Corporate governance performance ratings with machine learning

We use machine learning with a cross-sectional research design to predict governance controversies and to develop a measure of the governance component of the environmental, social, governance (ESG) metrics. Based on comprehensive governance data from 2,517 companies over a period of 10 years and investigating nine machine-learning algorithms, we find that governance controversies can be predicted with high predictive performance. Our proposed governance rating methodology has two unique advantages compared with traditional ESG ratings: it rates companies' compliance with governance responsibilities and it has predictive validity. Our study demonstrates a solution to what is likely the greatest challenge for the finance industry today: how to assess a company's sustainability with validity and accuracy. Prior to this study, the ESG rating industry and the literature have not provided evidence that widely adopted governance ratings are valid. This study describes the only methodology for developing governance performance ratings based on companies' compliance with governance responsibilities and for which there is evidence of predictive validity.     

Is the US Phillips curve stable? Evidence from Bayesian vector autoregressions*

It has been claimed that the fall in US inflation during the Great Recession was surprisingly small. One possible explanation for this is that the Phillips curve is unstable and that its slope was lower around the Great Recession. We investigate the importance of time-varying parameters using Bayesian vector autoregressions for inflation and unemployment. We find support for time variation in the inflation equation and an unstable Phillips curve that was somewhat flatter between 2005 and 2013. However, conditional forecasts mostly suggest that inflation was not unexpectedly high around the Great Recession, which puts the claim of a “missing disinflation” into question.     

A note of caution on the relation between money growth and inflation

We assess the bivariate relation between money growth and inflation in the euro area and the United States using hybrid time-varying parameter Bayesian VAR models. Model selection based on marginal likelihoods suggests that the relation is statistically unstable across time in both regions. The effect of money growth on inflation weakened notably after the 1980s before strengthening after 2020. There is evidence that this time variation is related to the pace of price changes, as we find that the maximum impact of money growth on inflation is increasing in the trend level of inflation. These results caution against asserting a simple, time-invariant relationship when modeling the joint dynamics of monetary aggregates and consumer prices.     

The Role of Money in Monetary Policy at the Lower Bound

In light of the current low-interest-rate environment, we reconsider the merits of strict money growth targeting (MGT) relative to conventional inflation targeting (IT) and to price level targeting (PLT). We evaluate these policies in terms of social welfare through the lens of a New Keynesian model and accounting for a zero lower bound (ZLB) constraint on the nominal interest rate. Although MGT makes monetary policy vulnerable to money demand shocks, MGT contributes to achieving price level stationarity and significantly reduces the incidence and severity of the ZLB relative to both IT and PLT. Furthermore, MGT lessens the need for fiscal expansions to supplement monetary policy in fighting recessions.     

Evaluating the effectiveness of labor market interventions on reducing the impacts of the COVID-19 pandemic

This paper evaluates whether different labor market policy interventions taken in response to the COVID-19 pandemic have been effective in reducing its adverse impacts. We construct a database covering 165 countries and 39 labor market interventions grouped into four pillars: stimulating the economy and jobs (pillar 1); supporting enterprises, employment, and incomes (pillar 2); protecting workers (pillar 3); and social dialogue (pillar 4). The results revealed that measures taken under pillars 1, 2, and 3 have reduced the impacts of the pandemic on economic growth; measures under pillar 4 were significantly associated with reducing its impacts on employment and those under pillar 2 with reducing its impacts on working hours.     

How Social Structures Influence the Labour Market Participation of Individuals with Mental Illness: A Bourdieusian Perspective

Adopting a Bourdieusian perspective, this paper examines the social structures that influence the labour market participation of individuals with mental illness. We draw on 257 qualitative surveys completed by individuals with diagnosed mental health conditions in Europe, North America, Oceania, Africa, and Asia. We employed thematic analysis to analyse the data. The findings reveal that the interplay of capital endowments, symbolic violence, habitus and illusio shape the labour market participation of individuals with mental illness. Capital endowments of individuals with mental illness are afforded less value in the labour market and these individuals internalize, legitimize and normalize their disadvantaged position, blaming themselves rather than questioning the social structures leading to the challenges they encounter. We highlight that social structures condition the opinion these individuals have of themselves and how this affects how they navigate the labour market. In sum, we show that Bourdieu's concepts provide a useful lens to study inequalities in the labour market, as they reveal the social structures that produce, sustain and reinforce the social order that disadvantages individuals with mental illness.