The impacts of households characteristics on the occupation opportunities of women the case study in vietnam

Problemstatement

Int h e past,Vietnamesefamilysizea p p e a r e d t o bem u c h biggert h a n t h a t i n t h e p r e s e n t Duringthedecadesof1980s,1990s,onecouldeasilyfindafamilywith8-

In modern Vietnamese society, traditional extended families have largely been replaced by nuclear families, with households typically consisting of four to six members Women, who were once primarily homemakers, have increasingly taken on roles as workers, officials, teachers, engineers, and leaders Despite this progress, labor force participation rates reveal a gender gap, with men participating at 82.5% compared to women's 73.3%, according to a 2014 investigation by the Vietnam General Statistics Office Additionally, the unemployment rate for young women is higher at 7.15% compared to 5.51% for young men, highlighting the need for greater attention to women's opportunities in the labor market from government and policymakers.

A study by Rahmah Ismail and Noorasiah Sulaiman (2014) revealed that while the participation of women in various economic sectors in Malaysia has increased over the years, their overall participation rate remains low Similarly, Julliffe (2004) noted that female careers constitute less than 50% of total careers in Sudan Factors such as the age of married women and the number of children significantly influence the supply of female labor Understanding the determinants of female labor supply is crucial, as these insights are essential for effective policy design and maximizing the benefits for women in the workforce.

Research by Francois (1998), Basu (2006), and Atal (2010) has explored the relationship between female labor supply and household decision-making processes They found that a woman's participation in the labor force is influenced by household dynamics and the endogenous distribution of power among members The occupational opportunities available to women significantly impact their income contributions, which in turn affects their power within the household As women gain access to better job opportunities, their influence increases, suggesting that decisions regarding whether to work outside the home or remain a housewife are driven by the collective utility of the household rather than solely by economic incentives like market wages Additionally, female participation in the labor market is contingent on various factors.

Researchobjectives

Thea i m oft h i s r e s e a r c h i s t o e x a m i n e t h e i m p a c t s ofhouseholdc h a r a c t e r i s ti c s , e s p e c i a l l y householdsizeonmarriedwomen’soccupationopportuni tiesinVietnam.ModelofProbit,O L S a n d Tobita r e usedt o a n a l y z e t h e decisiont o p a r ti c i p a t e i n w o r k f o r c e andthenumberofworkinghoursofwomen.

Householdsizeisdefinedasthenumberofmembersinthehouseholdinourstudy.B eside,theothercharacteristics ofthehouseholdandwomanarealsoa n a l y z e d , in cluding:household’sownershipasset,household’stotalincome,numberofc h i l d r e n i n household,householdpovertystatus,householdlocalitystatusorwoman’sm a r r i a g e s t a t u s , w o man’se d u c a ti o n , w o m a n ’ s a g e , h e r h u s b a n d ’ s w a g e , h e r husban d’seducation,herhusband’semploymentstatusandothervariables.

Scope ofthestudy

Weattempttoexaminetheemploymentoffemaleandmarriedwomenin63provincesinVietna mandbaseonthedatasettakenfromVietnamHouseholdLivingS t a n d a r d Survey(VHLSS) in2014.Subjectsofthestudyincludewomenfrom15to55y e a r s ofage.

Researchstructure

Theproceedsofthisstudyasfollows:Chapteroneistheintroduction Chaptertwop r e s e n t s theliterature reviewand empirical theories.Chapterthreeintroduces t hec o l l e c t e d d a t a s e t a n d t h e m e t h o d o l o g y w e usedt o a n a l y z e t h e d a t a i n t h i s study.Chapterfourpre s e nt s theestimationre s ult s and discussionsparts.Theconcl usion,p o l i c y implicationandlimitationarepresentedinthesectionfive.

Literaturereviewoflaboursupplymodels

The U.S Bureau of Labor Statistics (BLS) defines the labor force as all individuals classified as employed or unemployed Employed individuals are those aged 16 and older who work for an employer or are self-employed, excluding volunteers and those engaged in self-service or homemaking Unemployed individuals are those aged 16 and older who are jobless, available for work in the last four weeks, and have actively sought employment during that time While this definition provides a clear picture of the total labor force in an economy, it does not account for individuals who participate in the labor force but do not work during the surveyed period due to specific characteristics such as being students, retirees, disabled, or voluntarily idle, and it does not impose an upper age limit.

Accordingtothedefinition ofmacroeconomists,thelaborforceisdefinedast h e p eoplew h o a r e employeda n d u n e m p l o y e d c a l l e d w o r k i n g - a g e p e r s o n s Thew o r k i n g - a g e personsaredefinedaspeoplebetweentheagesof16-

The definition of the labor force excludes students, retirees, disabled individuals, houseworkers, and the voluntarily idle This limitation provides a clear ceiling age for categorization While it distinguishes between employed and unemployed individuals, it does not account for self-service workers or homemakers However, it is important to recognize that students, retirees, disabled individuals, and homemakers actively participate in economic activities, contributing to the production of goods, materials, and services, and earning income from their efforts.

The definitions of labor categories in Vietnam present certain challenges, particularly in assessing the roles of houseworkers, homemakers, and the part-time or full-time jobs held by students, retirees, and individuals with disabilities According to Vietnam's Labor Law No 10/2012/QH13, the working age is defined as individuals aged 15 and older, with specific age limits of 15-60 for men and 15-55 for women This study focuses on female and married participants in the Vietnamese labor market within the age range of 15 to 55 years.

Therearethreelaboursupply theoreticalmodels inthetraditionalmicroeco nomic.Theyareindividuallaboursupplymodel;householdmodelincludedt h e unitaryh ouseholdlabourmodelandcollectivehouseholdlabourmodel.

Theindividual laboursupplymodel is abasicmodelfollowsthe neoclassicaltheory.Iti s knownasthebasicmodelofatrade- off ofaconsumerbetweenconsumingmoreg o o d s orm o r e l e i s u r e I n 1 9 2 0 s , Ma rshallw r o t e t h a t , m o r e w o r k i n g hoursm e a n e a r n i n g moreincomeislimite dbytime spendingforleisureandreversewhichhadfoundi n hisresearch(Marshall,1

The individual labor supply decision is analyzed theoretically, assuming that each unit of labor maximizes utility through income and entertainment Work generates income, which consumers then use to purchase goods and services that enhance their utility Entertainment is viewed as a type of good, and consumption of it provides additional utility This theory highlights the trade-off between limited time, where more working hours lead to increased income but reduced leisure time These concepts are clearly articulated in the studies of Mincer (1962) and Becker (1965).

Theindividuallaboursupplym o d e l absolutest h e individuallabourdecisiont h a t h a v e noa n y r e l a ti v e t o otherm e m b e r s i n his/ herfamilyorhousehold.Thep a r ti c i p a ti o n decisionoft h i s i n d i v i d u a l i n t h e labo urm a r k e t i s a uniquea l t h o u g h ignoretheimpactofhouseholdfactors.Inthefact,the participationofamemberin thefamilycouldbeimpactedbyanyhis/ herrelativeintheirfamily.Thatisthereasonw h y theindividuallaboursupplymodelwasnota pproachedwidelynow.

The individual model of labor supply overlooks critical factors such as household production and family income, which can significantly influence labor supply decisions Participation in the labor market is not solely determined by an individual's income and available time; it also depends on variables like wages, the number of family members, shared household working hours, and the income of other family members, as well as fluctuations in the prices of goods and leisure Consequently, household models appear more suitable than individual models for understanding labor supply However, both models have inherent limitations; the individual model fails to account for the dynamics within households, while the household model neglects the unique characteristics of individual members.

Family’scharacteristicsandstructureshaveconsiderableinfluenceonthebehavi ourofitsmembers.Hence,theycandirectlyandindirectlyinfluenceoni n d i v i d u a l ’ s c h o i c e s Thestudieso f f a m i l y c h o i c e s h a v e b e e n i m p r o v e d a l o n g twodiffe rentlines.Thefirst oneistherulethatasinglefactorcanbebalancedwithitsfamilyth roughtheirutilityfunction.Itcalledunitaryhouseholdlaboursupplymodel.Thesecondone k n o w n t h a tt h e c o l l e c ti v e l y householdlabours u p p l y m o d e l t h a t m a k e s choiceswhichbasedonsomethingindividualdoes.Bythisway, thechoicesofe a c h memberisnotbeenlarged(orconstrained)byfamilialframeworkon ly,itmaybealsoinfluencedbytheothers.

Fortin and Lacroix (1997) demonstrated that within the unitary household labor supply model, households make labor market participation decisions to maximize their utility under budget constraints An individual's labor choices are influenced not only by personal factors but also by the characteristics of family members, such as household size, total income, and number of children Additionally, external societal factors, including unemployment rates and rural or urban conditions, play a significant role Over time, various household models have been developed to analyze household behavior from both general and specific perspectives However, substantial evidence from research by Blundell and Meghir (1986), Blundell and Walker (1986), and Lundberg (1988) has led to the rejection of the unitary model in favor of alternative frameworks.

Althoughtheunitarymodelwasrejected,itslimitationsalsoarethepremisesofthefurth erm o d e l d e v e l o p m e n t a ft e r t h a t T h e studiesofAppsa n d R e e s ( 1 9 8 8 ) , Chiappor i( 1 9 9 2 ) , Chiappori( 2 0 0 2 ) provedt h a t t h e r e i s a betterm o d e l w h i c h c a n c o n s i d e r individual’spreferencesandbargaintoParetoefficientoutcomeofallhousehold’ smembersinafamily.Thatisthecollectivehouseholdlabourmodel.Theh o u s e h o l d ’ s l abourmarketparticipationdecisionhastwostagesunderthecollectivelaboursupplymo del,thefirststageisthatno n- labour’sincomeisbalancedamongh o u s e h o l d ’ s membersbysharingprinciple,an dthesecondoneisthateachmemberofthehouseholdmakeshisorherownlaboursupply decisioninthewaycanm a x i m i z e hisorherownutilityunderthematchbudgetconstraint. FortinandLacroix( 1 9 9 7 ) , Blundellet.al.

