Student loans, fertility, and economic growth

June 24, 2016

By Koichi Miyazaki

The cost of attaining higher education is growing in some developed countries. More young people borrow larger amounts than before to finance their higher education. Several media reports indicate that student loans might affect young people’s decision making regarding important life events such as marriage, childbirth, purchasing a house, and so on. Specifically, this paper focuses on how the burden of student loans affects young people’s decision making with regard to the number of children to have, and studies the fertility rate, gross domestic product (GDP) growth rate, and growth rate of GDP per capita using a three-period overlapping generations model. A young agent needs to borrow to accumulate his/her human capital, although for some reason, s/he faces the borrowing constraint. In the next period, the agent repays his/her debt as well as determines the number of children to have. Under this setting, this paper analyzes how the tightness of the borrowing constraints affects the growth rates of the population, GDP, and GDP per capita. The paper finds that when rearing children is time-consuming, the population growth rate decreases as the borrowing constraints are relaxed. Moreover, the paper shows a case in which the GDP growth rate decreases as the borrowing constraints are relaxed, whereas the growth rate of GDP per capita still increases. In addition, I show that if the cost of rearing children is mainly monetary, then the population growth rate is not necessarily decreasing as the borrowing constraints are relaxed. The paper also calibrates the model using U.S. data.

Very interesting question. The paper is a nice exploratory work that shows what could matter. It has a lot of potential for further work, in particular extending the model to more than three periods, so that a richer life-cycle profile can be taken into account, including the other big choices mentioned in the abstract.

Solution and Estimation of Dynamic Discrete Choice Structural Models Using Euler Equations

June 16, 2016

By Victor Aguirregabiria and Arvind Magesan

This paper extends the Euler Equation (EE) representation of dynamic decision problems to a general class of discrete choice models and shows that the advantages of this approach apply not only to the estimation of structural parameters but also to the computation of a solution and to the evaluation of counterfactual experiments. We use a choice probabilities representation of the discrete decision problem to derive marginal conditions of optimality with the same features as the standard EEs in continuous decision problems. These EEs imply a fixed point mapping in the space of conditional choice values, that we denote the Euler equation-value (EE-value) operator. We show that, in contrast to Euler equation operators in continuous decision models, this operator is a contraction. We present numerical examples that illustrate how solving the model by iterating in the EE-value mapping implies substantial computational savings relative to iterating in the Bellman equation (that requires a much larger number of iterations) or in the policy function (that involves a costly valuation step). We define a sample version of the EE-value operator and use it to construct a sequence of consistent estimators of the structural parameters, and to evaluate counterfactual experiments. The computational cost of evaluating this sample-based EE-value operator increases linearly with sample size, and provides an unbiased (in finite samples) and consistent estimator the counterfactual. As such there is no curse of dimensionality in the consistent estimation of the model and in the evaluation of counterfactual experiments. We illustrate the computational gains of our methods using several Monte Carlo experiments.

Interesting approach that is not obvious at first sight. Indeed, why iterate on first order conditions in a discrete choice setup? But it works well.

The Limits of Central Bank Forward Guidance under Learning

June 10, 2016

By Stephen Cole

Central bank forward guidance emerged as a pertinent tool for monetary policymakers since the Great Recession. Nevertheless, the effects of forward guidance remain unclear. This paper investigates the effectiveness of forward guidance while relaxing two standard macroeconomic assumptions: rational expectations and frictionless financial markets. Agents forecast future macroeconomic variables via either the rational expectations hypothesis or a more plausible theory of expectations formation called adaptive learning. A standard Dynamic Stochastic General Equilibrium (DSGE) model is extended to include the financial accelerator mechanism. The results show that the addition of financial frictions amplifies the differences between rational expectations and adaptive learning to forward guidance. The macroeconomic variables are overall more responsive to forward guidance under rational expectations than under adaptive learning. During a period of economic crisis (e.g. a recession), output under rational expectations displays more favorable responses to forward guidance than under adaptive learning. These differences are exacerbated when compared to a similar analysis without financial frictions. Thus, monetary policymakers should consider the way in which expectations and credit frictions are modeled when examining the effects of forward guidance.

Forward guidance is all about expectation formation, thus understanding expectation formation is a pretty big deal if you want to apply such policies. And it matters, as this paper clearly shows.

Challenges for Central Banks’ Macro Models

June 8, 2016

By Jesper Lindé, Frank Smets and Rafael Wouters

In this paper we discuss a number of challenges for structural macroeconomic models in the light of the Great Recession and its aftermath. It shows that a benchmark DSGE model that shares many features with models currently used by central banks and large international institutions has difficulty explaining both the depth and the slow recovery of the Great Recession. In order to better account for these observations, the paper analyses three extensions of the benchmark model. First, we estimate the model allowing explicitly for the zero lower bound constraint on nominal interest rates. Second, we introduce time-variation in the volatility of the exogenous disturbances to account for the non-Gaussian nature of some of the shocks. Third and finally, we extend the model with a financial accelerator and allow for time-variation in the endogenous propagation of financial shocks. All three extensions require that we go beyond the linear Gaussian assumptions that are standard in most policy models. We conclude that these extensions go some way in accounting for features of the Great Recession and its aftermath, but they do not suffice to address some of the major policy challenges associated with the use of non-standard monetary policy and macroprudential policies.

The models of Smets and Wouters are the foundation of many of the models currently used in policy circles. Given that these models have been heavily criticized by some that they are lacking the very features that were relevant in the last crisis, it is interesting to see what Smets and Wouters (and Lindé) have come up with as a response. Given all the other work that has been done by others, and that has in part been highlighted on this blog, the response is disappointing, though. One could have in particular expected a more explicit modeling of the financial sector, for example, or there still seems to be a lot of linearity in the model even with the ZLB. My assessment, however, is based on a very quick reading of the paper, it is 95 pages long after all.

International competition and labor market adjustment

June 3, 2016

By João Paulo Pessoa

How does welfare change in the short- and long-run in high wage countries when integrating with low wage economies like China? Even if consumers benefit from lower prices, there can be significant welfare losses from increases in unemployment and lower wages. I construct a dynamic multi-sector country Ricardian trade model that incorporates both search frictions and labor mobility frictions. I then structurally estimate this model using cross-country sector-level data and quantify both the potential losses to workers and benefits to consumers arising from China’s integration into the global economy. I find that overall welfare increases in northern economies, both in the transition period and in the new steady state equilibrium. In import competing sectors, however, workers bear a costly transition, experiencing lower wages and a rise in unemployment. I validate the micro implications of the model using employer-employee panel data.

Good paper in the context of the current discussion of the virtues of free trade, and in particular the sectoral transition costs.