Re: Why Are Courting Signals Ambiguous?
The explanation below assumes that all women know what they're looking for whereas no men know what women are looking for. Which isn't plaus... h, wait, forget that remark. I think we could differentiate between flirting as a way of garnering information v flirting as a way of mitigating, as David says, the damage caused by rejected advances by the pattern of flirting. In places where relative strangers mingle (singles bars, say), I think the flirtation tends to border on direct propositioning. But where friends or co-workers interact, flirting is much more subtle. The latter type of environment is the one in which garnering information about potential mates is relatively unimportant (you already know them) whereas in the former information is scant, so if flirting were mostly a way of signalling personal characteristics we'd expect to see pattern reversed. This supports David's variant on Robin's theme 1. Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/> On Mon, 27 Nov 2000 [EMAIL PROTECTED] wrote: > > Robin Hanson's post was very interesting. I have wondered that ambiguous > signals might play another role. > > Suppose all women like men who wear red ties because those men, for some > reason, are nicer or richer than others. Assume that this is the only way > women can tell the nice guys from the jerks(the men who are not nice). So > women would avoid men who don't wear red ties. But if women told men that > they like men who wear red ties, then the jerks(the men who are not nice) > could wear red ties. If all men wore red ties, then women could not tell > which guys were really nice. So you might not want to give away what signals > you are looking for or what they mean. In your mating, dating, flirting > activity you wuold not come right out and say what you are looking for. > > Cyril Morong > San Antonio College >
Re: Murray/Hernstein
Bryan Caplan wrote: > Chris Auld wrote: > > outcome. Would it be accepted by any economics journal? I don't > > think so -- so why should I think that if we put a couple of dozen > > of these together, we arrive at something compelling? > > I think so. Anyone can get one marginally convincing result. But > getting hundreds shows something. Not if all of them are faulty! You ignored the first part of the paragraph: taken alone, are any of M/H's results up to the minimum standards of quantitative research in economics? You also ignored the question: how would M/H's results discern whether we live in a world where ability has no direct effect, but ability is correlated with any of the many relevant excluded regressors? > There is even a formal test using > this intuition - the p-lambda test. Eight results (out of 10) > significant the 20% level, for example, could almost never arise from > pure chance. Suppose I were to take price/quantity clouds from many markets. In each market, I regress price on quantity and announce I've measured the supply curve. If I do this two dozen times rather than once, have I found compelling evidence on the slope of supply curves? Your argument is indeed valid to show that it's very unlikely that AFQT is actually an irrelevant regressor in M/H's specifications. I'm not arguing that the true coefficient in their spefications is zero -- I'm arguing that they consistently misinterpret their findings. > > in each regression, rather then the ad hoc index. > > There's as much or as little reason to use an index in TBC as anywhere. > Should they have put in each test sub-scale separately, too? I don't understand this, Bryan. By this reasoning, why do we ever bother with more than one or two regressors? Why not always just create ad hoc indexes of everything we think we ought to be controlling for? We _should_ put in each sub-scale if we have reason to believe that, say, math scores affect outcomes much differently from verbal scores and we are interested in how these effects differ. We should not if our question is whether some generalized cognitive ability score affects outcomes. Also notice there is a natural way of aggregating test scores, whereas M/H's SES index is completely ad hoc. > I don't think it's possible for them to show there's "no way" to do it. > They could certainly point out data limitations, and offer their > judgment that these are insuperable. But I wouldn't call that judgment > a "finding." What they show is that time differences in returns to education and returns to ability are nonparametrically unidentified by data based on a single cohort. That's a theoretical finding. They then show that the structure typically imposed to identify these effects, linear seperability, is rejected by the NLS-Y data. Of course, M/H never even get in the ballpark of realizing that all this is even an issue. > If there's "no way" to do it for the whole population, how did they > manage? :-) They imposed the identification assumptions that everyone else does. This is at least useful for showing the