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This chapter studies the large sample properties of a subclassification-based estimator of the dose–response function under ignorability. Employing standard regularity conditions, it is shown that the estimator is root-n consistent, asymptotically linear, and semiparametric efficient in large samples. A consistent estimator of the standard-error is also developed under the same assumptions. In a Monte Carlo experiment, we investigate the finite sample performance of this simple and intuitive estimator and compare it to others commonly employed in the literature.

This second of a six‐part series presents a hierarchical scheme for classifying integral snap‐fits at the attachment level, bringing great order to where there appeared to be chaos. The scheme is then used to enumerate all possible design options. The proliferation of plastic parts, and the ability to mould such parts of great complexity at little cost penalty, has resulted in the growing use of integral attachment in the form of snap‐fit features in designs. Heretofore, the great diversity of part geometry and integral snap‐fit features has made it appear that design possibilities may be unbounded, and that attempts at optimization might be intractable. The result shows that options can quickly be reduced to a small enough number to allow designers to compare every possibility, thereby making true optimization a practical reality. As such, the scheme guides new designers and validates choices for experienced designers in ways never before possible.

This paper examines the going concern of integrated reporting <IR> as the pessimistic about its sustainable value relevance is gaining momentous. The study employs a quantitative approach to data analysis and mainly sourced secondary data from integrated reports of 83 sampled companies.

Utilising data from the companies' integrated reports from 2015 to 2018, the study analyses the impact of <IR> capitals disclosure on corporate sustainable value. <IR> was proxied by its six capital elements, which include financial, manufactured, human, intellectual, natural and social, and relationship capitals, while sustainable value was surrogated by the cost of financing and revenue growth rate. The study develops a checklist and utilises content analysis to score the quality of disclosure by sample companies during the period.

The longitudinal panel data analysis results reveal that on overall disclosure, <IR> capital has a significant positive effect on the revenue growth but fails to document such on the cost of financing. Meanwhile, on the individual level, human capital and natural capital disclosure have an indirect effect on the cost of financing, while all the six subclassifications affect the revenue growth of the sampled companies.

The study sampled only 83 companies across the region due to the limited availability of data. Therefore, the generalisation of findings might be hindered, and further examination might be considered as more data become available.

The study would support the regulators in developing countries to monitor <IR> practices for their domestic companies. It would assist the International Integrated Reporting Council (IIRC) to review the industry's current <IR> practices and give the reason for better <IR> implementation in the future, from both minority and majority economies.

The study is among the pioneer studies that would consider <IR> research across the Asian continent. The study contributes to the recent discussion about sustainable value relevance of <IR>. Also, it would provide some level of incentive to those charged with governance concerning the voluntary compliance with the <IR> framework.

This work aims to assess the potential of a Bio-District as a model for applying the circular economy concerning the waste scope. It aims to understand the capability of organic farms to manage waste with a circular perspective, starting with the use of indicators that directly or indirectly impact the waste scope.

This study is based on previous work that identified and systematised the circular indicators of the agri-food sector within a dashboard. With this research as a basis, the indicators within the waste scope in the dashboard were extracted. Cross-linked indicators with an indirect connection to the waste scope were also systematised and tested in a case study. Primary and secondary data were used for the study. The primary data came from a semi-structured interview, and the secondary data were from official databases.

The work highlights two important results. The first allows the definition of a subclassification of indicators by product and organisation, extracting those with a cross-linked characteristic concerning the waste scope. Secondly, the indicators' application shows the farm's circular and waste valorisation potential within the Bio-District. The study also made it possible to test a new indicator, the “Potential Energy Biomass Recovery”, to measure the farm's potential to produce energy from waste.

This research proposes a new circular economy approach to evaluate waste management in the agri-food sector.

Analyzing medicare data is a role undertaken by the government and commercial companies for accepting the appeals and sanctioning the claims of those insured under Medicare. As the data of medicare is robust and made up of heterogeneous typed columns, traditional approaches consist of a laborious and time-consuming process. The understanding and processing of such data sets and finding the role of each attribute for data analysis are tricky tasks which this research will attempt to ease. The paper aims to discuss these issues.

