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The existence of the regional Kuznets curve, i.e. an inverted U-shaped relationship between regional disparity and economic development is widely debated and discussed. The bell-shaped curve of the spatial growth process where during the initial phase inequality increases and then reduces is theoretically supported by Myrdal (1957), Hirschman (1958), and Williamson (1965). It becomes important to understand regional Kuznets curve globally. Understanding the relationship between regional disparity and economic development becomes essential for public policy for balanced regional growth.

Regional Kuznets Curve which is an inverted U-shaped relationship between regional disparity and economic development is not a new phenomenon. Theoretical framework by Myrdal (1957), Hirschman (1958), and Williamson (1965) support the an inverted U-shaped relationship. To understand the relationship between regional disparity and economic development, the authors investigate the regional Kuznets curve by using data for 184 countries and 1765 subnational regions. Using parametric, semi-parametric, and non-parametric, it is found that there exists an inverted U-shaped relationship between regional disparity and economic development. The presence of the regional Kuznets curve is observed. As the theoretical framework suggests, regional inequality increases with income initially and decreases after attaining a certain level of income. This study identifies two stages of divergence-convergence where in the first stage, divergence across regions in a country happens with increasing income and in the later stage, convergence across regions in a country occurs with increasing income.

Using the parametric approach (panel data analysis), semi-parametric and non-parametric approaches, it is found that there exists a regional Kuznets curve. It is found that there exists an inverted-U relationship between regional inequality and per capita GNI. This suggests that the divergence-convergence passes through two stages. In the first stage, divergence across regions in a country happens with increasing income while in the later stage convergence occurs.

This research work has done three important things which fill the research gap that exists in the literature: (1) constructing the Gini coefficient to measure the regional inequality for 184 countries using 1765 subnational regional data; (2) using a parametric approach (panel data analysis) to understand the regional Kuznets phenomenon and (3) using a semi-parametric approach and non-parametric approach to understand the regional Kuznets phenomenon.

Corporate managers spend on research and development (R&D) for reasons of growth and survival. However, they may be less willing to invest in R&D because of its long-term horizon, high failure rate and uncertain outcomes. This study aims to explore the extent to which managerial ownership influences R&D expenditure decisions.

Apart from the linear regression models, this study uses a semi-parametric quantile regression analysis for a sample of German non-financial firms throughout 2009–2018.

This study finds a nonmonotonic sensitivity of R&D spending to the level of managerial ownership over various quantiles of R&D distribution. That is, managerial ownership increases the expenditure on R&D at low R&D intensity firms. However, it decreases the expenditure on R&D at high R&D intensity firms. These results suggest the presence of a maximum level of R&D expenditure, after which owner-managers would be unwilling to spend on R&D.

The results confirm the importance of corporate ownership structure for firm R&D and innovation activities. It provides an implication for corporate policymakers to reform the corporate ownership structures to encourage corporate managers and owners to invest in R&D projects.

This study offers two distinct contributions study. First, it provides the first German shred of evidence on the nonlinear relationship between managerial ownership and R&D expenditure decisions by distinguishing between high and low R&D intensity firms. Second, unlike prior research, it uses a semi-parametric quantile regression analysis. This method is more efficient than least-squares estimators and produces robust estimators to heteroscedasticity of the residuals.

This paper aims to assess whether incubation affects the technical efficiency of innovative firms after entering the market. The study of efficiency allows firms to understand how well resources have been used in production processes. The research intends to contribute to the literature on the performance of incubated firms.

This study estimates the relative efficiency of innovative firms adopting a DEA-based two-stage semi-parametric method. Incubation, firm age and initial capital are used for explaining the relative performance of previously incubated firms compared to non-incubated ones over a six-year period of activity. This research focuses on Italian innovative firms using a large sample of companies.

Results show that incubators have a positive and significant effect on efficiency for firms that have been in the market for more than two years. Efficiency also improves with age and with the level of initial capital of the firm.

This analysis is limited to the quantitative dimension of inputs as reported in the balance sheets, without qualitative considerations.

Findings enhance firms' understanding of the role of incubators as neutral places to develop a business culture of efficiency. From an empirical standpoint, this study provides useful insights to start-uppers who intend to attend incubation programs. Overall, incubators matter to the extent that they enable new firms, net of those that fail to survive in the first two years of activity, to improve their efficiency in the use of inputs. This research also suggests incubators consider the start-ups’ potential of being efficient.

