It's All about Control: Worker Control over Schedule and Hours ... - LIS

6 American Sociological Review XX(X)have found schedule control to have no effecton work-life outcomes (Thompson and Prottas2006; Voydanoff 2004). These inconsistentresults suggest the need for further study.Consequences of Flexibility:Comparative Research on ContextualInfluencesSeveral comparative studies explore the influenceof a country’s gender egalitarian cultureand policy environment on workers’ worklifeconflict and similar constructs (e.g.,Lyness and Kropf 2005). Generally, withinand across European countries, women andmen who work longer hours, hold demandingjobs, and have younger children are morelikely to report WFC or lower work-life balance(Cousins and Tang 2004; Lyness andKropf 2005; Ruppanner 2011; van der Lippe,Jager, and Kops 2006). Schieman and colleagues(2009) found similar results in theUnited States. At the country level, however,results are surprising, with most researchshowing that parents in countries with themost egalitarian gender roles and highly supportivework-family policies—such asSweden—report more, not less, work-conflictthan do parents in countries with less generouswork-family policies, such as theAnglophone and Eastern European bloccountries (Cousins and Tang 2004; Cromptonand Lyonette 2006; Strandh and Nordenmark2006; van der Lippe et al. 2006). In trying tomake sense of this seemingly anomalousresult, scholars have suggested that more progressivegender norms may actually heightenwork-family conflict by increasing pressuresfor men and women to be involved in bothwork and family spheres. In contrast to thisprevailing pattern, Ruppanner’s (2011) recentmultilevel study of 10 Western countriesfound that work-family policies appear tomodulate WFC only for women who are parentsof young children. With regard to influenceson work-family conflict, the surprisingand contradictory results of prior research,based on a relatively small number of countries,also suggest the need for further study.Model And HypothesesWe estimate a multilevel model of workplaceflexibility or, as we conceptualize it, workercontrol over working time, defined as controlover one’s work schedule and hours. Ourmodel includes micro-level worker and jobcharacteristics that research shows are associatedwith flexibility. Our model also includeseffects of several country-level factors identifiedin the small prior cross-national literatureabout contextual antecedents of workers’flexibility. These include macroeconomic factors,labor market characteristics, and policyindicators related to social spending and regulationof working time. In light of claimsmade about the beneficial consequences ofworkers’ control over their working time, wealso evaluate the impact of our flexibilitymeasures on three crucial policy-relevant outcomes.Figure 1 shows our model, whichassesses both antecedents and consequencesof worker control over working time.At the micro-level, based on the foregoingliterature review, we hypothesize that womenwill have less flexibility or control. Wehypothesize that age, education, and having apartner will all be positively linked to workercontrol. We also include micro-level job characteristicsand hypothesize that working fulltimewill result in less control than workingpart-time, but that being self-employed orworking in exempt, high-paying jobs withopportunities for advancement will result inmore control.At the country level, we examine effects ofmany salient aspects of national context,including standard of living (GDP per capita),public social welfare effort (measured bysocial expenditures), and characteristics ofthe labor force (women’s labor force participation,service sector employment, and unioncoverage). We hypothesize that all thesemacro-level contextual features will be positivelyassociated with worker control. Wealso include two measures of working timeregulation: length of the standard work week(in hours) and amount of paid leave grantedannually (in days). Both directly affect theDownloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

