The Marginal Income Effect of Education on Happiness: Estimating the Direct and Indirect Effects of Compulsory Schooling on Well-Being in Australia

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1 DISCUSSION PAPER SERIES IZA DP No The Margnal Income Effect of Educaton on Happness: Estmatng the Drect and Indrect Effects of Compulsory Schoolng on Well-Beng n Australa Nattavudh Powdthavee Warn N. Lekfuangfu Mark Wooden Aprl 2013 Forschungsnsttut zur Zukunft der Arbet Insttute for the Study of Labor

2 The Margnal Income Effect of Educaton on Happness: Estmatng the Drect and Indrect Effects of Compulsory Schoolng on Well-Beng n Australa Nattavudh Powdthavee CEP, London School of Economcs, MIAESR, Unversty of Melbourne and IZA Warn N. Lekfuangfu Unversty College London Mark Wooden MIAESR, Unversty of Melbourne and IZA Dscusson Paper No Aprl 2013 IZA P.O. Box Bonn Germany Phone: Fax: E-mal: za@za.org Any opnons expressed here are those of the author(s) and not those of IZA. Research publshed n ths seres may nclude vews on polcy, but the nsttute tself takes no nsttutonal polcy postons. The IZA research network s commtted to the IZA Gudng Prncples of Research Integrty. The Insttute for the Study of Labor (IZA) n Bonn s a local and vrtual nternatonal research center and a place of communcaton between scence, poltcs and busness. IZA s an ndependent nonproft organzaton supported by Deutsche Post Foundaton. The center s assocated wth the Unversty of Bonn and offers a stmulatng research envronment through ts nternatonal network, workshops and conferences, data servce, project support, research vsts and doctoral program. IZA engages n () orgnal and nternatonally compettve research n all felds of labor economcs, () development of polcy concepts, and () dssemnaton of research results and concepts to the nterested publc. IZA Dscusson Papers often represent prelmnary work and are crculated to encourage dscusson. Ctaton of such a paper should account for ts provsonal character. A revsed verson may be avalable drectly from the author.

3 IZA Dscusson Paper No Aprl 2013 ABSTRACT The Margnal Income Effect of Educaton on Happness: Estmatng the Drect and Indrect Effects of Compulsory Schoolng on Well-Beng n Australa * Many economsts and educators favour publc support for educaton on the premse that educaton mproves the overall well-beng of ctzens. However, lttle s known about the causal pathways through whch educaton shapes people s subjectve well-beng (SWB). Ths paper explores the drect and ndrect well-beng effects of extra schoolng nduced through compulsory schoolng laws n Australa. We fnd the net effect of schoolng on later SWB to be postve, though ths effect s larger and statstcally more robust for men than for women. We then show that the compulsory schoolng effect on male s SWB s ndrect and s medated through ncome. JEL Classfcaton: I20, I32, C36 Keywords: schoolng, ndrect effect, well-beng, mental health, wndfall ncome, HILDA survey Correspondng author: Nattavudh Powdthavee Centre for Economc Performance London School of Economcs Houghton Street London, WC2A 2AE Unted Kngdom E-mal: n.powdthavee@lse.ac.uk * We thank Davd Johnston, Andrew Oswald, Jörn-Steffen Pschke, and Mchael Shelds for ther helpful comments. We are also grateful to Chrs Ryan for provdng us wth the STATA codes for changes to compulsory schoolng laws n Australa. Ths paper uses unt record data from the Household, Income and Labour Dynamcs n Australa (HILDA) Survey. The HILDA Project was ntated and s funded by the Australan Government Department of Famles, Housng, Communty Servces and Indgenous Affars (FaHCSIA) and s managed by the Melbourne Insttute of Appled Economc and Socal Research (Melbourne Insttute). The fndngs and vews reported n ths paper, however, are those of the author and should not be attrbuted to ether FaHCSIA or the Melbourne Insttute

4 Many economsts and educators favour publc support for educaton on the premse that educaton mproves the overall well-beng of ctzens. However, lttle s known about the causal pathways through whch more educaton shapes people s overall subjectve well-beng (SWB). Whle some studes of the effect of compulsory schoolng on people s future mental health and well-beng prmarly n the Unted Kngdom s context confrm the presence of a causal relatonshp, they do not systematcally explan the medatng pathways by whch the effect s transmtted. Consder, for example, the study by Oreopoulos (2007) n whch a change to the mnmum school leavng age law n the UK has been shown to sgnfcantly ncrease future overall lfe satsfacton of people who were affected by that law. What ths study does not reveal, however, s how much of ths postve effect s drect and how much s ndrectly channelled through potental medators of educaton such as ncome and health? These are dffcult questons, but they seem mportant for polcy makers to understand, especally f we assume that ther broad goal s the maxmsaton of people s well-beng. Ths paper focuses on a partcular ndrect effect of educaton on SWB an ncrease n the lfetme fnancal returns nduced by a change n the compulsory schoolng law and attempts emprcally to estmate ths causal chan. Usng the dfferent tmng of educaton laws across the states of Australa as nstruments for years of educaton, and shocks n personal ncome from bequest, nhertance, severance pay, and other rregular ncome (such as lottery wns) as nstruments for total real personal ncome, we estmate the drect and ndrect effects of educaton on three measures of SWB lfe satsfacton, mental health, and fnancal satsfacton for workng-age adults n Australa. We ntally fnd a postve causal effect of stayng an extra year n school on mental health, but not on the other two SWB measures. Further, ths effect s only statstcally sgnfcant for men. However, we later demonstrate that ths causal effect of compulsory schoolng on males SWB s not drect. 3

