Clauda Münch Sweder van Wjnbergen Arjan Lejour Educaton and Labor Market Actvty of Women: An Age-Group Specfc Emprcal Analyss Dscusson Paper 11/2009-050 November, 2009
Educaton and labor market actvty of women: an age-group specfc emprcal analyss Clauda Münch 1, Sweder van Wjnbergen Unversty of Amsterdam and the Tnbergen Insttute and Arjan Lejour CPB Abstract: We analyze the determnants of female labor market partcpaton for dfferent age-groups n the European Unon. We show that female partcpaton s postvely affected by tertary educaton at any age. But upper secondary educaton ncreases partcpaton only up to an age of 40 whle after that t has no effect or even a negatve mpact. The results are tested for robustness and controlled for endogenety. The results show that ncreasng educatonal attanment levels n the female populaton wll contrbute sgnfcantly to hgher aggregate partcpaton rates. However, n smulatons up to 2050 such benefts are partally offset by a negatve agng effect. 1 Ths paper bulds on the frst author s Master Thess for the Master of Scence n Economcs, Unversty of Amsterdam. The frst author also profted from an nternshp at the CPB, the Netherlands, whle workng on her thess.
1. Introducton Especally snce the Lsbon Agenda n 2000, polcy nterest has focused on ncreasng female labor market partcpaton n order to promote employment n the European Unon (European Councl, 2000). Facng the acceleratng agng of the populaton n Europe, hgher employment rates could allevate the pressure on the fscal sustanablty of PAYG penson systems (European Commsson, 2002). Aggregate female labor market partcpaton dd rse over the last decade, but partcpaton rates stll vary consderably accross countres. Focusng on women aged between 25 and 64 2, ther partcpaton rates range from about 80 percent n Scandnavan countres and Latva to about 60 percent n Greece and Hungary, 56 percent n Italy and only 38 percent n Malta (Eurostat-Labor Force Survey, 2009a). Moreover, partcpaton rates dffer not only among countres but also wthn countres by age-groups (see Fgure 1). Snce aggregate partcpaton rates follow from age-group specfc partcpaton rates (Balleer et al., 2009), a better understandng of why partcpaton rates dffer between age-groups may well be essental for any effort to ncrease aggregate partcpaton. All labor markets of the EU countres share one dstnct feature: ther female labor partcpaton rates vary wdely by the educatonal attanment level. It s strkng that, wth only few exceptons, female labor force partcpaton ncreases wth the level of educaton for all age-groups (see Fgure 2). The same relaton between educaton and partcpaton can therefore also be observed at the aggregate level n all countres. In 2008, for nstance, 53.4 percent of Dutch 25-64 old women wth prmary or lower secondary educaton degrees partcpated n the labor market, whereas, of the same age-group, 77.6 percent of women who fnshed upper secondary educaton and 87.2 percent of women wth tertary educatonal attanment partcpated n the labor market (Eurostat-Labor Force Survey, 2009a). A logcal queston s then whether promotng access to hgher educaton would lead to hgher partcpaton rates of all age-groups n the long run. 2 The aggregate workng-age populaton s normally defned over the age-group 15-64 (Eurostat, 2009). Ths defnton, however, s not approprate for the purpose of ths study where the focus s on the actual (potental) workforce wth fully formed educaton. Therefore we focus on the age-groups between 25 and 64 years. 1
Fgure 1: Female labor force partcpaton by country and age-group n 2008 Source: Eurostat-Labor Force Survey (2009a) 2
Fgure 2: Female labor force partcpaton by country, age and educatonal attanment level n 2008 Source: Eurostat-Labor Force Survey (2009a) 3
In order to assess the mpact of educaton on female labor market partcpaton of dfferent age-groups, the emprcal analyss estmates a separate female labor market partcpaton equaton for eght age-groups of fve years each between 25 and 64 years. The analyss s based on annual macro data for a panel of 23 EU countres over the perod 1995 to 2008. For educatonal attanment rates, the man explanatory varable, we use annual data for each agegroup. Such detaled data allow for a thorough analyss of the mpact of educaton on agegroup specfc partcpaton behavor of women. In addton to educatonal attanment levels, the emprcal analyss ncludes a range of aggregate control varables that consder the economc condtons, demographc ndcators, gender dscrmnaton and the nsttutonal framework. The results show that the educatonal attanment level matters for female labor force partcpaton rates, for all age-groups. Female labor force partcpaton s sgnfcantly and postvely affected by hgher (tertary) educaton. Upper secondary educaton, by contrast, ncreases the propensty to work only up to an age of 40 whle after that age t does not seem to play a role n partcpaton decsons; t even has a strong negatve mpact on partcpaton n case of women aged 55-59. The results are tested for robustness and controlled for endogenety. For the European countres that are faced wth an acceleratng agng populaton these results are of great mportance. The results ndcate that the educatonal attanment level has a long-term mpact on partcpaton. A smulated future scenaro for 2050 suggests that ncreasng the educatonal achevement of women could contrbute to ncrease aggregate partcpaton rates. However, the ncreases n partcpaton rates are partally offset by a strong negatve agng effect that wll occur whether there s a rse n educatonal achevement or not. In what follows, secton 2 dscusses three recent cross country studes of female labor market partcpaton behavor, secton 3 descrbes the data and the emprcal model before secton 4 explans the emprcal strategy and presents the estmaton results. Secton 5 smulates a possble future scenaro. Secton 6 concludes. 2. Settng the scene: the need for hgher female labor force partcpaton and recent cross country studes An agng populaton wll be one of the major economc and socal challenges n European countres n the upcomng decades. Rsng publc and prvate spendng for socal securty systems such as penson, health and elderly care wll put pressure on the fscal sustanablty 4
