1. Livelihoods analysis using SPSS

2. Why do we analyze livelihoods?  Food security analysis aims at informing geographical and socio-economic targeting  Livelihood analysis allows us to answer one of the key basic questions of food security analysis: “who are the food insecure?”  This analysis also allows us to create a socio-economic profile of the vulnerable households

3. How do we analyze livelihoods  The standard livelihood (income) module in a CFSVA allows for a few different types of analysis  We can analyze the main income activity followed by the second and third by simply running cross-tabulations with the main activity and other variables  We can also use multiple response analysis to analyze all of the reported income activities (regardless of order) and run cross- tabulations  We can analyze the number of income activities to see if there are significant differences between diversified households and single income households  And we can identify clusters of livelihood activities which offers a more powerful form of analysis

4. Types of cluster analysis available in SPSS  SPSS offers three methods for cluster analysis  Hierarchial clustering  Two-step clustering  K-means clustering

5. Types of analysis available in SPSS  Hierarchical clustering  Uses algorithms that are agglomerative (bottom-up) or divisive (top-down)  If agglomerative, each case is a cluster and then an algorithm is performed to either separate successive cases into clusters  Divisive algorithms first put all cases in a single cluster and then sequentially attempt to divide them

6. Types of analysis available in SPSS  Two-step clustering  As the name implies, clustering is done in two steps  First the cases are pre-clustered into many small sub-clusters  Then the sub-clusters are joined into the a specified number of clusters (SPSS can also find the number of clusters automatically)

7. Types of analysis available in SPSS  K-means clustering  Cases are placed into a partition and then iteratively relocated into another cluster  Iterations are repeated until the desired number of clusters are reached

8. Issue with SPSS cluster analysis  Two of the available procedures (hierarchical and k- means) require the user to know a priori the number of clusters desired  Only the two-step cluster option allows for automatic determination, however, from the WFP perspective it does not produce a useful result (too few clusters)  Therefore either another statistical software package needs to be used or a guess needs to be made on the number of clusters to include (and then run several iterations until a logical clustering is achieved)

9. Performing cluster analysis  As mentioned, there are several options available to perform cluster analysis  The analyst should chose the method that they are most familiar with  To give an example of one method to create the clusters, we will use the k-means method in SPSS

10. Prepare the dataset  It is imperative that the income activity module data is clean and without errors  The sum of all activities contributions must be 100  The same activity should not be repeated for a household  If an activity exists, the relative contribution must not be missing  Before the clustering can be performed, the contribution of each livelihood activity must be calculated for all households  To do so, syntax such as the following must be executed for all variables:  compute act01 = 0.  if (activity1 =1) act01 = act01+Activity1_Value.  if (activity2 =1) act01 = act01+Activity2_Value.  if (activity3 =1) act01 = act01+Activity3_Value.  The objective of this computation is to find out for every household, what is the relative contribution of each activity to their overall livelihood  After executing the syntax above for every activity, verify that the total for each household is exactly 100

11. Perform the first iteration of the cluster analysis  In this example, we will use the SPSS k-means method to perform cluster analysis using the contribution of each income activity as our variables of interest  In SPSS select:  Analyze > Classify > K-means cluster  Select all of the newly created income activity variables  The number of clusters is chosen at your discretion keeping in mind the number of activities listed in the survey and the knowledge that you will create a few iterations  Click the ‘save’ button and chose ‘cluster membership’  Click OK or Paste

12. Interpret the results  SPSS will produce a few outputs (based on the options you gave)  The iteration history will show you the number of iterations the change in the center of each cluster  The final clusters center table is the table we look at closely  Here, each variable is listed as a row and it’s average contribution to each cluster is noted in the columns  Paste this table into Excel

13. Interpret the results  Use conditional formatting to highlight cells with a value > 10 and examine the way the clusters have attempted to group the activities

14. Repeat the analysis  Repeat the cluster analysis this time increasing (or decreasing) the number of clusters by 1  Examine the final clusters table again  Continue to repeat this exercise until you have successfully created clusters that are logical  Livelihood clusters should be able to be described in a relatively simple fashion. Usually, there is one predominant income activity defining a group and some supplemental income from other activities  There is no ‘golden rule’ on the right number of clusters and some subjective but informed but decisions must be made

15. Describe the clusters  Once the clusters have been finalized, further examine the contribution of the activities to each cluster  Write a brief description of the composition of the cluster; for example:  A cluster which has a center of 78 from income from trading, selling and other commercial activity could be simply described as a ‘trader’  A cluster which has a center of 50 from cash crops and 30 from food crops could be summarized as ‘cash and food crops’  Appropriately label the final cluster variable in your dataset with the livelihood descriptions

16. Explore the clusters  Next, explore the livelihood clusters you’ve created  Look at the frequency of the clusters in the dataset  Some clusters may be combined if reasonable information allows you to do so  For example, people who are ‘remittance receivers’ and ‘pensioners’ may have very similar qualities and could possibly be combined

17. Analyze the clusters using cross-tabulations  The livelihood clusters can be used to examine ‘who are the food insecure’ and ‘where are they’  Cross-tabulate the livelihood clusters with Food Consumption Groups (you can also compare means of the FCS between clusters)  Cross-tabulate the clusters with all geographic strata  Wealth and livelihood are usually highly related and should be examined  Other indicators of interest: gender of household head, education of household head, etc.