Background: Learning health systems (LHSs) iteratively generate evidence that can

Objective: To implement use of obstetric (OB) hospitalist telemedicine services (TeleOB) to support clinicians facing OB emergencies in low-resource hospital settings. Methods: TeleOB was staffed b...

Objective A learning health system (LHS) uses data to generate evidence and answer questions required to continually improve system performance and patient care. Given the complexities of practice transformation, an area where LHS is particularly important is the study of primary care transformation (PCT) as PCT generates several practice-level questions that require study where the findings can be readily implemented. In May 2019, a large integrated health delivery system in Minnesota began implementation of a population management PCT in two of its 40 primary care clinics. In this model of care, patients are grouped into one of five service bundles based on their complexity of care; patient appointment lengths and services provided are then tailored to each service bundle. The objective of this study was to examine the use of a LHS in PCT by utilising the Consolidated Framework for Implementation Research (CFIR) to categorise implementation lessons from the initial two PCT clinics to inform further implementation of the PCT within the health system. Design This was a formative evaluation in which semistructured qualitative interviews were carried out. Observational field notes were also taken. Inductive coding of the data was performed and resultant codes were mapped to the CFIR. Setting Two suburban primary care clinics in the Twin Cities, Minnesota. Participants Twenty-two care team members from the first two clinics to adopt the PCT. Results Seventeen codes emerged to describe care team members’ perceived implementation influences. Codes occurred in each of the five CFIR domains (intervention characteristics, outer setting, inner setting, characteristics of individuals and process), with most codes occurring in the ‘inner setting’ domain. Conclusions Using an LHS approach to determine early-stage implementation influences is key to guiding further PCT implementation, understanding modifications that need to be made and additional research that needs to occur. Data are available upon reasonable request.

Research Summary: To grow, startup ventures often require the skills of professional managers familiar with running larger organizations. However, risk considerations may discourage such candidates from departing high-paying, stable jobs. Interpreting the manager's decision within a household holding a “portfolio” of jobs, we hypothesize that having a spouse whose career is prioritized mitigates risk as a barrier to joining a startup. Survey data corroborate that both men and women with a career-prioritized spouse are less likely to report risk as a barrier. However, having a career-prioritized spouse only translates to a greater interest in startup employment among men. Our findings have implications for understanding the challenges startup ventures face when attracting managerial talent, how dual careers and gender affect managers' careers, and regional entrepreneurial ecosystems. Managerial Summary: Using survey data from professional managers working in corporate headquarters, we show that being in a dual-career household increases one's willingness and lowers the perceived risk of leaving their job and joining a startup venture—especially if the household prioritizes their spouse's career. However, the increased willingness to join a startup in households that prioritize their spouse's career is only manifest for men. These findings highlight how dual-career households can be a talent source for startup ventures and suggest that regions with greater concentrations of dual-career households might be especially advantageous for startup ventures. Nevertheless, our results also suggest that gender norms are an impediment for dual-career women when considering employment in startup ventures.

Just as employers face uncertainty when hiring workers, workers also face uncertainty when accepting employment, and bad employers may opportunistically depart from expectations, norms, and laws. However, prior research in economics and information sciences has focused sharply on the employer's problem of identifying good workers rather than vice versa. This issue is especially pronounced in markets for gig work, including online labor markets, in which platforms are developing strategies to help workers identify good employers. We build a theoretical model for the value of such reputation systems and test its predictions on Amazon Mechanical Turk, on which employers may decline to pay workers while keeping their work product and workers protect themselves using third-party reputation systems, such as Turkopticon. We find that (1) in an experiment on worker arrival, a good reputation allows employers to operate more quickly and on a larger scale without loss to quality; (2) in an experimental audit of employers, working for good-reputation employers pays 40% higher effective wages because of faster completion times and lower likelihoods of rejection; and (3) exploiting reputation system crashes, the reputation system is particularly important to small, good-reputation employers, which rely on the reputation system to compete for workers against more established employers. This is the first clean field evidence of the effects of employer reputation in any labor market and is suggestive of the special role that reputation-diffusing technologies can play in promoting gig work, in which conventional labor and contract laws are weak.