8 American Sociological Review XX(X)decided by my employer; 2 = I can decide thetime I start and finish work, within certainlimits; 3 = I am entirely free to decide when Istart and finish work). We measured controlover work hours with two new variablesderived from an ISSP item that asked aboutpreference for time in a paid job using a fivepointscale (1 = much more time to 5 = muchless time). We collapsed the five responseoptions and created two dichotomous variablesreflecting different types of hoursmismatch: (1) hours deficit, that is, workerpreference for more work hours (coded 1 =hours deficit; 0 = no deficit, worker preferencefor same or fewer hours) and (2) hours excess,that is, worker preference for fewer workhours (coded 1 = hours excess; 0 = no excess,preference for same or more work hours).Worker outcome variables. Work-familyconflict (WFC) occurs when work interfereswith performance of nonwork roles; strainbasedconflict, an important type of WFC,occurs when work-related strain or fatigueinterferes with a nonwork role (Greenhaus andBeutell 1985). We measured strain-based conflictwith the ISSP item: “How often do youcome home from work exhausted?” (1 = neverto 5 = always). We measured job satisfactionwith the ISSP item: “How satisfied are you inyour job?” (1 = completely dissatisfied to 7 =completely satisfied). We measured organizationalcommitment by averaging two ISSPitems: (1) “I am willing to work harder than Ihave to in order to help the organization or firmI work for succeed” and (2) “I am proud to beworking for my firm or organization” (1 =strongly disagree to 5 = strongly agree). Thealpha coefficient for the scale was .65.Worker characteristics. Worker characteristicsincluded gender, coded as female (0 =no, 1 = yes), age (between 25 and 54 years),education measured in years, and partneredstatus (i.e., married/living with partner; 0 =no, 1 = yes).Job characteristics. Job characteristicsincluded full-time work status (0 = no, 1 = yes)and self-employment (0 = no, 1 = yes). We alsoincluded two characteristics associated withdesirable jobs, measured as the extent to whichthese characteristics described respondents’current jobs: high earnings (1 = strongly disagreeto 5 = strongly agree) and high opportunityfor advancement (1 = strongly disagree to 5 =strongly agree). Earnings and advancementopportunity, along with workers’ education,capture various aspects of social class, as wewill discuss later. Data about weekly hoursworked were available for only 20 countries(data not available for New Zealand) and wereused for supplementary analyses.National characteristics. Data are from1997, the first year of individual-level datacollection, unless otherwise noted. Weobtained gross domestic product (GDP) percapita (adjusted for purchasing power parityand measured in current dollars) from theUnited Nations (United Nations StatisticsDivision 1995). Social expenditure, whichmeasures public social welfare spending as apercentage of GDP, comes from the OECDFact Book 2007 (Organization for EconomicCooperation and Development 2007), anotherOECD source for Russia (Organization forEconomic Cooperation and Development2001), and an ILO source for Slovenia (1991),Bulgaria (1996), and Hungary (ILO Departmentof Statistics 1997a).Women’s labor force participation rateswere reported in an OECD statistical annex(Organization for Economic Cooperation andDevelopment 1997); for Bulgaria, Russia,and Slovenia, data come from the World BankGenderStats database (World Bank KeyDevelopment Data & Statistics 2008). Wecomputed the percentage of employees workingin industries classified as belonging to theservice sector from a yearly statistical classificationof industries into agricultural, manufacturing,and service sectors. We obtaineddata from the ILO Department of StatisticsLaborsta database (ILO Department of Statistics1997b); data for Bulgaria (1996) andFrance (1999) come from the CIA WorldFactbook (Central Intelligence Agency 2007).Downloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

Table 1. Means for Worker Control Variables and Outcome Variables by Gender within Country GroupsWorker Control Variables Worker Outcome VariablesnControl OverSchedule Hours Excess Hours DeficitStrain-BasedWork-FamilyConflict Job SatisfactionOrganizationalCommitmentGroup Male Female Male Female Male Female Male Female Male Female Male Female Male FemaleNordic 992 999 1.75 1.58 *** .52 .51 .06 .09 * 3.20 3.38 *** 5.34 5.36 3.66 3.62Continental European 1,691 1,413 1.72 1.67 * .47 .35 *** .10 .14 *** 3.19 3.27 ** 5.29 5.36 3.59 3.48 ***Southern European 729 507 1.63 1.48 *** .29 .31 .17 .19 3.40 3.56 ** 5.28 5.14 * 3.74 3.66Anglophone 699 764 1.65 1.59 .43 .48 .14 .15 3.33 3.42 * 5.14 5.29 * 3.78 3.80Eastern European 1,316 1,211 1.50 1.36 *** .25 .28 .25 .21 ** 3.45 3.53 * 4.99 5.01 3.45 3.41Asian/Pacific Rim 225 141 1.50 1.28 ** .51 .42 .04 .09 3.24 3.21 4.67 4.70 3.78 3.48 **Note: Significant gender differences within each cluster, based on two-tailed t-tests, are indicated.* p < .05; ** p < .01; *** p < .001.10Downloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

Table 2. Multilevel Analyses Predicting Control over Schedule with Individual- and Country-Level VariablesVariable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8Individual-Level Fixed EffectsIntercept (γ 00) 1.577 ***(.031)Worker CharacteristicsFemale (γ 10) –.033 *(.015)Age (γ 20) .004 **(.001)Years of Education (γ 30) .026 ***(.003)Partnered (γ 40) .023(.015)Job CharacteristicsFull-Time (γ 50) –.086 *(.036)Self-Employment (γ 60) .906 ***(.058)High Earnings (γ 70) .068 ***(.008)High Opportunity for Advancement (γ 80) .049 ***(.011)1.409 ***(.049)–.033 *(.015).004 **(.001).026 ***(.003).025(.015)–.085 *(.036).904 ***(.059).069 ***(.009).049 ***(.011)1.281 ***(.076)–.033 *(.016).004 **(.001).027 ***(.002).025(.015)–.085 *(.036).904 ***(.058).069 ***(.009).049 ***(.011)1.332 ***(.104)–.033 *(.015).004 **(.001).026 ***(.003).025(.015)–.085 *(.036).903 ***(.059).069 ***(.009).049 ***(.011)1.411 ***(.144)–.034 *(.015).004 **(.001).026 ***(.003).025(.015)–.085 *(.036).904 ***(.059).069 ***(.009).049 ***(.011)1.310 ***(.068)–.033 *(.015).004 **(.001).027 ***(.003).025(.015)–.085 *(.036).905 ***(.058).068 ***(.009).050 ***(.011).714(.589)–.033 *(.015).004 **(.001).026 ***(.002).025(.015)–.085 *(.037).904 ***(.058).069 ***(.009).049 ***(.011).734(.597).015(.378).004 **(.000).026 ***(.002).025(.012)–.085 *(.034).904 ***(.057).069 ***(.008).049 ***(.011)Country-Level Fixed EffectsIntercept x GDP per Capita (γ 01) .009 **.007 **(.002)(.002)Intercept x Social Expenditure (γ 02) .007 *Intercept x Women’s Labor ForceParticipation Rate (γ 02)Intercept x Service Sector (γ 02) –.006(.314)Intercept x Union Coverage (γ 02) .220 *(.003).008 *(.003).002(.002).009 *(.002).007 **(.002)(.078).010 ***(.002).010 **(.002)(continued)12Downloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

