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The monetary policy implementation and corporate investment are closely intertwined. The aim of modern monetary policy is to mitigate economic fluctuations and stabilise economic growth. One of the ways of influencing the real economy is influencing the level of investment by enterprises.

This chapter provides evidence on how monetary policy affected corporate investment in Poland between 1Q 2000 and 3Q 2022. We investigate the impact of Polish monetary policy on investment outlays in contexts of high uncertainty.

Using the correlation analysis and the regression model, we show the relation between the monetary policy and the investment outlays of Polish enterprises. We used the least squares method as the most popular in linear model estimation. The evaluation includes model fit, independent variable significance and random component, i.e. constancy of variance, autocorrelation, alignment with normal distribution, along with Fisher–Snedecor test and Breusch–Pagan test.

We find that Polish enterprises are responsive to changes in monetary policy. Hence, the corporate investment level is correlated with the effects of monetary policy (especially with the decision on the central bank's basic interest rate changes). We found evidence that QE policy has a positive impact on Polish investment outlays. The corporate investment in Poland is positively affected by respective monetary policies through Narodowy Bank Polski (NBP) reference rate, inflation, corporate loans, weighted average interest rate on corporate loans.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

Fiscal and monetary policies are essential to the development of a capital market. In this chapter, authors present how fiscal and monetary policy in Poland evolved and adjusted to economic challenges in 1998–2022. It is worth noticing that the Polish economy and financial market have been built from scratch after 45 years of socialism. Hence, it is scientifically interesting to study the relationship between fiscal and monetary policy, and capital market in a developing country, and in a relatively young economy.

Both – the macroeconomic policy mix and development of the capital market – are the subject of analysis how fiscal and monetary policy impacted the capital market. As so the main aim of the chapter is the assessment of the nexus and dependencies between fiscal and monetary policy and the capital market.

In the chapter, multiple linear regression was used for each dependent variable to discover which monetary and fiscal policy parameters significantly predicted selected variables describing the development of the capital market in Poland. Fiscal and monetary policy variables served as descriptors explaining capital market parameters in seven separate models.

Multiple regression models explain 77.3%–95.4% of the volatility of the capital market characteristics. The level of the central bank's reference rate is a variable that influences the capital market the most. In six out of seven models, the interest rate was a significant parameter. The development of the capital market was accompanied by a higher tax-to-GDP ratio. At the same time, a strong negative impact of the tax-to-GDP increase was noticed in domestic institutional investors' stock trading.

The burnout, stress, and work-life balance challenges faced by teachers have received renewed interest due to the myriad disruptions and changes to K-12 schooling brought about by the COVID-19 pandemic. Even prior to the pandemic, relatively little was known about teachers' time use outside of the classroom, the blurring of work and home boundaries, and how teachers compare to similar professionals in these regards. We use daily time-diary data from the American Time Use Survey (ATUS) for 3,168 teachers and 1,886 professionals in similarly prosocial occupations from 2003 to 2019 to examine occupational differences in time use. Compared to observationally similar non-teachers, teachers spend significantly more time volunteering at their workplace and completing work outside the workplace during the school year. On average, teachers spend 19 more minutes working outside of the workplace on weekdays than observably similar non-teachers and 38 more minutes on weekends. The weekend disparity is particularly large among secondary school teachers. This suggests that before the widespread switch to online and hybrid learning necessitated by the COVID-19 pandemic, teachers were already navigating blurrier work-life boundaries than their counterparts in similar professions. This has important implications for teacher turnover and for the effectiveness and wellness of teachers who remain in the profession.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

This chapter analyzes the process of surplus generation in Latin America based on the Penn World Tables, also with a comparison with the United States. The reference period is 1950–2019, revealing long-run evolution as well as certain differences between a State-driven industrialization strategy, the turn toward neoliberalism beginning in the 1980s, and a neo-developmentalist period in the twenty-first century. The research shows a steep decline in the rate of profit in Latin America until the early 1990s, with stabilization thereafter but without reversal of the downward trend. However, the turning point in terms of capital accumulation, surplus generation, and productivity indicators occurred in the early 1980s. In addition, divergence vis-à-vis the United States has been growing in the capacity to generate surplus, labor productivity, and GDP per capita.