5 Rather, we fnd evdence to suggest that most of the well-beng effect of educaton reforms n Australa s medated causally through ncome. The paper s structured as follows. Secton 1 brefly revews the lterature on educaton, mental health, and other measures of SWB. Secton 2 descrbes the Australan data we use. An emprcal model for causal analyss wth respect to educaton and ncome s outlned n Secton 3. Secton 4 reports the results. Secton 5 concludes. 1. Educaton, Mental Health, and Well-beng Whle t s theoretcally and emprcally well-establshed that there s a sgnfcant pecunary beneft to acqurng human captal, prevous studes on the lnk between educaton and SWB have produced mxed results. Usng hghest educaton qualfcaton dummes as control varables n cross-secton SWB equatons, many scholars have found a postve and statstcally sgnfcant assocaton between educaton and self-rated happness and lfe satsfacton across dfferent nternatonal data sets and tme perods (see, for example, Easterln, 2001; Graham and Pettnato, 2002; Blanchflower and Oswald, 2004; Ferrer-- Carbonell, 2005;). Yet there have also been other studes that have documented ether a negatve or a statstcally nsgnfcant effect of educaton on happness, job satsfacton, and dfferent measures of mental health (e.g., Clark and Oswald, 1996; Clark, 2003; Flour, 2004). One explanaton for these mxed fndngs s that schoolng may be correlated wth unobserved characterstcs of ndvduals, such as personalty trats, ntellgence, aspratons and motvaton, whch also jontly determne how ndvduals evaluate ther mental health and happness (Dolan et al., 2008; Oreopoulos and Salvanes, 2011). Moreover, the 4

6 coeffcent on educaton s lkely to depend on what else s beng ncluded n the estmaton model. For example, hghly educated ndvduals are, relatve to less educated ndvduals, lkely to be healther and earn hgher ncome, whle at the same tme have sgnfcantly hgher aspratons (Stutzer, 2004), are more lkely to feel rushed for tme (Oreopoulos and Salvanes, 2011), have greater responsbltes n the workplace, and spend more tme engagng n relatvely stressful actvtes such as commutng and pad employment. By controllng for these so-called bad controls (or varables that are themselves outcomes of educaton) n SWB regressons, we are lkely to ether underestmate the effect of educaton on mental health and well-beng when ncome and/or health are fully controlled for, or overestmate t when commutng tme and workng hours are fully controlled for. 1 In an attempt to establsh causalty, Oreopoulos (2007) and Oreopoulos and Salvanes (2011) explore the mpact of changes to compulsory schoolng laws n the Unted Kngdom on overall lfe satsfacton. Usng Eurobarometer data from 1973 to 1998, the authors show that an ncrease n the mnmum school leavng age from 15 to 16 ncreases mean overall lfe satsfacton, measured n adulthood, by approxmately of a pont (S.E.=0.010) on an 11-pont (0 to 10) satsfacton wth lfe scale. They also fnd the estmated coeffcent falls by less than half, to (S.E.=0.012), when condtonng on ndvdual ncome. In a separate study, Chevaler and Fensten (2006) use an nstrumental varables (IV) approach to estmate the effect of educaton on a measure of depresson among ndvduals n the UK s Natonal Chld Development Study. The authors fnd that educaton sgnfcantly reduces the rsk of adult depresson; on average, havng a secondary educaton qualfcaton reduces the rsk of depresson at age 42 by approxmately 5 to 7 percentage ponts. However, the postve effect of educaton on SWB could not be replcated n a more recent study by Banks and Mazzonna (2012). Usng the 1947 change to the mnmum school leavng age n England from 14 to 15 1 For a dscusson of bad controls, see Angrst and Pschke (2008). 5

7 as an IV for educaton, the authors fnd a large and sgnfcant effect of the school reform on males memory and executve functonng at older ages. Nonetheless, they do not fnd any statstcal evdence of a postve effect of educaton on the CASP-19, a measure of qualty of lfe that comprses four domans (control, autonomy, self-realsaton and pleasure) that has been wdely used by psychologsts. Evdence of a postve and statstcally well-determned effect of compulsory schoolng on lfe satsfacton s mportant n the recent human captal lterature (for a revew see Oreopoulos and Salvanes, 2011). Yet lke all prevous reduced-form estmates, the coeffcent on compulsory schoolng n a lfe satsfacton equaton only demonstrates that schoolng affects ndvduals evaluatons of ther lfe n a postve and statstcally sgnfcant manner. To the extent that there s a large pecunary return to educaton, we would deally want to be able to estmate the ndrect effect that schoolng has on SWB through ncome and nclude that n the total effect of educaton. Prevous research n economcs has focused prmarly on the estmaton of fnancal returns to educaton wthout ever attemptng to lnk those returns to mental health and wellbeng. For example, whle studes have successfully utlsed changes to compulsory schoolng laws n the Unted States and n the Unted Kngdom to estmate the causal effects of educaton on wages (e.g., Angrst and Krueger, 1991; Harmon and Walker, 1995; Oreopoulos, 2006), there have been no attempts to estmate how much these effects ndrectly cause a change n ndvdual SWB outcomes. The reason for ths s smple: estmatng causal ndrect effects s extremely dffcult (Pschke, 2009; Bullock et al., 2010; Green et al., 2010). For nstance, n order to establsh how much ncome medates the causal relatonshps between schoolng and lfe satsfacton, one needs both educaton to be randomly dstrbuted across the populaton, and ncome to be ndependent of unmeasured factors that affect lfe satsfacton. If randomsed educaton shfts ncome, and randomsed ncome shfts lfe 6

8 satsfacton, then n prncple (and assumng that the causal effects of educaton and ncome are the same across observatons) the drect and ndrect effects of educaton on lfe satsfacton va ncome can be estmated free of bas. However, what ths knd of double experments approach mples s that at least two sets of IVs or natural experments (or one of each), wth one nfluencng educaton but not ncome drectly, and the other nfluencng ncome but not lfe satsfacton drectly, are requred to be present n the same data set. Followng the framework set out n Fgure 1, the current study utlses changes to compulsory schoolng laws and data on wndfall ncome n Australa to estmate the drect and ndrect effects of schoolng on self-assessed lfe satsfacton, mental health, and fnancal satsfacton. The key assumptons used n the estmaton of the causal medaton effect of schoolng are: () changes to compulsory schoolng laws affect ndvdual level of schoolng drectly, but are otherwse uncorrelated wth total personal ncome; and () a shock n wndfall ncome affects total ncome of the respondent drectly, but s otherwse uncorrelated wth the level of educaton and other unobserved measures of SWB. 2. Data The data used n ths analyss come from the Household, Income and Labour Dynamcs n Australa (HILDA) Survey, a longtudnal survey that has been trackng members of a natonally-representatve sample of Australan households snce A total of 7,682 households partcpated n wave 1, provdng an ntal sample of 19,914 persons (Wooden et al., 2002). The members of these partcpatng households form the bass of the panel pursued n subsequent annual survey waves. Intervews are conducted wth all adults (defned as persons aged 15 years or older) who are members of the orgnal sample, as well as any other adults who, n later waves, are resdng wth an orgnal sample member. Annual re-ntervew 7