of Europeans welfare states. At the same tme, economc growth s at rsk due to the gradually shrnkng workng age populaton. 3 Increasng labor market partcpaton rates could attenuate the detrmental mpacts of the demographc change, mostly by broadenng the contrbuton base of PAYG penson suystems (European Commsson, 2002; Euwals et al., 2007). In most countres the partcpaton rate of women s stll low compared to male partcpaton rates and therefore consttutes an mportant potental source of addtonal labor supply. Increasng female labor force partcpaton through polces that strengthen the access to educaton would not only affect the quantty but also the qualty 4 of the labor force. Obvously, hgher educaton levels wll flter through slowly nto older age-groups. However, even f the mpact s slow to materalze, t can be an mportant n the long-term nstrument to ncrease labor force partcpaton whlst obtanng a better sklled workforce at the same tme. In ths way t mght promote long-term economc growth even n per capta terms (Gros and Roth, 2008). There s a growng emprcal lterature dealng wth female labor market partcpaton behavor. Although the use of mcroeconomc data s predomnant n the lterature 5, there are some recent cross-country studes usng macroeconomc data. Jaumotte (2003) provdes a comprehensve study on the determnants of the aggregate female labor force partcpaton n 17 OECD countres over the perod 1985-1999. Her fndngs suggest that hgher female educaton, the condtons of the labor market and cultural atttudes play a major role n determnng female labor market partcpaton. Besdes, polces pursung a more neutral famly taxaton, the avalablty of flexble tme arrangements, famly support n the form of chldcare subsdes and pad parental leave all encourage female labor force partcpaton. She does not however dstngush between dfferent age-groups. For the present paper, emprcal cross country studes that do not only analyze gender specfc partcpaton behavor but also dstngush between dfferent age-groups such as Bloom et al. (2009) and Genre et al. (2005) are of partcular nterest. Bloom et al. (2009) analyze the 3 For recent analyses of the economc mpacts of populaton agng see e.g. Balassone et al. (2009) for a study of the Euro area and Van Ewjk et al. (2006) for a study focusng on the Netherlands. 4 Increased enrolment rates may have to be complemented by hgher-qualty educaton. 5 For example Euwals et al. (2007) study female labor force partcpaton n the Netherlands usng mcro data from the Dutch Labor Force Survey. Ther results ndcate that ncreasng educaton, dmnshng negatve effects of chldren and unobserved cohort effects are mportant determnants of partcpaton behavor of Dutch women. 5
causal effect of fertlty on female labor force partcpaton drawng on a 5-yearly panel for 97 countres that covers the perod 1960 to 2000. For fve age-groups between 20 and 44 (n ncrements of fve years) a female labor force equaton s estmated. OLS estmatons ndcate a sgnfcant and negatve mpact of fertlty on female labor force partcpaton at any age. Female educaton enters postvely and statstcally sgnfcant n each age-group regresson. To take account of the potental endogenety bas of fertlty decsons, aborton legslaton s used as an nstrument for the total fertlty rate. Usng an IV approach, the coeffcent of fertlty stays negatve and sgnfcant, but female educaton loses ts sgnfcance. However, Bloom et al. fnd that female educaton stll appears to have a strong negatve mpact n the frst stage regresson mplyng an ndrect effect of educaton on female labor force partcpaton through fertlty. Genre et al. (2005), usng an annual panel of 12 EU15 countres over the perod 1980-2000, estmate a female partcpaton regresson for three age-groups: young, prme-age and elderly women. Ther results show that strct labor market nsttutons have a negatve mpact on female labor market actvty at any age, although less so at younger ages. Educaton decsons have, as can be expected, a negatve mpact on younger age-groups due to enrollment n educaton. Young women s partcpaton rates are postvely affected by the avalablty of part-tme work and negatvely by the unemployment rate. In the regresson for prme-age women, educaton s used as proxy for wages, but wthout conclusve results. Ther partcpaton rates are negatvely affected by the fertlty rate and, as n the case of younger women, the unemployment rate. Maternty leave polces (up to 10 months) and the avalablty of part-tme work stmulate prme-aged women s partcpaton decsons. In the regresson for elderly women, educaton s not ncluded as an explanatory varable. Ther partcpaton behavor s largely nfluenced by ther prevous behavor. Women that dd not partcpate durng ther prme-age are less lkely to partcpate when they grow older. The revewed studes use educaton data from the Barro and Lee (2001) dataset - a wdely used dataset n macroeconomc studes. The dataset provdes comparable cross country educaton ndcators for the years between 1960 and 2000. However, data are only avalable n ntervals of fve years, values for 2000 rely on projectons and there s a dfferentaton for gender but not between age-groups. The use of recent age-group specfc annual educaton data wthn a broad cross country analyss of female labor market partcpaton s the nnovatve feature of the present emprcal 6
study, allowng for more relable estmates of the mpact of educaton on female labor force partcpaton behavor. 3. Emprcal analyss In European countres, female labor force partcpaton rates vary by age (cf. Fgure 1) whch ndcates dfferences n the underlyng labor market behavor of women n dfferent agegroups. The emprcal work ams at dssectng the determnants of age-group specfc partcpaton rates of women wthn a cross-country analyss. For ths purpose t estmates a separate partcpaton equaton for each age-group. The followng secton frst dscusses the data used for the emprcal analyss before t turns to the emprcal model. 3.1 Data The data set used for the emprcal work s an annual panel 6 coverng the perod 1995-2008 for 23 European countres. The countres ncluded are the EU27 countres except for Bulgara, Estona, Lthuana and Malta because data of these countres are ether mssng or extremely unrelable. 7 The data used are taken from a number of publcly avalable data sources n order to construct a comprehensve data set. Table A1 n the Annex gves an overvew of all varables, ther defntons and sources. The dependent varable n the analyss s the age-group specfc female labor force partcpaton rate. Partcpaton data are taken from Eurostat-Labor Force Survey (2009a). The Eurostat-Labor Force Survey provdes age-group specfc comparable cross natonal data for female actvty rates. The female partcpaton rate s defned as the number of economcally actve women n the labor force n a gven age-group dvded by the female populaton of that age-group. Economcally actve s defned as ether workng or actvely lookng for work. 8 The focus of the emprcal analyss s on the mpact of educatonal attanment of women n dfferent age-groups on the partcpaton rates n these age-groups. Educatonal attanment data are also taken from Eurostat-Labor Force Survey (2009a). Informaton s gven n numbers of women (n thousands). In order to calculate the rate, age-group specfc educatonal attanment data are dvded by the female populaton of the respectve age-group. Populaton data are taken from Eurostat (2009) and are supplemented wth data from ILO- LABORSTA (2009) for the year 2008. Educaton levels are classfed accordng to 6 The panel s unbalanced due to data lmtatons. 7 Cf. Eurostat-Labor Force Survey (2009a). 8 There s no nformaton on whether the work s a full-tme or a part-tme occupaton. 7
