Miracle-dz/UniFAR-FTUs huggingface dataset:
Data in Action: Data-Driven Decision Making and Predictive Analytics in U.S. Manufacturing

Management in America has become significantly more data-intensive, yet the economic, organizational, and strategic implications of this shift are poorly understood. Working with the U.S. Census Bureau, we developed measures of how manufacturing firms have used data to guide decision making over the past decade. In our large and representative sample, data-driven decision making (DDD) is strongly associated with increased productivity. The benefits attributable to DDD are distinct from those associated with other structured management practices or investment in IT, though the latter is an important complement. Moreover, instrumental variables estimates and timing falsification tests suggest a causal relationship. Implications for firm strategy, however, are nuanced; we find evidence of significant advantages for early adopters of DDD, particularly in the 2005-2010 window, when adoption rates in the sector were lower. Yet we also observe timing-dependent complementarities. The frontier of data-centric practices shifts during our study period, with increased use of predictive analytics becoming the key driver of productivity gains from 2010 to 2015.
Person-Organization Fit and Employee Work Outcomes: Test of Social Exchange Model

Data obtained from subordinate-supervisor dyads (N ¼ 314) of a large manufacturing company in South Korea were used to test a moderated mediation model of the processes linking person-organization (P-O) fit and employee work attitudes and behaviors. The results revealed that the influence of P-O fit on work attitudes and behaviors was indirect through perceived social exchange with organization. In addition, the relationship between P-O fit and perceived social exchange with organization was moderated by leader-member exchange (LMX) quality. Specifically, a high-quality LMX enhanced the positive effects of P-O fit on perceived social exchange with organization.
Consistent Pricing of Options on ETFs and Options on Inverse ETFs

Given the growth of the inverse ETF markets, exchanges have begun to trade options on inverse ETFs, similar to the trading of options on regular ETFs. Since both regular and inverse ETFs on the same index trade the same underlying risk, the pricing of options on regular ETFs and the pricing of options on inverse ETFs should exhibit strong pricing relationships. In particular, the pricing of calls on regular ETFs should be closely related to the pricing of puts on inverse ETFs. The existing literature, as in Deng et al. (2014) and Leung and Sircar (2015), has not directly focused on these arbitrage relationships because it has focused on the more general issue of pricing options on leveraged ETFs, whether the leverage is positive or negative (inverse). Also, most of the empirical results derived in this literature have been derived relative to the S&P 500 index. The purpose of this paper will be to derive a model which explicitly reflects the arbitrage pricing relationships between options on regular ETFs and options on inverse ETFs, and to use market data to test whether these relationships hold in the market for several of the most liquid ETF option contracts.
Tax-Pass-through, Pricing Strategy and Consumer Spending Dynamics: The Indian GST Experience

Measuring the consumption response to a tax regime has been a central issue in the optimal design of fiscal policy. This study leverages a large-scale natural experiment in India, the 2017 Goods-Service-Tax (GST) Reform, to quantify its impact on suppliers’ pricing strategy and consumer spending. Using scanner data on purchasing behavior of a panel of households and a triple difference design, we find a spending reduction in quantities of 0.23 percent, on average, following one percentage point increase in the GST tax. Tax elasticity is not uniform across product categories and is highest for goods in the fresh and fashion class (2.1 and 1.7, respectively). Consumers exhibit intertemporal substitution by preponing spending during the announcement period and substitute consumption across products. We also find evidence of asymmetry for tax pass-through to consumers. The tax is shifted at a lower rate in the case of tax rise, but when the tax is reduced, the elasticity of the tax exclusive price is significantly smaller than -1, resulting in a higher final price and a lower level of spending.
Inheritance of Terminal Heat Tolerance in Two Spring Wheat Crosses

The objective of this study was to develop an understanding about the genetics of terminal heat tolerance in wheat (Triticum aestivum L.). The minimum number of genes was assessed using Mendelian and quantitative genetic approach. Two crosses were made between heat tolerant and heat susceptible bread wheat cultivars: NW1014 × HUW468 and HUW234 × HUW468. Heat susceptible HUW468 was common in both the crosses. The F4, F5 and F6 generations were evaluated including F1 in two different dates of sowing (normal and very late) under field conditions in year 2006–07. The data was recorded for grain fill duration (GFD) and thousand-grain weight (TGW). Based on data of two dates, decline% and heat susceptibility index (HSI) of GFD and TGW were estimated. Heat tolerance in F1 showed absence of dominance. Estimation of genes using Mendelian approach in F4, F5 and F6 progenies (148–157) of the two crosses suggested that heat tolerance was governed by a minimum of three genes. Quantitative approach also indicated simil.
How are Work-Related Characteristics Linked to Sickness Absence and Presenteeism? – Theory and Data

