A canonical finding from earlier research is that the cross-sectional variance of income increases sharply with age Deaton and Paxson (1994). However, the trend in this age profile is not separately identified from time and cohort trends. Conventional methods solve this identification problem by ruling out "time effects." This strong assumption is rejected by the data. We propose a new proxy variable machine learning approach to disentangle age, time and cohort effects. Using this method, we estimate a significantly smaller slope of the age profile of income variance for the US than conventional methods, as well as less erratic slopes for 11 other countries. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We provide new estimates of the evolution of productivity in England from 1250 to 1870. Real wages over this period were heavily influenced by plague-induced swings in the population. We develop and implement a new methodology for estimating productivity that accounts for these Malthusian dynamics. In the early part of our sample, we find that productivity growth was zero. Productivity growth began in 1600—almost a century before the Glorious Revolution. Post-1600 productivity growth had two phases: an initial phase of modest growth of 4% per decade between 1600 and 1810, followed by a rapid acceleration at the time of the Industrial Revolution to 18% per decade. Our evidence helps distinguish between theories of why growth began. In particular, our findings support the idea that broad-based economic change preceded the bourgeois institutional reforms of 17th century England and may have contributed to causing them. We also estimate the strength of Malthusian population forces on real wages. We find that these forces were sufficiently weak to be easily overwhelmed by post-1800 productivity growth.

In standard models, economic activity fluctuates symmetrically around a “natural rate” and stabilization policies can dampen these fluctuations but do not affect the average level of activity. An alternative view—labeled the “plucking model” by Milton Friedman—is that economic fluctuations are drops below the economy’s full potential ceiling. If this view is correct, stabilization policy, by dampening these fluctuations, can raise the average level of activity. We show that the dynamics of the unemployment rate in the US display a striking asymmetry that strongly favors the plucking model: increases in unemployment are followed by decreases of similar amplitude, while the amplitude of the increase is not related to the amplitude of the previous decrease. We develop a microfounded plucking model of the business cycle. The source of asymmetry in our model is downward nominal wage rigidity, which we embed in an explicit search model of the labor market. Our search framework implies that downward nominal wage rigidity is consistent with optimizing behavior and equilibrium. In our plucking model, stabilization policy lowers average unemployment and thereby yields sizable welfare gains.

We study the consequences of "regime-induced" exchange rate depreciations by comparing outcomes for peggers versus floaters to the U.S. dollar in response to a dollar depreciation. Pegger currencies depreciate relative to floater currencies and these depreciations are strongly expansionary. The boom is associated with a fall in net exports, and (if anything) an increase in interest rates in the pegger countries. This suggests that expenditure switching and domestic monetary policy are not the main drivers of the boom. We show that a large class of existing models cannot match our estimated responses and develop a model with imperfect financial openness that can. Following a depreciation, uncovered interest parity deviations lower the costs of borrowing from abroad and stimulate the economy, as in the data. The model is consistent with (unconditional) exchange rate disconnect and the Mussa fact, even though exchange rates have large effects on the economy.

Forecasts of professional forecasters are anomalous: they are biased, forecast errors are autocorrelated, and predictable by forecast revisions.Sticky or noisy information models seem like unlikely explanations for these anomalies: professional forecasters pay attention constantly and have precise knowledge of the data in question.We propose that these anomalies arise because professional forecasters don't know the model that generates the data.We show that Bayesian agents learning about hard-to-learn features of the data generating process (low frequency behavior) can generate all the prominent aggregate anomalies emphasized in the literature.We show this for two applications: professional forecasts of nominal interest rates for the sample period 1980-2019 and CBO forecasts of GDP growth for the sample period 1976-2019.Our learning model for interest rates also provides an explanation for deviations from the expectations hypothesis of the term structure that does not rely on time-variation in risk premia.

