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This chapter presents the indirect preferences for all full rank Gorman and Lewbel demand systems. Each member in this class of demand models is a generalized quadratic expenditure system (GQES). This representation allows applied researchers to choose a small number of price indices and a function of income to specify any exactly aggregable demand system, without the need to revisit the questions of integrability of the demand equations or the implied form and structure of indirect preferences. This characterization also allows for the calculation of exact welfare measures for consumers, either in the aggregate or for specific classes of individuals, and other valuations of interest to applied researchers.

Existing multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models either impose strong restrictions on the parameters or do not guarantee a well-defined (positive-definite) covariance matrix. I discuss the main multivariate GARCH models and focus on the BEKK model for which it is shown that the covariance and correlation is not adequately specified under certain conditions. This implies that any analysis of the persistence and the asymmetry of the correlation is potentially inaccurate. I therefore propose a new Flexible Dynamic Correlation (FDC) model that parameterizes the conditional correlation directly and eliminates various shortcomings. Most importantly, the number of exogenous variables in the correlation equation can be flexibly augmented without risking an indefinite covariance matrix. Empirical results of daily and monthly returns of four international stock market indices reveal that correlations exhibit different degrees of persistence and different asymmetric reactions to shocks than variances. In addition, I find that correlations do not always increase with jointly negative shocks implying a justification for international portfolio diversification.

This study aims to examine the effects of participating in physical activities on female college graduates' starting salaries. We used an instrumental variable (IV) approach to address the possible endogeneity problem. By using the Taiwan Higher Education Dataset, we discovered that participating in physical activities during college increased an individual's earnings by 3.06%. The significant positive effect of physical activity on salary demonstrated in this study is consistent with that in other relevant studies. This study also discovered that both the intensity and the persistence of participation in physical activities affected salary outcomes. Individuals earned 0.17%–2.41% more if they exercised for an additional hour per week, suggesting the importance of the intensity of participation in physical activities. In addition, persistent participation in physical activities was associated with a 3.08% higher salary.

We analyze the food security implications of off-farm labor reallocation decisions of rural farm households in transitional Albania. We accomplish this by examining local and nonlocal off-farm incomes for at-home food consumption expenditures.

An instrumental variable approach is employed to correct for endogeneity and censorship biases of off-farm income variables in a two-stage estimation of the food consumption expenditures.

We find that local off-farm income exerts a positive and significant effect on per capita food consumption expenditures of farm households, while private remittances from nonlocal off-farm income has the opposite effect on food consumption expenditures. In terms of regional heterogeneity, we discover that the mountain region spends significantly less on annual per capita food consumption compared to the central region. This confirms anecdotal evidence that food and nutrition insecurity in rural Albania is predominant in the mountain region.

Our findings suggest the need for policy makers to promote a development agenda that enables farm households to exploit the synergies among the various income-generating activities in the rural economy. This spectrum of income-generating activities forms complex livelihood strategies adopted by rural farm households to improve and maintain their food security.

We distinguish between local and nonlocal sources of off-farm income. Knowing which off-farm income source(s) has the largest impact on household welfare through improved food security status should be of interest to policy makers.

Introduction: The Internet has tremendously transformed the computer and networking world. Information reaches our fingertips and adds data to our repository within a second. Big data was initially defined as three Vs, where data come with greater variety, increasing volumes and extra velocity. Big data is a collection of structured, unstructured and semi-structured data gathered from different sources and applications. It has become the most powerful buzzword in almost all the business sectors. The real success of any industry can be counted based on how the big data is analysed, potential knowledge is discovered and productive business decisions are made. New technologies such as artificial intelligence and machine learning have added more efficiency to storing and analysing data. This big data analytics (BDA) becomes more valuable to those companies, focusing on getting insight into customer behaviour, trends and patterns. This popularity of big data has inspired insurance companies to utilise big data at their core systems and advance the financial operations, improve customer service, construct a personalised environment and take all possible measures to increase revenue and profits.

Purpose: This study aims to recognise what big data stands for in the insurance sector and how the application of BDA has opened the door for new and innovative changes in the insurance industry.