9 rates (the proporton of respondents from one wave who are successfully ntervewed the next) are reasonably hgh, rsng from 87% n wave 2 to over 96% by wave 9 (see Watson and Wooden, 2012). Alongsde soco-economc questons typcally asked n standard household surveys, the HILDA Survey regularly collects data on a number of SWB measures. Most notably, as part of the personal ntervew component of the survey, respondents are asked to rate, on a 0 to 10 bpolar scale, ther satsfacton wth eght aspects of lfe the home n whch they lve, ther employment opportuntes, ther fnancal stuaton, how safe they feel, the extent to whch they feel part of ther local communty, ther health, the neghbourhood n whch they lve, and the amount of free tme they have. Ths s then followed by a queston about overall lfe satsfacton. The queston reads: All thngs consdered, how satsfed are you wth your lfe? Agan, pck a number between 0 and 10 to ndcate how satsfed you are. A vsual ad s used n the admnstraton of these questons, whch nvolves a pctoral representaton of the scale wth the extreme ponts labelled totally dssatsfed and totally satsfed. In addton, as part of a separate self-completon questonnare gven to all persons ntervewed, the Short Form (SF36) Health Survey s admnstered. Descrbed n more detal by Ware et al. (2000), the SF36 s a survey of generc health concepts that has been extensvely tested and used around the world. It comprses 36 tems that can then be used to construct mult-tem scales measurng eght dfferent health concepts. One of those sub-scales s the Mental Health Inventory (MHI-5), a measure of psychologcal well-beng that has proven to be an effectve screenng nstrument for dentfyng persons wth mental health problems n large populatons (e.g., Rumpf et al., 2001; Hoeymans et al., 2004). It comprses fve tems that assess frequency (on a 6-pont scale) of symptoms of anxety and mood dsturbance over the 4-week perod precedng ntervew. The response optons range from all of the tme to none of the tme, wth all response optons fully labelled. Lke all SF36 8

10 sub-scales, raw scores on each tem are summed and then standardsed so that the scale values range from 0 to 100. Relatvely low scores are ndcatve of a poor mental health state. Whle the MHI-5 s measurng somethng dstnctly dfferent from overall lfe satsfacton, the two measures are qute strongly correlated, wth the wave specfc Pearson correlatons rangng from 0.44 to For the purpose of the current study, our measures of SWB are: () mental health (MHI-5); () overall lfe satsfacton; and () fnancal satsfacton. The ratonale for choosng fnancal satsfacton as a potental subjectve outcome s because ncome s beng consdered here as a potental medator of schoolng, and s expected to have a drect effect on fnancal satsfacton, even f t does not have a large mpact on overall lfe satsfacton. Further, fnancal satsfacton s an nterestng subjectve outcome n ts own rght. The measure has been used many tmes n economcs as an mportant component of one s doman satsfactons (e.g., Schwarze, 2003; Easterln, 2006). Followng Legh and Ryan (2008), estmates of years of educaton are derved from respondents hghest educatonal attanment. Thus a respondent reportng havng completed secondary school (Year 12) s assumed to have completed 12 years of educaton, a person completng an ordnary unversty degree s assumed to have completed 15 years of educaton, and so on. As s conventonal, we are not measurng actual years spent n educaton (whch would vary wth the tme wth whch qualfcatons are completed, the number of qualfcatons obtaned, and tme spent studyng that dd not lead to a qualfcaton) but nstead the tme typcally taken to obtan the hghest qualfcaton reported. Also followng Legh and Ryan (2008), we allow the varaton n schoolng to be nstrumented by the nteracton between the wthn-state varaton n compulsory school 9

11 leavng age and the brth year of the respondent. 2 The compulsory schoolng age by brth cohort for each of the Australan states and terrtores and how t has changed over tme s depcted n Appendx A. In addton, the HILDA Survey also asks ndvduals to report the amount of ther personal ncome by source (e.g., wages and salares, government benefts, dvdends, busness ownershp, etc.) for the precedng fnancal year (year ended 30 June). Ths ncludes amounts from rregular sources, or what we descrbe as wndfall ncome. We focus our attenton on partcular types of wndfall ncome; namely nhertances, bequests, redundancy or severance payments, and other rregular sources of payments such as ncome from lottery wns. About 4% of the entre sample (2,192 observatons) reported recevng these types of wndfall ncome at least once durng the frst ten years of the panel. Of these: 1,341 reported recevng t only once; 592 reported havng receved wndfall ncome n two dfferent years; and 259 reported havng receved wndfall ncome three or more tmes throughout the panel. The average real wndfall ncome across all ndvduals (wth and wthout wndfall ncome) n a gven year s A$1,029 per annum, wth a large standard devaton of A$13,792. There s also very lttle gender dfference n mean wndfall ncome. The average real annual wndfall ncome s A$1,135 for females and A$999 for males. When we exclude all observatons wth zero wndfall ncome n each of the surveyed years, the average real annual wndfall ncome among those wth postve wndfall ncome s A$33,980 for men and A$35,060 for women. The sample used n the analyss conssts of all adults aged 22 to 65 who partcpated n any of the frst ten survey waves and who responded to the lfe satsfacton, fnancal satsfacton, and mental health questons, recorded postve total annual personal ncome (regular plus rregular ncome), and completed secondary school n Australa. Ths leaves 62,780 observatons (9,799 persons). Of these, 29,528 (4,732 persons) are males and 33,252 2 See Legh and Ryan (2005) for a detaled dscusson of school leavng age polces n Australa. 10