Business cycle recoveries have slowed in recent decades. This slowdown comes entirely from female employment, as women’s employment rates converged toward men’s during the past half-century. But does the slowdown in the growth of female employment rates translate into a slowdown for overall employment rates? We estimate the extent to which women “crowd out” men in the labor market across US states, and find that it is small. Through the lens of a general equilibrium model with home production, we show this statistic implies that 60-75 percent of the slowdown in recent business cycle recoveries can be explained by female convergence.(JEL D13, E24, E32, J16, J21)

We study the macroeconomic effects of unemployment insurance (UI) benefit extensions in the United States at short and long durations. To do this, we develop a new state level dataset on trigger variables for UI extensions and a "UI benefit calculator" based on detailed legislative and administrative sources spanning five decades. Our identification approach exploits variation across states in the options governing the Extended Benefits program. We find that UI extensions during time periods when UI benefit durations are already long—such as in the Great Recession—have minimal effects. However, UI extensions when initial durations are shorter have substantial effects on the unemployment rate and the number of people receiving UI. Through the lens of a search-and-matching model, we show that our estimates are consistent with microeconomic estimates of the duration elasticity to UI, implying small general equilibrium effects of UI extensions.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We exploit a volcanic “experiment” to study the costs and benefits of geographic mobility. In our experiment, a third of the houses in a town were covered by lava. People living in these houses were much more likely to move away permanently. For the dependents in a household (children), our estimates suggest that being induced to move by the “lava shock” dramatically raised lifetime earnings and education. While large, these estimates come with a substantial amount of statistical uncertainty. The benefits of moving were very unequally distributed across generations: the household heads (parents) were made slightly worse off by the shock. These results suggest large barriers to moving for the children, which imply that labour does not flow to locations where it earns the highest returns. The large gains from moving for the young are surprising in light of the fact that the town affected by our volcanic experiment was (and is) a relatively high income town. We interpret our findings as evidence of the importance of comparative advantage: the gains to moving may be very large for those badly matched to the location they happened to be born in, even if differences in average income are small.

We live in an age of capitalism. Private property and voluntary exchange between private individuals play a central role in the organization of economic activity. We frequently describe economic activity as occurring on free “markets.” In actuality, however, few products are traded on organized markets. Rather, we have come to use of the term “markets” as a short-hand metaphor to describe voluntary exchange of goods and services by individuals (and groups formed by individuals). Markets, conceived of in this broad sense, are a central institution in the modern world. This heavy reliance on markets is a relatively recent phenomenon. Until a few hundred years ago, most economic activity consisted of small groups with strong kin ties, such as extended families, villages, or tribes, engaged in subsistence farming. Specialization and trade was limited due to high levels of violence, pervasive lack of freedom (e.g., serfdom, slavery, class and caste restrictions, etc.), absence of individual property rights (e.g., property rights over land), poor infrastructure, piracy, tolls, and various other obstacles to trade. The rise of markets over the past few hundred years has coincided with an enormous increase in economic well-being for a large and growing fraction of the world’s human population. Yet the growing importance of markets has been extremely controversial, and still is. Critics lament alienation and exploitation of the weak by the strong. Karl Marx, the most influential among these critics, argued that with the growth of capitalism “grows the mass of misery, oppression, slavery,

An amazing variety of items have been used as money throughout history. For many centuries, cowrie shells where a dominant form of money in large parts of Asia and Africa. In Icelandic (my mother tongue), the word for money is the same as the word for sheep (“fé”) because sheep were used as a medium of exchange for centuries. Similarly, the English word “pecuniary” comes from the Latin word “pecus,” which means cattle. Tobacco and beaver skins (among other objects) were used as money in the British colonies of North America in the 17th and 18th centuries. The most common form of money over the past millenium has probably been coins made of gold, silver, copper, and sometimes other metals. More recently, paper money, bank deposits, and other types of ledger entries have become the dominate form of money in much of the world. Most people are used to the monetary system employed in their own society, and may not give it much thought on a day-to-day basis. But the monetary system used in one society can strike people in other societies as puzzling and even illogical. Take, for example, the Micronesian island of Yap. It’s unusual monetary system was described in The Island of Stone Money by anthropologist Henry Furness (1910) as well as by Friedman (1992, ch. 1). The medium of exchange on Yap was called fei. Furness describes fei as “large, solid, thick, stone wheels, ranging in diameter from a foot to twelve feet, having in the center a hole varying in size with the diameter of the stone, wherein a pole may be inserted sufficiently large and strong to ∗I would like to thanks Amanda Awadey for excellent research assistance. I would like to thank Emi Nakamura, Kivanc Karaman, Nuno Palma, Angela Redish, Greg Simitian, and Alan Taylor for valuable comments and discussions. I would like to thank Adrian Baltazar, Emma Berman, and Rafael Silva for finding typos and errors. First posted in June 2021.