sensitivity of this type of result under the usual assumptions, even if we also know we are imposing unwarranted structure. Notice also the non-identification paper postdates this one. > Seriously, I'd expect that you could re-do almost anyone's > results this way. In each case, you would learn more, but unless the > whole-sample results drastically reversed I don't see why it's so > interesting. Because it directly contradicts M/H's interpretation of their findings? Here, for instance, is the abstract from Cawley et al 1997: This paper presents new evidence from the NLSY on the importance of meritocracy in American society. In it, we find that general intelligence, or g -- a measure of cognitive ability--is dominant in explaining test score variance. The weights assigned to tests by g are similar for all major demographic groups. These results support Spearman's theory of g. We also find that g and other measures of ability are not rewarded equally across race and gender, evidence against the view that the labor market is organized on meritocratic principles. Additional factors beyond g are required to explain wages and occupational choice. However, both blue collar and white collar wages are poorly predicted by g or even multiple measures of ability. Observed cognitive ability is only a minor predictor of social performance. White collar wages are more g loaded than blue collar wages. Many noncognitive factors determine blue collar wages. > > Many textbooks recommend dropping a regressor, then interpreting the > > coefficients on the remaining regressors _as if_ the
Re: Murray/Hernstein
Chris Rasch wrote: > My current level of understanding of econometrics and statistics is such that I > don't feel qualified to evaluate the arguments presented in the recent exchange > between Brian and Chris regarding the merits (or lack thereof) of Murry and > Herrnstein's research in The Bell Curve. Assuming I wanted to remedy that > situation, what texts would you recommend I study to learn the vocabulary and to > at least recognize when a good (or bad) argument is being made? I agree with Bryan's response, however, I would suggest that picking up a copy of Kennedy's _A Guide to Econometrics_ would be worthwhile. It's cheap and contains non-technical and very lucid discussion of the major issues. I use it as a required supplementary text for first year grad metrics. Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: Murray/Hernstein
On Sun, 29 Oct 2000, Alex Tabarrok wrote: > Different types of empirical research are convincing to different people. I, and > I gather Bryan, tend to like the type where a problem is approached from several > different angles using perhaps relatively simple econometrics and is integrated with > other types of evidence including historical evidence (non-quantitative empirics) > and a modicum of theory told with a plausible story. The Bell Curve does well on > this measure. I disagree TBC meets these criteria. The problem isn't that they don't use sophisticated methods; the problem is that they use very simple methods and then discuss as if they've obtained a variety of deep structural results. Again, let's suppose that M/H are completely wrong: suppose all that matters for all the social outcomes they investigate is income. Ability has no direct effect. How would we be able to tell whether we live in such a world from the results in TBC? But M/H are explicitly claiming we don't live in such a world, based mostly on their own results. > By the way, as a paradigmatic example of the first sort I would mention > Friedman and Schwart'z A Monetary History of the United States. Chris, I wonder > what you would say about the quality of this work? (And I mean today not circa > 1965). I still think it is great even thought it fails test number 2 very badly. I haven't read it so I can't comment. I'm not saying M/H are mistaken because they don't use the flashiest new techniques, and there're certainly lots of results in various literatures based on very simple techniques that are quite compelling. I'm saying M/H misinterpret their results. They do not show what M/H claim they show. Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: Murray/Hernstein
d said that, their discussion wouldn't be so grating, although then one would wonder why they bothered with nearly meaningless regression after nearly meaningless regression. > If anything your command of the details of their study exceeds mine. > The main thing I'd say in their defense is to step back and take stock > of what they did. Here's how I'd summarize it: > > 1. They put forward an intutively plausible hypothesis that virtually > no economists I knew at Berkeley or Princeton ever even mentioned. I agree that cognitive ability is a too-often omitted variable. But it isn't the case that it hasn't appeared in the economics literature before (see, for instance, that AER piece I've mentioned, which is more plausible than anything in TBC). > 