Table 2. (continued)Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8Intercept x Weekly Hours Policy (γ 02) .014(.014)Intercept x Paid Leave Policy (γ 03) .006 *Female Slope x GDP per Capita (γ 11) –.000(.002)Female Slope x Weekly Hours Policy (γ 12) –.001(.009)Female Slope x Paid Leave Policy (γ 13) .000(.001)(.003).014(.014).006(.003)Intercept Random Effect (μ ) .020 *** .013 *** .011 *** .012 *** .013 *** .013 *** .012 *** .012 ***10Female Slope Random Effect (μ ) .002 .002Note: Entries are unstandardized coefficients; standard errors are in parentheses. N = 21 countries. GDP = gross domestic product. Gross domestic product percapita was measured in thousands.* p < .05; ** p < .01; *** p < .001 (two-tailed tests).13Downloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

Table 3. Multilevel Analyses Predicting Hours Excess with Individual- and Country-Level VariablesVariable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8Individual-Level Fixed EffectsIntercept (γ 00) .562 **(–3.666)Worker CharacteristicsFemale (γ 10) 1.232 **(2.961)Age (γ 20) 1.004(1.026)Years of Education (γ 30) 1.056 ***(6.270)Partnered (γ 40) 1.078(.960)Job CharacteristicsFull-Time (γ 50) 3.232 ***(7.104)Self-Employment (γ 60) 1.030(.275)High Earnings (γ 70) 1.019(.506)High Opportunity forAdvancement (γ 80).905 **(–3.734).201 ***(–6.006)1.230 **(2.896)1.004(1.176)1.055 ***(6.171)1.099(1.215)3.454 ***(7.452)1.022(.204)1.016(.420).906 **(–3.643).109 ***(–5.222)1.230 **(2.891)1.004(1.178)1.054 ***(6.178)1.103(1.265)3.485 ***(7.403)1.026(.240)1.016(.418).905 **(–3.646).056 ***(–5.745)1.231 **(2.898)1.005(1.218)1.055 ***(6.215)1.102(1.285)3.462 ***(7.296)1.013(.119)1.012(.340).906 **(–3.614).028 ***(–4.844)1.231 *(2.890)1.005(1.281)1.055 ***(6.273)1.102(1.264)3.500 ***(7.640)1.011(.100)1.016(.404).904 **(–3.603).201 **(–3.985)1.230 **(2.895)1.004(1.179)1.055 ***(6.153)1.099(1.228)3.458 ***(7.462)1.022(.205)1.016(.415).906 **(–3.646).004(–1.629)1.223 *(2.785)1.004(1.204)1.055 ***(6.198)1.103(1.266)3.521 ***(7.530)1.022(.207)1.012(.319).906 **(–3.567).002(–1.841)7.970(1.040)1.004(1.201)1.056 ***(6.234)1.093(1.131)3.531 ***(7.560)1.022(.208)1.010(.261).907 **(–3.502)Country-Level Fixed EffectsIntercept x GDP per Capita (γ 01) 1.054 ***(4.355)1.052 ***(4.349)Intercept x Social Expenditure (γ 02) 1.032(1.754)Intercept x Women’s Labor ForceParticipation Rate (γ 02)1.016(1.107)1.033 **(3.108)1.017(.886)Intercept x Service Sector (γ 02) 68.128 *(2.648)Intercept x Union Coverage (γ 02) .982(–.040)1.054 **(4.239)1.065 ** *(4.499)1.066 ***(4.575)(continued)14Downloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

Table 3. (continued)Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8Intercept x Weekly Hours Policy (γ 02) 1.080(.964)Intercept x Paid Leave Policy (γ 03) 1.035 *Female Slope x Gross DomesticProduct per Capita (γ 11)Female Slope x Weekly HoursPolicy (γ 12)Female Slope x Paid LeavePolicy (γ 13)(2.181)1.097(1.175)1.036 *(2.223).990(–1.194).970(–.636).975 *(–2.725)Intercept Random Effect (μ ) .507 *** .310 *** .256 *** .231 *** .281 *** .319 *** .318 *** .321 ***10Female Slope Random Effect (μ ) .056 ** .042 *Note: Entries are odds ratios (and t-ratios) from unit-specific models. N = 21 countries. GDP = gross domestic product. Gross domestic product per capita wasmeasured in thousands.* p < .05; ** p < .01; *** p < .001 (two-tailed tests).15Downloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