(2007),Bloemen(2004)havealsoconcludedthatthecollectivelaboursupplymodelismorea dvantagethanunitaryhouseholdlabourmodelsthrought h e householdlaboursupplyd e c i s i o n behaviours.B y a d d i n g t h e i n c o m e sharingfactor,FortinandLacroix(19 97)testedanddevelopedthecollective modelont h e basicofu n i t a r y m o d e l Th estudyofChiapporie t a l

( 2 0 0 2 )i n h e r i t e d t h e p r e v i o u s resultsandimprovedthecollective modelwith workingspousesaswellas otherdistributionfactors.F u r t h e r , Donni( 2 0 0 3 ) t o o k t h e n o n - p a r ti c i p a ti o n oft h i s a s p e c t intothehouseholdmembers’unrestrictedworkinghourd ecision.

Reviewofempiricalstudies

Thissectionreviewpreviousempiricalstudiesonthedecisiontoparticipate inlaborforc eandlaborsupply.Almostallstudiesabouttheparticipation inlaborforeapplyt h e Logit /

Probitmodel,whilethoseaboutlaborsupplyusuallyapplyTobitmodelort h e Heckm anselection modelwhichanalyzebothdecisionontheparticipation andlaborsuppl ysimultaneously.Inthelabourmarket,thewomenlaboursupplyisrisingy e a r l y allover theworld.Specially,thelabourforceactivitiesofwomenarehigherind e v e l o p i n g coun triesnotonlyinnumberbutalsoinkindsofwork.Therearemajorofr e se a r c h e s intheyieldof womenlabourmarketparticipationwerestudiedbylotsofa u t h o r s intheapplicationscien ce.

The study by Khan and Khan (2009) reveals that women's participation in the labor market is significantly influenced by various factors, accounting for 75% of the variation in labor force participation Their findings indicate a positive correlation between a mother's age and the likelihood of labor force participation among married women, although the effect diminishes with age, peaking at 39.49 years The average age of participants in their study was 41.61 years, consistent with national statistics showing that women aged 40 to 45 have the highest labor force participation rates in Pakistan (Pakistan Bureau of Statistics, 2003).

This research focuses on married women's labor force participation (MLFP), a binary dependent variable where a value of 1 indicates participation in the labor market and 0 indicates non-participation The authors employ a Probit model to estimate the non-linear maximum likelihood of MLFP The study identifies a limited number of explanatory variables within the MLFP function, defining labor force participation as any engagement in paid employment, self-employment, or family enterprise for at least one hour during the research period, typically one week.

Theequationis:�=�(�) where,Yisthedecisionofmarriedwomentoparticipatelaborforce.Xisavectorof exogenoussocio- economicvariablesinfluencingthefemale’sdecisionoflaborforcep a r ti c i p a ti o n (alsocalledindependentvariablesorexplanationvariables).Theauthorsstudythedecision participation ofmarried womenattheageof16-

60yearsoldinlaborforcea c ti v i ti e s T h e r e a r e 3 , 9 1 1 observationsi n t h i s researcht h a t studiedi n twodistrictsofPunjabp r o v i n c e i n P a k i s t a n throught h e surveyw a s c o n d u c t e d i n 2 0 0 4 -

2 0 0 5 T h e y v a l u a t e d t h a t onew o m a n i f sheh a s m o r e oney e a r ofe d u c a ti o n th eirlabourforceparticipationmayincreaseby7.9%.Theresultsalsoshowthattheprobabilit yoflabourforceparticipationofmothersasheadofhouseholdsis3.7%.

D.BlauandM.Kahn(2006)increasedthesamplesize.Inaddition,theeffectofselected endpointsw as minim ized byt he i r m o d e l They ai me d t o m a ke cle arl y t he a r g u m e n t s oft h e c h a n g i n g i n laboursupply.T h e y usedt h r e e s e t s oft h r e e y e a r groups:t h e y a r e i n c l u d i n g 1 9 7 9 - 1981( c a l l “ 1 9 8 0 ” g r o u p ) , 1 9 8 9 -

2001( c a l l “ 2 0 0 0 ” g r o u p ) t o f o c u s o n f a c t o r s t h a t e ff e c t o n t h e pa r ti c i p a ti o n decisionofm a r r i e d women(int h e a g e groupof2 5 -

5 4 y e a r s old)i n labourmarket.TheMarchCurrentPopulationSurvey(CPS)datausedto observethelaboursupplyb e h a v i o u r ofUnitedS t a t e s m a r r i e d womenofa b o v e t h r e e groups.T h e r e are64,001observationsin“1980”group,58,987observationsin“1990”groupa n d 48,733observationsin“2000”group.

Theauthorsusedcross- sectionaldataonindividualstoestimatefunctionssuchasthestaticlaboursupplymodels.Firs tly,t h e a u t h o r s c o u n t e d t h e c h a n g e s ofm a r r i e d womeni nt h e labors u p p l y market f ro m 1980to2 0 0 0 Th e y t re atthe m o d e l s asa

The study employs both linear and Tobit models to assess the robustness of results, utilizing subgroup disaggregation based on education, mothers of young children, and age groups Additionally, the authors adjusted the extensive versus intensive margins and identified key explanatory variables through robustness testing They re-evaluated the models after accounting for tax incomes, wages, and salaries, while also modifying the selectivity of married couples to address omitted variable bias Grouped data was utilized to resolve these issues, with results presented through difference reports for three distinct periods, highlighting elasticity trends Notably, the elasticity of women's wages declined, and the long-term trend of spouse wage elasticity also showed a continuous decrease from 1980 onward.

2000,itisreally highestdecreasinginthe1980s.Theresultsoft his studyshowedt hatthewives’laborsupplywasimpactednegativelybytheeffectsofh u sb a n d s ’ wage andtheabsolutevalueofthesesignificantnegative effectsbecomeweakerbytheti me.Atthemeanpartoflaboursupply,theabsolutevalueaswellastheelasticityofthehu sbands’wageswasdeclinedconsiderablycomparetotherawhourimpacts.

The study by Ismail and Sulaiman (2014) evaluates the factors influencing the labor supply participation decisions of married women in Malaysia Utilizing a dummy variable to represent women's employment status, where 1 indicates employment and 0 indicates unemployment, the authors employed logistic regression to analyze the categorical dependent variable The research involved a field survey conducted in 2011, which randomly selected 4,000 households in Peninsular Malaysia Data collected included information about family heads, spouses, household heads, education, and occupation The study specifically focused on the participation decisions of 3,520 married women, aiming to understand the utility maximization in their employment choices.

Theestimationoft h e l o g i s ti c m o d e l u ti l i z e s t h e likelihoodr a ti o t e s t ( L R T ) a s a n i n d i c a t o r forfitnessofthemodelandthet- testforidentifyingthesignificantofthep a r a m e t e r s Theestimationmodelforthisstu dyisasfollows:

=β 0 +β 1 HW i +β 2 FW i +β 3 FEDU i +β 4 FAGE i +β 5 NLY i +β 6 NUMC i +β 7 G L O B i

The authors employed a logistic binomial form to investigate the probability of job changes among workers They utilized a logarithmic model to develop an equation model and conducted a likelihood ratio test (LRT) to assess the model's fitness and identify significant parameters The findings revealed that educational attainment, age of women, and number of children are key determinants of married women's labor supply, while husbands' and own wages were found to be insignificant Years of schooling positively influenced the labor supply of married women, whereas age had a negative effect The study highlighted the importance of education, showing that women with secondary and tertiary education are more likely to participate in the labor market compared to those with only primary education or no schooling Women's workforce participation is particularly beneficial for those with higher education levels.

EconomicPanel(SOEP)toconcentrate t h e u n d e r c u r r e n t t r e n d s t h a t t h e r e a r e somew o m e n groupss h o w i n g t h e i r subgroupa n d heterogeneousd e v e l o p m e n t

12 i n these.T h e y usedt h e r e p r e s e n t a ti v e sampleas theindividualhouseholdand pri vatewhoaretakenfromF e d e r a l RepublicofG e r m a n y Thea u t h o r s uset h e c o l l e c ti v e t h e o r y t o studyt h e i r r e s e a r c h Theydefinedthreedifferentcategories:fu ll-time,part-time andnon- employmentinthe20yearsperiodoftime from1984-

2004.Theycalculatetherealr a t e ofwagehoursofanindividualthroughthisannualcros s- sectionaldatasetasther a t e ofg r o s s m o n t h l y labouri n c o m e a n d t h e w o r k i n g - h o u r s oft h i s p e r s o n i n t h e r e sea r ch ed m o n t h s T h i s r a ti o c a l c u l a t e d byusin gt h e w o r k e d - h o u r s t h a t signedi n t h e i r contracts.Theseresultsarenotincludingtherealworking- hoursandothersalsoa r e notpresentedinthecontracts.ThepriceindexcomesfromGerma nSocio-

EconomicPanel(SOEP)astherealworkinghourswasusedtointerprettheresultsofwager ateintherealterms(20000).Theyfoundthatthegrossincomeshasbeendecreasedu ndertheeffectsofseveralindividualcharacteristicsassex,age,yearsofeducation,marita lstatus,nationalityaftertheregressionsofthelogonthreedummyv a r i a b l e s asfull- time employment,occupation andindustry.Thesamplesweredividedintotwogrou psas

WesternareofGermanyandEasternareofGermany.Theresultsw e r e explainedt h a tt h e r a ti o o c c u p a ti o n employmentofG e r m a n y m a r r i e d womennonstoprisinganddistin guishingwithhigh-middle- loweducationlevelsorift h e y haveyoungchildrenornot,thisemploymentrateevenwash ighestwithwomenw h o h a v e k i d s orw h o h a v e t h e highe d u c a ti o n l e v e l I n contras t,t h e womenw h o h a v e toworkmorehoursweeklytheirhusbandshavelesswagerat e.Theyalsofindoutthatthewomenwhohavehigher- wagehusbandscouldparticipateinthelabourm a r k e t asthesimilarastheotherwome n.Theyevencouldemploymorepart- timehoursoremployfewerweeklyhoursinthelabourmarketperweekthanwomenwhoh a v e lower-wagehusbands.

