The purpose of this paper is to examine and elucidate the effect of the announcement of winning a US federal contract by pharmaceutical companies, traded on the NASDAQ stock exchange on the share prices. The analysis is based on a sample of 327 event days from 12 pharmaceutical companies listed on the NASDAQ market for a given period (2010 – 2021). The sample is analysed by using the event study methodology and Capital Asset Pricing Model (CAPM) in order to examine the abnormal returns and cumulative abnormal returns during an event window of 10 days before the event and 10 days after the event. While prior researchers have identified significant positive abnormal returns following different types of contract announcements, this study aims to contribute to the existing literature by exploring the specific impact of the public contract in the pharmaceutical market on market shares. By investigating this unique aspect, the research fills a gap in previous studies and enhances our understanding of the relationship between public contract announcements and market performance. The data collection utilises secondary data from the official open data source of US federal spending information and daily stock prices. The collected data is analysed quantitatively using event study methodology, allowing for statistical examination of abnormal returns, such as ANOVA analysis, t-statistic, beta index, time series analysis and linear regression. The research findings indicate a significant impact of public contract-winning announcements on cumulative abnormal returns, suggesting a positive reaction from the market. However, average abnormal returns within the event window were positive but not statistically significant. These results imply that winning a government contract has a limited impact on the stock market. The implications of this research highlight the importance of public contract announcements in the pharmaceutical industry and their influence on share prices. The findings provide valuable insights for investors and stakeholders, aiding decision-making processes related to investment opportunities and market performance.

Objective: The paper, specifically examines whether the recently formed Regional Comprehensive Economic Partnership (RCEP) can potentially facilitate minimising the constraints to renewable energy goods exports at the regional level in Asia. Data, Methodology and Findings: Using the panel data from 11 RCEP members from 2006 to 2014, this study has applied the ‘meta frontier stochastic gravity frontier’ methodology and confirms that the establishment of RCEP has the potential to improve trade in renewable energy commodities within the RCEP region. Policy Implications: The policy implication is that when countries work together, it will lead to enormous benefits for national, regional, and worldwide prospects of a more sustainable energy future. Practical Policy Implications: In terms of practical policy implications, the developed RCEP member countries should actively engage in promoting R&D activities and protecting intellectual property rights concerning renewable energy production, which are essential for countries to integrate with the world market and to lift the export frontiers of both the developed and developing RCEP member countries to reach the unrestricted export of renewable energy technology.

Based on fully considering the actual differences in statistical indicators between Hong Kong, Macao, and the nine cities in the Pearl River Delta, this paper constructs a high-quality urban development evaluation system that is suitable for the actual development of the Guangdong-Hong Kong-Macao Greater Bay Area. The Entropy Weighted TOPSIS two-step method is used to process data and systematically investigate the changes in the high-quality development index of 11 cities in the Guangdong-Hong Kong-Macao Greater Bay Area from 2013 to 2020. The study found that the Guangdong-Hong Kong-Macao Greater Bay Area has thoroughly implemented the new development concept, and the level of high-quality development has continued to improve. The profound integration effect of the Guangdong-Hong Kong-Macao Greater Bay Area has appeared, and it has promoted the high-quality development of the region in coordination. The construction of the Guangdong-Hong Kong-Macao Greater Bay Area has strongly promoted the great practice of "one country, two systems" to achieve stability and prosperity.

The UK fully legalised open market share repurchases in 1981, and to our knowledge no study has investigated the business cycle’s influence on repurchase decision-making. We address this aspect and investigate the period 1985-2014. This is relevant as the business cycle factors impact the firm-specific variables such as cash flow, profitability, dividends and capital structure, and these factors traditionally influence repurchase decisions. This forms the paper’s theoretical intuition, and the empirical objectives test the business cycle’s influence on the decision to undertake a repurchase, and also its influence on repurchases values. The results find that the business cycle influences both the decision of undertaking repurchases and repurchases’ values, and this influence has aggregately remained positively associated with economic prosperity. Thus, the frequency of repurchase announcements by British firms is more probable during prosperous economic circumstances. The results also reveal that the repurchase-business cycle relationship witnessed a structural break in 1996:Q2, and the real difference following this break is the increase in the business cycle’s influence on the decision regarding repurchase values. The paper thus contributes to existing literature by directly testing the UK’s repurchase-business cycle relationship, and providing detailed empirical evidences that business cycle conditions strongly impact the repurchase decision-making.

