16 research outputs found

    Aspects of Farm Finances: Distribution of Income, Family Income, and Direct Payments, 1986

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    Farm program commodities are grown on farms with greatly differing input, output, and cost and income relationships. Financial conditions are thus widely diverse. The Farm Costs and Returns Survey of 1986 of the Economic Research Service has provided significant data on this diversity. For example, income is concentrated on large farms. Losses, however, tend to be distributed over many small farms. Direct income support for program commodities is also concentrated on large farms, which also are the major producers. Assets and debts tend to be associated with farms that are most able to repay debt. Farms with the highest value of sales and the highest gross family cash income tend to have the highest income-to-asset ratios. Conversely, farms with sales under 40,000yieldverylowincomesrelativetoassets.Negativeincomes(thatis,losses)werefoundin1986forabout11percentoffarmfamiliesevenwhenoff−farmincomeswereaddedin.Nevertheless,inthatyear,27percentoffarmfamilieshadatotalfamilycashincomesofover40,000 yield very low incomes relative to assets. Negative incomes (that is, losses) were found in 1986 for about 11 percent of farm families even when off-farm incomes were added in. Nevertheless, in that year, 27 percent of farm families had a total family cash incomes of over 40,000

    Four decades of ozonesonde measurements over antarctica

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    described and intercompared. Observations from the two sites reveal remarkable agreement, supporting and extending the understanding gained from either individually. Both sites exhibit extensive Antarctic ozone losses in a relatively narrow altitude range from about 12 to 24 km in October, and the data are consistent with temperaturedependent chemistry involving chlorine on polar stratospheric clouds as the cause of the ozone hole. The maximum October ozone losses at higher altitudes near 18 km (70 hPa) appear to be transported to lower levels near the tropopause on a timescale of a few months, which is likely to affect the timing of the effects of ozone depletion on possible tropospheric climate changes. Both sites also show greater ozone losses in the lowermost stratosphere after the volcanic eruption of Mt. Pinatubo, supporting the view that surface chemistry can be enhanced by volcanic perturbations and that the very deep ozone holes observed in the early 1990s reflected such enhancements. Sparse data from the Syowa station in the early 1980s also suggest that enhanced ozone losses due to the El Chichon eruption may have contributed to the beginning of a measurable ozone hole. Observations at both locations show that some ozone depletion now occurs during much if not all year at lower altitudes near 12–14 km. Correlations between temperature and ozone provide new insights into ozone losses, including its nonlinear character, maximum effectiveness, and utility as a tool to distinguish dynamical effects from chemical processes. These data also show that recent changes in ozone do not yet indicate ozone recovery linked to changing chlorine abundances but provide new tools to probe observations for the first such future signals

    Fractionalization and Inter-Group Differences

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    Fractionalization has been shown to have a detrimental effect on growth, public goods provision, and redistribution. The conventional measure of fractionalization is the Herfindahl index, which calculates the probability that two persons drawn at random belong to different groups. This measure implicitly assumes that all groups are equally distant. In this paper, I argue that a more appropriate measure of fractionalization should take into account that some groups are more different than others, so we need a measure of groups distance. We should then measure fractionalization as the average distance between every citizen, or equivalently the average distance between groups weighted by group size. I present a simple method to estimate these distances from opinion survey data by regressing stated opinions on indicator variables from group and a set of control variables. The coefficients on the group variables can then be interpreted as measures of distance. Finally, I apply the method to US data and show that we get more reasonable measures of fractionalization. Copyright 2007 Blackwell Publishing Ltd..
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