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To what extent are personal trust, mutual interests, and third parties important in enforcing agreements to trade? How do firms combine these to form transactional-governance structures? This article answers these questions in a whole-economy, cross-country setting that considers a full spectrum of transactional-governance strategies. The data collection requires a new survey question answerable in any context. The question is applied in six South American countries using representative samples, with the resultant survey weights facilitating a whole-economy analysis. Without imposing an a priori model, latent class analysis estimates meaningful governance structures. Bilateralism is always used. Law is never used alone. Bilateralism and formal institutions are rarely substitutes. Within country, inter-regional variation in governance is greater than inter-country variation. The usefulness of the data is shown by testing one element of Williamson's discriminating-alignment agenda: greater uncertainty in the transactional environment increases the involvement of third parties. [ABSTRACT FROM AUTHOR]

This study distinguishes itself from existing organizational culture studies by investigating the relatively under-studied theoretical relationship between organizational culture and employer attractiveness. To capture the holistic nature of organizational culture theory, this study adopts configurational analysis, which contrasts resourceful cultural attributes (collaborative, employee development, and fair-compensation cultures) with demanding attributes (result-oriented, overworked, and job-insecurity cultures). This study proposes three configurational propositions of employer attractiveness and employs review-based topic modeling and fuzzy-set quality comparative analysis (fsQCA) to overcome the limitations of traditional survey-based measurement and regression analysis. For the empirical analysis, this study constructs an industry-wide dataset comprising 2209 quarterly samples from 157 hotels over six years, utilizing 54,889 employee reviews posted on Glassdoor in the United States. Topic modeling analysis adopting Latent Dirichlet Allocation extracts the probabilities of six cultural attributes. Finally, the fsQCA generates three groups of 13 configurations, leading to employer attractiveness: a fully resourced culture, a resourced and low-demanding culture, and a fairly compensated overwork culture. The findings confirm the core concepts of configurational analysis in contrast to regression analysis and present novel theoretical, methodological, and practical implications.

The sustainability concept embraces a wide scope of linked practices: green thinking, sustainability informational and management assurance, ESG reporting and scoring, responsible and impact investment, stability measurements, and others. Significantly expanding beneficiary's and stakeholders' circle needs information to build and implement a policy providing ESG values creation and meeting their requirements in relevant sustainable agenda. This is why it is important to create a transparent information landscape and to shape new highlights in management and decision-making. The article is aimed at exploring specific ways to transform information and management practices, primarily accounting, ESG-reporting, analysis, and assurance to create a transparent information environment for sustainability.

In micro-level data concerning the housing market, the apartment quality may be signaled via textual statements or the attached descriptions/photos. It may be done using hard information related to the easier-to-measure structural characteristics or soft information related to the apartment condition and design - soft quality. This paper checks whether the choice of the approach to handling the issue of soft quality of apartments influences the properties of hedonic models and the course of hedonic rent indices. The study shows that hedonic models that account for soft quality have better statistical properties than those without soft-quality-related variables. Among them, the models that include the information on quality extracted from descriptions of apartments prove to be the best. Still, considerable differences in the indicated course of hedonic rent indices have not been detected. However, the paper concludes that utilizing information on apartments’ soft quality may be crucial to understanding the market adjustment process to economic shocks. It has been proven that the price reaction of the Poznań (Poland) rental market to the COVID-19 pandemic and the Russian aggression on Ukraine has been diversified in the quality-related market segments.

Purpose: European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders' structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic. Design/methodology/approach: The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach. Findings: XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood. Originality/value: This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective. [ABSTRACT FROM AUTHOR]

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness. [ABSTRACT FROM AUTHOR]

Firms may seek non-redundant information through inter-firm relations beyond their geographic and cognitive boundaries (i.e. relations with firms in other regions and active in different fields). Little is known about the conditions under which firms benefit from this high-risk/high-gain strategy. We created a digital layer of 600,000 German firms by using their websites' textual and relational content. Our results suggest that strong relations (relations with common third partners) between firms from different fields and inter-regional relations are positively associated with a firm's innovation level. We also found that a specific combination of weak and strong relations confers greater innovation benefits. [ABSTRACT FROM AUTHOR]

This article develops a demographic method to estimate the civilian noninstitutional population for counties and county equivalents in the U.S. While these data provide the key sampling frame for national labor market surveys and denominators for labor market prevalence rates, the data are thus far unavailable for small areas. I develop a modified cohort component method to produce novel, monthly estimates of the civilian noninstitutional population for all U.S. counties using publicly available data on population and vital statistics with minimal modifications. The resulting population data may be used by researchers and policymakers to study within-year population dynamics as they relate to economic and demographic factors. I further extend the method to produce short-term population projections that include the most current vital statistics. The method compares favorably to existing annual, midyear estimates by the U.S. Census Bureau, but is prone to error in areas with fewer vital events. [ABSTRACT FROM AUTHOR]

Maritime transport helps the development of the economy of countries. Improving the current situation in this type of transport requires the application of modern software tools for assessment, analysis and forecasting.

