Infrastructures and Societal Change. A View from the Large Technical Systems Field

Infrastructural and societal changes intertwine in multiple ways. This makes the societal implications of infrastructural projects difficult to assess and anticipate. Yet in present day network societies this task is particularly urgent. This paper first identifies two positions that tend to monopolize and deadlock debates. Next it examines two decades of research in the Large Technical System (LTS) research field for alternative approaches to this issue, before finally suggesting distinguishing between the four increasingly indirect moments of (co-) construction of infrastructures and societies. These may be related causally, but also add their own moments of contingency, agency, and choice.     

Are We Better Off if Our Politicians Have More Information?

The model of public policy studied in this paper has heterogeneous citizens/voters and two public goods: one (roads) chosen directly by an elected policy‐maker, and the other (pollution) stochastically dependent on the amount of roads. Both a one‐country and a two‐country version of the model are analyzed; the latter displays externalities across the countries which create incentives for free riding and strategic delegation. The welfare effects of providing the policy‐maker with information about the relationship between roads and pollution are investigated, and it is shown that more information hurts some—sometimes even all—citizens. In particular, the opportunity not to create an institution for information gathering can serve as a commitment device for a country, although with the unfortunate effect of making the overall outcome even worse. Implications for the welfare effects of “informational lobbying” are also discussed.     

Another view on optimal design for estimating the point of extremum in quadratic regression

In this paper we illustrate how certain design problems can be simplified by reparametrization of the response function. This alternative viewpoint provides further insights than the more traditional approaches, like minimax, Bayesian or sequential techniques. It will also improve a practitioner’s understanding of more general situations and their “classical” treatment.     

Money growth volatility and the demand for money in Germany: Friedman’s volatility hypothesis revisited

Money Growth Volatility and the Demand for Money in Germany: Friedman’s Volatility Hypothesis Revisited. — Recently, the Bundesbank claimed that monetary targeting has become considerably more difficult by the increased volatility of short-term money growth. The present paper investigates the impact of German money growth volatility on income velocity and money demand in view of Friedman’s money growth volatility hypothesis. Granger-causality tests provide some evidence for a velocity/volatility linkage. However, the estimation of volatility-augmented money demand functions reveals that — in contrast to Friedman’s hypothesis — increased money growth volatility lowered the demand for money.     

Global economy gathering strength

Encouraged by a highly expansionary economic policy, the global economy is enjoying a rapid upturn. Utilisation of capacity is high in many sectors, particularly in the growth centres of the USA and East Asia, where China leads the field. With inflationary risks on the increase, most countries are now shifting the focus of economic policy. Fiscal policy in particular can be expected to provide little in the way of further stimulus. In addition, there has been a turnaround in interest rates in many countries. Yet how quickly can the monetary reins be tightened without jeopardising growth or endangering price stability?     

On Labour Market Policies and Aggregate Unemployment Outflows

This paper is interested in the modelling of the relationship between active and passive labour market policies and the aggregate unemployment outflow rate. Our model is based on a matching function and includes a simple representation of the competition between various groups of job searchers. The empirical analysis uses Belgian data. Faced with variables that are often integrated of order 1 according to the usual tests but which cannot strictly speaking be integrated, we contribute to an important methodological debate by comparing the conclusions of a classical econometric analysis and a cointegration approach.     

Estimating the Size of a Criminal Population from Police Records Using the Truncated Poisson Regression Model

The truncated Poisson regression model is used to arrive at point and interval estimates of the size of two offender populations, i.e. drunk drivers and persons who illegally possess firearms. The dependent capture–recapture variables are constructed from Dutch police records and are counts of individual arrests for both violations. The population size estimates are derived assuming that each count is a realization of a Poisson distribution, and that the Poisson parameters are related to covariates through the truncated Poisson regression model. These assumptions are discussed in detail, and the tenability of the second assumption is assessed by evaluating the marginal residuals and performing tests on overdispersion. For the firearms example, the second assumption seems to hold well, but for the drunk drivers example there is some overdispersion. It is concluded that the method is useful, provided it is used with care.     

Analysing Materials and Energy Flows in an Industrial District using an Enterprise Input–Output Model

Recent approaches to sustainable development leave much room for policies at a local level. In fact, it is becoming evident that targets such as increasing resource productivity, preserving natural cycles, or extending the present level of welfare, are best pursued within the confines of a local area. In particular, environmental changes are best brought about by considering local systems of firms as cornerstones of cooperative strategies and using data on materials and energy use in physical terms. In this paper, an enterprise input–output model is developed for an industrial district, i.e. a local group of firms specialized in the production of a single final output. The model allows for a detailed quantitative analysis of materials and energy flows and the consequent generation of waste and pollution. As a planning tool, the model may be used to evaluate alternative scenarios, such as the possibility of re- using waste taking account of sustainability requirements. An empirical case study applies the model to an industrial district in Southern Italy producing leather sofas.