Berulavaa and Chikava (1983) utilized a collective framework to analyze individual behavior in household labor supply across various labor market contexts Their research, conducted in Georgian, French, and Romanian regions, relied on data from the Generations and Gender Survey for the years 2002 and 2005 They applied both unitary and collective household labor supply models to test the parametric restrictions, alongside conducting comparative research to identify key determinants of household labor supply Notably, household preferences were characterized by a unique, well-behaved utility function (Fortin and Lacroix, 1997) While estimating labor supply equations, the authors employed methods from Chiappori, Fortin, and Lacroix (2002) and the semi-log function form, but did not apply the collective model as previously established They established a set of restrictions from various functions to assess the validity of unitary and collective labor supply models.

Fortinand Lacroix(2002)usedin theirstudy.Beside,theauthorsalsoexaminetheunitarymodelaccordingthroughtesting t h e null hypothesisonnon- existence ofdistributionfactors(DFI ) Tote s tthe r e l e v a n c e oft h e collectivem o d e l t h e y m e a s u r e t h e v a l i d i t y twoe l e m e n t s a s t h e v a li d i t y ofcross- derivativeconditionandthevalidityofParetoaffection.

The study reveals that the primary determinants of household labor supply across the researched regions—France, Georgia, and Romania—include the wages of women and their spouses, cross-wage terms, and distribution factors In France, a significant negative impact on labor supply hours was observed at the 1% level, while in Georgia and Romania, women's wages positively influenced labor supply hours, showing statistical significance The findings indicate notable differences in spouse income levels, with Georgia exhibiting a more substantial prevalence of lower-income couples compared to France and Romania Additionally, Georgia displayed the widest gender gap in labor supply among the studied regions The authors emphasize that these factors cannot fully address the differences in preference structures across countries Results aligned with the collective model in France and Romania generally showed positive outcomes, despite restrictions from Pareto efficiency and cross-term conditions Conversely, the Georgian data largely rejected these model restrictions The unitary model, which disregards distribution factors, was successfully applied across all three countries, highlighting its value in analyzing household labor supply behaviors in transitional economies, particularly in developed contexts, and suggesting similar positive impacts as observed in previous studies.

InotherstudyofDostiea nd K r o m an n( 2 0 1 2 ) ,t he e l as tic i t y oflaboursupplythr ought h e o w n - w a g e , c o u p l e - w a g e , non- labouri n c o m e w e r e m e a s u r e d byusingt h e d a t a ofm a r r i e d womenw h o l i v e i n

SLID).ThedataofCanadianTaxandCreditSimulator( C t a C S ) a n d a l l i n c o m e l e v e l s ofhouseholdalsoc a l c u l a t e d i n thisr e s e a r c h Underinvestigatingattheleveloffed eralalsoatthelevelofprovince,working- hoursthatg a v e householdb a c k t h e r e a l e a r n i n g s during1 0 y e a r s (from1 9 9 6 t o 2005)h a v e b e e n taxedatthefourthlevelinsteadofthethirdlevelin2001bytheirgover nment,risingonelevelinthefact.Itisalsoakinin severalrecentlystudiesasthevariableCPSusedtoevaluatetheelasticityoflabour supply(Heim,2007;BlauandKahn,2007).Toe v a l u a t e theelasticityofthelaboursupplythe authorsusedtheeconometricmethodw i t h fourimportantstepsaliketoHeim(2007).

Firstly,theHeckman’stwostepproducewasappliedtoinvestigatetheinverseMills’sr a ti o (λit).Thechosenequation wasestimated bytheirProbitmodeltocalculateatt h e ti mecalledtforpermembercalledi(means- it )inthelaboursupplymarket: λ̂it Φ(δ ̂0 ϕ(δ̂

The study examines total non-labour income per family and the various factors influencing an individual's opportunity to participate in the labour force It employs the inverse Mills’s ratio to adjust for endogenous participation in the labour market Non-observed wages are imputed using traditional regression methods, incorporating individual characteristics and human capital Additionally, the analysis evaluates working hours, which are always positive, alongside other variables such as non-labour income and the inverse Mills’s ratio to explain the number of hours worked Finally, the participation of individuals in the labour supply market is analyzed using the Probit model, focusing on the wages of women in the labour force and those who are unable to participate but could potentially receive wages, as outlined in the relevant equation.

+δ 1 lnŵ it ++δ 2 lnŵ it +δ 3 I it +δX it )(3.4) Inabovetheequation,thewagesofwife, husbandandotherincomewerecalculated byt h e p a r ti c i p a ti o n ofp e r m e m b e r i n theirf a m i l y T h e h u s b a n d ’ s c o n s u m p ti o n behaviourisdifferencefromthewife’sconsumptionb ehaviourbydifferenceincomesourcesi n t h e similarfamily.I n t h e periodofti m e s a s 1 0 y e a r s (from1996-

In 2005, researchers recognized that the elasticity of labor supply in Canada significantly declined due to the government's income tax policy, resulting in an elasticity approaching zero Evidence suggests that the elasticity of the Canadian labor force is lower than that of the United States, indicating that changes in Canada are too minimal to measure effectively Their analysis revealed that fewer working hours among women correspond to a larger elasticity of female participation in the labor force They identified three hours as a critical threshold; if women work more than this, their elasticity diminishes to nearly zero When taxes are considered, women with incomes below this threshold also exhibit zero elasticity in their working hours The study concluded that the impacts of public policies on labor supply in Canada have weakened over the past decade Furthermore, women with fewer working hours tend to have a greater influence on their spouses' wages or their own Consequently, if the government aims to increase working hours for women in the labor force, it should address the concerns of those currently working low hours.

SmithandStelcner(1988)usedreleaseddatafromthe1981Censustore- testth e behaviourofmarriedwomeninlabourforceinCanada.TheauthorsusedH e c k m a n ' s proceduretoensureforselectivity biasandincorporating theinfluence ofi n c o m e t a x e s , t h i s s t u d y findss m a l l w a g e a n d i n c o m e e ff e c t s onC a n a d i a n wives'laboursupply.Dataa r e c o m e fromt he StatisticsCanada (1 9 8 5 ) showedt h e PublicUseSampleTape,Household/

The study analyzes data from the 1981 Census, focusing on a sample of 2,851 married couples with wives aged between twenty and fifty-four It provides insights into labor earnings and actual workweeks in 1980, although data on hours worked is limited to the week ending June 3, 1981 The hourly wage rate is calculated by dividing annual earnings from 1980 by the product of weeks worked that year and hours worked in the reference week While the division of annual working hours was not examined, the authors report small labor supply wage elasticity estimates that are not significantly different from zero Using Heckman's technique, the study addresses bias in selectivity and identifies factors influencing labor supply in relation to income taxes.

Researchmethodology

Ourstudyf o l l o w s t h e p a p e r ofK h a n a n d K h a n ( 2 0 0 9 ) i n w h i c h t h e a u t h o r s h a v e e v al uat e d thedecisionoflaborsupplyparticipationofmarriedwomenforthenorm alprobabilityusingProbitm o d e l O n e ofourtwod e p e n d e n t v a r i a b l e s i s c a l l e d a s women’sl a b o r f o r c e p a r ti c i p a ti o n i s s h o w e d b y a f u n c ti o n o f P r F ( x ) = WEs.I t r e c e i v e s v a l u e 1ifawomanparticipatedinthelaborforceandreceivesvalue0ifshedidnot.

Thefunctionis:Pr(Participation=WEs=Y=1)=F(x) where,Participation=Y=WEsisthedecisionofwomenlaborforceparticipation; x1………xna r et h e e x o g e n o u s s o c i o - economicv a r i a b l e s i n fl u e n c i n g t h e f e m a l e ’ s decisionoflaborforceparticipation.

Moreover,w e useT o b i t m o d e l t o e v a l u a t e t h e factorsw h i c h m a y i m p a c t ont h e n u m b e r ofworkinghoursofawoman(WORKHOURS).Theseconddependencev a r i a b l e presentstheworkingtimeofawomaninamonthattheinvestigatedperiod.Theunitofw orkingtime iscalculatedbyherrealworkinghourslimitedfrom0-

55yearsoldinlaborforceactivities.Thereare9,171observationsinthisstudythatis collectedfrom63provincesofVietnaminVHLSSin2014.Itiscalledasthefullsampleoffemale group.Werunthetworegressionspresentedabovewiththisfullsamplew h i c h h a s nineteeni n d e p e n d e n t variables.T h e y a r e i n v o l v e d byhouseholdc h a r a c t e r i s ti c s ( t h e fi r s t c h a r a c t e r i s ti c g r o u p ) a n d i n d i v i d u a l ’ s c h a r a c t e r i s ti c s o f a woman(thesecondcharac teristicgroup)

(presentingindetailintable1).TheProbitm o d e l isusedtorunthisfullsample(femaleg roup)withdependentvariableisWEsw i t h 9 , 1 7 1 observations.I n t h e Tobitm o d e l , w e r u n w i t h d e p e n d e n t v a r i a b l e s i s WORKHOURSwith9,161observationsonly becausethereare100observationsinthefemalegroupwhichhavenotenoughinformationtor unningthismodel.