Copyright of Sozialer Fortschritt is the property of Duncker & Humblot GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accidentally parallel at the beginning, the transition to value-based pricing and transition to pricing data science have blended harmoniously, changing the pricing landscape. Using the marketing capability approach, I show that the introduction of pricing data science is costly and requires higher management support. Despite its cost, algorithmic price optimisation allows one to react swiftly to changes in demand. The optimisation process is applied to inherently non-linear, multimodal, and right-skewed pricing data. Presenting the interactions between new computational techniques and value-data pricing, I concentrate on altered perceptions of price elasticity, value-driver estimations, and contract opportunity analysis.

Firm growth is an essential feature of market economies, shaping together macroeconomic performance and the evolution of industry structures. As a potential indicator of organizational "fitness" within a competitive environment, firm growth is also a central concern to both the practice and theory of business strategy. Despite both its theoretical and practical importance, though, growth remains a poorly understood property of firms. While previous studies have documented the fat-tailed nature of firm growth rates, we know far less about the patterns of growth rates over long periods of time. For instance, do "fast growers" tend to maintain their relative growth rates advantages over long periods or is superior growth a transitory phenomenon? Is, as predicted by evolutionary and capability-based theories of the firm, the process of firm growth path-dependent or is it more akin to a random walk? The answers to these questions are central to building a robust theory of firm growth. This article attempts to address this gap in our empirical knowledge of firm growth using a dataset that spans 50 years, which allows the abandonment of the assumption, common to all incumbent studies, that the stochastic paths of all firms stem from the same generating process. These exploratory results indicate that growth rate persistence is there and may be even substantial for some firms, but it is rare. We also study the links between the micro-properties of firm growth within sectors and the patterns of aggregate growth of these same sectors. Indeed, we find circumstantial but widespread evidence that heterogeneity across firms correlates with industry dynamism. [ABSTRACT FROM AUTHOR]

The "superstar economy" is characterized by payoff functions that depend in a discontinuous way on the quality level of the corresponding products and services. Firm A might generate much higher returns than firm B, although A's product is only marginally superior to B's product. We look at an investor who considers to invest into start-ups that want to become active in one particular technological segment. Consequently only the very best few projects generate high returns. The investor is faced with a sequence of investment opportunities, observes the objective relative rankings of the corresponding projects seen so far, and must decide whether and how much to invest into the currently observed opportunity. Returns are realized at the end of the investment horizon. We derive the value functions and optimal investment rules for risk-neutral and risk averse investors. Under weak assumptions, the expected infinite horizon utility exceeds that of initial wealth. We show that for a risk-neutral investor "invest all or nothing", depending on the project's ranking and time of occurrence, is an optimal strategy. For a risk-averse investor the optimal rule is non-linear and path dependent. A simulation study is performed for risk-neutral and log-utility investors. [ABSTRACT FROM AUTHOR]

Do people know their own risk preferences, or do risk choices change with experience and observation? We provide a straightforward test in the laboratory. People make an initial decision concerning a lottery choice and then experience 24 unpaid practice periods in which they roll the dice, record the outcome, and record the would-be payoff. They then make a final decision for the lottery choice; one of the first and last periods is randomly chosen for payment. Our primary hypothesis is that people will become less risk-averse by having made and experienced the practice rolls. We do find that people are significantly more likely to become less risk-averse than more risk-averse over time. We note that this move towards assuming increased risk goes in the opposite direction from what is at least arguably predicted by loss aversion and reference dependence. We find that women's preferences change much less during a session than men's preferences change. We feel that our literally hands-on approach ensures a degree of engagement that helps to accelerate the learning process. We argue that measures obtained after people have had experience with a mechanism are more meaningful, and that this principle might well extend more generally to other elicitation tasks. [ABSTRACT FROM AUTHOR]

Most studies on benefit sanctions within the German welfare system rely on established datasets about welfare receipt. This paper analyzes how using a dataset from the operational system of the German Federal Employment Agency for processing welfare claims can contribute to further research on benefit sanctions. For this purpose, I use a random sample of welfare recipients with at least one sanction between 2016 and 2018. First, this allows the detailed analysis of time lags between different steps in the sanction process. Second, linking this dataset with established datasets allows the identification of imposed sanctions for which sanction periods could not be (fully) implemented. This is largely explained by individuals leaving the welfare system between sanction events and sanction periods, e.g., by taking up employment. Third, the paper shows differences in benefit cuts across subgroups. This opens up paths for future research. [ABSTRACT FROM AUTHOR]

Aim/purpose – Not only have micro, small, and medium enterprises (MSMEs) been regarded as a driving force of the national economy, but they have also become important in promoting innovation and entrepreneurship. In general, today’s MSMEs face problems, such as high business costs, insufficient innovation, and inadequate management capabilities, that have all forced the digital transformation of MSMEs. However, in existing studies, the impact of digital transformation on performance is controversial, and the subjects are generally listed companies. In this paper, we have decided to use micro business survey data and analyze them from a spatial perspective to explore how MSMEs’ digital transformation plays a role in influencing performance and the path of its effect.

