Implicações macroeconômicas do COVID-19

 Resumo:

We present a theory of Keynesian supply shocks: supply shocks that trigger changes in aggregate demand larger than the shocks themselves. We argue that the economic shocks associated to the COVID-19 epidemic—shutdowns, layoffs, and firm exits—may have this feature. In one-sector economies supply shocks are never Keynesian. We show that this is a general result that extend to economies with incomplete markets and liquidity constrained consumers. In economies with multiple sectors Keynesian supply shocks are possible, under some conditions. A 50% shock that hits all sectors is not the same as a 100% shock that hits half the economy. Incomplete markets make the conditions for Keynesian supply shocks more likely to be met. Firm exit and job destruction can amplify the initial effect, aggravating the recession. We discuss the effects of various policies. Standard fiscal stimulus can be less effective than usual because the fact that some sectors are shut down mutes the Keynesian multiplier feedback. Monetary policy, as long as it is unimpeded by the zero lower bound, can have magnified effects, by preventing firm exits. Turning to optimal policy, closing down contact-intensive sectors and providing full insurance payments to affected workers can achieve the first-best allocation, despite the lower per-dollar potency of fiscal policy.

“Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages?” (with Guido Lorenzoni, Ludwig Straub, and Ivan Werning) July 2020, Revise & Resubmit, American Economic Review.

Divulgação sobre Capital Humano

 Resumo:

In 2020, the Securities and Exchange Commission revised human capital disclosure rules to improve shareholder understanding of how human capital management contributes to corporate value and strategy.

In this Closer Look, we examine early disclosure choices that companies have made under these rules to evaluate the information they share about employment practices.

We find that while some companies are transparent in explaining the philosophy, design, and focus of their HCM, most disclosure is boilerplate and lacks quantitative metrics.

As such, the new rules appear to contribute to the length but not the informativeness of 10-K disclosure.

Fonte: Human Capital Disclosure What Do Companies Say About Their “Most Important Asset”?

Decisões Humanas e Previsões das Máquinas

 Resumo:

We examine how machine learning can be used to improve and understand human decision making. In particular, we focus on a decision that has important policy consequences. Millions of times each year, judges must decide where defendants will await trial—at home or in jail. By law, this decision hinges on the judge’s prediction of what the defendant would do if released. This is a promising machine learning application because it is a concrete prediction task for which there is a large volume of data available. Yet comparing the algorithm to the judge proves complicated. First, the data are themselves generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the single variable that the algorithm focuses on; for instance, judges may care about racial inequities or about specific crimes (such as violent crimes) rather than just overall crime risk. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: a policy simulation shows crime can be reduced by up to 24.8% with no change in jailing rates, or jail populations can be reduced by 42.0%with no increase in crime rates. Moreover, we see reductions in all categories of crime, including violent ones. Importantly, such gains can be had while also significantly reducing the percentage of African-Americans and Hispanics in jail. We find similar results in a national dataset as well. In addition, by focusing the algorithm on predicting judges’ decisions, rather than defendant behavior, we gain some insight into decision-making: a key problem appears to be that judges to respond to ‘noise’ as if it were signal. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.

Human Decisions and Machine Predictions Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan NBER Working Paper No. 23180 February 2017

Divulgação Voluntária x Obrigatória

 Resumo:

We develop a theory of asymmetries between voluntary and mandatory disclosure. Efficiently designed mandatory disclosure policies are substitutes for excessive voluntary disclosures. The efficient policy takes the form of a lower threshold below which firms must disclose bad news and an upper threshold above which firms voluntarily disclose good news. Hence mandatory disclosures are asymmetric and feature conservative reporting of bad news. The threshold to recognize bad news increases when information is more precise. We also characterize interactions between disclosures and real decisions in environments where information has social value: investment decisions, optimal liquidations, and adverse selection in a lemons market.

Bertomeu, Jeremy and Vaysman, Igor and Xue, Wenjie, Voluntary versus Mandatory Disclosure (October 4, 2019). Review of Accounting Studies (2021). 

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Uma publicação compartilhada por Stevie Martin (@5teviem)

XVIII Seminário Internacional CPC - Normas Contábeis Internacionais

 

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Trabalhe enquanto eles dormem?

Atenção, workaholics. Em um estudo recente, a OMS e a OIT descobriram que longas jornadas de trabalho causaram, só em 2016, mais de 745.000 mortes por doenças cardíacas e derrames.

Como eles sabem que a causa foi o trabalho em excesso? Todas as 745 mil vítimas trabalhavam mais de 55 horas por semana nos anos anteriores. Inclusive, a situação é ainda mais grave nos homens, que representam 72% dos óbitos analisados.

Trabalhar por tantas horas está associado a um risco 35% maior de derrame e 17% mais chances de morrer por problemas cardíacos.

Para colocar atenção… Os efeitos da pandemia ainda não foram sentidos por completo, mas pense por você… Está trabalhando mais ou menos desde que foi pra casa?

O que o estudo aponta como solução?

Basicamente, que empresas, funcionários e governos estabeleçam limites que protejam a saúde dos trabalhadores. A maioria dos exemplos vistos envolve a redução da jornada para 4 dias — ou 32 horas — por semana:

A Microsoft fez isso, ainda em 2019, no Japão. Como resultado, a produtividade entre os empregados cresceu 40% e os custos com eletricidade caíram 23%.

A Unilever aderiu a mesma ideia na Nova Zelândia, com 81 funcionários da sua subsidiária.

Aqui no Brasil, foi a Zee.Dog, que anunciou o teste em fevereiro de 2020, retomado nesse ano.

Em termos governamentais, a Espanha vai investir 50 milhões de euros em um teste nacional para uma jornada de 4 dias por semana, por três anos, custeando a diferença na remuneração dos funcionários de mais de 100 empresas.

Na Nova Zelândia, a premiê recomendou o mesmo para aumentar o turismo local e reduzir o estresse. Tendência? Vale tentar?

Fonte: Aqui