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The outcome of any important macroeconomic policy change is the net effect of forces operating on different parts of the economy. A central challenge facing policymakers is how to assess the relative strength of those forces. Economists have a range of tools that can be used to make such assessments. Dynamic stochastic general equilibrium (DSGE) models are the leading tool for making such assessments in an open and transparent manner. We review the state of mainstream DSGE models before the financial crisis and the Great Recession. We then describe how DSGE models are estimated and evaluated. We address the question of why DSGE modelers—like most other economists and policymakers—failed to predict the financial crisis and the Great Recession, and how DSGE modelers responded to the financial crisis and its aftermath. We discuss how current DSGE models are actually used by policymakers. We then provide a brief response to some criticisms of DSGE models, with special emphasis on criticism by Joseph Stiglitz, and offer some concluding remarks.

For a large number of OECD countries we estimate the cross-country spillover effects of government purchases on output. Following the methodology in Auerbach and Gorodnichenko (2012a, b), we allow these multipliers to vary smoothly according to the state of the economy and use real-time forecast data to purge policy innovations of their predictable components. Our findings suggest that cross-country spillovers have an important impact. The findings also confirm those of our earlier papers--namely that fiscal shocks have a larger impact when the affected country is in recession.

At the onset of the recent global financial crisis, the workhorse macroeconomic models assumed frictionless financial markets. These frameworks were thus not able to anticipate the crisis, nor to analyze how the disruption of credit markets changed what initially appeared like a mild downturn into the Great Recession. Since that time, an explosion of both theoretical and empirical research has investigated how the financial crisis emerged and how it was transmitted to the real sector. The goal of this paper is to describe what we have learned from this new research and how it can be used to understand what happened during the Great Recession. In the process, we also present some new empirical work. We argue that a complete description of the Great Recession must take account of the financial distress facing both households and banks and, as the crisis unfolded, nonfinancial firms as well. Exploiting both panel data and time series methods, we analyze the contribution of the house price decline, versus the banking distress indicator, to the overall decline in employment during the Great Recession. We confirm a common finding in the literature that the household balance sheet channel is important for regional variation in employment. However, we also find that the disruption in banking was central to the overall employment contraction.

We estimate a state-of-the-art DSGE model to study the natural rate of interest in the United States over the last 20 years. The natural rate is highly procyclical, and fell substantially below zero in each of the last three recessions. Although the drop was of comparable magnitude across the three recessions, the decline was considerably more persistent in the Great Recession. We discuss the usefulness and limitations, particularly due to the zero lower bound, of the natural rate for the conduct of monetary policy.

Unemployment Insurance benefit durations were extended during the Great Recession, reaching 99 weeks for most recipients. The extensions were rolled back and eventually terminated by the end of 2013. Using matched CPS data from 2008-2014, we estimate the effect of extended benefits on unemployment exits separately during the earlier period of benefit expansion and the later period of rollback. In both periods, we find little or no effect on job-finding but a reduction in labor force exits due to benefit availability. We estimate that the rollbacks reduced the labor force participation rate by about 0.1 percentage point in early 2014.

Background: All-cause mortality is a global indicator of the overall health of the population, and its relation to the macro economy is thus of vital interest. The main aim was to estimate the short-term and the long-term impact of macroeconomic change on all-cause mortality. Variations in the unemployment rate were used as indicator of temporary fluctuations in the economy. Methods: We used time-series data for 21 OECD countries spanning the period 1960–2018. We used four outcomes: total mortality (0+), infant mortality (<1), mortality in the age-group 20–64, and old-age mortality (65+). Data on GDP/capita were obtained from the Maddison Project. Unemployment data (% unemployed in the work force) were sourced from Eurostat. We applied error correction modelling to estimate the short-term and the long-term impact of macroeconomic change on all-cause mortality. Results: We found that increases in unemployment were statistically significantly associated with decreases in all mortality outcomes except old-age mortality. Increases in GDP were associated with significant lowering long-term effects on mortality. Conclusions: Our findings, based on data from predominantly affluent countries, suggest that an increase in unemployment leads to a decrease in all-cause mortality. However, economic growth, as indicated by increased GDP, has a long-term protective health impact as indexed by lowered mortality.

