The Hospital Price Transparency Final Rule aims at reducing the cost of healthcare in the United States by making information about the prices of medical services more readily available to market participants. The economic rationale is that higher price transparency should reduce the level and the dispersion of prices by disincentivizing hospitals from charging higher prices than possible. Using data on 3789 prices for three diagnostic imaging procedures and on the compliance with the rule for 86 acute care hospitals in the Greater Los Angeles area, we test the hypothesis that price levels and price dispersion are lower among hospitals that are more compliant. We do not reject this hypothesis, although we do not observe strictly decreasing relationships in all analyses. Prices are lower at hospitals with high or medium compliance than at hospitals with low compliance, but there is not a clear difference between hospitals with high and hospitals with medium compliance. Prices are less dispersed among hospitals with high compliance than among hospitals with low compliance, but the evidence for hospitals with medium compliance is less clear. We conclude that high compliance with the Hospital Price Transparency Final Rule implies lower prices and less price dispersion among hospitals.

The digital transformation provides an opportunity for the development of a green and low-carbon economy. This study used panel data collected from 30 Chinese provinces between 2011 and 2018, and assessed the impact of digitization (Dig) on environmental efficiency (EE). Quantile regression is employed to scrutinize the evolution of the marginal effect. From the perspectives of population and institutional factors, this study empirically investigates nonlinear relationships and potential mechanisms using Hansen threshold and mediation models. The findings reveal several key insights. Overall, levels of digitization and environmental efficiency (EE) are increasing with regional dispersion expansion, indicating a “polarization” characteristic. The impact of digitization on EE exhibits noticeable stage and regional heterogeneity. Analysis of population factors reveals that population structure, population size, and human capital trigger a sharp “marginal increase” of positive effects with single thresholds of 0.8155, 7.2284, and 11.0497, respectively. Analysis of institutional factors highlights the significance of fiscal policy quality (tax proportion), education expenditure, and tax system structure as important intermediaries. Finally, this paper presents corresponding policy implications.

We offer an extensive analysis of the significance of information within the realm of Gaussian quadratic economies. We build upon the seminal papers of Morris and Shin (2002, 2007) and consider a signal game of incomplete information. Particularly, we question the suitability of partial transparency portrayed by fragmented information in addition to the private signal in terms of welfare effects. We can summarize our findings in two main points. First, fragmented information, in conjunction with a private signal, can reduce the reliance on public signals. Second, a conflicting effect arises between increasing full disclosure and increasing the precision of fragmented (semi-public) information when examining different complex scenarios, involving for example endogenous private information or imperfect correlated signals. For a critical threshold, an optimal communication strategy designed by fragmented information should be implemented whenever that kind of information is acquired at a high precision.

The use of cloud computing by firms can be expected to go hand in hand with higher productivity, more innovations, and lower costs, and, therefore, should be positively related to export activities. Empirical evidence on the link between cloud computing and exports, however, is missing. This paper uses firm level data for manufacturing enterprises from the 27 member countries of the European Union taken from the Flash Eurobarometer 486 survey conducted in February – May 2020 to investigate this link. Applying standard parametric econometric models and a new machine-learning estimator, Kernel-Regularized Least Squares (KRLS), we find that firms which use cloud computing do more often export, do more often export to various destinations all over the world, and do export to more different destinations. The estimated cloud computing premium for extensive margins of exports is statistically highly significant after controlling for firm size, firm age, patents, and country. Furthermore, the size of this premium can be considered to be large. Extensive margins of exports and the use of cloud computing are positively related.

In recent years, financial inclusion has taken center stage in policy discussions regarding achieving higher growth rates and reducing poverty levels. The existing literature analyzing the relationship between financial inclusion and GDP growth mostly assumes a one-way relation from financial inclusion to GDP, often ignoring potential reverse causality. Furthermore, the literature commonly adopts a financial inclusion index, or focuses on specific indicators such as the number of bank branches, ATMs or the share of people having an account. Because financial inclusion encompasses multiple dimensions, it is important to analyze the causal linkages between different financial inclusion indicators and GDP. In this paper, we analyze the nature and the direction of the causality between GDP and a large number of financial inclusion indicators for MENA (Middle East and North Africa) countries by adopting the recently developed nonlinear and nonparametric Kernel causality approach. Our analysis suggests that financial inclusion increases as the share of women having bank accounts increases. It also increases with the share of adults with primary education having an account and the share of adults having a mobile account. We also identify the relationship between main barriers to financial inclusion and GDP, and find that affordability and having insufficient funds are associated with GDP.

