An RCM Approach to Causal Inference with Two-level Data and Unobserved Social Contextual Heterogeneity: An application for the decomposition analysis of the gender income gap and the gender gap in positional rank in Japan

本稿は、処理確率の逆数の重みづけによるルービン流の因果分析を、社会コンテキストと個人の２つのレベルがあるデータ分析に用いる際の、新たな分析方法を開発し応用している。この方法は、社会コンテキストの観察されない効果が存在しているが、それに対して傾向スコアの推定に社会コンテキストの固定効果を推定する方法がデータ制約上問題があり望ましくない場合に、代わりに有効に用いることができる方法である。本稿は、この新たな方法の応用に、傾向スコアを用いるDFL法の不平等の要素分析法を併用し、ホワイトカラー正規雇用者の男女賃金格差と男女の職階格差の要素分析を行っている。分析の焦点は社会コンテキストである雇い主の企業がこれらの男女不平等に対して果たす役割である。結果は仮説通り、女性は賃金格差や係長クラスの職階への昇進に関する男女格差が少ない企業の場合には、そうでない企業に比べ、就業に留まる傾向があることを間接的に実証している。

This article introduces a new RCM method based on the inverse probability of treatment weighting for the analysis of two-level data of individuals and their social contexts when we expect unobserved contextual effects on the treatment and outcome. The method is an alternative to the use of fixed effects for social contexts in the estimation of propensity score when the fixed effects cannot be included in the estimation of propensity score due to small sample sizes for a non-negligible number of social contexts. The method is based on a novel ignorability assumption that may hold in many cases and permits the elimination of confounding unobserved contextual effects under such a situation.

An application of the new method to the decomposition analysis of inequality by combining it with the DiNardo-Fortin-Lemieux method focuses on the decomposition of the gender income gap and gender gap in positional rank among white-collar regular employees in Japan when their employers are the social contexts. The application provides findings that are consistent with the hypotheses that women tend to remain employed in firms for which their relative income and relative opportunity of being promoted to supervisory positions compared with men are better than in other firms, and that the gender gap is consequently reduced among those who remain employed.