Modern growth theory suggests that more than three-quarters of growth since 1950 reflects rising educational attainment and research intensity. As these transition dynamics fade, US economic growth is likely to slow at some point. However, the rise of China, India, and other emerging economies may allow another few decades of rapid growth in world researchers. Finally, and more speculatively, the shape of the idea production function introduces a fundamental uncertainty into the future of growth. For example, the possibility that artificial intelligence will allow machines to replace workers to some extent could lead to higher growth in the future.

[Display omitted] •Franchisors strategically modify contracts for new franchisees (contractual discrimination).•Positive discrimination reduces existing franchisees perceptions of equity.•Reduced perception of equity increases freeriding and hurts performance.•Greater number of existing franchisees worsens franchisor performance.•Negative discrimination reduces freeriding, increases performance only for more franchisees. Franchisors often modify the contract terms offered to prospective (new) franchisees – to incentivize growth in the number of franchisees, to access capital, or to improve their financial performance. We argue that changes in contract terms offered to new franchisees (contractual discrimination across franchisees) can alter existing franchisees’ perceived equity in their relationship with the franchisor, and affect their freeriding. Specifically, we hypothesize, and show, that positive (negative) discrimination towards new franchisees reduces (maintains) existing franchisees’ perceived equity in their relationship with the franchisor, motivating existing franchisees to increase (eschew) freeriding – with impact on franchisors’ performance. To do so, we first take advantage of an exogenous event (the great recession of 2007-09) to study how 120 restaurant franchisors changed their contract terms to new franchisees and how that affected their post-recession net income (Study 1). We show that changes in contracts for new franchisees impact franchisors’ post-crisis performance, as a function of the number of existing franchisees. Second, with two experiments (Studies 2 and 3) with entrepreneurs and franchisees, we document that the observed changes in performance occur because contractual discrimination affects existing franchisees’ perceived equity and their intentions to free-ride. Thus, we contribute to the literature on equity in franchising relationships, on contract evolution in franchising, and its impact on financial performance.

Modern business cycle theory focuses on the study of dynamic stochastic general equilibrium (DSGE) models that generate aggregate fluctuations similar to those experienced by actual economies. We discuss how these modern business cycle models have evolved across three generations, from their roots in the early real business cycle models of the late 1970s through the turmoil of the Great Recession four decades later. The first generation models were real (that is, without a monetary sector) business cycle models that primarily explored whether a small number of shocks, often one or two, could generate fluctuations similar to those observed in aggregate variables such as output, consumption, investment, and hours. These basic models disciplined their key parameters with micro evidence and were remarkably successful in matching these aggregate variables. A second generation of these models incorporated frictions such as sticky prices and wages; these models were primarily developed to be used in central banks for short-term forecasting purposes and for performing counterfactual policy experiments. A third generation of business cycle models incorporate the rich heterogeneity of patterns from the micro data. A defining characteristic of these models is not the heterogeneity among model agents they accommodate nor the micro-level evidence they rely on (although both are common), but rather the insistence that any new parameters or feature included be explicitly disciplined by direct evidence. We show how two versions of this latest generation of modern business cycle models, which are real business cycle models with frictions in labor and financial markets, can account, respectively, for the aggregate and the cross-regional fluctuations observed in the United States during the Great Recession.

Health care spending usually contributes to a large part of a developed country’s economy. In 2011, the U.S. consumed about 17.7% of its GDP on health care. As one of the most significant components of the health care industry, the hospital sector plays a key role to provide healthcare services. Healthcare services industry can be affected by many factors, of which economic downturn is a crucial one. As a result, it is worth investigating the condition and state of hospital management when economic downturn occurs. This paper aims to analyze how the Great Recession affects hospital performance in Pennsylvania during the period 2005–2012 by using data envelopment analysis (DEA). Specifically, we measure efficiency for hospitals in Pennsylvania, and use several DEA models to calculate the global Malmquist index (GMI). We find that: (1) 15.4% hospitals are always efficient while 36.9% hospitals are always inefficient for all years in 2005–2012; (2) The relative distance for a group of hospitals to the frontier is almost unchanged post-recession and pre-recession; (3) The average efficiency/GMI decreases by 2.43%/3.07% from pre-recession to post-recession. The analysis indicates that hospital performance slightly decreased due to the economic downturn in Pennsylvania.