We develop and test a theoretically informed and generalizable empirical framework for evaluating the performance gap between internally and externally hired workers. First, human capital theory predicts that internal hires will be immediately more productive than external hires. Second, contextual learning predicts that internal hires will be more productive with time. Finally, theories of commitment, which are rarely applied to this literature, predict that internal advancement enhances retention among high performers (“positive retention”). Applying a general empirical framework for quantifying the relative contributions of these mechanisms to a retailer with 109,063 commissioned salespeople and their 12,931 managers, we find that the gap in our setting is primarily driven by positive retention: high performers and internal hires are less likely to quit, and crucially, high performing internal hires are especially unlikely to quit. When high performing internal hires do quit, they tend to cite reasons unrelated to work rather than advancement opportunities. By typically examining erformance and retention in isolation, researchers and organizations may be underestimating the importance of internal advancement as a means of retaining of high performers

Technological advances and the big-data revolution have facilitated fine-grained, high-frequency, low-cost measurement of individuals’ work. Yet we understand little about the influences of such quantification of work on workers’ behavior and performance. This article investigates how and when quantification of work affects worker productivity. We argue that quantification affects worker productivity via auto-gamification, or workers’ inadvertent transformation of work into an independent, individual-level game. We further argue that quantification is likely to raise productivity in a context of simple work, where auto-gamification is motivating because quantified metrics adequately measure the work being performed. When work is complex, by contrast, quantification reduces productivity because quantified metrics cannot adequately measure the multifaceted work being performed, causing auto-gamification to be demotivating. To substantiate our argument, we study implementation of an RFID measurement technology that quantifies individual workers’ output in real time at a garment factory in India. Qualitative evidence uncovers the auto-gamification mechanism and three conditions that enable it; a natural experiment tests the consequences of quantification of work for worker productivity. This article contributes to the study of quantification, work games, technology, and organizations, and we explore the policy implications of further quantification of work.

Workers struggle to understand prospective employers. Through experienced workers' volunteered reviews, Glassdoor is a platform that seeks to provide information about prospective employers to jobseekers. We find that the content most valuable to job-seekers (negative information) is the kind most risky to supply, pointing to a Catch-22. Higher ratings increase labor supply to less well-known firms, creating an incentive for smaller firms to discourage negative reviews. Concerns about employer retaliation discourage negative reviews and motivate employees who do disclose to conceal aspects of their identity, degrading the information's value. Reputation institutions provide valuable but partial solutions to workers' information problems. JEL: J3, J28, D83, J62.

The best worker is not always the best candidate for manager. In these cases, do firms promote the best potential manager or the best worker in their current job? Using microdata on the performance of sales workers at 131 firms, we find evidence consistent with the Peter Principle, which proposes that firms prioritize current job performance in promotion decisions at the expense of other observable characteristics that better predict managerial performance. We estimate that the costs of promoting workers with lower managerial potential are high, suggesting either that firms are making inefficient promotion decisions or that the benefits of promotion-based incentives are great enough to justify the costs of managerial mismatch. We find that firms manage the costs of the Peter Principle by placing less weight on sales performance in promotion decisions when managerial roles entail greater responsibility and when frontline workers are incentivized by strong pay for performance.

This paper theorizes and provides evidence for the segregation of men into clustered occupations and women into dispersed occupations in advance of marriage and in anticipation of future colocation problems. Using the Decennial Census, and controlling for occupational characteristics, I find evidence of this general pattern of segregation, and also find that the minority of the highly educated men and women who depart from this equilibrium experience delayed marriage, higher divorce, and lower earnings. Results are consistent with the theory that marriage and mobility expectations foment a self-fulfilling pattern of occupational segregation with individual departures deterred by earnings and marriage penalties.

Empirical research on the family cites the tendency for couples to relocate for husbands' careers as evidence against the gender neutrality of household economic decisions. For these studies, occupational segregation is a concern because occupations are not random by sex and mobility is not random by occupation. I find that the tendency for households to relocate for husbands' careers is better explained by the segregation of women into geographically dispersed occupations rather than by the direct prioritization of men's careers. Among never-married workers, women relocate for work less often than men, and the gender effect disappears after occupational segregation is accounted for. Although most two-earner families feature husbands in geographically clustered jobs involving frequent relocation for work, families are no less likely to relocate for work when it belongs to the wife. I conclude that future research in household mobility should treat occupational segregation occurring prior to marriage rather than gender bias within married couples as the primary explanation for the prioritization of husbands' careers in household mobility decisions.

