No date for the weekend?Feeling a bit desperate? This NBER paper (No. 16517) tells you what you need to know (who says economics isn't useful?).
Terms of Endearment: An Equilibrium Model of Sex and Matching
Peter Arcidiacono, Andrew W. Beauchamp, Marjorie B. McElroy
We develop a directed search model of relationship formation which can disentangle male and female preferences for types of partners and for different relationship terms using only a cross-section of observed matches. Individuals direct their search to a particular type of match on the basis of (i) the terms of the relationship, (ii) the type of partner, and (iii) the endogenously determined probability of matching. If men outnumber women, they tend to trade a low probability of a preferred match for a high probability of a less-preferred match; the analogous statement holds for women. Using data from National Longitudinal Study of Adolescent Health we estimate the equilibrium matching model with high school relationships. Variation in gender ratios is used to uncover male and female preferences. Estimates from the structural model match subjective data on whether sex would occur in one's ideal relationship. The equilibrium result shows that some women would ideally not have sex, but do so out of matching concerns; the reverse is true for men.
Showing posts with label matching. Show all posts
Showing posts with label matching. Show all posts
Friday, November 12, 2010
Tuesday, September 28, 2010
Regression as a matching estimator: Oaxaca-Blinder rides again
Propensity score matching is a well known method of estimating treatments effects with observational data where the treatment is binary and it is assumed there is no selection on unobservables. To recall: one has data on individuals who have been treated. One would like to form a control who are otherwise identical (on average) but who were not treated.
One could match on characteristics but if the dimension of the X's is high that gets very difficult. It turns out that, due to a famous result of Rosenbaum & Rubin, given a key assumption, matching on the probability of being treated (the propensity) is equivalent.
So the norm is to model this probability with say a logit or probit, estimate the predicted probability and form your control group. There are several ways of doing this.
So what if you used a linear probability model instead? Well it turns out that, like speaking prose, you may have been doing this all along without realizing it. In a recent paper P. Kline shows that such a procedure is equivalent to our old friend the Oaxaca-Blinder estimator well known to de-composers. Aside from being easy to do it has several other nice features like being unbiased in finite samples and ensuring exact covariate balance between the two groups in circumstances where it is not guaranteed by other estimators.
For a nice introduction to matching methods see Conniffe et al.
One could match on characteristics but if the dimension of the X's is high that gets very difficult. It turns out that, due to a famous result of Rosenbaum & Rubin, given a key assumption, matching on the probability of being treated (the propensity) is equivalent.
So the norm is to model this probability with say a logit or probit, estimate the predicted probability and form your control group. There are several ways of doing this.
So what if you used a linear probability model instead? Well it turns out that, like speaking prose, you may have been doing this all along without realizing it. In a recent paper P. Kline shows that such a procedure is equivalent to our old friend the Oaxaca-Blinder estimator well known to de-composers. Aside from being easy to do it has several other nice features like being unbiased in finite samples and ensuring exact covariate balance between the two groups in circumstances where it is not guaranteed by other estimators.
For a nice introduction to matching methods see Conniffe et al.
Wednesday, September 30, 2009
The 'Incidence' and 'Wage Effects' of Mismatch among Immigrant and Ethnic Minority Graduates
A new ESRI working paper by Delma Byrne and Seamus McGuinness looks at mismatch in the graduate labour market among immigrants and second-generation ethnic minority groups. As in other studies of a similar nature (some of which have been discussed on this blog before: Bender and Heywood; 2006 (and Robst; 2007); Nordin/Persson/Rooth; 2008; Budria and Moro-Egido; 2004), the focus is to estimate the incidence and wage effects of over-education and overskilling.
In the Byrne and McGuinness paper, the graduates come from UK universities. The results show that immigrant and second-generation ethnic minority graduates are no more likely to experience education or skill mismatch relative to their native counterparts. Furthermore, graduates from immigrant and ethnic minority backgrounds incur overeducation and overskilling wage penalties that lie well below the level incurred by native graduates.
In the Byrne and McGuinness paper, the graduates come from UK universities. The results show that immigrant and second-generation ethnic minority graduates are no more likely to experience education or skill mismatch relative to their native counterparts. Furthermore, graduates from immigrant and ethnic minority backgrounds incur overeducation and overskilling wage penalties that lie well below the level incurred by native graduates.
Monday, April 27, 2009
Stephen Kinsella on Matching in Labour Markets
Here is a theoretical (working) paper by Dr. Stephen Kinsella (from the University of Limerick) on matching in labour markets. Dr. Kinsella presented this paper yesterday at the Irish Economics Association. The matching process described can also be thought of as a mate choice problem, as discussed in the paper. We discussed the geographic distribution of single females to males in Ireland on the blog before, here.
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