In 2008 all ECB publications feature a motif taken from the €10 banknote.

In the vehicle routing problem with time windows a number of identical vehicles must be routed to and from a depot to cover a given set of customers, each of whom has a specified time interval indicating when they are available for service. Each customer also has a known demand, and a vehicle may only serve the customers on a route if the total demand does not exceed the capacity of the vehicle. The most effective solution method proposed to date for this problem is due to Kohl, Desrosiers, Madsen, Solomon, and Soumis. Their algorithm uses a cutting-plane approach followed by a branchand -bound search with column generation, where the columns of the LP relaxation represent routes of individual vehicles. We describe a new implementation of their method, using Karger's randomized minimum-cut algorithm to generate cutting planes. The standard benchmark in this area is a set of 87 problem instances generated in 1984 by M. Solomon; making using of parallel processing in both the cutting-pla...

Abstract. This paper employs the data-sorting method developed by Hansen (2000) which allows the data to endogenously select regimes using di®erent variables. It is shown that openness, as measured by the trade share to GDP, is a threshold variable that can cluster middle-income countries into two distinct regimes that obey di®erent statistical models. Our result suggests that openness may not be as crucial in the growth process of low and high-income countries but it is instrumental in identifying middle-income countries into high and low-growth groups.

DSGD Discussion Papers contain preliminary material and research results, and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers will eventually be published in some other form, and that their content may also be revised. DSGD DISCUSSION PAPER NO. 13

The labour productivity differentials between manufacturing firms in Ghana and South Korea exceed those implied by macro analysis. Median value-added per employee is nearly 40 times higher in South Korea than Ghana. The most important single factor in explaining this difference is the Mincerian return to skills which differ by a factor of three between Ghana and South Korea. There is no significant difference in total factor productivity across the countries once we allow for human capital. Our results are consistent with those who have argued that rises in the return to education within developed countries can be explained by skill-biased technical progress in those economies. They are also consistent with work in developing countries which finds a convex return to education based on individual labour market data. Allowing for differences in the shape of the relationship between productivity and human capital across countries is crucial for understanding the role of human capital in increasing productivity. JEL Classification: O14, D24.

New macro empirical evidence is provided to assess the relative importance of object and idea gaps in explaining the world income distribution dynamics over a benchmark period 1960-1985. Results are then extended through 1995. Formal statistical hypothesis tests allow us to discriminate between two competing growth models: (i) the standard neoclassical growth model similar to that employed by Manldw, Romer, and Weil (1992), (ii) a Schumpeterian endogenous growth model closely related to the Nelson and Phelps' approach (1966) that emphasizes the importance of technology transfer in addition to factor accumulation as an opportunity to catch up. First, the latter can hardly be rejected and reveals itself to be a reliable either alternative or complementary model depending on the sample under study. Second, taking into consideration the impact of the technological catch-up phenomenon allows us to better capture and locally fit the pattern of income distribution dynamics that took place over the period.

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We thank David Albouy and Francesco Trebbi for kindly sharing their data as well as Chris Recent approaches to development accounting reflect substantial model uncertainty at both the instrument and the development determinant level. Bayesian Model Averaging (BMA) has been proven useful in resolving model uncertainty in economics, and we extend BMA to formally account for model uncertainty in the presence of endogeneity. The new methodology is shown to be highly efficient and to reduce many-instrument bias; in a simulation study we found that IVBMA estimates reduced mean squared error by 60 % over standard IV estimates. We also introduce Bayesian over and under-identification tests that are based on model averaged predictive p-values. This approach is shown to mitigate the reduction in power these tests experience as dimension increases. In a simulation study where the exogeneity of the instrument is compromised we show that the classical Sargan test has a power of 0.2 % while our Bayesian over-identification test has a power of 98 % at detecting the violation of the exogeneity assumption. An application of our method to a prominent development accounting approach leads to new insights regarding the primacy of institutions.

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As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semi-parametric and non-parametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledged and attempts have been made to take them into account either by integrating out their effects or by modeling the sources of heterogeneity when suitable panel data exists. The counterfactual considerations that underlie policy analysis and treat-ment evaluation have been given a more satisfactory foundation. New time series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Non-linear econometric techniques are used increasingly in the analysis of cross section and time series observations. Applications of Bayesian techniques to econometric problems have been given new impetus largely thanks to advances in computer power and computational techniques. The use of Bayesian techniques have in turn provided the investigators with a unifying framework where the tasks of forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process; thus paving the way for establishing the foundation of “real time econometrics”. This paper attempts to provide an overview of some of these developments.