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I. Introduction to the Study of the Economic Role of Government: Alternative Approaches to Law and Economics

Saving is essential to the health of economies and households, yet relatively little scholarship investigates saving behaviors among the urban working class in the nineteenth century. This chapter uses five surveys of industrial workers in 1880s New Jersey, an analysis of which reveals sophisticated saving behaviors consistent with life-cycle and precautionary theories. The mean saving rate was between 8% and 12% of annual income. Younger households saved less than older households. Householders with longer expected careers, on average, saved less. Life insurance and fraternal societies were the most popular saving vehicles, but workers also used savings banks and building and loan associations, alone and in combination.

Failure to rescue events, or events involving preventable deaths from complications, are a significant contributor to inpatient mortality. While many interventions have been designed and implemented over several decades, this patient safety issue remains at the forefront of concern for most hospitals. In the first part of this study, the development and implementation of one type of highly studied and widely adopted rescue intervention, algorithm-based patient assessment tools, is examined. The analysis summarizes how a lack of systems-oriented approaches in the design and implementation of these tools has resulted in suboptimal understanding of patient risk of mortality and complications and the early recognition of patient deterioration. The gaps identified impact several critical aspects of excellent patient care, including information-sharing across care settings, support for the development of shared mental models within care teams, and access to timely and accurate patient information.

This chapter describes the use of several system-oriented design and implementation activities to establish design objectives, model clinical processes and workflows, and create an extensible information system model to maximize the benefits of patient state and risk assessment tools in the inpatient setting. A prototype based on the product of the design activities is discussed along with system-level considerations for implementation. This study also demonstrates the effectiveness and impact of applying systems design principles and practices to real-world clinical applications.

Despite rising incomes and reduction of extreme poverty, the feeling of being poor remains widespread. Support programs can improve well-being, but they first require identifying who are the households that judge their income is insufficient to meet their basic needs, and what factors are associated with subjective poverty.

Households report the income level they judge is sufficient to make ends meet. Then, they are classified as being subjectively poor if their own monetary income is inferior to the level they indicated. Second, the study compares the performance of three machine learning algorithms, the random forest, support vector machines and least absolute shrinkage and selection operator (LASSO) regression, applied to a set of socioeconomic variables to predict subjective poverty status.

The random forest generates 85.29% of correct predictions using a range of income and non-income predictors, closely followed by the other two techniques. For the middle-income group, the LASSO regression outperforms random forest. Subjective poverty is mostly associated with monetary income for low-income households. However, a combination of low income, low endowment (land, consumption assets) and unusual large expenditure (medical, gifts) constitutes the key predictors of feeling poor for the middle-income households.

To reduce the feeling of poverty, policy intervention should continue to focus on increasing incomes. However, improvements in nonincome domains such as health expenditure, education and family demographics can also relieve the feeling of income inadequacy. Methodologically, better performance of either algorithm depends on the data at hand.

For the first time, the authors show that prediction techniques are reliable to identify subjective poverty prevalence, with example from rural China. The analysis offers specific attention to the modest-income households, who may feel poor but not be identified as such by objective poverty lines, and is relevant when policy-makers seek to address the “next step” after ending extreme poverty. Prediction performance and mechanisms for three machine learning algorithms are compared.

The two sets of notes, taken only three years apart are substantially similar in organization and content. We document differences identified in a line-by-line comparison in Table 1. Generally, the 1996 course notes reproduced here more prominently feature the work of legal scholars, from Oliver Wendell Holmes to St. George Tucker. Curiously, many of these references were removed from the later version, as well as nearly all discussion on legal precedent established by Supreme Court cases. The overall effect of these changes is a marked shift away from a critical legal studies approach to the economic role of government and toward a more focused neoclassical lens.

To analyze the role of price fairness perceptions as a construct underlying individuals' transactions.

The paper formulates and empirically tests the hypothesis that price fairness perceptions endogenously determine consumers' expenditures decisions. Economic transactions are viewed as an allocation choice problem with fairness perceptions being an endogenous variable determining problem outcome. A treatment effects model is utilized, allowing for the analysis of the effects that price fairness perceptions exercise upon both the consumers' decision to realize a transaction as well as upon their consequent level of spending.

Consumers do patronize stores and one important variable determining their level of spending is their perceptions of fairness underlying the transaction with a specific provider.

The small usable questionnaire sample may be considered as a limitation. However, the very satisfactory fit of the estimated model allows for the results to be a comparison basis with future findings.

Analysis of price fairness perceptions provides new insights regarding consumer behavior, enhancing the analytical validity of typical household demand models.

Analysis allows for price fairness perceptions to enter a consumer's expenditures equation usually expressed in terms of socio‐economic indicators.