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The purpose of this paper is to investigate the nonlinear relationship between shadow economy and income inequality and determine whether the size of shadow economy can influence the level of income inequality.

Both parametric (panel OLS) and nonparametric/semiparametric regression suggested by Robinson (1988) will be used to capture the dynamic nonlinear relationship between these variables using unbalanced panel data of 154 countries from 2000 to 2007. Additionally, the relationship between income inequality and shadow economy on both developed and developing countries will be analyzed and compared.

First, semiparametric analysis and nonparametric analysis are significantly different than parametric analysis and better in nonlinear analysis between income inequality and shadow economy. Second, income inequality and shadow economy resemble an inverted-N relationship. Third, the relationship between income inequality and shadow economy is different in developed countries (OECD countries) and developing countries, where OECD countries have similar inverted-N relationship as before. However, for developing countries, income inequality and shadow economy show an inverted-U relationship, similar to the original Kuznets hypothesis.

This study suggests that there is a possible trade-off between income inequality and shadow economy and helps policy makers in solving both problems effectively.

Despite the growing importance of income inequality and shadow economy, literature linking the two variables is scarce. To the best of the authors’ knowledge, there is no literature that nonlinearly links these two variables. Furthermore, the dynamics of the relationship between these two variables in developed countries and developing countries will be explored as well.

This chapter discusses new developments in nonparametric econometric approaches related to empirical modeling of demand decisions. It shows how diverse recent approaches are, and what new modeling options arise in practice. We review work on nonparametric identification using nonseparable functions, semi- and nonparametric estimation approaches involving inverse problems, and nonparametric testing approaches. We focus on classical consumer demand systems with continuous quantities, and do not consider approaches that involve discrete consumption decisions as are common in empirical industrial organization. Our intention is to give a subjective account on the usefulness of these various methods for applications in the field.

The aim of this paper is to examine product formulation screening at the industrial level in terms of multi‐trait improvement by considering several pertinent controlling factors.

The study adopts Taguchi's orthogonal arrays (OAs) for sufficient and economical sampling in a mixture problem. Robustness of testing data is instilled in this method by employing a two‐stage analysis where controlling components are investigated together while the slack variable is tested independently. Multi‐responses collapse to a single master response has been incurred according to the Super Ranking concept. Order statistics are employed to provide statistical significance. The slack variable influence is tested by regression and nonparametric correlation.

Synergy among Taguchi methodology, super ranking and nonparametric testing was seamless to offer practical resolution to product component activeness. The concurrent modulation of two key product traits due to five constituents in the industrial production of muffin‐cake is invoked. The slack variable, rich cream, is strongly active while the influence of added amount of water is barely evident.

The method presented is suitable only for situations where industrial mixtures are investigated. The case study demonstrates prediction capabilities up to quadratic effects for five nominated effects. However, the statistical processor selected here may be adapted to any number of factor settings dictated by the OA sampling plan.

By using a case study from food engineering, the industrial production of a muffin‐cake is examined focusing on a total of five controlling mixture components and two responses. This demonstration emphasizes the dramatic savings in time and effort that are gained by the proposed method due to reduction of experimental effort while gaining on analysis robustness.

This work interconnects Taguchi methodology with powerful nonparametric tests of Kruskal‐Wallis for the difficult problem of non‐linear analysis of mixtures for saturated, unreplicated fractional factorial designs in search of multi‐factor activeness in multi‐response cases employing simple and practical tools.

Equilibrium job search models allow for labor markets with homogeneous workers and firms to yield nondegenerate wage densities. However, the resulting wage densities do not accord well with empirical regularities. Accordingly, many extensions to the basic equilibrium search model have been considered (e.g., heterogeneity in productivity, heterogeneity in the value of leisure, etc.). It is increasingly common to use nonparametric forms for these extensions and, hence, researchers can obtain a perfect fit (in a kernel smoothed sense) between theoretical and empirical wage densities. This makes it difficult to carry out model comparison of different model extensions. In this paper, we first develop Bayesian parametric and nonparametric methods which are comparable to the existing non-Bayesian literature. We then show how Bayesian methods can be used to compare various nonparametric equilibrium search models in a statistically rigorous sense.