This study investigates the likelihood of time to bank failures in the US between 2001 and April 2023, based on data collected from the Federal Deposit Insurance Corporation's report on "Bank Failures in Brief - Summary 2001 through 2023". The dataset includes 564 instances of bank failures and several variables that may be related to the likelihood of such events, such as asset amount, deposit amount, ADR, deposit level, asset level, inflation rate, short-term interest rates, bank reserves, and GDP growth rate. We explore the efficacy of machine learning survival models in predicting bank failures and compare the performance of different models. Our findings shed light on the factors that may influence the probability of bank failures with a time perspective and provide insights for improving risk management practices in the banking industry.

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 performance of ETFs in Hong Kong that apply environmental, social and governance (ESG) criteria to make their investment decisions is examined in this study. The performance of these ETFs is assessed against the performance of competitive non-ESG ETFs. The study period spans from 1/12/2022 to 29/2/2024. From a methodological perspective, raw returns, tracking errors and risk-adjusted returns are computed. Raw returns are computed in percentage terms with daily close trade prices. Tracking error is calculated as the difference in daily returns between ETFs and Hang Seng Index, which is used as a proxy for the entire stock market of Hong Kong. Risk-adjusted returns include Sharpe, Treyor, Modigliani & Modigliani, and Information Ratios. A simple regression model is applied too, via which the daily excess return of each ETF, that is the return above the risk-free rate, is regressed on the corresponding return of the market index. The empirical results indicate that the ESG ETFs clearly underperform their non-ESG peers, also being a little riskier and more expensive than them. In particular, the average daily return of ESG ETFs is equal to -0.058%, while the respective return of non-ESG ETFs is slightly better at -0.033%. At the cumulative level, the average return of ESG ETFs is equal to -19.22%, while the respective return of the non-ESG ETFs amounts to -12.41%. When it comes to risk, the ESG group presents an average measure of 1.572%, whereas the non-ESG group has an average risk of 1.490%. In regard to expenses, the average management fee of the ESG ETFs is equal to 0.18%, while the average management fee of the non-ESG peers is 0.09%.

Broadband availability is generally lower in the Tribal areas of the United States than in other areas and several policies implemented over the years aim to close that gap. Using data on broadband deployment over the years 2014-2020, progress in closing the Tribal Gap is evaluated. Unmatched and matched sample are used, and a sample of census tracts within 30 miles of a Tribal area are also analyzed with and without matching. The gap between Tribal and non-Tribal census tracks has been getting smaller over time and by 2020 the Tribal Gap was near zero in all cases, especially when the deployment differences are conditioned on a few covariates.

Trust beneficiary protection constitutes an important legal system in the trust industry. This paper, based upon the study approach of the “Theory of Law and Finance”, establishes the trust beneficiary protection index of China. We also test the influence of trust beneficiary protection on the performance of trust companies. As indicated by our study’s results, an “inverted U-shaped” relationship exists between the degree of trust beneficiary protection and the performance of trust companies. In a period when the trust law system has not yet been well established, strengthening trust beneficiary protection could help facilitate the growth of trust company performance. However, this facilitation effect gradually diminishes as the trust law system becomes more perfect. This in turn means that, after reaching a certain peak, conferring the beneficiary with more rights of supervision and participation would cause the performance of trust companies to decline. Improving regional legal environments could also help facilitate the growth of trust company performance. In addition, this facilitation effect is subject to the influence of the nature of trust company controllers. In trust companies controlled by local state-owned capitals, we find that the facilitation effect of the improvement of regional legal environments on trust company performance is inhibited. In trust companies controlled by non-state-owned capitals, however, the facilitation effect of the improvement of regional legal environments on trust company performance is enhanced.

