Effects Of Unobserved Factors Research Articles

Estimating the Safety Effects of Congestion Warning Systems using Carriageway Aggregate Data

Is it possible to use just aggregate carriageway data for the evaluation of congestion warning systems (CWS) in large networks—or any system affecting traffic safety for that matter? In this paper, two hypotheses related to this question are tested. The first hypothesis is that it can be done by comparing large-scale congestion patterns on road stretches with and without CWS. The underlying rationale is that heterogeneous congestion patterns with many disturbances, frequent wide moving jams, and large speed differences result in more potentially unsafe traffic conditions than more homogeneous congestion patterns. The second hypothesis is that it is possible to compare differences in average (maximum) deceleration distributions into congestion waves between road stretches with and without CWS. Both hypotheses have been tested for similar bottlenecks with similar demand patterns and the results suggest the first hypothesis must be rejected. Although the idea seems plausible (CWS result in more homogeneous congestion patterns) there were too many confounding factors in the data to make the case. However, persuasive evidence was found for the second hypothesis. Statistically significant differences were found between (maximum) deceleration distributions on road stretches with and without CWS that suggest CWS do—as expected—contribute positively to traffic safety. It thus seems to be possible to monitor safety effects using just average speeds. However, the method is limited to providing relative comparisons. Furthermore, to fully rule out the effects of unobserved factors, more evidence and validation with microscopic data are needed.

Do Voluntary Associations Matter for the Spread of Civic Activism in Russia? Matching Technique Applied to Survey Data

ObjectiveThis article develops an empirical model that tests whether in post‐communist Russia prior membership in a civil society organization (CSO) motivates civic activism. Revisiting Tocquevillian notions of a nurturing effect by formal civil society organizations of civic activism, we aim to help explain recent research findings showing that civic activism is sustainable and vital in Russia.MethodsTo test the main hypothesis of the study, we use the cross‐sectional survey of Russians aged 18 and older in the fall of 2014 (N = 1,500) representing 43 regions conducted by the Russian Civil Society Monitoring Survey of the Higher School of Economics' Centre for Studies of Civil Society and the Nonprofit Sector (2006–). We use the propensity score matching (PSM) technique to estimate the main effects. We also show that the effect of unobserved factors on our PSM estimates is limited using Rosenbaum bounds analysis.ResultsThe PSM analysis suggests that for all four indicators of civic activism, that is, willingness to integrate with other members of society, participation in tenant meetings, engagement in charity activities, and engagement in home improvement, CSO participation positively impacts civic activism. The impact varies from a 7.2 to 15.8 percent higher propensity of civic activism for CSO participants.ConclusionWe conclude that prior participation in a CSO can have a motivating influence on civic activism such as charity work, residential home improvement, willingness to integrate with other members of the society, and participation in tenant meetings.

Hypomethylating agent (HMA) therapy use and survival in older patients with higher risk myelodysplastic syndromes (HR-MDS) in the United States (USA): A large population-based study.

7057 Background: The HMA azacitidine improved overall survival (OS) compared to conventional care in patients with HR-MDS in the landmark randomized clinical trial AZA-001 by a median of 9.5 months compared to conventional care regimens. However recent registry data from USA (Zeidan et al, 2016) and Spain (Bernal et al, 2015) did not show substantial population-level gains in OS. We used HMA market entry in the US (2004-06) as a natural experiment to assess the effect of HMA on OS. Methods: We conducted a retrospective cohort study using Surveillance, Epidemiology, and End Results-Medicare data.Individuals were included if they were 66 years or older in age, had refractory anemia with excess blasts (RAEB, considered to have HR-MDS), and were diagnosed in 2001-11.We performed 2-stage residual inclusion instrumental variable (IV) analysis, using diagnosis year as the IV. Residuals from the first stage model were added to the second stage regression to control for effects of unobserved factors between HMA treatment and OS. We also adjusted for demographic characteristics, comorbidity, disability status and transfusion receipt. Results: Among 2,581 patients with RAEB, the median OS from diagnosis was 9 months, and 35.8% received HMAs. In the first stage model, diagnosis year was a strong predictor of HMA (2001-06 vs. 2011, Odds Ratio = 0.004-0.56, P-value < .01). In IV analysis, patients who received HMAs showed no gain in OS (Hazard Ratio (HR) = 0.93, 95% Confidence Interval (CI) = 0.76-1.14), while there was a strongly protective effect of unobservable characteristics on OS (HR = 0.58, 95% CI = 0.47-0.73). Conclusions: In our US population-based analysis,HMA use was not associated with survival prolongation in older adults with HR-MDS. It is unclear if this is related to suboptimal use of HMAs (alternate schedules, doses, number of cycles), use of decitabine (which did not show survival benefit in randomized studies), or the unselected nature (compared to strict eligibility in clinical trials) of patients receiving HMAs at the community level. Better understanding of these issues is vital for optimal HMA patient selection and administration.

Measuring unobservable factors in residential developments: a structural equation approach

Purpose The purpose of this paper is to investigate how to measure the effect of unobservable factors on residential choice behaviour in an attempt to advance the understanding of how to perform advanced market analysis. This research is important to residential developers, as the diversity of preferences is increasingly driven by lifestyle-based households and affluent households. Design/methodology/approach Information about the pros and cons of renting an apartment in an ongoing residential development project in Stockholm came from interviews using the laddering technique. Qualitative data were subsequently analysed using exploratory and confirmatory factor analysis to confirm which factors had the highest effect on an overall assessment of living in the development and a value-for-money conclusion. Findings Among the potentially important factors identified, respondents who became residents perceived the development to be more noise-free than those who did not when stating the overall value of living in the development and making a value-for-money conclusion. The perception of noise differed between the two groups, meaning that those who did not become residents believed the development to be more exposed to noise. The standard of the apartment was the second most influential factor when stating the overall value of living in the development and making a value-for-money conclusion. The belief of being able to relax in the home environment had no significant effect on overall value for either group. Originality/value The results show that confirmatory factor analysis can be used in measuring the effect of unobservable factors in residential choice behaviour. The methodology presented may advise developers, architects or planners in evaluating those attributes that create value-for-money to improve, for example, overall design solutions in urban development projects.