16 American Sociological Review XX(X)job characteristics, full-time work has a positiverelationship to hours excess and opportunitiesfor advancement has a negative relationship;the nonsignificant random effect suggests thatthe advancement opportunities relationship doesnot vary across countries. 1GDP per capita has a significant positiverelationship to hours excess (Table 3, Model2). Holding constant the other predictors andthe random effect, for every $1,000 increase inGDP per capita, the expected odds that arespondent would report hours excessincreases by about 5 percent. Controlling forGDP per capita, women’s labor force participationand service sector employment havepositive relationships to hours excess (Models4 and 5). Also, GDP per capita in combinationwith each of these characteristics reduces thecross-national random effects for the modelintercept by approximately half, and thusexplains meaningful cross-national variationin workers’ reported hours excess. Neithersocial expenditure nor union coverage is asignificant predictor (Models 3 and 6).Of the two policy variables, paid leavepolicy has a significant positive relationshipto hours excess, but weekly work hours policyis not significantly related (Model 7).Also, paid leave policy has a significant negativemoderating effect on the micro-levelrelationship between gender and hours excess,such that the overall gender relationship is nolonger significant when paid leave policiesare taken into account (Model 8). The gendergap thus varies depending on paid leave policies;in countries with more generous paidleave policies (e.g., Sweden with 25 days),women are more similar to their male counterpartsin likelihood of reporting an hoursexcess, whereas in countries with less generousleave policies (the United States being theextreme with no guaranteed leave) there is alarger gender gap, with women more likelythan men to report an hours excess.Control over hours: hours deficit. We alsoused HGLM to estimate multilevel logisticmodels for hours deficit (worker preference formore work hours). Based on a model with nopredictors, the estimated log odds of reportingan hours deficit (γ 00) = –1.838 (SE = .159),which corresponds to a probability of .137.Of the worker characteristics, women andworkers with partners (married or living asmarried) were less likely to report an hoursdeficit than were men or workers without partners(Table 4, Model 1). Based on its randomeffect, the relationship of gender to hours deficitdoes not vary significantly across countries.Three job characteristics are significantlyrelated to hours deficit (Model 1): full-timework and high income work have negativerelationships to hours deficit, and being selfemployedis positively related. 2 Only the relationshipwith self-employment does not appearto vary significantly across countries.GDP per capita has a negative relationshipto hours deficit (Table 4, Model 2). The oddsratio indicates that holding constant the otherpredictors and the random effect, for every$1,000 increase in GDP per capita, theexpected odds that a respondent would reportan hours deficit decrease by about 7 percent.After controlling for GDP per capita, socialexpenditure and women’s labor force participationhave significant negative relationshipsto hours deficit (Models 3 and 4). GDP percapita, as well as GDP per capita in combinationwith these other two national characteristics,reduce the cross-national random effectsfor the intercept by over one-third, and thusexplain meaningful cross-national variationin workers’ reported hours deficit. Servicesector proportion and union coverage are notsignificant predictors (Models 5 and 6).Of the two policy variables (Model 7),paid leave policy has a negative relationshipto hours deficit, but weekly hours policy isnot significantly related. Both paid leave policyand weekly hours policy are significantpositive moderators of the relationshipbetween gender and hours deficit. After controllingfor effects of these interactions, therelationship between gender and hours deficitis weaker but still statistically significant.Tests of gender differences. To see ifrelationships of worker and job characteristicsDownloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

Table 4. Multilevel Analyses Predicting Hours Deficit with Individual- and Country-Level VariablesVariable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8Individual-Level Fixed EffectsIntercept (γ 00) .134 ***(–11.353)Worker CharacteristicsFemale (γ 10) .699 ***(–4.491)Age (γ 20) .993(–1.452)Years of Education (γ 30) .968(–1.797)Partnered (γ 40) .795 *(–2.394)Job CharacteristicsFull-Time (γ 50) .248 ***(–7.909)Self-Employment (γ 60) 1.446 **(3.331)High Earnings (γ 70) .861 **(–2.921)High Opportunity forAdvancement (γ 80)1.040(1.069).511 *(–2.425).703 ***(–4.412).993(–1.320).968(–1.817).795 *(–2.350).243 ***(–8.277)1.444 **(3.356).868 *(–2.837)1.038(1.009)1.423(.831).706 ***(–4.364).993(–1.279).968(–1.761).793 *(–2.375).244 ***(–8.310)1.438 **(3.326).868 *(–2.771)1.038(1.019)1.468(.667).700 ***(–4.456).993(–1.326).968(–1.793).798 *(–2.324).242 ***(–8.312)1.435 **(3.288).867 *(–2.861)1.038(1.021).823(–.227).703 ***(–4.403).993(–1.303).968(–1.797).795 *(–2.367).242 ***(–8.324)1.438 **(3.316).868 *(–2.801)1.038(1.011).540(–1.401).704 ***(–4.402).993(–1.327).968(–1.816).795 *(–2.351).243 ***(–8.280)1.443 **(3.347).868 *(–2.830)1.038(1.009)210.905(1.418).697 ***(–4.526).993(–1.243).968(–1.768).784 *(–2.521).240 ***(–8.325)1.450 **(3.393).869 *(–2.802)1.042(1.130)8.929(.619).000 *(–2.910).994(–1.254).968(–1.761).780 *(–2.503).238 ***(8.406)1.451 **(3.460).866 *(–2.848)1.043(1.166)Country-Level Fixed EffectsIntercept x GDP per Capita (γ 01) .934 ***.937 ***(–5.821)(–5.566)Intercept x Social Expenditure (γ 02) .949 *Intercept x Women’s Labor ForceParticipation Rate (γ 02)(–2.748).959 *(–2.562).975 *(–2.102).943 *(–2.615).933 ***(–5.481)Intercept x Service Sector (γ 02) .356(–.552)Intercept x Union Coverage (γ 02) .946(–.104).921 ***(–6.001).923 ***(–6.193)(continued)17Downloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

