Knowledge that Transforms

Much economic growth research has been devoted to determining the explanatory variables that explain cross-country variation in growth rates. A frequently cited problem with this literature is that the number of potential growth regressors is vast, potentially exceeding the number of countries available for study. Thus, researchers are faced with the task of arbitrarily specifying which explanatory variables to include in their growth regressions, raising concerns about how confident we can be in their results. These concerns were magnified by the influential paper of Ross Levine and David Renelt (1992), in which they employ a variation of Edward E. Leamer’s (1983) extreme bounds analysis to test the robustness of conventional growth regression coefficients to changes in the set of conditioning variables. They conclude that the results of this literature are extremely fragile, with the only robust determinants of growth being physical capital investment, initial income, and secondary school enrollment. In contrast, they demonstrate the fragility of a host of fiscal, monetary, and trade policy variables, as well as measures of political and economic stability and economic distortions. There have been two main responses to their findings. The pessimistic response has been to conclude, given the lack of a reliable statistical relationship between conventional

The dynamics of a linear (or linearized) dynamic stochastic economic model can be expressed in terms of matrices (A, B, C, D) that define a state space system for a vector of observables. An associated state space system (A, ^ B,C, ^D) determines a vector autoregression for those same observables. We present a simple condition for checking when these two state space systems match up and when they do not when there are equal numbers of economic and VAR shocks. We illustrate our condition with a permanent income example.

An organization makes collective decisions through neither markets nor contracts. Instead, rational agents voluntarily choose to follow a leader. In many cases, incentive problems are solved: the unique nondegenerate equilibrium achieves the first best, even though every agent has incentives to free ride. The leader has no special talents but is distinguished by getting exclusive access to information. A crucial feature is that the leader reveals part but not all of her information. It is this maintenance of informational asymmetry that permits achieving the first best.

Bounding Revenue Comparisons across Multi-Unit Auction Formats under ε-Best Response by James T. E. Chapman, David McAdams and Harry J. Paarsch. Published in volume 97, issue 2, pages 455-458 of American Economic Review, May 2007

During the last several decades, a growing body of laboratory research has shown that human subjects do not always choose to maximize material payoffs. Economists following the lead of

Situations in which agents’ choices depend on choices of those in close proximity, be it social or geographic, are ubiquitous. Selecting a new computer platform, signing a political petition, or even catching the flu are examples in which social interactions have a significant role. While some behaviors or states propagate and explode within the population (e.g., Windows OS, the HIV virus) others do not (e.g., certain computer viruses). Our goal in this paper is twofold. First, we provide a general dynamic model in which agents’ choices depend on the underlying social network of connections. Second, we show the usefulness of the model in determining when a given behavior expands within a population or disappears as a function of the environment’s fundamentals. We study a framework in which agents face a choice between two actions, 0 and 1 (e.g., whether to pursue a certain level of education, switch to Linux OS, etc.). Agents are linked through a social network, and an agent’s payoffs from each action depend on the number of neighbors she has and her neighbors’ choices. The diffusion process is defined so that at each period, each agent best responds to the actions taken by her neighbors in the previous period, assuming that her neighbors follow the population distribution of actions (a mean-field approximation). Steady states correspond to equilibria of the static game. Under some simple conditions, equilibria take one of two forms. Some are stable, so that a slight perturbation to any such equilibrium would lead the diffusion process to converge back to that equilibrium point. Other equilibria are unstable, so that a slight change in the distribution of actions leads to a new distribution of actions and eventually to a stable steady state. We call such equilibria tipping points. We analyze how the environment’s fundamentals (cost distribution, payoffs, and network structure) affect the set of equilibria, and characterize the adoption patterns within the network. The paper relates to recent work on network games and network diffusion, including work by Stephen Morris (2000); Pastor-Satorras and Vespignani (2000); Mark E. J. Newman (2002); Dunia López-Pintado (2004); Jackson and Brian W. Rogers (2007); Jackson and Yariv (2005); and Andrea Galeotti et al. (2005, henceforth GGJVY). Its contribution is in characterizing diffusion of strategic behavior and analyzing the stability properties of equilibria, and employing methods that allow us to make comparisons across general network structures and settings. Given that social networks differ substantially and systematically in structure across settings (e.g., ethnic groups, professions, etc.), understanding the implications of social structure on diffusion is an important undertaking for a diverse set of applications.

The paper generalizes the Taylor principle—the proposition that central banks can stabilize the macroeconomy by raising their interest rate instrument more than one-for-one in response to higher inflation—to an environment in which reaction coefficients in the monetary policy rule change regime, evolving according to a Markov process. We derive a long-run Taylor principle which delivers unique bounded equilibria in two standard models. Policy can satisfy the Taylor principle in the long run, even while deviating from it substantially for brief periods or modestly for prolonged periods. Macroeconomic volatility can be higher in periods when the Taylor principle is not satisfied, not because of indeterminacy, but because monetary policy amplifies the impacts of fundamental shocks. Regime change alters the qualitative and quantitative predictions of a conventional new Keynesian model, yielding fresh interpretations of existing empirical work.

The empirical literature on aid effectiveness has yielded unclear and ambiguous results. This is not surprising given the heterogeneity of aid motives, the limitations of the tools of analysis, and the complex causality chain linking external aid to final outcomes. The causality chain has been largely ignored and as a consequence the relationship between aid and development has been mostly handled as a kind of 'black box'. Making further progress on aid effectiveness requires opening that box. This paper examines the causality chain linking aid flows to development outcomes. It argues that many of the questions that policy makers and economists would like to squeeze data into answering simply cannot be answered due to the complexity and ‘noise ’ along links in the chain, and hence the problem of attribution. It then examines what is known about aid effectiveness along different links in the causality chain. Finally, it turns to recent trends in the way aid is delivered and the new model that appears to be emerging. I. The ‘causality chain’: aid, effectiveness and results The debates around the impact of aid on development have typically aggregated

We develop an equilibrium business cycle model where nonconvex delivery costs lead firms to follow (S, s) inventory policies. Calibrated to postwar US data, the model reproduces two-thirds of the cyclical variability of inventory investment. Moreover, it delivers strongly procyclical inventory investment, greater volatility in production than sales, and a countercyclical inventory-to-sales ratio. Our model challenges several prominent claims involving inventories, including the widely held belief that they amplify aggregate fluctuations. Despite the comovement between inventory investment and final sales, GDP volatility is essentially unaltered by inventory accumulation, because procyclical inventory investment diverts resources from final production, thereby dampening fluctuations in sales.

We utilize graphical representations of Dictator Games which generate rich individual-level data. Our baseline experiment employs budget sets over feasible payoff-pairs. We test these data for consistency with utility maximization, and we recover the underlying preferences for giving (trade-offs between own payoffs and the payoffs of others). Two further experiments augment the analysis. An extensive elaboration employs three-person budget sets to distinguish preferences for giving from social preferences (trade-offs between the payoffs of others). And an intensive elaboration employs step-shaped sets to distinguish between behaviors that are compatible with well-behaved preferences and those compatible only with not well-behaved cases.