Internatonal Standard classfcaton 1997 - ISCED 97 - from UNESCO (1997). The ISCED 97 provdes a cross-natonal classfcaton framework for harmonzng educatonal programs and qualfcatons. 9 The data provded by the Eurostat-Labor Force Survey dstngush between prmary and lower secondary educaton, upper secondary educaton and tertary educaton. Prmary educaton (ncludng pre-prmary educaton) and lower secondary educaton provde basc mathematcal, wrtng and readng sklls. Lower secondary educaton usually ends after some nne years after the begnnng of prmary educaton. Prmary and lower secondary educaton s compulsory n most countres. Upper secondary educaton follows lower secondary educaton and conssts of several tracks and programs (general, techncal and vocatonal programs). Pupls startng upper secondary educaton are normally about 15 to 16 years old. 10 Tertary educaton requres the successful completon of upper secondary educaton and conssts of theoretcally based, occupaton- orented and advanced research programs such as PhD or doctorate studes. The defnton of prmary and lower secondary educaton refers to ISCED 97 levels 0-2, upper secondary educaton to levels 3 and 4 and tertary educaton to levels 5 and 6. It should be mentoned that, n the dataset, the levels prmary/lower secondary, upper secondary and tertary educaton do not sum up to 1 but close to. Ths can have several reasons, namely coverage errors, measurement errors, processng errors, non-responses n the surveys, changes n the survey characterstcs and estmates of mssng data n the surveys. 11 Errors may also occur because of nconsstences between the two datasets used (educaton dataset and populaton dataset). As the dfferences n most cases are mnor, we stll consder the shares of prmary/lower upper secondary, upper secondary and tertary educatonal attanment rate as dependent on each other. A hgher percentage for one educaton level as the hghest one attaned mples a decrease for at least one of the two remanng educaton levels. The emprcal model thus ncludes only two educaton levels namely upper secondary and tertary educaton. 9 ISCED 97 s the modfed verson of the frst verson ISCED 76 (UNESCO 1997). 10 The category upper secondary also ncludes post secondary non-tertary educaton. The classfcaton refers to programs that serve to broaden the knowledge of the pupls after completng upper secondary educaton but do not provde an essentally hgher content. For a more detaled descrpton of the classfcaton standards see UNESCO (1997). 11 See Eurostat-Labor Force Survey (2009b) for a dscusson of problems concernng the metadata used for the Labor Force Survey. 8
The emprcal analyss further ncludes explanatory varables representng economc, demographc, socal polcy and gender dscrmnaton ndcators. These are the total female unemployment rate, total female part-tme employment, logarthm of GDP per capta and ts square, crude marrage and dvorce rate, total fertlty rate, duraton of pad maternal leave, publc expendture for pensons (n percent of GDP), publc expendture for formal chld day care (n percent of GDP), proporton of seats n natonal parlaments held by women and unadjusted gender paygap. Female labor force partcpaton data and educaton data cover eght age-groups: 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64. 12 For the control varables, the analyss uses data that are aggregated over age-groups. Table A2 n the Annex gves summary statstcs for partcpaton and educaton data. The dataset contans 322 observatons for each age-group specfc partcpaton rate. It shows that mean partcpaton rates are declnng wth age. Whle the (mean) partcpaton rate of women up to the age-group 45-49 s about 75 percent, the (mean) partcpaton rate of the agegroup 60-64 s only 19 percent. The data also show a large varaton n partcpaton rates across countres for each agegroup. For nstance, for the age-group 35-39, partcpaton rates range from 52 percent n Luxembourg (1995) to 97 percent n Slovena (1998). For the age-group 60-64, partcpaton rates vary between 2.7 percent of women aged 60-64 n Slovaka (1999) 13 to 59 percent n Sweden (2007). Scandnavan countres show also the hghest partcpaton rates for the agegroups 50-54 and 55-59 (Fnland n 2008 and Sweden n 2008, respectvely). A comparson of the age-group specfc female partcpaton rates of 1995 wth those n 2008 (see Fgure 3) shows a general ncrease n partcpaton rates although the ntegraton of women s not evenly dstrbuted over age-groups and dffers n sze as well as between countres. Romana s a clear excepton from ths pattern wth decreasng partcpaton rates n all age-groups. 12 The choce of the age-group s explaned n footnote 1. 13 Note that the penson elgblty age n Slovaka s lower than 65. Up to a penson reform n 2004, the pensonable age was 53-57, snce then t s gradually ncreasng to 62 (OECD, 2005; eronlne, 2004). The partcpaton rates of the age-groups between 25 and 55 are relatvely hgh n Slovaka. 9
Fgure 3: Female labor force partcpaton by age and country n 1995 and 2008 10
