Livelihoods activitiesLivelihoods activities

Food Security Indicators TrainingFood Security Indicators Training

Bangkok, 12-17 January 2009Bangkok, 12-17 January 2009

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.)

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

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.

HOW? Livelihood module

• 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

• 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)

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

Example: Laos CFSVA (2006)

1 =Production & sale of agricultural crops 10 =Collection and/or sale of Forest Products

2 =Livestock rearing and/or selling 11 =Hunting

3 =Brewing (lao lao) 12 =Petty trading

4 =Fishing 13 =Seller, commercial activity

5 =Collection of aquatic animal resources 14 =Remittances

6 =Unskilled wage labour – agriculture 15 =Salaries, Wages (employees, longer-term)

7 =Unskilled wage labour – non agriculture 16 =Collecting scrap metal/explosive powder

8 =Skilled wage labour 17 =Government allowance (pension, disability benefit)

9 =Handicrafts /Artisan 18 =Others, specify_______________

Livelihood module preparation

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

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.

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.

Modifications (cont’d)

activities collected as proportion

activities collected as cash value

Easier to explain / collect Difficult to get reliable data – people tend to under estimate

Easier to analyze More complex to analyze

Less details More 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?

Proportions or cash values?

• 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)

HOW to analyse livelihood data?

Livelihood data can be analysed in different way, according to:

• The structure of the module

• Analyst’s skills

• 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

Number of activities

Number of activities by demographic characteristics of HH head

51% 53% 56% 51%

44% 42% 43%54%

40% 45%

53% (*)39% (*)

0%10%20%30%40%50%60%70%80%90%

100%

Male Female head < 60 head 60+ No Yes

Sex of HH head age HH head Literacy of HH head

one tw o three/four

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)

Main income activity

main livelihood activity by region

0% 20% 40% 60% 80% 100%

DRD

Gbao

Khatlon

Sughd

% HHs

w heat/potato prod

vegetable/fruit prod

Agricultural w age labour

Non-agricultural w age labour

Self-employed

salary

animal production

Petty trade/handicraft

Pension

Remittances

Other

You may focus on the first activity and analyse its distribution by key socio-demographic and economic indicators.

(Source: Tajikistan rural EFSA, 2008)

Change over the time

change in the N of livelihood sources

14%1% 4% 1% 5%0%

10%20%30%40%50%60%70%80%90%

100%

DRD Gbao Khatlon Sughd

Region TOTAL

% H

Hs

increased

same

decreased

The output depends upon the type of “change” questions in the questionnaire:

Change in the number of livelihood activitiesChange in the main livelihood activityChange in the relative contribution of each activity to total income

(source: Tajikistan rural EFSA 2008)

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 2nd, the 3rd, etc.

Multiple responses

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

Multiple responses: analysis

2. run the frequency or a crosstab on the defined set ($activities) asking percentages based on cases

N PercentPetty trade, street vending 660 32% 49%Regular salary 508 25% 38%Unskilled/casual labour 226 11% 17%Skilled labour 189 9% 14%support from Liberia 173 8% 13%Shop ow ner, commerce 89 4% 7%support from outside Liberia 79 4% 6%Food crop production 36 2% 3%Renting out 31 2% 2%Pension 20 1% 2%Fishing 12 1% 1%Charcoal prod. 11 1% 1%Other 6 0% 1%

Responses

Main Sources of Insome: multiple response analysis

Percent of HHs

Multiple responses: output

Simple frequency: % based on cases (HHs) and responses

Main Income Sources: results from multiple response analysis

49%

38%

17%

14%

13%

7%

6%

3%

2%

2%

1%

1%

1%

0% 10% 20% 30% 40% 50% 60%

Petty trade, street vending

Regular salary

Unskilled/casual labour

Skilled labour

support from Liberia

Shop ow ner, commerce

support from outside Liberia

Food crop production

Renting out

Pension

Fishing

Charcoal prod.

Other

% HHs

Multiple responses: output

Food crop production Fishing Petty trade

Unskilled labour Skilled labour

Regular salary from employer

Shop owner, commerce Renting

support from

outside Liberia

support from inside

Liberia Pension total

New Kru Town 0% 0% 60% 18% 13% 35% 3% 6% 4% 12% 3% 154%Clara Town 1% 4% 41% 17% 12% 25% 4% 0% 0% 13% 0% 119%West Point 0% 10% 69% 21% 28% 24% 3% 0% 0% 10% 3% 169%

We can cross-tabulate against several variables (province, female/male headed HHs, etc)

Multiple responses: output

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).

crop 1 crop 2 crop 3 crop 4HH 1 maize beans groundnuts sorghumHH 2 groundnuts maize beans -HH 3 maize beans groundnuts -

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)

crop 1 crop 2 crop 3 crop 4HH 1 maize beans groundnuts sorghumHH 2 groundnuts maize beans -HH 3 maize beans groundnuts -

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

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

1. compute act01 = 0 .IF (Activity1 = food crop production) act01 = act01+ contribution of the 1st activity.IF (Activity2 = food crop production) act01 = act01+ contribution of the 2nd activity .IF (Activity3 = food crop production) act01 = act01+ contribution of the 3rd activity.IF (Activity4 = food crop production) act01 = act01+ contribution of the 4th 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

• 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

• 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

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

SPSS output reports the mean relative contribution to total income of the activities.

Total is 100.

Results are percentages.

11,4 6,1

24,1 19,4

5,5 4,7

16,3 14,9

,9 ,7

1,9 1,3

1,6 1,0

13,2 8,2

4,3 3,3

4,4 2,5

9,1 5,1

,4 ,2

,1 ,0

,4 ,6

1,8 29,6

,7 ,0

4,0 2,4

share of income fromremittance

share from food crops

share from cash crops

share from casual labour

share from begging

share from livestock

share from skilled trade

share from small businss

share from petty trade

share from pension

share from salary

share from fishing

share from gold panning

share from vegetablesales

share from foodassistance

share from brewing

share from other sources

Mean

no

Mean

yes

hhold recorded as abeneficiary as per CP

records?

contribution of each activity:output

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.

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.

Socio-economic profiles: example

Food Consumption Groups (FCS) by Livelihood Profiles

2%

9%

12%

7%

9%

0%

0%

7%

10%

0%

13%

22%

30%

23%

29%

13%

25%

30%

12%

54%

85%

69%

58%

71%

62%

87%

75%

63%

79%

46%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Employees

Petty trade

Casual Labourers

Skilled Labourers

Support receivers

Traders

renting

Food Crop Farmers

remittance

pensioners

poor borderline acceptable

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

university

some university

vocational

secondary

some secondary

pre-secondary

some pre-secondary

primary

some primary

None

Education of Household Head

Socio-economic profiles: example

Questions?

Let’s practice!