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. 

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.

We explore the connectedness and portfolio implications between Islamic and conventional bonds in global and GCC regions. We also compare which bonds performed better during our sample period. Unlike previous studies, we focus on Islamic bond markets compared to their conventional counterparts and highlight the GCC bonds (Islamic and conventional) in respect of global bonds. We apply the DCC-GJR-GARCH (1,1) method, the Sharpe ratio, and the portfolio implications strategy over the period from September 1, 2013, to February 23, 2022. Our time-varying results suggest that the relationship among all variables varies over time, but most of them are positive, suggesting that there are fewer diversification opportunities between Islamic and conventional bonds. Hedging and diversification benefits are found only in the limited period among these variables, especially between GCC bonds and global bonds, and global Sukuk and GCC Sukuk. The findings of risk-adjusted returns reveal that Islamic bonds outperform their conventional counterparts. Moreover, mixed results are found in the case of hedging costs, and the majority of the fund, based on optimal weights, should be invested in Islamic bonds. Our study endows investors and regulators in the global and GCC markets with new insights on how to shield their investments and the financial system from financial crises through a hedging strategy with Islamic finance.

In this paper I will show that budget deficit (or fiscal deficit) is necessary to achieve full employment under constant prices or inflation, using a model of endogenous growth in which consumers hold money for the reason of liquidity and live forever. Budget deficit need not be offset by future budget surpluses. I consider the continuous time case by taking the limit of the discrete time case when the time interval approaches zero. A continuous time dynamic model seems to be more general than a discrete time model. When the actual budget deficit is greater (smaller) than the value which is necessary and sufficient for full employment under constant prices, an inflation (a recession) occurs. The main argument of this paper is that a growing economy requires the continuation of budget deficit, and that we should not think of paying off the resulting government debt with taxes.

We present two approaches to forecasting parameters in the SABR model. The first approach is the vector autoregressive moving-average model (VARMA) for the time series of the in-sample calibrated parameters, and the second is based on machine learning techniques called epsilon-support vector regression (ε-SVR). Using daily data of S&P 500 ETF option prices from January 1, 2014, to December 31, 2018, we first calibrate the daily values of the model parameters from the training sample, then conduct out-of-sample forecasting of parameters and pricing of options. Both approaches produce good fits between the forecasted and calibrated parameters for out-of-sample dates. A comparison study shows that using forecasted parameters as inputs, the SABR model generates better pricing results than assuming constant parameters or using lag parameters. We also discuss the market conditions under which one approach outperforms the other.

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 study explores online consumers’ impulse buying intentions and behaviors on live streaming platforms. Unlike traditional shopping modes, the development of real-time video streaming provides online consumers with a distinct approach to interacting with live streamers and browsing online products in real-time, potentially causing their impulse buying intentions. To understand online consumers’ impulse buying intentions and behaviors, the paper establishes the research model based on the theory of planned behavior (TPB) model and analyses influencing factors from attitude, subject norm and perceived control aspects. Through the data analysis based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the research results show that, three factors positively affect online consumers’ impulse buying intentions and lead to their final behaviors. Meanwhile, control variables, including gender, age, and income level, demonstrate insignificant effects across the model. Unlike existing literature, the current study displays the distinct features of live streaming platforms and discovers online consumers’ impulse buying intention based on the TPB model. The results are helpful for related scholars and departments to pay more attention to the live shopping environment and understand online consumers’ impulse buying issues.

The answer to how to stop economic growth must be political rather than economic. Economic growth made by men has an impetus which can only be stopped by a deliberate choice. It takes national and international decisions to stop it. In order to organize a moratorium on economic growth (which eventually must lead to a stop on growth) I suggest to create blocks of nations in the world. The European Union can function as a model for such a block. The E.U. is on its way of becoming a supra-national government which was created to ensure a level playing field for its members. Now it is meant to create economic growth for its members, in the future its administration should be used to stop it. Blocks should agree on the targets to reach a durable economy. Globally or between certain more advanced blocks a date should be set to stop economic growth after that date. Each block should implement the goals to reach a durable economy and prevent products reaching its market that violate the goals of durability. Blocks in developing countries cannot be expected to stop economic growth. They need that growth for two reasons. Eventually they must reach the level of existence in the more advanced block and secondly they must have the time to improve the standard for durability of their products. They need the aid of the developed countries to reach a higher but also more durable level of production, but this aid comes with a quid pro quo, In the meantime they must do their utmost to stop wars in their regions and conduct a responsible population-policy to reduce the growth of their population and let it reduce on the long term.

