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%.

The paper examines how the growth of entrepreneurship affects the generation of employment opportunities in Nasarawa State. Even though entrepreneurship is crucial for generating employment, there are still obstacles in the way of its growth. These include limited support from governmental and non-governmental organizations, difficulty obtaining financing and microcredit, proper training and enlightenment etc. The objective of this paper is to investigate how the growth of entrepreneurship affects the creation of jobs in Lafia, Nasarawa State. The study used a descriptive research design, and the means used for gathering data was a questionnaire given to new entrepreneurs in Lafia, Nasarawa State. A sample size of 375 was determined from a population of 1604 using a Smith (1984) sampling formula; this sample represented the number of questionnaires that were delivered. This findings of the research demonstrated that the creation of jobs in Nasarawa State is significantly impacted by the growth of entrepreneurship. This implies that through entrepreneurship development, self-reliance and employment opportunities have been created in Lafia, Nasarawa State. Through this, youths have been able to develop a way to create value in the society. More so, this study revealed that access to credit (micro credit) has a significant effect on employment creation in the State. Having acquired knowledge on how to manage finance they make sound financial decisions. Lastly, the study showed that the number of new businesses established has a significant effect on employment creation in Nasarawa State. This result implies that youths who apply their entrepreneurship skills and creativity in one nature of business or the other have been able to earn a living out of it in Nasarawa State. This paper thus suggests that the Nasarawa State government should genuinely recognize the relevance of entrepreneurship development and provide an enabling environment. Both the government and institutions should ensure that youths get adequate financial credit (micro-credit).

Using data from the Japanese government bonds and stock markets, this study examines the predictability of Japanese recessions based on a probit model with instrument variables. By decomposing the term spread into the expected short-term interest rate and the term premium, this study analyzes the relationship between the components of the term spread and recessions. The results show that the predictive power of the term spread for recessions has declined since 1999—when Japan began employing an unconventional monetary policy. Additionally, stock market capitalization relative to nominal GDP is a useful predictor of recessions.

This study investigates the impact of COVID-19 on bank borrowers across 22 Arab economies from 2017 to 2022, employing the two-step system GMM estimator. The findings reveal a significant negative effect of COVID-19 on the number of bank borrowers, highlighting a sharp decline in borrowing activity due to the pandemic’s economic disruptions. Notably, the decline was more pronounced in non-GCC countries, where economic structures and pandemic responses differed. Robustness tests, including subsample analysis and alternative econometric models (pooled OLS, fixed effects, random effects, and 2SLS), consistently confirm these results. The study emphasizes the need for targeted policy interventions to stabilize financial systems, reduce borrowing costs, and control exchange rate fluctuations to maintain credit access during crises. The findings provide crucial insights for policymakers aiming to support economic recovery and strengthen the resilience of the banking sector in the face of future external shocks.

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.