Unconditional quantile regression has quickly become popular after being introduced by Firpo, Fortin, and Lemieux (2009, Econometrica 77: 953-973) and is easily implemented using the user-written command rifreg by the same authors. However, including high-dimensional fixed effects in rifreg is quite burdensome and sometimes even impossible. In this article, I show that when the number of fixed effects is large, the computational speed is massively increased by using xtreg rather than regress to fit the unconditional quantile regression models. I also introduce the xtrifreg command, which should be considered a supplement to rifreg. The xtrifreg command has many of the same features as rifreg but can be used to include a large number of fixed effects, to estimate cluster-robust standard errors, and to estimate cluster-bootstrapped standard errors.

Schools and residential neighbourhoods constitute key contexts of development beyond the family of origin. Yet, few prior studies address whether the overall impact of these childhood contexts on adult life chances has changed over time. In this article, we investigate changes in socio-economic resemblance between former schoolmates and neighbouring children using Norwegian administrative data covering three decades. We use cross-classified multilevel models to decompose the variance in children’s educational attainment and adult earnings into the contributions found within and between their school and neighbourhood contexts in adolescence. We find that unadjusted school and neighbourhood correlations in educational attainment are relatively modest and declining over time. These trends largely reflect declining socio-economic segregation between schools and neighbourhoods over time. After adjusting for sorting by family background, schools account for 2 per cent or less of the total variation in completed years of education in the more recent cohorts and neighbourhoods even less. For adult earnings, the adjusted school correlations are very low, accounting for around 1 per cent of the total variance, while the contribution of neighbourhoods is close to zero. Our findings suggest that adolescent school and neighbourhood contexts are not major determinants of children’s later-life socio-economic attainments in the Norwegian welfare state setting.

Studies typically find large variation in labor market outcomes not only between educational levels, but also among individuals with a higher education. However, the importance of different types of horizontal divisions in higher education is mostly treated in separate literatures. In this paper, we use multilevel models and an outcome-based approach to investigate the relative importance of institution (college), department, and field of study in the Norwegian labor market. We find that the effects of field of study on wages are generally strong. The overall effects of institution are also quite large, but they emerge to a considerable extent at the level of departments; the effects of institution over and above the effects of department are small. We also show that the effects of horizontal divisions are greater at the graduate than at the undergraduate level, and that the effects of horizontal divisions increase over individuals' work careers.

The identification of unconditional quantile treatment effects (QTE) has become increasingly popular within social sciences. However, current methods to identify unconditional QTEs of continuous treatment variables are incomplete. Contrary to popular belief, the unconditional quantile regression model introduced by Firpo, Fortin, and Lemieux (2009) does not identify QTE, while the propensity score framework of Firpo (2007) allows for only a binary treatment variable, and the generalized quantile regression model of Powell (2020) is unfeasible with high-dimensional fixed effects. This paper introduces a two-step approach to estimate unconditional QTEs where the treatment variable is first regressed on the control variables followed by a quantile regression of the outcome on the residualized treatment variable. Unlike much of the literature on quantile regression, this two-step residualized quantile regression framework is easy to understand, computationally fast, and can include high-dimensional fixed effects.

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