Table 4. (continued)Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8Intercept x Weekly Hours Policy (γ 02) .883(–1.381)Intercept x Paid Leave Policy (γ 03) .955 *Female Slope x Gross DomesticProduct per Capita (γ 11)Female Slope x Weekly HoursPolicy (γ 12)(–2.838).951(–.598).967 *(–2.158)Female Slope x Paid Leave Policy (γ 13) 1.027 *1.003(.348)1.201 *(2.738)(2.348)Intercept Random Effect (μ ) .628 *** .392 *** .320 *** .344 *** .398 *** .402 *** .351 *** .345 ***10Female Slope Random Effect (μ ) .036 .024Note: Entries are odds ratios (and t-ratios) from unit-specific models. N = 21 countries. GDP = gross domestic product. Gross domestic product per capita wasmeasured in thousands.* p < .05; ** p < .01; *** p < .001 (two-tailed tests).18Downloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

Lyness et al. 19to the three types of control over working timediffer by gender, we conducted additionalmicro analyses with main effects for genderand the seven micro characteristics, as well asinteractions of gender with each characteristic.We found little evidence that relationshipsdiffer by gender (results not shown here).None of the gender interactions predictinghours excess or hours deficit are significant.For schedule control, however, there is onenotable exception: the gender by educationinteraction. Examination of separate level-lmodels for each gender reveals that, althougheducation has a significant positive relationshipto schedule control for both genders, therelationship is somewhat stronger for menthan for women, indicating that men reportedslightly more schedule control for each additionalyear of education than did women. 3Social class comparisons. A final set ofexploratory analyses examined whether thethree types of control differ depending onworker social class, based on a self-reportedmeasure of social class that was available foronly 19 countries; data were missing for theUnited Kingdom and Hungary. (These exploratoryanalyses, which rely on subjectiveassessments of class, supplement our findingsreported above, based on three other indicatorsthat capture facets of class [i.e., educationand jobs with high earnings or advancementopportunities]). Because the social class measurehad different response options acrosscountries, we aggregated responses into threecategories: below middle class, middle class,and above middle class, and created twodichotomous variables: (1) lower class (1 =below middle class, 0 = middle class or above)and (2) upper class (1 = above middle class, 0= middle class or below). We conductedlevel-1 analyses that included the two socialclass variables, controlling for the other eightmicro characteristics (not shown).We found a significant negative relationshipbetween the measure of lower class and schedulecontrol; lower class workers reported lessschedule control than did workers from middleor upper classes, and the nonsignificant randomeffect suggests that the relationship is invariantacross the 19 countries. 4 Consistent with findingthat lower class workers have less schedulecontrol, the other class-related indicators (education,high earnings, and advancement opportunities)all have positive relationships toschedule control. The lower class measure isalso positively related to an hours deficit, indicatingthat lower class workers were more likelyto report this type of hours mismatch than weremiddle- or upper-class workers. Among theother class indicators, only high earnings has asignificant negative relationship to hours deficit.The lower social class measure is not related tohours excess, and two other class indicatorshave inconsistent relationships, with a positiverelationship for education and a negative relationshipfor advancement opportunities. Theupper-class measure is not significantly relatedto any of the three control measures, suggestingthat, after controlling for the eight individuallevelworker and job characteristics, controlover schedule and working time do not differbetween upper- and middle-class workers.Multilevel Analyses: Consequences ofWorker Control over Working TimeIn the next set of analyses, we test the consequencesof worker control by examining microlevelrelationships of each type of workercontrol to the three outcome variables, controllingfor the eight worker and job characteristics(see Table 5). We used HLM analyses to testthese relationships, and to determine whetherthe relationships hold across countries.Worker schedule control is positivelyrelated to both job satisfaction and organizationalcommitment. Moreover, the nonsignificantrandom effects for these two relationshipssuggest a lack of meaningful cross-nationalvariation. Schedule control is not significantlyrelated to strain-based WFC for thetotal sample but, as we will discuss later, therelationship is significant for female workers.Hours excess has significant relationshipsto all three outcomes, with a positive relationshipto strain-based WFC, and negative relationshipsto job satisfaction and organizationalDownloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