This paper investigates how changes in work-related factors affect workers' absence and presenteeism behavior. Previous studies (implicitly) assume that there is a substitutive relationship, i.e. a change in a work-related factor decreases the level of absence and simultaneously increases presenteeism (or vice versa). We set up a theoretical model in which work-related characteristics not only affect a worker's absence decision but also the individual-specific sickness definition. Since work-related factors affect presenteeism through these two channels, non-substitutive relationships between absence and presenteeism are also conceivable. Using European cross-sectional data, we find only few substitutive and complementary relationships, while the bulk of the work-related characteristics is related only to one of the two sickness states.
Firms’ Risk Endogenous to Strategic Management Choices

Use of variability of profits and other accounting-based ratios in order to estimate a firm's risk of insolvency is a well-established concept in management and economics. This paper argues that these measures fail to approximate the true level of risk accurately because managers consider other strategic choices and goals when making risky decisions. Instead, we propose an econometric model that incorporates current and past strategic choices to estimate risk from the profit function. Specifically, we extend the well-established multiplicative error model to allow for the endogeneity of the uncertainty component. We demonstrate the power of the model using a large sample of U.S. banks, and show that our estimates predict the accelerated bank risk that led to the subprime crisis in 2007. Our measure of risk also predicts the probability of bank default both in the period of the default, but also well in advance of this default and before conventional measures of bank risk.
Deep Learning Approach for the Detection of Depression in Twitter

Depression is a common and serious mental illness that negatively affects from all the ways like how you feel, the way you think and how you act. And also it has very complicated nature. These reasons makes it difficult to find out a better method for detecting mental illness such as depression among social media users. Recent research studies have shown that the popularity of online social media networks among one’s life is getting increased day by day. Also online social media users makes it as a good platform for expressing their emotions and feelings. Choosing appropriate supervised machine learning approaches for identifying mental illnesses through social media platforms (especially Twitter) are somewhat difficult due to the unavailability of proper amounts of annotated training data. So here try to create some sufficient amount of data and implement deep neural architecture for the same. A combination of Long Short Term Memory and Convolutional Neural Network, and Support Vector Machine (SVM) are used to detect users with signs of mental illnesses (depression in this case) from the Twitter social media platform. The evaluation results shows that Deep Neural Network is producing more accurate results compared to Machine Learning algorithm like SVM.
DNA Fingerprint of Carica papaya L. Genotypes by RAPD Markers

Abstract DNA fingerprints and phylogenetic relationships were established for ten Carica papaya L. genotypes based on RAPD markers. The plant materials included seven parents from the Solo and Formosa groups and three hybrids from the cross between these two groups. Some genotypes could be fingerprinted by the presence or absence alone of some bands, but in general the DNA fingerprint could only be obtained by a set of RAPD markers. The phylogenetic analyses showed greater similarity among the parents of each group and the intermediary position of the hybrids, as expected.
Fiscal Issues and Central Banking in Emerging Economies

Central banks have an incentive to monitor the fiscal position of the government for at least two reasons. First fiscal policy can constrain the implementation of monetary policy, particularly when governments call on the central bank to fund government programmes. Second, fiscal policy measures can affect aggregate demand as well as the condition of the financial sector. Fiscal measures therefore affect the central bank's ability to achieve macroeconomic and financial stability. Three key issues are: (i) How should central banks assess fiscal positions? (ii) What is the experience with countercyclical fiscal policy? and (iii) How do fiscal operations affect central bank balance sheets, and does this raise any problems? Senior officials from a number of emerging economies discussed these issues at a meeting in Basel in December 2002. This volume contains revised versions of the papers prepared by participants describing experiences in their own economies, and the background papers prepared by BIS staff.
Random Intercept Item Factor Analysis

The common factor model assumes a linear relation between the observed variables and a set of underlying latent traits. It also assumes that the linear coefficients, intercepts and slopes (factor loadings), linking the observed variables to the latent traits are fixed coefficients (i.e., common for all subjects). When the observed variables are subjects' direct responses to stimuli, such as their responses to the items of a questionnaire, the assumption of common linear coefficients may be too restrictive. This may occur, for instance if respondents to questionnaire items consistently use the response scale idiosyncratically. To account for this phenomenon we partially relax the fixed coefficients assumption by letting the intercepts in the factor model change across subjects while keeping the factor loadings fixed. We show that, under suitable assumptions on this random component of the intercept, the covariance structure implied by a model with p factors and random intercepts is equivalent to the covariance structure model implied by a model with p + 1 factors model where one of the factors have common loadings.
Modeling Flash Crash Behavior in a Stock Market Using Multivariate Hawkes Processes

