Publications

Redistributive justice is based on the premise that it is unfair for people to be better or worse off relative to others simply because of their fortune or misfortune. It assumes equal opportunities arising from four factors: social circumstances, effort, option luck and brute luck. This paper seeks to investigate how differences in perceived brute luck influence individual preferences for redistribution in favour of two public policies: “health intervention” and “environmental actions”. These policies are viewed somewhat differently: the environment is considered a pure “public good” and health, more as a “private good” with a strong public good element. Consequently, potential self-serving biases inherent in the preferences for redistributive policies are expected to differ, more likely favouring health than the environment. The perceived degree of brute luck may capture such a difference—those perceiving themselves as luckiest should be less amenable to redistribution in favour of health than the unluckiest. Data from the three waves (2000, 2006 and 2008) of a French population survey are used to examine this self-serving bias. A Generalised Ordered Logit (GOL) model is found to be statistically more relevant compared to other logistic regression models (multinomial and ordered). We find that a perceived low degree of brute luck is significantly associated with a decreased preference of redistributive environmental policies but the reverse is true for redistributive health policies, i.e., association with an increased preference. Assuming that all inequalities due to differing luck are unjust, this empirical validation gives redistributive justice grounds for equalisation policies regarding health.

Although the Covid-19 crisis has shown how high-frequency data can help track the economy in real time, we investigate whether it can improve the nowcasting accuracy of world GDP growth. To this end, we build a large dataset of 718 monthly and 255 weekly series. Our approach builds on a Factor-Augmented MIxed DAta Sampling (FA-MIDAS), which we extend with a preselection of variables. We find that this preselection markedly enhances performances. This approach also outperforms a LASSO-MIDAS—another technique for dimension reduction in a mixed-frequency setting. Though we find that a FA-MIDAS with weekly data outperform other models relying on monthly or quarterly data, we also point to asymmetries. Models with weekly data have indeed performances similar to other models during “normal” times but can strongly outperform them during “crisis” episodes, above all the Covid-19 period. Finally, we build a nowcasting model for world GDP annual growth incorporating weekly data that give timely (one per week) and accurate forecasts (close to IMF and OECD projections but with 1- to 3-month lead). Policy-wise, this can provide an alternative benchmark for world GDP growth during crisis episodes when sudden swings in the economy make usual benchmark projections (IMF's or OECD's) quickly outdated.

This paper estimates trade barriers in government procurement, a market that accounts for 12 percent of world GDP. Using data from inter-country input–output tables in a gravity model, we find that home bias in government procurement is significantly higher than in trade between firms. However, this difference has decreased over time. Results also show that trade agreements with provisions on government procurement increase cross-border flows of services, whereas the effect on goods is small and not different from that in private markets. Provisions on transparency and procedural requirements are particularly instrumental in increasing cross-border government procurement.

Tests of labor supply models often rely on wages. However, wage variation alone generally cannot disentangle the classical time separable model and its extensions: reference dependent preferences (income targeting) and time nonseparable preferences (disutility spillovers; timing-specific preferences). We set up a novel laboratory experiment in which individuals choose their working time. We vary, independently, wages, historical income paths, and cumulative past work. We also vary the timing of experimental sessions. Statistical tests and stochastic revealed preference methods cannot reject the classical model in favor of income targeting or disutility spillovers, but the data suggest that labor supply varies by time-of-the-day.