Sibling spillover effects in school achievement

This paper provides empirical evidence on sibling spillover effects in school achievement using administrative data on 230,000 siblings in England. We extend previous strategies to identify peer effects by exploiting the variation in school test scores across subjects observed at ages 11 and 16 as well as variation in peer quality between siblings. We find a statistically significant positive spillover effect from the older to the younger sibling. Sibling spillovers account for a non‐negligible proportion of the attainment gap between low‐ and higher income pupils in England.     

Intergenerational mobility and sample selection in short panels

Using data from the first 11 waves of the BHPS, this paper measures the extent of the selection bias induced by standard coresidence conditions—bias that is expected to be severe in short panels—on measures of intergenerational mobility in occupational prestige. We try to limit the impact of other selection biases, such as those induced by labour market restrictions that are typically imposed in intergenerational mobility studies, by using different measures of socio‐economic status that account for missing labour market information. We stress four main results. First, there is evidence of an underestimation of the true intergenerational elasticity, the extent of which ranges between 12% and 39%. Second, the proposed methods used to correct for the selection bias seem to be unable to attenuate it, except for the propensity score weighting procedure, which performs well in most circumstances. This result is confirmed both under the assumption of missing‐at‐random data as well as under the assumption of not‐missing‐at‐random data. Third, the two previous sets of results (direction and extent of the bias, and differential abilities to correct for it) are also robust when we account for measurement error. Fourth, restricting the sample to a period shorter than the 11 waves under analysis leads to a severe sample selection bias. In the cases when the analysis is limited to eight waves, this bias ranges from about 40% to 65%. Copyright © 2006 John Wiley & Sons, Ltd.     

Nonresponse in dynamic panel data models

To verify whether data are missing at random (MAR) we need to observe the missing data. There are only two exceptions: when the relationship between the probability of responding and the missing variables is either imposed by introducing untestable assumptions or recovered using additional data sources. In this paper, we briefly review the estimation and test procedures for selectivity in panel data. Furthermore, by extending the MAR definition from a static setting to the case of dynamic panel data models, we prove that some tests for selectivity are not verifying the MAR condition.     

Explaining personality pay gaps in the UK

Using the British Household Panel Survey we estimate the effect on pay of each of the Big Five personality traits for employed men living in the UK. We add to the existing literature by estimating the role of factors such as education and occupation in explaining personality pay gaps, by allowing the personality traits to affect wage differently across occupations, education levels and other workers characteristics, and by investigating personality pay gaps for high- and low-paid workers. We find that openness to experience is the most relevant personality trait in explaining wages, followed by neuroticism, agreeableness, extroversion and conscientiousness. Openness and extroversion are rewarded while agreeableness and neuroticism are penalized, but the openness pay gap is totally explained by differences in worker characteristics, particularly education and occupation.     

The Use of Instrumental Variables in Peer Effects Models

Instrumental variables are often used to identify peer effects. This paper shows that instrumenting the ‘peer average outcome’ with ‘peer average characteristics’ requires the researcher to include the instrument at the individual level as an explanatory variable. We highlight the bias that occurs when failing to do this.     

Interpreting Wage Gaps of Disabled Men: The Roles of Productivity and of Discrimination

Using the UK Labour Force Survey, we study wage gaps for disabled men after the introduction of the Disability Discrimination Act. We estimate wage gaps at the mean and at different quantiles of the wage distribution and decompose them into a part explained by differences in workers' and job characteristics, a part that can be ascribed to health‐related reduced productivity, and a residual part. The large original wage gaps reduce substantially when we control for differences in education and occupation, although significant residuals remain. However, when we isolate productivity differences between disabled and nondisabled workers, the residual wage gap becomes insignificant in most cases.     

The effects of input tariffs on productivity: panel data evidence for OECD countries

Based on a panel of 16 OECD countries and 10 manufacturing industries over 1996–2007, this paper investigates the impact of tariffs on foreign intermediate goods on productivity growth in downstream manufacturing industries. The results show that imposing tariffs on imported intermediate goods is particularly harmful for industries that operate close to the global technological frontier. We also consider the possibility that input-tariff liberalisation may have different effects depending on the technological content of imported intermediate goods. Our findings suggest that protecting imports of high-technological goods is more harmful for productivity improvements in industries that operate close to global best practice.     

Poverty analysis with missing data: alternative estimators compared

Poverty models are generally estimated by using sample surveys affected by missing data problems. Most methods proposed to take account of missing data problems consider point estimators which typically impose restrictive assumptions. However, it is possible to identify a range of logically possible values for the poverty probability, an identification interval, without imposing any assumption. It is then of interest to check whether the point estimates lie within the identification interval. This is a way to check the validity of the assumptions imposed by point estimators. Using the ECHP we perform this check to assess different estimation methods.