Economic Fluctuations and Growth
Xavier Gabaix, Harvard University and NBER
In this paper, Gabaix presents a framework for analyzing how bounded rationality affects monetary and fiscal policy. The model is a tractable and parsimonious enrichment of the widely-used New Keynesian model with one main new parameter, which quantifies how poorly agents understand future policy and its impact. That myopia parameter in turn affects the power of monetary and fiscal policy in a microfounded general equilibrium. A number of consequences emerge. First, fiscal stimulus or "helicopter drops of money" are powerful and, indeed, pull the economy out of the zero lower bound. More generally, the model allows for the joint analysis of optimal monetary and fiscal policy. Second, the model helps solve the "forward guidance puzzle," the fact that in the rational model, shocks to very distant rates have a very powerful impact on todays consumption and inflation: because the agent is de facto myopic, this effect is muted. Third, the zero lower bound is much less costly than in the traditional model. Fourth, even with passive monetary policy, equilibrium is determinate, whereas the traditional rational model generates multiple equilibria, which reduce its predictive power. Fifth, optimal policy changes qualitatively: the optimal commitment policy with rational agents demands "nominal GDP targeting"; this is not the case with behavioral firms, as the benefits of commitment are less strong with myopic firms. Sixth, the model is "neo-Fisherian" in the long run, but Keynesian in the short run something that has proven difficult for other models to achieve: a permanent rise in the interest rate decreases inflation in the short run but increases it in the long run. The non-standard behavioral features of the model seem warranted by the empirical evidence.
Alessandro Gavazza, London School of Economics; Simon Mongey, New York University; and Giovanni Violante, New York University and NBER
Gavazza, Mongey, and Violante develop a model of firm dynamics with random search in the labor market where hiring firms exert recruiting effort by spending resources to fill vacancies faster. Consistent with micro evidence, in the model fast-growing firms invest more in recruiting activities and achieve higher job-filling rates. In equilibrium, individual decisions of hiring firms aggregate into an index of economy-wide recruiting intensity. The researchers use the model to study how aggregate shocks transmit to recruiting intensity, and whether this channel can account for the dynamics of aggregate matching efficiency around the Great Recession. Productivity and financial shocks lead to sizable pro-cyclical fluctuations in matching efficiency through recruiting effort. Quantitatively, the main mechanism is that firms attain their employment targets by adjusting their recruiting effort as labor market tightness varies. Shifts in sectoral composition can have a sizable impact on aggregate recruiting intensity. Fluctuations in new-firm entry, instead, have a negligible effect despite their contribution to aggregate job and vacancy creations.
Francois Geerolf, University of California at Los Angeles
A strong empirical regularity is that firm size and top incomes follow a Pareto distribution. A large literature explains this regularity by appealing to the distribution of primitives, or by using dynamic "random growth" models. In contrast, Geerolf demonstrates that Pareto distributions can arise from production functions in static assignment models with complementarities, such as Garicano's (2000) knowledge-based production hierarchies model. Under very limited assumptions on the distribution of agents' abilities, these models generate Pareto distributions for the span of control of CEOs and intermediary managers, and Zipf's law for firm size. Geerolf confirms this prediction using French matched employer-employee administrative data. This novel justification of Pareto distributions sheds new light on why firm size and labor income are so heterogeneous despite small observable differences. In the model, Pareto distributions are the benchmark distributions that arise in the case of perfect homogeneity, while heterogeneity in primitives should be inferred from deviations from Pareto distributions.
Neil Mehrotra, Federal Reserve Bank of Minneapolis, and Dmitriy Sergeyev, Bocconi University
Mehrotra and Sergeyev argue that the creation and destruction margins of employment (job flows) at the aggregate level and disaggregated across firm age and size can be used to measure the employment effects of disruptions to firm credit. Using a firm dynamics model, the researchers establish that a tightening of credit to firms reduces employment primarily by reducing gross job creation, exhibiting stronger effects at new/young firms and middle-sized firms (20-99 employees). The researchers find that 18% of the decline in U.S. employment during the Great Recession is due to the firm credit channel. Using MSA-level job flows data, they show that the behavior of job flows overall and across firm size and age categories in response to identified credit shocks is consistent with their model's predictions and hold within tradable and non-tradable industries.
Pablo Kurlat, Stanford University and NBER
Kurlat studies expertise acquisition in a model of trading under asymmetric information. He proposes and implements a method to estimate the ratio of social to private marginal value of expertise. This can be decomposed into three sufficient statistics: traders' average profits, the fraction of bad assets among traded assets and the elasticity of good assets traded with respect to capital inflows. For venture capital, the ratio is between 0.64 and 0.83 and for junk bond underwriting, it is between 0.09 and 0.26. In both cases this is less than one so at the margin financial expertise destroys surplus.
Tobias Adrian, Nina Boyarchenko, and Domenico Giannone, Federal Reserve Bank of New York
Adrian, Boyarchenko, and Giannone study the conditional distribution of GDP growth as a function of economic and financial conditions. Deteriorating financial conditions are associated with an increase in conditional volatility and a decline in the conditional mean of GDP growth leading to a highly skewed distribution, with the lower quantiles of GDP growth exhibiting strong variation as a function of financial conditions and the upper quantiles stable over time. While measures of financial conditions significantly forecast downside vulnerability, measures of economic conditions have significant predictive power only for the median of the distribution. These findings are robust both in- and out-of-sample and to including different measures of financial conditions. The researchers quantify GDP vulnerability as relative entropy between the empirical conditional and unconditional distribution. They show that this measure of vulnerability is highly asymmetric: the contribution to the total relative entropy of the probability mass below the median of the conditional distribution is larger and more volatile than the contribution of the probability mass above the median. The asymmetric response of the distribution of GDP growth to financial and economic conditions with adverse financial conditions increasing downside vulnerability of growth but not the median forecast is challenging for standard models of the macroeconomy. The researchers argue that the inclusion of a financial sector is crucial for generating the observed dynamics of growth vulnerability.