Underpaid or Overpaid? Wage Analysis for Nurses Using Job and Worker Attributes

The nursing labor market presents an apparent puzzle. Hospitals report chronic shortages, yet standard wage analysis shows that nursing wages have increased over time and greatly exceed those received by other college-educated women. This paper addresses this puzzle. Data from the Current Population Survey (CPS) are matched with detailed job content descriptors from the Occupational Information Network (O*NET). Nursing jobs require higher levels of skills and more difficult working conditions than do jobs for other college educated workers. A standard CPS-only wage regression shows a registered nurse (RN) wage advantage of .22 log points compared to a pooled male/female group of college-educated workers. Control for O*NET job attributes reduces the RN gap to .08, while an arguably preferable nonparametric estimator produces a wage gap estimate close to zero. We conclude that nurses receive compensation close to long-run opportunity costs, narrowing if not resolving the RN wage-shortage puzzle.nursing, wage differentials, job attributes

Dominance relations when both quantity and quality matter, and applications to the\r\ncomparison of US research universities and worldwide top departments in economics

In this article, we propose an extension of the concept of stochastic dominance intensively\r\nused in economics for the comparison of composite outcomes both the quality and the\r\nquantity of which do matter. Our theory also allows us to require unanimity of judgement\r\namong new classes of functions. We apply this theory to the ranking of US research\r\nuniversities, thereby providing a new tool to scientometricians (and the academic\r\ncommunities) who typically aim to compare research institutions taking into account both\r\nthe volume of publications and the impact of these articles. Another application is provided\r\nfor comparing and ranking academic departments when one takes into account both the size\r\nof the department and the prestige of each member.Ranking, dominance relations, citations.

Underpaid or Overpaid? Wage Analyses For Nurses Using Jobs versus Worker Attributes

Nursing shortages are common despite the fact that nurses earn far higher wages than other college-educated women. Our analysis addresses the puzzle of high nursing wages. Employee data from the Current Population Survey are matched with detailed job descriptors from the Occupational Information Network. Nursing requires high levels of compensable skills and demanding working conditions. Standard log wage regression estimates indicate nursing wage advantages of about 40%. Accounting for job attributes reduces estimates to roughly 20%. Rather than transforming ordinary least squares log gaps to percentages, alternative methods measuring Mincerian gaps produce estimates of 15% or less. We conclude that nurses receive compensation that is much closer to opportunity costs than that seen in standard analyses, narrowing the shortage puzzle. Supply constraints in nurse licensing can produce wages above long-run opportunity costs but that are too low to clear short-run labor markets during periods of growing demand. The analysis provides broader implications for the conduct of wage analyses

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q2-index, and hr-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area

Peer assessment or promotion by numbers? A comparative study of different measures of researcher performance within the UK Library and Information Science research community

Hirsch’s h-index, Egghe’s g-index, total citation and publication counts, and five proposed new metrics were correlated with one another using Spearman’s Rank Correlation for one hundred randomly selected academics and researchers working in UK Library and Information Science departments. Metrics were compared for individuals of different genders and at institutions awarded different RAE (2001) grades. Individuals’ metrics were rank-correlated against academic ranks and RAE (2001) grades of their employing departments. Metrics calculated using Web of Science and Google Scholar data were compared. Peer- and h-index metric-ranked orders of researchers were rank-correlated. Citation behaviour and attitudes towards peer and citation-based assessment of 263 academics and researchers were investigated by factor analysis of online attitudinal survey responses. h increased curvilinearly with total citation and publication counts, suggesting that h was constrained by the activity in the field preventing individuals producing enough heavily cited publications to increase their h-index scores. Most individuals therefore shared similar h-index scores, making interpersonal comparisons difficult. Total citation counts and Bihui’s a-index scores distinguished between more individuals, though whether they could confidently identify differences between individuals is uncertain. Both databases arbitrarily omitted individuals and publications, systematically biasing citation metrics calculated using them. In contrast to studies of larger fields, no citation metrics correlated with RAE grade, academic rank, or direct peer-assessment, suggesting that citation-based assessment is unsuitable for research fields with relatively little research activity. No gender bias was evident in academic rank, esteem or citedness. At least nine independent factors influence citation behaviour. Mertonian factors dominated. The independence of the factors suggested different individuals have different combinations of non-Mertonian motivations. The overriding meaning of citations was confirmed as signals of relevance and reward. Recommendations for future research include a need to develop simple, robust methods to identify subfields and normalise citations across subfields, to quantify the impact of random bias and to determine whether it varies across subfields, and to study the rate of accumulation of citations and citation distribution changes for individuals (and departments) over time to determine whether career age can be controlled for, in particular

Global assessment of university research comprehensiveness

The demand for global university league tables has been high over the past two decades. However, significant criticism of their methodologies is accumulating without being addressed. I revisit global university league tables by normalizing each field as to create a uniform distribution of value. Then, the overall performance of an institution is interpreted as the probability of having a high score in any given academic field. I focus on the similarity of institutions across ten criteria related to academic performance in eighty subjects of all fields of knowledge. The latter does not induce a zero-sum game, removing one of the most prominent negative features of established league tables. The present assessment shows that the main difference between hundreds of leading global research universities is whether their coverage of all areas of human knowledge is comprehensive or specialized, as their mean performance per subject is nearly indistinguishable. I compare the results with the main league tables and found excellent agreement, suggesting that regardless of their methodologies, research-intensive institutions perform well in rankings if they are comprehensive. This comprehensiveness is ultimately dependent on institutional age, privileged funding allocation and regional academic culture. Consequently, when the size of an institution is taken out of the picture, I found no correlation between comprehensiveness and quality, and no difference can be found in the mean quality of institutions regionally or globally. Furthermore, I find the reputation and prestige of several famous institutions to far exceed their performance within the present methodology, while numerous institutions with less reputation and visibility perform better than expected