Findings provide tips to policymakers when they are called upon to propose funding programs to support prominent firms entering the business scalability.

This study contributes to the literature on the relative performance of post-incubated firms, highlighting the efficiency frontier analysis. This methodological approach is relatively new in this field. It allows researchers to study the innovative firms' performance in relative terms, that is with respect to the input level. It integrates the performance-based with efficiency frontier analysis. Also, this study reinforces the idea that incubators prepare start-ups to develop capacities and managerial skills, which will be useful in post-incubation life to improve their cost competitiveness.

In the corporate world, there is no certainty of survival. This research aims to identify firm-level factors that increase or decrease a firm's probability of exit and survival.

The study examines 153 listed textile sector firms in Pakistan over a 10-year period from 2009 to 2018, comprising 1,413 observations. The semi-parametric Cox regression model is used to process the results.

The study finds that larger and exporting firms are more likely to survive, while those with a high ratio of fixed assets to total assets, high expenditure on advertising and variable costs are less likely to survive. The relationship between age and firm survival is inconclusive.

Adaptability to the external environment provides a competitive advantage that is crucial for textile firms to reduce their chances of exit. The research is valuable for strategic managers and policymakers to identify focus areas to prevent firm exit.

This study supports the active learning theory, which suggests that new entrants in the textile sector of Pakistan should focus on becoming active market players, increasing efficiency and reducing variable costs to survive.

This study aims to investigate whether temperature affects the product quality of exporters and whether the effect is non-linear. More specifically, whether the impact of high temperatures differs from the impact of low temperatures, and whether different types of companies or industries are affected differently.

The paper uses detailed data covering all Chinese exporters from 2000 to 2016 to estimate the effects of temperature on the product quality of export firms. To clarify the relationship between them, the authors use a semi-parametric regression method, trying to test whether there is a non-linear relationship between temperature and the export quality of firms.

The increase in the number of high temperature days significantly reduces the quality of exported products, and this negative effect increases as the temperature rises. High temperature has the most significant negative impact on export quality for firms with low technical complexity, private firms and firms with no intermediate imports and located in historical hot cities. Product quality of both labor-intensive and capital-intensive firms will be affected by heat. High temperatures have the greatest negative impact on the export quality of newly entering products, followed by exiting products, with the least negative impact on persisting product.

To the best of the authors’ knowledge, this paper is the first to examine the impact of temperature on the quality of economic development. The findings of this paper again show that the potential economic impacts of global warming are huge. In addition to some potentially devastating impacts in the future, global warming is already causing imperceptible impacts in the present. Public and economic agents need to fully understand the possible adverse impacts of climate change and take corresponding adaptation measures to cope with global warming.

This paper generalizes the quadratic framework introduced by Le Courtois (2016) and Sumpf (2018), to obtain new credibility premiums in the balanced case, i.e. under the balanced squared error loss function. More precisely, the authors construct a quadratic credibility framework under the net quadratic loss function where premiums are estimated based on the values of past observations and of past squared observations under the parametric and the non-parametric approaches, this framework is useful for the practitioner who wants to explicitly take into account higher order (cross) moments of past data.

In the actuarial field, credibility theory is an empirical model used to calculate the premium. One of the crucial tasks of the actuary in the insurance company is to design a tariff structure that will fairly distribute the burden of claims among insureds. In this work, the authors use the weighted balanced loss function (WBLF, henceforth) to obtain new credibility premiums, and WBLF is a generalized loss function introduced by Zellner (1994) (see Gupta and Berger (1994), pp. 371-390) which appears also in Dey et al. (1999) and Farsipour and Asgharzadhe (2004).

The authors declare that there is no conflict of interest and the funding information is not applicable.

This work is motivated by the following: quadratic credibility premium under the balanced loss function is useful for the practitioner who wants to explicitly take into account higher order (cross) moments and new effects such as the clustering effect to finding a premium more credible and more precise, which arranges both parts: the insurer and the insured. Also, it is easy to apply for parametric and non-parametric approaches. In addition, the formulas of the parametric (Poisson–gamma case) and the non-parametric approach are simple in form and may be used to find a more flexible premium in many special cases. On the other hand, this work neglects the semi-parametric approach because it is rarely used by practitioners.