20 American Sociological Review XX(X)Table 5. Multilevel Analyses Predicting Strain-Based Work-Family Conflict, Job Satisfaction,and Organizational Commitment with Control over Schedule, Hours Excess, and HoursDeficitVariable (1) Strain-Based WFC (2) Job Satisfaction(3) OrganizationalCommitmentFixed EffectControl over Schedule (γ 90) –.032(.021).122 ***(.016).125 ***(.016)Hours Excess (γ 90) .233 ***(.019)–.284 ***(.030)–.156 ***(.025)Hours Deficit (γ 90) –.074 *(.031)–.028(.040).140 **(.037)Random EffectControl over Schedule (μ 9) .005 ** .003 .001Hours Excess (μ 9) .001 .009 * .007 **Hours Deficit (μ 9) .008 * .013 .017 **Note: Entries are unstandardized coefficients; standard errors are in parentheses. N = 21 countries.All three models control for individual-level effects of worker characteristics (gender, age, education,and partner) and job characteristics (full-time status, self-employed, high earnings, and advancementopportunities), but these coefficients are not shown.* p < .05; ** p < .01; *** p < .001 (two-tailed tests).commitment. The nonsignificant randomeffect suggests that the relationship to strainbasedWFC does not vary across countries.Hours deficit has a negative relationship withstrain-based WFC and a positive relationshipto organizational commitment, but is not significantlyrelated to job satisfaction.Tests of gender differences. To see ifthese relationships differ by gender, werepeated these analyses and included interactionsof gender with each type of workercontrol (not shown). Seven of the nine interactionsare statistically significant; weexamined separate models by gender to clarifythe findings. Of particular note are ourfindings that, for women, control over workschedule has a significant negative relationshipto strain-based WFC, but this is not thecase for men. Women with more control overtheir work schedules thus reported less WFC,whereas for men it made no difference. Wealso found that actual weekly work hourswere related to schedule control for men butnot for women (not shown); this raises thepossibility that schedule control is negativelyrelated to strain-based WFC for women butnot men because men with schedule controlare working longer hours, as has been seen inthe United States. 5 However, adding a controlfor weekly work hours does not change ourpattern of gender results; schedule controlstill has a significant negative relationship tostrain-based WFC for women, and a nonsignificantrelationship for men (not shown).Among the three types of control, onlyhours excess shows a consistent pattern of genderdifferences across all three outcome measures,with stronger relationships for womenthan for men. For women, hours excess has astronger positive relationship to strain-basedWFC and stronger negative relationships to jobsatisfaction and organizational commitmentthan is the case for men. The other significantgender interactions reflect small gender differencesin effect sizes of relationships, but thedirections of these relationships are consistentwith (i.e., in the same direction as) results basedon the total sample (see Table 5).Social class comparisons. We also conductedexploratory micro analyses thatDownloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

Lyness et al. 21included interactions of the two social classmeasures with each type of worker control tosee if social class moderates relationshipswith the three worker consequences (notshown). None of these interaction terms arestatistically significant, suggesting that therelationships of control to worker consequencesdo not differ according to workers’social class.DiscussionCountry-Level Influences and WorkerControl over Working TimeOur study considerably extends prior researchto document the importance of country contextin understanding workers’ control overwork schedules and hours, independent of themore often studied effects of individual characteristicsand job features. Our multilevel,multivariate analyses enable us to look insidethe black box of country to begin to understandwhich country-level features affect control.For example, Nordic countries stand outas having, on average, the highest per capitaGDP, public social expenditure, and women’slabor force participation rates, and a comparativelylarge service sector, a relatively highunion coverage rate, and relatively short standardwork weeks—and, in general, morefavorable outcomes with respect to workers’control over their working time. In contrast,Eastern European countries have quite differentmacroeconomic and labor market features(including comparatively long standard workweeks) and, overall, less favorable workercontrol outcomes.Two intriguing findings emerge withregard to country-level influences. Factorsthat we typically interpret as indicators ofaffluence and economic independence forworkers are associated, as we predicted, withgreater control over starting and stoppingtimes (i.e., schedule control). Paradoxically,these same factors are also associated with agreater likelihood of workers’ reporting anhours excess, which we, in common withother researchers, have argued is a form ofmismatch and signals workers’ having lowerlevels of control. Higher per capita GDP,higher rates of women’s labor force participation,and a larger service sector are all associatedwith more widespread preferences forfewer hours, as are higher public socialexpenditures and more generous annual leavepolicies. All of these features are indicators ofvarious types of economic security for workersand their families, through greater householdincome or more widespread publicbenefits and services that provide alternativesto earned income, thus making shorter workhours more economically feasible. Here ourfindings support Reynolds (2004:94), whonoted that generous social policy “softens theconsequences of cutting back on one’s workhours,” and Stier and Lewin-Epstein(2003:320) who concluded that “in countrieswith a high rate of decommodification, workingmen and women prefer to reduce theirhours of work.” With regard to schedule control,our results also support and extend thoseof Berg and colleagues (2004) by empiricallydocumenting unions’ significant role in helpingworkers gain control over their workschedules, although not, we find, in addressinghours mismatch.Although generous welfare regimes mayindeed soften the economic consequences ofcutting back on hours, our findings of a linkbetween excess hours and negative workerconsequences raise questions about the abilityof even generous welfare regimes to offset thetime bind of long-hour jobs, many of whichare in service, managerial, and professionaloccupations that make up a growing share ofemployment in the countries studied. Themicro-level association of more educatedworkers and features of good jobs (advancementopportunities) with schedule control andhours excess highlights the emergence of aglobal pattern whereby workers, especiallymore elite workers, appear to have gainedcontrol over when they work at the expense ofhow long they work. This has negative consequencesfor these workers in the form ofincreased strain-based work-family conflictand less job satisfaction, as well as for theirDownloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

