This paper examines the empirical evidence on the impact of performed R&D and of R&D embodied in intermediate and capital goods on productivity performance in 10 major OECD countries over the last two decades. To quantify intersectoral and international technology flows, industry-level embodied R&D variables were constructed from an input-output (IO) R&D embodiment model. The productivity variables used are discrete Divisia growth indexes of total factor productivity (TFP), which were estimated from an IO growth accounting model. The results from pooled regressions indicate that the rates of return of the R&D variables were positively significant and increasing in the 1980s. In particular, embodied R&D is an important source for TFP growth in services, indicating very high social returns of the flows of capital-embodied technology into this sector. Moreover, the information and communi-cation technology (ICT) cluster of industries played a major role in the generation and cquisition of new technologies at the international level.R&D, embodied R&D, productivity, ICT sector, OECD countries,

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This article considers a modular approach to the design of integrated social surveys. The approach consists of grouping variables into 'modules', each of which is then allocated to one or more 'instruments'. Each instrument is then administered to a random sample of population units, and each sample unit responds to all modules of the instrument. This approach offers a way of designing a system of integrated social surveys that balances the need to limit the cost and the need to obtain sufficient information. The allocation of the modules to instruments draws on the methodology of split questionnaire designs. The composition of the instruments, that is, how the modules are allocated to instruments, and the corresponding sample sizes are obtained as a solution to an optimisation problem. This optimisation involves minimisation of respondent burden and data collection cost, while respecting certain design constraints usually encountered in practice. These constraints may include, for example, the level of precision required and dependencies between the variables. We propose using a random search algorithm to find approximate optimal solutions to this problem. The algorithm is proved to fulfil conditions that ensure convergence to the global optimum and can also produce an efficient design for a split questionnaire.

In this paper, we build on the "residual-based block bootstrap unit root testing" (RBB) method, proposed by Paparoditis and Politis (2003). We develop an extension of this method to allow for bootstrap unit root testing in a model defined by an augmented Dickey--Fuller (ADF) equation that contains linear combinations of arbitrary dummies as its deterministic part. The main application of such an extension is that it allows for unit root testing in the presence of arbitrary multiple trend breaks, such as jumps or changes of the slope of a linear trend. The model framework used here is an extension of Perron's "innovational outlier model", and allows as well for gradual transitions of the expectation of the series when such breaks or outliers occur; this assumption is particularly appealing in the context of analysing economic time series. Our extension of the bootstrap method involves specifying a drift term and adjustments for the expectation of the residuals and the pseudo-differences, which all appropriately take into account the dependency structure. We prove asymptotic validity of the proposed modified bootstrap procedure in the case of a single break in slope. The small sample behaviour of the proposed methodology is studied in a simulation experiment. Copyright 2005 Royal Economic Society

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