Hundreds of articles have been written that include empirical estimates of the dis-employment effects of minimum wages; however, many of these articles find statistically insignificant effects, some find significant negative effects, and a few find significant positive effects. Most of these studies use multivariate analyses which can be criticized for omitting key variables. The omitted variables problem ruins all statistics and estimates. This paper uses reiterative truncated projected least squares (RTPLS), a solution to the omitted variables problem, to estimate the percentage increase in unemployment due to a one percent increase in the real minimum wage using monthly data for the 50 states of the USA from 1987 to 2021. RTPLS produces a separate elasticity for every observation where differences in these estimates are due to omitted variables. We argue that RTPLS solves most of the econometric problems that David Neumark identified in his keynote address at a minimum wage conference in Berlin in 2018. We find that the percentage change in the unemployment rate due to a one percent change in the minimum wage ranges between 1.156 and 3.389, that the elasticities for different states tend to move together over time, and that all these elasticities are statistically significant at a 95 percent confidence level.

Keeping in view the concept of agglomeration economies and the New Economic Geography (NEG) angle, this paper makes an attempt to examine the rural to urban population movement at the district level in India. The findings do not favour a strong positive association between levels of urbanization and migration rates.  However, there exists a cluster of districts which are able to attract migrants on a large scale in spite of being already urbanized. The work participation rate, share of services and construction work, and literacy rate all form parts of this positive nexus, indicating that opportunities exist with increased levels of urbanization which in turn prompt people to migrate. The positive spill-over effects of higher levels of urbanization are not limited to the urban spaces only as the adjoining rural areas also seem to have undergone   a significant transformation. The land use pattern and activities are changing and some of the developmental impact is evident. However, the regional variations in this respect are evident. There are sharp differences in the relationship between urbanization level and migration rates across regions. The nature of urbanization and its determinants and the outcome variables of urbanization and migration also unravel regional variations. Besides, there are many districts with higher levels of urbanization; yet, they are not able to attract migrants at a rapid pace. New investment opportunities can be created in these spaces to reduce the cost of growth and make employment generation process more effective, facilitating the rural population to take the benefits of agglomeration economies.

Credit markets around the world are undergoing digital transformation which has led to the rise in FinTech and BigTech lending. FinTech and BigTech lending is the provision of credit by FinTech and BigTech providers who have more capital, cutting-edge IT systems, worldwide recognition, greater online presence and are able to handle more big data on computers and mobile phones than traditional banks. FinTech and BigTech lending is growing in importance, but the determinants of FinTech and BigTech lending have received little attention in the literature. This study investigates the determinants of FinTech and BigTech lending. The study focused on the effect of financial inclusion and financial development on FinTech and BigTech lending. Using data for 18 countries from 2013 to 2019 and employing the difference-GMM and 2SLS regression methods, the findings reveal that financial inclusion and financial development are significant determinants of FinTech and BigTech lending. Financial development is a positive determinant of FinTech and BigTech lending while financial inclusion has a significant effect on FinTech and BigTech lending. Also, FinTech and BigTech lending lead to greater banking sector stability and also poses the risk of rising nonperforming loans. There is also a significant positive correlation between financial development and FinTech and BigTech lending. These findings add to the emerging literature on the role of FinTech and BigTech in financial intermediation. This research is significant because it provides insights into the role of financial inclusion and financial development in the digital transformation of credit markets.

The goal of this paper is to work out a poststratification method for estimating in-time indicators for international end-to-end delivery processes when

• collected data cannot cover all the strata making up the logistics universe to be surveyed,
• the true weights of the strata - needed to correct biases in representativeness caused by disproportionate sampling and incomplete coverage - are unknown but can be inferred from marginal subtotals related to stratification criteria considered separately, rather than jointly, and conditional on each end of the delivery journey: outbound- versus inbound-specific. Within this perspective, poststratification is used here to mean a statistical correction of measurements derived from incomplete stratified samples, an ex-post calibration aimed at yielding more accurate estimates based on an analysis of the data. Thus, we tackle instances where ex-ante assignment to strata is not a problem, but when surveying all strata is out of the question.

For that purpose, an econometric model is designed

• to link the discrete transport lead times, counted in days, of tested items to the specifics of their material characteristics (e.g. size/weight), as well as those of the routes they take through the distribution network (e.g. origin and destination zones),
• and provide performance predictions for each of the strata, covered as well as non-covered.

Benchmarking the multinomial cumulative logit regression against the negative binomial one reveals that delivery time had better be treated as an ordinal categorical system’s response, rather than as a ratio-scaled count.

The model-based fitted and extrapolated estimates are then used as inputs to the ex-post weighting stage, which produces robust point- and interval-estimates of aggregate key performance indicators (KPIs) through bootstrapping. Simple linear programs provide two extreme weighting sets, one per country-to-country path: the first minimizes the KPIs’ values, while the second maximizes them. 

