Three Essays on International Trade and Informal Employment

In this dissertation I use theory, empirics, and calibration with Vietnamese firm-level data which I obtain from the Statistics General Office of Vietnam to examine the effects of international trade and foreign direct investment on the hiring of informal employment. In the first chapter, I study the effects of export opportunities on the share of informal employment across firms in an environment in which tariffs through demand volume and volatility can affect firms\u27 hiring decisions. I demonstrate that the heterogeneity in productivity is a relevant factor in explaining changes in informality share. It is predicted that access to international trade reduces the incidence of informality if the effects of raising output on the demand for formal workers dominate the increased demand for informal workers due to the greater volatility. Using tariff data on the United States-Vietnam Bilateral Trade Agreement together with the Vietnam Enterprise Surveys, I find that greater export opportunities are significantly related to reductions in relative demand for informal employment, and focusing on cross section variation, more productive firms hire a lower share of informal workers. Larger firms respond less to tariff liberalization, so my findings suggest that access to international markets may be the most effective for smaller exporters to reduce informal employment. The second chapter provides firm-level evidence of the relationship between foreign investment and informal employment. I examine the informality differentials between FDI and domestic firms and the informality spillover effect of FDI in the Vietnamese manufacturing sector. The results indicate that FDI firms not only create more jobs but also reduce informality by creating relatively more formal jobs. Foreign multinationals offer higher wages than domestic firms even after controlling for differences in informality share. The prevalence of foreign direct investment generates a negative spillover effect in terms of informality share in domestic firms within an industry, but increases the informality level of domestic firms within a province. In the third chapter, I analyze the effects of trade liberalization and foreign direct investment on labor informality in a dynamic general equilibrium model. I show that escalating import competition increases the size of the informal sector, inducing a reallocation of workers from the formal sector to informal firms. More specifically, the informal sector grows by 1.5\% in response to a 10\% drop in import tariffs. Alternatively, export opportunities diminish the level of informality, suggesting that a 10\% drop in export tariffs reduces the size of the informal sector by 0.1\%. Comparative advantage in wages motivates agents to reallocate labor between the two sectors. Moreover, the FDI analysis shows that a decline in the size of the formal sector is associated with increases in foreign direct investment. Quantitatively, a 10\% increase in FDI reduces informal output and employment by 2\%. This is because foreign firms bring intense competition that drives out both formal and informal domestic firms, creating a smaller informal labor market. The policy implication is that export and FDI liberalization should be employed to improve labor conditions by reducing the level of informality

Website Quality and Intention to Use Real Estate Website in Housing Market

The purpose of this study is to operationalize the impact of some factors of real estate website quality on behavioral intention to use in searching information about housing market. Research model is the integration of extended Technology Acceptance Model (TAM) of Davis and Information Success System of DeLone and McLean. The data of 847 real estate website users from Hanoi and Ho Chi Minh City was analyzed by Structural Equation Model (SEM) and multiple group analysis. The findings indicate that most of all hypotheses received support from data, specifically, Perceived Enjoyment has the most positive impact on Attitude and Behavioral Intention of real estate website users. Moreover, there is difference in the degree of impact of website quality between the perception of users in Hanoi and Ho Chi Minh City. Keywords: real estate website, website quality, housing market, searching information DOI: 10.7176/EJBM/13-11-09 Publication date:June 30th 202

A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence

This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction (FLSVR TSP ). The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR's tuning parameters, the FLSVR TSP incorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that the FLSVR TSP has achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction

Boosting Stock Price Prediction with Anticipated Macro Policy Changes

Prediction of stock prices plays a significant role in aiding the decision-making of investors. Considering its importance, a growing literature has emerged trying to forecast stock prices with improved accuracy. In this study, we introduce an innovative approach for forecasting stock prices with greater accuracy. We incorporate external economic environment-related information along with stock prices. In our novel approach, we improve the performance of stock price prediction by taking into account variations due to future expected macroeconomic policy changes as investors adjust their current behavior ahead of time based on expected future macroeconomic policy changes. Furthermore, we incorporate macroeconomic variables along with historical stock prices to make predictions. Results from this strongly support the inclusion of future economic policy changes along with current macroeconomic information. We confirm the supremacy of our method over the conventional approach using several tree-based machine-learning algorithms. Results are strongly conclusive across various machine learning models. Our preferred model outperforms the conventional approach with an RMSE value of 1.61 compared to an RMSE value of 1.75 from the conventional approach

