A DYNAMIC ANALYSIS OF

Transcription

1 A DYNAMIC ANALYSIS OF THE DEMAND FOR LIFE INSURANCE Andre P. Lebenberg (contact author) Faculty of Fnance The Unversty of Msssspp Oxford, MS Tel: James M. Carson Faculty of Rsk Management and Insurance Florda State Unversty Tallahassee, FL Emal: Randy E. Dumm Faculty of Rsk Management and Insurance Florda State Unversty Tallahassee, Florda March 15, 2010

2 A DYNAMIC ANALYSIS OF THE DEMAND FOR LIFE INSURANCE ABSTRACT Pror research suggests that nether the choce to own lfe nsurance nor the amount purchased s consstently related to the presence of chldren n the household. Whle these perplexng fndngs are based on a statc framework, we alternatvely examne lfe nsurance demand n a dynamc framework as a functon of changes n household lfe cycle and fnancal condton. Our results, based on a unque panel dataset from the Survey of Consumer Fnances, ndcate both a statstcally and economcally sgnfcant relaton between lfe events and the demand for lfe nsurance. In partcular, we fnd that new parents are 40 percent more lkely to ntate term nsurance coverage than are other households, and that these new parents purchase two-thrds more coverage than do other households. We also provde new evdence n support of the emergency fund hypothess: Households n whch ether spouse has become unemployed are more than twce as lkely as other households to surrender ther whole lfe nsurance. Keywords: Lfe Insurance Demand, Famly Lfe Cycle, Survey of Consumer Fnances, Emergency Fund Hypothess INTRODUCTION Research has emprcally examned lfe nsurance demand for over 50 years. Based on pror fndngs, Hau (2000) noted conflctng evdence for varous tradtonal determnants of lfe nsurance demand (e.g., age, martal status, number of chldren, educaton, workng wfe, famly ncome, and net worth). 1 Smlarly, Zetz (2003) found that publshed research shows conflctng results for certan varables that are expected to be determnants of the demand for lfe nsurance (e.g. educaton, famly sze, spouse workng outsde home, and brth order). More recently, whle Bernhem et al. (2003a and 2003b) found no correlaton between lfe nsurance ownershp and fnancal vulnerablty (ther IMPACT factor), Ln and Grace (2007) used an alternatve IMPACT 1 For example, Berekson (1972) found a postve relatonshp between age and lfe nsurance demand, whle subsequent research (Ferber and Lee, 1980; Auerbach and Kotlkoff, 1989; and Bernhem, 1991) found a negatve relatonshp between age and demand for lfe nsurance. Addtonally, some research found an nsgnfcant result on the age varable (Duker, 1969, Anderson and Nevn, 1975, Ftzgerald, 1987, Gandolf and Mners, 1996). 1