Inaddition,w e alsor u n t h e twom o d e l s a b o v e w i t h s u b - s a m p l e ofm a r r i e d womengroup.Themarriedwomensamplewith4,077individualswh oaremarriedatt h e investigatedperiodcollectedfromthefullone(socallmarriedwo mengrouporsub- sample).Besidet h e similareightteeni n d e p e n d e n t v a r i a b l e s a s t h e y a r e i n fullsam ple(notincludingmarriagestatusvariable),weaddhusband’sfactorsasthethirdc h a r a c t e

Data

DataSource

55years)top a r ti c i p a t e i n laborforcea c ti v i ti e s a n d t h e factorst h a t i m p a c t on t h e decisionofwomen(alsointheagegroupof15-

55years)toparticipate inlaborforceactivities T h e womenareunderstoodasthemar riedwomen(inthesubsample)orthefemale(inthefullsample)aswell.Theresearchstud yin63provincesandcitiesinVietnamthrought h e d a t a ofVietnamHouseholdLivingS t a n d a r d s S u r v e y ( V H L S S ) w a s c o n d u c t e d in2014.Inaddition,wealsoinvestigatet heimpactsofhouseholdc h a r a c t e r i s ti c s onthenumberofworkinghoursofwome n.

From 2002 to 2014, the General Statistics Office of Vietnam (GSO) conducted the Vietnam Household Living Standards Survey (VHLSS) every two years to systematically monitor the living standards of the Vietnamese population This survey aimed to evaluate the implementation of the Comprehensive Poverty Reduction and Growth Strategy and assess the achievement of the Millennium Development Goals (MDGs) and Vietnam's socio-economic development objectives Utilizing direct interviews, investigators visited households to gather information from household heads and members through structured questionnaires The survey team leader also interviewed local commune leaders and officials to record relevant data To ensure the quality of the collected information, the survey strictly avoided indirect methods or reliance on information from other sources during the interviews.

The VHLSS dataset provides comprehensive household-level survey information, capturing key demographic characteristics of household members such as age, gender, ethnicity, and marital status It details household income, including various sources like wages, self-employment in agriculture, forestry, fisheries, and other sectors The dataset also outlines household expenditures categorized by purpose, including essential expenses for food, clothing, housing, travel, education, healthcare, and cultural activities Additionally, it collects data on education levels, professional qualifications, health issues, employment status, working hours, property ownership, and amenities like electricity and water Participation in poverty reduction programs, credit situations, and aspects of operational and risk management are also included The survey encompasses households across 63 provinces and central cities, ensuring a broad representation of the population.

20 upofhouseholds,householdmembers,andcommunes/ wards.Eachsurveyunitisaselectedhouseholdorward 1

Variablesdescription

Ourstudyincludestwodependentvariablesaswomenemploymentstatus(WEs)andwomen workinghours(WORKHOURS)

The employment status of women is categorized as a binomial variable, where a value of 1 indicates participation in the labor force and a value of 0 indicates non-participation Labor force participation is defined as activities performed for at least one hour during the reference period (one week), including paid employment, self-employment, or work in a family enterprise In Vietnam, the employment status of women is recorded as either one or zero only for the investigated period, based on statistics from the General Statistics Office (GSO) in 2014 The average monthly working hours for women are calculated by multiplying the average daily working hours by the number of actual working days over the past 30 days, with total working hours ranging from 0 to 480 hours per month.

Therearetwentyfiveindependentvariablesinourstudytheyaredividedintot h r e e groups:thefirstgroupishousehold’scharacteristicsgroupincludinghouseholdsize- hhsize,household’s,household’spovertystatus- poverty,schoolingmembersoft h e household- attendsch,n u m b e r ofc h i l d r e n i n t h e household– c h i l d r e n 1 5 , n u m b e r ofoldpeopleinthehousehold– oldpp60,totalincomesofthehousehold- t o t a l i n c , a v e r a g e p e r c a p i t a m o n t h l y i n c o m e – a v e r a g e c a p i n c , n u m b e r ofseverelyillnessorinjuredmembersinthehousehold– illness,household’sownershipassets

The VHLSS employs a two-level sampling method, selecting multiple households for various surveys or survey years in rotation panels The first level consists of communes, while the second level includes three enumeration areas (EAs) per commune Communes are stratified by province and categorized as rural or urban Both commune and EA selections are conducted using Probability Proportional to Size (PPS) sampling techniques.

20 le ,the sizeofeachcommuneoreachEAwasbasedonthenumberofhouseholdsbasedonthe results oftheCensusIn1999o r 2009,surveyedhouseholdsineachEAinthesurveywereselectedbasedonEA'slatestlistofho useholds(3m o n t h s priortothesurvey).TheVHLSSsurveyserieswereconductedfrom2002 to2010basedonasample oft h e sampleselection.Thebasics a m p l e i s a r a n d o m s a m p l e fromt h e enumerateda r e a s (EAs)o f t h e 1 9 9

9 P o p u l a ti o n Census.Similarly,t h e P o p u l a ti o n Censuso f 2 0 0 9 willp r o v i d e i n f o r m a ti o n for t h e d e s i g n a n d implementationofnewmodelsfortheyearsafter2012onwards(GSO).

The article discusses dependent variables related to assets, focusing on individual characteristics such as women's age, membership in the Vietnam Women Union, marital status, and whether women are heads of households It also examines women's education levels through four binary variables: primary school status, secondary school status, high school certification, and higher education certification Additionally, it includes societal characteristics, encompassing locality status and economic area codes.

Inthesub- sampleweuseagainalmostindependentvariableswhich appiedinth efullonebutr emovethemarriagevariableandaddthehusband’scharacteristicstogether Thefa ctorsoft h e spousesofm a r r i e d womeni n c l u d i n g h u s b a n d ’ s e m p l o y m e n t s t a t u s c a l l e d h u s b a n d w k , h u s b a n d ’ s w a g e o r s a l a r y p e r m o n t h c a l l e d husba ndwage,andhusband’seducationcategorywithfourvariables:husband’sp r i m a r y s choolcertification-hprimaryshc,husband’ssecondaryschoolcertification- hsecondsch,h u s b a n d ’ s h i g h s c h o o l c e r ti fi c a ti o n - h h i g h s h c , h u s b a n d ’ s h i g h e r h i g h schoolcertification-hhigherhsch(table1).

No.Variable Notation Definition/explainationUnit

WEs 1i f shew o r k s a t l e a s t one houraweek,0=otherwise

1 Householdsize hhsize Numberofm e m b e r s i n h e r Person household

2 Members attending attendsch Total members in the Person

15Husband high schoolhhighsch1 if her husband has highBinary

No Variable Notation Definition/explaination Unit school householdattendingschool

4 Numberofoldpeople oldpp60 Numberofpersonsatleast60 yearsofage person

5 Severelillness/injured illness 0i f familyh a s z e r o seriousillne ssorinjured,1otherwise

6 Householdincome totalinc Total income ofherhouseholdpermonth

Location locality Locationofherhousehold,1ifur ban,0otherwise

8 Houseownershi p assets 1i fh e r familyh a s a t l e a s t 1 house,0otherwise

9 Household’s povertystatus poverty 1 if poor household,

10 Average per capitain co me averagecapi nc

12 Husband’semploymen tstatus husbandwk 1i f he r husbandhasjob,0 other wise

14 Husband secondary hsecondsch 1ifherhusbandhassecondary Binary school school certification, 0 otherwise school otherwise certification, 0

23 Woman school secondarysecondsch 1 if she has Low secondaryBinary school otherwise certification, 0

25Woman higherHighhigherhsch1 if she has higher High schoolBinary schoolcertification, 0 otherwise

No Variable Notation Definition/explaination Unit

1ifhe r husbandh a s higherhigh schoolc e r ti fi c a ti o n , 0 oth erwise

17 WomenUnion Vwunion 1i f shei s t h e m e m b e r ofVie tnamWomenUnion,0otherwise

Headh 1i f shei s t h e h e a d ofh e r house hold,0otherwise

19 Womanage age Ageofawomanaroundeduntil monthofinterview years

20 Womanmaritalstatus married 1ifsheismarried,0otherwise Binary

Primaryschool primarysch 1i f sheh a s p r i m a r y schoolcer tification, 0otherwise

24 WomanHighschool highsch 1i f sheh a s Highs e c o n d a r y school certification,

Collective household laborframe-work

In this study, we utilize a collective framework to analyze the variables relevant to both Probit and Tobit models The participation of individuals in the labor supply and workforce is influenced by three primary groups of factors: individual characteristics, household characteristics, and societal characteristics Additionally, the collective household labor framework introduces a new factor group that considers the characteristics of spouses when individuals are in a marital status The conceptual framework is outlined as follows:

The characteristics of a spouse are integral to the household dynamics, as they share both the burdens and joys of family life A spouse is expected to support the woman in both spiritual and material aspects, typically living alongside her throughout their lives, except in special circumstances Consequently, the traits of a spouse are more pronounced than those of other household members, warranting their classification into a distinct group for analysis This conceptual framework serves as the guiding principle for evaluating and analyzing the study's findings.

Approached models

Toe s ti m a t e t h e i m p a c t s ofh o u s e h o l d ’ s andi n d i v i d u a l ’ s c h a r a c t e r i s ti c s o n women’semployment,weruntheProbitmodelwiththedependentvariableis�𝐸� 𝑓 andusethebelowequation(1)withfull-sample(9,171observations):

WEsfisthewomen’semploymentinthefull-sample(fs) fisthefull-sample(fs) b0…b19isthecoefficientsoftheindependencevariablesintheequation(1)

TheTobitmodelisusedtocensoredthe full-samplewiththedependent variableis���� �𝐻 𝑈�� 𝑓a n d theformispresentedinthee quation(2)with9,161 observationsasbelow:

WORKHOURSfisthewomen’sworkinghourspermonthinthefull-sample(fs)d0… d19isthecoeffitionsoftheindependencevariablesintheequation(2)

To estimate the impacts of household’s, spouse’s and individual’sc h a r a c t e r i s ti c s o n m a r r i e d w o m e n ’ s e m p l o y m e n t , w e r u n P r o b i t m o d e l a g a i n w i t h dependentvariableis� �𝐸 𝑚a n d usetheequation (3)withsub-sample( 4 , 0 7 7 observations)toanalysistheresults,asbelow:

WEsmisthewomen’semploymentinthemarriedsub-sample mismarriedsub-sample a0…a24iscoefficientsoftheindependencevariablesintheequation(3)

TheindependencevariablesaresimilarvariablesusedforProbitmodelinthee q u a ti o n (1)afterweremovethemarriagestatusandfurtherincludingthehusband’scharact eristics ashusband’s employmentstatuscalledhusbandwk,husband’swage o rsalaryp e r m o n t h c a l l e d h u s b a n d w a g e , a n d h u s b a n d ’ s e d u c a ti o n c a t e g o r y w i t h fourv a r i a b l e s : h u s b a n d ’ s p r i m a r y s c h o o l c e r ti fi c a ti o n s t a t u s , h u s b a n d ’ s s e c o n d a r y sc h o o l certification status,husband’shighschoolcertificatio nstatus,husband’shigherhighschoolcertification status.Theindependentvariableso fthissubsamplea r e alsodetailexplainedasattheendofthis“Approachedmodels”part.

