October 18, 2017
By Elizabeth Caucutt, Nezih Guner and Christopher Rauh
The differences between black and white households and family structure have been a concern for policy makers for a long time. The last few decades, however, have witnessed an unprecedented retreat from marriage among black individuals. In 1970, about 89% of black women between ages 25 and 54 were ever married, in contrast to only 51% today. Wilson (1987) suggests that the lack of marriageable black men due to incarceration and unemployment is behind this decline. In this paper, we take a fresh look at the Wilson Hypothesis. We argue that the current incarceration policies and labor market prospects make black men much riskier spouses than white men. They are not only more likely to be, but also to become, unemployed or incarcerated than their white counterparts. We develop an equilibrium search model of marriage, divorce and labor supply that takes into account the transitions between employment, unemployment and prison for individuals by race, education, and gender. We calibrate this model to be consistent with key statistics for the US economy. We then investigate how much of the racial divide in marriage is due to differences in the riskiness of potential spouses, heterogeneity in the education distribution, and heterogeneity in wages. We find that differences in incarceration and employment dynamics between black and white men can account for about 76% of the existing black-white marriage gap in the data. We also study how “The War on Drugs” in the US might have affected the structure of black families, and find that it can account for between 13% to 41% of the racial marriage gap.
I chose to highlight this paper because it addresses an important question and it shows how the DSGE methodology allows to study scenarios that you cannot run in real life. This paper shows that if incarceration policies were different, there would be quite different outcomes on the labor and marriage markets, and many people would be living in a very different world.
October 18, 2017
By Diego Daruich
Standard macroeconomic analysis of inequality focuses on the optimal choice of progressive taxation. However, early childhood environment has been shown to significantly impact adult outcomes. Using children’s time diaries, we show that parental quality time with children is strongly associated with children’s skills—which is later associated with their education. To compare the quantitative role of standard policies to ones that target early childhood, we extend the standard general-equilibrium heterogeneous-agent life-cycle model with earnings risk and credit constraints to allow for endogenous education, parental time and money investments towards children’s skill development, and family transfers. The model includes two types of college majors: STEM and non-STEM. We evaluate three policies: progressive taxation, college tuition subsidies, and parenting education. Progressive taxation is the most effective at reducing disposable income inequality, but it does not promote the development of skills necessary to increase college graduation or social mobility. College subsidies promote only non-STEM graduation, since STEM is a better alternative only for high-skilled individuals. Parenting education is the most effective at increasing intergenerational mobility and the only one able to promote STEM graduation.
There is hardly anything surprising in the results of this paper, but they really need to be emphasized: Reducing college tuition is not helping with subsequent income inequality and the very first years of life are the most important for adult outcomes. Public policies should focus on helping parents doing the right thing when they are catapulted into parenthood.
October 17, 2017
By Henrik Jensen, Ivan Petrella, Søren Hove Ravn and Emiliano Santoro
We document that the U.S. economy has been characterized by an increasingly negative business cycle asymmetry over the last three decades. This finding can be explained by the concurrent increase in the financial leverage of households and firms. To support this view, we devise and estimate a dynamic general equilibrium model with collateralized borrowing and occasionally binding credit constraints. Higher leverage increases the likelihood that constraints become slack in the face of expansionary shocks, while contractionary shocks are further amplified due to binding constraints. As a result, booms become progressively smoother and more prolonged than busts. We are therefore able to reconcile a more negatively skewed business cycle with the Great Moderation in cyclical volatility. Finally, in line with recent empirical evidence, financially-driven expansions lead to deeper contractions, as compared with equally-sized non-financial expansions.
The fact that increasing financial leverage can explain the Great Moderation and the last recession is nothing new. The fact that it could explain the strong asymmetry between the sharp drop ten years ago and the slow recovery since is, however, new. By now, it should be clear that models relying on symmetry around a steady-state can be shelved for good.
October 9, 2017
By Stefan Laséen, Andrea Pescatoriand Jarkko Turunen
We introduce time-varying systemic risk (à la He and Krishnamurthy, 2014) in an otherwise standard New-Keynesian model to study whether simple leaning-against-the-wind interest rate rules can reduce systemic risk and improve welfare. We find that while financial sector leverage contains additional information about the state of the economy that is not captured in inflation and output leaning against financial variables can only marginally improve welfare because rules are detrimental in the presence of falling asset prices. An optimal macroprudential policy, similar to a countercyclical capital requirement, can eliminate systemic risk raising welfare by about 1.5%. Also, a surprise monetary policy tightening does not necessarily reduce systemic risk, especially during bad times. Finally, a volatility paradox à la Brunnermeier and Sannikov (2014) arises when monetary policy tries to excessively stabilize output.