Entrepreneurial firms contribute to economic growth, but the potential gendered nature of this contribution must be investigated as outcomes of male-owned and female-owned firms differ. The study investigates the female underperformance hypothesis in a cross-country analysis of Schumpeterian entrepreneurs. Next, it investigates if there is a gendered dimension of Schumpeterian firm contribution to economic growth.

The study utilizes both nonparametric and parametric methodologies. Through nonparametric methods, the success of female-owned and male-owned firms is compared. Next, a parametric ordinary least squares regression model tests if there is a gendered nature of an entrepreneurial firm's economic contribution.

In nonparametric analyses, female-owned entrepreneurial firms in developed countries perform similarly to male-owned firms, while in developing countries male-owned firms significantly outperform female-owned firms. The author also finds strong evidence that the gender of the Schumpeterian entrepreneur does not matter in the contribution in economic growth.

In all countries, the number of female-owned entrepreneurial firms was significantly lower than that of male-owned firms. The findings point to consistent cultural barriers for women in innovation-related fields and persistent gendered norms in entrepreneurship. Thus, removal of cultural barriers and continued support for Schumpeterian entrepreneurship will benefit women and contribute to a country's economic growth.

The data for this study is a unique utilization of the Enterprise World Survey to identify Schumpeterian entrepreneurial firms. Additionally, the study challenges the female underperformance hypothesis and contributes to the literature on the role of entrepreneurship in economic growth.

The purpose of this paper is to empirically test if the interaction between universities and spin-offs, as proxy of knowledge translation (KT), which is based in particular on negotiation, semantics and pragmatics, has a positive impact on spin-off performance – in terms of greater distance from the “valley of death” – and allows access to credit and financial instruments.

The authors adopt an appropriate nonparametric conditional efficiency approach for panel data. The authors provide a unique picture of the innovation environment in Italy using an original dataset. These data provide information regarding the intensity of interaction between universities and spin-offs along with financial balance datasheet of the spin-offs. The nonparametric approach is particularly suitable for nonlinear relationships typical for this type of data.

The results of the analysis confirm that the translation of knowledge, based on negotiation of interests, semantics of the text and pragmatism, favored by the interaction between universities and spin-offs, improves the productivity performance of the spin-off by allowing them to move away from the “valley of death”. Therefore, universities must pay particular attention to the way they work with spin-offs by making use of the translation of knowledge, based on semantics and pragmatism, in order to encourage an understanding of knowledge, sharing of interests among the partners and stakeholders of the spin-off, often belonging to different backgrounds. These are processes that favor the transfer and development of research outcomes to the market, improving the spin-off competitiveness of the territory and strengthening relations with universities and their stakeholders (banks and financial intermediaries, local and national politicians, institutions and the community at large).

Implications for research can be identified at policy and managerial level and refer to the effectiveness of the so called “Third Mission”, university entrepreneurship through the creation of profitable spin-offs which contribute to innovation, and to the socioeconomic development of the territory. In turn, spin-offs with good performances are more likely to have access to external financing to allow the growth of their business in the market. Further studies can investigate the organizational way of the universities that promote these virtuous results, distinguishing them by spin-off efficiency clusters.

For universities, the results make it possible to envisage organizational processes to support spin-offs that are important both for compliance with the regulations and for the Third Mission. Researchers, teachers and PhD academics have the opportunity to exploit the results of their innovative research on the market. Spin-offs and start-up founders should note that the results of scientific transfer can create value for the firm and the territory. Useful information also derives for banks and financial intermediaries that intend to improve the credit risk assessment of the spin-offs during the loan assessment phase.