3 measure and found a postve relatonshp between fnancal vulnerablty and the amount of lfe nsurance purchased. 2,3 Table 1 categorzes and summarzes emprcal results for lfe nsurance demand varables. For lfe cycle varables, dfferences exst for age and educatonal levels. For famly lfe cycle varables, dfferences exst for martal status and number of chldren. For the fnancal varables, dfferences exst n the relatonshp of the famly s fnancal vulnerablty and the nsurance coverage purchased to address that vulnerablty. 4,5 A common thread among pror emprcal analyses of lfe nsurance demand s that researchers have employed a statc (rather than dynamc) framework. Such an approach s reasonable gven that household survey data are largely crosssectonal and generally do not follow the same households over several tme perods. Whle crosssectonal data are useful n explanng current levels of nsurance holdngs n terms of current household characterstcs (lke martal status, employment status, and number of chldren), crosssectonal data do not allow for an analyss of the mpact of lfe events (e.g., gettng marred, changng jobs, or havng a chld) on lfe nsurance demand. 6 <INSERT TABLE I HERE> 2 Bernhem et al. (2003a) use data from the 1992 Health Retrement Study (older workers) along wth ndvdual fnancal vulnerablty, whle Bernhem et al. (2003b) use 1995 Survey of Consumer Fnances (SCF) data to examne the relatonshp between fnancal vulnerablty and lfe nsurance usage for couples over the lfe cycle. 3 Ln and Grace (2007) use SCF data from 1992, 1995, 1998, and 2001 n ther analyss and they use a dfferent approach (vs a vs Bernhem et al.) to calculate fnancal vulnerablty. The authors argue that ther IMPACT measure, whch they use n a Tobt model, captures the standard of lvng declne at the death of a spouse n a way that s more transparent and uses weaker assumptons. 4 For example, two of the nne artcles referenced reported a negatve result for the number of chldren varable whle one study found a postve result. Of the remanng sx studes, four found no sgnfcant result for the number of chldren varable whle two reported mxed results (e.g., dfferent result based on model or varable subset). 5 Recent publshed work also examnes lfe nsurance demand from an nternatonal perspectve. L et al. (2007) nvestgate lfe nsurance demand n 30 OECD countres for the perod from 1993 to They fnd a postve ncome elastcty of nsurance demand and a postve relatonshp between demand and number of dependents and educaton. Hwang and Gao (2003) examne lfe nsurance demand n Chna and fnd that mprovng economc securty and educaton levels help to explan the rapd ncrease n lfe nsurance sales n Chna. 6 Data sources used n emprcal studes of lfe nsurance demand vary wdely and nclude ndustry publcatons and surveys (e.g. Lfe Insurance Fact Book, Best s Lfe Reports, LIMRA survey), Unted States government surveys (e.g. Survey of Consumer Fnances, Retrement Hstory Survey), and other sources. Addtonally, some studes develop addtonal fnancal vulnerablty nput data through the use of a modelng process (e.g. Bernhem et al. 2003a, 2003b). 2

4 Lmtatons of data used n prevous lfe nsurance demand research are mportant because the lfe cycle lterature suggests that lfe nsurance purchases are lkely to follow varous lfe events such as gettng marred, havng a chld, purchasng a home, and gettng a new job, for whch cross-sectonal data are not well-suted. Smlarly, termnaton of lfe nsurance s lkely to follow other lfe events such as gettng dvorced, havng a spouse de, becomng unemployed, and retrng. Whle prevous lterature has provded a strong theoretcal foundaton as well as emprcal evdence on the determnants of lfe nsurance demand based on cross-sectonal data (wthn a statc framework), the lterature has not employed panel data (wthn a dynamc framework) to examne the varous lfe events hypotheszed to relate to lfe nsurance demand. Thus, the use of crosssectonal data n pror studes coupled wth the dsparty n results that have been reported n prevous lterature suggest that a dynamc analyss based on panel data may provde the bass for a deeper understandng of the determnants of lfe nsurance demand. The purpose of ths study s to capture the effects of varous lfe events that are hypotheszed to relate to lfe nsurance demand. We examne lfe nsurance demand n a dynamc settng usng data from the Survey of Consumer Fnances panel study. Thus, we examne changes n lfe nsurance holdngs for the tme perod between 1983 and A comparson of our results wth the fndngs from pror research llustrates the mportant nsght that s ganed by consderng the effect of changes n demand determnants on new nsurance demand. Whle prevous research based on SCF cross-sectonal data ndcated that nether the choce to own lfe nsurance nor the amount of lfe nsurance owned s related to the presence of chldren n the household (Hau, 2000, Gutter and Hatcher, 2008), our analyss of the SCF panel data provdes evdence of a postve relatonshp between havng a new chld and purchasng term lfe nsurance. In partcular, new parents are 40 percent more lkely to ntate term nsurance coverage 3

