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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.

The purpose of this paper is to illustrate the value of extended time span coverage of state longitudinal education and workforce data system to inform and improve the effectiveness of future high impact expenditure decisions.

It used an analytical 29-year data file created by the author that links seven already-in-place education and workforce administrative record sources. Relying on the path dependency theory, multi-level mixed-effect logistic and multi-level mixed-effect linear regression models are used to test three hypotheses.

The findings are consistent with the hypotheses: inclusion of the multiple steps along a post-secondary education pathway and prior job histories are both critical to understanding workforce outcomes mechanisms; it takes time for the employment outcome effect to be evident and strong following education attainment.

The study concludes with research limitations and implications for decision makers to call for retaining and investing in administrative records with extended time span coverage, particularly for the already-in-place historical administrative records.

The paper is one of the first to demonstrate the value of extended time span coverage in a longitudinal state integrated data system through econometric modeling, using longitudinally integrated data linking seven administrative records covering continuously for 29 years. No matter for prior education or employment pathway, it is only through extended time span coverage that employment outcomes can be well measured and the rich nuances interpreting the mechanisms of education return on investment can be revealed.

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 current research explores the structural elaboration of municipal American police organizations, specifically, the structural complexity of police organizations and its relationship to time. The purpose of this paper is to describe and test essential elements of the structural elaboration hypothesis.

The authors explore the structural elaboration hypothesis utilizing a sample of 219 large police departments across the USA. Data are drawn from multiple waves of the Law Enforcement Management and Administrative Statistics survey and are analyzed using tobit and OLS regression techniques.

While there is some evidence that police departments are becoming more elaborate, little evidence for the structural elaboration hypothesis as a function of time is found.

This project is the first to specifically explore the structural elaboration hypothesis across multiple time points. Additionally, results highlight structural trends across a panel of large American police organizations and provide potential explanations for changes. Suggestions for large-scale policing data collection are also provided.

The purpose of this paper is to provide international data on the occurrence (and rates) of clinical errors, identified by type and consequence in the Lombardy region, and to assess empirically the association between hospital accreditation‐type measures and clinical error rates by merging hospital discharge records and medical malpractice claim data in the Lombardy region (Italy).

Data were drawn from the regional database collecting claims and demands for reimbursement declared by patients hospitalized in regional healthcare structures and regional archives collecting hospital discharge records. To model the variability of clinical errors rates, binomial negative regression models were applied. For improved interpretation of the results, a regression tree methodology was used.

The results demonstrated that the rate of readmission for the same major diagnostic category and the rate of discharges against medical advice significantly affect the incidence of errors causing patient death, whereas the rate of unscheduled surgical readmission in the operating room significantly affects the rate of surgical error.

The findings confirm that claims data is problematic in nature because of the limited number of claims generally emerging from administrative sources. The article proposes using proper regression models for count data, taking into account over‐dispersion and excess zeroes and classification tree methods for a better interpretation of empirical evidence.

Health structures where quality outcomes have a significant impact on clinical error rates should be monitored in depth, investigating the medical charts of involved patients to identify quality problems and problematic areas.

As a risk management strategy, the combined use of claims data and clinical administrative data is proposed to shed light on the more problematic, error‐prone areas, allowing regional stakeholders to receive relevant, highly cost‐effective and timely information and an in‐depth understanding of the problematic areas in the assessment of risk.

The present paper constructed a new framework for government data governance based on the concept of a data middle platform to elicit the detailed requirements and functionalities of a government data governance framework.

Following a three-cycle activity, the design science research (DSR) paradigm was used to develop design propositions. The design propositions are obtained based on a systematic literature review of government data governance and data governance frameworks. Cases and experts further assessed the effectiveness of the implementation of the artifacts.

The study developed an effective framework for government data governance that supported the digital service needs of the government. The results demonstrated the advantages of the framework in adapting to organizational operations and data, realized the value of data assets, improved data auditing and oversight and facilitated communication. From the collection of data to the output of government services, the framework adapted to the new characteristics of digital government.

Knowledge of the “data middle platforms” generated in this study provides new knowledge to the design of government data governance frameworks and helps translate design propositions into concrete capabilities. By reviewing earlier literature, the article identified the core needs and challenges of government data governance to help practitioners approach government data governance in a structured manner.

This paper aims to present the IZA Evaluation Dataset, a newly created data source allowing for the evaluation of active labor market policies in Germany.

The paper's approach is a description of the sampling and contents of the IZA Evaluation Dataset and an outline of its research potential.

The evaluation of active labor market policies is often confronted with a lack of adequate empirical data. The IZA Evaluation Dataset may serve as a role model for the provision of such data.

The scope of active labor market policy instruments that can be analyzed with the IZA Evaluation Dataset is mainly restricted to measures for unemployed individuals.

In recent years, many countries have opened their administrative databases for evaluation studies. However, information that might be relevant for economic modeling is often absent. The IZA Evaluation Dataset aims to overcome such limitations for Germany by complementing administrative data from the Federal Employment Agency with innovative survey data.

The purpose of this paper is to understand the landscape of data curation services among public and academic libraries in the USA, with a focus on library capacity for providing data curation services.

The authors conducted an online survey by employing stratified sampling from the American Library Directory. A total of 198 responses were analyzed.

The authors’ findings provide insight into the current landscape of libraries’ data curation services. The survey participants evaluated six capacity dimensions for both public and academic libraries – value, financial, administrative, technical infrastructure, human resources and network. The ratings the participants gave to these capacity dimensions were significantly different between academic and public libraries.

This study suggests several areas in which libraries will benefit from further developing their capacity to successfully run data curation services.

This is among the first research study to address the concept of capacity in the context of libraries’ data curation services.