Working Papers

The People’s Bank of China’s network of local currency swap arrangements provide Asian countries with a much-needed safety net, while also strengthening China’s diplomatic position

Formerly incarcerated Black people emerge from prison with far less education and social skills than white ex-cons. And they have great trouble forming families or earning a good living.

Mortality and economic data show how constraints to government spending and a skepticism of redistributive policies have made the pandemic far worse

INET’s Commission on Global Economic Transformation - Interim Report on the Global Response to the Pandemic

The New Keynesian Dynamic Stochastic General Equilibrium (DSGE) is a straightjacket for macroeconomics

A large and growing percentage of households are missed in the monthly Current Population Survey (CPS).

Conservative legal theory is based on a shoddy definition of what constitutes “efficiency”

Labor-saving advances in AI may undo the gains from globalization and pose new challenges for economic development

Recognizing that inflation of the value of output and its costs of production must be equal, we focus on a cost-based macroeconomic structuralist approach in contrast to micro-oriented monetarist analysis.

This paper analyzes the role of local spending, particularly on social welfare, and local inequality as factors in the Italian political crisis following the adoption in 2011 of more radical national austerity measures.

“Build back” means restoring the government and business investments in the productive capabilities of the U.S. labor force that created a growing middle class in the three decades after World War II

Even if psychological factors influence participants’ decision-making, as behavioral economists compellingly argue, incorporating such factors into economic theory would seem to require that market participants adhere to elementary logical rules.

How electric and self-driving cars could change the industry

Carbon pricing still has the potential to be a powerful tool contributing to emissions reductions, but it is clearly no panacea.

In this paper we analyze the Gilens dataset using the complementary tools of Random Forest classifiers (RFs), from Machine Learning.