Research
2024
- Digital Technologies and Firms’ Employment and Training2024Work in Progress
This study examines the causal influence of digital technologies, specifically operational (ODT) and information digital technologies (IDT), on firms’ employment structure using Italian firm-level data. It employs a unique empirical approach, constructing instrumental variables based on predetermined employment composition and global technological progress, proxied by patents. Findings indicate that IDT investment positively affects employment, favoring a skilled, IT-competent workforce, as supported by firms’ training and recruitment plans. Conversely, ODT investment does not significantly alter total employment but skews the workforce towards temporary contracts. The study contributes methodologically by distinguishing between ODT and IDT and highlighting nuanced employment dynamics within firms.
@misc{Caselli_FourrierNicolai_Fracasso_Scicchitano_2024b, title = {{Digital} {Technologies} and {Firms}' {Employment} and {Training}}, author = {Caselli, M. and Fourrier-Nicolaï, E. and Fracasso, A. and Scicchitano, S.}, year = {2024}, note = {Work in Progress} } - Digital Technology Adoption: The Dual Role of Firm Leadership2024submitted to Industrial & Corporate Change
This work empirically examines the adoption of Fourth Industrial Revolution (4IR) technologies, using firm-level data from Italy for the period 2015-17. The analysis shows that technology adoption varies together with the scope of technological opportunities by industry, measured by the number of worldwide patents in a given 4IR domain and industry during the period 2000-2014, and that the extent to which firms are receptive to the technological progress depends on firms’ absorptive capacity. The latter, in turn, varies across firms according to the characteristics of the firm’s leaders, as these influence firms’ exploratory capabilities and their internal organization. However, while the direct effects of leader characteristics on technology adoption are significant in all technological domains, their moderating effects vary significantly across technologies.
@misc{Caselli_FourrierNicolai_Fracasso_Scicchitano_2024a, title = {{Digital} {Technology} {Adoption}: {The} {Dual} {Role} of {Firm} {Leadership}}, author = {Caselli, M. and Fourrier-Nicolaï, E. and Fracasso, A. and Scicchitano, S.}, year = {2024}, note = {submitted to Industrial \& Corporate Change} }
2023
- Bayesian Inference for Non-Anonymous Growth Incidence Curves using Bernstein Polynomials: An Application to Academic Wage DynamicsE. Fourrier-Nicolaï, and M. LubranoStudies in Nonlinear Dynamics & Econometrics, 2023Publisher: De Gruyter
The paper examines the question of non-anonymous Growth Incidence Curves (na-GIC) from a Bayesian inferential point of view. Building on the notion of conditional quantiles of Barnett (1976. “The Ordering of Multivariate Data.” Journal of the Royal Statistical Society: Series A 139: 318–55), we show that removing the anonymity axiom leads to a complex and shaky curve that has to be smoothed, using a non-parametric approach. We opted for a Bayesian approach using Bernstein polynomials which provides confidence intervals, tests and a simple way to compare two na-GICs. The methodology is applied to examine wage dynamics in a US university with a particular attention devoted to unbundling and anti-discrimination policies. Our findings are the detection of wage scale compression for higher quantiles for all academics and an apparent pro-female wage increase compared to males. But this pro-female policy works only for academics and not for the para-academics categories created by the unbundling policy.
@article{FourrierNicolai_Lubrano_2023, title = {{Bayesian} {Inference} for {Non-Anonymous} {Growth} {Incidence} {Curves} using {Bernstein} {Polynomials}: {An} {Application} to {Academic} {Wage} {Dynamics}}, shorttitle = {{Bayesian} {Inference} for {Non-Anonymous} {Growth} {Incidence} {Curves} using {Bernstein} {Polynomials}}, author = {Fourrier-Nicolaï, E. and Lubrano, M.}, journal = {Studies in Nonlinear Dynamics \& Econometrics}, year = {2023}, note = {Publisher: De Gruyter}, doi = {10.1515/snde-2022-0109}, issn = {1558-3708}, keywords = {academic wage formation, Bayesian inference, conditional quantiles, gender policy, non-anonymous GIC} }
2021
- Bayesian Inference for Parametric Growth Incidence CurvesE. Fourrier-Nicolaï, and M. LubranoIn Research on Economic Inequality: Poverty, Inequality and Shocks, 2021
The growth incidence curve of Ravallion and Chen (2003) is based on the quantile function. Its distribution-free estimator behaves erratically with usual sample sizes leading to problems in the tails. The authors propose a series of parametric models in a Bayesian framework. A first solution consists in modeling the underlying income distribution using simple densities for which the quantile function has a closed analytical form. This solution is extended by considering a mixture model for the underlying income distribution. However, in this case, the quantile function is semi-explicit and has to be evaluated numerically. The last solution consists in adjusting directly a functional form for the Lorenz curve and deriving its first-order derivative to find the corresponding quantile function. The authors compare these models by Monte Carlo simulations and using UK data from the Family Expenditure Survey. The authors devote a particular attention to the analysis of subgroups.
