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The main purpose of this paper is to analyse the influence of capital efficiency on the economic growth of Bangladesh using the Harrod-Domar (H-D) model.

We use annual data from 1980 to 2019 for this paper. Three steps are taken in the data analysis. First, to check the existence of a unit root, we use the augmented Dickey-Fuller (ADF) test and to determine co-integration among the variables, we use the Johansen-Juselius co-integration test. Next, for long-run estimation, we use the dynamic ordinary least square (DOLS) estimator. The sensitivity of the long-run estimations is further checked by the fully modified OLS (FMOLS) and autoregressive distributed lag (ARDL) estimators. Lastly, we use the Granger causality test to determine the long-run causality among the variables.

The long-run co-integration test validates the co-integrating relationship among the variables. DOLS estimations reveal that the economic growth of Bangladesh is negatively associated with the incremental capital output ratio (ICOR), validating the notion that capital efficiency matters for achieving higher economic growth. On average, an increase in ICOR by a unit tends to reduce economic growth in the long term by 0.75 percent. Our results also reveal no significant relationship between savings and economic growth when the model is extended. Finally, causality results indicate unidirectional causality between ICOR and economic growth.

Based on the results obtained, we argue that the enhancement of capital productivity could bring efficiency because ICOR is an inverse of capital productivity. Since Bangladesh’s capital productivity is considerably low compared with other neighbouring countries, it is suggested that firms should gradually move towards technological advancement and enhance economies of scale, etc. in the long run. Moreover, policies in favour of continuous skill development programmes could be highly effective in increasing capital productivity given that capital follows a vintage structure.

This is the first paper to analyse the economic growth pattern of Bangladesh using the traditional H-D model by incorporating variables such as savings and ICOR and also by relaxing the assumption of time-invariant (i.e. fixed) data of the variables. Moreover, this paper extends the traditional H-D empirical model by introducing key indicators and time breaks for Bangladesh’s economy through a stepwise regression process.

Business creation is an important measure of real economic activity as it shows the dynamics with which new firms are born, create jobs, move their capital, innovate and compete with old firms. In this sense, this paper aims to analyze the short-term impact of the lockdown policies implemented to stop the spread of the COVID-19 on the creation of new formal firms in Ecuador.

This paper uses a regression discontinuity in time (RDiT) design jointly with official administrative real-time data. This data is collected by the supervisory and regulatory institution of formal companies in Ecuador. The authors use real-time data from January 13, 2020, to May 15, 2020. This period allows to use the President’s order of effective lockdown on March 16, 2020, as the exogenous event. This gives 43 working days on each side of the cutoff date on the baseline model.

The authors find: an overall large drop in the creation of new formal firms (−73%) and a decrease in the total amount of initial capital coming from the new formal firms (−40%). Additionally, the results suggest that the negative impact of the COVID-19 lockdown on the creation of new formal firms seems not to decrease in the short term. The main conclusion is that lockdown policies have a negative impact on firm creation, a result that is of high policy relevance and can be a tool to design business attraction policies.

The analysis is carried out in a short period because on May18, 2020, a new policy was applied in Ecuador that allowed firms to be created more quickly, with 1 USD of capital, and 1 shareholder, among other benefits, and this may affect the outcomes analyzed in this document, so extending the analysis of the impact of the lockdown to a longer period could result in biased results due to this policy. Additionally, studying daily sales would be of the utmost importance; however, these data are not found in the database of the supervising institution.

This study contributes to the empirical literature and the policy debate in various aspects. First, it is important to generate facilities for the creation of new formal firms, from the reduction of days it takes to create one (using technology as a support in this matter) to the decrease of the minimum capital to formalize a company. Second, improve the business conditions of the new formal firms that were born during the pandemic, but also that these conditions create stimulus for the creation of new companies. Third, the authors show that induced-lockdown policies have a negative impact on the creation of new formal firms and the total amount of initial capital from new formal firms; this effect could be a full-blown recession if governments do not apply mechanisms to revert this situation that could be a drag on the economy.

This paper opens the debate on the effects of the COVID-19 lockdown on the creation of new formal firms; therefore, future research could study the impact in a broader time window to analyze medium and long-run effects, but also in different economic sectors and in the effects on firm bankruptcy, which added to an analysis of job loss, will show a total effect of damage in the economy.

The objective of this study is to investigate how the implementation of an Emission Trading Scheme (ETS) influences an ETS-regulated firm’s level of earnings smoothness.

Using a staggered difference-in-differences model based on China’s ETS pilots commencing in 2013, this study investigates how the implementation of ETS pilots affects regulated firms’ earnings smoothing relative to non-regulated firms. The sample period spans from 2008 to 2019. This model incorporates time-invariant firm-specific heterogeneity, time-specific heterogeneity, and a series of firm characteristics to establish causality. Robustness tests justify findings.

The results show that after implementing an ETS pilot, regulated firms increase their earnings smoothness relative to non-regulated firms. Regulated firms strategically smooth their earnings to obtain additional financial resources and meet compliance costs arising from an ETS. Further analysis reveals that regulated firms’ earnings smoothing activity is a function of environmental regulations, managerial integrity, and capital market incentives.

