Reforming the Social Security Earnings Cap: The Role of Endogenous Human Capital

March 10, 2017

By Adam Blandin

Old age Social Security benefits in the US are funded by a 10.6% payroll tax up to a cap, currently set at $118,500. Despite calls from policy circles to eliminate the cap on taxable earnings, there has been little work examining the likely outcomes of such a policy change. I use a life-cycle human capital model with heterogeneous individuals to investigate the aggregate and distributional steady state impacts of several policy changes to the earnings cap. I find: (1) Eliminating the earnings cap generates large reductions in aggregate output and consumption, between 2.1- 3.1%. (2) The role of endogenous human capital is first order: when I do not allow the life-cycle human capital profiles of workers to adjust across policy regimes, the change in economic aggregates is roughly cut in half. (3) While eliminating the earnings cap increases revenues from the payroll tax by 12%, the decline in output lowers tax revenues from other sources, so that total federal tax revenues never increase by more than 1.2%. (4) Eliminating the earnings cap produces modest welfare gains for about 2/3 of workers, while about 1/3 of workers experience welfare losses, which are typically large. (5) Lowering the earnings cap to a level near the mean of earnings increases aggregate output by 1.3%, and also increases welfare for the vast majority of workers.

While I do not think such a policy experiment is likely in the current political climate, this paper presents an interesting thought experiment. As with studies about the estate tax, I would though whether the models reflect the cluelessness of youth making schooling choice that will matter only much later in life. Sure, the models use discount rates, but it is hard to think that a teenager thinking about college is pondering whether his income decades in the future will hit the payroll cap tax.

Why mandate young borrowers to contribute to their retirement accounts?

February 21, 2017

By Troebn M. Andersen and Joydeep Bhattacharya

Many countries, in an effort to address the problem that too many retirees have too little saved up, impose mandatory contributions into retirement accounts, that too, in an age-independent manner. This is puzzling because such funded pension schemes effectively mandate the young, who wish to borrow, to save for retirement. Further, if agents are present-biased, they disagree with the intent of such schemes and attempt to undo them by reducing their own saving or even borrowing against retirement wealth. We establish a welfare case for mandating the middle-aged and the young to contribute to their retirement accounts, even with age-independent contribution rates. We find, somewhat counter-intuitively, that pitted against laissez faire, mandatory pensions succeed by incentivizing the young to borrow more and the middle-aged to save nothing on their own, in effect, rendering the latter’s present-biasedness inconsequential.

This paper challenges your intuition. The story is more complex than what the abstract can convey, so do read the paper to find how the twisted logic of cornering the middle-aged to save nothing on their own and making the young borrow like crazy ends up with the old being able to afford a comfortable retirement, even though everybody exhibits a bias for the present.

Jobless Recoveries: The Interaction between Financial and Search Frictions

February 16, 2017

By Dennis Wesselbaum

This paper establishes a link between labor market frictions and financial market frictions. We present empirical evidence about the relation between search and financial frictions. Then, we build a stylized DSGE model that features this channel. Simulation excercises show that the model with this channel generates a strong internal propagation mechanism, replicates stylized labor market effects of the Great Recession, and, most importantly, creates a jobless recovery.

Nice to see a paper that ties together the jobless recoveries of the last few cycles with the financial frictions that have been so important in the last one.

February 2017 calls for papers

February 13, 2017

Some of them have really close deadlines, so do not delay your submissions!

Flipping in the Housing Market

February 9, 2017

Charles Ka Yui Leung and Chung-Yi Tse

We add arbitraging middlemen — investors who attempt to profit from buying low and selling high — to a canonical housing market search model. Flipping tends to take place in sluggish and tight, but not in moderate, markets. To follow is the possibility of multiple equilibria. In one equilibrium, most, if not all, transactions are intermediated, resulting in rapid turnover, a high vacancy rate, and high housing prices. In another equilibrium, few houses are bought and sold by middlemen. Turnover is slow, few houses are vacant, and prices are moderate. Moreover, flippers can enter and exit en masse in response to the smallest interest rate shock. The housing market can then be intrinsically unstable even when all flippers are akin to the arbitraging middlemen in classical finance theory. In speeding up turnover, the flipping that takes place in a sluggish and illiquid market tends to be socially beneficial. The flipping that takes place in a tight and liquid market can be wasteful as the efficiency gain from any faster turnover is unlikely to be large enough to offset the loss from more houses being left vacant in the hands of flippers. Based on our calibrated model, which matches several stylized facts of the U.S. housing market, we show that the housing price response to interest rate change is very non-linear, suggesting cautions to policy attempt to “stabilize” the housing market through monetary policy.

Interesting. The next question is then: can flippers trigger a bubble?

Learning Efficiency Shocks, Knowledge Capital and the Business Cycle: A Bayesian Evaluation

January 31, 2017

By Alok Johri and Muhebullah Karimzada

We incorporate shocks to the efficiency with which firms learn from production activity and accumulate knowledge into an otherwise standard real DSGE model with imperfect competition. Using real aggregate data and Bayesian inference techniques, we find that learning efficiency shocks are an important source of observed variation in the growth rate of aggregate output, investment, consumption and especially hours worked in post-war US data. The estimated shock processes suggest much less exogenous variation in preferences and total factor productivity are needed by our model to account for the joint dynamics of consumption and hours. This occurs because learning efficiency shocks induce shifts in labour demand uncorrelated with current TFP, a role usually played by preference shocks. At the same time, knowledge capital acts like an endogenous source of productivity variation in the model. Measures of model fit prefer the specification with learning efficiency shocks.

Conceptually, I much prefer learning efficiency shocks to preference shocks, which have become a catch-all for anything that cannot be measured and which are as close to a black box as can be. Learning efficiency shocks have a much better defined theoretical story that can be tested.

Retirement Behavior in the U.S. and Europe

January 27, 2017

By Jochem de Bresser, Raquel Fonseca and Pierre-Carl Michaud

We develop a retirement model featuring various labor market exit routes: unemployment, disability, private and public pensions. The model allows for saving and uncertainty along several dimensions, including health and mortality. Individuals’ preferences are estimated on data from the U.S. and Europe using institutional variation across countries. We analyze the roles of preferences and institutions in explaining international heterogeneity in retirement behavior. Preliminary estimates suggest that a single set of preferences for individuals from the U.S., the Netherlands and Spain does not fit the data well. Were Europeans to have the same preferences as Americans, they would save less than they actually do. Furthermore, the Dutch and Spanish would work more hours than is observed in the data.

Interesting that the one-size-fits-all approach for preferences does not apply, at least in this case. I wonder how those differences in preferences can be explained. If this is not from an estimation issue (model miss-specification, for example), then what drives them? Tradition/history? Demographics? Anything else?