@incollection{FourrierNicolai_Lubrano_2021, title = {{Bayesian} {Inference} for {Parametric} {Growth} {Incidence} {Curves}}, author = {Fourrier-Nicolaï, E. and Lubrano, M.}, series = {Research on {Economic} {Inequality}}, volume = {29}, booktitle = {Research on {Economic} {Inequality}: {Poverty}, {Inequality} and {Shocks}}, editor = {Bandyopadhyay, Sanghamitra}, publisher = {Emerald Publishing Limited}, pages = {31--55}, year = {2021}, isbn = {978-1-80071-558-5 978-1-80071-557-8}, url = {https://doi.org/10.1108/S1049-258520210000029003}, doi = {10.1108/S1049-258520210000029003}, keywords = {Bayesian inference, C11, D31, Distributional changes, Growth incidence curve, I31, Inequality, Lorenz curves, Mixtures of log-normals} }
2020
- Bayesian Inference for Distributional Changes: The Effect of Western TV on Wage Inequality and Female Participation in Former East-GermanyE. Fourrier-Nicolaï, and M. Lubrano2020Work in Progress
This paper investigates the evolution of wage formation in a Mincer model with sample selection for which we develop Bayesian inference and growth incidence and poverty growth curves. We estimate the effect of an exogenous exposure to Western TV broadcasts on labour market participation and wage inequality in East Germany after the German reunification. Using the GSOEP, we find evidences that Western television had significantly increased wage inequality among males while it has significantly affected female labour participation and led the less productive females to drop out from the market, hiding thus a large increase in wage inequality among females.
@misc{FourrierNicolai_Lubrano_2020b, title = {{Bayesian} {Inference} for {Distributional} {Changes}: {The} {Effect} of {Western} {TV} on {Wage} {Inequality} and {Female} {Participation} in {Former} {East-Germany}}, shorttitle = {{Bayesian} {Inference} for {Distributional} {Changes}}, author = {Fourrier-Nicolaï, E. and Lubrano, M.}, year = {2020}, note = {Work in Progress}, url = {https://shs.hal.science/halshs-02909932} } - Bayesian Inference for TIP Curves: An Application to Child Poverty in GermanyE. Fourrier-Nicolaï, and M. LubranoThe Journal of Economic Inequality, 2020
TIP curves are cumulative poverty gap curves used for representing the three different aspects of poverty: incidence, intensity and inequality. The paper provides Bayesian inference for TIP curves, linking their expression to a parametric representation of the income distribution using a mixture of log-normal densities. We treat specifically the question of zero-inflated income data and survey weights, which are two important issues in survey analysis. The advantage of the Bayesian approach is that it takes into account all the information contained in the sample and that it provides small sample credible intervals and tests for TIP dominance. We apply our methodology to evaluate the evolution of child poverty in Germany after 2002, providing thus an update the portrait of child poverty in Germany given in Corak et al. (Rev. Income Wealth 54(4), 547–571, 2008).
@article{FourrierNicolai_Lubrano_2020a, title = {{Bayesian} {Inference} for {TIP} {Curves}: {An} {Application} to {Child} {Poverty} in {Germany}}, shorttitle = {{Bayesian} {Inference} for {TIP} {Curves}}, author = {Fourrier-Nicolaï, E. and Lubrano, M.}, journal = {The Journal of Economic Inequality}, volume = {18}, number = {1}, pages = {91--111}, year = {2020}, url = {https://doi.org/10.1007/s10888-019-09426-6}, doi = {10.1007/s10888-019-09426-6}, issn = {1573-8701}, keywords = {Bayesian inference, Child poverty, Mixture model, Survey weights, Zero-inflated model} } - How Family Transfers Crowd-out Social Assistance in GermanyE. Fourrier-Nicolaï2020Work in Progress
The non-take-up of social assistance has been receiving increased attention among policy makers in recent years as it would apparently underpin the effectiveness of public intervention in alleviating poverty. We examine whether receipt of private transfers affects the household decision to take-up social assistance in Germany between 2009 and 2011. We exploit the follow-up of households in the SOEP to reconstruct family links and estimate a model of welfare participation with endogenous private transfers and sample selection of the instruments. We find that 20% of the non-take-up rate is due to monetary substitution of private transfers lowering the welfare program costs. However, we find that social assistance is more effective in alleviating poverty and its intensity than private transfers.
@misc{FourrierNicolai_2020, title = {{How} {Family} {Transfers} {Crowd-out} {Social} {Assistance} in {Germany}}, author = {Fourrier-Nicolaï, E.}, year = {2020}, note = {Work in Progress}, url = {https://shs.hal.science/halshs-02874852} }