Despite its fast rise on the world stage and rapid economic growth, there has been a dramatic increase in wealth inequality in India in recent years. Nonetheless, the issue of wealth inequality and wealth distribution in India has been relatively less explored. In this context, we use an extension of Blinder–Oaxaca decomposition method based on the recentered influence function (RIF) proposed by Firpo et al. (Econometrics 6(2):28, https://doi.org/10.3390/econometrics6020028, 2018) to analyze the role of household characteristics such as location, household’s composition by age, social groups, as well as education or employment on various wealth distributional measures during the period 1991–2012. We examine the effect of changing distribution of different household characteristics on wealth at various distributional measures (like ventile, Gini index, and variance). In our analysis, we observe the effect of household characteristics on wealth at different ventiles. Using counterfactual distribution analysis, we decomposed the inter-period difference in inequality into characteristics (explained) and coefficients (unexplained) effects. We then decompose changes in wealth at each ventile, Gini index, the variance of log of wealth, 90–10 percentile wealth gap, 50–10 wealth gap, and 90–50 wealth gap during 1991–2002 and 2002–2012 to see the composition effect and wealth structure effect. The composition effect shows the effect of change in the considered household characteristics, whereas the wealth structure effect shows the effect of change in returns to these characteristics. We noticed that during 1991–2002, wealth at lower quantile has decreased whereas it has increased for other quantiles, whereas during 2002–2012, we observe that wealth increase is highest at lower quantiles and is positive for all ventiles. In the case of inequality measures, we observe that all inequality measures have increased during 1991–2002, whereas during 2002–2012, only Gini and 90–50 gap has increased while other measures have decreased.

尽管印度在世界舞台上迅速崛起，并且经济快速增长，但近年来印度的贫富差距却在急剧增加。尽管如此，相对较少地探讨了印度的财富不平等和财富分配问题。在这种情况下，我们使用了基于Firpo等人提出的最近影响函数（RIF）的Blinder-Oaxaca分解方法的扩展。（Econometrics 6（2）：28，https://doi.org/10.3390/econometrics6020028，2018）分析住户特征的作用，例如位置，按年龄，社会群体划分的住户构成，以及各种教育或就业1991-2012年期间的财富分配措施。我们研究了在不同的分配措施（例如通货膨胀，基尼系数和方差）。在我们的分析中，我们观察了家庭特征对不同持币者财富的影响。使用反事实分布分析，我们将不平等的期间间差异分解为特征（解释）和系数（无法解释）效应。然后，我们在1991-2002年和2002-2012年期间分解每个变动点的财富变化，基尼系数，财富对数方差，90-10％的财富差距，50-10的财富差距和90-50的财富差距，组成效应和财富结构效应。构成效应显示了所考虑的家庭特征变化的影响，而财富结构效应显示了这些特性的收益变化的影响。我们注意到，在1991-2002年期间，低分位数的财富减少了，而其他分位数的财富增加了，而在2002–2012年期间，我们观察到，低分位数的财富增加最高，并且所有通货膨胀都是正数。对于不平等措施，我们观察到所有不平等措施在1991–2002年期间都在增加，而在2002–2012年期间，只有基尼和90–50的差距有所增加，而其他措施则有所减少。