I think it is no surprise that adding a policy objective requires adding a policy instrument. The question here is whether the new instrument messes up the others in a major way. If things are difficult with a double mandate, imagine what this becomes with a triple one.
October 5, 2017
By Hélène Desgagnés
I examine the impact of non-regulated lenders in the mortgage market using a dynamic stochastic general equilibrium (DSGE) model. My model features two types of financial intermediaries that differ in three ways: (i) only regulated intermediaries face a capital requirement, (ii) non-regulated intermediaries finance themselves by selling securities and cannot accept deposits, and (iii) non-regulated intermediaries face a more elastic demand. This last assumption is based on empirical evidence for Canada revealing that non-regulated intermediaries issue loans at a lower interest rate. My results suggest that the non-regulated sector contributes to stabilize the economy by providing an alternative source of capital when the regulated sector in unable to fulfill the demand for credit. As a result, an economy with a large non-regulated sector experiences a smaller downturn after an adverse financial shock.
As I was reading this abstract, its conclusion caught me completely wrong-footed. Why would you want a large unregulated financial sector? Well, of course, the larger it is the more it can lend and at lower cost. But this hides the reason that regulation is put in place: to avoid systemic crashes. Those events seems to be absent from considerations in this paper. Its financial shock is just a perturbation to the cost of capital, and thus unlikely to trigger a financial crisis. The paper is thus not turning everything I knew about banking upside down after all.
September 26, 2017
By Michal Rubaszek and Margarita Rubio
The size of the rental housing market in most countries around the globe is low. In this article we claim that this may be detrimental for macroeconomic stability. Toward this aim we, determine the reasons behind rental market underdevelopment by conducting an original survey among a representative group of 1005 Poles, a country that is characterized by high homeownership ratio. We find that households’ preferences are strongly influenced by economic and psychological factors. Next, we propose a DSGE model in which households satisfy housing needs both by owning and renting. We use it to show that reforms enhancing the rental housing market contribute to macroeconomic stability. This micro-macro approach allows us to dig into the causes of rental market underdevelopment and design appropriate policy recommendations.
I have been puzzled that in economies with more risk, people were owning their homes rather than renting them. The reason of my puzzlement was that owning makes you less mobile, as you are tied to an illiquid asset whose value correlates strongly with local economic conditions (which may be the reason to move away). This paper seems to resolve this through reverse causation: where rental rates are higher, economies are more stable as a result. To make this happen in general equilibrium, though, the choice of residential status has then to be at least partly exogenous of economic considerations.
September 22, 2017
By Warwick J. McKibbin and Andrew Stoeckel
Macro models have come under criticism for their ability to understand or predict major economic events such as the global financial crisis and its aftermath. Some of that criticism is warranted; but, in our view, much is not. This paper contributes to the debate over the adequacy of benchmark DSGE models by showing how three extensions, which are features that have characterized the global economy since the early 2000s, are necessary to improve our understanding of global shocks and policy insights. The three extensions are to acknowledge and model the entire global economy and the linkage through trade and capital flows; to allow for a wider range of relative price variability by moving to multiple sector models rather than a single good model; and to allow for changes in risk perceptions which propagate through financial markets and adjustments in the real economy. These extensions add some complexity to large scale macromodels, but without them policy models can oversimplify things, allowing misinterpretations of shocks and therefore costly policy mistakes to occur. Using oversimplified models to explain a complex world makes it more likely there will be “puzzles”. The usefulness of these extensions is demonstrated in two ways; first, by briefly revisiting some historical shocks to show how outcomes can be interpreted that make sense within a more complex DSGE framework; then, by making a contemporary assessment of the implications from the proposed large fiscal stimulus and the bans on immigration by the Trump administration which have both sectoral and macroeconomic implications that interact.
A frequent criticism of DSGE models is that the simplest model cannot address all sorts of empirical regularities (“puzzles”) that often can be dealt with through appropriate extensions of the model. I think this paper is trying to make the point that some of those extensions should be systematically added to the canonical DSGE model. I do not think I can agree with that. First, adding all sorts of bells and whistles to a model makes it very difficult to understand. As a researcher we should strive to understand why something happens, not just observe that it happens. Models are abstractions of the reality that keep just what is needed to understand the empirical phenomenon. Of course, one can debate whether the assumption of the model are right, and this is what peer review, for example, is supposed to do. Second, having a too complex model makes it difficult to solve. A model that goes after a simple question and need a cluster to run is not a good model. Say you what to study the impact of unemployment insurance extension. Having trade and capital flow linkage in a global economy is then going to be of second or third order importance. Stay with the simplest possible model that can answer your research question.