This paper uses multivariate Hawkes processes to model the transactions behavior of the U.S. stock market as proxied by the 30 Dow Jones Industrial Average stocks before, during and after the May 6, 2010 flash crash, which lasted 36 minutes. The basis for our analysis is the excitation matrix, which describes the network of interactions among the stocks. Using high-frequency transactions data for individual stocks, we find, among other things, strong evidence of contagion that is self- and asymmetrically cross-induced. Our descriptive findings have implications for stock trading and corresponding risk management strategies, as well as stock market microstructure design. <br>
A Macroeconometric Model for Kazakhstan

The paper builds a structural macroeconometric model for Kazakhstan to generate short-term and medium-term forecasts for main macroeconomic variables and conduct scenario analyses based on dynamic simulation of the model. Due to the poor quality of quarterly data on GDP and its expenditure components, they have been adjusted using volume indexes. The model consists of aggregate supply, aggregate demand, labor market, asset market, the central bank policy and government side equations. Most equations are estimated via econometric techniques and identities are explicitly introduced in line with economic theory. We combine all the regression equations into a single model and solve for the baseline scenario from 2003 to 2017. The simulation results show that the structural macroeconometric model approximates Kazakhstani economy reasonably well. Ex-ante forecasts under oil prices remaining around 50 and 60 US dollars per barrel are generated and compared with the baseline forecast of the National Bank of the Republic of Kazakhstan.
Aggregate Productivity Growth and Firm Dynamics in Korean Manufacturing 2007-2017

<b>English Abstract:</b> We study aggregate productivity growth of the Korean manufacturing industry for the 2007-2017 period. We find that the nature of such growth was quite different for two measures of productivity. For labor productivity, most of growth comes from productivity changes among surviving firms. On the other hand, for TFP, most of the productivity growth comes from that of new entrants in recent years. Our work illustrates the different nature of two productivity measures in terms of their growth paths. We also show interesting industry dynamics for both productivity measures, as exiting firms contributed positively to aggregate productivity growth with increasing trends, which suggests that the market had gradually eliminated firms of lower productivity. Using the dynamic Olley and Pakes (1996) decomposition, we also find that for both productivity measures, a substantial productivity growth after the 2008 global financial crisis was due to market share reallocations between firms, but this between-firm contribution has somewhat slowed or been decreasing since then. Our industry sector level study also shows that there has been fundamentally different heterogeneous productivity growth patterns and components across manufacturing sectors. <br><br>Finally, we find that the wage level also plays a role in moderating or as an accelerating factor for different productivity growth paths among surviving, entering, and exiting firms. We find that higher wage groups had disproportionately higher entry and exit rates, and that the contributions of these industry dynamics to aggregate productivity growth were largest for the highest wage group while the productivity growth from the between firm component was substantially higher for lower wage groups. Therefore, we find that not only a timely change in input and output, but also in the wage, is a necessary ingredient for the pace and magnitude of reallocation to be effective in aggregate productivity growth.
Patent Mapping on Electrical Energy Sector Through Visualization Approach

In this paper authors have tried to highlight some new areas of patent analysis by using visualizing and neural network based approaches. This paper creates patent map that represents all technological categories and a method to locate patent data of electrical energy sector. This mapping technique may support competitive intelligence and policy decision-making. The author used visualization approach to generate patent map based on weight of keyword in patent documents and their corresponding filling date. The author also used neural network based approach to detect importance of patents particularly on electrical energy sector. Evaluation and capturing temporal changes in sector specific innovation activities or patterns of technological changes and broader changes from hierarchical to structured knowledge in technology domain performed with help of these tools.
Stock Price Volatility in National Stock Exchange of India

Economic status of India is greatly imitated by the introduction of new economic policy in 1991. The Indian Capital Market has perceived a marvelous progression. There was an outburst of investor interest during the nineties and an equity cult emerged in the country. Foreign Exchange Regulations Act is one such legislation in this direction. An important recent development has been the entry of Foreign Institutional Investors as participants in the primary and secondary markets for industrial securities. In the past several years, investments in developing countries have increased remarkably. Among the developing countries, India has received considerable capital inflows in recent years. We apply the GARCH (1, 1) (General Autoregressive Conditional Heteroscedasticity) framework to on selected representative stock indices. The findings reveal that the GARCH (1, 1) model successfully captures nonlinearity and existence of volatility. The analysis suggests indicates a long persistence of volatility in Indian stock market especially National Stock Exchange (NSE) of India. The preliminary analysis of data set suggests that volatility in the Indian stock market is time varying in nature, persist to form clusters and has a long memory process. These findings of the data characteristics have been consistent with previous studies of Indian markets and justify the application of GARCH type models. The detailed analysis shows that the TGARCH (1,1) model outperforms in estimating, predicting and forecasting the stock market volatility.
Conceptual Model: A Framework for Institutionalizing the Vigor in Business Research

