Essays In Labor Economics

This thesis consists of three chapters. They explore develop and estimate economic models to analyze questions of interests to public policies. Chapter 1 develops and estimates a spatial general equilibrium job search model to study the effects of local and universal (federal) minimum wage policies. In the model, firms post vacancies in multiple locations. Workers, who are heterogeneous in terms of location and education types, engage in random search and can migrate or commute in response to job offers. The model is estimated by combining multiple databases including the American Community Survey (ACS) and Quarterly Workforce Indicators (QWI). The estimated model is used to analyze how minimum wage policies affect employment, wages, job postings, vacancies, migration/commuting, and welfare. Empirical results show that minimum wage increases in local county lead to an exit of low type (education\u3c12 years) workers and an influx of high type workers (education\u3e12 years), which generates negative externalities for workers in neighboring areas. The model is used to simulate the effects of a range of minimum wages. Minimum wage increases up to $14/hour increase the welfare of high type workers but lower the welfare of low type workers, expanding inequality. Increases in excess of$14/hour decrease welfare for all workers. Two counterfactual policies are further evaluated under this framework: restricting labor mobility and preempting local minimum wage laws. For a certain range of minimum wages, both policies have negative impacts on the welfare of high type workers, but benecial effects for low type workers. Chapter 2 poses a dynamic discrete choice model of schooling and occupational choices that incorporates time-varying personality traits, as measured by the so-called Big Five traits. The model is estimated using the Household Income and Labor Dynamics in Australia (HILDA) longitudinal dataset from Australia. Personality traits are found to play a critical role in explaining education and occupational choices over the lifecycle. The traits evolve during young adult years but stabilize in the mid-30s. Results show that individuals with a comparative advantage in schooling and white-collar work have, on average, higher cognitive skills and higher personality traits, in all ve dimensions. The estimated model is used to evaluate two education policies: compulsory senior secondary school and a 50% college subsidy. Both policies are found to be effective in increasing educational attainment, but the compulsory schooling policy provides greater benets to lower socioeconomic groups. Allowing personality traits to evolve with age and with years of schooling proves to be important in capturing policy response heterogeneity. Chapter 3 develops and estimates a model of how personality traits affect household time and resource allocation decisions and wages. In the model, households choose between two behavioral modes: cooperative or noncooperative. Spouses receive wage offers and allocate time to supply labor market hours and to produce a public good. Personality traits, measured by the so-called Big Five traits, can affect household bargaining weights and wage offers. Model parameters are estimated by Simulated Method of Moments using the Household Income and Labor Dynamics in Australia (HILDA) data. Personality traits are found to be important determinants of household bargaining weights and of wage offers and to have substantial implications for understanding the sources of gender wage disparities

What is the meaning of physical quantity ν\nu in the expression of photon energy hνh\nu?

It is well known that, for an incident light of not so high intensity and in a certain range of frequency, the stopping voltage of photoelectric effect is independent of the intensity but dependent on the frequency of the light, which is described by the equation $V = h\nu /e - {W_0}/e$, where $V$ is the stopping voltage, $h$ is the Planck constant, $\nu$ is the frequency of incident light, $e$ is the electron charge, and $W_0$ is the work function. It means that the larger the frequency of incident light, the higher the stopping voltage is. However, the present experiment finds that for a non-monochromatic incident light, the stopping voltage is not determined by the maximum frequency component of the incident light, but by the maximum center frequency of all the wave train components (with different center frequencies) involved in the incident light, that is to say, in the photon energy expression $h\nu$, physical quantity $\nu$ does not refer to the frequency of a monochromatic light, but represents the center frequency of a wave train spectrum. The spectral bandwidth of a wave train component can be as large as 122 nm in visible and near-infrared region. This should arouse more attention in the study of energy exchange between light and matter.Comment: 7pagers,4figure

Anisotropy of Localized Corrosion in AA2024-T3

This work was supported by the United States Air Force Office of Scientific Research through grant no. F49620-99-1-0103

Attention-based Dynamic Graph Convolutional Recurrent Neural Network for Traffic Flow Prediction in Highway Transportation