Income and population growth are key determinants of housing demand, while land use regulations are designed to affect housing supply. Previous studies of housing price determinants focus either on specific regulations in particular cities/regions, or on selective subsets of major cities and regulations. This study examines the impact of land use regulations on housing prices from 1989 to 2006 in an unusually large sample of 250 major US cities. Aside from factors that are commonly associated with housing demand (income, population growth and density), housing prices are found to be associated with local cost-increasing land use regulations (approval delays) and with statewide regulations. Since statewide regulations factor prominently into the results, specific examples of the impact of different types of land use regulations are provided for 5 cities in the state of Washington. The estimated increase in housing prices associated with regulations is, on average (over 250 cities), substantially larger than the effects of income and population growth. While the estimated dollar costs associated with regulations may be sizable at times, the results are remarkably consistent with previous studies that were based on smaller cross sections.

The recent interest rate policy decisions of the South African Reserve Bank have been criticized significantly by left-leaning political parties and civic society organisations for being anti-poor, anti-labour, and pro-capital because of their implications for household debt. Existing literature has established that interest rates and house prices are insignificant determinants of household debt dynamics in South Africa. Taking advantage of additional data for the period 2013-2022, and contrary to the previous studies, the paper maintains that house prices and the central bank policy rate play a crucial role in household debt dynamics. Applying a Markov Switching regression to quarterly data for the period 1981Q1 to 2022Q1, the paper finds that house prices and the policy rate have a significant influence on household debt dynamics. It establishes the existence of a ‘house price boom, low policy rate burden’ regime and a ‘high policy rate burden, low house price’ regime. The coexistence of the ‘house price boom’ and the ‘low policy rate’ explains the debt euphoria characterised by significant household leveraging.

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.

Non-labor income is a crucial factor influencing time allocation, and prior studies have primarily concentrated on the linear association between non-labor income and labor hours. Utilizing micro-survey data from the CFPS in 2018 and 2020 and employing the panel threshold model, this paper empirically identifies a double threshold with the wage rate as the threshold variable. This finding reveals a non-linear relationship between non-labor income and labor hours. The two thresholds categorize the relationship into three intervals. In the first interval, non-labor income significantly promotes labor hours, while in the second and third intervals, non-labor income significantly decreases labor hours, exhibiting slightly varying degrees of influence. In general, the relationship between non-labor income and labor hours demonstrates an irregular inverted U-shaped pattern. Upon dividing the workers in the sample into three groups based on the two thresholds, it is observed that wage rates exhibit a positive correlation with non-labor income.

This research delves into the temporal dynamics of a nation's pursuit of a targeted GDP per capita level, employing five different survival machine learning models, remarkably Deep Learning algorithm (DeepSurv) and Survival Random Forest. This nuanced perspective moves beyond static evaluations, providing a comprehensive understanding of the developmental processes shaping economic trajectories over time. The economic implications underscore the intricate balance required between calculated risk-taking and strategic vulnerability mitigation. These findings guide policymakers in formulating resilient economic strategies for sustained development and growth amid the complexities inherent in contemporary economic landscapes.

Population, as a fundamental element in urban development, often reflects a city's economic development pattern through its spatial distribution and dynamic changes. Studying population spatial distribution is pivotal for bolstering the economic activity capacity in urban agglomerations and guiding regional economic health. Using the Changsha-Zhuzhou-Xiangtan urban agglomeration as a case study, this paper analyzes its overall spatial structure and the spatiotemporal evolution of population at the district and county levels. This analysis utilizes population density, population redistribution index, and population geographic concentration as key indices. Additionally, a spatial econometric model is constructed to assess the impact of economic, social, and environmental factors on population spatial patterns. Findings reveal several key points: (1) Furong District serves as the primary central area, boasting a population geographic concentration of 25.1% in 2021. Tianxin District, Kaifu District, Yuhua District, Shifeng District, Yuelu District, and Hetang District constitute the secondary central areas, while Yutang District, Tianyuan District, Lusong District, Yuhu District, Wangcheng District, and Changsha County form the tertiary level areas. (2) Population density within the Changsha-Zhuzhou-Xiangtan urban agglomeration gradually decreases from Furong District outward. The first central area and sub-central areas experience increasing population density, highlighting a polarization trend in the population distribution. (3) The overall Moran's index for the spatial distribution of population in the Changsha-Zhuzhou-Xiangtan urban agglomeration is significantly positive, indicating a strong spatial autocorrelation and a deepening spatial agglomeration of population distribution. (4) Per capita disposable income, financial expenditure, and education level positively influence the geographical concentration of population in the urban agglomeration, while GDP per capita, road area per capita, and environmental quality exert a negative impact. Notably, the most influential factors shaping population spatial distribution are GDP per capita, disposable income per capita, and air quality.

