1. Livelihoods activities Food Security Indicators Training Bangkok, 12-17 January 2009

2. Objectives Explain WHY we collect data on livelihood activities Suggest HOW to collect this information (standard module) Suggest HOW to analyse livelihood data Show HOW to use results in the food security analysis (CFSVAs, EFSAs, etc.)

3. Livelihood activities are activities that households engage in to earn income and make a living (i.e., on- farm and off-farm activities providing a variety of procurement strategies for food and cash) Livelihood/economic activities

4. WHY do we collect? Because food security analysis aims at informing geographical AND socio-economic targeting. To answer one of the key basic questions of food security analysis: “who are the food insecure?” Because a socio-economic profile of the vulnerable HHs need to be identified.

5. HOW? Livelihood module

6. The module detects the activities and their relative importance Main indicators from this module are: a.main economic livelihood activities (3 or 4 max); b.percent contribution of the main activities to HH income If absolute values on income are collected, the module helps distinguishing between subsistence and commercial activities Livelihood module info

7. Note that the module asks to consider both activities that: –generate cash (e.g., food/cash crop production, unskilled labour, pension, etc.), AND –Sustain livelihood even though don’t generate cash (e.g., food production only for autoconsumption) For the latter, HHs are supposed to estimate the cash value of the output directly consumed by the household. Livelihood module info (cont’d)

8. 1.Prepare a list of economic activities List should be based on secondary data, previous studies and local expert knowledge. Important to include atypical sources that vulnerable households would exploit. List should be exhaustive to better differentiate households and minimize the reporting of undefined “others” activities. Livelihood module preparation

9. Example: Laos CFSVA (2006) 1 =Production & sale of agricultural crops10 =Collection and/or sale of Forest Products 2 =Livestock rearing and/or selling11 =Hunting 3 =Brewing (lao lao)12 =Petty trading 4 =Fishing13 =Seller, commercial activity 5 =Collection of aquatic animal resources14 =Remittances 6 =Unskilled wage labour – agriculture15 =Salaries, Wages (employees, longer-term) 7 =Unskilled wage labour – non agriculture16 =Collecting scrap metal/explosive powder 8 =Skilled wage labour17 =Government allowance (pension, disability benefit) 9 =Handicrafts /Artisan18 =Others, specify_______________ Livelihood module preparation

10. 2.Collect main activities & relative importance HHs report the main activities (max 3 or 4), using the list prepared in advance. HHs estimate the relative importance of the activities in contributing to the household’s income, food and access to services (proportional piling). The sum of the proportions for the 3-4 activities has to be 100%. Do not duplicate categories. Example: if men undertake a type of agriculture and women undertake another type of agriculture, the two activities should be grouped as the level of analysis is the household. Data collection

11. Modifications Recall period is typically one year. Depending on the survey context, it can be reduced (EFSA). Change over the time can be collected (before/after) Key actor(s) for each activity can be collected. Seasonality of activities can be included. Instead of the relative contribution (%), the absolute cash value of each activity can be collected.

12. Modifications (cont’d) We can ask to estimate the % of results/goods from each activity that is directly consumed by the HH (to estimate the relative importance of auto- consumption). But… –concept is difficult to explain –analysis is complex –it is based on the assumption that HH’s income can be measured through expenditure plus produced and consumed goods.

13. Modifications (cont’d) activities collected as proportion activities collected as cash value Easier to explain / collectDifficult to get reliable data – people tend to under estimate Easier to analyzeMore complex to analyze Less detailsMore details Allow to differentiate between subsistence and commercial level activities If absolute values are collected→ the sum of these values should not be considered as an income level for the household. This derived income is not intended for poverty analysis. Proportions or cash values?

14. IF there is capacity → cash values IF capacity is low / time is short → proportions MONITORING might consider to use the easiest/quickest tool to be expanded during large assessments. Modifications (cont’d)

15. HOW to analyse livelihood data? Livelihood data can be analysed in different way, according to: The structure of the module Analyst ’ s skills

16. Main income activity Number of income activities Change over the time (e.g., main activity, number, relative contribution) Relative contribution of each activity Multiple response analysis Identification of homogeneous clusters (i.e., cluster analysis) Types of analysis/output

17. Number of activities  create a new variable “number of activities” (‘count’). Analyse the distribution of the number of activities by key socio-demographic and economic indicators. (source: Liberia CFSNS 2008)

18. Main income activity  You may focus on the first activity and analyse its distribution by key socio- demographic and economic indicators. (Source: Tajikistan rural EFSA, 2008)

