Transcript Livelihoods Analysis using SPSS - VAM Resource Center

Livelihoods analysis using SPSS
Why do we analyze livelihoods?
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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
How do we analyze livelihoods
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The standard livelihood (income) module in a CFSVA
allows for a few different types of analysis
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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 crosstabulations
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
Types of cluster analysis available in SPSS
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SPSS offers three methods for cluster analysis
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Hierarchial clustering
Two-step clustering
K-means clustering
Types of analysis available in SPSS
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Hierarchical clustering
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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
Types of analysis available in SPSS
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Two-step clustering
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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)
Types of analysis available in SPSS
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K-means clustering
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Cases are placed into a partition and then iteratively relocated
into another cluster
Iterations are repeated until the desired number of clusters are
reached
Issue with SPSS cluster analysis
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Two of the available procedures (hierarchical and kmeans) 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)
Performing cluster analysis
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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
Prepare the dataset
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It is imperative that the income activity module data is clean and without
errors
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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:
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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
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
Perform the first iteration of the cluster
analysis
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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:
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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
Interpret the results
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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
Interpret the results
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Use conditional formatting to highlight cells with a value
> 10 and examine the way the clusters have attempted to
group the activities
Repeat the analysis
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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
Describe the clusters
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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:
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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
Explore the clusters
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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
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For example, people who are ‘remittance receivers’ and
‘pensioners’ may have very similar qualities and could possibly
be combined
Analyze the clusters using cross-tabulations
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The livelihood clusters can be used to examine ‘who are
the food insecure’ and ‘where are they’
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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.