Approximating income inequality dynamics given incomplete information: an upturned Markov chain model

Daniel Arreola () and Luis V. Montiel ()
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Daniel Arreola: Universidad Nacional Auntónoma de México - UNAM
Luis V. Montiel: Universidad Nacional Auntónoma de México - UNAM

Computational Statistics, 2024, vol. 39, issue 2, No 10, 629-651

Abstract: Abstract This article aims to understand mobility within income distribution in cases where there is incomplete information about how individuals transit between income distribution brackets. Understanding these transitions is crucial for evaluating and designing economic policies that affect the population in the long run. For this reason, we propose a methodology that may assist decision-makers to improve policies related to poverty reduction. We start by assuming that the income distribution bracket a person holds exclusively depends on the previous generation’s income bracket, i.e. it has the memoryless property. Therefore, our model resembles a Markov chain model with a steady state distribution that describes the distribution of the income brackets in the long run, and a transition matrix that describes the transitions between income distribution brackets from generation to generation. In contrast to a Markov chain, we assume a given steady state, in order to analyze the space of consistent transition matrices that could generate the steady state proposed. Additionally, we used the joint distribution simulation algorithm developed by Montiel and Bickel (Decis Anal 9:329–347, https://doi.org/10.1287/deca.1120.0252 , 2012) to analyze the transition matrix, which allows us to understand the effects of partial information. We test the model with official data from the National Institute of Statistics and Geography and the Social Mobility Survey in Mexico.

Keywords: Social mobility; Maximum entropy; Vector simulation; Joint distribution simulation algorithm (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-022-01305-5

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