TheTobitm o d e l i s continuouslya p p l i e d t o r u n t h e s u b - s a m p l e w i t h 4 , 0 7 7 obsevationsinwhichtheworkinghoursofthemarriedwomen(���� � ��𝐻 𝑈 𝑚) isth edependentvariable.Thenumberofhourst h a t amarriedwomanhastoworkper monthalsolimitedfrom0-

WORKHOURSmisthewomen’sworkinghourspermonthinthesub-sample(mss)c0… c24iscoefficientsoftheindependencevariablesintheequation(4)

The article discusses various independent variables related to household characteristics, including household size (hhsize), the number of school-attending members (attendshci), and the count of children aged 15 (children15) It also examines the number of elderly individuals (oldpp60) and the presence of severely ill or injured members (illness) Additionally, it covers total monthly household income (totatlinc), the household's location (locality), and asset ownership (assets) The poverty status of the household (poverty) is analyzed alongside average per capita monthly income (averagecapinc) The husband's wages (husband wage) and employment status (husbandwk) are highlighted, as well as his educational qualifications, including primary (hprimarysch), secondary (hsecondarysch), high school (hhighsch), and higher education (higherhsch) The article also notes the status of the husband in relation to the Vietnam Women Union (vunion) and the marital status (marriage) and age (age) of married women who head the household (head) Finally, it includes educational qualifications for women, covering primary (primarysch), secondary (secondarysch), high school (highsch), and higher education (higherhsch) certifications.

+£24ℎ𝑖ℎ��ℎ��ℎ 1(6) where,thefemalelaboursupplystatuswhetherworkingornotworkingcalledWEsf andtheindependentvariablessimilartotheindependentvariablesintheequation (1). Themarriedwomenlaboursupply statuswhetherworkingornotworkingcalledWEs ma n d t h e i n d e p e n d e n t v a r i a b l e s s i m i l a r t o t h e i n d e p e n d e n t variablesi n t h e equation( 3 ) £ i st he p e r c e n t a g e pointsofprobabilityofa m a r r i e d w o m a n participationi n t h e laborforcea n d β i s t h e p e r c e n t a g e pointsofprobabilityofa femaleparticipationinthelaborforce.

Thisc h a p t e r p r e s e n t s t h e researchresults,i n c l u d i n g t h e s u m m a r y statistics,s o m e bivariatea n a l y s e s a n d t h e r e g r e s s i o n r e s u l t s Forr e g r e s s i o n results,w e r e p o r t t h e ProbitregressionandTobitregressionforthetwosamples:thefullsamplea ndthesub-sampleofmarriedwomen.

SummaryStatistics

Table2presentsthesummarystatisticsforthefullsampleconsistingof9,171women.The averageofemploymentwomenis0.817withstandarddeviation0.386.I t canbeexplain edthattherate ofemploymentfemaleis81.75%whiletherateofunemploymentfem aleis18.25%.Theaverageofworkinghoursofawomanis1 1 1 8 7 3 hourspermonth.Itm eansawomanworkw i t h average of3.73hoursperd a y Itislowermorethanahal foftheaveragework- hourofapersonaccordingtor e g u l a ti o n ofVietnamLaborLawindicateis8hoursperday (table2).

TheresultsofsurveyinVHLSSshowt h at the av erage households i z e is3.81perso ns.Thistrendslowlyreducesoverthenearest12years(2002-

In our study, the largest household size consists of 13 members, with an average of 4.46 persons per household, which is higher than the average household size of 3.91 persons reported in the General Office of Statistics (GOS) data Each household has an average of 1.68 schooling members, with the number of children ranging from 0 to 7, and an average of 1.03 children per household This highlights the presence of schooling adults aged 15 and older, a common phenomenon in Vietnam and globally, where schooling ages often extend beyond 15 years, sometimes reaching 20, 30, or even 40 years Additionally, the prevalence of elderly individuals and those with severe illnesses is comparable to the average.

0.29and0.251peopleperhousehold.Thereare7,081householdsdonothaveoldpeop lel i v e t o g e t h e r by77.21%,w h i l e t h e householdsw h i c h h a v e from1 t o 4 old

In a study of 7,340 households, it was found that only 22.79% of individuals live together with similar illness conditions Among these households, 81.02% of women are without illness, and 81.81% of this group are employed The employment opportunities for women show minimal variation across households with one to five ill members The minimum household income is reported at 463.25 VND per month, particularly for a family of three where the husband is unemployed and the child is under 15 years old Conversely, the maximum household income reaches 334,500.00 VND per month, which pertains to a household of four adults and one elderly person, with no children present.

In our study, 69.46% of females reside in rural areas, while 28.43% live in urban locations, indicating a significant presence of women in rural settings (6,370 individuals versus 2,801) The employment rate for rural women is notably higher, with 84.46% engaged in the labor force, compared to 75.58% of urban women Additionally, 6.78% of females live in poverty (622 individuals), suggesting a low overall poverty rate; however, the average per capita income reflects a concerningly low level across the full sample, with 7,711 out of 9,171 individuals (84.08%) affected.

84.25%)infull- samplewhohaveaveragepermonthlycapitaincomearebelowtheVietnamnation a v e r a g e percapitaincomeline 2 Itmaybethepressureofwomenthatleadthemtoworkin thelaborforce.Theresultsofourstudyalsoprovedthisdiscussion,there are11.32%ofheadofhouseholdarewomeniftheyareinthefemalegroup.Although,

2 According toreportsofGSOandWorldBank(WB),theaverageyearlyincomepercapitaofVietnamof2014is2 , 0 2 8 0 0 u s d / person/year.B a l an c e averagem o n t h l y in co m e percap i ta o f Vietnamof2014is169usd/per/ monthequal3,937,856.00vnd/per/ month.Vietnampopulationof 2014isabout90,730,000people.GrossDomesticProductivity(GDP)in2014of

Vietnamis184,000.44thousandusd.TheVietnamdonga n d USdolarexchangeratesbyDecember31 st 2014 byVietn amnationalBankis21,400.00vnd/usd.

Dependent variables thereisoneofthreeoffemalewhoaremembersofVietnamWomenUniononlyby35.33%o ft o t a l femalei n ourfull-sample.Ther a t e s ofw o r k i n g femalea n d non- w o r k i n g femalehavethedistinctdifferenceswhentheyarebalancedbetween96.3 0%ofw o r k i n g f e m a l e w h o a r e t h e m e m b e r s ofVietnamWomenUniona n d 73 80%ofworkingfemalewhoarenotthemembersofVietnamWomenUnioninourstudy(thedi fferencesare22.50%).

No Variable Unit Obs Mean Std.

No Variable Unit Obs Mean Std.

10 Average perc ap ita income

The summary statistics for the sub-sample of married women indicate a higher employment rate compared to the full sample, with an average employment status of 0.969 versus 0.817 The average household size among married women in this sub-sample is 4.30 members, which is still 0.49 points higher than the average household size reported in the 2014 VHLSS Married women work an average of 112.08 hours per month, although this remains below the Labor Law's regulation of 3.74 hours per day compared to the standard 8 hours The average monthly household income is 10,225.09 VND, with 98.08% of rural married women employed and 93.96% of urban married women participating in the labor force In 2014, 8.14% of married women lived in poverty, while 3,446 out of 4,077 individuals in the sub-sample had average monthly per capita income below the national average The highest percentage of working women in this sub-sample is found among those aged 33 to 36 years, at 99.36%.

1 0 0 % Thisisstillintherangeofthechildbirth- ageofwomen.Theemploymentratioofm a r r i e d w o m e n a l w a y s i s t h e h i g h e s t r a ti o w i t h 96.91%.I t m e a n s forp e r 1 0 women,thereistheaverageofmoretha n9womencangetajobbutthereisonlyb e lo w 1womanisunemploymentinthissample.

Variable Unit Obs Mean Std.Dev Min Max

No Variable Unit Obs Mean Std.Dev Min Max employments t a t u s

Previous studies, such as those by Pangestu and Hendy (1997), overlooked women engaged in family enterprises and self-employment, failing to recognize these roles as market participation However, Khan and Khan (2009) defined working women as those who engaged in paid employment, self-employment, or household enterprises for at least one hour during the study period, typically one week This research focuses on these forms of employment While other activities like unpaid employment and home care are considered in the analysis, this study specifically evaluates the economic tasks that women participate in.

Vietnam's labor laws permit individuals aged 15 and older to work under a labor contract, provided they are capable and subject to employer management A dataset from the Vietnam Household Living Standards Survey (VHLSS) 2014, which included 3,133 communes and 36,081 individuals across 63 provinces and cities, was analyzed The study focused on a sample of 9,171 observations, specifically targeting women aged 15-55 Additionally, a sub-sample of 4,077 married women was examined The analysis aimed to identify factors affecting these women, particularly those employed in the past 12 months in wage or salaried positions, engaged in household production activities, or involved in various business or service sectors.