The value of the work entails in offering a unique overview of university innovation, through an original dataset and a robust methodology. By adopting a different approach, the contribution of KT at national level was assessed, measuring the impact on the technical performance and on the probability of survival of the companies. Originality of the paper lies not only in the approach but also in the fact that this is the first attempt to use the KT as a key factor for the economic sustainability from a financial perspective of start-up companies.

Since there is no agreement on the consistency of their estimates, the purpose of this paper is to investigate whether parametric stochastic frontier analysis (SFA) and nonparametric data envelopment analysis (DEA) generate consistent bank efficiency assessments.

The authors utilize four alternative efficiency computation models: two DEA technical efficiency models based on constant and variable returns to scale, and two SFA cost efficiency models employing Translog and Fourier functional specifications. An unbalanced panel of 59 Indian banks over 1990‐2007 is employed as a model, developing country, banking market.

The Translog and Fourier specifications in SFA and the constant and variable returns to scale assumptions in DEA are found to rank and identify “best‐practice” and “worst‐practice” approximately in the same order. The association between DEA efficiency estimates and non‐frontier standard performance measures, however, is mixed and inconclusive. Unlike DEA scores, SFA efficiency assessments were found to be consistent with cost and profit ratios and hence are “believable”.

For regulators and bankers alike, the authors' findings highlight the importance of investigating the consistency of efficiency scores across various research methods. They should ensure that frontier‐based efficiency assessments are not simply “artificial constructs” of models' assumptions/specifications.

This paper extends the existing literature by checking jointly the statistical consistency of both DEA technical efficiency scores and SFA cost efficiency scores. The prior studies focus either on technical efficiency or cost efficiency, but not both. Moreover, as far as the authors are aware, this is the first cross‐methodological validation study to focus on bank efficiency in the context of a developing country banking market.

The study seeks to shed light on the estimates of the carbon shadow price in the literature relying on frontier techniques. The shadow price of undesirable outputs, such as greenhouse gas emissions, assists policymakers in determining the most cost-effective methods for reducing emissions.

The study relies on the PSALSAR and PRISMA approaches for a systematic literature review. The Web of Science and Scopus databases were used for the references.

Both parametric and nonparametric methods have been employed in the literature to estimate the shadow prices of undesirable outputs. Also, results were discussed according to the methodological and application aspects, and broad conclusions on obtained results were provided, bridging climate change mitigation policies and the shadow price of undesirable outputs.

The present study applies an integrated method, PSALSAR, to conduct a systematic review of 53 studies published between 2014 and 2023 in which efficiency models were applied to estimate the shadow price of undesirable outputs, especially CO₂. After presenting the most applicable parametric and nonparametric estimation models, a systematic summary of included articles was provided, highlighting the key features of publications.

Heavy-tailed distributions present a tough setting for inference. They are also common in industrial applications, particularly with internet transaction datasets, and machine learners often analyze such data without considering the biases and risks associated with the misuse of standard tools. This chapter outlines a procedure for inference about the mean of a (possibly conditional) heavy-tailed distribution that combines nonparametric analysis for the bulk of the support with Bayesian parametric modeling – motivated from extreme value theory – for the heavy tail. The procedure is fast and massively scalable. The work should find application in settings wherever correct inference is important and reward tails are heavy; we illustrate the framework in causal inference for A/B experiments involving hundreds of millions of users of eBay.com.

Weak separability is an important concept in many fields of economic theory. This chapter uses Monte Carlo experiments to investigate the performance of newly developed nonparametric revealed preference tests for weak separability. A main finding is that the bias of the sequentially implemented test for weak separability proposed by Fleissig and Whitney (2003) is low. The theoretically unbiased Swofford and Whitney test (1994) is found to perform better than all sequentially implemented test procedures but is found to suffer from an empirical bias, most likely because of the complexity in executing the test procedure. As a further source of information, we also perform sensitivity analyses on the nonparametric revealed preference tests. It is found that the Fleissig and Whitney test seems to be sensitive to measurement errors in the data.