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1 Wallet Study Readers Digest, over a period of years, lost more than 1,100 wallets in various cities. Each wallet contained $50 in local currency and.

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Presentation on theme: "1 Wallet Study Readers Digest, over a period of years, lost more than 1,100 wallets in various cities. Each wallet contained $50 in local currency and."— Presentation transcript:

1 1 Wallet Study Readers Digest, over a period of years, lost more than 1,100 wallets in various cities. Each wallet contained $50 in local currency and a name and phone number so it could be returned. The wallets were dropped in various places (phone booths, sidewalks, restaurants, etc.).

2 2 Wallets Returned Norway100%Denmark100% Singapore90%New Zealand83% Finland80%Scotland80% Australia70%Japan70% South Korea70%Spain70% Austria 70%Sweden70% U.S.67%England67% India65%Canada64% France60%Brazil60% Netherlands60%Thailand55% Belgium50%Taiwan50% Malaysia50%Germany45% Portugal45%Argentina44% Russia43%Philippines40% Wales40%Italy35% Switzerland35%Hong Kong30% Mexico21%AVERAGE: 56%

3 3 U.S. Cities Wallets Returned Wallets Kept Seattle 9 1 St. Louis 7 3 Atlanta 5 5 Boston* 7 3 Los Angeles* 6 4 Houston* 5 5 Greensboro, N.C. 7 3 Las Vegas 5 5 Dayton, Ohio 5 5 Concord, N.H. 8 2 Cheyenne, Wyo. 8 2 Meadville, Pa. 8 2 (*wallets dropped in suburbs of these cities)

4 4 Social Outputs Economic outputs depend on social capital, to be sure, but what crime, illegitimacy, loneliness, etc.? Social outputs can also be modelled using production functions. Social outputs can also be assigned dollars values: how much would people pay to increase a social output? The variability in social outputs from differing levels of social capital may be much greater than the variability in economic outputs.

5 5 Regressions 1. Specification search (which variables?) 2. Using simple bivariate correlations 3. Weighted regressions 4. Dropping outliers 5. Adding quadratic or log terms for curvature

6 6 Available Data pop malework murder cartheft illegit divorce metro retired midage unemp teachsal homeown income bachelor abortion black southern churchm voting

7 7 Bivariate Correlations with the Murder Rate malework-.25 cartheft.71 illegit.79 divorce.03 metro.27 retired-.03 midage.14 unemp.38 teachsal.15 homeown -.52 income.23 bachelor.32 abortion.84 black.77 southern.38 churchm.06 voting -.27

8 8 Source: Robert Putnam: Bowling Alone, Selected statistical trend data (12/8/01)

9 9 Correlation Matrix | putnam orgs member trust putnam | 1.00 orgs | member | trust |

10 10 Bivariate Correlations with putnam Social Capital malework.58 murder-.74 cartheft-.45 illegit-.56 divorce-.35 metro-.36 retired.15 midage-.05 unemp-.44 teachsal-.13 homeown.05 income.09 bachelor.34 abortion-.30 black -.71 southern-.62 churchm-.02 voting.69

11 11 Different Measures Murder = putnam (7.55) R 2 =.55 N = 48 Murder = trust -.16 (6.03) R 2 =.48 N=41 Murder = orgs (5.17) R 2 =.35 N=50 Murder = member (3.35) R 2 =.22 N=40 Murder = putnam orgs trust member (1.89 ) (0.07) (0.47) (0.71) R 2 =.52 N=40

12 12 Weighting Murder = putnam income -2.69(7.51) (1.75) Murder = putnam income -2.48(5.58) (2.05) (weight = pop) Murder = putnam income -2.46(8.00) (2.38) (weight = 1/pop) Murder = putnam income -2.69(9.97) (1.60) ( White robust standard errors )

13 13 A Regression Murder =.68* Illegit -.25*Homeown (7.78) (2.89)

14 14 Residuals from Illegit,Homeown state v1 1. AL AK AZ AR CA CO CT DE DC FL GA HA ID IL IN IA KA KE LA ME MD MA MI MN MS MO MT NE NV NH NJ NM NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY

15 15 Residuals from putnam, income 1. AL AK. 3. AZ AR CA CO CT DE DC. 10. FL GA HA. 13. ID IL IN IA KA KE LA ME MD MA MI MN MS MO MT NE NV NH NJ NM NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY.87

16 16 Dropping Outliers Murder = putnam income (7.51) (1.75) R 2 =.55 Murder = putnam income (8.20) (2.33) R 2 =.63 (WV dropped) Murder = putnam income (8.37) (2.66) R 2 =.65 (WV,MD,LA dropped) Not much happens if outliers are dropped.

17 17 Dropping Outliers Murder = illegit homeown (7.78) (2.89) R 2 =.67 Murder = illegit homeown (7.31) 0.05 (0.98) R 2 =.53 (DC dropped) The effect of illegitimacy becomes smaller, and home ownership becomes insignificant.

18 18 Squared and Log Terms Murder = illegit homeown (7.78) -.25 (2.89) R 2 =.67 Murder = log(illegit) homeown (5.56) -.33 (3.33) R 2 =.55 Murder = illegit illegit 2 homeown (6.27) (9.14) (1.23) R 2 =.88

19 19 Murder Regression 1 R 2 =.55 Putnam (7.55) Regression 2 R 2 =.68 Putnam (3.06) Income (2.75) Black 0.15 (3.45) Metro (1.18) Southern (1.83)

20 20 Illegitimacy Regression 1 R 2 =.34 Putnam (4.91) Regression 2 R 2 =.50 Putnam (1.40) Income (1.81) Black 0.34 (3.16) Metro (0.42) Southern (1.32)

21 21 Male Labor Participation Regression 1 R 2 =.35 Putnam 2.60 (5.08) Regression 2 R 2 =.42 Putnam 2.45 (2.89) Income (0.74) Black (0.11) Metro (0.42) Southern (0.55)

22 22 Car Theft Regression 1 R 2 =.21 Putnam (3.53) Regression 2 R 2 =.61 Putnam (1.04) Income (2.92) Black 0.13 (0.04) Metro 8.03 (5.76) Southern (2.97)

23 23 Aristotle contra Kelvin Kelvin: ``When you measure what you are speaking about and express it in numbers, you know something about it, but when you cannot express it in numbers your knowledge about is of a meagre and unsatisfactory kind.' Aristotle: ``...it is the mark of an educated man to look for precision in each class of things just so far as the nature of the subject admits; it is evidently equally foolish to accept probable reasoning from a mathematician and to demand from a rhetorician scientific proofs.' Kelvin: ``In science there is only physics; all the rest is stamp collecting.'' Kelvin: ``I can state flatly that heavier than air flying machines are impossible.'


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