Sustainable Development and a Dwindling Carbon Space

Curbing global warming by setting long term maxima for temperature rise or concentrations of greenhouse gases defines spaces within which further emissions of these gases are to remain (referred to here as ‘carbon spaces’). This paper addresses questions related to how to share between countries the carbon space and/or efforts to stay within it, in the perspective of sustainable development; different allocation mechanisms are reviewed, responding to criteria such as ‘responsibility’ for climate change, ‘capability’ to engage in abating it, and ‘potential’ or future contribution. The carbon space remaining at any time will depend on effective mitigation up till that time, and will condense if more stringent maxima are to be set; per capita this space becomes smaller with rising population. Sharing the carbon space in a fair way requires “convergence” of currently widely unequal per capita emissions. If the world is to stay within the carbon space consistent with <2° warming, then developed economies—the wealthiest sources of greenhouse gases should quickly and deeply engage in mitigation. Also, substantial mitigation is to take place in developing countries and that this will require substantial support to developing countries (financially, technologically). Changing development paths can make a major contribution to climate change mitigation; this requires changes in investment, production and consumption patterns. Green New Deals as proposed in the context of a widened response to the current economic crisis could become a first phase of a fundamental transition towards a decarbonised global economy worldwide. Concerns to do with equity as well as sustainability must be incorporated and integrated into coherent transitory strategies.     

Revealed smooth nontransitive preferences

In the present paper, we are concerned with the behavioural consequences of consumers having nontransitive preference relations. Data sets consist of finitely many observations of price vectors and consumption bundles. A preference relation rationalizes a data set provided that for every observed consumption bundle, all strictly preferred bundles are more expensive than the observed bundle. Our main result is that data sets can be rationalized by a smooth nontransitive preference relation if and only if prices can normalized such that the law of demand is satisfied. Market data sets consist of finitely many observations of price vectors, lists of individual incomes and aggregate demands. We apply our main result to characterize market data sets consistent with equilibrium behaviour of pure-exchange economies with smooth nontransitive consumers.     

Common socio-economic cycle periods

We present a meta-analysis of cycle periods in historical socio-economic data found in the K-wave literature. The literature on stochastic and deterministic cycles in variables such as the consumer price index, employment, interest rates, commodity prices, GDP, war and hegemony is huge and scattered. However our meta-analysis reveals various commonalities. Our key finding is that there is a common set of cycle periods that is common across most socio-economic variables.     

Corporate foresight: Its three roles in enhancing the innovation capacity of a firm

In the last three decades much knowledge has been produced on how best to conduct foresight exercises, but little is known on how foresight should be integrated with the innovation effort of a company. Drawing on empirical evidence from 19 case studies and 107 interviews, we identify three roles that corporate foresight should play to maximize the innovation capacity of a firm: (1) the strategist role, which explores new business fields; (2) the initiator role, which increases the number of innovation concepts and ideas; and (3) the opponent role, which challenges innovation projects to increase the quality of their output.     

Analyzing a panel of seasonal time series: Does seasonality in industrial production converge across Europe?

In this paper we consider deterministic seasonal variation in quarterly industrial production for several European countries, and we address the question whether this variation has become more similar across countries over time. Due to economic and institutional factors, one may expect convergence across business cycles. When these have similar characteristics as seasonal cycles, one may perhaps also find convergence in seasonality. To this aim, we propose a method that is based on treating the set of production series as a panel. By testing for the relevant parameter restrictions for moving window samples, we examine the hypothesis of convergence in deterministic seasonality while allowing for seasonal unit roots. Our main empirical finding is that there is no evidence for convergence in seasonality.     

Labor income and risky investments: can part-time farmers compete?

In the developed countries, a majority of farm households receive at least as much income from nonfarm sources as from the farm. Such part-time farms have survived inspite of lower returns than full-time farms. This paper considers when lower returns to part-time farming could be compensated by risk-reduction due to diversification of income sources. The paper uses a dynamic portfolio choice model with labor income. The model and results could be applied in other contexts as well.     

Spurious principal components

The principal component regression (PCR) is often used to forecast macroeconomic variables when there are many predictors. In this letter, we argue that it makes sense to pre-whiten the predictors before including these in a PCR. With simulation experiments, we show that without such pre-whitening, spurious principal components can appear and that these can become spuriously significant in a PCR. With an illustration to annual inflation rates for five African countries, we show that non-spurious principal components can be genuinely relevant in empirical forecasting models.     

Estimating Transition Probabilities from a Time Series of Independent Cross Sections

This paper considers the implementation of a nonstationary, heterogeneous Markov model for the analysis of a binary dependent variable in a time series of independent cross sections. The model, previously considered by M offitt (1993), offers the opportunity to estimate entry and exit transition probabilities and to examine the effects of time-constant and time-varying covariates on the hazards. We show how ML estimates of the parameters can be obtained by Fisher's method-of-scoring and how to estimate both fixed and time-varying covariate effects. The model is exemplified with an analysis of the labor force participation decision of Dutch women using data from the Socio-economic Panel (SEP) study conducted in the Netherlands between 1986 and 1995. We treat the panel data as independent cross sections and compare the employment status sequences predicted by the model with the observed sequences in the panel. Some open problems concerning the application of the model are also discussed.