This study focuses on listed and over-the-counter (OTC) companies in the Taiwan Stock Exchange. It found that an increase in the ownership proportion of institutional investors (INs), including foreign investors, investment trusts, and dealers can enhance the informativeness of stock prices. The relationship between these factors follows an inverted U-shaped pattern, indicating that excessively high ownership ratios can actually lead to a decrease in the informativeness of stock prices. Additionally, increasing the ownership proportions of foreign investors and investment trusts can reduce the risk of stock price collapse, while dealers show no significant relationship in this regard. This study also reveals that the technical variable of the price deviation rate is an important explanatory factor for post-collapse returns. It is positively correlated with the magnitude of the price decline after a collapse, meaning that stocks with weaker pre-collapse performance experience larger post-collapse declines. When the data during the 2020 pandemic period are excluded, changes in foreign ownership ratios show a significant positive correlation with postcrash returns in both the long and short term. The significant correlation in the short term may be due to a high proportion of foreign ownership. Any reduction in this could put pressure on stock prices, and retail investors may follow suit and sell-off, using foreign investors as a reference. The significant correlation in the long term might be due to foreign investors themselves possibly also trying to avoid the pressure that their own short-term sell-offs could exert on stock prices. The changes in the ownership ratios of investment trusts and dealers indicate that medium and long-term changes have a significant impact on postcrash returns, while the changes in the major players' ownership show no significant correlation. When data from 2020 are included in the analysis, the significance of all INs decreases.

Climate change is a bitter truth for the entire humanity, and it vehemently calls for thoughtful means for environmental protection along with sustainable economic growth. International trade blocs fundamentally represent amalgamation of countries to achieve unified goals like higher living standards, reduced trade barriers, freer labour mobility across member states, social and cultural upliftment, political allegiance to regional association, etc. Throughout the 1990s, these trade blocs have committed to reducing environmental pressures and shifting towards cleaner forms of energy. This chapter examines the relationship between rate of change in carbon dioxide (CO₂) emissions per capita and rate of change in per capita gross domestic product (GDP) in linear, quadratic and cubic polynomial forms with the other control variables like inflow of foreign direct investment (FDI), export of goods and services, population density, urban population percentage and location dummies for the 66 countries falling in seven regional trade blocs. Other than the European Union and North American Free Trade Agreement (NAFTA), the remaining five trade blocs in the study – Association of South-East Nations (ASEAN), South Asian Association for Regional Cooperation (SAARC), Common Market for Eastern and South Africa (COMESA), Mercado Común del Sur (MERCOSUR) and Commonwealth of Independent States (CIS) – contain mostly the developing and some of the fastest growing economies of the world. The panel regression result finds an inverse relationship between rate of change in per capita CO₂ emissions and rate of change in GDP per capita (in linear and cubic polynomial forms), exports and population density, while the other coefficients of the explanatory variables are positive. The study also establishes an Environmental Kuznets Curve (EKC) which is opposite to N-shape during 2005–2019, and that contradicts with the original EKC of inverted U-shaped. However, this shape admits the collective efforts of region-specific trade blocs towards achieving clean environment which is one of the important global goals.

This chapter investigates the cost of public debt for firms using a comprehensive sample consisting of 17,368 industrial bond issues from 1970 to 2011. The empirical evidence shows that yield spreads for seasoned bond issues are significantly lower than those for initial bond issues. This seasoning effect is robust across different sample periods, subsamples, and model specifications. On average, the yield spreads for seasoned bond issues are around 50 bps lower than those for initial bond issues. This difference cannot be explained by other bond and firm characteristics. The seasoning effect is more pronounced for firms with higher levels of uncertainty, lower information disclosure quality, and longer time intervals between the first and subsequent issues. Our empirical findings provide supportive evidence for the extant theories that aim to rationalize the information role in determining the cost of capital.

This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.