2. They showed that this hypothesis explained an extremely diverse and > seemingly unconnected sets of facts. They didn't even come within the ballpark of holding all else equal, so I disagree they "showed" this result at all. Consider all their outcomes but poverty, and suppose we live in a world where cognitive ability has zero impact on whatever the outcome of interest is, but income has a large impact of the same sign as M/H find with respect to AFQT. How would we be able to discern whether we live in such a world from the regressions in TBC? > 3. They directly challenged almost the entire "socialization" > literature within psych, and implicitly called much of the ROR to > education literature in econ into question. I'm not sure why. It's long been extremely well-known that education is an endogenous outcome in wage equations. I don't recall M/H ever actually running an income wage equation (I could be wrong, don't have the book handy at the moment), but they certainly woulnd't have been the first to stick proxies for ability in to try to trick out the direct effect of education. > 4. Almost all of the econometric problems you cite seem at least as > severe in almost every study I can think of. Correlated independent > variables, measurement error, using indices, etc. If these were enough > for summary rejection, most of the ROR to education literature would > remain unpublished. It's not that these problems exist in TBC per se that leads to me the conclusion it's all but worthless, it's the sheer magnitude of these problems, combined with the analysis that seems blithely unaware that these problems undercut their conclusions. All they're holding constant in most specifications is the SES index and age. All else aside, endogeneity via the dozens of surely relevant but omitted variables is enough to render their results meaningless. When was the last time you saw an economist run a wage equation and include only age and an ad hoc index of parents' socioeconomic status constant, in addition to some variable of particular interest in the study? How about a determinants of criminal activity equation that doesn't control for _gender_? > Of course, saying "*I* learned a lot from it" might just be a sign of > how clueness I was or am. But I'm pretty sure that even today most > labor economists are happily ignoring the issues M/H raise. In short, > most economists would learn a lot from TBC. How many published articles > can you say that about? I think we're talking across each other to some extent here. I'm not saying TBC doesn't discuss lots of interesting questions (although I don't think most of those questions are attributable to M/H). I'm certainly not saying I didn't learn a lot from it (although that view is tempered somewhat by oft-made claims that M/H's review of the literature is biased). What I'm saying is that the original statistical results in the TBC are so rotten that they can't be used to show much of anything, and they certainly don't show what M/H think they show. This doesn't necessarily imply that M/H are wrong in their qualitative conclusions, of course. But we will have to look elsewhere to find if their results hold under the much more rigorous analysis required to get a solid handle on these question. Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: Murray/Hernstein
On Fri, 27 Oct 2000, Bryan Caplan wrote: > Unfortunately Bill is too busy to weigh in here. I still haven't > untangled your simulation, but I'm skeptical that measurement error > could radically effect the results I showed (or M/H's much more detailed > results). Notice your results are stronger than anything in the entire Bell Curve: you at least contolled for education and some important characteristics such as marital and fertility status. M/H make no attempt to control for income, health, marital status, fertility, experience, region of residence, gender, nationality, and so on, and so on. Even if they had, there would still be some pretty serious endogeneity problems, but as it stands their analysis is completely worthless as far as I can tell. Recall too that folks like Heckman have shown the results in M/H change dramatically when the analysis is done right. > If you're right on this, then I'd better start greeting > almost all results with greater skepticism - in the real world, what is > better measured than IQ and education? Well, it's not so much measurement error as the other endogeneity problem (and recall that was modest) that caused the inconsistency in my little experiment. That said, I'm not sure education, at least, is particularly well measured in most datasets, as we generally ignore quality measures. And recall M/H "solve" the colinearity problem they have between education and IQ by *dropping* education. Some solution! Granted, they also repeat most of their regressions with a high school only sample, but notice the results generally change markedly when that sample is contrasted with the whole sample. Also recall they admit that education causally increases performance on IQ tests (although I looked up their cite, a 1989 AER article, and that piece actually assumes such a relationship exists, but differences it out so they don't have to measure it, so I don't know what M/H were talking about). That implies that a simultaneous approach is necessary, particularly recalling that the endogeneity problem here is compounded by the colinearity problem. Perhaps you could explain why you find the evidence they array compelling. Maybe I'm missing something. Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: Murray/Hernstein