12 (5,067 persons) are females. A potental drawback of the current HILDA Survey dataset s that we do not have nformaton about the state n whch the respondent attended school, and so we proxy ths by the current state of resdence. See Appendx B for a table of descrptve statstcs for each of the varables used n our analyss. 3. Emprcal Estmaton Strategy 4.1. Establshng Causal Pathways The current study contrbutes to the lterature by attemptng to establsh the causal mechansms of the schoolng effects on mental health and well-beng through ncome n Australa. To determne the drect and ndrect effects of educaton on SWB, we are nterested n estmatng an equaton of the followng form: 3 SWB = α 0 + beduc + cincome + ε, = 1, 2,..., N (1) Educ s the ndvdual s total number of years of educaton; Income s log of real total personal ncome of ndvdual ; SWB s a measure of subjectve well-beng; and ε s the error term. Suppose that both Educ and Income are not randomly assgned. Also suppose that Educ causes Income. Then: Income = λ0 + aeduc + u (2) Now suppose that we have a varable varable Z 1, whch s a vald nstrument for Educ n (2), and a Z 2, whch s a vald nstrument for Income n (1). Then a system of nstrumental varables (IV) regressons of SWB on Income and Educ wth Z 1 as the nstrument for Educ 3 For a summary of how to model causal mechansms n emprcal economcs, see Pschke (2009). 11

13 and Z 2 as the nstrument for Income dentfes b and c. To llustrate ths, consder the followng reduced forms: Educ = γ0 + γ 1Z1 + µ (3) Income = 0 + Π γ2z 2 Π Z + η (4) SWB =Λ 0 +Λ 1Z1 +Λ 2Z2 + v (5) The reduced forms have causal nterpretatons, where the coeffcents are gven by: Π0 Π = λ 0 + aγ0 ; 1 1 = aγ ; = α + aγ + b( λ + a ); Λ γ ( 1 ac + ); Λ Λ γ0 1 = b 2 2 = cγ. Snce Z 1 and Z 2 are ndependent by random assgnment, and assumng that b and c are constant, then IV wll dentfy b and c n (1). To calculate the ndrect (or medatng) effect of Educ on SWB through Income, we estmate the followng reduced form ncome equaton: Income = ζ 0 + ζ 1 Z 1 + υ, (2 ) where ζ 0 = Π 0 = λ 0 + aγ0 ; ζ 1 = a. Ths enables us to dentfy a n (2). The ndrect effect can γ 1 then be calculated smply as the product of a and c (Baron and Kenny, 1986) Instrumentng for Years of Educaton Prevous research has used a wde range of varables to nstrument for schoolng. Included here are dstance from home to college (Card, 1995), the ntroducton of restrctve compulsory schoolng laws (Harmon and Walker, 1995), and regonal spendng on educaton n regons where the ndvdual was a student (Berger and Legh, 1989). In ths paper, we follow Legh and Ryan (2008) and use changes n the school leavng laws n Australa (whch were ntroduced n dfferent states at dfferent tmes) nteracted wth the brth year of the 12

14 respondent to nstrument for Educ. The ratonale behnd our decson to nteract the two ndcator varables together s that t allows the effect of compulsory schoolng laws on years of educaton to operate dfferently for dfferent brth cohorts. 4 Provded the nstruments strongly predct Educ but not Income and SWB drectly, we can treat the predcted Educ as randomly dstrbuted among those who compled wth the laws Instrumentng for ncome The ssue of ncome endogenety when ncome s an explanatory varable s tradtonally dffcult to deal wth. Studes n psychology have consstently shown that people who are extroverted and reslent are more lkely to be happer wth lfe, as well as more productve n the labour market (e.g., Judge et al, 1997; Kvmak et al., 1997; Salgado, 1997). Ths s reflected n evdence that happer people earn more than others n general (see, for example, Graham et al., 2004; DeNeve and Oswald, 2012), resultng n upward bas n the estmate of c. By contrast, ncome s lkely to be hghly correlated wth work hours, hgher aspratons for more ncomes and status, tme spent away from famly and loved ones, and tme spent commutng to and from work, as well as subject to the usual attenuaton bas from msmeasurement, all of whch could potentally bas the estmated ncome effect towards zero. What ths mples s that a pror the drecton of the overall bas on the estmate of c s unclear. Understandably lttle has been done to address the ncome endogenety ssue n happness research. Luttmer (2005), Pschke (2011) and Levnson (2012) use ndustry codes or ndustry wage dfferentals to nstrument for famly ncome n lfe satsfacton equatons. Snce ndustry wage dfferentals most lkely reflect rents rather than dfferences n 4 See Legh and Ryan (2008) for a full descrpton of the changes n the school leavng law n Australa; e.g., the extent and tmng of each law n each dfferent state. 13

15 unobserved sklls and worker sortng, ther argument s that workers n a hgh wage and a low wage ndustry may not be very dfferent n terms of unobserved characterstcs but workers n hgh wage ndustres wll tend to have hgher wages, earnngs, and famly ncomes. 5 Very dfferently, Powdthavee (2010) nstruments famly ncome usng the proporton of household members showng the ntervewer ther payslps. The ratonale behnd ths nstrument s that famly ncome s lkely to be more accurately measured when payslps are shown, thereby mnmsng measurement error bas, whle there s no reason to beleve the reference to a payslp should have any mpact on reported lfe satsfacton. Overall, these studes have shown that IV estmates of the ncome effect are ether smlar to or slghtly larger than those obtaned usng ordnary least squares (OLS). In fndng an approprate approxmaton for Z 2, the current study utlses the longtudnal property of the HILDA Survey data and uses wthn-person varatons n wndfall ncome to nstrument for the total ncome of an ndvdual. The dentfcaton strategy here s that whle people wth certan fxed characterstcs may be more lkely than others to receve wndfall ncome, a postve wthn-person shock n the level of wndfall ncome should not have a drect effect on SWB beyond ts effects on total ncome. Ths s a plausble assumpton, and receves support from other survey evdence showng that, holdng ncome and other thngs constant, SWB does not ncrease n the year of a lottery wn (Gardner and Oswald, 2007; Oswald and Wnkelmann, 2008; Apouey and Clark, 2010). 6 In other words, our excluson restrcton reles on the assumpton (and statstcal evdence) that there s no mmedate net effect that runs from wnnng a lottery to people s mental health and well-beng. More specfcally, we allow Z 2 (that s, annual personal wndfall ncome) to 5 Pschke and Schwandt (2012), however, report evdence that casts doubt on the exogenety of ndustry afflaton as a vald nstrument for ncome. 6 Although there s some evdence of a delayng effect of a lottery wn on SWB of approxmately two years. 14

