University at Albany

Ph.D. Job Market Candidates

Placement Director:
Professor Kajal Lahiri
Phone: (518) 442-4735

Job Market Candidates (2018-2019)

Yuan Fang        CV | E-mail

Primary Fields: Health Economics
Secondary Fields: Applied Microeconomics, Applied Econometrics
Job Market Paper: Work till Well: Coping Health Shocks with Health Insurance

References: Kajal Lahiri (Advisor), Pinka Chatterji, Chun-Yu Ho

Jun Soo Lee        CV | E-mail | Homepage

Primary Fields: Health Economics
Secondary Fields: Labor Economics, Applied Econometrics
Job Market Paper: Does Seasonality of Birth Outcomes Really Exist?: Evidence from Weather Conditions during Pregnancy

References: Pinka Chatterji (Co-chair), Byoung Park (Co-chair), Chun-Yu Ho, Kajal Lahiri

Fangning Li        CV | E-mail | Homepage

Primary Fields: Econometrics
Secondary Fields: Applied Econometrics, Macroeconomics
Job Market Paper: Consistent Model Averaging Using Elastic-Net

References: Zhongwen Liang , Daiqiang Zhang, Ulrich Hounyo

Job Market Papers (2018-2019)

Yuan Fang

Title: Work till Well: Coping Health Shocks with Health Insurance
Abstract: This paper examines the economic consequences of health shocks on individuals with employer-sponsored insurance or without insurance based on Survey of Income and Program Participation (SIPP) data 2008 Panel. In response to a health shock, the insured have a greater incentive than the uninsured to maintain a paid job to keep their health insurance, even though the insured tend to reduce their working hours. On the other hand, the uninsured overcome the effect of health shocks by earning income from non-labor activities. Although both groups of individuals experience similar initial reduction in total income, the insured increase their health care utilization and out-of-pocket (OOP) medical expenses in response to a health shock more than the uninsured. It suggests that holding the employer-sponsored insurance rather than maintaining the income is an important incentive to work in response to a health shock. Further, the insured tend to obtain supplement for their financial need from non-labor income and job-related disability benefits, whereas the uninsured tend to rely on the receipt of public insurance, job-related disability benefits and borrowing. In case the health shock persists for another year, the insured are able to increase the health care utilization for another year, but the uninsured are unable to utilize health care requiring OOP expenses. Finally, our results are robust to the use of alternative specification, measure of health shock and estimation method, and several placebo tests.

Jun Soo Lee

Title: Does Seasonality of Birth Outcomes Really Exist?: Evidence from Weather Conditions during Pregnancy
Abstract: Previous studies demonstrate that month of birth is highly correlated with birth outcomes and later economic outcomes. Infants born in winter months experience lower educational attainment and worse socioeconomic status later in life compared to infants born during summer months. The common limitation of previous studies is that most document the association between month of birth and various outcomes, but the mechanism is poorly understood. In the first part of this paper, I document that the seasonality of birth outcomes is driven by weather conditions during pregnancy. My results show that up to 81% of the effects of seasonality on birth outcomes disappears in most specifications of models that include weather conditions during pregnancy as covariates. In fact, the seasonality birth effects for summer periods either become negative in sign or become statistically insignificant. This evidence supports the idea that the month of birth effects absorb the effects of weather conditions during pregnancy on birth outcomes. In the second part of the paper, I contribute to the small but growing literature on the effects of weather on birth outcomes. Previous studies have found negative effects of solar radiation during the second trimester on birth outcomes. I find that the negative association is driven by the adverse effect of solar radiation during the second trimester on the length of the gestation period. The negative effects on gestation periods are significant and stronger for Non-Latino White mothers but statistically insignificant and positive for African-American mothers. The heterogeneity of the effects of solar radiation on the length of gestation period is consistent with previous findings of racial differences in effects of solar radiation on birth outcomes. After taking the length of the gestation period into account, solar radiation has a direct positive influence on babies' birth outcomes for both Non-Latino White and African-American mothers. The increase in vitamin D may improve birth outcomes, but, on the other hand, increases in solar radiation reduce the length of gestation periods, detracting from birth outcomes.

Fangning Li

Title: Consistent Model Averaging Using Elastic-Net
Abstract: This paper proposes a new model averaging method in the linear model setup named consistent model averaging (CMA) based on Tikhonov regularization. The CMA estimator is consistent in the sense that it converges to the infeasible optimal model weight that minimizes conditional risk in finite sample. Given the number of regressors p, first we show that ideally model averaging over the maximal collection of 2p models is equivalent to averaging over a subcollection of only singleton and pairwise models in the sense of achieving the same minimum risk, which reduces computational burden substantially. Then we propose the CMA estimator based on Tikhonov penalty. The Tikhonov penalty turns out to be essential for the consistency of the CMA estimator. We derived the √n-consistency and asymptotic normality of the CMA estimator in fixed-p case, as well as its deterministic L2 error bound when p diverges with sample size n. Interestingly the CMA estimated model weight can be interpreted as probability amplitude. An additional elastic-net penalty is motivated in CMA estimation to stabilize solution and encourage sparsity. Further issues such as heteroscedasticity and sparse coefficients are addressed, so that CMA can handle heteroscedastic errors and cooperate nicely with variable selection procedures such as lasso and SIS. Simulation results show that CMA with elastic-net penalty performs better than the original elastic-net estimator and Mallows' model averaging estimator when population R2 is moderate. We also illustrate the better performance of CMA with an application of predicting wages.