Foreign Direct Investment and Local Firm Productivity: Evidence from Thailand

Abstract The enormous costs incurred to government for foreign direct investment (FDI) inflows raised question whether its benefits are worthwhile. In this dissertation, I use productivity estimates as outcomes to explore the direct and indirect impacts of FDI inflows on local firms in manufacturing sector of Thailand during 2001 to 2006. Chapter 1, I introduce the overview of the entire dissertation. Chapter 2, I briefly reviewed investment climates and FDI conditions in Thailand. Then I constructed a comprehensive firm-level dataset from several data sources for FDI examination. The main dataset offers quantity and capacity outputs along with revenues at product-level. Chapter 3, I modify two existing productivity estimation approaches to compute firm productivity in terms of value added, quantity and full capacity outputs. The new productivity estimation approach corrects for endogeneity, multicollinearity and allows for multi product firm assumption. I estimate productivity for three output types: value added, quantity and full capacity. The production function coefficients exhibit unskilled labor intensive technology in all sectors. Chapter 4, with productivity estimates from chapter 3, I examine the FDI direct impacts on productivity of local affiliates. I adopt two selection bias correction methods: average treatment effects on the treated based on propensity score matching (ATT), and a control function based on second moment conditions proposed by Farre, Klein and Vella (2009). The results from ATT exhibit the existence of FDI direct effects in ten sectors. The results from the second method are significant in eleven sectors. This finding suggests that FDI direct effects depend on specific factors across sectors. Chapter 5, I investigate whether FDI spillovers exist through horizontal and vertical relationships. The agglomeration-spatial weights are introduced in spillover variables to capture the contributions of clusters and geographical distances. When controlling for agglomeration and geographical distance, the results indicate positive spillover effects through backward linkages, and negative effects through forward linkages and horizontal relationships. This finding reflects the positive benefits from FDI to Thai suppliers, negative impacts on Thai buyers and competitors who located in close to clusters. This means FDI spillover exists only when FDI firms are located close to clusters

Understanding Thai fansubbing: collaboration in fan Communities translating a Korean TV show

The present research seeks to shed light on collaboration in the largely unexplored area of Thai fansubbing practices with the main focus placed on fansubbers. The interrelationships within the fansubbing community, which develop in particular between fansubbers and non-translating fans, have generally been overlooked by scholars. Furthermore, the significant roles played by digital technologies in facilitating such interdependencies have not been fully discussed in the literature. In an attempt to fill these gaps, this thesis explores how Thai fansubbers exploit technology and collaborate with community members to demonstrate the mechanism in which fan community collaboration is sustained, despite ever present legal threats. The current study takes a socio-technical approach to focus on the interactions of humans, i.e. fansubbers and non-translating fans, with particular attention placed on the role of technologies. In order to gain insights into such interactions, research data were collected through a virtual ethnography method via online fan surveys and interviews with fansubbers. The data were analysed on the basis of Actor-Network Theory (ANT) (Latour 2005, 1987), combined with concepts which bring into focus the interrelationships formed in digital network environments: affinity spaces (Gee and Hayes 2011), networked affect (Hillis, Paasonen and Petit 2015), and epistemic trust (e.g. Origgi 2013, 2012). The study presents empirical evidence to show that affects, such as attachment to favourite programmes and engagement in fansubbing in particular, which underlie fansubbing communities, are the driving force behind collaborative effort so that the community can produce and circulate fansubs. The findings further suggest that these shared affects between fansubbers and non-translating fans develop into common ethical values, which are in turn influenced by specific cultural contexts of the practice. Such values develop into mutual trust among the fan community members, strengthening their interrelationships which can sustain collaboration in fansubbing

Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses

<p>Abstract</p> <p>Background</p> <p>Purely epistatic multi-locus interactions cannot generally be detected via single-locus analysis in case-control studies of complex diseases. Recently, many two-locus and multi-locus analysis techniques have been shown to be promising for the epistasis detection. However, exhaustive multi-locus analysis requires prohibitively large computational efforts when problems involve large-scale or genome-wide data. Furthermore, there is no explicit proof that a combination of multiple two-locus analyses can lead to the correct identification of multi-locus interactions.</p> <p>Results</p> <p>The proposed 2LOmb algorithm performs an omnibus permutation test on ensembles of two-locus analyses. The algorithm consists of four main steps: two-locus analysis, a permutation test, global <it>p</it>-value determination and a progressive search for the best ensemble. 2LOmb is benchmarked against an exhaustive two-locus analysis technique, a set association approach, a correlation-based feature selection (CFS) technique and a tuned ReliefF (TuRF) technique. The simulation results indicate that 2LOmb produces a low false-positive error. Moreover, 2LOmb has the best performance in terms of an ability to identify all causative single nucleotide polymorphisms (SNPs) and a low number of output SNPs in purely epistatic two-, three- and four-locus interaction problems. The interaction models constructed from the 2LOmb outputs via a multifactor dimensionality reduction (MDR) method are also included for the confirmation of epistasis detection. 2LOmb is subsequently applied to a type 2 diabetes mellitus (T2D) data set, which is obtained as a part of the UK genome-wide genetic epidemiology study by the Wellcome Trust Case Control Consortium (WTCCC). After primarily screening for SNPs that locate within or near 372 candidate genes and exhibit no marginal single-locus effects, the T2D data set is reduced to 7,065 SNPs from 370 genes. The 2LOmb search in the reduced T2D data reveals that four intronic SNPs in <it>PGM1 </it>(phosphoglucomutase 1), two intronic SNPs in <it>LMX1A </it>(LIM homeobox transcription factor 1, alpha), two intronic SNPs in <it>PARK2 </it>(Parkinson disease (autosomal recessive, juvenile) 2, parkin) and three intronic SNPs in <it>GYS2 </it>(glycogen synthase 2 (liver)) are associated with the disease. The 2LOmb result suggests that there is no interaction between each pair of the identified genes that can be described by purely epistatic two-locus interaction models. Moreover, there are no interactions between these four genes that can be described by purely epistatic multi-locus interaction models with marginal two-locus effects. The findings provide an alternative explanation for the aetiology of T2D in a UK population.</p> <p>Conclusion</p> <p>An omnibus permutation test on ensembles of two-locus analyses can detect purely epistatic multi-locus interactions with marginal two-locus effects. The study also reveals that SNPs from large-scale or genome-wide case-control data which are discarded after single-locus analysis detects no association can still be useful for genetic epidemiology studies.</p