Probabilities of delivery within deadlines summarize distributions of delivery times better than their means and standard deviations, because logistical efforts to cut transit by one day must be enhanced more and more as it gets shortened. Three types of graphs are proposed to help visualize this exponential increase in the service quality required. The applicability of the methodology developed is demonstrated on the 2023 database of the International Post Corporation. In this case, the imprecision of the KPI estimates depends much more on the uncertainty caused by disturbances occurring during the first- and last-miles, than on the imperfection of the information about the real weights of the strata. 

This paper presents a model of bank runs and evaluates relevant policy tools. The model is founded on the historical pattern of banking panics, involving an economic boom, an adverse shock, prominent bank failures, and runs on both insolvent and solvent banks. The model analyzes various ways in which solvency information affects the likelihood of systemwide bank runs. An interesting result is that partial bank-specific information can be worse than no bank-specific information. The model can also explain runs driven by liquidity concern based on incomplete solvency information. The main policy implication derived from the model and the evaluation of policy tools is that policy actions to contain a financial crisis should incorporate weeding out insolvent institutions and assuring the solvency of remaining institutions.

The high price of energy due to green energy policy will cause adjustments across the US economy predicted in the present general equilibrium model that includes energy Btu input with capital and labor to produce manufactures and services. This same model in trade theory examines the effects of a tariff on an imported factor of production such as a natural resource or capital.  Corresponding error correction estimates of the reduced form equations of the model in annual 1970-2018 data prove robust and suggest model modifications. A parametric approach to noncompetitive pricing based on unit cost diminishing with output brings the model much closer to the estimates. Manufactures are revealed to have a higher degree of noncompetitive pricing than services. Assuming constant elasticity of substitution production, a weak degree is revealed to provide the best fit to the error correction estimates. The high price of green energy will cause an inelastic decrease in energy input resulting in increased energy revenue. Outputs of both sectors fall, more in manufactures due to its energy intensity. The capital return and wage also fall given the weak substitution of capital and labor relative to the price of energy. The wage rises slightly in the model but falls considerably in the error correction estimate. The only clear winner is energy input with increased revenue. The government owns a large share of hydrocarbon reserves and will benefit from a higher price of energy.

The paper focuses on younger online viewers’ motivation to watch video game live streaming on live streaming platforms. Unlike existing scholars, it analyses younger online viewers’ watching motivation through a positive perspective and draws on the Play-Others-Downtime theory (P-O-D theory) and the motivation theory to establish the research model. By analysing 397 samples based on the variance-based structural equation modelling and partial least squares path modelling (SEM-PLS), the results present that younger viewers’ entertainment, social and leisure purposes positively affect their watching motivation. Control variables (i.e., gender, education background, and income level) demonstrate insignificant effects across the model. Considering the influence of the COVID-19 pandemic, watching video game live streaming is essential entertainment and social activities for younger adults. Future studies should identify the positive impact of video game live streaming and guide younger viewers to participate appropriately in this activity.

Motivated by the Blackorby-Schworm (1993) observation that market outcomes may differ from those originating in market-actor optimization, this paper claims that the number of banks in the market is larger than the number justified by bank profit maximization alone or in combination with bank depositor welfare maximization. This claim is made within the context of bilateral monopoly banks and intertemporal utility maximization by bank depositors. The basic policy implication towards bank population rationalization is a minimization of the deviation away from the optimal interest rate margin at every stage of the business cycle. It is meant to be an acyclical policy though the target of optimal bank population is attainable by active countercyclical policy as well. The nature of this policy issue makes the use of macroprudential measures imperative, jointly perhaps with a fiscal-monetary policy mix. A dynamic version of the model in a Cournot environment is akin to the modeling of Minsky's hypothesis of financial fragility.

This paper presents an analytical framework to assess the probability of achieving nationally determined contributions (NDC). The prediction model based on the Kaya identity is used to simulate the pathway of carbon emission until the target year. Applying the modified STIRPAT framework (named CO-STIRPAT) to data observed in South Korea shows that the probability that the predicted pathway with existing climate technology will stay above the NDC target pathway is significantly high. The result suggests that it is necessary to design a climate policy to improve energy intensity and carbon intensity by accelerating the advance in climate technology.

This paper empirically investigates the relation between firm performance in corporate social responsibility (CSR) and the need and likelihood of external financing to test the predictions of agency and stakeholder theories. Empirical results from Logit, Linear Probability Model, OLS and Firm fixed effects regressions indicate that CSR is negatively related to the likelihood and level of external financing. Further analysis indicates that CSR has a negative and significant effect on both net equity issued (NEI) and net debt issued (NDI), the two components of external financing. Overall, the empirical results support the predictions of agency theory.