16 vary by t and decompose t nto ts mean over the observaton perod and the devaton from that mean as follows: Z = Z + ( Z Z ) (6) 2t 2 2t 2 Introducton of the wthn-person mean wndfall ncome ( Z 2 ) n both frst-stage and second-stage regresson equatons helps to correct for the fact that people wth some unobserved fxed trats are more lkely than others to receve wndfall ncome (Mundlak, 1978). Provded that Z 2t Z 2 (the wthn-person devaton n annual wndfall ncome) s exogenous, we can use t to predct ncome n the frst-stage regresson equaton. Moreover, n order to get a better predcted ncome varable that s representatve across people who were exposed to dfferent compulsory schoolng laws at dfferent tmes, we also allow the effect of Z t Z 2 2 on ncome to operate dfferently for dfferent laws at dfferent tmes. What ths mples s that the IV for Income becomes Z t Z 2 2 nteracted wth Z 2 (or the wthn- person change n annual wndfall ncome compulsory schoolng laws brth year). All of the equatons set out above can be estmated usng the random effects estmator. However, gven we would also want to account for clusterng at both the state-brth year level and the ndvdual level, t seems better to follow the emprcal strategy outlned n Cameron et al. (2006) and allow for a non-nested two-way clusterng by personal dentfcaton and by state brth year n all of our regressons. Fnally, the system of smultaneous equatons that we can estmate to gauge the drect and ndrect effects of schoolng on SWB s gven by ' α0 + β0educ + γ0income + X λ0 + φ0w ε0 SWB = + (7) Educ = + (8) ' α1 + δ1 (Compulsory schoolng Brth year ) + X λ1 + φ1w ε1 ' α2 + λ2 ( wt Compulsory schoolng Brth year ) + X λ2 + φ2w ε2 Income = + (9) 15

17 where: ' X represents a vector of exogenous control varables, ncludng lnear and quadratc age effects, year dummes, brth year dummes, and state dummes; w denotes the wthnperson mean of yearly personal wndfall ncome; and w t s the yearly personal wndfall ncome measured n perod t. Followng Devereux and Hart (2010), who conclude that the effect of compulsory schoolng on wages n the UK s only postve for males but not for females, we estmate the drect and ndrect effects of schoolng on SWB by gender. 4. Results Is there a causal lnk between schoolng and SWB n Australa? To make a frst pass at ths, Table 1 estmates and reports smple lnear regressons n whch SWB s the dependent varable and years of educaton s the ndependent varable of nterest. The regresson equatons are estmated usng OLS wth standard errors corrected for two-way clusterng by personal dentfcaton and by state brth year. The exogenous control varables n these equatons are respondent age, age-squared (/100), brth year dummes, state dummes, and year dummes. Lookng at the frst panel of Table 1 (the frst three numerc columns), n whch schoolng s assumed to be exogenous n the SWB equatons, we can see that, for both men and women, years of educaton enters postvely and statstcally sgnfcantly at conventonal confdence levels n mental health and fnancal satsfacton equatons. On the other hand, the estmated relatonshp between educaton and overall lfe satsfacton s postve and statstcally sgnfcant for men but not women. These postve assocatons are, however, lkely to be confounded by omtted varables such as ncome, abltes, and personalty trats. 16

18 We begn nstrumentng for years of educaton wth Compulsory School Law Brth Year as IVs n the second panel of Table 1 (the last three columns). Whle many of the ndvdual nteractons between Compulsory School Law and Brth Year are not statstcally sgnfcant n both male and female samples, a test of the jont sgnfcance of the excluded varables produces a large F-statstc for both samples (F (79, 298) = n the male sample and F (77, 300) = n the female sample), ndcatng that the IVs are jontly sgnfcant predctors of years of educaton. Instrumentng for years of educaton leads to an ncrease n the sze of the estmated schoolng coeffcent n the mental health equaton for men but not for women; the coeffcent on years of educaton ncreases almost three-fold from 0.7 to 1.8 n the mental health equaton for men. However, an extra year of educaton s no longer statstcally sgnfcantly assocated wth hgher levels of fnancal satsfacton for men after nstrumentaton. The estmated local average treatment effect (LATE) of educaton on SWB s postve and statstcally well-determned only n the mental health equaton for men. By contrast, the LATE of educaton s negatve and margnally sgnfcant n the lfe satsfacton equaton, and statstcally nsgnfcantly dfferent from zero n the mental health and fnancal satsfacton equatons for women. Hence, what we have here s emprcal evdence that changes n the compulsory schoolng laws have a postve and statstcally sgnfcant net effect on only one of the three measures of SWB for men and zero net effect on all three measures of SWB for women n Australa. One nterpretaton of these results s that an extra year of educaton contrbutes very lttle to the overall well-beng of people who compled wth the law n the long-run. Our hypothess, however, s that compulsory schoolng affects SWB through dfferent postve and negatve channels, one of whch s the postve effect t has on ncome, whch may cancel each other out on aggregate. To test ths hypothess, we estmate an equaton n whch the 17

19 dependent varable s log of total real annual personal ncome and report the estmates for men and women n Table 2. Educaton appears to affect men s and women s ncome dfferently. Consstent wth the dfferental effects of compulsory schoolng on wages by gender found by Devereux and Hart (2010), the estmated LATE of schoolng n Australa s hgher for men than for women: the rates of return to personal ncome of one more year of schoolng are estmated to be 15.5% for men and 10.9% for women. Table 3 moves on to estmate the drect and ndrect effects of schoolng on SWB by gender. The frst three columns estmate, for men and women, SWB equatons n whch schoolng s exogenous (through nstrumentaton) but real total personal ncome s not. The last three columns then estmate SWB equatons n whch both schoolng and real total personal ncome are smultaneously nstrumented by ther respectve IVs. As descrbed n the precedng secton, the IV for real total personal ncome here s the shock n personal wndfall ncome, w, nteracted wth the exogenous varatons n compulsory schoolng law and brth t year. It s worth notng here that a test of the jont sgnfcance of the excluded varables produces a large F-statstc for both samples (F (90, 298) = n the male sample and F (90, 300) = n the female sample), ndcatng that the IVs are jontly hghly sgnfcant predctors of ncome for both genders. The frst three columns of Table 3 show that there s a postve and statstcally sgnfcant assocaton between ncome and SWB n most cases for men and women. However, t s also worth notng that the magntudes of the correlaton are notably smaller n the female sample than n the male sample when the only control varables other than the nstrumented years of educaton are the exogenous characterstcs of the respondent that are age, age-squared, brth year, and state of resdence. After nstrumentng for ncome, we can see from the last three columns that ncome contrbutes to hgher SWB n all specfcatons 18