Do regional primary‐care organisations influence primary‐care performance? A dynamic panel estimation

The role of regional primary-care organizations (PCOs) in health-care systems is not well understood. This is the first study to attempt to isolate the effect of regional PCOs on primary-care performance. We examine Divisions of General Practice in Australia, which were established in 1992. A unique Division-level panel data set is used to examine the effect of Divisions, and their activities, on various aspects of primary-care performance. Dynamic panel estimation is used to account for state dependence and the endogeneity of Divisions' activities. The results show that Divisions were more likely to have influenced general practice infrastructure than clinical performance in diabetes, asthma and cervical screening. The effect of specific Division activities, such as providing support for practice nurses and IT support, was not directly related to changes in the level of general practice performance. Specific support in the areas of diabetes and asthma was associated with general practice performance, but this was due to reverse causality and the effect of unobservable factors, rather than the direct effect of Divisions.

PMD14 Vox Populi: Stated Preferences for Pharmaceutical Innovations

This paper outlines a decision model for evaluating the effect of information regarding toxicity, efficacy, cost, and alternative treatments on anticipated adoption rates of developmental compounds. METHODS: The paper discusses the relationship between multiattribute utility theory and people's preferences for health outcomes. Health outcomes arising from pharmaceutical interventions are viewed as multiattribute commodities. A method for reliable quantification of health-state preferences is described. This procedure requires estimating utility weights from stated-preference (SP) data. By including health cost as an SP attribute, marginal utilities are converted to marginal dollar values to explicitly account for cost in determining market share. Additionally, SP allows for a nonlinear association between willingness to pay and health states reflecting the diminishing marginal utility of health. Furthermore, this technique permits estimating weights for combined health states. A recent SP design for eliciting respondent trade-offs among episode duration, symptom, daily-activity limitations, and cost is presented. Additionally, various utility specifications and simplifying assumptions are described. Finally, a rule for simulating aggregate choice behaviour is presented. The prediction rule employed draws upon random utility theory. This method addresses the probabilistic nature of choices by including a residual term to represent the effect of unobserved factors on utility. A numerical example demonstrates the capability of the decision model. In this example, a general, preference-based form for health-related utility facilitates direct estimation of health attribute utility weights arising from pharmaceutical product choice. This example demonstrates how the model evaluates the impact of cost, outcomes, and competition on anticipated drug adoption rates. CONCLUSION: Integrating stated-preference data, random-utility modeling, and cost-utility analysis provides a reliable way to describe preferences and predict choice behaviour. This capability provides important information for developmental pharmaceutical product marketing decisions.

Antecedents of Executive Compensation: The Use of Covariance Structure Modeling

The use of covariance structure modeling is explored as a means of moving toward a resolution of the debate over the antecedents of executive compensation. The major strength of this methodology is that it enables researchers to measure the effects of unobserved factors on measured variables. It is suggested that covariance structure modeling is a promising way of studying the effect of institutional isomorphism on executive compensation. The popular business press has questioned repeatedly the justification for and the performance effects of prevailing executive compensation systems (Crystal, 1988; Loomis, 1982; Patton, 1985). These articles argue that executives are more interested in creating wealth for themselves than for stockholders. They also underscore the absence of an obvious link between executive compensation and firm performance. Recent academic research on executive compensation adopts an agency perspective that emphasizes potential conflicts of interest between managers and stockholders. It contends that, in the absence of effective disciplining and monitoring systems, executive compensation plans may direct managers' efforts toward personal wealth enhancement to the detriment of firm value (Baumol, 1958; Berle & Means, 1932). In response, scholars have urged that executive compensation plans contain monetary incentives that only accrue to executives when shareholder wealth is maximized (Kerr, 1985; Rappaport, 1983; Tehranian & Waegelein, 1985). However, designing compensation systems that effectively align the interests of managers and stockholders requires a knowledge of the role and effect of relevant driving forces on compensation. Statistical research on executive compensation has been guided predominantly by a search for tangible, observable determinants (Ciscel & Carroll, 1980), examples of which have been firm size or growth rate (Baumol, 1967; Marris, 1963), inter‐firm and inter‐in‐dustry differences (Coughlan & Schmidt, 1985), and performance (Murphy, 1986). The emphasis on such tangible explanations is not surprising given the overwhelming use of econometric techniques, such as ordinary least squares regression (Ciscel & Carroll, 1980; Finkelstein & Hambrick, 1988), logistic regression (Walking & Long, 1984), time series analysis (Murphy, 1985), and event studies (Brickley, Bhagat & Lease, 1985; Coughlan & Schmidt, 1985; Tehranian & Waegelein, 1985). This paper argues that the focus on tangible, observable variables by compensation researchers is a methodologically ‐ driven practice that constrains theory building and testing. As a result, we may have ignored interesting and relevant theoretical frameworks for the study of executive compensation. We also have overlooked the use of analytical techniques that allow us to examine the role of potentially relevant latent constructs. In this paper, we will describe and illustrate the use of covariance structure modeling for the study of institutional pressures on executive compensation.