For educatonal attanment rates, the dataset contans 301 observatons for the age-groups between 25 and 59. For the age-group 60-64, data for Unted Kngdom had to be left out because of nconsstences n the data set, leavng 287 observatons. Educaton data show large varatons across countres for each age-group and educaton level. In order to gve an dea of the change whch occurred over tme, fgure 4 shows the dfference n the hghest educatonal attanment of women aged 25-29 between the year 1995 and the year 2008, to the extent that data for 1995 were avalable. Otherwse, the earlest avalable year was taken. Wth only very few exceptons, the countres show a strkng pattern: the dfference of tertary educatonal attanment level s postve for all countres (Luxembourg shows the hghest ncrease wth 30 percentage ponts), whle the dfferences of upper secondary and prmary/lower secondary educaton tend to be negatve. Obvously, unlke partcpaton, educatonal attanment s not reversble when growng older whch means that today s young age-groups wll have a hgher educatonal attanment when they are old as compared to today s older age-groups. Yet, fgure 4 should not hde the fact that European countres stll feature consderable dfferences n ther educatonal attanment rates. In Portugal 39.3 percent of the female populaton aged 25-29 had only prmary/lower secondary educaton n 2008, 25.3 percent acheved at most upper secondary educaton and 32.5 percent acheved tertary educaton. By contrast, n Sweden only 8.6 percent of women aged 25-29 acheved at most prmary/lower secondary educaton, 47.2 percent of women acheved at most upper secondary educaton and 45.9 percent acheved tertary educaton. Table 1 shows the dstrbuton for the educatonal attanment rates n 2008. 11
Fgure 4: Dfference of hghest educaton level attaned of female populaton (age-group 25-29) between 1995 and 2008 For the Netherlands and Slovena the base year s 1996, for Hungary, Poland and Romana 1997, for the Czech Republc, Latva and Slovaka 1998 and for Cyprus 1999. Table 1: Educatonal attanment rates of women aged 25-29 n 2008 Country Prmary/lower secondary Upper secondary Tertary Austra 0.127 0.717 0.186 Belgum 0.153 0.399 0.517 Cyprus 0.111 0.323 0.562 Czech Republc 0.058 0.721 0.249 Denmark 0.132 0.401 0.459 Fnland 0.081 0.511 0.392 France 0.164 0.395 0.459 Germany 0.146 0.656 0.235 Greece 0.167 0.452 0.341 Hungary 0.127 0.559 0.303 Ireland 0.113 0.397 0.573 Italy 0.257 0.534 0.271 Latva 0.150 0.457 0.380 Luxembourg 0.193 0.427 0.460 Netherlands 0.149 0.428 0.438 Poland 0.047 0.500 0.397 Portugal 0.393 0.253 0.325 Romana 0.239 0.542 0.245 Slovaka 0.050 0.712 0.248 Slovena 0.039 0.584 0.391 Span 0.320 0.290 0.451 Sweden 0.086 0.472 0.459 Unted Kngdom 0.187 0.448 0.415 12
3.2 The emprcal model At the ndvdual level, the decson of a woman to partcpate n the labor market s a dscrete choce (Elhorst and Zelstra, 2007). A woman decdes to partcpate f the benefts from workng exceed the loss of benefts from home producton and the possble (value of the) loss of lesure. Otherwse, she stays outsde the labor market (or leaves the market). Benefts n the labor market are determned by the remuneraton from work n form of earnngs whle benefts from non-partcpaton depend on non-labor ncome from home producton and the utlty of lesure. Both forms of benefts are equally shaped by preferences. The possbltes and earnng power of women n the labor market depend on ther sklls and the avalablty of sutable jobs. The restrctons women are faced wth are gven by ndvdual tme and budget constrants, restrctons mposed by the labor market and socal atttudes/norms towards women (Vlasblom and Schppers, 2004). 14 At the country level, the partcpaton rate s the proporton of women (of a certan age-group) n the female workng-age populaton (of that age-group) that decded to partcpate n the labor market. It s therefore bounded between 0 and 1 (Elhorst and Zestra, 2007). A bounded dependent varable needs to be treated wth cauton when the explanatory varables and the error terms are unrestrcted because f one assumes a smple lnear relaton the predctons may take values outsde the fnte nterval allowed. The emprcal analyss takes account of the boundedness of the partcpaton rate by estmatng the female partcpaton rate usng a logstc regresson model. 15 Specfyng the proporton of women that partcpate as a logstc regresson model (1) ensures that the predcted values of the dependent varable le nsde the nterval 0-1 even f the explanatory varables take unrestrcted values and thus allows for more reasonable estmates. 16 e 1 ( X ) ( 1 ) FLP ( X ) ( X 1 e 1 e ) where FLP s the female labor market partcpaton rate and X s a vector contanng the explanatory varables. An alternatve and more convenent form of the logstc regresson model s n terms of the odds of the dependent varable. 14 For a comprehensve dscusson of theoretcal models of female labor supply see Heckman and Kllngsworth (1986). 15 The estmaton approach s based on Genre et al. (2005). 16 The explanaton of the logstc regresson model s based on Aldrch and Nelson (1984) and Gujarat (2003). 13
If FLP s the proporton of women that partcpate n the labor market, (1-FLP) s the proporton of women that stay outsde the market (2). ( X ) ( 2 ) (1 FLP ) ( X ) ( X 1 e 1 1 e e ) Dvdng (1) by (2) yelds the odds of female partcpaton (3). ( 3 ) 1 FLP FLP e 1 ( X ) e ( X ) / 1 e ( X ) e ( X ) e ( X ) Takng the logs from (3) gves: ( 4) Log FLP 1 FLP X Equaton (4) shows that the log odds of the partcpaton rate are lnearly related to the explanatory varables and can take values on the entre real axs. The female labor force partcpaton, however, s nonlnearly related to the explanatory varables and can only take values between 0 and 1. The emprcal analyss estmates the followng female labor force equaton for each age-group = 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64: FLPjt Log 1 FLPjt US jt Ter jt Dem jt Eco jt Gen jt Pol jt where the dependent varable s the logstc transformaton of the female labor force partcpaton rate ( FPR ) and j and t are the ndces for country and tme, respectvely. For each age-group the emprcal model contans a range of general explanatory varables to test f the margnal reactons to changes of these varables vary between age-groups. The man explanatory varables are upper secondary (US) and tertary (Ter) educatonal attanment rate of women n the respectve age-group. The emprcal analyss controls for aggregate demographc (Dem), economc (Eco) and gender dscrmnaton (Gen) ndcators. The analyss further controls for socal polces (Pol) that address only specfc age-groups. j s an unobserved country effect and jt s the error term. j jt 14