We provide new time-varying estimates of the housing wealth effect back to the 1980s. We use three identification strategies: ordinary least squares with a rich set of controls, the Saiz housing supply elasticity instrument, and a new instrument that exploits systematic differences in city-level exposure to regional house price cycles. All three identification strategies indicate that housing wealth elasticities were if anything slightly smaller in the 2000s than in earlier time periods. This implies that the important role housing played in the boom and bust of the 2000s was due to larger price movements rather than an increase in the sensitivity of consumption to house prices. Full-sample estimates based on our new instrument are smaller than recent estimates, though they remain economically important. We find no significant evidence of a boom–bust asymmetry in the housing wealth elasticity. We show that these empirical results are consistent with the behaviour of the housing wealth elasticity in a standard life-cycle model with borrowing constraints, uninsurable income risk, illiquid housing, and long-term mortgages. In our model, the housing wealth elasticity is relatively insensitive to changes in the distribution of loan-to-value (LTV) for two reasons: first, low-leverage homeowners account for a substantial and stable part of the aggregate housing wealth elasticity; second, a rightward shift in the LTV distribution increases not only the number of highly sensitive constrained agents but also the number of underwater agents whose consumption is insensitive to house prices.

We live in an era of economic growth. Over our lifetimes, the lifetimes of our parents, and grandparents, output per person in North America and much of Western Europe has grown on average by roughly 2% per year. This steady growth has led to a staggering transformation of material wellbeing. Ordinary workers in North America and Western Europe earn about 15 times more than they did two hundred years ago. After several generations of steady progress, it may seem inevitable that economic growth will continue throughout our life-times and the life-times of our children and grandchildren. It is important, however, to realize that the era of rapid economic growth that we live in is a very recent phenomenon. Before 1750, economic growth was less than one tenth as rapid as it is today; and before 1500, economic growth proceeded at a truly glacial pace (as far as we can tell using current historical knowledge). Our species has dominated the earth for thousands of years. Massive empires have risen and fallen. But over the millenia before 1500, the material wellbeing of ordinary workers changed very slowly if at all. Then, in a blink of an eye (from a long-term historical perspective), economic growth increased from close to zero to

We estimate the slope of the Phillips curve in the cross section of U.S. states using newly constructed state-level price indices for nontradeable goods back to 1978. Our estimates indicate that the slope of the Phillips curve is small and was small even during the early 1980s. We estimate only a modest decline in the slope of the Phillips curve since the 1980s. We use a multiregion model to infer the slope of the aggregate Phillips curve from our regional estimates. Applying our estimates to recent unemployment dynamics yields essentially no missing disinflation or missing reinflation over the past few business cycles. Our results imply that the sharp drop in core inflation in the early 1980s was mostly due to shifting expectations about long-run monetary policy as opposed to a steep Phillips curve, and the greater stability of inflation between 1990 and 2020 is mostly due to long-run inflation expectations becoming more firmly anchored.

This paper presents new estimates of gross worker flows for Canada for the sample period 1978 to 2016. We use administrative data from the Canadian Record of Employment in combination with the Canadian Labor Force Survey to estimate employer-to-employer flows in addition to flows between labor market states. We highlight three main results: Roughly two-thirds of all job separations are employer-to-employer flows. Employer-to-Employer flows are highly procyclical. The combination of these two results means that total job separations are procyclical. If employer-to-employer flows improve match quality, our results imply that recessions have a sullying effect on the labor market.

An analytical (heterogeneous-agent New-Keynesian) HANK model allows a closed-form treatment of a wide range of NK topics: determinacy properties of interest-rate rules, resolving the forward guidance FG puzzle, amplification and fiscal multipliers, liquidity traps, and optimal monetary policy. The key channel shaping all the model’s properties is that of cyclical inequality : whether the income of constrained agents moves less or more than proportionally with aggregate income. With countercyclical inequality, good news on aggregate demand gets compounded, making determinacy less likely and aggravating the FG puzzle (the resolution of which requires procyclical inequality)– a Catch-22, because countercyclical inequality is what HANK (and TANK) models need to deliver desirable amplification. The dilemma can be resolved if a distinct, "cyclical-risk" channel is procyclical enough. Even when both channels are countercyclical a Wicksellian rule of price-level targeting ensures determinacy and cures the puzzle. Optimal monetary policy is isomorphic to RANK and TANK but calls for less inflation stabilization. In a liquidity trap, even with countercyclical inequality and FG amplification, optimal policy does not imply larger FG duration because as FG power increases, so does its welfare cost. JEL Codes: E21, E31, E40, E44, E50, E52, E58, E60, E62