This study examined the determinants of the exchange rate in a mono-resource economy, during the period of currency and oil price fall in Nigeria. The aim of the study is to ascertain the potentiality of economic diversification (non-oil export) in saving the falling value of the domestic currency, as well as, its stability. The study employed the use of monthly data from the Central Bank of Nigeria and Nigeria National Petroleum Corporation from January 2008 to December 2020 using the ARDL and NARDL models to achieve the stated objectives. In the literature, several empirical studies analyzed the relationship between exchange rates, oil price, and oil export and came to the logical conclusion that a rise in the price of oil would bring about a rise in the exchange rate, all things being equal while the reverse is the case. To add to the existing body of knowledge this study did not only look at the relationship between the variables but also examined the determinants of the exchange rate in a mono-resource economy and ascertained the potentiality of economic diversification in bringing succor to the falling exchange rate in the midst of falling oil price. The NARDL result corroborates the findings of previous studies on the relationship between exchange rate, oil price, and the performance of non-oil export. It maintains that positive changes in oil prices would lead to the appreciation of domestic currency while negative changes in oil prices would lead to the depreciation of the domestic currency. With this analysis, the study recommends the diversification of the economy and revitalization of the non-oil sector. Since import proves to have a negative impact on the exchange rate, the revitalization of the non-oil sector stimulates domestic consumption and thus reduces import bills which raise the demand for the dollar and weakens the naira.

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.

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.

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.

This research paper examines changes in patient care management in acute care hospitals between 2001 and 2011. During this time, there were two opposing factors at play: the competition effect of the reform and the policymaker’s decision to reduce public hospitals across France. By studying the trends, it is evident that there has been a significant overall shift in patient care management during this period. This change could be attributed to the global competition effect and the concentration of in-patients in specific public facilities. Through the difference-in-difference method, the study analyzed time variations in the intensity of local competition. It was found that local competition had a negligible impact on patient care management. Additionally, the study revealed that there was a significant positive competition effect on high-technical procedures for the private sector, which is in line with the market segment where private sector hospitals have a leadership position and the pro-competitive reform intensified this position. The study also uncovered a negative competition effect on the length of stay for public hospitals. Prior to the implementation of the DRG-based payment reform, public sector hospitals were paid a global budget. However, after the reform was implemented, they had to shorten the length of stay to increase the number of stays. For-profit hospitals have always been paid based on the number of stays. The results are robust and consistent when alternative measures of local competition are used.

All things being equal, and in a truly democratic world, one will expect that the fact that women are the majority in almost every single community of the world should guarantee them at least equal control or ownership over resources and enjoy most of the rights and privileges in that particular community. But things seem not to be working that way as evidence from the literature shows that women are always relegated to the background when it comes to the issue of control or ownership over, and sometimes even access to property in most societies which include the right to own and use land. This undoubtedly has a consequence on the productive and overall development of such a community. In recent times, the issue of gender discrimination, disparities, and inequalities has attracted the attention of researchers over the past decade with enormous literature in this area. There are, however, few studies that provide a comparative analysis of the situation between two or more countries. This paper seeks to fill this void of knowledge based on a review of relevant literature on land rights with an emphasis on the situations in Kenya and Tanzania. The results have shown the prevalence of four types of land rights such as communal property rights, non or open-access property rights, state or crown property rights, and private property rights. The results have further indicated that the controversial land rights of Kenya have led to the reduction in the lands of certain groups of people, increasing land disputes, and also discriminating against women in their land rights. In the case of Tanzania, there were several cases of bias against women concerning the ownership of land, especially under the customary system of Tanzania. Issues of discrimination against women were also identified in the inheritance laws of the land which is a paramount feature within the norms and cultures of the people of Tanzania. The lack of equal access to land by women in both countries is found to harm productivity in several sectors of their economies, especially in agriculture. Policymakers are therefore admonished to ensure equal access by both genders to land as this will lead to efficiency in the management and usage of land which is certainly good for sustainability in the development of any economy.

