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This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

As Czech export is widely considered the key to the economic development of Czechia, this chapter explores the relationship between microeconomic profitability among companies in selected TOP10 export industries and the macroeconomic development of the export itself. An investigation was carried out to compare the differences caused by the COVID-19 pandemic. In addition, the comparison is developed according to the size and concentration of ownership among exporting companies. Annual data are obtained from the Bureau van Dijk Orbis database to analyse profitability among 4,283 companies in 10 NACE industries from 2012 to 2021. We have obtained encouraging results, demonstrating that not only those less profitable companies affected export development. However, in general, our results emphasise the importance of those less profitable medium-sized companies for Czech export, within the manufacture of machinery and equipment, and the manufacture of motor vehicles in particular.

Czechia is a small open economy and a member state of the European Union. Several important trends and episodes that have determined economic growth can be identified over the last two decades. This chapter deals with some macroeconomic features like macroeconomic and labour market performance within the business cycle, the Czech National Bank (CNB) exchange rate commitment and interest rate policy, increasing indebtedness and budget deficits, foreign trade and the international investment position. We applied publicly available data from Eurostat, the Organisation for Economic Co-operation and Development and CNB databases. The data show that the Czech economy was significantly converging to the average economic level of the European Union. We also identified key turning points in business cycles. Macroeconomic data on economic development of the economy indicate an atypical course of the business cycle between 2020 and 2022, which can be evaluated as different from the one that followed the global financial crisis.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

Introduction: The government has taken many initiatives for the overall growth of India after liberalisation and remarkably performed to make India an emerging economy. Due to changes in macroeconomic conditions, investment in companys’ shares includes the possibility of bearing high risk, which cannot be eliminated but, to some extent, minimised. The persistence of risks motivates investors to invest in different available options of investment. Gearing measures, a company’s financial leverage, represent the risk afforded within the company’s capital structure.

Purpose: The research aims to identify the risk-return analysis of financial geared stocks of Nifty 50 companies in India, which have debt equity ratios of more than 1.

Methodology: Convenience and cluster sampling techniques were used to identify companies with debt equity ratios of more than 1. The considered time period is 2010–2019.

Findings: This research found capital structure ratios, debt equity ratio, and total debt ratio. The total equity ratio does not have any visible effect on any of the dependent variables, i.e., Return on equity (ROE), Return on Assets (ROA), Earnings per share (EPS), Return on capital employed (ROCE). It explains the impact of high-levered firms’ performance on profitability and functioning. The study highlights that highly geared companies do not significantly impact the ROA, proving Modigliani and Miller’s (1958) irrelevant theory.

The authors examine whether or not applicants and recipients of federal disability insurance (DI) inflate their self-assessed health (SAH) problems relative to others. To do this, the authors employ a technique which uses anchoring vignettes. This approach allows them to examine how various cohorts of the population interpret survey questions associated with subjective self-assessments of health. The results of the analysis suggest that DI participants do inflate the severity of a given health problem, but by a small but significant degree. This tendency to exaggerate the severity of disability problems is much more apparent among those with more education (especially those with a college degree). In contrast, racial minorities tend to underestimate severity ratings for a given disability vignette when compared to their white peers.