5 than are other households, and they purchase two-thrds more coverage than do other households. Thus, the results for the effect of havng a new chld on both the decson to ntate term nsurance as well as the amount of coverage purchased are not only statstcally sgnfcant, but also are economcally sgnfcant. More broadly, we fnd that households who were more lkely to ntate new term lfe nsurance coverage were not only those that added a chld snce 1983, but also those households that started a new job and those that surrendered whole lfe coverage. For households who termnated lfe nsurance, our fndngs suggest that the decson to drop coverage s related to several lfe events and the purchase of alternatve coverage. In partcular, we fnd that households n whch ether spouse became unemployed were more than twce as lkely as other households to surrender ther whole lfe nsurance, provdng new evdence n support of the emergency fund hypothess. The next secton presents the sample, data, and methodology. We then present and dscuss our emprcal results. The fnal secton summarzes and concludes. SAMPLE, DATA, AND METHODOLOGY We nvestgate determnants of changes n household lfe nsurance demand by examnng household survey data from the Survey of Consumer Fnances (SCF) panel study. The panel study s the only source of panel data n the hstory of the SCF. 7 In the 1989 survey, 1,479 households that partcpated n the 1983 survey were re-ntervewed. 8 In addton to several hundred questons regardng household demographcs, wealth, ncome, and pensons, the 7 The trennal cross-sectonal SCF data have been used n recent lfe nsurance studes ncludng Ln and Grace (2007) and Gutter and Hatcher (2008). 8 For more nformaton regardng the panel survey, see the onlne codebook at: See also Kennckell and Starr-McCluer (1997) for a dscusson of the advantages of studyng changes n fnancal holdngs usng the SCF panel study rather than usng other household panel data sources such as the Survey of Income and Program Partcpaton, the Panel Study of Income Dynamcs, or the Health and Retrement Survey. 4

6 surveys contan a seres of questons that address lfe nsurance holdngs. 9 The survey nstrument used for the 1989 re-ntervew ncluded all of the questons asked n the 1983 survey and also ncluded a number of questons regardng famly, fnancal, and other events that occurred n the past few years. Ths panel data set allows for an dentfcaton of changes n lfe nsurance polcy demand and an examnaton of the factors that affect new lfe nsurance demand. The SCF panel data also have been used n the context of savngs behavor (Dynan, Sknner, and Zeldes, 2004), stockholdng behavor (Bertaut, 1998), and lfe nsurance polcy loan demand (Lebenberg, Carson, and Hoyt, 2009), but have not been used to study changes n lfe nsurance holdngs. As llustrated by Lebenberg et al. (2009), the analyss of household-level panel data can reveal relatonshps between events affectng households (such as unexpected major expenses) and the demand for fnancal products or optons (such as lfe nsurance polcy loans) that may not be apparent usng less detaled data sources. Whle pror studes usng aggregate economc and loan demand data had faled to fnd any support for the ntutvely appealng emergency fund hypothess for lfe nsurance polcy loans, Lebenberg et al. (2009) were able to fnd support for the hypothess usng the SCF panel data. The SCF panel data are partcularly well-suted to a dynamc analyss of lfe nsurance demand because the SCF samplng frame s desgned to oversample wealther households that are most lkely to hold varous fnancal assets (such as lfe nsurance) and the samplng frame ncludes households at varous stages of the famly lfe cycle. As descrbed earler, the lfe cycle lterature suggests that lfe nsurance purchases are lkely to follow varous lfe events, such as marrage, the brth of a chld, purchasng a home, and a new job. Smlarly, termnaton of lfe nsurance s lkely to follow other lfe events such as dvorce, death of a spouse, unemployment, and retrement. 9 The wordng of the key survey questons regardng lfe nsurance holdngs appears n Appendx A. 5