Chaotic initialized multiple objective differential evolution with adaptive mutation strategy (CA-MODE) for construction project time-cost-quality trade-off

Time, cost and quality are three factors playing an important role in the planning and controlling of construc­tion. Trade-off optimization among them is significant for the improvement of the overall benefits of construction pro­jects. In this paper, a novel optimization model, named as Chaotic Initialized Multiple Objective Differential Evolution with Adaptive Mutation Strategy (CA-MODE), is developed to deal with the time-cost-quality trade-off problems. The proposed algorithm utilizes the advantages of chaos sequences for generating an initial population and an external elitist archive to store non-dominated solutions found during the evolutionary process. In order to maintain the exploration and exploitation capabilities during various phases of optimization process, an adaptive mutation operation is introduced. A numerical case study of highway construction is used to illustrate the application of CA-MODE. It has been shown that non-dominated solutions generated by CA-MODE assist project managers in choosing appropriate plan which is other­wise hard and time-consuming to obtain. The comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previ­ous results verify the efficiency and effectiveness of the proposed algorithm. First published online: 24 Aug 201

npInv: accurate detection and genotyping of inversions using long read sub-alignment

BACKGROUND: Detection of genomic inversions remains challenging. Many existing methods primarily target inzversions with a non repetitive breakpoint, leaving inverted repeat (IR) mediated non-allelic homologous recombination (NAHR) inversions largely unexplored. RESULT: We present npInv, a novel tool specifically for detecting and genotyping NAHR inversion using long read sub-alignment of long read sequencing data. We benchmark npInv with other tools in both simulation and real data. We use npInv to generate a whole-genome inversion map for NA12878 consisting of 30 NAHR inversions (of which 15 are novel), including all previously known NAHR mediated inversions in NA12878 with flanking IR less than 7kb. Our genotyping accuracy on this dataset was 94%. We used PCR to confirm the presence of two of these novel inversions. We show that there is a near linear relationship between the length of flanking IR and the minimum inversion size, without inverted repeats. CONCLUSION: The application of npInv shows high accuracy in both simulation and real data. The results give deeper insight into understanding inversion

AMRViz enables seamless genomics analysis and visualization of antimicrobial resistance

We have developed AMRViz, a toolkit for analyzing, visualizing, and managing bacterial genomics samples. The toolkit is bundled with the current best practice analysis pipeline allowing researchers to perform comprehensive analysis of a collection of samples directly from raw sequencing data with a single command line. The analysis results in a report showing the genome structure, genome annotations, antibiotic resistance and virulence profile for each sample. The pan-genome of all samples of the collection is analyzed to identify core- and accessory-genes. Phylogenies of the whole genome as well as all gene clusters are also generated. The toolkit provides a web-based visualization dashboard allowing researchers to interactively examine various aspects of the analysis results. Availability: AMRViz is implemented in Python and NodeJS, and is publicly available under open source MIT license at https://github.com/amromics/amrviz

Efficient inference of large prokaryotic pangenomes with PanTA

Pangenome inference is an indispensable step in bacterial genomics, yet its scalability poses a challenge due to the rapid growth of genomic collections. This paper presents PanTA, a software package designed for constructing pangenomes of large bacterial datasets, showing unprecedented efficiency levels multiple times higher than existing tools. PanTA introduces a novel mechanism to construct the pangenome progressively without rebuilding the accumulated collection from scratch. The progressive mode is shown to consume orders of magnitude less computational resources than existing solutions in managing growing datasets. The software is open source and is publicly available at https://github.com/amromics/panta and at 10.6084/m9.figshare.23724705

AMRomics: a scalable workflow to analyze large microbial genome collections

Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, an optimized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license