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This study investigates the stability of the “hospital effect” on performance over time by administrative health data as a source of evidence. Using 78,907 heart failure adult records from 117 hospitals in the Lombardy Region (Northern Italy) over three years (2010–2012), we analyzed hospital performance in terms of 30-day mortality and 30-day unplanned readmissions to gather evidence about the stability of the “hospital effect.” Best/worst performers were identified through multi-level models that combine both patient and hospital covariates. Our results confirm that managerial choices affect hospital performance, and that the “hospital effect” is not, contrary to expectations, stable over the short term. Performance improvement/worsening over the three years has been also analyzed.

The goal of this contribution is to shed light on the benefits for research in health care coming from the use of administrative data, especially in terms of measuring hospitals’ outcomes. The main approaches to health outcome evaluation are reviewed and the possible improvements deriving from the use of administrative data are highlighted. Administrative data may be an essential element in the process of gathering to the public true rankings of health care organizations, reducing the degree of asymmetric information that typically arises in health care. Patients will be more aware of the best institutions, which will induce most of them to demand to be admitted in them, taking into account the costs associated with distance and with the severity of the illness. This in turn may ask for a reorganization of the sector, closing some organizations and expanding others, having as final goal to improve the health status of the population, without income barriers. This is one of the first attempts to provide an overview of the advantages that administrative data may gather in health care.

Ideal estimates of the intergenerational elasticity (IGE) in income require a large panel of income data covering the entire working lifetimes for two generations. Previous studies have demonstrated that using short panels and covering only certain portions of the life cycle can lead to considerable bias. I address these biases by using the PSID and constructing long time averages centered at age 40 in both generations. I find that the IGE in family income in the United States is likely greater than 0.6 suggesting a relatively low rate of intergenerational mobility in the United States. I find similar sized estimates for the IGE in labor income. These estimates support the prior findings of Mazumder (2005a, b) and are also similar to comparable estimates reported by Mitnik et al. (2015). In contrast, a recent influential study by Chetty, Hendren, Kline, Saez (2014) using tax data that begins in 1996 estimates the IGE in family income for the United States to be just 0.344 implying a much higher rate of intergenerational mobility. I demonstrate that despite the seeming advantages of extremely large samples of administrative tax data, the age structure, and limited panel dimension of the data used by Chetty et al. leads to considerable downward bias in estimating the IGE. I further demonstrate that the sensitivity checks in Chetty et al. regarding the age at which children’s income is measured, and the length of the time average of parent income used to estimate the IGE suffer from biases due to these data limitations. There are also concerns that tax data, unlike survey data, may not adequately reflect all sources of family income. Estimates of the rank–rank slope, Chetty et al.’s preferred estimator, are more robust to the limitations of the tax data but are also downward biased and modestly overstate mobility. However, Chetty et al.’s main findings of sizable geographic differences within the US in rank mobility are unlikely to be affected by these biases. I conclude that researchers should continue to use both the IGE and rank-based measures depending on their preferred concept of mobility. It is also important for researchers to have adequate coverage of key portions of the life cycle and to consider the possible drawbacks of using administrative data.

The goal of the Convention on the Rights of Persons with Disabilities (CRPD) is to secure the rights of persons with disabilities as full and equal members of society. Achieving these objectives requires appropriate and high-quality data for identifying disability gaps and areas of exclusion. Such data can be then used to develop policies and programmes to address that exclusion, and then to monitor and evaluate their effectiveness. However, researchers and policy makers face a number of challenges when collecting this data. This chapter reviews the various approaches to collecting data on disability, identifying potential sources of relevant data, including through surveys and repurposing of data collated for administrative means. It discusses at length the legal and ethical issues involved in their collection and use for alternative purposes, providing a comprehensive exploration of issues relating to participation, privacy, big data and rights of access. It concludes by suggesting ways forward for creating disability data systems that can fully support efforts to achieve a fully inclusive society.

The UK Coalition government introduced a raft of welfare reforms between 2010 and 2015. As part of its response to the financial crisis, reforms were designed to cut public expenditure on social security and enhance work incentives. Policy makers are required by legislation to have due regard to the need to eliminate discrimination, advance equality of opportunity and foster good relations between different people. This Public Sector Equality Duty is an evidence-based duty which requires public authorities to assess the likely effects of policy on vulnerable groups. This chapter explores the extent to which the Department for Work and Pensions adequately assessed the equality impacts of key welfare reforms when policy was being formulated. The chapter focuses on the assessment of the impact of reductions to welfare benefits on individuals with protected characteristics – age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race, religion and belief, sex and sexual orientation – including individual and cumulative impacts. It also considers mitigating actions to offset negative impacts and how the collection of evidence on equality impacts was used when formulating policy. The chapter shows that the impacts of the reforms were only systematically assessed by age and gender, and, where data were available, by disability and ethnicity with no attempt to gauge cumulative impacts. There is also evidence of Equality Impact Assessments finding a disproportionate impact on individuals with protected characteristics where no mitigating action was taken.

Sustainable Development Goal (SDG) Indicator 4.2 calls for all girls and boys to have access to high-quality early childhood education by 2030. This global mandate establishes a new framework of accountability to increase access to preprimary education in low- and middle-income countries through measurement and reporting. As with other global indicators, however, the measurement of preprimary education access is more complex and nuanced than may be supposed. This data-oriented chapter delves deeply into the measurement of SDG 4.2 and explores the accuracy of the indicator being used: the adjusted net enrollment ratio, one year before the official age of primary entry. The chapter analyzes data from both education management information systems (EMIS) and household surveys to triangulate information about children’s access to preprimary education before they begin primary school. The analysis concludes that the indicator used to measure SDG 4.2 is overestimating access to preprimary education, because it includes large numbers of children who enroll in primary school before the official age of entry. This suggests that parents “vote for preschool” by sending their under-age children to primary school when access to affordable preprimary is limited. Implications for SDG measurement and preprimary policy are discussed.

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

The aim of this article is to present the project “Information System on Disability”, carried out by ISTAT (Italian National Statistics Institute). The background context affecting its implementation is described, in terms of both the policies and the main information sources on disability available in Italy. The paper does not attempt to develop a new theoretical explanation of disability, but underlines the steps and actions that must be taken, and problems that must be overcome in building a statistical information system on disability able to satisfy the information needs of diverse users.