Methodology: This study describes the field of BDA in the insurance sector, discusses the benefits, outlines tools, architectural framework, the method, describes applications in general and specific and briefly discusses the opportunities and challenges.

Findings: The study concludes that BDA in insurance is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however, there remain challenges to overcome.

Efficiency and productivity has always being a key issue in economic science. The analysis of the impact of research and development (R&D) has been extensively studied in industries and countries of more or less aggregated level. This chapter aims to investigate the impact of corporate R&D in performance of low-tech industries, medium-tech, and high-tech in OECD countries.

This chapter aims to answer the questions: Is the impact of R&D significant for all types of industries? If so, what are the differences and the magnitude of these effects in each of these types of industries?

To this end, an unbalanced data set from 2000 to 2011 was collected for the main countries of Europe and the United States concerning low-, medium-, and high-tech to analyze the impact of the magnitude of corporate R&D and capital accumulation on productivity of these industries. The productivity of industries was measured by stochastic parametric frontier functions, in order to measure the efficiency of R&D and accumulation of capital on labor productivity.

The main results highlight the impact of corporate R&D on productivity of high-tech industries, but for other industries those relations are not clear. However, capital accumulation became crucial on low technology to improve their performance. These results, although needing to include a more extensive data set of industries across countries, refer the need for policy and decision makers to allocate public funds for R&D in high-tech industries, while the investment in capital seems crucial, particularly in low-tech industries to improve the productivity.

Empirical studies on household-level inflation inequality have so far only focused on periods with positive inflation rates. However, the major concern on the policy agenda since the most recent financial crisis has been deflation rather than inflation. This naturally raises the question regarding the effect of deflation on the distribution of real income when households spend their budget on different consumption bundles. This chapter compiles annual household-level inflation rates in Denmark from 1930 to 1935 based on microdata from the Expenditure and Saving Survey of 1931 and price data from the official Retail Price Index. The results indicate that lower-income households faced a larger decline in prices on their consumption of goods and services during the deflation years 1930–1932 than higher-income households did. The deflation thus contributed to narrowing the difference in real incomes between the top and bottom parts of the income distribution during the recession. In the years 1933–1935 with positive inflation rates, the lower-income households experienced higher inflation rates than higher-income households. Over the period 1930–1935 seen as a whole, the price development contributed slightly to reducing real income inequality. The low degree of medium-term persistence of differences in household-specific inflation rates is consistent with previous findings in various time periods from the 1960s to the 2000s without any persistent deflation events. The chapter at hand is the first empirical study of the direct distributional effects of price developments at the household level in a period with persistent deflation.

In order to survive beyond 2020, historically black colleges and universities (HBCUs) will need to strengthen their financial standing. Compared to predominately white institutions, HBCUs have substantially weaker financial resources. Without strong fundraising and effective financial management, HBCUs are doomed beyond 2020. The importance of hiring astute financial managers at HBCUs cannot be overstated. History, tradition, and reputation are irrelevant at an institution if the finances are not optimally managed. Moreover, state and federal higher education policies can damage the financial standing of HBCUs, as seen in the 2013 PLUS loan crisis. This chapter will be divided into two sections. The first section will provide a historical and contemporary perspective on financing HBCUs, including how state higher education policies impact HBCUs. The second section of the chapter will provide an overview of budget management at HBCUs.

Little work has directly addressed the potential to control food waste. This chapter focuses on behavioral nudges and their potential to reduce food waste and, in turn, implications for food security.

Key methodological and definitional challenges that must be met to make effective use of interventions to reduce food waste are examined. Chief among these challenges are determining welfare measures that are robust to the behavioral anomalies and apparently inconsistent preferences observed under behavioral interventions.

Targeted reductions in food waste can be significantly impacted by simple behavioral interventions either in institutional settings or within the home. Some evidence suggests that food waste is rampant not only in developed countries, but also among developing countries.

Our findings highlight the need to create a research program addressing the behavioral causes of food waste both in developed and developing country contexts.