Sectoral Agglomeration Economies in a Panel of European Regions

We estimate agglomeration economies, defined as the effect of density on labour productivity in European regions. The analysis of Ciccone (2002) is extended in two main ways. First, we use dynamic panel estimation techniques (system GMM), thus offering an alternative methodological treatment of the inherent endogeneity problem. Second, the sector dimension in the data allows for disaggregated estimation. Our results confirm the presence of significant agglomeration effects at the aggregate level, with an estimated long-run elasticity of 13 percent. Repeated crosssection regressions suggest that the strength of agglomeration effects has increased over time. At the sector level, the dominant pattern is of cross-sector "urbanisation" economies and own-sector congestion diseconomies. A notable exception is financial services, for which we find strong positive productivity effects from own-sector density.employment density; productivity; european regions; dynamic panel GMM

Energy Abundance, Trade and Industry Location

We study the effect of countries’ energy abundance on trade and sector activity, conditional on sector’s energy intensity, using an unbalanced panel with 14 high-income countries from Europe, America and Asia, 10 broad sectors, and years 1970-1997. We find that (i) countries with large energy endowments have low energy prices, and are thus energy abundant both on micro and macro level. (ii) Energy abundant countries have a high level of energy embodied in exports relative to imports. (iii) Energy intensive sectors export from and (iv) have higher economic activity in energy abundant countries. (v) The trade and location effects increase with a sector’s exposure to international trade. In short, energy is a major driver for sector location through specialisation. We show that capital and energy are complements in the production function and use various controls in our analysis. The results give insights into delocalisation effects that may take place among rich countries with heterogeneous energy policy.Trade and the Environment, Pollution Haven, Factor Endowments, Industry Location

Unraveling the worldwide pollution haven effect

This paper contributes to the debate on the existence of pollution haven effects by systematically measuring the pollution content of trade (measured by the pollution content of imports, PCI) and decomposing it into three components-a"deep"component (unrelated to the environmental debate but including variables traditionally present in the gravity model) and two components (factor endowments and environmental policies) that occupy center stage in the debate on trade and the environment. The decomposition is carried out for 1986-88 for an extensive data set covering 10 pollutants, 48 countries, and 79 ISIC 4-digit sectors. Illustrative decompositions presented for three of the 10 pollutants in the data set indicate a significant pollution haven effect which increases the PCI of the North because of stricter environmental regulations in the North. At the same time, the factor endowment effect decreases the PCI of the North as the North is relatively well-endowed in capital and pollution-intensive activities are also capital-intensive. On a global scale, because the bulk of trade is intraregional with a high North-North share, these effects are small relative to the"deep"determinants of the worldwide pollution content of trade. In sum, although the impact has been stronger on vertical (North-South) trade flows, differences in factor endowments and environmental policies have only marginally affected the pollution content of world trade during the 1986-88 period.Environmental Economics&Policies,Water and Industry,Brown Issues and Health,Transport Economics Policy&Planning,Water Resources Assessment

Unraveling the World-Wide Pollution Haven Effect

This paper contributes to the debate on the existence of pollution haven effects by systematically measuring the pollution content of trade (measured by the polluction content of imports (PCI)) and decomposing it into three components: a 'deep' (i.e. unrelated to the environmental debate) component and two components (factor endowments and environmental policies) that occupy centerstage in the debate on trade and the environment. The decomposition is carried out for 1986-88 for an extensive data set covering 10 pollutants, 48 countries and 79 ISIC 4-digit sectors. Illustrative decompositions presented for 3 of the 10 pollutants in the data set indicate a significant pollution haven effect and highlight the role of factor endowments in each region's PCI. However, because the bulk of trade is intra-regional with a high North-North share, these effects are small relative to the 'deep' determinants of the worldwide pollution content of trade.trade and the environment; pollution haven

