Policy Options on Technology:       Statistical t-test          Source: Babu and Sanyal (2009)
Technological Progress & Implications for                  FNS• Policy options for agricultural Growth:  Technological pro...
Technological Progress & Implications for                  FNS• Questions:1. Identify the process and quantify the extent ...
Evidence from Malawi• Household level data from Malawi on the  impact of adoption of hybrid maize technology  on household...
Empirical evidence on Technological                  Impact• Zambia: Land is not a constraint; still scope for  growth by ...
Empirical evidence on Technological                  Impact• Constraints:• Low adoption rate due to limited availability a...
Empirical evidence on Technological               Impact• Impact:  Benefit for small farmers & their food consumption.  ...
Empirical evidence on Technological                  Impact• International Food Policy Research Institute and  Internation...
Empirical evidence on Technological                 Impact• Madagascar:   Strong association between better agricultural ...
Post-harvest Technology & Food Security• ‘Post-harvest crop loss’:  Crop losses occur during pre-processing, storage   (e...
Empirical verification• Data source:    Socioeconomic household survey data of Malawi.• Question:    Does food security ...
Empirical verification• Options:   (i) Panel data: Survey the same set of households before      and after technology adop...
Statistical Procedure• Test for the statistical significance of the  observed differences in food security between  techno...
Testing: Different Steps1. Data description and analysis.2. Descriptive statistics.3. Threshold of food insecurity by each...
Data Description & Analysis• Sample size: 604 households from regions Mzuzu, Salima and  Ngabu out of 5069 households• Cri...
Data Description & Analysis• Comprehensiveness of information:    All households provided information on food intake, qua...
Measures for Analysis: Technology• Technology: HYBRID (Dummy variable)- adoption of  hybrid maize (a value of 1); non-adop...
Measures for Analysis• Food Security: Income and consumption components(i) ‘Income component’ is determined by total lives...
Table 2.1 Tropical livestock unit values for different                       animals  Animal type                         ...
Table 2.2 Scaled values for livestock owned Data value of livestock units (TLUs)                        Scaled value      ...
Food Security Index(ii) Consumption components:      Number of meals (NBR) that the household consumes       during a giv...
Table 2.3 Scaled values for number of meals per day    Number of meals per day                                   Scaled va...
Food Security Index• Food Security Index: A weighted average of the  three components - (1) the number of livestock  owned...
Food Security IndexFOODSEC = 0.2798*NBR + 0.4821*RUNDUM + 0.2381  *LIVSTOCKSALE where 0.2798, 0.4821 and 0.2381 are respec...
Table 2.4 Group Distribution of FOODSEC                                                              Standard    Standard ...
Food Security by Technology• Hybrid maize adopters have a higher mean for food  security compared to non-adopters.• Adopti...
Threshold of food security by each           individual component• Problem with a continuous indicator of food  insecurity...
Table 2.5 Threshold of food security                  components        Indicator      Cut-off point                    Cu...
Nature of Food Insecurity• NBR: About 13 per cent of the population is food  insecure.• RUNDUM(variable when food stock ru...
Table 2.6 Levene’s test of equality of             variances      Variables             F-statistic               p value ...
Student t-test for testing the equality                of means• Ho : μ1 – μ2 = 0• H1 : μ1 – μ2 ≠ 0• Null hypothesis (Ho) ...
Student’s t-test for equality of means• Next step: Specify the sampling distribution  of the test statistics              ...
Standard error of the difference between the two                        means :                            2              ...
t-test statistic     X1         X2 (                1    2   )t                 SX X                         1      2     ...
Table 2.7 Student’s t-test for equality of                means                                                           ...
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Topic 10 technology

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Topic 10 technology

  1. 1. Policy Options on Technology: Statistical t-test Source: Babu and Sanyal (2009)
  2. 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 2 Dimension
  3. 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 3 Dimension
  4. 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 4 Dimension
  5. 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 5 Dimension
  6. 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 6 Dimension
  7. 7. Empirical evidence on Technological Impact• Impact: Benefit for small farmers & their food consumption. Adverse impact on womens 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 7 Dimension
  8. 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 8 Dimension
  9. 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 9 Dimension
  10. 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 10 Dimension
  11. 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 11 Dimension
  12. 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 12 Dimension
  13. 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 13 Dimension
  14. 14. Testing: Different Steps1. 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 14 Dimension
  15. 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 15 Dimension
  16. 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 16 Dimension
  17. 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 17 Dimension
  18. 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 18 Dimension
  19. 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. 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 Food Security Profile: Technology 20 Dimension
  21. 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 21 Dimension
  22. 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 Food Security Profile: Technology 22 Dimension
  23. 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 23 Dimension
  24. 24. Food Security IndexFOODSEC = 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 24 Dimension
  25. 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 Food Security Profile: Technology 25 Dimension
  26. 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 26 Dimension
  27. 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 27 Dimension
  28. 28. Table 2.5 Threshold of food security components Indicator Cut-off point Cumulative percentageNBR 0.33 13.4RUNDUM 0.33 69.8LIVSTOCKSCALE 0.16 74.7 Food Security Profile: Technology 28 Dimension
  29. 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 29 Dimension
  30. 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 Food Security Profile: Technology 30 Dimension
  31. 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 31 Dimension
  32. 32. Student’s t-test for equality of means• Next step: Specify the sampling distribution of the test statistics Food Security Profile: Technology 32 Dimension
  33. 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. Food Security Profile: Technology 33 Dimension
  34. 34. t-test statistic X1 X2 ( 1 2 )t SX X 1 2 Food Security Profile: Technology 34 Dimension
  35. 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 0.02INSECURE Equal variance not 2.363 0.019 assumed Equal variance assumed -2.064 0.04FOODSEC Equal variance not -2.011 0.04 assumed Food Security Profile: Technology 35 Dimension

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