This study deviates from past research focusing on the environmental consequences of ETS by indicating that an ETS affects regulated firms’ financial reporting decisions. Specifically, regulated firms resort to earnings smoothing as a short-term exit strategy from financing concerns arising from environmental regulations. This finding expands prior literature primarily focusing on the effect of tax and financial reporting regulations on earnings smoothness. This study also indicates that firms utilize earning smoothing to lower their short-term cost of capital, which enables them to access additional financing at a lower cost and reconfigure their operations to meet stakeholder environmental demands.

This study aims to investigate the linkage of employee treatment and trade credit for a sample of 45 countries from 2003 to 2018. It explores the trade credit from a receivable perspective.

The estimations are performed using panel regression with fixed effects for both country and year. A batter of robustness tests is also performed to validate the findings.

The results reveal a positive and highly significant relation between employee treatment and trade credit. The authors observe that firms from labor-intensive and highly competitive industries are likelier to extend trade credit to their customers. The authors also find that firms from developed countries are more likely to extend trade credit to their customers.

First, to boost trade credit, the firms need to materialize fair employee treatment. Second, firms from labor-intensive firms and highly competitive industries need to care more about employee treatment which promotes trade credit.

The findings offer novel evidence of the relationship between employee treatment and trade receivables.

The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018.

We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure.

Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input.

These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation.

Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018.

This study aims to transcend geographical boundaries and provide insights into innovative strategies used by Indian Asset Reconstruction Companies (ARCs) in managing distressed assets. The study examines the origins, evolution, challenges and opportunities faced by ARCs to derive lessons that can be universally applicable and serve as a valuable blueprint for global investors and institutions seeking effective strategies in managing distressed assets. From a legal and compliance angle, this opens up many perspectives that would help plug loopholes and grey zones within the legal ambit for organisations and institutions.

The study invokes a critical review of existing literature, news, discussions and publicly available information from reliable sources such as the central bank’s websites to develop the viewpoints and provide recommendations.

ARCs face challenges, recovering only 19.15% of distressed assets in 2022. Despite constraints like funding, governance issues and regulatory hurdles, there is a substantial opportunity for investors in the Rs. 9.6 lakh crore non-performing assets. The study suggests strategic assessments by banks, emphasises ARCs’ roles in specific sectors and calls for regulatory adjustments. With diverse investors and favourable regulations, this evolving landscape offers significant global opportunities for policymakers and investors in distressed assets.

This study serves as a valuable guide for shaping resilient policies, fostering cross-border collaborations and optimising distressed asset management strategies on a global scale.

This study breaks new ground by examining the private ARCs sector within an emerging economy’s dynamics, presenting insights relevant to global distressed markets. This study serves as a unique resource for those navigating the complexities of distressed markets globally, providing insights that can inform strategies, policies and academic discussions in the broader financial landscape.

The purpose of this study is to evaluate whether a diversification by grape varieties may help wine producers reduce uncertainty in quantity and quality variations due to increasingly erratic climate conditions.

This study hand-collects granular quantity and quality data from wine harvest reports for vintages 2003 to 2017 for the Valais region in Switzerland. The data allows us to obtain detailed data on harvested kilograms/liters and Oechsle/Brix degrees. It is then merged with precise meteorological data over the same sample period. The authors use this data set to capture weather conditions and their impact on harvested quantities and quality. Finally, they build portfolios including different grape varieties to evaluate whether this reduces variations in quality and quantity over vintages.

The findings highlight that the weather varies relatively strongly over the sample period and that climate hazards such as hail, frost or ensuing vine diseases effectively occur. These strongly impact the harvested quantities but less the quality of the wine. The authors further show that planting different grape varieties allows for a significant reduction in the variation of harvested quantities over time and thus acts as a good solution against climate risk.

The effect of climate change on viticulture is becoming increasingly important and felt and bears real economic and social consequences. This study transposes portfolio diversification which is central to reducing risk in the finance industry, into the wine industry and shows that the same principle holds. The authors thus propose a novel idea on how to mitigate climate risk.

In social marketplaces, follower ego networks are integral social capital assets for online sellers. While previous research has underscored the positive impact of the follower number on seller performance, little attention has been given to the structure of follower networks and their value implications. This research investigates two structural properties of follower networks—network centralization and density—and examines their main and contingent effects on sellers’ sales performance.

A 13-month panel dataset of 1,150 sellers in Etsy, a social marketplace for handmade and vintage products, was collected and analyzed. A fixed effects model was adopted to validate the hypotheses on the main effect of centralization and density, as well as the moderating effects of two store attributes: store age and product diversification.

We find that both network centralization and density negatively impact sellers’ sales performance, and these effects vary across store age and product diversification levels. Specifically, the negative effect of network centralization is less pronounced for older stores than young ones, whereas the negative effect of density is more severe for stores with high product diversification.

This research contributes to social commerce research by highlighting the significance of network structure, alongside network size, in assessing the value of followers and offers practical guidance for sellers in social marketplaces seeking to optimize their follower networks.