19. Change over the time The output depends upon the type of “change” questions in the questionnaire:  Change in the number of livelihood activities  Change in the main livelihood activity  Change in the relative contribution of each activity to total income (source: Tajikistan rural EFSA 2008)

20. In the data collection module: we ask to identify the main (3 or 4) activities. In SPSS: we have a column for the main activity, one for the 2 nd, the 3 rd, etc. Multiple responses

21. Multiple responses: analysis  With “multiple responses” we pull all the responses into a set ($activities) and analyse them all together. 1.Analyse → multiple response → define sets

22. Multiple responses: analysis 2.run the frequency or a crosstab on the defined set ($activities) asking percentages based on cases

23. Multiple responses: output Simple frequency: % based on cases (HHs) and responses

24. Multiple responses: output

25. We can cross-tabulate against several variables (province, female/male headed HHs, etc) Multiple responses: output

26. Percentages based on cases  When we analyse responses as a set, we can compute 2 types of percentages: based on responses or on cases  The percentage based on cases (HHs) tells us the prevalence (%) of HHs that cultivate a specific crop (disregarding the order)  Household is the denominator.  E.g., 100% of the HHs cultivate maize (3/3*100).

27. Perc. based on responses  The percentage based on responses (crops) compares one crop against all the cultivated crops.  Here the denominator is all the cultivated crops.  E.g., Maize represents 30% of the cultivated crops (3/10*100)

28. In the data collection module: the percent contribution of the main activities The initial data look like the picture below: source 1, contribution 1; source 2, contribution 2; source 3, contribution 3; etc. contribution of each activity

29. Restructure the initial dataset: create as many new variables as the livelihood activities listed in the module Values of the new variables indicate the relative contribution (%) of each source to total income. For each household the total is 100. How do we do this? contribution of each activity

30. 1.compute act01 = 0. IF (Activity1 = food crop production) act01 = act01+ contribution of the 1 st activity. IF (Activity2 = food crop production) act01 = act01+ contribution of the 2 nd activity. IF (Activity3 = food crop production) act01 = act01+ contribution of the 3 rd activity. IF (Activity4 = food crop production) act01 = act01+ contribution of the 4 th activity. 2.Label act01 'Food crop production/gardening'. 3.Repeat this procedure for each income activity By doing so, if an activity is listed in more than one activity variable, their values are summed up and not lost as if overwritten. contribution of each activity: data management

31. compute act01 = 0. IF (Activity1 =1) act01 = act01+Activity1_Value. IF (Activity2 =1) act01 = act01+Activity2_Value. IF (Activity3 =1) act01 = act01+Activity3_Value. IF (Activity4 =1) act01 = act01+Activity4_Value. compute act02 = 0. IF (Activity1 =2) act02 = act02+Activity1_Value. IF (Activity2 =2) act02 = act02+Activity2_Value. IF (Activity3 =2) act02 = act02+Activity3_Value. IF (Activity4 =2) act02 = act02+Activity4_Value. contribution of each activity: data management

32. Once you have repeated the procedure for each activity → sum all the contributions (%) and check the total. –if total is 100  ok –If total is not 100  check and change the initial data. contribution of each activity: final check

33. Relative contribution (%) of each source to total income is a continuos variable:  Compute the mean  Compare means of different categories (e.g., provinces) contribution of each activity: analysis

34. SPSS output reports the mean relative contribution to total income of the activities. Total is 100. Results are percentages. contribution of each activity: output

35. Data quality 1.The sum of all the % contributions has to be 100. If not, modify the values. 2.The share from the 1 st source has to be higher than the share from the 2 nd source, etc. If not, fix the problem by changing the order

36. Data quality (cont’d) 3.Livelihood activities have to be mentioned once. If not … … sum up the related contributions and delete one of the two answers.

37. Data quality (cont’d) 4. We should not have missing data on the activity contributions. If missing data are present, modify the data To do so, take into consideration the contributions from the other activities

38. HOW do we use livelihood data? Livelihood activities help understand the sustainability of households and their vulnerability to shocks  Some livelihood activities are less likely to provide continuous access to food (e.g., begging, casual labour, etc.).  The impact of natural- and human-induced hazards (e.g., floods, food price increase) depend upon the livelihood activities HHs engage into.

39. HOW do we use livelihood data? Exploring the association between livelihood activities and:  food consumption  nutritional outcomes  other indicators of human, social, economic, natural and physical assets is crucial to inform socio-economic targeting.

40. Socio-economic profiles: example

42. Questions?

43. Let’s practice!