22 American Sociological Review XX(X)employers in the form of less organizationalcommitment. This pattern of findings providesadditional support for what Schiemanand colleagues (2009:970) identify in theUnited States as the “stress of higher status”hypothesis, in which high status, long-hourjobs increase work-family border permeability,making it harder, not easier, for workers tomanage work and family. Our results suggestthis phenomenon is widespread throughoutupper-income industrialized countries.The effects of working time policies aresomewhat surprising but might help explainthe foregoing paradox. The indicator that weexpected would have the stronger effect, thelength of the standard work week, is not significantlyassociated with any of the threecontrol measures, including the two that concernlack of control as reflected in hoursmismatch. This unexpected finding is probablydue to the limited variation on this indicator;the observed values across these 21countries ranged only from 37 to 40 hours. Incontrast, the generosity of paid leave policy isassociated with more schedule control, morehours excess, and less hours deficit. Theeffects of paid leave policy could operate in anumber of ways. Although we see no directlink to daily starting and stopping times, havingmore paid days off might contribute to aperception of having more control, becausepaid leave days are largely discretionary(workers can choose when to take them). Theassociation between more generous leavepolicy (which reduces hours spent at theworkplace over the course of the year) andhours excess (i.e., a preference for fewerhours) may arise partly due to a “social multipliereffect” (Alesina, Glaeser, and Sacerdote2005:4), such that workers will wantmore leisure time when family, friends, andcolleagues have more leisure time or becausetime away from paid work is less stigmatized.Finally, more generous leave policies mightbe part of a larger package of more progressivegender policies, as in the Nordic countries.Although our analysis cannot identifythe specific policy mechanisms involved, theinvariant nature of the link between controland favorable employee/employer outcomesunderscores the benefits of policies, both publicand private, that foster greater employeecontrol.Gender and Control over WorkingTimeA key contribution of our research is theexamination of the consequences of workershaving control over their time, which previouscross-national studies have not investigated.Understanding these consequences gives usinsight into whether increased worker controllives up to the claims made for it. Our answeris a qualified yes. We found that across allcountries, control over one’s work schedule ispositively related to job satisfaction and organizationalcommitment for both women andmen, but it has a significant relationship tostrain-based WFC only for women, for whomgreater control results in reduced levels ofWFC. Although workers’ reported hours deficit(underwork) shows mixed outcomes, theirreported hours excess (an expression of overwork)is consistently linked to negative outcomes:lower job satisfaction, less organizationalcommitment, and more strain-based WFC.Importantly, we generally found across countriesthat hours excess (overwork) is linkedmore strongly for women than men to all threeof the micro-level consequences, suggestingthat lack of control over work hours hasgreater negative consequences for women,both in terms of the impact of work on theirlives outside of work and their attitudes abouttheir jobs and organizations.Our results, which are consistent with findingsof gender differences in WFC from limitedprior comparative research (e.g., Moenand Forest 1990; Ruppanner 2011), makeclear that gender plays a significant role inworkers’ capacity to gain control over workingtime in industrialized economies—and towomen’s disadvantage. In juggling work andfamily, not only do women have less controlthan men over their starting and stoppingtimes, they are more likely to report overwork,that is, they want to work fewer hours. We alsoDownloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