On Fri, 27 Oct 2000, Bryan Caplan wrote: > more information. Better if Bill summarizes, but on the whole I'd say > he concluded that M/H's SES moderately understates the importance of > SES, but intelligence still matters a great deal. Don't get me wrong: I'm not claiming that intelligence doesn't matter. What I'm claiming is that disentangling the marginal effect of intelligence from other factors that influence various outcomes is not "pretty easy." In particular, the approach taken by M/H is so statistically inept that it's impossible to discern from their work whether intelligence matters directly, and if it does, by how much. Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: Murray/Hernstein
> Chris, could you summarize the alleged deficiencies of the Bell Curve? > -fabio Many others have critiqued their methods, their interpretation of the psychometric literature, and their analysis of their own original results. You can find lengthy criticism in: Kincheloe et al (1996) Measured Lies: the Bell Curve Examined. Devlin et al (1997) Intelligence and Success: Is it All in the Genes? Scientists Respond to the Bell Curve. Here's my take on some aspects: almost all of the original results are based on logit models that look like y* = constant + b(SES) + c(AFQT) + d(age) + extreme value noise. There are rarely other covariates, where there they aren't exactly exhaustive. SES is simply a weighted sum of father's and mother's income, family income (of the respondent's parents), and an index of parents' occupational status. The weights are more or less ad hoc, so including SES is the same as including all these covariates, but then adding three ad hoc linear restrictions. Now, the outcomes y* include: unemployment, levels of educational attainment, poverty, marital status, illegitimate births, welfare dependency, low birth weight children, criminal activities, and so on. Consider any one of these outcomes, say, "the subject was in the top decile on an index of self-reported crime." Suppose anyone on this list took NLS-Y data, constructed an indicator for that condition, and typed: logit criminal age mothersed fathersed faminc occind afqt into an econometric package (remembering income and occupation refer to the family where the respondent grew up, not to the respondent). We then arbitrarily add three restrictions to get logit criminal age ses afqt. We then interpret the coefficient on AFQT as the causal effect of higher cognitive ability on propensity to be a criminal, all else equal, write up our result, and send it off to a journal. Of course, it barely stops moving across the editor's desk before being popped back in the mail, rejected. Where to start documenting the problems with this interpretation of the regression above? The respondent's own income, gender, occupation, marital status, health, and so on have been excluded. Since all of these outcomes are related to the distibution of intelligence, the coefficient on AFQT reflects all these effects, not the marginal impact of intelligence. It is highly doubtful SES is controlling for background adequately, and we haven't even controlled for education! Education! It is true that the authors stratify by coarse educational groupings, but that's nowhere near good enough. And remember that, as my little Monte Carlo showed, even a little endogeneity causes big problems in this context, even if they'd gone to the trouble of properly trying to hold all else equal. So, take a whole bunch of more or less uninterpretable logit regressions, make some lousy conclusions from them, and write a book: that's the bulk of the Bell Curve. the other part concerns how race factors into all this, and I'm not even going to go there. Individually, none of Herrnstein and Murray's results would pass muster as an undergraduate term paper in economics, much less a study in a refereed journal. And if you sum junk, you just get aggregate junk. Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: Top 10 Economic Puzzles
