Calendrier du 24 novembre 2023
Casual Friday Development Seminar - Brown Bag Seminar
Du 24/11/2023 de 13:00 à 14:00
R1-09
RICHARD Marion ()
Conflict, Road Insecurity and Migration in Mali
This study aims to examine how insecurity along main migration routes in Mali affects households’ ability to rely on migration as a risk-coping and income-smoothing strategy. Exploiting detailed data on the stock of economic migrants and the origin of remittances received from a set of 13 destinations in neighboring countries and regions of Mali, I first estimate additional migration costs generated by insecurity with a gravity model. In a second step, I analyse the effect of average road insecurity faced by each of the localities on their migration outcomes. The findings show that both the aggregate stock of economic migrants and new seasonal departures in migration are reduced for households in localities with high average road insecurity, so that reduction in accessibility of some destinations due to conflict is not fully offset by migration to alternative destinations. These effects are significantly larger for localities affected by droughts.
EU Tax Observatory Seminar
Du 24/11/2023 de 12:00 à 13:00
Salle R1-14
WAMSER Georg (Tübingen University)
Effective Corporate Income Taxation and Corruption
We show that effective corporate income taxes are lower in EU NUTS 2 regions where citizens perceive corruption to be comparatively more prevalent. We develop a new approach for calculating region-industry-year-specific empirical effective income tax rates (EEITRs) using firm-entity-level income statement data. Controlling for proxies for deductions that could legally be claimed (e.g., depreciation allowances, deduction of interest payments, potential for loss carryforwards, preferential treatment of patent revenues) and additional controls (e.g., regional GDP), as well as country-industry-year fixed effects, our benchmark model suggests that a one standard deviation increase in corruption leads to a statistically significant decrease in EEITRs of approximately 0.4 percentage points. From an economic point of view, this effect is sizeable given that the between-region within-country differences in corruption are significant. Our findings suggest more tax evasion in regions with high corruption via overstated tax-base deductions.
PSE Internal Seminar
Du 24/11/2023 de 12:00 à 13:00
FLEURBAEY Marc(PSE)
GORDON Matthew(PSE)
Aggregation, beyond GDP
1.This paper explores the possibility to replace the GDP-shareholder-value nexus, which has been under severe criticism for decades, with a combination of social-welfare compatible objectives at the macroeconomic level and at the firm level. Can GDP be replaced by social welfare and shareholder value be replaced with stakeholder value, in an integrated and consistent fashion? It is shown that money-metric utilities and their aggregation can indeed provide interesting tools to conceive of this integration of micro with macro objectives. But while consistent measures of the contribution of each firm to social welfare can be derived from this analysis, there is little hope to build new national welfare accounts in which micro-level contributions to social level could be simply added up to form social welfare at the macro level****************************************************************************************2.Advances in machine learning and the increasing availability of satellite imagery have led to the proliferation of social science research that uses remotely sensed measures of human activity or environmental outcomes to infer the impact of policy. However, standard machine learning loss functions can yield predictions that, when used in causal inference, may introduce bias. In this paper, we show how this bias can arise, and we demonstrate the use of an adversarial debiasing algorithm in order to correct this issue when generating machine learned predictions for use in causal inference. This method is widely applicable beyond satellite data to any setting where machine learned predictions are the dependent variable in a regression. We conduct simulations and empirical exercises using data on forest cover in Western Africa, to show that our method is able to generate predictions that recover the regression parameters that are estimated from ground truth data, in contrast to a naive model. We then use the method to study the relationship between economic development and deforestation in Western Africa and find a positive relationship between increases in wealth and forest cover.