Lyness et al. 23find that working part-time, other things beingequal, is associated with a desire for morehours, suggesting that women, who are morelikely to work part-time, fall back on lessdesirable part-time jobs to gain flexibility andan hours fit consonant with their personal andfamily demands (Bardasi and Gornick 2008;Crompton 1997; Stone 2007).Men, in contrast, have greater flexibilitywith respect to their starting and stoppingtimes and a lower likelihood of wanting fewerhours, that is, they are less likely to be workinglonger hours than desired. Although menmay be increasingly using these advantagesto shoulder more family responsibilities, ourresults also show that they are more likelythan women to desire more hours than theyhave, presumably to fulfill the traditionalmale breadwinner role. Moreover, in countrieswith higher rates of female labor forceparticipation, workers (both women and men)desire fewer hours, suggesting that in thesecountries workers are trying to relieve thefamily time bind—and that men may beshouldering more family responsibilities(Jacobs and Gerson 2004). In the process,men may be changing cultural attitudes bymaking shorter work hours more sociallyacceptable in their countries, complementingthe social multiplier effect discussed earlier.We thus discern three striking gender gapswith regard to control over working time. Thefirst is a schedule control gap such that women,who presumably need it more, have less controlthan men over their work schedules. The secondis an hours preference gap, reflecting a neotraditionalhousehold division of labor, whichfinds women unable to realize their preferencefor fewer hours and men unable to realize theirpreference for more. These gaps, in turn, areassociated with, and help create a third gap, thatwe call a work and family well-being gap,whereby women are less likely than men toexperience the favorable outcomes—enhancedjob satisfaction and organizational commitmentand less strain-based WFC—associated with theability to control working time.Were women able to gain more schedulecontrol, our analysis suggests they wouldrealize attendant attitudinal and organizationalbenefits for themselves, their employers,and their families. Our results alsoindicate that public policies can moderatesome gender effects, and thus might hastenthe closing of some work-related gender gaps.In fact, after the time period covered in thisstudy, several of these countries enacted policiesthat explicitly grant workers more controlover their work schedules or work hours(Hegewisch and Gornick 2008). Assessingthe impact of these policies, including theireffects on gendered divisions of labor in paidand unpaid work, should be a priority infuture research.FundingThis research was supported by grants from the CUNYCollaborative Research Program and the Alfred P. SloanFoundation.AcknowledgmentsWe gratefully acknowledge the able research assistanceof Meg Lovejoy, Kelsey Schwarz, Mary Ignagni, andRebecca Ray. We also thank the ASR editors and ouranonymous reviewers for their insights and helpfulsuggestions.Notes1. We also tested the possibility that these relationshipsreflect effects of current work hours, with additionallevel-1 analyses (not shown) using weekly hoursworked in place of employment status (i.e., full-timeversus part-time). These two variables are related, r =.59, p < .001, but the work hours variable was availableonly for 20 countries (New Zealand wasmissing). Consistent with the positive relationship tofull-time work, we found a positive relationshipbetween weekly hours worked and hours excess, oddsratio = 1.05, indicating that holding other predictorsand the random effect constant, for each one-hourincrease in weekly work hours, the expected odds thata worker would report hours excess increases byabout 5 percent. Controlling for hours worked, relationshipsof other variables to hours excess are similarto those based on full-time work (see Table 3, Model1). We thus conducted the remaining analyses usingthe full-time employment variable based on 21 countriesand the larger sample.2. Based on the subset of respondents with data forweekly work hours, we also examined these relationshipsin a level-1 model that controlled for weeklyhours worked in place of full-time employmentDownloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

24 American Sociological Review XX(X)status. The micro predictors all have similar relationshipsto hours deficit (not shown) as in the model withfull-time employment. We conducted the remaininganalyses using the full-time employment status variableand data from 21 countries.3. Based on the 18 countries with data about children, weconducted additional analyses with parental statusadded to level-l models estimated separately by gender.We found that living with children is not significantlyrelated to any of the three types of control over workingtime for either women or men (not shown).4. Research in the United States reports that workers’access to workplace benefits and rights, includingflexible work arrangements, is positively correlatedwith social class (see, e.g., Heymann 2001; Heymannet al. 2002; Williams and Boushey 2010). Our findingsare largely consistent with that claim, especiallywhen we assess the impact of being less than middleclass, according to self-report.5. 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Her research focuses on women in managementand leadership roles, work-family interface,organizational careers, cross-cultural comparisons, racial/ethnic groups, and other issues related to workforcediversity. Her research has been published in Journal ofApplied Psychology, Academy of Management Journal,Human Relations, Organizational Research Methods,and Journal of Vocational Behavior.Janet C. Gornick is Professor of Political Science andSociology at the CUNY Graduate Center, and Director ofLIS, a cross-national data archive in Luxembourg. Mostof her research is comparative, and concerns the impactof social policies on family well-being and genderDownloaded from asr.sagepub.com at Mina Rees Library/CUNY Graduate Center on November 5, 2012

13. Plemomorphic Adenoma14. Teratoma15. Osteo Sarcoma16. Osteo Clastoma17. Chondroma18. Melanoma19. C.M.L20. C.L.L21. Iron Deficiency Anemia22. Filarial Lymph Adenitis23. Red-Hepatisation – Lung24. Gray – Hepatisation – Lung25. C.V.C. Lung26. Ameloblastoma (Adamantinoma)27. Fibro Adenoma28. Rhinosporidiosis29. Capillary Angioma – Skin30. Cavernous Angioma31. Colloid Goitre32. Secondary Deposits L.N33. Lipoma34. Toxic Goitre35. Thrombus2. SPECIMENS:1. Fatty Liver2. Fibroid Uterus3. Pyelo Neophritis4. Squamous cell Carcinoma (Maxilla)5. Ductal Carcinoma Breast6. Chondroma (Hand)7. Lymphoma (Lymphnode)8. Carcinoma Thyroid9. Ovarian CystI. CLINICAL PATHOLOGY INSTRUMENTS:1. RBC, WBC, Hemoglobin Pipette2. Wintrobe Tube (PVC and ESR tube)3. Westergren tube (E.S.R. Tube)4. Urinometer5. New Baur ChamberII.URINE EXAMINATION:1. Test for Protein (Heat coagulation Test)2. Test for Sugar (Benedicts Test)Page 27 of 74