Bryan Caplan wrote: > Chris, would you mind telling us what the variances of u1, u2, and u3 > were? I think you gave us all the necessary info, but I'm too lazy. :-) The standard deviations were about 1, 10, and 2 in the log-wage, AFQT, and years of education equations, respectively. > A while back I said that the Card/Krueger minimum wage study was at the > 90th percentile of quality for published empirical work. I would put > Murray and Herrnstein at the 99th percentile of quality. Applied work > is never perfect, but I have to think you're being way too harsh. And I turn think you're being way, way to quick to turn a blind eye to all the serious problems here. I doubt most of the Bell Curve would ever have got through peer review at any refereed economics journal, for very good reasons. I'd put the Bell Curve in the bottom 5% of the distribution of published empirical work in economics. And I'm probably being far too generous. > What empirical problems *do* you think are "pretty easy"? Most questions that do not implicitly or explicitly require causation to be determined. Causal questions where there exists large truly exogenous variation, such as in solid controlled experiments or good natural experiments. Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: Top 10 Economic Puzzles
guide in how not to proceed with scientific inquiry, but not much more. And now for something completely different: Playstation 2 was introduced today, with a retail price of $300 and "only" 500,000 units available. They're selling on Ebay for over $1,500. Sure wish I'd pre-ordered a thousand or so Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: Top 10 Economic Puzzles
Bryan Caplan wrote: > I'll agree there are problems here, but I still think some simple > regressions bound the answer fairly well. How do you figure that? Strip away everything from the problem and suppose that we live in a world where: wages = bIQ + u1 afqt = cIQ + u2 educ = dIQ + u3. By assumption, all that's driving all three outcomes is sheer brain power, but brain power is measured with noise, and that noise and unobervables affecting educational choices and wages may all be correlated. A regression of wages on afqt and education could easily come up with large values for the coefficient on the latter, and very misleading estimates for the return to afqt. Or, of course, some other structural model might imply that regressions such as were provided underestimate the true returns to educations, and would generally, therefore mis-estimate the returns to cognitive ability. I don't see any sense in which the regression supplied bounds the true values. For formal estimates of somewhat related models, see for instance Cawley, Heckman, and Vytlacil's work, which specifically demonstrates that estimates such as you supply are very misleading. Again, the empirical literature on this question is vast -- it's a tough problem, and just running OLS with an coarse measure of cognitive abilility doesn't bound anything. This puzzle is so important, and tough to "solve," that is has been and remains a testbed for the latest and greatest econometric techniques, as well as the target of numerous attempts to find truly exogenous variation in various places to help identify the causal mechanisms. Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: Top 10 Economic Puzzles
On Wed, 25 Oct 2000, Bryan Caplan wrote: > > 2- What % of wages is due to i.q. and what are the other factors? > > That one is pretty easily answered using NLSY data. Here's one fairly > canonical rate of return to education regression: > In answer to your question, then, each IQ percentile on average raises > your annual labor earnings by .5%; each year of school raises them by > 10.3%. These are correlations and causation is anything but obvious, so the phrasing "each year of school raises wages by 10.3%" is misleading. There are very well-known endogeneity problems here, in addition to the observation that the human capital variables held constant are themselves affected by cognitive ability, such that the partial derivative estimated likely underestimates the total return to such ability dramatically. The question above has been and continues to be the subject of much research; it is not a "fairly easy" problem at all. Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: Top 10 Economic Puzzles
fabio guillermo rojas wrote: > What are the big unsolved puzzles of economic empirical research? > What economic phenoma seem pretty darn important, but have not > been adequately explained by current economic theories? All of macroeconomics? Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: women
Alex Tabarrok wrote: >Non-working women are likely to have husbands who earn more than the > husbands of working women (all else equal) - this says the probability > of a woman working increases with a *decrease* in *husband* income. But > the finding is that the probability of a woman working increases with an > *increase* in the *sister's husband's* income. Two different results. It's hard to know how to interpret this result without knowing what was actually estimated. Ray's point is quite correct if the model looks like y* = Xb + c*1(SH>H) + noise, where y* is a latent variable governing employment, X contains whatever else they controlled for aside from husbands' incomes, and SH and H are sister's husband's income and own-husband's income respectively. This model confounds changes in H and SH -- we'd expect c to be very large simply because 1(SH>H) is highly (negatively) correlated with with H. If the model looks like, y* = Xb + dH + eSH + c*1(SH>H) + noise, then a value of c implying an odds ratio of 16 to 25 is pretty startling. Does anyone know the actual cite? Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: patenting
