MEASURING QUALITY OF LIFE: ECONOMIC,SOCIAL, AND SUBJECTIVE INDICATORS

Thinkers have discussed the “good life” and the desirable society for millennia. In the last decades, scientists offered several alternative approaches to defining and measuring quality of life: social indicators such as health and levels of crime, subjective well-being measures (assessing people's evaluative reactions to their lives and societies), and economic indices. These alternative indicators assess three philosophical approaches to well-being that are based, respectively, on normative ideals, subjective experiences, and the ability to select goods and services that one desires. The strengths and weaknesses of the various approaches are reviewed. It is argued that social indicators and subjective well-being measures are necessary to evaluate a society, and add substantially to the regnant economic indicators that are now favored by policy makers. Each approach to measuring the quality of life contains information that is not contained in the other measures.     

Lottery Rather than Waiting-line Auction

This paper investigates the allocative efficiency of two non-price allocation mechanisms – the lottery (random allocation) and the waiting-line auction (queue system) – for the cases where consumers possess identical time costs (the homogeneous case), and where time costs are correlated with time valuations (the heterogeneous case). We show that the relative efficiency of the two mechanisms depends critically on a scarcity factor (measured by the ratio of the number of objects available for allocation over the number of participants) and on the shape of the distribution of valuations. We show that the lottery dominates the waiting-line auction for a wide range of situations, and that while consumer heterogeneity may improve the relative allocative efficiency of the waiting-line auction, the ranking on relative efficiency is not reversed.     

Strategic Maneuvers for Outsourcing Flexibility: An Empirical Assessment

Tan and Sia (2006) conceptualize outsourcing flexibility within a set of four dimensions: robustness, modifiability, new capability, and ease of exit, and propose a portfolio of preemptive, protective, exploitive, and corrective maneuvers to manage flexibility in outsourcing. In this article, we build upon their proposed framework by anchoring the strategic maneuvers through a multitheoretic lens and empirically validate the research propositions. A survey of 171 outsourcing projects in Singapore was conducted. The findings bear evidence that the four proposed dimensions of outsourcing flexibility are conceptually distinct and that different strategic maneuvers contribute differently to the various flexibility dimensions. The empirical findings also affirm the importance of structuring flexibility for outsourcing success, with some flexibility dimensions manifesting different intensity in enabling outsourcing success for IT outsourcing versus business process outsourcing. We conclude by proposing a more holistic approach for effective flexibility management in outsourcing and highlighting research gaps that may need deeper theorizing and empirical extension.     

The effects of nonnormality on the analysis of supersaturated designs: a comparison of stepwise,SCAD and permutation test methods

Supersaturated designs (SSDs) are useful in examining many factors with a restricted number of experimental units. Many analysis methods have been proposed to analyse data from SSDs, with some methods performing better than others when data are normally distributed. It is possible that data sets violate assumptions of standard analysis methods used to analyse data from SSDs, and to date the performance of these analysis methods have not been evaluated using nonnormally distributed data sets. We conducted a simulation study with normally and nonnormally distributed data sets to compare the identification rates, power and coverage of the true models using a permutation test, the stepwise procedure and the smoothly clipped absolute deviation (SCAD) method. Results showed that at the level of significance α=0.01, the identification rates of the true models of the three methods were comparable; however at α=0.05, both the permutation test and stepwise procedures had considerably lower identification rates than SCAD. For most cases, the three methods produced high power and coverage. The experimentwise error rates (EER) were close to the nominal level (11.36%) for the stepwise method, while they were somewhat higher for the permutation test. The EER for the SCAD method were extremely high (84–87%) for the normal and t-distributions, as well as for data with outlier.     

Using generalized doubly robust estimator to estimate average treatment effects of multiple treatments in observational studies

The generalized doubly robust estimator is proposed for estimating the average treatment effect (ATE) of multiple treatments based on the generalized propensity score (GPS). In medical researches where observational studies are conducted, estimations of ATEs are usually biased since the covariate distributions could be unbalanced among treatments. To overcome this problem, Imbens [The role of the propensity score in estimating dose-response functions, Biometrika 87 (2000), pp. 706–710] and Feng et al. [Generalized propensity score for estimating the average treatment effect of multiple treatments, Stat. Med. (2011), in press. Available at: http://onlinelibrary.wiley.com/doi/10.1002/sim.4168/abstract] proposed weighted estimators that are extensions of a ratio estimator based on GPS to estimate ATEs with multiple treatments. However, the ratio estimator always produces a larger empirical sample variance than the doubly robust estimator, which estimates an ATE between two treatments based on the estimated propensity score (PS). We conduct a simulation study to compare the performance of our proposed estimator with Imbens’ and Feng et al.’s estimators, and simulation results show that our proposed estimator outperforms their estimators in terms of bias, empirical sample variance and mean-squared error of the estimated ATEs.     

