Knowledge that Transforms

We construct a model to study the implications of changes in political institutions for economic institutions. A change in political institutions alters the distribution of de jure political power, but creates incentives for investments in de facto political power to partially or even fully offset change in de jure power. The model can imply a pattern of captured democracy, whereby a democratic regime may survive but choose economic institutions favoring an elite. The model provides conditions under which economic or policy outcomes will be invariant to changes in political institutions, and economic institutions themselves will persist over time. (JEL D02, D72) The domination of an organized minority … over the unorganized majority is inevitable. The power of any minority is irresistible as against each single individual in the majority, who stands alone before the totality of the organized minority. At the same time, the minority is organized for the very reason that it is a minority. —Gaetano Mosca (1939, 53).

We show that the week after selling a large-prize Texas Lotto winning ticket, a retailer experiences a 12 to 38 percent relative increase in ticket sales. Some increase persists for up to 40 weeks. We document that the sales response increases with jackpot size and is larger in areas with more economically disadvantaged populations. Sales patterns across games and across retailers are not consistent with most advertising explanations. Furthermore, response patterns are not consistent with representativeness-based explanations for the hot hand or gambler's fallacy; we suggest an alternative explanation for the observed "lucky store" effect.

Rethinking Global Environmental Governance to Deal with Climate Change: The Multiple Logics of Global Collective Action by Daniel C. Esty. Published in volume 98, issue 2, pages 116-21 of American Economic Review, May 2008

Is the 2007-2008 U.S. sub-prime mortgage financial crisis truly a new and different phenomena? Our examination of the longer historical record finds stunning qualitative and quantitative parallels to 18 earlier post-war banking crises in industrialized countries. Specifically, the run-up in U.S. equity and housing prices (which, for countries experiencing large capital inflows, stands out as the best leading indicator in the financial crisis literature) closely tracks the average of the earlier crises. Another important parallel is the inverted v-shape curve for output growth the U.S. experienced as its economy slowed in the eve of the crisis. Among other indicators, the run-up in U.S. public debt and is actually somewhat below the average of other episodes, and its pre-crisis inflation level is also lower. On the other hand, the United States current account deficit trajectory is worse than average. A critical question is whether the U.S. crisis will prove similar to the most severe industrialized-country crises, in which case growth may fall significantly below trend for an extended period. Or will it prove like one of the milder episodes, where the recovery is relatively fast? Much will depend on how large the shock to the financial system proves to be and, to a lesser extent, on the efficacy of the subsequent policy response.

Economics has traditionally relied on revealed preferences (and, occasionally, on verbal reports) to understand the desires of people. Another source of information has been developed in recent years: the direct observation of choice processes. This mechanism, possible thanks to the improvements in the designs and techniques to measure brain activity, is explored in the bur geoning field of experimental neuroeconom ics (see Paul W. Glimcher and Aldo Rustichini (2004) and Colin Camerer, George Loewenstein, and Drazen Prelec (2005) for recent surveys). In this article, we argue that the evidence on brain activity can also be used to build theo retical models that help us understand choices and predict behaviors. We label this research “Neuroeconomic Theory.” In Section I, we describe the procedure, discuss some advan tages over traditional methodologies, and estab lish some facts that motivate our approach. In Section II, we illustrate the methodology with two brain-based models of decision making. I. What is “Neuroeconomic Theory”? Neuroeconomic theory is an interdisciplin ary line of investigation that combines research from neuroscience, neurobiology, and econom ics. Experimental neuroscience and neurobiology provide detailed evidence of the functionality, interconnectivity, and physiological constraints of the brain systems involved in decision making. Microeconomic theory supplies the toolkit to build simple optimization models that incorporate these network interactions and well-defined con straints into the mechanisms of choice.

Widening and Deepening: Reforming the European Union by Erik Berglof, Mike Burkart, Guido Friebel and Elena Paltseva. Published in volume 98, issue 2, pages 133-37 of American Economic Review, May 2008

Beyond DSGE Models: Toward an Empirically Based Macroeconomics by David Colander, Peter Howitt, Alan Kirman, Axel Leijonhufvud and Perry Mehrling. Published in volume 98, issue 2, pages 236-40 of American Economic Review, May 2008

Policymakers often prescribe that microfinance institutions increase interest rates to eliminate their reliance on subsidies. This strategy makes sense if the poor are rate insensitive: then microlenders increase profitability (or achieve sustainability) without reducing the poor's access to credit. We test the assumption of price inelastic demand using randomized trials conducted by a consumer lender in South Africa. The demand curves are downward sloping, and steeper for price increases relative to the lender's standard rates. We also find that loan size is far more responsive to changes in loan maturity than to changes in interest rates, which is consistent with binding liquidity constraints.

This article examines the stock market's changing valuation of corporate patentable assets between 1910 and 1939. It shows that the value of knowledge capital increased significantly during the 1920s compared to the 1910s as investors responded to the quality of technological inventions. Innovation was an important driver of the late 1920s stock market runup, and the Great Crash did not reflect a significant revaluation of knowledge capital relative to physical capital. Although substantial quantities of influential patents were accumulated during the post-crash recovery, high technology firms did not earn significant excess returns over low technology firms for most of the 1930s.