policy options on technology: statistical t-test

Policy Options on Technology: Statistical t-test

Source: Babu and Sanyal (2009)

Food Security Profile: Technology Dimension

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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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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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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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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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• 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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• 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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• 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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• 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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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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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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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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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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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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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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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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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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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).

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)


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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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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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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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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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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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Table 2.4 Group Distribution of FOODSEC

Hybrid maize N Mean Standard deviation

Standard error mean

FOODSEC Non-adopters 131 0.3439 0.144 0.01261

Adopters 43 0.397 0.152 0.02318


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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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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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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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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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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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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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Student’s t-test for equality of means

• Next step: Specify the sampling distribution of the test statistics


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Standard error of the difference between the two means :

• where

• s12 and s2

2 are the estimates of the within group variability of the first and second group, respectively.

2

2

1

2

21 ns

ns

S pooledpooledXX

2

11

21

222

2112

nn

snsnspooled


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t-test statistic

21

)( 2121

XXSXXt


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Table 2.7 Student’s t-test for equality of means

Variables Assumptions t-statistic Attained significance (2-tailed)

INSECURE Equal variance assumed 2.33 0.02

Equal variance not assumed 2.363 0.019

FOODSEC Equal variance assumed -2.064 0.04

Equal variance not assumed -2.011 0.04

policy options on technology: statistical t-test

Documents

food security profile

household food security

food nutrition security

household food consumption

hybrid maize technology

technology adopters

technology dimensionevidence

major food crop