LaborLaw-No.10/2012/QH13,ChapterI,section3,article1.

Frequency Percent Frequency Percent Employedatleastonehourperweek 7,497 81.75 3,951 96.91

The data presented in Table 4 reveals the employment status of women, indicating that women who choose to participate in the labor force are assigned a value of 1, while those who do not participate receive a value of 0 Among a sub-sample of 4,077 married women, an impressive 96.91% are employed, with only 126 women, or 3.09%, classified as unemployed Furthermore, 61.88% of husbands are employed, reflecting a high employment rate of 97.03% among working husbands Conversely, 1,554 husbands, representing 38.12%, are unemployed, while the employment rate for their wives stands at 96.72% In a broader analysis of 9,171 observations, the overall employment rate for women is 81.75%, contrasted with an unemployment rate of 18.25%.

Wepurposetocollecttheworkinghoursofwomentoestimatetheeffectsofabovei n d e p e n d e n t variablesontheotherfactorsofwomencorrelatedtolaborsupplyofthese twogroups.Tobece rt ai n theimpacts are negative orpositive,significantorinsignific ant?

The group named WORK HOURS approached the Tobit model to analyze the working hours of married women within a sample of 0 to 480 hours per month Utilizing data from the VHLSS, the study identifies two types of employment for individuals each month, allowing for the calculation of average working hours for both main and supplementary employment This analysis focuses on the most time-consuming jobs to derive a combined daily working hour value (referred to as D value) Additionally, the research includes the number of days a woman has taken off in the past three months, providing a comprehensive view of their work patterns.

0 d a y s a tt h e investigatedperiod(includingt h e m a i n employmenta n d supplementary employment)

(callEvalue).Then,toreachtheaveragemonthlyw o r k i n g hoursofawomanperm o n t h , weput Dtimesto E(D*E=average monthlyworking hours).Itissurprisethatthemax imumofaverageworkinghoursofawomana t 720hourspermonth meansshehastowork24hoursper day.Thisindicates tobenotreasonablesoweremove theobservationswhichhaveworkinghoursishig hert h a n 16hoursperdays.Wejustkeeptheindividualswhohavetheaveragemonthly w o r k i n g hoursequalorbelow16hoursperdaysforourbothfullandsubones.

Household size is defined as the number of individuals living together in a single dwelling, according to business online dictionaries A household typically includes one or more people who share meals and living accommodations, which may consist of a single family or other groupings (Haviland, W.A., 2003) Merriam-Webster simply describes a household as those who reside under the same roof and form a family, representing a social unit of individuals living together Additionally, a household can be characterized by members who are married, related by lineage, or have lived together for an extended period, often sharing financial resources (Nguyen Dinh Tan and Le Lieu Lai, 1999, page 18).

4 Sources:https://www.merriam-webster.com/dictionary/household

Standardde viation Employed at leastonehourperwee

Int h i s study,w e followthedefinitionofG O S i n t h e VHLSSa s householdmemb ersarethosewhoshareaccommodationandmealsduringnearest6monthsorm o r e overthe last12monthsandsharethepoolofincomesandexpenditures.TheresultsofsurveyinV HLSSshowthattheaveragehouseholdsizeis3.81persons.Thistrendslowlyreducesovert henearest12years(2002-

2014).Thehouseholdsizehasl a r g e s t c r o p i s t h e 4 memberhouseholdg r o u p I n ourobservationsgroups,thea v e r a g e households i z e i s 4 4 6 p e r s o n s a t t h e bigon ea n d i t is4.304p e r s o n s p e r householdatt he s m a l l one.Thesera t e s are highert han t he abo vei ndex ofVHLSSaround0.5persons(showingintable5).

Toe v a l u a t e t h e m e m b e r s ofattendingschoolv a r i a b l e , t h e firststep,w e p u t t h e m e m b e r s whopresentthe“yes- có”informationreceivingthevalues1andthem e m b e r s who presentthe “noorno information-không/ whitespace”bothreceive0va lu e s 5 Thesecondstep,weusesumifcommandtocountthe numberofmembersw h o areattendingschoolinahouseholdthroughthehouseholdpr ivateindex.Andt h e lastly,w e findt h e n u m b e r ofm e m b e r s attendingschoolofa w omanbyt h e

5 I n thebasicdataset,wefoundthecolumn(m2c5)ofVHLSSdatabasewhichshowtheindividualwhoisorisnotattendingschoolornoinf ormationoverthe past12 months(yes=có,no=không/noinformation).

41 privateindexofherselfin herhousehold.In thefull sample,therearemorethan77%oft o t a l householdsw h i c h h a v e 1 -

9membersattend schoolisnotover10%.Thehouseholdsh a v e nomemberattendingsc hoolwith16.13%.Therateofparticipationofwomeninlabormarketis90.74%withthehouseh oldswhichhavenomemberattendingschool.Thisr a t e i s from9 0 9 0 % t o e v e n 1 0 0 % w i t h householdsw h i c h h a v e 6-

81.26%ofparticipation ofwomeninthelaborforceonly.W h i l e therateofemployment marriedwomenisatleast95.09%evenat100%inoursub-sampleofmarriedwomen.

Peoplesaidthatitisnotunifiedinthe definitionsofchildreninVietnamalsointheworld.T heUnitedN a ti o n s Conventionont h e r i g h t s oft h e c h i l d ( C R C ) r e g u l a t e s a c h i l d whoisbelow18yearsold 6 TheSave,CareandEducateChildLawofVietnamshowsth at achildwhois below

Vietnamese labor law stipulates that the minimum working age is 15 years, allowing individuals capable of working to engage under a labor contract with compensation and employer oversight This regulation complicates the identification of children's status within households due to inconsistencies Our study emphasizes women's participation in the labor force, utilizing the concept of Vietnamese labor law to define children as citizens under 15 years old We analyze the number of children in households by summing individual data and maintaining values for a women's private index In our comprehensive sample, we examine the rates of women with varying numbers of children, ranging from zero upwards.

2arepeoplearethehighestrates(93.73%oftotalfemaleinthefullsample).Thesenum b e rs are m a j o r i t y , w h i l e t he re a re fivewo m e n who h a v e 6

TheUnitedNationsConventionontherightsofthechild(CRC)regulatesachildwhoisbelow18yearsold(Part1 , section2 CRCno.182,,1999).

7 Source :LawofTheSave,CareandEducateChildofVietnamNo.25/2004/QH11,15/06/2004Chapter1,section1 childrenandthereisonlyonewomanwhohas7childrenthatishighestnumberofc h i l d r e n ofhouseholdsinthisproxy.Itisremarkable thingisthenum be rs ofchild- freewomenh a v e r i s i n g trendby3 6 2 6 % oft o t a l w o m e n w i t h 3 , 3 2 5 indivi dualsincluding2,187singlewomen(shownintable6).

Average Standard Average Standard numberof deviation numberof deviation children children

Nowadays,theyarefoundthatthechild- freefamilyphenomenoniscommonw i t h ratiolaborparticipated womenisextremel yhigh.Inthemarriedwomengroupw e recognizethat therearenearlyper10m arri e dwomenwould have9.3married womenworkingwithout child(inthechild- freewomengroup).I t i s a n a d v e r s e phenomenonthatthesocialist,policymakersandrelatedstatesofficialsne edtopaya tt e n ti o n t o controlt h i s phenomenon.T h e s e womenp r e f e r h a v i n g a w o r k t h a n havingachild,donotthey?

Individuals aged 60 years and older are commonly referred to as old adults or seniors, marking the final stage of the normal human lifespan Definitions of old age vary across biological, demographic, employment, retirement, and sociological perspectives For statistical and administrative purposes, old age is often defined as 60 or 65 years and older, according to sources like the Encyclopedia Britannica In Vietnam, the Old People Law (2009) specifies that individuals who are 60 years old or older and have completed their biological cycle are classified as old people Thus, the group of old adults includes those aged 60 and above.

Average Standard Average Standard numberof deviation numberof deviation elderly elderly

Astheresultspresentedbytable7,inthefullsample,theratio betweenoneempl oymentwomenandnumberofoldpeopleinherhouseholdis 1and 0.28. Whilet h e ratiobetweenanunemploymentwomenandnumberofoldpeopleinherhouse holdishigherat1and0.32.Thereare7,081householdsdonothaveoldpeoplel i v e together by77.21%,whilethehouseholdswhichhavefrom1to4oldageslivet o g e t h e r a r e o nly22.79%.Beside,i n t h e subsample,t h e r a ti o betweena n employmentmarriedw omanandnumberofelderlypeopleis1and0.31,theratio betweenanunemploym entmarriedwomenandnumberofelderlypeopleis1and

0.34.There a r e 3 ,0 7 2 householdsdono t hav e oldpeoplel i v e t o ge t he r i n t h e sub- sampleofmarriedwomenby75.35%.Itisthehighestnumberwhilethehouseholdsw h i c h havefrom1to3oldageslivetogetherareonly24.65%.Itmaybeprovedthat

VietNamSeniorCommanderNo.23/2000/PL-UBTVQH10,28/4/2000,ChapterI,article1. twoore v e n onepedigreef a m i l i e s a r e m a j o r i t y phenomenoni n Vietnamn o w I n thesek i n d s ofhousehold,t h e r a t e ofe m p l o y m e n t womeni s n e a r l y highestw i t h 97.01%oftotalmarriedwomenattheagefrom15to55yearsoldinourstudy.Inthefullsampl e,therateofaverageemploymentfemaleis

We utilize binary values to assess the number of individuals categorized as having severe illness or serious injury within a household An individual is assigned a value of 1 if they are classified as severely ill or injured, and 0 otherwise Subsequently, we employ the "sumif" command to calculate the total number of severely ill or injured individuals residing in similar households, based on both individual and household private indexes Individuals living in the same household share a similar count of severe illnesses or injuries We then identify and retain these members for married women, using their private index While the rate of employment opportunities for women shows minimal variation among groups with 0 to 3 ill members, there is a significantly higher percentage of women's participation in the labor market among households with 4 or more severely ill individuals.

