Concerns that technological progress degrades job opportunities have been expressed over much of the last two centuries by both professional economists and the general public. These concerns can be seen in narratives both in scholarly publications and in the news media. Part of the expressed concern about jobs has been about the potential for increased economic inequality. But another part of the concern has been about a perceived decline in job quality in terms of its effects on monotony vs creativity of work, individual sense of identity, power to act independently, and meaning of life. Public policy should take account of both of these concerns, inequality and job quality.
We consider the Rothschild-Stiglitz model of insurance but without the exclusivity constraint. It turns out that there always exists a unique equilibrium, in which the reliable and unreliable consumers take out a primary insurance up to its quantity limit, and the unreliable take out further secondary insurance at a higher premium. We provide a simple proof of this result (extended to multiple types of consumers) with the hope that it may be pedagogically useful.
This comment points out mismeasurement of three of the variables in the DSGE model in Del Negro, Giannoni, and Schorfheide (2015). These errors began with the model in Smets and Wouters (2007), and they also exist in other models that use the Smets-Wouters model as a benchmark. The mismeasurement appears serious enough to call into question the reliability of empirical results using these variables.
How do shocks to economic fundamentals in the world economy affect local labor markets? In a framework with a flexible structure of spatial linkages, we characterize the model-consistent shock exposure of a local market as the exogenous shift in its production revenues and consumption costs. In general equilibrium, labor outcomes in any market respond directly to the market’s own shock exposure, and indirectly to other markets shocks exposures. We show how spatial linkages control the size and the heterogeneity of these indirect effects. We then develop a new estimation methodology - the Model-implied Optimal IV (MOIV) - that exploits quasi-experimental variation in economic shocks to estimate spatial linkages and evaluate their counterfactual implications. Applying our methodology to US Commuting Zones, we find that difference-in-difference designs based on model-consistent measures of local shock exposure approximate well the differential effect of international trade shocks across CZs, but miss around half of the aggregate effect, partly due to the offsetting action of indirect effects.
Parents everywhere want their children to be happy and do well. Yet how parents seek to achieve this ambition varies enormously. For instance, American and Chinese parents are increasingly authoritative and authoritarian, whereas Scandinavian parents tend to be more permissive. Why? Love, Money, and Parenting investigates how economic forces and growing inequality shape how parents raise their children. From medieval times to the present, and from the United States, the United Kingdom, Germany, Italy, Spain, and Sweden to China and Japan, Matthias Doepke and Fabrizio Zilibotti look at how economic incentives and constraints—such as money, knowledge, and time—influence parenting practices and what is considered good parenting in different countries.
Through personal anecdotes and original research, Doepke and Zilibotti show that in countries with increasing economic inequality, such as the United States, parents push harder to ensure their children have a path to security and success. Economics has transformed the hands-off parenting of the 1960s and ’70s into a frantic, overscheduled activity. Growing inequality has also resulted in an increasing “parenting gap” between richer and poorer families, raising the disturbing prospect of diminished social mobility and fewer opportunities for children from disadvantaged backgrounds. In nations with less economic inequality, such as Sweden, the stakes are less high, and social mobility is not under threat. Doepke and Zilibotti discuss how investments in early childhood development and the design of education systems factor into the parenting equation, and how economics can help shape policies that will contribute to the ideal of equal opportunity for all.
We provide evidence that democracy has a positive effect on GDP per capita. Our dynamic panel strategy controls for country fixed effects and the rich dynamics of GDP, which otherwise confound the effect of democracy. To reduce measurement error, we introduce a new indicator of democracy that consolidates previous measures. Our baseline results show that democratizations increase GDP per capita by about 20 percent in the long run. We find similar effects using a propensity score reweighting strategy as well as an instrumental-variables strategy using regional waves of democratization. The effects are similar across different levels of development and appear to be driven by greater investments in capital, schooling, and health.
We describe a methodology for making counterfactual predictions in settings where the information held by strategic agents is unknown. The analyst observes behavior assumed to be rationalized by a Bayesian model, in which agents maximize expected utility, given partial and differential information about payoff-relevant states of the world. A counterfactual prediction is desired about behavior in another strategic setting, under the hypothesis that the distribution of the state and agents’ information about the state are held fixed. When the data and the desired counterfactual prediction pertain to environments with finitely many states, players, and actions, the counterfactual prediction is described by finitely many linear inequalities, even though the latent parameter, the information structure, is infinite dimensional.
We describe a methodology for making counterfactual predictions when the information held by strategic agents is a latent parameter. The analyst observes behavior which is rationalized by a Bayesian model, in which agents maximize expected utility, given partial and differential information about payoff-relevant states of the world, represented as an information structure. A counterfactual prediction is desired about behavior in another strategic setting, under the hypothesis that the distribution of the state and agents’ information about the state are held fixed. When the data and the desired counterfactual prediction pertain to environments with finitely many states, players, and actions, there is a finite dimensional description of the sharp counterfactual prediction, even though the latent parameter, the information structure, is infinite dimensional.
We describe a methodology for making counterfactual predictions when the information held by strategic agents is a latent parameter. The analyst observes behavior which is rationalized by a Bayesian model in which agents maximize expected utility, given partial and differential information about payoff-relevant states of the world. A counterfactual prediction is desired about behavior in another strategic setting, under the hypothesis that the distribution of and agents’ information about the state are held fixed. When the data and the desired counterfactual prediction pertain to environments with finitely many states, players, and actions, there is a finite dimensional description of the sharp counterfactual prediction, even though the latent parameter, the type space, is infinite dimensional.
We describe a methodology for making counterfactual predictions when the information held by strategic agents is a latent parameter. The analyst observes behavior which is rationalized by a Bayesian model in which agents maximize expected utility given partial and differential information about payoff-relevant states of the world. A counterfactual prediction is desired about behavior in another strategic setting, under the hypothesis that the distribution of and agents’ information about the state are held fixed. When the data and the desired counterfactual prediction pertain to environments with finitely many states, players, and actions, there is a finite dimensional description of the sharp counterfactual prediction, even though the latent parameter, the type space, is infinite dimensional.