7 By examnng the SCF panel we are able to dentfy such events n a natonally representatve sample. 10 Appendx B shows that when frst surveyed n 1983, 55% of sample households held term nsurance and 53% held whole-lfe nsurance. 11 The medan household was 50 years old, had one chld, owned $40,000 ($25,000) 12 of term (whole lfe) nsurance, had annual ncome of $32,000, and net worth of roughly $180,000. The vast majorty of households were workng (90%), whte (87%), marred (74%), homeowners (80%), and hgh-school graduates (84%). Approxmately 40% of households expected to leave a large bequest, and 8% were very rsk averse. 13 We dentfy four categores of households that experenced changes n lfe nsurance holdngs (between the ntal 1983 ntervew and the subsequent 1989 rentervew) new term lfe owners, new whole lfe owners, households droppng term nsurance, and households droppng whole lfe nsurance. 14 We then construct varables that capture changes n demand determnants by comparng household characterstcs between the ntal ntervew n 1983 and the 1989 rentervew. 15 Whle several pror studes have examned the determnants of lfe nsurance ownershp usng Tobt regresson, we use the approach ntroduced by Cragg (1971) to separately analyze the 10 Other sources of household panel data regardng nsurance purchases (such as the HRS or the NSIP) are composed of predomnantly older households for whch several of these lfe events would be less relevant. 11 Approxmately 22% of households owned both term and whole lfe polces. 12 These amounts are based on only those households wth ether type of lfe nsurance. For the full sample that ncludes those households wthout ether type of nsurance the medan amount of term nsurance s $3,000 and the medan amount of whole lfe nsurance s $2, As explaned later, the bequest motve and rsk averson varables are actually only measured n the 1989 survey nstrument. However, we nclude them n Table 1 as household characterstcs. 14 The other possble categores are (1) households that held nether term lfe nor whole lfe nsurance n 1983 and purchased both by 1989, and (2) households that owned both term lfe and whole lfe nsurance n 1983 and dropped them both by In our sample of 1479 households, only 8 households fall nto the frst category and 19 households fall nto the second. 15 We are unable to capture the effect of changes for certan demand determnants. Change n age s dentcal for the vast majorty of sample households (t dffers only for those households wth spousal changes); household race, educaton, and rsk averson are constant across the panel; expectatons regardng bequests are observable only n 1989; and home ownershp status s constant across the panel because a requrement for ncluson n the re-ntervew s that 1983 households were stll lvng at the same address n

8 determnants of ownershp status and extent. 16 Cragg s two-part model s well-suted to studes of partcpaton (or status) and volume (or extent). 17 Cragg s model has been used n pror nsurance research to examne partcpaton and volume decsons regardng dervatves usage (Cummns, Phllps, and Smth, 2001) and long-term care nsurance ssuance (McShane and Cox, 2008). 18 We use the Cragg regresson methodology to examne the mpact of changes n fnancal, demographc, and employment characterstcs on both the choce to purchase new lfe nsurance (or drop exstng coverage) and the amount of new coverage purchased (or the sze of the polcy dropped). For new lfe nsurance purchases, the followng models are estmated: NEWTERM β WORTH + β DROPPEDWHOLE + controls + ε 5 = α + β NEWKID + β NEWMARRIED + β NEWJOB + β INCOME (1) LN$ NEWTERM = α + β NEWKID + β NEWMARRIED + β NEWJOB + β INCOME + β WORTH + β LN$ WHOLEDROPPED + controls + ε (2) NEWWHOLE = α + β NEWKID + β NEWMARRIED + β NEWJOB + β INCOME + β WORTH + β DROPPEDTERM + controls + ε (3) LN$ NEWWHOLE = α + β NEWKID + β NEWMARRIED + β NEWJOB + β INCOME + β WORTH + β LN$ TERMDROPPED + controls + ε (4) 16 For example, see Gutter and Hatcher (2008), Ln and Grace (2007), and Hau (2000). The Tobt model s approprate when there s substantal censorng or truncaton of the dependent varable. Ths s usually the case n survey samples where many households n the sample do not own lfe nsurance and therefore have zero values for lfe nsurance holdngs. Whle the Tobt model s more approprate than smple ordnary least squares, t does not allow for separate analyss of the household decsons regardng ownershp status and ownershp extent. If the factors that explan lfe nsurance ownershp status are dfferent from those that explan ownershp extent, the Tobt model wll be msspecfed. 17 An alternatve two-part (or two-step) method used as a robustness check by Gutter and Hatcher (2008) and Ln and Grace (2007) s the Heckman model that controls for sample selecton bas. The frst step of the Heckman model s a probt regresson for ownershp status. The second step ncludes a sample selecton parameter (the nverse mlls rato) that s estmated n the probt regresson. For robustness we also estmate Heckman models for term and whole lfe nsurance and fnd the nverse mlls rato s nsgnfcant mplyng an absence of sample-selecton bas. 18 We also estmate Tobt regressons (on the full sample) so that we can test whether Cragg s model s more approprate for our analyss than the Tobt model. If the coeffcent vectors for the ownershp status and extent regressons do not dffer sgnfcantly, then the Tobt model s more effcent. If determnants of ownershp extent and status do dffer sgnfcantly then the Cragg model s approprate. Followng the method presented by Greene (2003, p770) we use a lkelhood rato test to evaluate the null hypothess of equalty of the coeffcent vectors n the Tobt and Cragg models. Our test results strongly reject the null and support the estmaton approach outlned here. 7