5i l l n e s s membersgroupsby100.00%.Itmaybethepressureofearningmoneyform a r r i e d womeni s highert h a n t h e othersw h e n t h e m a r r i e d womenh a v e t o t a k e c a r e t h e othersevereillnessori n j u r e d individualsi n h e r household.I n o u r s u b - s a m p l e ofm a r r i e d women,thepercentagesofhouseholdhavezeroillnessby79.15

%.Thereis96.81%ofmarriedwomenofthisrate(79.15%)havingjob.Whilethereare96 97%-1 0 0 0 0 % ofworkingmarriedwomenofhouseholdwithin1-

5illnesspeople.Inthefullone,therateofoccupationopportunitiesofawomanisnotalmostd ifferencewithinhouseholdshave from1 t o 5 illnessm e m be rs Thepe rc e n t a ge s ofhou seholdh av e z e r o illnesspeopleby8 0 0 3 % T h e r e i s 8 1 8 1 % oft h i s r a t e ( 8 0 0 3

To calculate the total monthly income of a household, we follow a five-step process First, we determine the yearly total revenue by summing various income sources, including educational aids, healthcare assistance, salaries, and revenues from agricultural and forestry activities Next, we identify all household members with similar private indices and aggregate their yearly revenues to ascertain the household's total yearly revenue, referred to as A value The third step involves calculating the total costs of production and business by adding expenses related to agriculture, husbandry, and other services In the fourth step, we group members with similar private indices again and sum their yearly costs to find the total yearly costs of the household, known as B value Finally, we compute the monthly total income (C value) using the formula C = (A - B) / 12.

12.Theminimumtotalincomesofanindividual’shouseholdreceivethevalue463.25v ndpe r mo nt h.W hi le t he m ax i m um totali n c o m e s ofanindividual’shouseholdreache supthevalue334,500.00vndpermonth.

The household locality determines whether a household is classified as urban or rural, assigning a value of 1 for urban households and 0 for rural ones In our survey of married women, 71.57% (2,918 individuals) reside in rural areas, while 28.43% (1,159 individuals) live in urban settings Interestingly, rural women face higher working pressures, with 98.08% participating in the labor force compared to 93.96% of urban women In the full sample, the ratio of rural to urban women is similar, with 69.46% (6,370 individuals) from rural areas and 30.54% (2,801 individuals) from urban areas Employment rates also reflect this trend, as 84.46% of rural women are employed versus 75.58% of urban women, suggesting that rural women may have more occupational opportunities than their urban counterparts.

Ori s itmoreworkingpressureofthemwhentheyliveinurbanplaceswiththeirchildrena n d att endingschoolmembersleadurbanwomendecidestayingathomeinsteadofw o r k outsid e?

Theque s tio n is “w ho o w n s t he m a i n ho us i ng accom modatio ni n y o u r ho use h o l d”?

The VHLSS data categorizes household ownership assets into seven distinct types: owned by the household, rented or borrowed from the government, rented or borrowed from a private landlord, collectively owned, owned by a religious establishment, co-financed by both the state and the people, and unclear ownership status This research aims to estimate the number of women who are considered "owned by the household" during the investigated period, assigning a value of 1 for those owned and 0 for others Among the 4,077 observations in the subsample, 96.03% of women live in households with ownership assets The labor force participation rate for women is comparable between those who own and do not own household assets; for every 10 married women in the non-owned asset group, approximately 9.57 have job opportunities, while in the owned asset group, about 9.70 married women can find employment Overall, the full sample indicates that the ratio of working women is lower than that of the subsample, at 8.2%.

8.6womenhavingajobwithin10womeninbothgroupsw h e t h e r t h e y owna s s e t s o rn o t T h e womenw h o l i v e int h e householdsowninga s s e t s a r e majorityw i t h

Researchresults

ResultsofProbit, Tobitregression,marginaleffectsforthefull sample

The Probit model analysis reveals that household size does not significantly influence women's labor supply participation across the full sample However, various household characteristics, such as the number of elderly members, average monthly income, and asset ownership, do have notable effects Specifically, a higher number of ill members and schooling members, as well as total household income and location, negatively impact a woman's likelihood of participating in the labor market Statistically significant findings indicate that women are more likely to work when their households have fewer schooling or ill members, with probabilities increasing by 3.90 percentage points and 1.8 percentage points, respectively Conversely, factors such as living in urban areas, belonging to low-income households, being older, married, serving as household heads, being part of a Women’s Union, and holding primary or higher education certifications positively influence women's employment opportunities, demonstrating statistically significant effects at the 0% level.

Women with multiple children are more likely to participate in the labor force, with a 4.08% increase in participation probability for each additional child Rural women face fewer job opportunities compared to urban women, who have an 8.71% higher likelihood of employment Individual characteristics such as age, marital status, and membership in Women’s Union significantly influence women's labor market participation, with a statistically significant impact at 0% Membership in the Vietnam Women’s Union increases job opportunities by 9.97% Education also plays a crucial role; women with primary or higher secondary education have better employment prospects, and those with secondary or higher qualifications face a higher likelihood of being jobless As women's age increases, their probability of labor force participation rises, with a 0.44% increase for each additional year Married women are more likely to work than unmarried women, enjoying a 17.28% higher chance of participating in the workforce However, relocating to a different economic area can decrease a woman's working probability by 1.34%.

Whenawomenistheheadofherhouseholditwillleadhertoparticipatethelabormark e twith3.68%ofhigherworkingprobabilitythantheotherwomenwhoarenott h e heado fhousehold.

The study employs a Tobit model to analyze the factors influencing married women's monthly working hours, utilizing a sample of 9,161 observations The analysis reveals that working hours are constrained between 0 and 480 hours per month Key variables affecting these hours include age, total household income, area code, average per capita income, and marital status Notably, age, economic area code, and total household income exhibit a negative correlation with women's working hours, statistically significant at 5% and 10% Conversely, marital status and average per capita monthly income show a positive relationship with working hours, also significant at these levels Other variables, such as household size and assets, do not significantly impact women's working hours Furthermore, the results indicate that if a household's total income exceeds 1,000,000 VND, a woman’s working hours decrease by 1.81 hours per month, while married women work 2.46 hours more per month compared to their unmarried counterparts.

ResultsofProbit, TobitRegression,marginaleffectsforthesub-

Ast h e resultsofP r o b i t r e g r e s s i o n p r e s e n ti n t a b l e 9 , w e findt h a t m a j o r i t y h o u s e h o l d ’ s characteristicsasmembersattendingschool,oldpeople,illnessmembers,mon thlytotalincome,ownershipassets,povertystatus,aswellasalmosth u s b a n d ’ s fact orsashusband’sworkandeducationlevelhaveno anyimpactonthema rrie d w o m e n ’ s e m p l o y m e n t p r o b a b i l i ti e s i n o u r s u b - samplew i t h 4 , 0 7 7 observations.Themarriedwomen’sindividualcharacteristicsalmostha veparticularlyi m p a c t s ont h e p r o b a b i l i ti e s ofw o r k i n g p a r ti c i p a ti o n of

In our study involving 64 households, we found that key characteristics such as household size, number of children, location, and average per capita monthly income have contradictory effects on married women's employment probabilities While larger household sizes and higher average incomes positively influence married women's participation in the labor force at a statistically significant level of 5%, the number of children and the household's location negatively impact their working probabilities, with significance levels of 10% and an absolute significance of 0%, respectively Specifically, having one more child decreases a woman's likelihood of participating in the labor force by 0.43% Conversely, when a married woman shares her household with an additional member, her participation probability increases by 0.52% This suggests that financial pressure encourages married women to seek employment Furthermore, rural women are more likely to participate in the labor force compared to their urban counterparts, with urban married women's participation probability being 1.74% lower.

The characteristics of a husband have minimal impact on the labor force participation of married working women, with the exception of their wages Specifically, if a husband's salary is higher, the likelihood of his wife participating in the labor market decreases significantly Conversely, a husband with a higher education level, such as a high school diploma, can motivate his wife to work outside the home, increasing her participation by 10% Additionally, factors like a woman's age, her role as head of the household, and her education level generally correlate negatively with her labor market opportunities However, membership in the Vietnam Women Union positively influences married women's participation in the labor supply market Notably, for every additional year in age, a married woman's employment probability decreases by 0.18%, and those who are heads of households face a decline in working probabilities by 3.56%.

% c o m p a r e tothe marriedwomen whoarenottheheadofhousehold.Theaverageper capitaincom e ofhouse hol d m ay bea l s o t he motivationofmarriedwo m e n’s labor f orceparticipation(5%ofstatisticssignificant).

The Tobit model analysis indicates that the p-value of 0.0000 demonstrates the statistical significance of the factors influencing married women's working hours Key factors include household size, total income, average capital income, location, and the husband's employment status Notably, household size and average per capita monthly income negatively impact working hours, with household size showing statistical significance below 10% This suggests that married women work fewer hours when living in larger households, likely due to increased financial support from additional members Conversely, the average capital income also has a negative relationship with working hours, statistically significant below 5% In contrast, factors such as location, total income, and the husband's employment positively correlate with married women's working hours, with statistical significance at 0% for the husband's work and below 5% for location and total income However, other variables like the number of children, elderly members, illness, poverty status, and educational levels of both the women and their husbands do not significantly influence married women's working hours in this study.

