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If you have data on GINI after taxes/transfers, I'd love to see it. Umm, Google, Wikipedia? http://en.wikipedia.org/wiki/File:GiniPlots_USA.png http://en.wikip

If you have data on GINI after taxes/transfers, I'd love to see it. Umm, Google, Wikipedia? http://en.wikipedia.org/wiki/File:GiniPlots_USA.png http://en.wikipedia.org/wiki/File:GiniPlots_USA.png See also [1] for a list of countries by Gini both before and after taxes/transfers. But please recognize that you are in a minority here - most left wing sorts don't look at data after taxes and transfers. For example, anyone who talks about poverty is talking about pre-tax/transfer data (after taxes and transfers, poverty is almost nonexistent). Say what? For example, Germany actually has higher inequality than the US before taxes and transfers, but significantly lower inequality after taxes and transfers [1]. And even egalitarian Finland's pre-tax Gini isn't much different from that of the US (taxes and transfers cut it almost in half). Inequality before taxes and transfers is practically unavoidable in a developed countries; taxes and transfers are the difference between the European social democracies and the US. And no, poverty is not non-existent after taxes and transfers; about one in six Americans lives in a food-insecure household [2]. Ever been to Gary, IN [3], East St. Louis, or Chicago's South Side? America has not only a high relative poverty rate, but also a high absolute poverty rate (compared to the rest of the developed world). Also, why do you seem to assume that I'm a "left wing sort" (other than by tea party standards where Rockefeller Republicans are proto-communists)? You still have not clearly stated what data would falsify your theory. Should I conclude that your claims are unfalsifiable, and therefore closer to a religious claim than a scientific one? Huh? Just because a random variable that is the aggregate of many other random variables (college enrollment) is not sufficient to make statements about one of the component random variables (college tuition) does not mean that it's unfalsifiable. You do have to control for the other variables, though. But there's nothing inherently unfalsifiable about a relation between tuition and college enrollment/completion. Just because you can't find it easily on Google doesn't mean that you couldn't design a study to examine that relation minus confounding factors. And the evidence is strong that there is such a relation (it's also common sense - if a good becomes more expensive, fewer people will purchase this good, unless there are countervailing effects). If you look at it more closely, for example, you will learn that income affects college graduation rates and college degree attainment rates (and, conversely, having a college degree affects income and social mobility) [4]. Higher tuition both negatively affects graduation rates and also segregates low-income students into lower quality colleges (or kept out of college entirely). The underlying paper by Bailey and Dynarski [5] documents pretty clearly that income inequality translates into inequality in educational attainment and that low-income students dropping out disproportionately often is a major driver of this effect. [1] http://en.wikipedia.org/wiki/List_of_countries_by_income_equality http://en.wikipedia.org/wiki/List_of_countries_by_income_equ... [2] http://feedingamerica.org/hunger-in-america/hunger-facts/hunger-and-poverty-statistics.aspx http://feedingamerica.org/hunger-in-america/hunger-facts/hun... [3] http://www.businessinsider.com/gary-indiana-photos-2012-7 http://www.businessinsider.com/gary-indiana-photos-2012-7 [4] http://money.cnn.com/2011/11/21/news/economy/income_college/ http://money.cnn.com/2011/11/21/news/economy/income_college/ [5] http://users.nber.org/~dynarski/Bailey_Dynarski.pdf http://users.nber.org/~dynarski/Bailey_Dynarski.pdf