20 for men but not for women. For example, a postve shock n ncome ncreases lfe satsfacton, mental health and fnancal satsfacton n the male sample. Women, on the other hand, only report a sgnfcantly hgher level of fnancal satsfacton and a margnally hgher level of mental health wth nstrumented ncome. In vrtually all cases for men, we also fnd that t makes lttle dfferences to the sze of the estmated ncome coeffcent whether one assumes ncome to be exogenous or endogenous (and therefore requres nstrumentaton) n the SWB regresson equatons. The male sample s results are consstent wth prevous studes whch have found IV estmates to be smlar to OLS estmates, suggestng that most of the assocaton between ncome and SWB s causal (Luttmer, 2005; Powdthavee, 2010; Pschke, 2011). The postve and statstcally well-determned LATE that we observed earler for male s mental health n Table 1 contnues to be statstcally sgnfcantly dfferent from zero when randomsed ncome s ncluded as a regressor, although there s a reducton n the magntude of the effect from to What ths mples s that educaton affects male s mental health both drectly and ndrectly through ncome. By contrast, most of the causal effect of educaton on male s lfe satsfacton and fnancal satsfacton, but not female s, s ndrectly channeled through the effect of ncome. In an attempt to determne the statstcal sgnfcance of the estmated ndrect effects, Table 4 uses the coeffcents reported n Tables 2 and 3 to calculate the ndrect effect of schoolng on SWB through ncome. The standard errors for the ndrect effect are calculated usng the Sobel test (Preacher and Leonardell, 2013), an approprate method for testng the sgnfcance of the medaton effect gven suffcently large N (MacKnnon et al., 2002). 7 7 Unfortunately, gven the unusually complex nature of our IV and mult-way clusterng analyss (rather than the smultaneous equaton modelng technque typcally appled n the lterature), we were unable to apply the bootstrappng method n order to generate the standard errors for our product coeffcents here. 19

21 The ndrect effect of changes n the compulsory schoolng laws on lfe satsfacton, mental health, and fnancal satsfacton are gven by the product of the years of educaton coeffcent obtaned n Table 2 and the ncome coeffcents reported n Table 3. Assumng that the estmated effect of ncome s homogeneous across people wthn the same gender group who compled wth the compulsory schoolng laws, we fnd the estmated ndrect effect of an extra year of educaton on lfe satsfacton to be postve and statstcally welldetermned for men but not for women: the product coeffcents are [S.E.=0.014] n the male sample and [S.E.=0.006] n the female sample. A smlar pattern s also observed for mental health: the estmated ndrect effect of schoolng on mental health s [S.E.=0.163] for men and [S.E.=0.079] for women. On the other hand, the ndrect effect of schoolng on fnancal satsfacton appears to be postve and statstcally welldetermned for both genders, though notceably larger for males than females: the estmated ndrect effect of schoolng on fnancal satsfacton s [S.E.=0.032] n the male sample and [S.E.=0.027] n the female sample. Gven the dstrbuton of SWB, these are not small effects. They are equvalent to around 2%, 3%, and 6% of the standard devatons of male s lfe satsfacton, mental health, and fnancal satsfacton, respectvely. These statstcally sgnfcant ndrect effects of educaton through ncome further strengthen our earler concluson that most of the causal effect of schoolng on SWB for men s medated almost completely through ncome. One possble objecton to our results s that the effect of ncome may not be constant across all ndvduals n the sample, especally those who were affected by the changes n the compulsory schoolng laws. Ths s a far objecton as () dfferent nstruments wth dfferent complers were used to estmate the smultaneous equatons, and () only a mnorty of people was affected by shocks n wndfall ncome. What ths mples s that t wll be no 20

22 longer possble to decompose the LATE of educaton on SWB nto drect and ndrect effects f we cannot assume a constant average treatment effect (ATE) for ncome. Whle t may not be possble to completely address ths ssue, we can nevertheless try to make an nference about the sze of the ATE of ncome on SWB based on prevous fndngs and our own estmates, and use that as a bass for our calculatons of the ndrect effects of schoolng. Usng dfferent IVs and natural experments to gauge the effect of ncome on SWB, scholars workng n ths area have reached the conclusons that () ncome causally affects SWB n a postve and statstcally sgnfcant way, and () the magntude of the IV coeffcent on ncome s ether the same as or larger than the ncome coeffcent obtaned n the OLS estmaton (Frjters et al., 2004; Luttmer, 2005; Powdthavee, 2010; L et al., 2011; Pschke, 2011). Note that () s also the same as what had been found earler n our Table 3, thus suggestng that what had been found n terms of the LATE of ncome on SWB may already be close to mrrorng the populaton ATE. Table 5, as a further check, re-estmates Tables 2 and 3 s specfcatons but on smaller sub-samples of relatvely more homogenous groups of men and women who report postve personal wndfall ncome, thus gnorng the majorty of people who report zero ncome from bequest, nhertance, severance payment, lottery wns and other rregular sources n any gven year. In other words, we now have a populaton n whch everyone s affected by one nstrument (a shock n wndfall ncome), although not necessarly by the other nstrument (compulsory schoolng). As can be seen, the magntudes of the estmated ndrect effects on male s SWB are, wth the excepton of mental health, very smlar to what had been prevously observed n when usng the full male sample (see Table 4). However, the same cannot be sad for women, where all of the estmated ndrect effects of educaton on SWB are now mprecsely estmated usng the small sub-sample. 21

