Jonathan James |

(Former) Research Economist


Jonathan James, (Former) Research Economist

Jonathan James is a former research economist in the Research Department of the Federal Reserve Bank of Cleveland.

  • Fed Publications
Title Date Publication Author(s) Type

 

2013-14 ; Economic Commentary
Abstract: The economic returns to education are well documented. It is also well-known that college graduates with certain majors will earn more than others and find it easier to land a job. But surprisingly, the courses students take in high school also make a difference, when the courses are mathematics. Even among workers with the same level of education, those with more math have higher wages on average and are less likely to be unemployed. These findings suggest that even students ending their formal education after high school can increase their future earnings by investing in more math courses while in high school.

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November, 2012 Federal Reserve Bank of Cleveland, working paper no. 12-25 ; Working Papers
Abstract: This paper develops and estimates a model of occupational choice and learning that allows for correlated learning across occupation specific abilities. In the labor market, workers learn about their potential outcomes in all occupations, not just their current occupation. Based on what they learn, workers engage in directed search across occupations. The estimates indicate that sorting occurs in multiple dimensions. Workers discovering a low ability in their current occupation are signicantly more likely to move to a new occupation. At the same time, workers discovering a high ability in some occupations are more likely to move up the occupational ladder into managerial occupations. By age 28 this sorting process leads to an aggregate increase in wages similar to what would occur if all workers were endowed with an additional year of education.

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September, 2012 Federal Reserve Bank of Cleveland, working paper no. 12-19 ; Working Papers
Abstract: The mixed logit is a framework for incorporating unobserved heterogeneity in discrete choice models in a general way. These models are difficult to estimate because they result in a complicated incomplete data likelihood. This paper proposes a new approach for estimating mixed logit models. The estimator is easily implemented as iteratively re-weighted least squares: the well known solution for complete data likelihood logits. The main benefit of this approach is that it requires drastically fewer evaluations of the simulated likelihood function, making it signicantly faster than conventional methods that rely on numerically approximating the gradient. The method is rooted in a generalized expectation and maximization (GEM) algorithm, so it is asymptotically consistent, efficient, and globally convergent.

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2012-10 ; Economic Commentary
Abstract: The return on educational investments has risen substantially in the past 30 years. While the primary focus has been on the college wage premium, new evidence shows that the value of going to college is affected by a host of other important educational decisions, each of which has a potential large effect on future earnings. This Commentary examines the impact of two of these other decisions on earnings: the choice of a college major and the pursuit of an advanced degree. In some cases, differences in the college major premium are as large as the college wage premium itself.

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April, 2012 Federal Reserve Bank of Cleveland, working paper no. 12-10 ; Peter Arcidiacono; Patrick Bayer; Federico A Bugni; Working Papers
Abstract: Many dynamic problems in economics are characterized by large state spaces, which make both computing and estimating the model infeasible. We introduce a method for approximating the value function of high-dimensional dynamic models based on sieves and establish results for the: (a) consistency, (b) rates of convergence, and (c) bounds on the error of approximation. We embed this method for approximating the solution to the dynamic problem within an estimation routine and prove that it provides consistent estimates of the model?s parameters. We provide Monte Carlo evidence that our method can successfully be used to approximate models that would otherwise be infeasible to compute, suggesting that these techniques may substantially broaden the class of models that can be solved and estimated.

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October, 2011 Federal Reserve Bank of Cleveland, Working Paper no. 11-25 ; Working Papers
Abstract: This paper develops and estimates an individual model of occupational choice and learning that allows for correlated learning across occupation-specific abilities. As an individual learns about their occupation-specific ability in one occupation, this experience will be broadly informative about their abilities in all occupations. Workers continually process their entire history of information, which they use to determine when to change careers, as well as which new career to go to. Endogenizing information in this manner has been computationally prohibitive in the past. I estimate the model in an innovative way using the Expectation and Maximization (EM) algorithm. The model is estimated on the National Longitudinal Survey of Youth 1997. The estimates suggest that both direct and indirect learning play an important role in early career wage growth, with those with the lowest levels of education achieving the largest increases.

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