Educatonal attanment: Hgher educaton s supposed to ncrease potental earnngs by ncreasng human captal and productvty (Vlasblom and Schppers, 2003). 17 In addton to potental earnngs, educaton may have an nfluence on female labor market behavor va tastes. The attanment of hgher educaton mght change the tradtonal dea of ndvdual fulfllment of a women by creatng the desre to realze an own professonal career. Work s not only seen as an oblgaton but as an aspect of lfe satsfacton and socal lfe. The decson to work thus mples a decson of personal dentty (Goldn, 2006). The pursut and the duraton of ndvdual fulfllment through a career on the labor market are expected to ncrease wth the level of educaton. Demographc ndcators: As demographc ndcators the emprcal analyss contans the martal status represented by the crude marrage and dvorce rate and the total fertlty rate. Increasng dvorce rates nduce a woman to stay economcally ndependent when marred n order to hedge aganst the dvorce rsk and are thus expected to have a postve effect on female labor force partcpaton. Marrage rates are expected to operate n the opposte drecton (Bremmer and Kesselrng, 2004). Female labor force partcpaton s often lnked to declnng fertlty rates. Fewer chldren reduce the value of tme spent at home. However, the pcture n the European Unon s dffuse. Some countres show low fertlty rates and low partcpaton rates as can be seen n Italy and Greece whle others agan show hgh fertlty rates and hgh partcpaton rates as manly observed n the Scandnavan countres (Jaumotte, 2003; Del Boca et al., 2005). Economc stuaton and avalablty of jobs: The labor market stuaton mpacts the avalablty of jobs (Genre et al., 2005). An unfavorable labor market stuaton mght nduce women to wthdraw from the labor market n the belef that they would not fnd a sutable job. The emprcal analyss controls for the female unemployment rate expectng a negatve, dscouragng, effect on female labor partcpaton decson (Euwals et al., 2007). Furthermore, the flexblty of workng tme arrangements could be a decsve factor of female labor market partcpaton decsons. The share of female part-tme work n total female employment s used as a proxy for the flexblty of workng tme (Jaumotte, 2003; Genre et al., 2005). 17 For recent emprcal studes of the mpact of educaton on earnngs see Brunello and Com, 2004; Gørgens, 2002. 15
Economc lterature fnds that female labor force partcpaton frst declnes wth ncreasng ncome per capta due to the shft away from agrculture and a transton towards a male breadwnner famly. After a certan threshold, partcpaton starts to ncrease agan due to an expandng servce sector wth more workng opportuntes for women, better educated women and changng deologes (e.g. Goldn, 1994). The logarthm of GDP per capta and ts square are ncluded to control for a U-shape relatonshp of female labor force partcpaton and economc growth (Luc, 2009). Dscrmnaton n the labor market: Dscrmnaton aganst women n the labor market s expected to negatvely nfluence female labor market behavor by decreasng ther chances to fnd a sutable job and ther potental earnngs. The emprcal model tres to capture gender dscrmnaton by ncludng the proporton of seats n natonal parlaments held by women (as used n Genre et al., 2005) and the (unadjusted) gender paygap n the regresson. The former s expected to postvely mpact female partcpaton whle the latter should have a negatve mpact. Socal Polces: Socal polces that am at facltatng a woman s re-entrance n the labor market and allow reconclng famly wth a professonal career are expected to have a postve effect on prme-age women s partcpaton decsons (Genre et al., 2005; Jaumotte, 2003). For controllng famly frendly polces, the emprcal model ncludes pad maternal (parental) leave (and ts square to control for a potentally changng behavor dependng on the length of maternal leave) as well as publc expendture for chldcare arrangements. Furthermore, the natonal famly taxaton system s expected to affect female labor force decsons. Jaumotte (2003) fnds that a neutral taxaton of the second earner relatve to sngle ndvduals enhances female labor force partcpaton. However, due to data lmtatons, the emprcal model msses a varable descrbng the tax system. Generous penson systems are expected to nduce a strong ncome effect and thus negatvely nfluence partcpaton decson of elderly women (Boer, 2005; Genre et al., 2005). The emprcal model ncludes socal protecton expendture for old age pensons as an admttedly crude proxy for the generosty of penson system. 4. Econometrc strategy and results The frst set of estmatons s the basc specfcaton of the emprcal analyss. As explanatory varables, t contans the age-group specfc upper secondary and tertary educatonal attanment 16
rate, the total female unemployment rate (women aged 15 to max), the share of female part-tme employment n total female employment (women aged 15 to max), the share of seats n natonal parlament held by women, the logarthm of GDP and ts square and the crude marrage and dvorce rate. In order not to restrct the already small dataset even more, the basc regresson excludes varables wth mssng values (.e. publc expendture for pensons, publc expendture for chld day care, duraton of pad maternal leave and unadjusted gender paygap). The basc specfcaton also excludes fertlty as an explanatory varable. Due to potental endogenety of fertlty, ths ssue s addressed n a separate estmaton. The varables ncluded n the basc specfcaton allow a sample of 301 observatons for the age-groups between 25 and 59 and 287 observatons for the age-group 60-64. A concern when estmatng the mpact of educaton on female partcpaton decsons s an omtted varable bas due to the potental correlaton of educaton and unobserved cultural atttudes whch are also lkely to determne the female partcpaton rate. But the ncluson of fxed effects n the regressons allows capturng cultural atttudes and thus mnmzes the rsk of bas from that source. It s assumed that ncreasng dvorce rsk nduces women to partcpate n the labor market to hedge aganst the possble ncome loss. However, t s also plausble that f the earnngs of a women rse, she can decde more freely over marrage and dvorce. The drecton of causalty s therefore ambguous (Bremmer and Kesselrng, 2004). One year lagged dvorce and marrage rates are used to avod based estmates. 18 Wth respect to the female unemployment rate and part-tme work, the rsk of a bas through reverse causalty s taken nto account by defnng for both varables a larger age-group (women aged 15 to maxmum nstead of 25 to 64) (Genre et al., 2005; Jaumotte, 2003). Followng the same argumentaton, t s not expected that the age-group specfc female partcpaton rates do affect natonal GDP per capta to the extent that they cause a bas of the estmates. A fnal concern relates to the structure of the dataset. Usng annual data together wth fve-year age-groups mples that the female labor force moves n and out of each age-group over tme. These slowly movng cohorts are lkely to cause a movng average structure n the resduals. Because there s no lagged endogenous varable n the equaton, there s no consstency problem, but a movng average structure of the resduals mght cause a loss of effcency. 18 The value for the year 1995 corresponds to the actual value so as not to lose observatons. 17