On Tue, 10 Oct 2000, Robin Hanson wrote: > If the monopolist equals the paper authors, and the product > over which there is a monopoly has its primary value in producing > this paper, then I think the journal should require that the > algorithm be made available free to others, but only for the > purpose of trying to reproduce the result. Is that feasible? Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
patenting
In the Sep 2000 Econometrica, Hal White has a paper called "A Reality Check for Data Snooping." The acknowledgments read in part: "Computer implementations of the methods described in this paper are covered by U.S. Patent 5,893,069." 1. Should academic journals publish papers with patented algorithms? Isn't the whole point ostensibly to generate reproducible results? 2. Would more widescale patenting of such algorithms lead to increased or decreased aggregate research output? 3. Does anyone know if such a patent is actually enforceable? Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: Card/Krueger Revisited
On Tue, 3 Oct 2000, Alex Tabarrok wrote: >If Card/Krueger is such a bad study where is the locus classicus of a > reply? I have heard for years of a Finis Welch reply but have never > seen anything published. Where indeed is the reply to their book which > includes a lot more questioning the miniumum wage than their paper? The paper I obliquely referred to yesterday is: Kennan, J. (1995) "The Elusive Effects of the Minimum Wage," JEL 33:4. There's also a whole sequence of papers by Neumark and Wescher. However, if I recall correctly, Card and Krueger published a later piece (circa 1998?) that addressed many of the points critics brought up. The key critical point, in my mind anyways, is that they didn't find that the minimum wage increased employment. They found is had no effect, whereas in the "control" State employment increased. If there are unobserved differences between the two States, that would reconcile the result and suggest the effect of the minimum wage was small, rather than positive. The other point is that, essentially, n=2 -- despite the fact there are lots of restaurants, they aren't really using any more information than is in aggregate employment in the two States before and after the minimum wage change. Large panel studies, while having other problems, tend to find small negative effects on employment. >I don't happen to believe their result (and where I was educated (and > Bryan teaches) Card and Kruger are known as "whores for the political > classes" ). "whores for the political classes" ??? Care to elaborate? >Card and Krueger is indeed an original and clever study and if it had Well, Kennan points out that a very similar study was published in 1915. None of this should be taken to infer that I think Card and Krueger deserve the derision that another list member has poured on them. I don't, however, think they gave us compelling evidence demonstrating perverse effects of minimum wages. Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: Card/Krueger Revisited
Wasn't it John Kennan who wrote a piece which summarized the original Card and Krueger results as: the placebo had a big positive effect, the treatement had no effect, and the sample size is n=2? Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
Re: DNA and the Death Penalty
On Wed, 26 Jul 2000, Alex Tabarrok wrote: > > In the last few years support for the death penalty has declined > (from 80% in 1994 to 67% today) as DNA technology has revealed that the > number of innocent people on death row is higher than we wanted or > expected. > Does this make sense rationally? Surely, the correct response is > that DNA technology makes the conviction of an innocent person *less* > likely and thus support for the death penalty should increase! Using DNA tests to prove innocence is applicable only in a minority of cases, usually rape/murder. It has been shown that in these type of cases a significant proportion of convictions were of innocents. If that proportion, as in Alex's story, is higher than prior beliefs, then we should also update our beliefs as to the proportion of innocents in cases where DNA evidence cannot provide conclusive proof. Further, the availability of DNA testing does not reduce the error rate in such cases, as applied either to the stock or the flow. Chris Auld (403)220-4098 Economics, University of Calgary<mailto:[EMAIL PROTECTED]> Calgary, Alberta, Canadahttp://jerry.ss.ucalgary.ca/>