This thesis is composed of three papers, each relating to labor market imperfections and their implications for firms staffing practices. In the first paper, I examine why hospitals provide direct financial support to nursing schools and faculty. This support is striking because nursing education is clearly general, clearly paid by the firm, and information asymmetries appear minimal. Using AHA and survey data, I find hospitals employing a greater share of their MSAs registered nurses are more likely to provide such support, net of size and other institutional controls. I interpret this result as evidence that technologically-general skills training may be made de facto-specific by mobility frictions.In the second paper, I present a theory of couples job search whereby women sort into lowerpaying geographically-dispersed occupations due to expectations of future spouses geographically-clustered occupations and (thereby) inability to relocate for work. Results confirm men segregate into geographically-clustered occupations, and that these occupations involve more-frequent early career relocations for both sexes. I also find that the minority of the men and women who depart from this equilibrium experience delayed marriage, higher divorce, and lower earnings. Results are consistent with the theorys implication that marriage and mobility expectations foment a self-fulfilling pattern of occupational segregation, with individual departures deterred by earnings and marriage penalties.In the third paper, I examine the use and misuse of authority and incentives in organizational hierarchies. Through a principal-supervisor-agent model inspired by sales settings, I propose organizations delegate authority over salespeople to front-line sales mangers because they can decompose performance measures into ability and luck. The model yields the result that managers on the cusp of a quota have a unique personal incentive to retain and adjust quotas for poor performing subordinates, permitting me to distinguish managers' interests from those of the firm. I parametrically estimate the model using detailed person-transaction-level microdata from 244 firms that subscribe to a ?cloud?-based service for automating transaction processing and compensation. I estimate 13-15% of quota adjustments and retentions among poor performers are explained by the managers' unique personal interest in meeting a quota. I use agency theory to evaluate firms mitigation practices.

This study uses recent data from the Bureau of Labor Statistics (BLS) and Registered Nurses (RNs) licensing exam to examine the recession's effect on the RN labor market. It then reports results of a survey of 518 hospital nursing officers conducted in 2008 and 2010 matched with institutional data from the American Hospital Association (AHA). These unique data show how the recession led hospitals to slow hiring despite accelerating attrition of retirement-age nurses; shift away from H1-B, agency, and, overtime work; and reduce training, and other benefits for new hires. More broadly, results show how nurse-staffing practices adapt to market conditions. Results also suggest reduced hospital support for nursing education may strain the supply of managerial and specialty nurses as baby-boom nurses retire.

In this section we discuss the sensitivity of our results in several dimensions. Productivity. In Section 4.2 we estimate our baseline productivity regression (11) via weighted-least squares. Tables 3 and 4 shows the resulting supermodularity statistics and confidence intervals from 10,000 bootstraps. As stated in the main text, mean productivity is supermodular, especially for white-Hispanic and black-Hispanic pairs, and productivity variance is log-submodular, especially for black-Hispanic pairs. Thus, the black-Hispanic pairs indicate screening discrimination (97%) while white-Hispanic indicate screening or complementarity (99.1%). Table B1 shows the regression coefficients underlying these results. As discussed in Section 4.2, Table B2 conducts several robustness checks and Figure B2 recalculates Figure 4 for (a) the first six months of workers' tenure, and (b) later months. Turnover. Section 4.3 considered turnover after 6 months, showing that x ⇥⇥ 0 < 0 for all three pairs, with white-Hispanic being significant. The underlying regression is shown in Table B3. Table B4(a) shows that the results are robust to measuring turnover at 3, 9 and 12 months. All the coefficients are negative except black-Hispanic at 6 months; only the white-Hispanic coefficients are significantly negative. Table B4(b) then includes the worker's average productivity, ¯ e i , as a control. As expected, this has a negative sign; e.g. with the "6 month" regression, the coefficient is 0.0609, meaning a one standard deviation increase in ¯ e i of 0.439 lowers turnover by 2.7% from a base of 36.1%. In the model, a worker is fired when their realized productivity falls below a threshold. Under screening discrimination and complementary production, same-race workers have higher productivity than cross-race workers, so controlling for SPH should increasê x ⇥⇥ towards zero. Under taste-based discrimination, same-race workers have lower productivity than cross-race workers, so controlling for SPH should lowerˆxlowerˆ lowerˆx ⇥⇥. Table B4(b) shows thatˆxthatˆ thatˆx ⇥⇥ tends to increase for white-Hispanic and black-Hispanic pairs.