You have a product or service for which the world has been waiting. You've scraped together enough cash to get your idea off the ground. You are confident that your business will succeed. And yet, it is often the case that a short time later you are out of business. Numbers from the U.S. Bureau of Labor Statistics indicate that about half of new businesses fail within the first five years. And with those business failures go the life savings and the dreams of the person who started the business. This article describes five finance and accounting pitfalls that should be avoided in order to increase the likelihood that a small business will succeed. Of course, the most common reason for a small business to fail is that customers don't want the product or service. But small businesses are also often killed because of predictable and preventable finance and accounting mistakes: insufficient capital, poor cash management, poor record keeping and controls, improper product pricing, and uncontrolled growth.

High levels of trust are positively correlated with increased collaboration, prosocial actions, and heightened adherence to preventive behaviors during the COVID-19 pandemic. Previous studies on trust during the pandemic have primarily focused on either cross-sectional data or its impact in conjunction with other related variables, such as political party affiliations or vaccine availability. In this study, we employed a national survey panel comprising data from 760 individuals interviewed at three intervals between July 2020 and January 2021. We used pooled datasets, panel datasets, and dependent variable lags to control for time-invariant unobservable variables and endogeneity. Our findings reveal that trust in government influences individuals’ behavior when they are requested to follow public interventions. Notably, trust in local government is associated with increased adherence to COVID-19 preventive behaviors, similar to the effect observed with an annual income exceeding $100,000.

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.

This study examines the role of news sentiments in the GCC equity markets’ connectedness. We collected news titles for the period from 22nd June 2006 until 31st December 2020 from Gulf News, which is the most widely read English newspaper in the Arab World. We filter these news titles using a carefully designed list of keywords that capture public sentiment on matters related to financial markets. Next, we classify the news titles to compute the geographically distinguished sentiment indexes that allow for a detailed analysis of the source of news sentiment spillovers to compare the impact of domestic versus regional sentiments on the equity markets of GCC countries. Our quantile regression results reveal that equity markets in the GCC are most sensitive to news sentiments when underperforming. Moreover, our results from the connectedness approach suggest that the UAE equity markets are most influenced by domestic sentiments, whilst the KSA equity market is most influenced by regional sentiments from the other GCC countries. Mixed results are found for other countries. The time-varying component of this study also shows that the influence of news spillovers intensified during the major crises events, including the COVID-19 outbreak.

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.

Green innovation, driven by China's new development concept, plays a crucial role in high-quality economic development. In line with the green development trend, businesses increasingly prioritize whether their corporate environmental responsibilities (CER) can effectively enhance corporate green innovation (CGI) levels. This paper examines the influence and mechanism of CER on CGI using a dynamic perspective, drawing from 1,640 manually-collected panel data of Shanghai and Shenzhen A-share listed companies between 2010 and 2017. The primary findings indicate that the impact of CER on CGI possesses phase-specific characteristics and a dual effect of "crowding in" and "crowding out." The current phase of CER negatively affects green innovation, while the lag phase has a positive effect. CER's impact on various CGI types is heterogeneous: specifically, it follows an "inverted-N" trajectory (inhibition-promotion-inhibition) for "strategic green innovation" and has a promotional effect on "substantive green innovation," which is stronger and has a longer time lag. The mechanism analysis reveals that financing constraints play a critical mediating role. A heterogeneity analysis based on multiple dimensions (ownership, industry, and location) suggests that CER has a more significant driving force for CGI among state-owned firms, high-polluting industries, and enterprises in inland areas. Finally, the paper presents corresponding suggestions for government and corporate entities.