9 NEWTERM s an ndcator varable equal to one for households that dd not have any term nsurance n 1983 but do have term nsurance n 1989, and zero otherwse. NEWWHOLE s an ndcator varable equal to one for households that dd not have any whole lfe nsurance n 1983 but do have whole lfe nsurance n 1989, zero otherwse. LN$NEWTERM (LN$NEWWHOLE) s the natural log of the face value of new term (whole lfe) nsurance. NEWKID s equal to one for households that had a new chld snce 1983, zero otherwse. NEWMARRIED s equal to one for households that became marred snce 1983, 0 otherwse. NEWJOB s equal to one for households that changed employers snce 1983, 0 otherwse. These three ndependent varables capture the effect of lfe events on the demand for new term or whole lfe nsurance. INCOME and WORTH reflect the mpact of changes n the household s fnancal wellbeng on new nsurance demand. They are equal to the percentage change (from 1983 to 1989) n real ncome and net worth, respectvely. DROPPEDTERM (DROPPEDWHOLE) s equal to one for households that had term (whole lfe) nsurance n 1983 but not n 1989, zero otherwse. LN$TERMDROPPED (LN$WHOLEDROPPED) s the natural log of the face value of term (whole lfe) nsurance dropped snce These varables are ncluded as ndependent varables to capture substtuton effects between term and whole lfe nsurance. Fnally, n order to control for the relaton between household characterstcs and changes n lfe nsurance demand, we nclude as control varables a set of varables that commonly appear n statc models of lfe nsurance demand. Specfcally, we control for household ncome (LNINCOME), net worth (LNWORTH), number of chldren (KIDS), martal status (MARRIED), age (AGE50), 19 race (WHITE), educaton (HSCHOOL), employment (WORKING), homeownershp (OWNHOME), bequest motve (BEQUEST), and rsk averson (RISKAVERSE) Pror studes (such as Ln and Grace, 2007) use categorcal varables to capture the potentally non-lnear relaton between AGE and lfe nsurance demand. For our analyss t s especally mportant to control for the dffculty n 8

10 follows. The choce to drop nsurance coverage, and the amount of coverage dropped, s modeled as DROPPEDTERM = α + β NEWDIVORCED + β NEWWIDOWED + β NEWSEPARATED + β NEWUNEMP + β NEWRETIRED + β INCOME + β WORTH + β NEWWHOLE + controls + ε (5) LN$ TERMDROPPED = α + β NEWDIVORCED + β NEWWIDOWED + β NEWSEPARATED + β NEWUNEMP + β NEWRETIRED + β INCOME + β WORTH + β LN$ NEWWHOLE + controls + ε (6) DROPPEDWHOLE = α + β NEWDIVORCED + β NEWWIDOWED + β NEWSEPARATED + β NEWUNEMP + β NEWRETIRED + β INCOME + β WORTH + β NEWTERM + controls + ε (7) LN$ WHOLEDROPPED = α + β NEWDIVORCED + β NEWWIDOWED + β NEWSEPARATED + β NEWUNEMP + β NEWRETIRED + β INCOME + β WORTH + β LN$ NEWTERM + controls + ε (8) NEWDIVORCED, NEWWIDOWED, and NEWSEPARATED, are equal to one for households that became dvorced, wdowed, or separated snce 1983, zero otherwse. NEWUNEMP and NEWRETIRED are equal to one for households where ether the respondent or spouse became unemployed or became retred snce 1983, zero otherwse. INCOME and WORTH are defned as before. 21 NEWTERM (NEWWHOLE) and LN$NEWTERM (LN$NEWWHOLE) also are defned as before. The same set of control varables (dscussed above) s ncluded n these models. Descrptve statstcs and varable defntons appear n Table 2. Approxmately 21 (13) percent of households that were rentervewed n 1989 had ntated term (whole lfe) nsurance snce The mean amount of term (whole lfe) nsurance purchased was $19,028 ($18,389). 22 Further, roughly 15 (17) percent of households that owned term (whole lfe) nsurance n 1983 no longer owned any n The mean sze of term (whole lfe) nsurance dropped was $19,390 obtanng new lfe nsurance at an advanced age. For smplcty we use a sngle ndcator varable (equal to 1 for households over 50, and 0 otherwse) to capture the dfferental mpact of age on changes n lfe nsurance demand. As a senstvty test we replace the smple ndcator varable wth the seres of ndcators for age categores used by Ln and Grace (2007). Our results are robust to ths alternatve approach to capturng the relaton between age and new lfe nsurance demand. 20 These varables are measured at the tme of the ntal (1983) ntervew, wth the excepton of BEQUEST and RISKAVERSE whch are only captured n the 1989 rentervew. 21 These two varables are wnsorzed at the 5 th and 95 th percentles to reduce the mpact of outlers. 22 Unreported medans for $NEWTERM and $NEWWHOLE are $20,000 and $23,000, respectvely. 9