As one of the important tools for spatial feature extraction, graph convolution has been applied in a wide range of fields such as traffic flow prediction. However, current popular works of graph convolution cannot guarantee spatio-temporal consistency in a long period. The ignorance of correlational dynamics, convolutional locality and temporal comprehensiveness would limit predictive accuracy. In this paper, a novel Attention-based Dynamic Graph Convolutional Recurrent Neural Network (ADGCRNN) is proposed to improve traffic flow prediction in highway transportation. Three temporal resolutions of data sequence are effectively integrated by self-attention to extract characteristics; multi-dynamic graphs and their weights are dynamically created to compliantly combine the varying characteristics; a dedicated gated kernel emphasizing highly relative nodes is introduced on these complete graphs to reduce overfitting for graph convolution operations. Experiments on two public datasets show our work better than state-of-the-art baselines, and case studies of a real Web system prove practical benefit in highway transportation

Regiodivergent enantioselective C-H functionalization of Boc-1,3-oxazinanes for the synthesis of beta(2)- and beta(3)-amino acids

beta(2)- and beta(3)-amino acids are important chiral building blocks for the design of new pharmaceuticals and peptidomimetics. Here, we report a straightforward regio- and enantiodivergent access to these compounds using a one-pot reaction composed of sparteine-mediated enantioselective lithiation of a Boc-1,3-oxazinane, transmetallation to zinc and direct or migratory Negishi coupling with an organic electrophile. The regioselectivity of the Negishi coupling was highly ligand-controlled and switch-able to obtain the C4- or the C5-functionalized product exclusively. High enantioselectivities were achieved on a broad range of examples, and a catalytic version in chiral diamine was developed using the (+)-sparteine surrogate. Selected C4- and C5-functionalized Boc-1,3-oxazinanes were subsequently converted to highly enantioenriched beta(2)- and beta(3)-amino acids with the (R) or (S) configuration, depending on the sparteine enantiomer employed in the lithiation step

MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning

Advancements in deep learning-based 3D object detection necessitate the availability of large-scale datasets. However, this requirement introduces the challenge of manual annotation, which is often both burdensome and time-consuming. To tackle this issue, the literature has seen the emergence of several weakly supervised frameworks for 3D object detection which can automatically generate pseudo labels for unlabeled data. Nevertheless, these generated pseudo labels contain noise and are not as accurate as those labeled by humans. In this paper, we present the first approach that addresses the inherent ambiguities present in pseudo labels by introducing an Evidential Deep Learning (EDL) based uncertainty estimation framework. Specifically, we propose MEDL-U, an EDL framework based on MTrans, which not only generates pseudo labels but also quantifies the associated uncertainties. However, applying EDL to 3D object detection presents three primary challenges: (1) relatively lower pseudolabel quality in comparison to other autolabelers; (2) excessively high evidential uncertainty estimates; and (3) lack of clear interpretability and effective utilization of uncertainties for downstream tasks. We tackle these issues through the introduction of an uncertainty-aware IoU-based loss, an evidence-aware multi-task loss function, and the implementation of a post-processing stage for uncertainty refinement. Our experimental results demonstrate that probabilistic detectors trained using the outputs of MEDL-U surpass deterministic detectors trained using outputs from previous 3D annotators on the KITTI val set for all difficulty levels. Moreover, MEDL-U achieves state-of-the-art results on the KITTI official test set compared to existing 3D automatic annotators.Comment: 6 pages Main, 1 page Reference, 5 pages Appendi

Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets

Short-term load forecasting (STLF) plays a significant role in the operation of electricity trading markets. Considering the growing concern of data privacy, federated learning (FL) is increasingly adopted to train STLF models for utility companies (UCs) in recent research. Inspiringly, in wholesale markets, as it is not realistic for power plants (PPs) to access UCs' data directly, FL is definitely a feasible solution of obtaining an accurate STLF model for PPs. However, due to FL's distributed nature and intense competition among UCs, defects increasingly occur and lead to poor performance of the STLF model, indicating that simply adopting FL is not enough. In this paper, we propose a DRL-assisted FL approach, DEfect-AwaRe federated soft actor-critic (DearFSAC), to robustly train an accurate STLF model for PPs to forecast precise short-term utility electricity demand. Firstly. we design a STLF model based on long short-term memory (LSTM) using just historical load data and time data. Furthermore, considering the uncertainty of defects occurrence, a deep reinforcement learning (DRL) algorithm is adopted to assist FL by alleviating model degradation caused by defects. In addition, for faster convergence of FL training, an auto-encoder is designed for both dimension reduction and quality evaluation of uploaded models. In the simulations, we validate our approach on real data of Helsinki's UCs in 2019. The results show that DearFSAC outperforms all the other approaches no matter if defects occur or not