Poverty comparisons—an increasingly important starting‐point for welfare policy analysis‐are almost always based on household surveys. Therefore they require that one be able to distinguish underlying differences in the populations being compared from sampling variation: standard errors must be calculated. This has typically been done assuming that the household surveys are simple random samples. However, household surveys are more complex than this. We show that taking into account sampling design has a major effect on estimated standard errors for well‐known poverty measures. In our samples they increase by around one‐half. We also show that making only a partial correction for sample design (taking into account clustering, but not stratification, whether explicit or implicit) can be as misleading as not taking any account of sampling design at all.

Rapid global economic growth, centred in Asia but now spread across the world, is driving rapid greenhouse-gas emissions growth, making earlier projections unrealistic. This paper develops new, illustrative business-as-usual projections for carbon dioxide (CO 2 ) from fossil fuels and other sources and for non-CO 2 greenhouse gases. Making adjustments to 2007 World Energy Outlook projections to reflect more fully recent trends, we project annual emissions by 2030 to be almost double current volumes, 11 per cent higher than in the most pessimistic scenario developed by the Intergovernmental Panel on Climate Change (IPCC), and at a level reached only in 2050 in the business-as-usual scenario used by the Stern Review. This has major implications for the global approach to climate-change mitigation. The required effort is much larger than implicit in the IPCC data informing the current international climate negotiations. Large cuts in developed country emissions will be required, and significant deviations from baselines will be required in developing countries by 2020. It is hard to see how the required cuts could be achieved without all major developing as well as developed countries adopting economy-wide policies.

Crowding out" is a widely accepted claim in migration analysis, which posits that the preference of profitmaximising employers for irregular and minimally regulated migrants overregulated alternatives will undermine, if not condemn to failure, well-regulated temporary migration schemes. In this paper, we test the crowding out hypothesis by examining the experience with well-regulated seasonal migrant worker programs in the horticultural sectors of Australia and New Zealand. This experience, which in both countries has involved recruitment of workers from the Pacific Islands, has been divergent, despite the two programs being similar in design. Our findings suggest that the relative attractiveness of regulated and unregulated migrant labour sources depends on a range of factors, including the export orientation of the sector, the costs of collective action and regulation, differences in policy design and implementation, and external factors.Depending on industry and economy-wide characteristics, quality and reputational benefits for employers can offset the cost of regulation.

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