11 ($10,236). 23 Snce they were last ntervewed n 1983, approxmately 12 percent of households had a new chld, 4 percent became marred, 3 percent became dvorced, 5 percent became wdowed, and 1 percent became separated. Regardng employment status, 18 percent of households started a new job, 16 percent became retred and 2 percent became unemployed. Fnally, mean real household ncome and net worth ncreased substantally. 24 For the sake of brevty we do not expand on the control varables here. Parwse correlatons reported n Table 3 reveal an absence of potentally problematc correlatons between ndependent varables. 25 <INSERT TABLE 2 HERE> <INSERT TABLE 3 HERE> The thrd column of Panel A n Table 4 reports dfferences n means of demand determnants for households that ntated term nsurance snce A sgnfcantly larger proporton of new term purchasers (NEWTERM=1) had a new chld, became marred, and started a new job snce 1983, than dd other households (NEWTERM=0). Household ncome of new term nsurance purchasers ncreased by more than that of other households, and the proporton of new term purchasers droppng whole lfe nsurance was sgnfcantly greater than other households (wth almost 40 percent of new term owners droppng exstng whole lfe nsurance). The sxth column of Panel A n Table 4 reports fewer sgnfcant dfferences n demand determnants based on new whole lfe nsurance purchases. Smlar to our fndngs for new term nsurance ownershp, the proporton of marred households that ntated whole lfe nsurance (NEWWHOLE=1) s sgnfcantly larger than the control sample (NEWWHOLE=0). Household net worth of new whole 23 Unreported medans for $TERMDROPPED and $WHOLEDROPPED are $22,000 and $15,000, respectvely. 24 However, unreported medans for these varables are and , respectvely. 25 Varance nflaton factors also ndcate that correlaton among the ndependent varables s not problematc. All varables have VIFs below

12 lfe owners ncreased by more than that of other households, and the proporton of new whole lfe owners that dropped exstng term lfe nsurance was sgnfcantly hgher than other households. Panel B of Table 4 reports dfferences n means of determnants of droppng term or wholelfe nsurance. Households that owned term nsurance n 1983 and were subsequently wdowed are lkely to no longer report term lfe nsurance n force n 1989 (DROPPEDTERM=1) f the survvng spouse does not have term lfe nsurance. Newly retred households are also more lkely than others to drop term lfe nsurance because term nsurance s more often ted to employment (versus wholelfe nsurance), and also because retrement ncome serves as a substtute for lfe nsurance. The proporton of households that became unemployed and dropped (surrendered) whole lfe polces (DROPPEDWHOLE=1) s sgnfcantly greater than those households that dd not become unemployed. Ths behavor s consstent wth the emergency fund hypothess that has been used to explan polcy surrenders (Outrevlle, 1990) and polcy loans (Lebenberg, Carson, and Hoyt, 2009). Fnally, households that dropped whole lfe nsurance snce 1983 experenced less growth n net worth than dd other households, and households that surrendered whole lfe polces were three tmes more lkely to ntate new term coverage than were other households. <INSERT TABLE 4 HERE> EMPIRICAL RESULTS Lfe Insurance Purchases Cragg regresson results for new lfe nsurance purchased, reported n Table 5, confrm the unvarate dfferences whle holdng constant other determnants of new nsurance demand. 26 The Status results for new term lfe nsurance ndcate that households that had a new chld snce 26 We test the robustness of our results to an alternatve measure of new lfe nsurance demand by measurng whether households ncreased the face value of term or whole lfe nsurance between 1983 and 1989, regardless of whether they already owned ether type of nsurance. Regresson results were consstent wth what we report n Table 5. 11

