Policy Options on Technology: Statistical t-test

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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 . Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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 Food Security Profile: Technology Dimension

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). Food Security Profile: Technology Dimension

19. Table 2.1 Tropical livestock unit values for different animals

20. Table 2.2 Scaled values for livestock owned Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

22. Table 2.3 Scaled values for number of meals per day Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

25. Table 2.4 Group Distribution of FOODSEC Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

28. Table 2.5 Threshold of food security components Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

30. Table 2.6 Levene’s test of equality of variances Food Security Profile: Technology Dimension

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. Food Security Profile: Technology Dimension

32. Student’s t-test for equality of means • Next step: Specify the sampling distribution of the test statistics Food Security Profile: Technology Dimension

33. Standard error of the difference between the two means : • where • s12 and s22 are the estimates of the within group variability of the first and second group, respectively. Food Security Profile: Technology Dimension

34. t-test statistic Food Security Profile: Technology Dimension

35. Table 2.7 Student’s t-test for equality of means Food Security Profile: Technology Dimension