Measurement in Economics and Social Science

The paper discusses measurement, primarily in economics, from both analytical and historical perspectives. The historical section traces the commitment to ordinalism on the part of economic theorists from the doctrinal disputes between classical economics and marginalism, through the struggle of orthodox economics against socialism down to the cold-war alliance between mathematical social science and anti-communist ideology. In economics the commitment to ordinalism led to the separation of theory from the quantitative measures that are computed in practice: price and quantity indexes, consumer surplus and real national product. The commitment to ordinality entered political science, via Arrow’s ‘impossibility theorem’, effectively merging it with economics, and ensuring its sterility. How can a field that has as its central result the impossibility of democracy contribute to the design of democratic institutions? The analytical part of the paper deals with the quantitative measures mentioned above. I begin with the conceptual clarification that what these measures try to achieve is a restoration of the money metric that is lost when prices are variable. I conclude that there is only one measure that can be embedded in a satisfactory economic theory, free from unreasonable restrictions. It is the Törnqvist index as an approximation to its theoretical counterpart the Divisia index. The statistical agencies have at various times produced different measures for real national product and its components, as well as related concepts. I argue that all of these are flawed and that a single deflator should be used for the aggregate and the components. Ideally this should be a chained Törnqvist price index defined on aggregate consumption. The social sciences are split. The economic approach is abstract, focused on the assumption of rational and informed behavior, and tends to the political right. The sociological approach is empirical, stresses the non-rational aspects of human behavior and tends to the political left. I argue that the split is due to the fact that the empirical and theoretical traditions were never joined in the social sciences as they were in the natural sciences. I also argue that measurement can potentially help in healing this split

Trade Potential and UN Peacekeeping Participation

The determinants of a country's UN peacekeeping troop contribution have been persistently studied. Trade, as a crucial self-interest motivation, is one of the important explanatory variables in the extant literature. However, the existing literature presents mixed results on the influence of trade on peacekeeping troop contributions. To capture the effect of trade on contributions precisely, we need to model expectations about future trade volume in a better way. Countries are pressured by the economic and political risks caused by the trade disruption and lobby groups to send peacekeeping troops to enable future trade or secure future investments. Therefore, trade potential, rather than realized trade, drives peacekeeping troop contributions. A gravity model is used to measure the trade potential between the UN peacekeeping mission countries and contributors, and test its relationship with the UN peacekeeping participation. Based on this measurement and a dyadic troop contribution dataset covering the period from 1990 to 2012, this article demonstrates that the counter-factual predictive trade volume is a relevant predictor of UN peacekeeping troop contributions

Generalized Canonical Regression

This paper introduces a generalized approach to canonical regression, in which a set of jointly dependent variables enters the left-hand side of the equation as a linear combination, formally like the linear combination of regressors in the right-hand side of the equation. Natural applications occur when the dependent variable is the sum of components that may optimally receive unequal weights or in time series models in which the appropriate timing of the dependent variable is not known a priori. The paper derives a quasi-maximum likelihood estimator as well as its asymptotic distribution and provides illustrative applications

What Drives the Productive Efficiency of a Firm? The Importance of Industry, Location, R&D, and Size

This paper investigates the factors that explain the level and dynamics of manufacturing firm productive efficiency. In our empirical analysis, we use a unique sample of about 39,000 firms in 256 industries from the German Cost Structure Census over the years 1992-2005. We estimate the efficiencies of the firms and relate them to firm-specific and environmental factors. We find that (1) about half the model's explanatory power is due to industry effects, (2) firm size accounts for another 20 percent, and (3) location of headquarters explains approximately 15 percent. Interestingly, most other firm characteristics, such as R&D intensity, outsourcing activities, or the number of owners, have extremely little explanatory power. Surprisingly, our findings suggest that higher R&D intensity is associated with being less efficient, though higher R&D spending increases a firm's efficiency over time