13 1983, those that experenced relatvely hgh ncome growth, those that started a new job snce 1983, and those that surrendered whole lfe coverage, were more lkely to ntate term nsurance coverage than were other households. <INSERT TABLE 5 HERE> <INSERT TABLE 6 HERE> As shown n Table 6, the margnal effect for NEWKID mples that households wth a new chld are 40% more lkely to ntate term nsurance coverage than are other households, holdng all other new nsurance demand determnants at ther means. Smlarly, households that started a new job are 39% more lkely to ntate term nsurance than other households. Households that surrendered whole lfe nsurance were more than three tmes as lkely to ntate term nsurance as other households, holdng other determnants at ther means. 27 The Status results for new whole lfe nsurance ndcate households that are newly marred, those that experenced growth n net worth, and those that dropped term nsurance coverage, were more lkely to ntate whole lfe nsurance coverage. Probabltes reported n Table 6 mply that households that became marred snce 1983 were more than twce as lkely to purchase whole lfe nsurance than other households. Further, households that dropped term nsurance snce 1983 were more than four tmes as lkely to ntate whole lfe nsurance, holdng other determnants at ther means. The Extent results for term nsurance ndcate that the face value of term nsurance purchased s sgnfcantly larger for those households that had a chld, changed jobs, and experenced an ncrease n ncome, than for other households. The coeffcent estmates for the categorcal varables n the extent regresson (where the dependent varable s the natural logarthm 27 Probabltes reported n Table 6 (and n Table 8) are calculated usng the prtab command n STATA. For more detals see Long and Freese (2006) and Agrest (2007). 12

14 of term coverage ntated) can be nterpreted by exponentaton. Thus, new term nsurance purchases made by new parents are roughly two-thrds larger than are those made by other households. 28 The Extent results for new whole lfe nsurance suggest that the amount of new whole lfe nsurance purchased s ndependent of most hypotheszed demand determnants, wth the excepton of growth n ncome and the amount of term nsurance dropped. Lfe Insurance Termnatons Table 7 reports that for both term and whole lfe nsurance, the decson to drop coverage s related to several lfe events and the purchase of alternatve coverage. However, the sze of the term polcy dropped s related only to the amount of new whole lfe coverage purchased, and the sze of the whole lfe polcy dropped s related only to retrement. Respondents that have become wdowed snce 1983 are more lkely than others to no longer own term nsurance n 1989, most lkely due to many households holdng term nsurance on only one spouse; thus when a spouse passes, the term polcy pays out and the household no longer owns any term nsurance. <INSERT TABLE 7 HERE> <INSERT TABLE 8 HERE> Households that retred snce 1983 are more lkely to drop ther term coverage, lkely because term nsurance often s ted to employment and also because the need to nsure the potental loss of future ncome no longer exsts n retrement. For whole lfe nsurance, recent dvorcees and households that recently became unemployed are more lkely to surrender ther whole lfe nsurance polces than are other households. The postve relaton between becomng unemployed and surrenderng whole lfe nsurance s consstent wth the emergency fund hypothess (Outrevlle, 1990; and Lebenberg, Carson, and Hoyt, 2009). Table 8 reports that households n whch ether 28 The effect of NEWKID=1 s calculated as exp(0.517), whch equals