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Livelihoods analysis using SPSS

Livelihoods analysis using SPSS. 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 ?”

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Livelihoods analysis using SPSS

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  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.

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