23 Fnally, Table 6 explores whether the statstcally nsgnfcant ndrect effects obtaned n the female sample s regressons are due to the decson to use personal ncome and not equvalsed household ncome as the medator of schoolng on SWB. One could magne, for nstance, that schoolng affects personal and household ncome dfferently for females, and that t s household ncome and not personal ncome that s most relevant for SWB. In Table 5, the log of real total annual personal ncome s replaced by the log of equvalsed real annual household ncome. 8 The IV for equvalsed household ncome, the average shock of pooled wndfall ncome, and pooled wndfall ncome. Z 2, s Z 2 s the wthn-household average of Replacng personal ncome wth equvalsed household ncome makes lttle dfference to the results for men. Wth equvalsed household ncome as the medator, the magntudes of the ndrect effects of educaton on male s lfe satsfacton, mental health, and fnancal satsfacton are, at 0.035, 0.318, and 0.117, respectvely smlar to the specfcaton where personal ncome s used as the medator. For women, however, the use of equvalsed household does make more of a dfference. The estmated LATE of schoolng on equvalsed household ncome s postve and statstcally sgnfcant for women at [S.E.=0.031]. The IV estmate of the ncome effect s now postve and statstcally sgnfcant n all SWB equatons for women, thus suggestng that t s the shared household ncome rather than personal ncome that contrbutes to hgher levels of SWB for women. As a result, we now observe an estmated ndrect effect of educaton that s postve and margnally sgnfcant at the 10% level n the lfe satsfacton equaton, as well as contnung to observe a statstcally sgnfcant ndrect effect that runs from years of educaton to fnancal satsfacton. Hence, 8 The equvalsed real annual household ncome s calculated usng the followng formula: real annual household ncome/( *(number of adult household members-1) + 0.3*(number of chldren aged 0-4 n the household + number of chldren aged 5-9 n the household + number of chldren aged n the household)). 22

24 we were able to margnally mprove upon our results for women by substtutng personal ncome wth equvalsed household ncome. 5. Conclusons Accordng to the tradtonal nvestment model, people nvest n educaton n hopes of greater lfetme wealth and consumpton n return. And whle evdence of a sgnfcant fnancal return to schoolng s well-establshed n the economc lterature, we contnue to know very lttle about the extent to whch ths effect mght contrbute to ncreases n the overall wellbeng of ndvduals. In ths paper, we emprcally demonstrate that, for ndvduals who were affected by changes n the compulsory schoolng laws n Australa, an extra year of educaton mproves the SWB of these ndvduals prmarly by rasng ncomes. In other words, wth the excepton of men s mental health, we fnd no evdence of an extra year of educaton causng hgher SWB ndependently of ncome even when the causal effect of ncome s properly modeled n the estmaton of SWB regresson equatons. We also fnd that men are sgnfcantly more affected by the ndrect effect of compulsory schoolng laws than women, although for both males and females an extra year of educaton statstcally sgnfcantly enhances satsfacton wth ther overall fnancal stuaton wth the effect beng larger for men than for women. Why do we fnd the net effect of an extra year of educaton on lfe satsfacton and fnancal satsfacton to be statstcally nsgnfcant when we also fnd the effect of educaton on ncome and the effect of ncome on SWB to be sgnfcantly dfferent from zero? The most lkely reason s straghtforward; changes n compulsory schoolng laws do very lttle to 23

25 mprove a person s lfe beyond ts mpact on hs or her lfetme ncome. Alternatvely, t mght be argued that there are sgnfcant non-pecunary benefts of schoolng nduced through compulsory schoolng laws e.g., better health and more job securty but these postve effects on SWB have been completely offset by other negatve effects that typcally come from havng more years of educaton. Ths mght nclude, for example, hgher aspratons and more work-related stress (Stutzer, 2004; Shelds et al., 2009). Future research may need to extend from the current analyss and estmate a model of multple ndrect effects that would allow us to determne the relatve mportance of medatng factors of schoolng other than ncome on overall well-beng. Also, gven that years of educaton s not a partcularly good measure of educaton, t seems mportant for future researchers to examne the drect and ndrect effects of other measures of educaton such as school qualty and degree attanment on SWB. More generally, we beleve that future researchers workng n the area wll beneft from a smlar modelng strategy n ther attempts to better understand the causal pathways through whch schoolng affects ther outcome varables of nterest. 24

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31 Salgado, J.F. (1997). The fve factor model of personalty and job performance n the European Communty, Journal of Appled Psychology, vol. 82(1), pp Shelds, M.A., Wheatley Prce, S. and Wooden, M. (2009). Lfe satsfacton and the economc and socal characterstcs of neghbourhoods, Journal of Populaton Economcs, vol. 22(2), pp Schwarze, J. (2003). Usng panel data on ncome satsfacton to estmate equvalence scale elastcty, Revew of Income and Wealth, vol. 49(3), pp Stutzer, A. (2004). The role of ncome aspratons n ndvdual happness, Journal of Economc Behavor & Organzaton, vol. 54(1), pp Watson, N. and Wooden, M. (2012). The HILDA Survey: a case study n the desgn and development of a successful household panel study, Longtudnal and Lfe Course Studes, vol. 3(3), pp Wooden, M., Fredn, S. and Watson, N. (2002). The Household, Income and Labour Dynamcs n Australa (HILDA) Survey: wave 1, The Australan Economc Revew, vol. 35(3), pp Ware, J.E., Snow, K.K., Kosnsk, M. and Gandek, B. (2000). SF-36 Health Survey: Manual and Interpretaton Gude, Lncoln, RI: QualtyMetrc Inc. 30

32 Fg. 1: Drect and Indrect effects of Schoolng on Subjectve Well-beng a Total real personal Income c Years of schoolng b Subjectve well-beng Note: A represents the effect of schoolng on total real personal ncome; B represents the effect of real total personal ncome on measures of subjectve well-beng; and C represents the drect effect of schoolng on measures of subjectve well-beng (gven earnngs). 31