This paper proposes a Hierarchical Grouping (HG) with an experimental model to handle the complex data and analysis of the categorical data which consist of heterogeneous typed columns. The HG methodology starts with feature subset selection. HG forms a structure by quantitatively estimating the similarities and forms groups of the features for data. This is carried by applying metrics like decomposition; it splits the dataset and helps to analyze thoroughly under different labels with different selected attributes of Medicare data. The method of fixed regression includes metrics of re-indexing and grouping which works well for multiple keys (multi-index) of categorical data. The final stage of structure is applying multiple aggregation function on each attribute for quantitative computation.

The data are analyzed quantitatively with the HG mechanism. The results shown in this paper took less computation cost and speed, which are usually incurred on the publicly available data sets.

The motive of this paper is to provide a supportive work for the tasks like outlier detection, prediction, decision making and prescriptive tasks for multi-dimensional data.

It provides a new efficient approach to analyze medicare data sets.

When decision makers encounter new assurance services that can be customized for individual clients, they must include them in their pre-existing categorization of assurance, a cognitive task known as postclassification. This paper draws upon three literatures (classification research in accounting, theory of assurance, and cognitive psychology) in order to suggest how this task might be modeled and studied empirically, using the example of SysTrust™. The role of a necessary condition for successful postclassification called the category use effect (Ross, 2000), in which decision makers are reminded of pre-existing categories when they learn to use new categories, is explained.

Part IV provides readers with the extant requirements for the application of materiality to recognition, measurement, presentation, and disclosure in the financial statements. This part also includes a detailed critical review of the recent Practice Statement on materiality, the FASB’s proposed ASU on the notes and the amendments to the Conceptual Framework proposed by the IASB and the FASB.

The part expands to issues that are typical of Management Commentary, including the SEC guidance on materiality in Management Discussion and Analysis.

It informs about the complexities and subtle differences between financial statements and bookkeeping and the different standards of reasonableness versus materiality.

A section moves from materiality to material misstatements and covers the application of materiality in auditing.

Another section goes in depth on internal control over financial reporting, showing the linkages between materiality and risk appetite and risk tolerance and the related application guidance.

“The Elephant in the Corner: A Cautionary Tale About Measurement Error in Treatment Effects Models” by Daniel L. Millimet discusses the current use of the unobserved-outcome framework to estimate population-averaged treatment effects, and it exposes the sensitivity of these estimators to assumption of no measurement error. The Monte Carlo simulation evidence in this chapter indicates that “nonclassical measurement error in the covariates, mean-reverting measurement error in the outcome, and simultaneous measurement errors in the outcome, treatment assignment, and covariates have a dramatic, adverse effect on the performance of the various estimators even with relatively small and infrequent errors” (Millimet article, p. 1–39). To some extent, all the estimators analyzed by Millimet are based on weak functional form assumptions and use semiparametric or nonparametric methods. Millimet's results indicate the need for measurement error models be they parametric or nonparametric models, see Schennach (2007), Hu and Schennach (2008), and Matzkin (2007) for some recent research in nonparametric approaches. Chapter 7 develops a Bayesian estimator that can handle some of the measurement errors discussed in this chapter.

In this paper, we present a critical survey of experiments on political clientelism and vote-buying. We claim that through randomization and control, field experiments represent an important tool for answering causal questions, whereas list experiments provide useful methods that improve the hard task of measuring clientelism. We show that existing experimental research gives answers to the questions of why clientelism is effective for getting votes and winning elections, who relies more on this strategy – incumbents or challengers – how much clientelism takes place, and who tend to be the favorite targets of clientelistic politicians. The relationship between clientelism and other illicit strategies for getting votes, such as electoral violence and fraud, has also been analyzed through experimental interventions. Experiments have also studied mechanisms and policies for overcoming clientelism. Finally, we show that external validity is a major source of concern that affects this burgeoning literature.

Part II contrasts the views of materiality in the Conceptual Frameworks of the IASB, FASB, IPSAS, and other framework such as the Integrated Reporting. In particular, it analyzes at what level and how differently that concept interacts with the qualitative characteristics of financial information in each of those frameworks. It looks at its pervasiveness and entity specificity, the interlock with the concept of relevance, reliability and faithful representation, completeness, understandability, neutrality, and drills down to the link to recognition.

This part then compares the definitions of materiality in different standards and contexts, to then draw a taxonomy of materiality and its attributes, such as the subject matter, thecontext of assessment, the addressees, the assessor, and the materiality test. A large part of the analysis involves the comparison between legal definitions of materiality and characterizations in the accounting, financial, and larger management contexts.