Contemporary global media circulation based on fan translation: a particular case of Thai fansubbing

This article argues that fan translation serves as a contemporary, alternative mechanism for the circulation of global media texts. By focusing on the fan subtitling practice in the case of Thai fansubbing of a Korean TV programme, we observe unique ways in which fans exploit semiotic resources to produce fansubs in relation to professional subtitling norms. For example, novel features of fansubs include the treatment of “impact captions” prevalent on Korean TV which are typically untranslated in official Thai translations as these captions are not common in Thailand. Using Actor Network Theory (ANT) as a framework, we analyse survey and interview data collected from Thai fansubbing communities of the Korean TV show Running Man ([Author] 2018). The data indicate the close interrelationship which seems to develop between fansubbers and “nontranslating” members of fan communities. The previously less recognised importance of such an inter-dependency points to the building of trust, especially epistemic trust, which underlies fansubbing practices. We thus argue that this modern alternative circulation mechanism for global media texts can be characterised by the particular way fans exploit semiotic resources and the way in which it is supported by epistemic trust within the fan community

Inferring combinatorial association logic networks in multimodal genome-wide screens

Motivation: We propose an efficient method to infer combinatorial association logic networks from multiple genome-wide measurements from the same sample. We demonstrate our method on a genetical genomics dataset, in which we search for Boolean combinations of multiple genetic loci that associate with transcript levels

Bioinformatics challenges for genome-wide association studies

Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population

Genome-Wide Interaction-Based Association Analysis Identified Multiple New Susceptibility Loci for Common Diseases

Genome-wide interaction-based association (GWIBA) analysis has the potential to identify novel susceptibility loci. These interaction effects could be missed with the prevailing approaches in genome-wide association studies (GWAS). However, no convincing loci have been discovered exclusively from GWIBA methods, and the intensive computation involved is a major barrier for application. Here, we developed a fast, multi-thread/parallel program named “pair-wise interaction-based association mapping” (PIAM) for exhaustive two-locus searches. With this program, we performed a complete GWIBA analysis on seven diseases with stringent control for false positives, and we validated the results for three of these diseases. We identified one pair-wise interaction between a previously identified locus, C1orf106, and one new locus, TEC, that was specific for Crohn's disease, with a Bonferroni corrected P<0.05 (P = 0.039). This interaction was replicated with a pair of proxy linked loci (P = 0.013) on an independent dataset. Five other interactions had corrected P<0.5. We identified the allelic effect of a locus close to SLC7A13 for coronary artery disease. This was replicated with a linked locus on an independent dataset (P = 1.09×10−7). Through a local validation analysis that evaluated association signals, rather than locus-based associations, we found that several other regions showed association/interaction signals with nominal P<0.05. In conclusion, this study demonstrated that the GWIBA approach was successful for identifying novel loci, and the results provide new insights into the genetic architecture of common diseases. In addition, our PIAM program was capable of handling very large GWAS datasets that are likely to be produced in the future

The Impact of Phenocopy on the Genetic Analysis of Complex Traits

A consistent debate is ongoing on genome-wide association studies (GWAs). A key point is the capability to identify low-penetrance variations across the human genome. Among the phenomena reducing the power of these analyses, phenocopy level (PE) hampers very seriously the investigation of complex diseases, as well known in neurological disorders, cancer, and likely of primary importance in human ageing. PE seems to be the norm, rather than the exception, especially when considering the role of epigenetics and environmental factors towards phenotype. Despite some attempts, no recognized solution has been proposed, particularly to estimate the effects of phenocopies on the study planning or its analysis design. We present a simulation, where we attempt to define more precisely how phenocopy impacts on different analytical methods under different scenarios. With our approach the critical role of phenocopy emerges, and the more the PE level increases the more the initial difficulty in detecting gene-gene interactions is amplified. In particular, our results show that strong main effects are not hampered by the presence of an increasing amount of phenocopy in the study sample, despite progressively reducing the significance of the association, if the study is sufficiently powered. On the opposite, when purely epistatic effects are simulated, the capability of identifying the association depends on several parameters, such as the strength of the interaction between the polymorphic variants, the penetrance of the polymorphism and the alleles (minor or major) which produce the combined effect and their frequency in the population. We conclude that the neglect of the possible presence of phenocopies in complex traits heavily affects the analysis of their genetic data