33 Table 1 Reduced-form Regressons Wth and Wthout Compulsory School Laws as Instruments for Years of Educaton OLS Regressons IV Regressons A) Men (N = 29,629) LSAT MH FSAT LSAT MH FSAT Years of educaton 0.015* 0.663*** 0.168*** *** [0.008] [0.097] [0.012] [0.052] [0.649] [0.098] B) Women(N = 33,404) LSAT MH FSAT LSAT MH FSAT Years of educaton *** 0.156*** * [0.007] [0.092] [0.011] [0.053] [0.777] [0.117] Notes: LSAT = Lfe Satsfacton; MH = Mental Health; FSAT = Fnancal Satsfacton. All equatons nclude age, age-squared, brth year fxed effects, state fxed effects, and wave fxed effects. Instrumental varables for schoolng = Compulsory School Law Brth Year (see Legh and Ryan, 2008). All equatons also allow for multple clusterng by () personal dentfcaton and (2) State Brth Year (Cameron et al., 2008). The sample conssts only of males and females wth postve personal ncome. *<10%; **<5%; ***<1%. Standard errors are n parentheses. 32

34 Table 2 Estmatng the Causal Schoolng Effect on Log of Real Total Annual Personal Income All men (N = 29,629) All women (N = 33,404) Years of educaton 0.155*** 0.109** [0.033] [0.043] Notes: All equatons nclude age, age-squared, brth year fxed effects, state fxed effects, and wave fxed effects. Instrumental varables for schoolng = Compulsory School Law Brth Year. **<5%; ***<1%. Standard errors are n parentheses. 33

35 Table 3 Estmatng the Causal Schoolng Effect on Mental Health and Well-beng Through Income All men (N = 29,629) All men (N = 29,629) A) Men LSAT MH FSAT LSAT MH FSAT Log of real total personal ncome (assumed exogenous) 0.200*** 2.422*** 0.871*** [0.056] [0.633] [0.091] Log of real total personal ncome(iv) 0.239*** 3.041*** 0.841*** [0.072] [0.831] [0.106] Years of educaton (IV) ** [0.062] [0.709] [0.101] [0.049] [0.555] [0.078] All women (N = 33,404) All women (N = 33,404) B) Women LSAT MH FSAT LSAT MH FSAT Log of real total personal ncome (assumed exogenous) *** 0.288*** [0.041] [0.519] [0.091] Log of real total personal ncome(iv) *** [0.052] [0.666] [0.087] Years of educaton (IV) [0.058] [0.753] [0.132] [0.042] [0.603] [0.096] Notes: See Table 2. Mean wndfall ncome over the prevous fnancal year s ncluded as an addtonal control varable here. The nstrumental varable for total real personal ncome s the shock n ncomes receved from bequest, nhertance, redundancy payment, lottery wns and other rregular ncomes and ts nteracton wth Compulsory School Law Brth Year to allow for the heterogeneous effects of wndfall ncome on real total annual personal ncome by people who were exposed to dfferent compulsory schoolng laws. **<5%; ***<1%. Standard errors are n parentheses. 34

36 Table 4 Indrect Effects of Schoolng on Mental Health and Well-beng Through Indvdual Income All men (N = 29,629) All women (N = 33,404) LSAT MH FSAT LSAT MH FSAT Path coeffcent a: Educ Income 0.155*** 0.155*** 0.155*** 0.109** 0.109** 0.109** [0.033] [0.033] [0.033] [0.043] [0.043] [0.043] Path coeffcent c: Income SWB 0.239*** 3.041*** 0.841*** *** [0.072] [0.831] [0.106] [0.052] [0.666] [0.087] Calculatng ndrect effects Indrect effect of schoolng a c 0.037*** 0.471*** 0.130*** ** [0.014] [0.163] [0.032] [0.006] [0.079] [0.027] S.D. of the dependent varable Indrect effects as % of that S.D. 2.50% 2.85% 5.91% 0.40% 0.47% 1.87% Notes: Standard errors of the ndrect effect are calculated usng the Sobel test and reported n parentheses (Preacher and Leonardell, 2013). **<5%; ***<1%. 35

37 Table 5 Indrect Effects of Schoolng on Mental Health and Well-beng for People wth Postve Personal Annual Wndfall Income Men wth postve personal wndfall ncome (N = 999) Women wth postve personal wndfall ncome (N = 948) LSAT MH FSAT LSAT MH FSAT Path coeffcent a: Educ Income 0.150*** 0.150*** 0.150*** [0.043] [0.043] [0.043] [0.050] [0.050] [0.050] Path coeffcent c: Income SWB 0.302*** *** *** [0.097] [1.078] [0.130] [0.085] [0.954] [0.111] Calculatng ndrect effects Indrect effect of schoolng a c 0.045** *** [0.019] [0.176] [0.040] [0.006] [0.062] [0.062] S.D. of the dependent varable Indrect effects as % of that S.D. 3.03% 1.53% 5.68% 0.21% -0.13% 1.54% Notes: Standard errors of the ndrect effect are calculated usng the Sobel test and reported n parentheses (Preacher and Leonardell, 2013). **<5%; ***<1%. 36

38 Table 6 Indrect Effects of Schoolng on Mental Health and Well-Beng Through Equvalsed Annual Household Income All men (N = 29,892) All women (N = 33,948) LSAT MH FSAT LSAT MH FSAT Path coeffcent a: Educ Income 0.124*** 0.124*** 0.124*** 0.062** 0.062** 0.062** [0.027] [0.027] [0.027] [0.031] [0.031] [0.031] Path coeffcent c: Income SWB 0.280*** 2.563*** 0.940*** 0.189*** 1.620*** 0.771*** [0.078] [0.909] [0.120] [0.063] [0.625] [0.098] Calculatng ndrect effects Indrect effect of schoolng a c 0.035*** 0.318** 0.117*** 0.012* * [0.012] [0.132] [0.029] [0.007] [0.063] [0.025] S.D. of the dependent varable Indrect effects as % of that S.D. 2.36% 1.93% 5.28% 0.79% 0.58% 2.07% Notes: Standard errors of the ndrect effect are calculated usng the Sobel test and reported n parentheses (Preacher and Leonardell, 2013). *<10%; **<5%; ***<1%. 37

39 Appendx A: Compulsory School Leavng Age by Brth Year Source: Legh and Ryan (2005). 38

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