Comparison of clustering algorithms on generalized propensity score in observational studies: a simulation study

In observational studies, unbalanced observed covariates between treatment groups often cause biased inferences on the estimation of treatment effects. Recently, generalized propensity score (GPS) has been proposed to overcome this problem; however, a practical technique to apply the GPS is lacking. This study demonstrates how clustering algorithms can be used to group similar subjects based on transformed GPS. We compare four popular clustering algorithms: k-means clustering (KMC), model-based clustering, fuzzy c-means clustering and partitioning around medoids based on the following three criteria: average dissimilarity between subjects within clusters, average Dunn index and average silhouette width under four various covariate scenarios. Simulation studies show that the KMC algorithm has overall better performance compared with the other three clustering algorithms. Therefore, we recommend using the KMC algorithm to group similar subjects based on the transformed GPS.     

DOES EARLY ACCESS TO PENSION WEALTH IMPROVE HEALTH?

We examine the health impacts of early access to public pension wealth by exploiting a unique policy in Singapore allowing individuals to withdraw a proportion of their pension savings after their 55th birthday. For the identification, we employ a regression discontinuity design by comparing individuals before and after their 55th birthday. To address anticipated and lagged health impacts, we adopt the donut regression discontinuity approach. Using nationally representative monthly panel data, we find that early access to pension wealth improves self-reported overall health. (JEL I10, H55, D15)     

Cultural mediation through vernacularization: framing rights claims through the day‐off campaign for migrant domestic workers in Singapore

Scholarship on civil society in Singapore has tended to emphasize the structural and institutional constraints on civil society space. Conversely, little attention is paid to the broader cultural and discursive realms in which civil society and state actors operate. This article seeks to address this gap by analysing the day‐off campaign for migrant domestic workers in Singapore. We demonstrate that by employing the cultural mediation strategy of vernacularization, civil society was able to frame migrant rights claims in a manner that resonated with the institutional logics and cultural repertoire of Singapore society. Civil society actors gained headway by adapting the discourse on migrant rights to Singapore's socio‐cultural and political context in three ways: by reframing rights claims into a moral appeal; by appealing to the cost‐benefit logics of Singaporean employers of migrant domestic workers; and by situating the provision of migrant labour protections as a relative market position.     

Is specialization desirable in committee decision making?

Committee decision making is examined in this study focusing on the role assigned to the committee members. In particular, we are concerned about the comparison between committee performance under specialization and non-specialization of the decision makers. Specialization (in the context of project or public policy selection) means that the decision of each committee member is based on a narrow area, which typically results in the acquirement and use of relatively high expertise in that area. When the committee members’ expertise is already determined, specialization only means that the decision of each committee member is based solely on his/her relatively high expertise area. This form of specialization is potentially inferior relative to non-specialization under which the decision of each committee member is based on different areas, not just his/her relatively high expertise area. Given that the expertise of the committee members is already determined but unknown, our analysis focuses on non-specializing individuals whose decision is based on a decision rule that does not require information on the decision-making skills. Under these realistic assumptions, non-specialization is shown to be preferable over specialization, depending on the aggregation rule applied by the committee. The significance of our approach is not limited to the specific results that we obtain. Rather, it should be viewed as a first step toward a deeper examination of the role of individual decision makers in enhancing the performance of collective decision making.     

Testing the Interaction Effects of Task Complexity in Computer Training Using the Social Cognitive Model

Using a Modified Social Cognitive Theory framework, this study examines the behavior modeling and lecture‐based training approaches to computer training. It extends the existing Social Cognitive Model for computer training by adding the task complexity construct to training method, prior performance, computer self‐efficacy, outcome expectations, and performance. A sample of 249 students from a large state university served as participants in a laboratory experiment that was conducted to determine the task complexity*training method and task complexity* self‐efficacy interaction effects on performance. Structural equation modeling with interaction effects was used to analyze the data. The results show that behavior modeling outperforms lecture‐based training in a measure of final performance when task complexity is high. Further, it is found that computer self‐efficacy has a greater positive effect on performance when task complexity is high than when task complexity is low. Prior performance is also found to be an important variable in the model.