Knowledge that Transforms

The theory of monotone comparative statics and supermodular games is presented as the appropriate tool to model complementarities. The approach, which has not yet been fully incorporated into the standard toolbox of researchers, makes the analysis intuitive and simple, helps in deriving new results and in casting new light on old ones. The paper takes stock of recent contributions and develops applications to industrial organization (oligopoly, R&D, and dynamics), finance (currency and banking crisis) and macroeconomics (adjustment and menu costs). Particular attention is devoted to Markov games and to games of incomplete information (including global games).

Neuroeconomics uses knowledge about brain mechanisms to inform economic analysis, and roots economics in biology. It opens up the “black box” of the brain, much as organizational economics adds detail to the theory of the firm. Neuroscientists use many tools— including brain imaging, behavior of patients with localized brain lesions, animal behavior, and recording single neuron activity. The key insight for economics is that the brain is composed of multiple systems which interact. Controlled systems (“executive function”) interrupt automatic ones. Emotions and cognition both guide decisions. Just as prices and allocations emerge from the interaction of two processes—supply and demand— individual decisions can be modeled as the result of two (or more) processes interacting. Indeed, “dual-process” models of this sort are better rooted in neuroscientific fact, and more empirically accurate, than single-process models (such as utility-maximization). We discuss how brain evidence complicates standard assumptions about basic preference, to include homeostasis and other kinds of state-dependence. We also discuss applications to intertemporal choice, risk and decision making, and game theory. Intertemporal choice appears to be domain-specific and heavily influenced by emotion. The simplified ß-d of quasi-hyperbolic discounting is supported by activation in distinct regions of limbic and cortical systems. In risky decision, imaging data tentatively support the idea that gains and losses are coded separately, and that ambiguity is distinct from risk, because it activates fear and discomfort regions. (Ironically, lesion patients who do not receive fear signals in prefrontal cortex are “rationally” neutral toward ambiguity.) Game theory studies show the effect of brain regions implicated in “theory of mind”, correlates of strategic skill, and effects of hormones and other biological variables. Finally, economics can contribute to neuroscience because simple rational-choice models are useful for understanding highly-evolved behavior like motor actions that earn rewards, and Bayesian integration of sensorimotor information. Who knows what I want to do? Who knows what anyone wants to do? How can you be sure about something like that? Isn't it all a question of brain chemistry, signals going back and forth, electrical energy in the cortex? How do you know whether something is really what you want to do or just some kind of nerve impulse in the brain. Some minor little activity takes place somewhere in this unimportant place in one of the brain hemispheres and suddenly I want to go to Montana or I don't want to go to Montana. (White Noise, Don DeLillo)

Around the world, large corporations usually have controlling owners, who are usually very wealthy families. Outside the U.S. and the U.K., pyramidal control structures, cross shareholding and super voting rights are common. Using these devices, a family can control corporations without making a commensurate capital investment. In many countries, such families end up controlling considerable proportions of their countries'' economies. Three points emerge. First, at the firm level, these ownership structures vest dominant control rights with families who often have little real capital invested n creating agency and entrenchment problem simultaneously. In addition, controlling shareholders can divert corporate resources for private benefits using transactions within the pyramidal group. The result is a poor utilization of resources. At the economy level, extensive control of corporate assets by a few families distorts capital allocation and reduces the rate of innovation. The result is an economy-wide misallocation of resources, and slower economic growth.

We survey and assess the literature on the positive and negative effects of ethnic diversity on economic policies and outcomes. Our focus is on communities of different size and organizational structure, such as countries, cities in developed countries, and villages and groups in developing countries. We also consider the endogenous formation of political jurisdictions and highlight several open issues in need of further research, in particular the endogenous formation of ethnic identity and the measurement of ethnic diversity.

We estimate the aggregate long-run elasticity of substitution between more educated workers and less educated workers (the slope of the inverse demand curve for more relative to less educated workers) at the U.S. state level. Our data come from the (five) 1950–1990 decennial censuses. Our empirical approach allows for state and time fixed effects and relies on time- and state-dependent child labor and compulsory school attendance laws as instruments for (endogenous) changes in the relative supply of more educated workers. We find the aggregate long-run elasticity of substitution between more and less educated workers to be around 1.5.

February 01 2005 “Measuring Poverty”: Discussion Michael Kremer Michael Kremer Harvard University Search for other works by this author on: This Site Google Scholar Author and Article Information Michael Kremer Harvard University Received: May 26 2004 Accepted: July 28 2004 Online ISSN: 1530-9142 Print ISSN: 0034-6535 © 2005 President and Fellows of Harvard College and the Massachusetts Institute of Technology2005 The Review of Economics and Statistics (2005) 87 (1): 23–25. https://doi.org/10.1162/0034653053327630 Article history Received: May 26 2004 Accepted: July 28 2004 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn Email Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Michael Kremer; “Measuring Poverty”: Discussion. The Review of Economics and Statistics 2005; 87 (1): 23–25. doi: https://doi.org/10.1162/0034653053327630 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThe Review of Economics and Statistics Search Advanced Search This content is only available as a PDF. © 2005 President and Fellows of Harvard College and the Massachusetts Institute of Technology2005 Article PDF first page preview Close Modal You do not currently have access to this content.

We estimate returns to human capital during communism and the transition using data on 2,284 men in the Czech Republic. We show: (a) extremely low and constant rates of return to education under the communist wage grid and dramatic increases in transition, which do not differ by firm ownership, (b) radical changes in returns to several fields of study and “sheepskin effects” in both regimes, (c) identical wage experience profile in both regimes, (d) similar 1996 returns to human capital obtained in communism and in transition, and (e) changes in the interindustry wage structure. A decomposition of the variance of wages finds individuals' unobservable effects from communism to persist into transition, but most of the variance is due to unobservable effects introduced in the transition.

We analyze the technology shocks identified by two different structural VAR models and compare them with classical and refined Solow residuals. The measures of technology are reasonably highly correlated. Alternative identifying restrictions in the VARs, however, have different theoretical as well as empirical consequences for the technology shocks. King et al.'s (1991) model and the classical Solow residual capture a mixture of technology and labor supply shocks, whereas the technology shocks from Galí's model and the refined Solow residuals are robust to the latter phenomenon. Moreover, we find that the two robust measures of technology are negatively correlated with hours worked.

Why have some countries done so much better than others over the recent past? This paper sheds light on this issue by providing a decomposition of the change in the distribution of output per worker across countries over the period 1960–1998. We find that most of the change in shape of the world distribution of income can be accounted for by a very substantial increase in the social returns to capital accumulation. In contrast, we do not find significant effects coming through changes in the effect of initial conditions or through increases in the importance of education.

Recent research documents the importance of uncertainty in determining macroeconomic outcomes, but little is known about the transmission of uncertainty across such outcomes. This paper examines the response of uncertainty about inflation and output growth to shocks documenting statistically significant size and sign bias and spillover effects. Uncertainty about inflation is a determinant of output uncertainty, whereas higher growth volatility tends to raise inflation volatility. Both inflation and growth volatility respond asymmetrically to positive and negative shocks. Negative growth and inflation shocks lead to higher and more persistent uncertainty than shocks of equal magnitude but opposite sign.