The emprcal analyss estmates a female partcpaton for each age-group separately usng Feasble Generalzed Least Squares. The structure of the error term s corrected for heteroskedastcty. 19 Each regresson ncludes fxed effects. Table 2 reports the results for all age-groups. Table 2: Fxed effect model basc specfcaton Dependent varable: Female labor market partcpaton rate Age-group 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 Upper secondary educaton 0.463*** 0.734*** 1.000*** 0.588*** 0.120-0.024-0.656*** -0.113 (0.121) (0.146) (0.181) (0.159) (0.151) (0.130) (0.205) (0.344) Tertary educaton 0.519*** 0.478** 1.054*** 0.831*** 1.169*** 1.503*** 2.285*** 5.058*** (0.186) (0.212) (0.226) (0.213) (0.218) (0.254) (0.376) (0.576) Total female unemployment rate 0.341 0.537** 0.382* -0.034-0.342-0.633*** 0.248-0.225 (0.243) (0.240) (0.208) (0.211) (0.228) (0.184) (0.202) (0.318) Share of total female part-tme employment 0.279 0.960*** 1.295*** 2.180*** 2.147*** 2.082*** 1.996*** 1.629*** (0.192) (0.202) (0.187) (0.177) (0.173) (0.182) (0.198) (0.362) Share of women n parlament 0.102 0.720*** 0.431** 0.340** 0.598*** 0.062-0.174 0.004 (0.184) (0.189) (0.169) (0.150) (0.148) (0.166) (0.191) (0.292) Logarthm of GDP per capta -3.039*** -5.405*** -4.926*** -2.574*** -3.646*** -3.328*** -3.499*** -4.067** (0.731) (0.787) (0.665) (0.709) (0.686) (0.732) (1.169) (1.589) Squared logarthm of GDP per capta 0.163*** 0.281*** 0.248*** 0.135*** 0.201*** 0.192*** 0.217*** 0.204** (0.037) (0.041) (0.035) (0.037) (0.035) (0.037) (0.058) (0.081) Lagged crude marrage rate 0.551-0.298-1.988*** -2.901*** -1.464* -2.319*** 0.374 3.232*** (0.638) (0.895) (0.669) (0.612) (0.769) (0.775) (0.771) (0.856) Lagged crude dvorce rate 5.390*** 15.189*** 14.438*** 10.092*** 14.818*** 16.754*** 8.325*** 7.214** (2.034) (2.301) (2.133) (1.962) (1.961) (1.718) (2.022) (3.102) Constant 14.360*** 25.250*** 23.600*** 11.445*** 14.983*** 12.254*** 10.543* 18.634** (3.583) (3.819) (3.197) (3.437) (3.354) (3.597) (5.835) (7.785) Observatons 301 301 301 301 301 301 301 287 Number of countres 23 23 23 23 23 23 23 22 Wald test: Prob>ch2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Standard errors n parentheses * sgnfcant at 10%; ** sgnfcant at 5%; *** sgnfcant at 1% The regressons are estmated usng FGLS. Each regresson ncludes country dummes. Before startng the dscusson of the results, one should note that, due to the nterdependent relatonshp of educatonal attanment rates, ncreasng ether upper secondary or tertary educaton holdng other ncluded varables constant mples a lower value for the varable percentage wth prmary/lower secondary educaton as hghest qualfcaton obtaned. 19 The varance of the error term n the logstc regesson s 18 [ N 1 FLP (1 FLP )]. As FLP vares n the explanatory varables, the varance s not constant. In ths nstance, OLS estmators are stll unbased but not effcent. Usng weghted least squares estmators nstead yelds unbased and effcent estmates (Aldrch and Nelson, 1984, p. 69).
The results strongly ndcate that the level of educatonal attanment has a strong mpact on female labor force partcpaton for all age-groups. The margnal effect of upper secondary educaton on female labor force partcpaton s postve and hghly sgnfcant up to the age 44. For the age-group 45-49 the effect s stll postve but no longer statstcally sgnfcant. For the age-groups 50-54, 55-59 and 60-64 the coeffcents turn negatve, although they are only sgnfcant for the age-group 55-59. The margnal effect of tertary educaton s postve and statstcally sgnfcant throughout all age-groups. For the age-group 30-34 the coeffcent s sgnfcant at a 5 percent level; for all other age-groups, the coeffcents are even sgnfcant at the 1 percent level. The dfference between the sgns of the coeffcents of dfferent educaton levels n the regressons of older agegroups s conspcuous. The results suggest that elderly women that acheved upper secondary educaton as hghest educatonal level are more lkely to drop out of the labor market whle elderly women wth tertary educaton wll contnue to partcpate. The sze of the coeffcents should be nterpreted wth cauton. Because of the logstc transformaton of the partcpaton rate and the underlyng non-lnear relaton wth the explanatory varables, the mpact of a change n one explanatory varable depends on the correspondng value of the explanatory varable. Calculatng the mpact of tertary educaton for example for the age-groups 60-64 shows that ncreasng tertary educatonal attanment by 10 percentage ponts translates n a mean ncrease n partcpaton rates of 7 percent (rangng from 2 to 12 percent). Annex II shows the (partal) frst order dervatve of the female partcpaton rate wth respect to the tertary educatonal attanment rate. Table 2 also presents the effects of the other control varables on female partcpaton. The total female unemployment rate gves mxed results. Its mpact s negatve for the age-groups 40-44, 45-49 50-54 and 60-64 but, the coeffcent s sgnfcant only for the age-group 50-54. For the remanng (mostly younger) age-groups, the female unemployment rate has a postve effect and for the age-groups 30-34 and 35-39 the coeffcent s statstcally dfferent from zero. In contrast, the parameters of the share of female part-tme employment as a proxy for the flexblty of workng tme arrangements have the expected postve sgn and are (hghly) sgnfcant, except for the age-group 25-29. Apparently the avalablty of part-tme work s less mportant for women n ther 20s, who have just started ther professonal career,. 20 20 Ths study only looks at the decson to partcpate n the labor market. Due to data lmtatons the analyss cannot dstngush between part-tme and full-tme partcpaton. For studes ncludng ths dstncton see e.g. Jaumotte (2003) or Bosch et al. (2005). In the study of Jaumotte, educaton has a sgnfcant postve effect on the aggregate 19
