.
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24. Food Security Index
FOODSEC = 0.2798*NBR + 0.4821*RUNDUM + 0.2381
*LIVSTOCKSALE
where 0.2798, 0.4821 and 0.2381 are respectively
the variances of the components NBR, RUNDUM,
and LIVSTOCKSCALE.
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25. Table 2.4 Group Distribution of FOODSEC
Standard Standard error
Hybrid maize N Mean
deviation mean
Non-adopters 131 0.3439 0.144 0.01261
FOODSEC
Adopters 43 0.397 0.152 0.02318
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26. Food Security by Technology
• Hybrid maize adopters have a higher mean for food
security compared to non-adopters.
• Adoption of new technology improves food
security.
• Issue: it the observed differences of mean and
variance are statistically significant.
• In other words, we want to determine if the
differences among the sample of technology
adopters and non-adopters on food security is
relevant for the population too.
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27. Threshold of food security by each
individual component
• Problem with a continuous indicator of food
insecurity.
• (FOODSEC) is that it does not contain rules or
information to identify the food insecure households
from the rest.
• In order fully to understand the households that are
food insecure in each of the above components
(namely livestock ownership, number of meals
consumed per day and the month when the stock of
food runs out), it is important to determine the cut-
off point for each of the above components.
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28. Table 2.5 Threshold of food security
components
Indicator Cut-off point Cumulative percentage
NBR 0.33 13.4
RUNDUM 0.33 69.8
LIVSTOCKSCALE 0.16 74.7
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29. Nature of Food Insecurity
• NBR: About 13 per cent of the population is food
insecure.
• RUNDUM(variable when food stock runs out):
Almost 70 per cent of the population is food
insecure.
• LIVSTOCKSALE: Almost 75 per cent of the
population does not own any livestock.
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30. Table 2.6 Levene’s test of equality of
variances
Variables F-statistic p value
INSECURE 0.566 0.452
FOODSEC 0.174 0.677
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31. Student t-test for testing the equality
of means
• Ho : μ1 – μ2 = 0
• H1 : μ1 – μ2 ≠ 0
• Null hypothesis (Ho) asserts that the population
parameters are equal. The statistic is the difference
between the sample means.
• If it differs significantly from zero, we will reject the null
hypothesis and conclude that the population parameters
are indeed different.
• Since the two random samples are independent, i.e.
probabilities of selection of the elements in one sample
are not affected by the selection of the other sample, we
want to verify.
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32. Student’s t-test for equality of means
• Next step: Specify the sampling distribution
of the test statistics
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33. Standard error of the difference between the two
means :
2 2
s pooled s pooled
SX1 X2
n1 n2
• where
2 2
2 n1 1 s n2 1 s
1 2
s pooled
n1 n2 2
• s12 and s22 are the estimates of the within group variability of
the first and second group, respectively.
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34. t-test statistic
X1 X2 ( 1 2 )
t
SX X
1 2
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35. Table 2.7 Student’s t-test for equality of
means
Attained significance (2-
Variables Assumptions t-statistic
tailed)
Equal variance assumed 2.33 0.02
INSECURE Equal variance not
2.363 0.019
assumed
Equal variance assumed -2.064 0.04
FOODSEC Equal variance not
-2.011 0.04
assumed
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