Marc Gurgand

Selection Bias Corrections Based on the Multinomial Logit Model: Monte-Carlo Comparisons

This survey presents the set of methods available in the literature on selection bias correction, when selection is specified as a multinomial logit model. It contrasts the underlying assumptions made by the different methods and shows results from a set of Monte Carlo experiments. We find that, in many cases, the approach initiated by Dubin and MacFadden (1984) as well as the semi-parametric alternative recently proposed by Dahl (2002) are to be preferred to the most commonly used Lee (1983) method. We also find that a restriction imposed in the original Dubin and MacFadden paper can be waived to achieve more robust estimators. Monte Carlo experiments also show that selection bias correction based on the multinomial logit model can provide fairly good correction for the outcome equation, even when the IIA hypothesis is violated.

Migrants as second-class workers in urban China? A decomposition analysis

In urban China, urban resident annual earnings are 1.3 times larger than long-term rural migrant earnings as observed in a nationally representative sample in 2002. Using microsimulation, we decompose this difference into four sources, with particular attention to path-dependence and statistical distribution of the estimated effects: (1) different allocation to sectors that pay different wages (sectoral effect); (2) hourly wage disparities across the two populations within sectors (wage effect); (3) different working times within sectors (working time effect); and (4) different population structures (population effect). Although sector allocation is extremely contrasted, with very few migrants in the public sector and very few urban residents working as self-employed, this has no clear impact on earnings differentials, because the sectoral effect is not robust to the path followed for the decomposition. The second main finding is that the population effect is robust and significantly more important than wage or working time effects. This implies that the main source of disparity between the two populations is pre-market (education opportunities) rather than on-market.

Adjusting Your Dreams? High School Plans and Dropout Behaviour

At the end of middle school, many low-achieving students realise that they do not have the ability to get into selective high school programmes, which may be a source of disengagement and eventually lead them to drop out of high school. Based on a randomised controlled trial, this article shows that a series of meetings facilitated by the school principals can help low-achievers to formulate educational objectives better suited to their academic aptitudes. By changing the high school plans of the less realistic students, the intervention reduces grade repetition and dropout by 25% to 40%.