The share of women n natonal parlaments, ndcatng the relatve absence of female dscrmnaton n the labor market, does enter postvely and sgnfcantly for the age-groups between 30 and 49. Even f the varable s only a rough proxy for gender dscrmnaton n the labor market t does have explanatory power for the labor market behavor of prme age women, wth sgns as expected: more dscrmnaton (less women n parlament) leads to lower partcpaton rates. The coeffcents of the logarthm of GDP per capta and ts square have to be nterpreted together. For a U-shaped relatonshp of female labor force partcpaton and economc growth, the logarthm of GDP per capta should be negatve and ts square postve (Luc, 2009). In all age-groups ths pattern can be observed and the coeffcents are sgnfcant at the 1 percent level for the age-groups up to 59 and at the 5 percent level for the age-group 60-64. Table 3 presents the threshold value of GDP per capta for each age-group after whch a growth n GDP per capta causes a postve margnal reacton of female labor market partcpaton. It shows that the values are ncreasng up to the age-group 35-39, after whch they start declnng up to the agegroup 55-59. The value jumps agan for the age-group 60-64. One nterpretaton of ths pattern could be that early chldhood care systems are better developed n rcher countres. Women that have small chldren (mostly women n the 30s) thus have a hgher GDP threshold value. However, the extreme jump at the age 60 s surprsng. Table 3: GDP per capta threshold value Age-group 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 Mnmum GDP per capta (n current 11182 15024 20567 13812 8687 5806 3172 21355 nternatonal US$) The margnal effect of the (lagged) crude marrage rate s, as expected, negatve for all agegroups between 25 and 54 but only sgnfcant for the age-groups between 35 and 54. For the hghest two age-groups the coeffcent turns postve and s even hghly sgnfcant for the agegroup 60-64, whch s somethng of a puzzle. The (lagged) crude dvorce rate s postve throughout, as expected, and statstcally sgnfcant at the 1 percent level up the age 59 and at the 5 percent level for the age-group 60-64. female full-tme partcpaton rate and a sgnfcant negatve effect on the aggregate female part-tme partcpaton rate. For the calculaton of part-tme and full-tme female partcpaton rates, Jaumotte assumes the same dstrbuton between part-tme and full-tme for the labor supply as can be observed for employment. Smlarly, n a study on labor market behavor of women n the Netherlands, Bosch et al. fnd that a woman s almost twce as lkely to work full-tme f she attaned tertary educaton compared to prmary educaton. 20
In a separate step, the unadjusted gender paygap as a further ndcator for gender dscrmnaton n the labor market s ntroduced nto the regressons. However, the varable does not yeld satsfyng results. It s only sgnfcant n the age regressons 25-29 and 40-44 and n both cases unexpectedly postve. Interpretng these results as a postve reacton to dscrmnaton mght be rather msleadng snce t s not known f the paygap stll holds after adjustng for sklls. Unfortunately, the data for the adjusted gender gap are not avalable for most countres n the data sample. The second set of estmatons addresses the problem of the potental endogenety of fertlty. The causal relatonshp of fertlty and labor market partcpaton s ambguous snce gettng chldren and partcpatng n the labor market can be seen as a jont decson. Hence, fertlty as a potentally endogenous varable nvolves the rsk smultanety bas (Bloom et al., 2009; Genre et al., 2005). Genre et al. (2005, p. 13) argue that causalty s more lkely to go from fertlty to partcpaton because havng chldren s a permanent decson, whle partcpaton s reversble, and therefore can adjust n the short run. In order to mtgate the rsk of based estmates, they use lagged fertlty rates. Jaumotte (2003) reasons that for European countres there s no evdence of a negatve mpact of female partcpaton decsons on fertlty rates. Therefore, her emprcal study does not nclude fertlty n the regressons but polces that help reconclng work and famly. The present emprcal analyss tres nevertheless to take account of a possble endogenety bas by nstrumentng the fertlty rate wth chld mortalty under fve. 21 For techncal reasons, the analyss refrans from a GLS correcton at ths pont, although at the expense of effcency of the estmates (cf. Aldrch and Nelson, 1984, p.69). Emprcal studes show that declnng nfant and chld mortalty s an mportant factor n explanng declnng fertlty rates (e.g. Ecksten et al., 1998). Parents apparently have a desre for a certan number of chldren. The consequent precautonary demand for chldren depends on the mortalty rate of chldren. A hgher expected mortalty rate wll accordng to ths vew lead to hgher fertlty rates to ensure that the desred number of chldren survves. Kaleml- Ozcan (2008) also fnd that declnng mortalty rates lead to declnng fertlty rates. So emprcal evdence seems to support the use of chld mortalty as an nstrument for fertlty rates. For each regresson fertlty s nstrumented wth chld mortalty under fve. In all frst stage regressons chld mortalty enters postvely and statstcally sgnfcant. In all estmatons, the F- statstc n the frst stage exceeds 10 ndcatng that chld mortalty under fve s not a weak 21 Elhorst and Tanveer (2008) also usse chld mortalty as an nstrument for fertlty. 21
nstrument (Stager and Stock, 1997). Table 4 presents the regresson results for all age-groups ncludng fertlty as an explanatory varable. Table 4: Fxed effect model ncluson of total fertlty rate as an explanatory varable Dependent varable: Female labor market partcpaton rate Age-group 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 Upper secondary educaton 0.417*** 0.734*** 0.979*** 0.595*** 0.122-0.051-0.785*** -0.307 (0.116) (0.141) (0.180) (0.160) (0.149) (0.133) (0.208) (0.343) Tertary educaton 0.474*** 0.508** 1.003*** 0.847*** 1.146*** 1.426*** 1.988*** 5.213*** (0.180) (0.209) (0.229) (0.213) (0.216) (0.267) (0.386) (0.545) Total female unemployment rate 0.092 0.338 0.221-0.143-0.229-0.575*** 0.666*** 0.025 (0.257) (0.251) (0.230) (0.221) (0.235) (0.196) (0.217) (0.348) Share of total female part-tme employment 0.310* 1.027*** 1.295*** 2.150*** 2.139*** 2.090*** 1.972*** 1.627*** (0.188) (0.203) (0.189) (0.174) (0.173) (0.182) (0.202) (0.364) Share of women n parlament 0.047 0.786*** 0.458*** 0.366** 0.566*** 0.071-0.114 0.018 (0.181) (0.185) (0.171) (0.151) (0.149) (0.166) (0.191) (0.289) Logarthm of GDP per capta -3.130*** -5.568*** -5.094*** -2.781*** -3.540*** -3.345*** -3.647*** -3.647** (0.737) (0.793) (0.687) (0.723) (0.673) (0.731) (1.196) (1.568) Squared logarthm of GDP per capta 0.169*** 0.289*** 0.257*** 0.146*** 0.196*** 0.193*** 0.226*** 0.182** (0.038) (0.041) (0.036) (0.037) (0.034) (0.037) (0.060) (0.080) Lagged crude marrage rate 0.268-0.165-2.176*** -2.801*** -1.701** -2.341*** 0.110 2.878*** (0.598) (0.821) (0.690) (0.581) (0.781) (0.777) (0.738) (0.885) Lagged crude dvorce rate 4.769** 14.165*** 14.187*** 9.907*** 14.872*** 16.718*** 7.916*** 7.361** (1.972) (2.306) (2.174) (1.983) (1.958) (1.716) (1.953) (3.306) Total fertlty rate -0.218*** -0.243*** -0.128-0.123* 0.150* 0.077 0.365*** 0.311** (0.080) (0.090) (0.088) (0.074) (0.082) (0.087) (0.101) (0.129) Constant 15.054*** 26.516*** 24.623*** 12.646*** 14.279*** 12.186*** 10.472* 16.290** (3.623) (3.877) (3.338) (3.530) (3.296) (3.591) (5.953) (7.700) Observatons 301 301 301 301 301 301 301 287 Number of countres 23 23 23 23 23 23 23 22 Wald test: Prob>ch2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Standard errors n parentheses * sgnfcant at 10%; ** sgnfcant at 5%; *** sgnfcant at 1% The regressons are estmated usng FGLS. Each regresson ncludes country dummes. To test whether the OLS or IV regresson technque s approprate, we apply the Hausman test to each regresson n order (Wooldrdge, 2002). The Hausman test generates an F-Statstc wth the null hypothess that there are no systematc dfferences n the coeffcents of the OLS (effcent estmaton) and the IV (consstent estmaton) regresson. For all regressons, the null hypothess 22
cannot be rejected, ndcatng that the OLS estmates are approprate. 22 Table A3 n the Annex shows the results of the Hausman test. 23 Fertlty enters negatvely up to the age 44 and turns postve for the age-groups between 45 and 64 and s sgnfcant for the age-groups 25-29, 30-34, 40-44, 45-49, 55-59 and 60-64. In a way the hgh sgnfcance levels for the elderly age-groups are surprsng snce the total fertlty rate s only defned over the fertle lfe of a woman. The results ndcate that wth a hgher total fertlty rate, other thngs beng equal, the dstrbuton of female labor market partcpaton s skewed towards the elderly whch could be taken as negatve socal atttudes towards workng mothers, and an ntergeneratonal dvson of labor. In order to examne the effect of elderly women s own fertlty rates, the regressons for the agegroups 45-49, 50-54, 55-59 and 60-64 are re-estmated usng past fertlty rates matchng each age-group. For the age-group 45-49 the perod refers to the years 1975-1988, for the age-group 50-54 to the years 1970 to 1983, for the age-group 55-59 to the years 1965 to 1978 and for the age-group 60-64 to the years 1960 to 1973,.e. to the peak fertle years of women of the respectve age-group. It turns out that the ncluson of past fertlty rates changes the coeffcents n the expected drecton. The coeffcent of the age-groups 45-49, 50-54 and 60-64 turn negatve and are hghly sgnfcant. The fertlty rate of the age-group 55-59 loses sgnfcance. The results suggest that the effect of own fertlty s not just temporary but that t trggers a persstent negatve effect on partcpaton rates of women over ther entre lfe tme. Ths could be nterpreted as a hysteress effect of fertlty. Table 5 presents the results for the four agegroup regressons. The results for the educatonal varables from the basc model are robust to the ncluson of both concurrent and own fertlty rates. In the regressons of the age-groups 45-49 and 50-54 wth past fertlty rates, the sgns of upper secondary educaton change but stay nsgnfcant. In all remanng age-group regressons the sgns and sgnfcance levels of both educaton levels reman unchanged. Wth respect to the control varables, t s notceable that, for many age-groups, the emprcal results of the total female unemployment rate shows changed sgns and/or sgnfcance levels once fertlty rates are ncluded. Ths may be due to a correlaton between fertlty and the female unemployment rate. However, t s also possble that the choce of the total female 22 The Hausman test s conducted for every estmaton that follows. In all cases the null hypotheses statng no systematc dfferences between the OLS and IV estmates cannot be rejected. 23 However, t should be kept n mnd that the Hausman test depends on the choce of the nstrument (Jordahl et al., 2009) and the rsk of potental bas s not entrely precluded. 23
unemployment rate nstead of the age-group specfc female unemployment rate does not totally solve the ssue of endogenety of unemployment and the assocated potental feedbacks of the partcpaton rate on unemployment (Genre et al., 2005). Table 5: Fxed effect model re-estmaton for the age-groups 45-49, 50-54, 55-59 and 60-64 wth past fertlty Dependent varable: Female labor market partcpaton rate Age-group 45-49 50-54 55-59 60-64 Upper secondary educaton -0.080 0.012-0.582*** -0.069 (0.149) (0.143) (0.225) (0.321) Tertary educaton 1.044*** 1.278*** 2.123*** 2.214*** (0.202) (0.253) (0.430) (0.686) Total female unemployment rate 0.316-0.298 0.229-1.008*** (0.207) (0.186) (0.252) (0.260) Share of total female part-tme employment 2.199*** 1.695*** 1.985*** 1.463*** (0.168) (0.190) (0.239) (0.321) Share of women n parlament 0.487*** -0.06-0.174-0.198 (0.136) (0.170) (0.217) (0.259) Logarthm of GDP per capta -3.558*** -2.964*** -3.241*** -0.781 (0.636) (0.734) (1.186) (1.611) Squared logarthm of GDP per capta 0.193*** 0.171*** 0.204*** 0.043 (0.032) (0.037) (0.060) (0.082) Lagged marrage rate -1.509* -1.879** 0.443 3.975*** (0.817) (0.785) (0.801) (0.846) Lagged dvorce rate 5.058** 12.185*** 7.903*** 7.956*** (2.026) (1.877) (2.350) (2.430) Total fertlty rate 20 years ago -0.309*** (0.032) Total fertlty rate 25 years ago -0.237*** (0.042) Total fertlty rate 30 years ago -0.001 (0.048) Total fertlty rate 35 years ago -0.549*** (0.070) Constant 16.537*** 12.546** 10.365* 2.745 (3.162) (3.666) (5.948) (7.917) Observatons 301 301 301 287 Number of countres 23 23 23 22 Wald test: Prob>ch2 0.0000 0.0000 0.0000 0.0000 Standard errors n parentheses * sgnfcant at 10%; ** sgnfcant at 5%; *** sgnfcant at 1% The regressons are estmated usng FGLS. Each regresson ncludes country dummes. The analyss untl now assumes that effects of external varables may dffer across age-groups, but assumes that all cohorts behave the same once they reach a partcular age-group. But changng socal mores may lead to dfferent behavour for younger cohorts. If they react 24