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Experiments should be designed to facilitate the detection of experimental measurement error. To this end, we advocate the implementation of identical experimental protocols employing diverse experimental modes. We suggest iterative nonparametric estimation techniques for assessing the magnitude of heterogeneous treatment effects across these modes. And we propose two diagnostic strategies—measurement metrics embedded in experiments, and measurement experiments—that help assess whether any observed heterogeneity reflects experimental measurement error. To illustrate our argument, first we conduct and analyze results from four identical interactive experiments: in the lab; online with subjects from the CESS lab subject pool; online with an online subject pool; and online with MTurk workers. Second, we implement a measurement experiment in India with CESS Online subjects and MTurk workers.

Increasing longevity causes an upward trend in the dependency ratio in many countries. This raises concerns about the financial sustainability of social security schemes, and reform initiatives and proposals abound. It is shown that a fundamental policy choice inevitably arises since a given social security system cannot be maintained by simply indexing retirement ages and benefits to longevity. The political reform process is analysed using the so-called legislative procedure. When longevity increases, the young generation contributes more, and the old generation faces lower benefits and an retirement age that increases more than proportionally to the increase in longevity.

This paper proposes a method to simultaneously estimate both measurement and nonresponse errors for attitudinal and behavioural questions. The method uses a Multi-Trait Multi-Method (MTMM) approach, which is commonly used to estimate the reliability and validity of survey questions. The classic MTMM model is in this paper extended to include the effects of measurement bias and longitudinal nonresponse. Measurement and nonresponse errors are expressed on a common metric in this model, so that their relative sizes can be assessed over the course of a panel study. Using an example about political trust from the Dutch LISS panel, we show that measurement problems lead to both small errors and small biases, that dropout in the panel study does not lead to errors or bias, and that therefore, measurement is a more important source of both error and bias than nonresponse.

This is the author's final draft of an article for which the publisher's official version is available electronically from: http://dx.doi.org/10.1017/S1365100509090166 ; This paper compares the different dynamics of the simple-sum monetary aggregates and the Divisia monetary aggregate indices over time, over the business cycle, and across high and low inflation and interest-rate phases. Although traditional comparisons of the series sometimes suggest that simple-sum and Divisia monetary aggregates share similar dynamics, there are important differences around turning points that cannot be evaluated by their average behavior. We use a factor model with a regime-switching model that separates the common movements underlying the monetary aggregate indices from idiosyncratic variations in each series. We find that the major differences between the simple-sum aggregates and Divisia indices occur around the beginnings and ends of recessions and during some high-interest-rate phases. We note the inferences' policy relevance, which is particularly dramatic at the broadest (M3) level of aggregation. Indeed, as Belongia [Journal of Political Economy, 104 (5) (1996), 1065–1083] has observed in this regard, "measurement matters."