There are several examples of actuarial science (credibility).

In this paper, the authors used the WBLF and a quadratic adjustment to obtain new credibility premiums. More precisely, the authors construct a quadratic credibility framework under the net quadratic loss function where premiums are estimated based on the values of past observations and of past squared observations under the parametric and the non-parametric approaches, this framework is useful for the practitioner who wants to explicitly take into account higher order (cross) moments of past data.

The purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.

Within spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m² cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.

The findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).

These results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.

The paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.

This article investigates the determinants of cross-section variation of initial public offerings' (IPOs) first-day returns in a sample of 710 issues across seven emerging markets between 2013 and 2017.

Ordinary least squares regression (OLS) and the semi-parametric quantile regression (QR) technique are employed. QR enables to analyse beyond the explanatory variables' relative mean effect at various points in the endogenous variable distribution. Furthermore, parameter estimates under QR are robust to the existence of outliers and long tails in the data distribution.

Underpricing varies across countries with an average of 78%. According to the OLS results, independent variables explain 26% of the variation of IPOs' first-day returns. Findings show that employing QR is important, given the non-normality of the data and because each quantile is associated with a different effect of explanatory variables.

In addition to firm-specific, market-specific and issue-specific factors, the paper extends IPOs' underpricing literature through studying the impact of country-specific characteristics, largely neglected by literature, on IPO underpricing.

The authors analyse the determinants of loan defaults in micro, small and medium enterprises (MSME) loans in India from the survival duration perspective to draw inferences that have implications for lenders and policymakers.

The authors use the Kaplan–Meier survivor function and the Cox Proportional Hazard model to analyse 4.29 lakhs MSME loan account data originated by a large bank having a national presence from 1st January 2016 to 31st December 2020.

The estimated Kaplan–Meier survival function by various categories of loan and socio-demographic characteristics reflects heterogeneity and identifies the trigger points for actions. The authors identify the key identified default drivers. The authors find that the subsidy amount is more effective at the lower level and its effectiveness diminishes significantly beyond an optimum level. The simulated values show that the effects of rising interest rates on survival rates vary across industries and types of loans.

The identified points of inflection in the default dynamics would help banks to initiate actions to prevent loan defaults. The default drivers identified would foster more nuanced lending decisions. The study estimation of the survival rate based on the simulated values of interest rate and subsidy provides insight for policymakers.

This study is the first to investigate default drivers in MSME loans in India using micro-data. The study findings will act as signposts for the planners to guide the direction of the interest rate to be charged by banks in MSME loans, interest subvention and tailoring subsidy levels to foster sustainable growth.

This research aims to evaluate the accuracy of several Value-at-Risk (VaR) approaches for determining the Minimum Capital Requirement (MCR) for Islamic stock markets during the pandemic health crisis.

This research evaluates the performance of numerous VaR models for computing the MCR for market risk in compliance with the Basel II and Basel II.5 guidelines for ten Islamic indices. Five models were applied—namely the RiskMetrics, Generalized Autoregressive Conditional Heteroskedasticity, denoted (GARCH), fractional integrated GARCH, denoted (FIGARCH), and SPLINE-GARCH approaches—under three innovations (normal (N), Student (St) and skewed-Student (Sk-t) and the extreme value theory (EVT).

The main findings of this empirical study reveal that (1) extreme value theory performs better for most indices during the market crisis and (2) VaR models under a normal distribution provide quite poor performance than models with fat-tailed innovations in terms of risk estimation.

Since the world is now undergoing the third wave of the COVID-19 pandemic, this study will not be able to assess performance of VaR models during the fourth wave of COVID-19.

The results suggest that the Islamic Financial Services Board (IFSB) should enhance market discipline mechanisms, while central banks and national authorities should harmonize their regulatory frameworks in line with Basel/IFSB reform agenda.

Previous studies focused on evaluating market risk models using non-Islamic indexes. However, this research uses the Islamic indexes to analyze the VaR forecasting models. Besides, they tested the accuracy of VaR models based on traditional GARCH models, whereas the authors introduce the Spline GARCH developed by Engle and Rangel (2008). Finally, most studies have focus on the period of 2007–2008 financial crisis, while the authors investigate the issue of market risk quantification for several Islamic market equity during the sanitary crisis of COVID-19.