Journal of Economic and Social Measurement

Most data sets used by economists are collected with after-the-fact surveys and the time aggregation is done by the survey respondents who produce, for example, monthly aggregates not actual transactions. 21st century digital transaction technologies will increasingly allow the collection of actual transactions, which will create an important new set of opportunities for forming time aggregates. This paper uses a transaction-by-transaction data set on purchases of diesel fuel by over-the-road truckers to form a monthly diesel volume index from 1999 to 2012 purged of weekday, holiday and calendar effects. These high-frequency data allow new and more accurate ways to correct for (1) the variability in the weekday composition of months and (2) the drift of holiday effects between months. These corrections have substantial effects on month-to-month comparisons.

Event study is a powerful tool for analyzing the dynamic effects of policy and other shocks in microeconomics. However, there is little understanding of how to apply this method when individuals or locations experience multiple events in close succession. We explore methods of estimating a multiple event study with Monte Carlo simulations. Allowing multiple event-time dummies to be turned on at once generally produces unbiased estimates, while ignoring subsequent events or duplicating observations to have one observation per individual-event-time create trends in the outcome variable before and after an event that can be misleading to the researcher. We present empirical applications which show that the choice of method can make important differences in practice.

Lifestyle data are rarely used in multivariate economic and social studies because the data describe the probability of having a categorical attribute. We propose a novel conversion of lifestyle data into metric scale values. Examining the 2001 referendum on the Allianz-Arena in Munich, our analysis demonstrates that refined indicators of value and strata orientation outperform the typical oriented indicators of economic wealth, in terms of capturing the spatial distribution of support and opposition to the project.

Numerous studies have documented a positive association between information technology investments and business- and establishment-level productivity. Most of these studies, however, rely on empirical specifications that over-represent small businesses. In this paper, we revisit one piece of evidence, the Computer Network Use Supplement to the 1999 U.S. Annual Survey of Manufactures, which has previously been used to show that there is a positive relationship between computer networks and productivity in manufacturing plants. We show that this is only true for small- and medium-sized plants, and that for larger plants the relationship is negative. We give critical consideration to alternative methods for weighting these data, and show that employment-weighted estimates indicate the presence of a computer network has, on average, a negative relationship with the productivity of employees.

In this paper we take a closer look at a potential flaw in the measurement of Texas Real Gross Domestic Product (RGDP) – value added in the oil and gas industry. BEA estimates of Texas RGDP in oil and gas extraction have a negative correlation with factors of production and units of output. In this paper we use several different approximations of RGDP in oil and gas extraction to see which seems to be a good substitute for the BEA estimates. We find that a measure based on changes in Texas physical production of oil and gas results in an estimate of total state RGDP that is more highly correlated with Texas job growth and closer to the correlation of these measures nationally. This adjusted measure of Texas RGDP should be a better measure of Texas economic performance.