Excessive volatility in financial markets can disrupt economic activity, affect investor and consumer confidence, and potentially lead to financial crises in an economy. Due to this backdrop, this study examined the link between asset class volatility and the output gap in Nigeria. The asset classes were categorized into stock, crude, gold, and bitcoin. The study adopted the GARCH and Bayesian VAR approach and found that all share index has an initial negative impulse with output gap while other asset classes have a positive impulse on output gap. The outcome of this study revealed to both policymakers and economists the potential risks and vulnerabilities of asset class volatility in the economy. Based on this result, recommendations are made amongst which is the strengthening of the Nigerian stock market to help with the inflationary pressures this is because the Nigerian stock market hurt the output gap also, the government should prioritize investing in crude, gold, and bitcoin to push the actual output to full capacity, which brings about employment.

The study investigates the impact of COVID-19 on African exchange rates, employing a panel dataset over the period 2016-2022 from 13 African economies and employing pooled OLS estimator. The findings reveal a noteworthy positive correlation between COVID-19 levels and exchange rates within the region. However, this association necessitates deeper exploration due to its multifaceted implications, including economic disruptions, government interventions, global market interconnections, regional stability perceptions, and investor behavior. Economic indicators, such as GDP, inflation rate, real interest rate, and unemployment rate, significantly influence exchange rates. While GDP positively impacts exchange rates through economic growth, investor confidence, and trade strength, controlled inflation fosters investor confidence and export competitiveness, positively affecting exchange rates. Moreover, a pronounced positive impact of the real interest rate indicates economic stability, attracting investment and contributing to currency appreciation. Conversely, high unemployment negatively affects exchange rates, signaling economic weaknesses and eroding investor confidence. These findings emphasize nuanced policy responses for managing health crisis-induced economic disruptions and suggest future research directions focusing on specific country-level analyses and multifaceted factors influencing currency valuations within African economies.

This paper examines the efficiency, in its weak form, of the clean energy stock indices, Clean Coal Technologies, Clean Energy Fuels, and Wilderhill, as well as the cryptocurrencies classified as "dirty", due to their excessive energy consumption, such as Bitcoin (BTC), Ethereum (ETH), Ethereum Classic (ETH Classic), and Litecoin (LTC), from January 2020 to May 30, 2023. In order to meet the research objectives, the aim is to answer the following research question, namely whether: i) the events of 2020 and 2022 accentuated the persistence in the clean energy and dirty energy indices? The results show that clean energy indices such as digital currencies classified as "dirty" show autocorrelation in their returns; the prices are not independent and identically distributed (i.i.d). In conclusion, arbitrage strategies can be used to obtain abnormal returns, but caution is needed as prices can rise above their real market value and reduce trading profitability. This study contributes to the knowledge base on sustainable finance by teaching investors how to use forecasting strategies on the future values of their investments.

This paper investigates the effect of economic policy uncertainty (EPU) on the systemic risk of the fintech industry. To achieve this goal, we first estimate the evolution of system-wide systemic risk using the CatFin method. We further examine whether EPU significantly affects systemic risk. Our findings demonstrate that the systemic risk of the fintech industry is time-variant and sensitive to major crisis events. Systemic risk tends to increase after major crises, especially the outbreak of the COVID-19 pandemic. EPU has a considerable impact on systemic risk, notably during periods of turmoil.