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Ph.D. Barrales-Ruiz, Jose
Nombre de publicación
Ph.D. Barrales-Ruiz, Jose
Nombre completo
Barrales Ruiz, Jose Antonio
Email
jbarrales@ucsc.cl
ORCID
2 results
Research Outputs
Now showing 1 - 2 of 2
- PublicationThe distributive cycle: Evidence and current debates(Wiley, 2022)
; ;Mendieta‐Muñoz, Ivan ;Rada, Codrina ;Tavani, Danielevon Arnim, RudigerThis paper surveys current debates on the distributive cycle. The literature builds on Goodwin's seminal 1967 chapter titled “A growth cycle.” We review theoretical motivations for the distributive cycle, which, despite significant differences, all imply that macroeconomic activity leads the labor share in a counterclockwise cycle in the activity‐labor share plane. Subsequently, we summarize and update evidence on the existence of a distributive cycle, with a focus on the post‐war U.S. macroeconomy. We analyze activity and labor share series and their interaction in the frequency domain, and also employ standard vector autoregressions. Results confirm the distributive cycle for the U.S. post‐war period. We contextualize results vis‐à‐vis current debates: (1) we consider a financial cycle, to rebut the theoretical possibility of “pseudo‐Goodwin” cycles, (2) demonstrate that a suppressed labor share and stagnation are compatible with short‐run Goodwin cycles, and argue that this link presents the way forward for research on secular stagnation. - PublicationEndogenous fluctuations in demand and distribution: An empirical investigationThis paper empirically investigates the possibility of self-sustained oscillations at business cycle frequency. The theoretical model considers aggregate economic activity and the functional income distribution in the spirit of Goodwin (1967). In the empirical investigation, we utilize four measures each of economic activity and the labor share for the US post-war macroeconomy. Using Schuster’s periodogram, we first show that these time series have an important frequency peak at about forty quarters. We therefore detrend them with wavelet methods. To allow for nonlinear dynamic interaction, we introduce a feedforward neural network (FNN). This method is first shown to correctly identify stability or a limit cycle in simulations of the theoretical model with reasonable noise and sample size. Estimation results provide some support for a limit cycle in the post-war US, but this evidence is not independent of detrending methods used.