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Published in: Social Indicators Research 2/2016

16-10-2015

Estimating Capabilities with Structural Equation Models: How Well are We Doing in a ‘Real’ World?

Authors: Jaya Krishnakumar, Florian Chávez-Juárez

Published in: Social Indicators Research | Issue 2/2016

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Abstract

Measuring capabilities is a major challenge for the operationalization of the capability approach. Structural equation models (SEM) are being increasingly used as one possible methodology for estimating capabilities, but a certain skepticism remains about their appropriateness. In this paper, we perform a unique simulation experiment for testing the validity of such estimators. Using an agent-based modeling tool, we simulate a ‘real’ life scenario with individuals of heterogeneous characteristics and behaviors, having different capability sets, and making different decisions. We then run a SEM (MIMIC) model on the data generated in this simulated world to estimate the individual capabilities. Thus our data generating process is completely disconnected with the econometric model used for estimation. Our results support the idea that SEM can coherently estimate the true capabilities. We find that the linear predictor from the structural part of the SEM provides better results than the ‘classical’ factor scores based on the full model.

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Appendix
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Footnotes
1
Some examples of direct capability questions are ‘At present, how easy or difficult do you find it to enjoy the love, care and support your immediate family’, ‘I am able to express my political views’, ‘Is your current accommodation adequate or inadequate for your current needs’, ‘At work, have you recently felt that you were playing a useful part in things’, see e.g. Anand et al. (2009, 2011).
 
2
See for instance Anderson and Gerbing (1988) and MacCallum and Austin (2000) for its use in the psychological literature and Judd and Milburn (1980) or Hurwitz and Peffley (1987) for an application to political attitudes.
 
3
For a general introduction to agent-based models see Gilbert (2008) and Epstein and Axtell (1996).
 
4
The extension of our study to a general SEM is relatively straightforward and should not alter the qualitative conclusions of our study. This can be envisaged in a follow-up work.
 
5
For instance, the access to a certain service can depend on the location of the agent.
 
6
The supply of education can be thought of as a combined package of quality and quantity. Though it is not complicated to distinguish between the two in our set-up and even introduce school ‘fees’, with all these aspects varying from one school to another, these extensions will not significantly alter the conclusions obtained in our simpler setting.
 
7
It would be easy to include schooling fees in the model. However, for the purpose of our study it is not useful and would only make the model unnecessarily more complex.
 
8
Using a t-test, we can reject the null hypothesis of equal means at less than the 0.1 % level.
 
9
We standardize the two variables to have them on the same scale for comparison. This does not alter the analysis, because the latent factor has neither inherent scale nor level.
 
10
We mean by fractional rank the rank measured on the interval between 0 (first individual) and 1 (last individual).
 
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Metadata
Title
Estimating Capabilities with Structural Equation Models: How Well are We Doing in a ‘Real’ World?
Authors
Jaya Krishnakumar
Florian Chávez-Juárez
Publication date
16-10-2015
Publisher
Springer Netherlands
Published in
Social Indicators Research / Issue 2/2016
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI
https://doi.org/10.1007/s11205-015-1148-6

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