We present estimates of monetary non-neutrality based on evidence from high-frequency responses of real interest rates, expected inflation, and expected output growth. Our identifying assumption is that unexpected changes in interest rates in a 30-minute window surrounding scheduled Federal Reserve announcements arise from news about monetary policy. In response to an interest rate hike, nominal and real interest rates increase roughly one-for-one, several years out into the term structure, while the response of expected inflation is small. At the same time, forecasts about output growth also increase-the opposite of what standard models imply about a monetary tightening. To explain these facts, we build a model in which Fed announcements affect beliefs not only about monetary policy but also about other economic fundamentals. Our model implies that these information effects play an important role in the overall causal effect of monetary policy shocks on output.

This paper discusses empirical approaches macroeconomists use to answer questions like: What does monetary policy do? How large are the effects of fiscal stimulus? What caused the Great Recession? Why do some countries grow faster than others? Identification of causal effects plays two roles in this process. In certain cases, progress can be made using the direct approach of identifying plausibly exogenous variation in a policy and using this variation to assess the effect of the policy. However, external validity concerns limit what can be learned in this way. Carefully identified causal effects estimates can also be used as moments in a structural moment matching exercise. We use the term “identified moments” as a short-hand for “estimates of responses to identified structural shocks,” or what applied microeconomists would call “causal effects.” We argue that such identified moments are often powerful diagnostic tools for distinguishing between important classes of models (and thereby learning about the effects of policy). To illustrate these notions we discuss the growing use of cross-sectional evidence in macroeconomics and consider what the best existing evidence is on the effects of monetary policy.

Unexpected events can have lasting effects on uncertainty. This paper presents a model in which occurrences of rare events endogenously result in lower levels of private information. Lower levels of information propagate within the model, as uncertainty makes it harder for agents to acquire information about future periods, resulting in uncertainty persistence. This model of uncertainty is applied to an economy with a financial market, yielding implications for the dynamics of financial uncertainty, asset demand, expected wealth, dispersion of beliefs, bid-ask spreads, and volatility. ∗Thanks to Ricardo Reis, Michael Woodford, Larry Glosten, Jose Scheinkman, and Jennifer La’O for invaluable guidance, assistance and advice; to Geert Bekeart, Patrick Bolton, Robert Hodrick, Michael Johannes, Marcin Kacperczyk, Emi Nakamura, Jaromir Nosal, Andrea Prat, Jon Steinsson, Tano Santos, Stephanie Schmitt-Grohe, Suresh Sundaresan, Martin Uribe, Laura Veldkamp, and Neng Wang for discussions and comments that shaped the content of this work; to seminar and conference participants at CEPR, Columbia, Columbia GSB, Emory, UC Davis, Northwestern Kellogg, London Business School, Imperial, Johns Hopkins Carey, and the Federal Reserve Board; and to Cynthia Mei Balloch, Keshav Dogra, Stephane Dupraz, Sergey Kolbin, Shaowen Luo, Antonio Miscio, Paul Piveteau, Evan Plous, Samer Shousha, Xing Xia, and Daniel Villar for all their help. All errors are my own. †Business School, Imperial College London; Email: s.sundaresan@imperial.ac.uk

We provide new estimates of the importance of growth-rate shocks and uncertainty shocks for developed countries. The shocks we estimate are large and correspond to well-known macroeconomic episodes such as the Great Moderation and the productivity slowdown. We compare our results to earlier estimates of "long-run risks" and assess the implications for asset pricing. Our estimates yield greater return predictability and a more volatile price-dividend ratio. In addition, we can explain a substantial fraction of cross-country variation in the equity premium.. An advantage of our approach, based on macroeconomic data alone, is that the parameter estimates cannot be viewed as backward engineered to fit asset pricing data. We provide intuition for our results using the recently developed framework of shock-exposure and shock-price elasticities. (JEL E21, E32, E44, G12, G35)