This study explores the cause and effect of endogenous risk aversion in land pricing, where state intervention through taxation remains a general practice. Using a consumption-based asset pricing model incorporating taxation, it is shown that high taxation, due to the indexation effect, supporting land prices and reducing individuals' risk expectations, could lead to an endogenous decrease in risk aversion, which could result in market dysfunction because risk aversion plays a key role in the market mechanism. China, with its wholly state-owned land and the general use of land sales to cover financial deficits, is a typical case for empirical tests. The tests confirm that there, the rise in land prices was driven by the increase in reserve prices set by local governments, a strong means of taxation, and not by the market, indicating the endogenous decrease in risk aversion.

This study investigates the impact of institutions and public agricultural Research and Development (R&D) on agricultural productivity in low- and middle-income countries. The paper employs panel data with fixed-effects models, spanning 12 years and including 49 low-and middle-income countries. Agricultural productivity is measured by cereal yield, while institutional factors are assessed through four key indicators: judicial independence, property rights protection, contract enforcement, and impartial public administration. Public agricultural R&D investments serve as the measure for agricultural R&D. The findings show that property rights protection positively influences agricultural productivity, whereas legal contract enforcement has a negative impact across these countries. Additionally, public agricultural R&D significantly and positively affect productivity in Asia and the LAC regions, but not in sub-Saharan Africa.

The construction and optimization of a portfolio is a complex process that has been a historically active research area in finance. For portfolios with highly correlated assets, the performance of traditional risk-based asset allocations methods such as the mean-variance (MV) model is limited when numerous assets are correlated. A novel clustering-based asset allocation method, called Hierarchical Risk Parity (HRP), provides an opportunity to mitigate these limitations in portfolio construction. HRP utilizes the hierarchical relationships between the covariance of assets in a portfolio to determine weight distributions, eliminating the need for the inversion of the covariance matrix that is required by most traditional risk-based asset allocation methods. This research examines the viability of Hierarchical Risk Parity (HRP) method in portfolio construction of a US equity portfolio and compares the performances of HRP to traditional asset allocation methods exemplified by the mean-variance (MV) method. The results of this research show that the performance of the HRP method is comparable to the performance of the MV method. Given these findings, HRP provides an advantageous approach for portfolio construction in practical scenarios where correlated assets are present in the portfolios.

Deposit dollarization in Turkey has been on the rise, reaching record levels in 2022. This was caused by the worsening macroeconomic fundamentals along with the transition to the presidential system in 2018 and the associated deviation from the monetary policy stance afterward. The unique case of Turkey presents an excellent natural experiment to explore the dynamics of deposit dollarization and how it is affected by the changes in the institutional structure as well as various social, economic, and financial shocks in a relatively short time. This study examines the time varying reactions of deposit dollarization for the period between 2013 and 2022 based on rolling window maximum entropy bootstrap estimates. Our findings reveal that all the model variables had significant and sometimes asymmetric effects on deposit dollarization during the different stages of this turbulent period. In particular, we observe diminishing effects of real exchange rate, policy credibility and consumer confidence over time. The findings provide ample new evidence regarding the dynamics of deposit dollarization in general, and how it is affected by loss of monetary policy credibility.

Bank capital requirements would entail large social costs if they made resource allocation suboptimal and banking services costly by unduly limiting the banks’ ability to lend. This paper considers three main factors that may make capital requirements relevant, namely, deposit insurance subsidies, stock valuation errors, and tax shields derived from debt financing. The theoretical model analyzes the combined effects of the three factors on the banks’ incentives to make fairly priced loans, which should also be socially optimal loans. A key finding is that the long-term cost of capital requirements is likely to be very small when deposit insurance is underpriced. Increased funding costs resulting from higher capital requirements are absorbed by shareholders of banks, rather than passed on to borrowers. Under some reasonable assumptions, higher capital requirements improve resource allocation by countervailing distortionary effects of deposit insurance subsidies. Short-term adjustment costs can still be large, but it should be relatively easy to mitigate the short-term effects.

This paper examines the information content of selected US industries focusing on the dynamic linkages among these industries, the stock market and a number of fundamental variables. The period of investigation spans from January 1960 to December 2021. The empirical strategy includes several methodologies such as regressions, vector autoregressions and volatility models. The idea is to investigate the dynamic linkages among these series at both the mean and the volatility levels. The results point to significant industry returns’ explanatory power for many predictors of economic activity including the stock market. Further, time-varying analysis of the linkages among the industries and the stock market’s returns reveal that certain industries such as Oil and Financials provide consistent information leadership over other industries and across decades. Further, upon assessing the industry–market return volatility spillovers, it was found that a market risk–return profile may not always be economically significant and timely for investors. Finally, crises, financial or otherwise, affect industries but to differing degrees.