State University JiM GUNDLACH, Auburn University Abstract T7his article assesses the link between country music and metropolitan suicide rates. Country music is hypothesized to nurture a suicidal mood through its concerns with problems common in the suicidal population, such as marital discord, alcohol abuse, and alienation from work. The results of a multiple regression analysis of 49 metropolitan areas show that the greater the airtime devoted to country music, the greater the white suicide rate. The effect is independent of divorce, southernness, poverty, and gun availability. The existence of a country music subculture is thought to reinforce the link between country music and suicide. Our model explains 51% of the variance in urban white suicide rates. Sociological work on the relationship between art and society has been largely restricted to speculative, sociohistorical theories that are often mutually opposed. Some theorists see art as creating social structure (Adorno 1973), while Sorokin (1937) suggests that society and art are manifested in cyclical autono- mous spheres; and still others contend that art is a reflection of social structure (Albrecht 1954). Little empirical work has been done on the impact of music on social problems. While some research has linked music to criminal behavior (Singer, Levine & Jou 1990), the relationship between music and suicide remains largely unexplored. Music is not mentioned in reviews of the literature on suicide (Lester 1983; Stack 1982, 1990b); instead, the impact of art on suicide has been largely restricted to analyses of television movies and soap operas (for a review, see Stack 1990b). ty and art are manifested in cyclical autono- tend that art is a reflection of social structure ork has been done on the impact of music on arch has linked music to criminal behavior ationship between music and suicide remains mentioned in reviews of the literature on 90b); instead, the impact of art on suicide has s of television movies and soap operas (for a link between a particular form of popular opolitan suicide rates. We contend that the ter a suicidal mood among people already at eby associated with a high suicide rate. The ubculture and a link between this subculture creased suicide risk. her variables were provided by the Inter-University search, University of Michigan, Ann Arbor. We are pirations and helpful discussions, to the anonymous to Mitch Henryfor his help in gathering the data on Steven Stack, Department of Sociology, Wayne State s Social Forces, September 1992, 71(1):211-218
paper explores the link between economic development and penile length between 1960 and 1985. It estimates an augmented Solow model utilizing the Mankiw-Romer-Weil 121 country dataset. The size of male organ is found to have an inverse U-shaped relationship with the level of GDP in 1985. It can alone explain over 15% of the variation in GDP. The GDP maximizing size is around 13.5 centimetres, and a collapse in economic development is identified as the size of male organ exceeds 16 centimetres. Economic growth between 1960 and 1985 is negatively associated with the size of male organ, and it alone explains 20% of the variation in GDP growth. With due reservations it is also found to be more important determinant of GDP growth than country's political regime type. Controlling for male organ slows convergence and mitigates the negative effect of population growth on economic development slightly. Although all evidence is suggestive at this stage, the `male organ hypothesis' put forward here is robust to exhaustive set of controls and rests on surprisingly strong correlations. JEL Classification: O10, O47 Keywords: economic growth, development, male organ, penile length, Solow model Tatu Westling Department of Political and Economic Studies University of Helsinki P.O. Box 17 (Arkadiankatu 7) FI-00014 University of Helsinki FINLAND e-mail: [email protected] • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 10 12 14 16 18 0 5000 10000 15000 20000 Male organ (cm) GDP 1985 ($) Figure 2: GDP ratio between 1985 and 1960 and the size of male organ countries, ORGAN in linear form, ¯ R2=0.20 • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 10 12 14 16 18 0 1 2 3 4 5 6 Male organ (cm) GDP 1985/1960 12 GDP 1985/1960 Male organ (cm)