yummyfajitas 13y ago
According to your graph, GINI went up (from 34 to 38%) after taxes and transfers as well. Just because a random variable that is the aggregate of many other random variables (college enrollment) is not sufficient to make statements about one of the component random variables (college tuition) does not mean that it's unfalsifiable. So how do you falsify it? Also, I didn't ask about a relation between tuition and graduation. I asked about a relation between inequality and enrollment. The underlying paper by Bailey and Dynarski [5] documents pretty clearly that income inequality translates into inequality in educational attainment... Looking at educational attainment* as opposed to enrollment is a bit, um, confusing. Is it your assertion that a higher GINI somehow makes the poor (specifically poor women, if we go by that paper) less willing to study than their richer counterparts? Not that it matters, since the paper doesn't look at inequality at all. All it shows is that higher quintiles have higher graduation rates - this is completely unrelated to the question of whether increasing the size of quintiles effects graduation rates. Also, if you read your source [5] (the middle, not the conclusions), you'd discover that the strongest explanation they have of their results is that children of poorer parents are not as smart as children of rich parents, which explains half the gap. So unless your claim is that higher GINI makes poor kids stupid, it's nonsensical to claim inequality has anything to do with graduation rates. I give up. You have unfalsifiable beliefs which are somehow supported by academic papers on completely unrelated topics. Not anything for me to learn here.
- rbehrends 13y ago
  According to your graph, GINI went up (from 34 to 38%) after taxes and transfers as well. I didn't say that it didn't change; I said it didn't change much. So how do you falsify it? As with any other such case, by controlling for those variables you want to eliminate. Is it your assertion that a higher GINI somehow makes the poor (specifically poor women, if we go by that paper) less willing to study than their richer counterparts? Looking at educational attainment as opposed to enrollment is a bit, um, confusing. It was you who brought up enrollment, not I. I was speaking in general terms about tuition as a barrier to access to postsecondary education. Enrollment is a very poor proximate variable for success at college due to increasingly high dropout rates. Not that it matters, since the paper doesn't look at inequality at all. From the paper: "In this paper we document changes over time in inequality in educational attainment by family income [...]". Differences in family income, of course, mean income inequality. So unless your claim is that higher GINI makes poor kids stupid As it so happens, precisely that may be the case (we'd need further corroboration to be fully certain, but it'd hardly be surprising - stress of all kinds is known to have a deleterious effect on IQ, so stress from poverty shouldn't be any different): http://www.businessinsider.com/poverty-effect-on-intelligence-2013-8 http://www.businessinsider.com/poverty-effect-on-intelligenc... It's not something I had talked about in this thread, so this is not "my claim" in that sense, but it is another example of the pernicious effects of inequality. I give up. You have unfalsifiable beliefs which are somehow supported by academic papers on completely unrelated topics. I think you simply do not understand what "unfalsifiable" means. Practically any statement that relies on observations of natural phenomena is in principle falsifiable by making observations counter to that statement; it may just be difficult to do that. Statistical phenomena also slightly complicate the situation, though the principle remains the same. That you have a different opinion or disagree is fine - the data is hardly conclusive either way, though your interpretation does make some very counterintuitive assumptions (in particular, that people are more likely to pay for a good or service - namely, college – the less they can afford it); that you try to give this disagreement a veneer of objectivity by misusing terminology isn't so cool. Especially when you handwave away anything that doesn't fit your preconceptions.

yummyfajitas 13y ago

According to your graph, GINI went up (from 34 to 38%) after taxes and transfers as well. Just because a random variable that is the aggregate of many other random variables (college enrollment) is not sufficient to make statements about one of the component random variables (college tuition) does not mean that it's unfalsifiable. So how do you falsify it? Also, I didn't ask about a relation between tuition and graduation. I asked about a relation between inequality and enrollment. The underlying paper by Bailey and Dynarski [5] documents pretty clearly that income inequality translates into inequality in educational attainment... Looking at educational attainment* as opposed to enrollment is a bit, um, confusing. Is it your assertion that a higher GINI somehow makes the poor (specifically poor women, if we go by that paper) less willing to study than their richer counterparts? Not that it matters, since the paper doesn't look at inequality at all. All it shows is that higher quintiles have higher graduation rates - this is completely unrelated to the question of whether increasing the size of quintiles effects graduation rates. Also, if you read your source [5] (the middle, not the conclusions), you'd discover that the strongest explanation they have of their results is that children of poorer parents are not as smart as children of rich parents, which explains half the gap. So unless your claim is that higher GINI makes poor kids stupid, it's nonsensical to claim inequality has anything to do with graduation rates. I give up. You have unfalsifiable beliefs which are somehow supported by academic papers on completely unrelated topics. Not anything for me to learn here.

rbehrends 13y ago
According to your graph, GINI went up (from 34 to 38%) after taxes and transfers as well. I didn't say that it didn't change; I said it didn't change much. So how do you falsify it? As with any other such case, by controlling for those variables you want to eliminate. Is it your assertion that a higher GINI somehow makes the poor (specifically poor women, if we go by that paper) less willing to study than their richer counterparts? Looking at educational attainment as opposed to enrollment is a bit, um, confusing. It was you who brought up enrollment, not I. I was speaking in general terms about tuition as a barrier to access to postsecondary education. Enrollment is a very poor proximate variable for success at college due to increasingly high dropout rates. Not that it matters, since the paper doesn't look at inequality at all. From the paper: "In this paper we document changes over time in inequality in educational attainment by family income [...]". Differences in family income, of course, mean income inequality. So unless your claim is that higher GINI makes poor kids stupid As it so happens, precisely that may be the case (we'd need further corroboration to be fully certain, but it'd hardly be surprising - stress of all kinds is known to have a deleterious effect on IQ, so stress from poverty shouldn't be any different): http://www.businessinsider.com/poverty-effect-on-intelligence-2013-8 http://www.businessinsider.com/poverty-effect-on-intelligenc... It's not something I had talked about in this thread, so this is not "my claim" in that sense, but it is another example of the pernicious effects of inequality. I give up. You have unfalsifiable beliefs which are somehow supported by academic papers on completely unrelated topics. I think you simply do not understand what "unfalsifiable" means. Practically any statement that relies on observations of natural phenomena is in principle falsifiable by making observations counter to that statement; it may just be difficult to do that. Statistical phenomena also slightly complicate the situation, though the principle remains the same. That you have a different opinion or disagree is fine - the data is hardly conclusive either way, though your interpretation does make some very counterintuitive assumptions (in particular, that people are more likely to pay for a good or service - namely, college – the less they can afford it); that you try to give this disagreement a veneer of objectivity by misusing terminology isn't so cool. Especially when you handwave away anything that doesn't fit your preconceptions.