Geographical Influences on Long-Run Development

We show that geographical factors influence long-run per capita income directly, as well as indirectly, through the quality of institutions. The direct influence of geography on per capita incomes is robust to the inclusion of a sub-Saharan Africa dummy and other tests. We obtain our results by replacing the usual instrument (settlers’ mortality (SM)) by stronger instruments for institutional quality (latitude, the share of the country in the temperate climatic zone). We also provide evidence that SM suffers from endogeneity with respect to institutional quality for early colonies, because of its dependence on nineteenth-century mortality data.

Simple method for the rapid simultaneous screening of photocatalytic activity over multiple positions of self-cleaning films

An intelligent ink, previously shown to be capable of rapidly assessing photocatalytic activity, was simply applied via a felt-pen onto a commercially available piece of Activ (TM) self-cleaning glass. The ink, comprising of redox dye resazurin and the sacrificial electron donor glycerol within an aqueous hydroxy ethyl cellulose (HEC) polymer media, was photocatalytically degraded in a two-step process. The key initial stage was the photo-reductive conversion of resazurin to resorufin, whereby a colour change from blue to pink occurred. The latter stage was the subsequent photo-reduction of the resorufin, where a slower change from pink to colourless was seen. Red and green components of red-green-blue colour extracted from flat-bed scanner digital images of resazurin ink coated photocatalytic films at intervals during the photocatalysis reaction were inversely proportional to the changes seen via UV-visible absorption spectroscopy and indicative of reaction kinetics. A 3 x 3 grid of intelligent ink was drawn onto a piece of Activ (TM) and a glass blank. The photocatalysis reaction was monitored solely by flat-bed digital scanning. Red-green-blue values of respective positions on the grid were extracted using a custom-built program entitled RGB Extractor (c). The program was capable of extracting a number of 5 x 5 pixel averages of red-green-blue components simultaneously. Allocation of merely three coordinates allowed for the automatic generation of a grid, with scroll-bars controlling the number of positions to be extracted on the grid formed. No significant change in red and green components for any position on the glass blank was observed; however, the Activ (TM) film displayed a homogenous photo-reduction of the dye, reaching maxima in red and minima in green components in 23 +/- 3 and 14 +/- 2 min, respectively. A compositionally graded N-doped titania film synthesised in house via a combinatorial APCVD reaction was also photocatalytically tested by this method where 247 positions on a 13 x 19 grid were simultaneously analysed. The dramatic variation in photocatalysis observed was rapidly quantified for all positions (2-3 hours) allowing for correlations to be made between thicknesses and N : Ti% compositions attained from Swanepoel and WDX analysis, respectively. N incorporation within this system was found to be detrimental to film activity for the photocatalysis reaction of intelligent ink under 365 nm light.

Red-shifts and red herrings in geographical ecology

I draw attention to the need for ecologists to take spatial structure into account more seriously in hypothesis testing. If spatial autocorrelation is ignored, as it usually is, then analyses of ecological patterns in terms of environmental factors can produce very misleading results. This is demonstrated using synthetic but realistic spatial patterns with known spatial properties which are subjected to classical correlation and multiple regression analyses. Correlation between an autocorrelated response variable and each of a set of explanatory variables is strongly biased in favour of those explanatory variables that are highly autocorrelated - the expected magnitude of the correlation coefficient increases with autocorrelation even if the spatial patterns are completely independent. Similarly, multiple regression analysis finds highly autocorrelated explanatory variables "significant" much more frequently than it should. The chances of mistakenly identifying a "significant" slope across an autocorrelated pattern is very high if classical regression is used. Consequently, under these circumstances strongly autocorrelated environmental factors reported in the literature as associated with ecological patterns may not actually be significant. It is likely that these factors wrongly described as important constitute a red-shifted subset of the set of potential explanations, and that more spatially discontinuous factors (those with bluer spectra) are actually relatively more important than their present status suggests. There is much that ecologists can do to improve on this situation. I discuss various approaches to the problem of spatial autocorrelation from the literature and present a randomisation test for the association of two spatial patterns which has advantages over currently available methods.

Geographical variation in total and inorganic arsenic content of polished (white) rice

An extensive data set of total arsenic analysis for 901 polished (white) grain samples, originating from 10 countries from 4 continents, was compiled. The samples represented the baseline (i.e., notspecifically collected from arsenic contaminated areas), and all were for market sale in major conurbations. Median total arsenic contents of rice varied 7-fold, with Egypt (0.04 mg/kg) and India (0.07 mg/kg) having the lowest arsenic content while the U.S. (0.25 mg/kg) and France (0.28 mg/kg) had the highest content. Global distribution of total arsenic in rice was modeled by weighting each country's arsenic distribution by that country's contribution to global production. A subset of 63 samples from Bangladesh, China, India, Italy, and the U.S. was analyzed for arsenic species. The relationship between inorganic arsenic contentversus total arsenic contentsignificantly differed among countries, with Bangladesh and India having the steepest slope in linear regression, and the U.S. having the shallowest slope. Using country-specific rice consumption data, daily intake of inorganic arsenic was estimated and the associated internal cancer risk was calculated using the U.S. Environmental Protection Agency (EPA) cancer slope. Median excess internal cancer risks posed by inorganic arsenic ranged 30-fold for the 5 countries examined, being 0.7 per 10,000 for Italians to 22 per 10,000 for Bangladeshis, when a 60 kg person was considered.