Re-examining female labor supply responses to the 1994 Australian pension reform

Many governments are aiming to extend working lives by raising the age at which people can claim retirement pensions. This makes it vital to understand how these policies affect retirement decisions. In this paper, I revisit the labor supply effects of a major Australian reform that increased women’s pension age from 60 to 65. Atalay and Barrett (2015) studied these effects using repeated household surveys and a differences-in-differences design in which male cohorts form the comparison group. They estimate that the reform increased female labor force participation by 12 percentage points. Using earlier data, I show that the parallel-trends assumption did not hold before the reform because of a strong female-specific trend in participation rates across the relevant cohorts. Accounting for this trend, the estimated effect on female participation falls by two-thirds and becomes statistically insignificant at conventional levels. This highlights the importance of carefully assessing and controlling for trends across cohorts when evaluating pension reforms, which are typically phased in across cohorts.

     

Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose–Response Uncertainty

Model averaging for dichotomous dose–response estimation is preferred to estimate the benchmark dose (BMD) from a single model, but challenges remain regarding implementing these methods for general analyses before model averaging is feasible to use in many risk assessment applications, and there is little work on Bayesian methods that include informative prior information for both the models and the parameters of the constituent models. This article introduces a novel approach that addresses many of the challenges seen while providing a fully Bayesian framework. Furthermore, in contrast to methods that use Monte Carlo Markov Chain, we approximate the posterior density using maximum a posteriori estimation. The approximation allows for an accurate and reproducible estimate while maintaining the speed of maximum likelihood, which is crucial in many applications such as processing massive high throughput data sets. We assess this method by applying it to empirical laboratory dose–response data and measuring the coverage of confidence limits for the BMD. We compare the coverage of this method to that of other approaches using the same set of models. Through the simulation study, the method is shown to be markedly superior to the traditional approach of selecting a single preferred model (e.g., from the U.S. EPA BMD software) for the analysis of dichotomous data and is comparable or superior to the other approaches.     

The Effects of Knowledge Management on Team Members' Ratings of Project Success and Impact*

Many organizations are trying to improve the generation and utilization of knowledge. The activities associated with these efforts are identified as organizational knowledge management (KM). While much has been written about knowledge management from the organizational level, the success of such efforts over the long run will depend upon on how KM activities impact important outcomes as perceived by those at the employee level who actually implement the activities. This study used the input‐process‐output framework of team effectiveness to investigate the relationship between selected KM‐related activities on integrated product and process development team members' satisfaction with their project's success and the impact they expected it to have on the organization. The results indicate that team‐level leadership and support (i.e., inputs), along with knowledge generation and dissemination (i.e., processes), are key drivers of member performance‐related ratings (i.e., outputs). Finally, and possibly most importantly, a number of interactions were evident suggesting that the KM processes moderate the effects of the KM inputs. These findings and their implications are discussed.     

Fiscal Foresight and Information Flows

News—or foresight—about future economic fundamentals can create rational expectations equilibria with non‐fundamental representations that pose substantial challenges to econometric efforts to recover the structural shocks to which economic agents react. Using tax policies as a leading example of foresight, simple theory makes transparent the economic behavior and information structures that generate non‐fundamental equilibria. Econometric analyses that fail to model foresight will obtain biased estimates of output multipliers for taxes; biases are quantitatively important when two canonical theoretical models are taken as data generating processes. Both the nature of equilibria and the inferences about the effects of anticipated tax changes hinge critically on hypothesized information flows. Different methods for extracting or hypothesizing the information flows are discussed and shown to be alternative techniques for resolving a non‐uniqueness problem endemic to moving average representations.