A conceptual model is a framework that is initially used in research to outline the possible courses of action or to present an idea or thought. When a conceptual model is developed in a logical manner, it will provide a rigor to the research process. This paper examines the process of building a conceptual model for business research. Conceptual models, being an intermediate theory, a suitable theory building process can be adopted for its development. The basic definitions of a theory, its elements and characteristics of a good theory are first understood and the different theory-building processes are evaluated. The various concepts are then synthesized and the process of conceptual model development is explained systematically with an illustration. This paper guides the researchers to develop a conceptual model with a good level of abstraction that establishes vigor in business research process.
Voting on Urban Land Development

In this paper, we analyze several local referendums on land development and land-use regulation in the City of Erlangen (Germany) in the period between 2011 and 2018. To identify the positive influence of the travel distance on approval for the development of land, we employ a two-way fixed-effects model and use spatial instruments. Also, we analyze the heterogeneity of city dwellers' preferences for the development of residential and commercial areas. In particular, we examine the homeownership and expenditure-crowding-out hypotheses.
Studies of the recognition sequence of φX174 gene A protein. Cleavage site of φX gene A protein in St-1 RFI DNA

Abstract It is already known that phi X gene A protein converts besides phi X RFI DNA also the RFI DNAs of the single-stranded bacteriophages G4, St-1, alpha 3 and phi K into RFII DNA. We have extended this observations for bacteriophages G14 and U3. Restriction enzyme analysis placed the phi X gene A protein cleavage site in St-1 RF DNA in the HinfI restriction DNA fragment F10 and in the overlapping HaeIII restriction DNA fragment Z7. The exact position and the nucleotide sequence at the 3'-OH end of the nick were determined by DNA sequence analysis of the single-stranded DNA subfragment of the nicked DNA fragment F10 obtained by gelelectrophoresis in denaturing conditions. A stretch of 85 nucleotides of St-1 DNA around the position of the phi X gene A protein cleavage site was established by DNA sequence analysis of the restriction DNA fragment Z7F1. Comparison of this nucleotide sequence with the previously determined nucleotide sequence around the cleavage site of phi X gene A protein in phi X174 RF DNA and G4 RF DNA revealed an identical sequence of only 10 nucleotides. The results suggest that the recognition sequence of the phi X174 gene A protein lies within these 10 nucleotides.
Molecular Diversity and Assessment of Biological Characteristics of Greek Colletotrichum lindemuthianum Populations

Colletotrichum lindemuthianum isolates collected in Greece were characterized by the temperature effect on their biological characteristics (mycelial growth, sporulation and spore germinability) and by molecular diversity revealed by RAM and ERIC-BOX PCR analysis. The temperature effect on the assessed biological characteristics resulted in a similar classification according to the origin and virulence patterns of isolates. Colletotrichum lindemuthianum isolates originating from the areas of Nevrokopi and Vrodou showed better adaptation at the lower temperatures exposure (12 and 18°C) compared to isolates originating from the Municipality of Hrisoupolis, which showed better adaptation at the highest temperature tested (24°C). Molecular diversity was detected using RAM and ERIC-BOX PCR primers. Both methods revealed, in a similar way (r = 0.58, P = 0.05), two main clusters of isolates, in agreement with previous findings using RAPD and RFLP analysis. The majority of the tested isolates were grouped in the same main cluster (29 out of 35 Greek isolates for both methods) underlying high levels of genotypic similarities between Greek populations of C. lindemuthianum. This study, an extension of previous research, provides further information on population diversity of C. lindemuthianum required for developing more efficient control strategies of bean anthracnose disease.
Regulation and Purchase Diversity: Empirical Evidence from the U.S. Alcohol Market

Abstract The repeal of the Prohibition Act in 1933 introduced state-level regulations on the retail availability of alcoholic beverages. Recently there has been much debate among industry stakeholders on how changes to these laws will affect consumer choices. We develop an index to measure purchase diversity for alcoholic beverages that considers similarities in product attributes. Following a set of households that moved between regulatory environments during the 2004 to 2016 period, we examine the effect of alcohol availability on purchase diversity. Our key finding shows that consumers further diversify their product selections in states that allow alcohol sales in grocery stores.