We examine how 17 behavioural biases relate to each other, to three standard measures of risk and time preferences, to cognitive skills, personality, and demographics, and to outcomes in household finance, well-being, and health. Most consumers in our nationally representative panel data exhibit multiple biases, with substantial cross-person heterogeneity. Biases are positively correlated within person, especially after adjusting for measurement error. From that correlation structure, we reduce our 20 bias and standard preference measures to four behavioural common factors. Each BCF reflects a group of related biases regarding beliefs, decision quality, discounting, or risk/uncertainty attitudes. The first two BCFs also strongly correlate with each other (positively) and cognitive skills (negatively). The first three BCFs and cognitive skills strongly correlate with various outcomes in the expected directions. Our results support processing-based models where basic limitations in cognition and/or attention produce multiple biases, and they have several other implications for theory and practice. [ABSTRACT FROM AUTHOR]

Copyright of AStA Wirtschafts- und Sozialstatistisches Archiv is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Gender differences in risk attitudes have recently been shown to be context-dependent rather than ubiquitous. We manipulate three widely used risk elicitation tasks to test whether the presence of a safe option among the set of alternatives can explain the heterogeneity of the findings. We find that the availability of a safe option induces significant effects in two out of three tasks. Despite the well-known instability of elicited risk preferences, we show with a structural model that the effect on risk attitudes is rather stable across tasks, but not sufficiently strong to reach traditional significance levels. [ABSTRACT FROM AUTHOR]

Smart networks, e.g., smart grids (SGs) as complex interconnected systems have become more complex and require greater processing and communication capabilities due to the growing use of renewable energy (RE). In addition to being a reliable method for collecting and managing information, wireless networks (WNs) are capable of enhancing the performance of RE management. As a result of wireless data transfer risks and centralized power trades, SGs using WNs are unable to provide the security of the electricity market or ensure a high level of RE utilization. Blockchains (BCs), which are new technologies that enable data sharing, are gaining popularity, and it is thought to be able to address these issues. A BC-based, reliable power-exchange scheme is presented for SGs using WNs in this study. Using the BC for recording power data obtained from the WNs allows the smart contract for making appropriate trades according to the information. Energy trading and RE consumption are more efficient in a dual-chain architecture that consists of local energy trade BCs and RE trade BCs. A BC-based RE incentive framework is designed to have greater stability and scale among RE producers. Overall, the proposed technique not only can provide higher security and reliability to the system but also can mitigate fraud, as well as providing less operation cost for the system. Ultimately, the 56-bus test feeder layout is used to validate the framework.

Accurately measuring government benefit receipt in household surveys is necessary when studying disadvantaged populations and welfare programs. The Food Stamp Program is especially important given its size and recent growth. To validate survey reports, we link administrative data on participation in two states to three key household surveys. We find that between 23 and 50 percent of true food stamp recipient households do not report receipt. A substantial number of true nonrecipients are also recorded as recipients. We examine reasons for these errors, including imputation, an important source of error. Error rates vary with household characteristics, implying complicated biases in multivariate analyses, such as regressions. We directly examine biases in common survey-based estimates of program receipt by comparing them to estimates from our linked data. We find that the survey estimates understate participation among single parents, nonwhites, and low-income households and also lead to errors in multiple program receipt and time and age patterns of receipt. [ABSTRACT FROM AUTHOR]

The Stochastic Becker-DeGroot-Marschak (SBDM) mechanism is a theoretically elegant way of eliciting incentive-compatible beliefs under a variety of risk preferences. However, the mechanism is complex and there is concern that some participants may misunderstand its incentive properties. We use a two-part design to evaluate the relationship between participants' probabilistic reasoning skills, task complexity, and belief elicitation. We first identify participants whose decision-making is consistent and inconsistent with probabilistic reasoning using a task in which non-Bayesian modes of decision-making lead to violations of stochastic dominance. We then elicit participants' beliefs in both easy and hard decision problems. Relative to Introspection, there is less variation in belief errors between easy and hard problems in the SBDM mechanism. However, there is a greater difference in belief errors between consistent and inconsistent participants. These results suggest that while the SBDM mechanism encourages individuals to think more carefully about beliefs, it is more sensitive to heterogeneity in probabilistic reasoning. In a follow-up experiment, we also identify participants with high and low fluid intelligence with a Raven task, and high and low proclivities for cognitive effort using an extended Cognitive Reflection Test. Although performance on these tasks strongly predict errors in both the SBDM mechanism and Introspection, there is no significant interaction effect between the elicitation mechanism and either ability or effort. Our results suggest that mechanism complexity is an important consideration when using elicitation mechanisms, and that participants' probabilistic reasoning is an important consideration when interpreting elicited beliefs. [ABSTRACT FROM AUTHOR]