Julieta Peveri*, Mélina London**

phd seminar

Julieta Peveri*, Mélina London**

AMSE
The effects of interest groups' contributions on political competition and selection: Evidence from Brazil*
Identification of early-warning signaling networks through granger-causality: Can production interdependencies explain the links?**
Événement annulé
Lieu

IBD Salle 16

Îlot Bernard du Bois - Salle 16

AMU - AMSE
5-9 boulevard Maurice Bourdet
13001 Marseille

Date(s)
Mardi 17 mars 2020| 12:45 - 14:15
Contact(s)

Anushka Chawla : anushka.chawla[at]univ-amu.fr
Laura Sénécal : laura.senecal[at]univ-amu.fr
Carolina Ulloa Suarez : carolina.ulloa-suarez[at]univ-amu.fr

Résumé

*While the role of interest groups' contributions in shaping policies has been well-studied, little is known about their effects on political competition and selection. This paper exploits a Brazilian reform that banned campaign contributions made by firms. I compare a range of political outcomes between municipalities and individuals that were more dependent on interest groups' contributions prior to the reform with the ones that weren't. I find that forbidding firms' contributions decreases the probability for incumbents to run for reelection, which encourages the entry of new candidates. Incumbents are also damaged by a drop in votes when they decide to rerun. Both effects give a large advantage to insiders challengers.

**Today’s economies are characterized by a high-level of interconnections across sectors. Monitoring inter-dependent sectors has become crucial in early-warning analysis aiming at forecasting developments in a specific sector of interest. Such reasoning had been mainly conducted on a case-by-case basis, relying on the understanding of sector-specific production dependencies. However, recent developments in the literature provided a more global perspective.  Latest research has highlighted production linkages as key drivers of the co-evolution across sectors. To what extent can production networks explain early-warning signals across sectors? To answer this question, I use an exclusive dataset from Coface, a leading trade credit insurer, on firms’ payment default on trade credit in four major European countries at the sector level. Using high-dimensional Granger causality tests, I identify which sectors send early-warning signals to better forecast short-term developments in others. In the resulting directed network, up-chain sectors, whose outputs are widely used in a set of other sectors, are over-represented among key signaling sources. Moreover, production linkages help understand early-warning relations, but only when restricting focus on up-chain sectors. However, they fall short of explaining a wide part of signals for middle-and-down-chain sectors.