a predictor, once we know the other predictors? • What is value of knowing marriage rate, once we already know median age at marriage? • What is value of knowing median age marriage, once we know marriage rate? U TFFN WBSJBUF OPUBUJPO .VMUJWBSJBUF SFHSFTTJPO GPSNVMBT MPPL B MPU MJLF UIF NPEFMT BU UIF FOE PG UIF QSFWJPVT DIBQUFSUIFZ BEE NPSF QBSBNFUFST T UP UIF EFĕOJUJPO PG µJ ćF TUSBUFHZ JT TUSBJHIUGPSXBSE NJOBUF UIF QSFEJDUPS WBSJBCMFT ZPV XBOU JO UIF MJOFBS NPEFM PG UIF BO S FBDI QSFEJDUPS NBLF B QBSBNFUFS UIBU XJMM NFBTVSF JUT BTTPDJBUJPO I UIF PVUDPNF VMUJQMZ UIF QBSBNFUFS CZ UIF WBSJBCMF BOE BEE UIBU UFSN UP UIF MJOFBS EFM F BMXBZT OFDFTTBSZ TP IFSF JT UIF NPEFM UIF QSFEJDUT EJWPSDF SBUF VT SSJBHF SBUF BOE BHF BU NBSSJBHF %J ∼ /PSNBM(µJ, σ) >OLNHOLKRRG@ µJ = α + βN NJ + βB BJ >OLQHDUPRGHO@
BTTPDJBUJPO I UIF PVUDPNF VMUJQMZ UIF QBSBNFUFS CZ UIF WBSJBCMF BOE BEE UIBU UFSN UP UIF MJOFBS EFM F BMXBZT OFDFTTBSZ TP IFSF JT UIF NPEFM UIF QSFEJDUT EJWPSDF SBUF VT SSJBHF SBUF BOE BHF BU NBSSJBHF %J ∼ /PSNBM(µJ, σ) >OLNHOLKRRG@ µJ = α + βN NJ + βB BJ >OLQHDUPRGHO@ divorce rate marriage rate median age marriage “slope” for marriage rate “slope” for median age marriage
additional value in knowing marriage rate. • Once we know marriage rate, still value in knowing median age marriage. • If we don’t know median age marriage, still useful to know marriage rate. 4163*064 "440$* sigma ba bm a -2 0 2 4 Estimate 'ĶĴłĿIJ ƍƋ 2VBESBUJD BQQSPYJNBUF QPTUFSJP FBDI QPTUFSJPS NPEF BOE TPMJE CMBDL MJOF T WBMT
with outcome, “controlling” for other predictors • Recipe: 1. Regress predictor on other predictors 2. Compute predictor residuals 3. Regress outcome on residuals
residual marriage rate? States with fast/slow rates of marriage (for age of marriage) do not (on average) have fast/slow divorce rates -2 -1 0 1 2 3 -1 0 1 MedianAgeMarriage.s Marriage. JT B S SJBHF QSFEJ BMPOF MJOF I BDDPS IBWF -1.5 -0.5 0.5 1.0 1.5 6 8 10 12 14 Marriage rate residuals Divorce rate faster slower 'ĶĴłĿIJ ƍƍ 1SFEJDUPS SFTJEVBM QMPUT G
• Check model fit — golems do make mistakes • Find model failures, stimulate new ideas • Always average over the posterior distribution • Using only MAP leads to overconfidence • Embrace the uncertainty
(c) (b) 6 8 10 12 6 8 10 12 Observed divorce Predicted divorce ID UT TX MI DE DC NC OH IA KS MD MA WA NM WV VT OR SD AZ TN NH IN MS LA RI CO OK GA KY AK AL AR ME 4 ME
residual: less divorce than expected • positive residual: more divorce than expected (c) 6 8 10 12 6 8 10 12 Observed divorce Predicted divorce ID UT ID NJ MN ND CT UT NE SC WI PA NY CA FL MT IL WY MO VA HI TX MI DE DC NC OH IA KS MD MA WA NM WV VT OR SD AZ TN NH IN MS LA RI CO OK GA KY AK AL AR ME -6 -4 -2 0 2 4 0 10 20 30 40 -4 -2 0 2 4 Waffles per capita Divorce error AL AR GA ID ME MS SC
12 Observed divorce Predicted divorce ID UT ID NJ MN ND CT UT NE SC WI PA NY CA FL MT IL WY MO VA HI TX MI DE DC NC OH IA KS MD MA WA NM WV VT OR SD AZ TN NH IN MS LA RI CO OK GA KY AK AL AR ME -6 -4 -2 0 2 4 0 10 20 30 40 -4 -2 0 2 4 Waffles per capita Divorce error AL AR GA ID ME MS SC (c) 6 8 10 12 Observed divorce ID NJ MN ND CT UT NE SC WI PA NY CA FL MT IL WY MO VA HI TX MI DE DC NC OH IA KS MD MA WA NM WV VT -6 -4 -2 0 2 4 0 10 20 30 40 -4 -2 0 2 4 Waffles per capita Divorce error AL AR GA ID ME MS SC 'ĶĴłĿIJ ƍƏ 1PTUFSJPS QSFEJDUJWF QMPUT GPS UIF NVMUJWBSJBUF EJWPSDF NPEFM (ǀǏƾ B 1SFEJDUFE EJWPSDF SBUF BHBJOTU PCTFSWFE XJUI DPOĕEFODF JO UFSWBMT PG UIF BWFSBHF QSFEJDUJPO ćF EBTIFE MJOF TIPXT QFSGFDU QSFEJDUJPO
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