Unravelling the Worldwide Pollution Haven Effect

This paper tackles the “pollution haven” argument by estimating the pollution content of imports (PCI). The PCI is then decomposed into three components: (i) a “deep” component (i.e. traditional variables unrelated to the environmental debate); (ii) a factor endowment component and (iii) a “pollution haven” component reflecting the impact of differences in environmental policies. The estimation is carried out for 1987 for an extensive data set covering 10 pollutants, 48 countries and 79 ISIC 4-digit sectors. Decompositions based on cross-section econometric estimates suggest a significant pollution haven effect which increases the PCI of the North because of stricter environmental regulations in the North. At the same time, the factor endowment effect lowers the PCI of the North, as the North is relatively well-endowed in capital and pollution-intensive activities are capital intensive. On a global scale, because the bulk of trade is intra-regional with a high North-North share, these effects are small relative to the “deep” determinants of the worldwide PCI. In sum, differences in factor endowments and environmental policies have only marginally affected the PCI of world trade at the end of the eighties.Trade and The Environment, Pollution Haven Effect, Factor Endowment Effect

Global Manufacturing SO2 Emissions: Does Trade Matter?

A growth-decomposition (scale, technique and composition effect) covering 62 countries and 7 manufacturing sectors over the 1990-2000 period shows that trade, through reallocations of activities across countries, has contributed to a 2-3 percent decrease in world SO2 emissions. However, when compared to a constructed counterfactual no-trade benchmark, depending on the base year, trade would have contributed to a 3-10 percent increase in emissions. Finally adding emissions coming from trade-related transport activities, global emissions are increased through trade by 16 percent in 1990 and 13 percent in 2000, the decline being largely attributable to a shift of dirty activities towards cleaner countries.embodied emissions in trade, environment, growth decomposition, transport, world trade

Unraveling the World-Wide Pollution Haven Effect

This paper contributes to the debate on the existence of pollution haven effects by systematically measuring the pollution content of trade (measured by the polluction content of imports (PCI)) and decomposing it into three components: a ‘deep’ (i.e. unrelated to the environmental debate) component and two components (factor endowments and environmental policies) that occupy centerstage in the debate on trade and the environment. The decomposition is carried out for 1986-88 for an extensive data set covering 10 pollutants, 48 countries and 79 ISIC 4-digit sectors. Illustrative decompositions presented for 3 of the 10 pollutants in the data set indicate a significant pollution haven effect and highlight the role of factor endowments in each region’s PCI. However, because the bulk of trade is intra-regional with a high North-North share, these effects are small relative to the ‘deep’ determinants of the worldwide pollution content of trade.Trade and the Environment, Pollution Haven

Trade, Technique and Composition Effects: What is Behind the Fall in World-Wide SO2 Emissions 1990-2000?

Combining unique data bases on emissions with sectoral output and employment data, we study the sources of the fall in world-wide SO2 emissions and estimate the impact of trade on emissions. Contrarily to concerns raised by environmentalists, an emission-decomposition exercise shows that scale effects are dominated by technique effects working towards a reduction in emissions. A second exercise comparing the actual trade situation with an autarky benchmark estimates that trade, by allowing clean countries to become net importers of emissions, leads to a 10% increase in world emissions with respect to autarky in 1990, a figure that shrinks to 3.5% in 2000. Additionally, back-of-the-envelope calculations suggest that emissions related to transport are of the same magnitude. In a third exercise, we use linear programming to simulate extreme situations where world emissions are either maximal or minimal. It turns out that effective emissions correspond to a 90% reduction with respect to the worst case, but that another 80% reduction could be reached if emissions were minimal.Trade, Growth, Environment, Decomposition, Embodied Emissions in Trade, Transport

Measurement of the cosmic ray spectrum above 4×10184{\times}10^{18} eV using inclined events detected with the Pierre Auger Observatory

A measurement of the cosmic-ray spectrum for energies exceeding $4{\times}10^{18}$ eV is presented, which is based on the analysis of showers with zenith angles greater than $60^{\circ}$ detected with the Pierre Auger Observatory between 1 January 2004 and 31 December 2013. The measured spectrum confirms a flux suppression at the highest energies. Above $5.3{\times}10^{18}$ eV, the "ankle", the flux can be described by a power law $E^{-\gamma}$ with index $\gamma=2.70 \pm 0.02 \,\text{(stat)} \pm 0.1\,\text{(sys)}$ followed by a smooth suppression region. For the energy ($E_\text{s}$) at which the spectral flux has fallen to one-half of its extrapolated value in the absence of suppression, we find $E_\text{s}=(5.12\pm0.25\,\text{(stat)}^{+1.0}_{-1.2}\,\text{(sys)}){\times}10^{19}$ eV.Comment: Replaced with published version. Added journal reference and DO