Marginal P>IzI P>ItI effects coef (z) coef (t)

Household size significantly influences married women's labor force participation, with larger households often leading to increased work commitments Research by Khan and Khan (2009) and McGrattan and Rogerson (2004) indicates that as family size grows, the economic pressures on mothers intensify, prompting them to seek employment Specifically, McGrattan and Rogerson found that average work hours shift from single women to married women as household size increases Conversely, Khan and Khan's study showed that married women in smaller households are not significantly more inclined to participate in the labor force than those in larger households, suggesting that participation is influenced by other household members as well Alderman and Christie (1989) further noted that a higher number of female household members can reduce women's economic participation, while more male or child members may necessitate increased work from women Thus, the second concept highlights that larger household sizes may negatively impact married women's decisions to engage in economic activities, as increased family members often provide more support in domestic responsibilities Studies by Naqvi and Shahnaz (2002) and Lokshin et al corroborate these findings, emphasizing the complex relationship between household composition and women's labor market participation.

(2000)separated toPakistanandKenyaprovedthatt h e womenw ho liveinthelessmemberhouseholdh av e moreopportunitiesp a r ti c i p a t e workingoutsidethantheothers.

Theresultscomesfromoursub- sampleofmarriedwomenareinterrelatedthefirstconceptthat havepositiverelationshi pbetweenprobabilityofoccupation opportunitiesofm o t h e r s a n d householdsize(tabl e9 ) I t alsop r e s e n t s a n e g a ti v e i m p a c t between theworkinghoursofmarriedwomenandhouseholdsize.Increasinga memberinhousehol dhigherprobabilitiesofoccupation opportunitiesofmarriedwomenandreducingthe workinghoursforthesewives.Incontrast,wedonotfindoutthesimilarevidencesinthef ullsample inwhichwedonotdistinguishthefemalew h o aresingle,married,separated,divorcedorwido winthisstudy.

The impact of household members on a mother's work labor is a topic of debate, with differing views on whether their influence is positive or negative In developed countries such as Spain, Germany, and Italy, high-quality childcare options have been linked to a positive relationship between the number of children or schooling members and mothers' labor supply decisions Research by Iacovou (2001) indicates that there is no significant correlation between the number of children in a household and the labor market participation of married women Conversely, a higher number of children may enhance mothers' employment opportunities.

Cura( 1 9 9 8 ) provedaninversesituationthatthe lowmembersarechildrenorattendi ngs c h o o l inahouseholdincreasingtheirmother’sprobabilityofhavinganoutsidej ob.MonikaMerz(2006)whohadstrongevidencesprovethatinfantsandsmallerc h i l d r e n inquirytheirmother,eventheirfatherspendingmuchmoretime forthemt h a n theelderchildren.Itisthereasonwhythesecouples– speciallythewiveshavelesspositioninthelaborsupplymarket.

InthedevelopingeconomicsasMalaysia,Kenya,Pakistanthenegativeimpactsofn u m b e r ofc h i l d r e n ont h e m a r r i e d women’sl a b o r f o r c e p a r ti c i p a ti o n h a v e a l s o im proved.Therearelesschild- carecentersinthesecountriessothemothersc e r t a i n l y choosetobeartheirinfa ntsandchildren ratherthangoouttowork.Them o r e detailimprovementsshowedinKhanandKhan(2009)’sstudy.Itevenindicated

71 thatthemotherswhohavedifferentgenderchildrenhavedifferentopportunitiesint h e l aborforce.T h e i r studyp r e s e n t e d t h a ti f a motherh a s a n attendingschooldaug hterheropportunitiestoworkoutsidearehigher.Inverse,apresenceofasoninh e r familyt h e m o t h e r m o r e l i k e l y t o t a k e - c a r e t h e i r c h i l d r a t h e r t h a n p a r ti c i p a t e laborforce.

The presence of children under 15 in a household significantly influences married women's participation in the labor force, particularly through the support of other adults Khan and Khan (2009) highlight that elder daughters and mothers-in-law in extended families provide essential assistance, allowing married women to engage in shopping, childcare, and household chores This support enables them to seek employment and contribute to their family's income Conversely, Aamir (2004) argues that married women may be confined to household duties and childcare due to the responsibilities placed on them by in-laws and other family members In such cases, their ability to engage in economic activities may be limited, restricting their participation in the workforce.

0 9 ) ’ s study.T h e e l d e r p e o p l e donothaveanyimpactonmarriedwomen’sworking hourseitherthep r o b a b il it y ofmarriedwomen’semploymentinthefamiliarhouse holdinbothsamples.Fort h e fullsample,t h e r e isa positivestronglye ff e c t betweenscho olingm e m b e r s , povertystatus,women’sage,women’smarriagestatus,womenas aheadofhousehold,w o m e n w h o h a v e higherhighschoolc e r ti fi c a ti o n a n d f e m a l e

’ s employmentr a t e s a t t h e l e v e l of0 % significant.I t c a n beexplainedt h a t w h e n a womanlivesinthepovertyhouseholdorherhouseholdhavemorechild,herh o u s e h o l d ’ s financial pressureswillrisehigherandthisleadsherintotheeconomica c ti v i ti e s toearnmoremoneybecauseoftheburdenofherhousehold’sconsumptionandexpe nditures.OurresultsareoppositewiththeresultsofMonika

Whenahouseholdhasmoreonememberattendingschooltheopportunitieso f af emale’slaborforce participationreduceintheir householdinthefullsample.

In a study of married women, the presence of household members attending school does not affect their work hours or participation in economic activities This trend is consistent across the entire sample, indicating that those in school may also be engaged in full-time or part-time jobs outside of their studies In recent decades, it has become increasingly common in Vietnam for adults to pursue continuous learning opportunities Despite holding current jobs, many individuals seek additional courses to enhance their knowledge or to discover better job prospects with higher salaries.

Our study reveals surprising findings regarding the influence of children under 15 on women's working hours, indicating no significant effect in both samples However, women's labor decisions are inversely affected by household children: a negative impact is observed in the subsample, while a positive impact is noted in the full sample The relationship between the number of children and a woman's likelihood of participating in the labor force is positive, yet married women's labor force participation decreases as the number of children increases This suggests that married women bear the primary responsibility for child-rearing and household chores, leading to increased family pressures Consequently, as married women have more children, they allocate more time to childcare rather than external economic activities Our findings, supported by a case study in Vietnam, indicate that married women's working hours remain relatively unchanged despite fluctuations in household child numbers, with shifts in their working time primarily occurring between economic activities and household responsibilities.

Fortheseverelyillnessorseriousinjuredstatusofmembersinthehousehold,o u r re sultspresenttheprobabilityofawoman’seconomicactivitiesparticipationjustinversecha ngedinthefullsample.Thefemalemayhavemoreheavyduties fortheillnessmembers thanthemaleintheirhousehold.

Inourstudy,thelocalityexplainswhetherawomen’shouseholdisinurbanorrural.Ther elationshipbetweenlocalitystatusofafemaleinthefullsampleaswellofthem a r r i e d womeni n t h e sub- sampleofm a r r i e d womenalsoh a v e statisticallysignificant(0%)withnegativeimpacton theprobabilityoflaborforceparticipationoff e m a l e a n d m a r r i e d women’sw o r k i n g h o u r s T h e l o c a ti o n d o e s n o t affectont h e w o r k i n g hoursoffemaleinthefulls amplebutonmarriedwomeninthesubsamplew i t h significantbelow5%

(positive).Thisresultcorrelatestothe estimationsofS a l w a y ( 2 0 0 3 ) orofF l e i s c h e r a n d Applebaum( 1 9 9 2 ) W e a n a l y s e t h a tt h e u r b a n marriedwomenhave to workw it h higherwo rking hoursco mparetorural marriedwomen.Inversely,theruralwomenmaypreferorhavemuc hmorefamilypressurel e a d themtolaborforcethantheurbanwomen,theseconclu sionsaresimilarwithstudiesofJamil(2001)andKhanandKhan(2009).

Azidet.al(2001)presentedthattheirestimatedeconometricresultsgavesignificantstatis ticst h a t a h o u s e h o l d ’ s a v e r a g e p e r c a p i t a i n c o m e i n P a k i s t a n h a s n e g a ti v e e ff e c t s onwomen’slaborsupplyopportunities Whenthehousehol d’saveragec a p i t a l incomesreducing,leadingtheparticipation ofawomaninlab orforcerising( a l s o seeAldermanandChristie,1989).Inotherstudies,theworkingmotiva tionofawomanwill behigher whenherhousehold’sincomesrisingup(Salway, 2003;Khan

Nationtoarguethatthemarried womenw ho liveinthepoorhouseholdandrural f a m i l i e s aremoreliketoworktoimprovetheirhousehols’sfinancialstatus 15

This analysis examines the impact of poverty status, average per capita monthly income, and total household income on the employment status of married women While the poverty line does not significantly affect the employment status of married women in the subsample, it has a strong positive influence on their occupational probability in the full sample Specifically, a household's poverty status may compel women to work outside the home to address financial challenges The average per capita income positively affects married women's working hours and their participation in the labor force, with a notable significance at the 10% level However, an increase in average household monthly income is associated with a decrease in working hours for married women in the subsample Our findings indicate that for every one million increase in average per capita income, married women's working hours decline by 2.09 hours per month, while their labor force participation probability increases by 2.26 points This suggests that improved financial stability allows married women greater opportunities to engage in economic activities, thereby enhancing their household's overall income.

15 Khan andKhan(2009)presentedthatthepovertylineforPakistanisestimatedatRs.848.79percapita,permonth(GOP,2 0 0 4 ) B y thisstandard,almostthemarriedwomenwhoa r e workingoutsidea r e livingint h e poorhousehold.Theirestimatedresultsha veshownacontradictedrelationshipbetweenmarriedwomen’sparticip ationinlaborsupplyandthehousehold’s percapitaincome.

Our research indicates that poverty status, food security, and minimum consumption expenditures compel married women to seek employment We gathered detailed poverty data from provinces with established poverty standards and applied national standards for others The findings reveal a positive correlation between household poverty status and the likelihood of women engaging in employment, particularly significant at a 1% level Specifically, in Vietnam, there is a 21% probability that women will work if their household is living below the poverty line However, within the subset of married women, we found no evidence of a significant relationship between household poverty status and either work hours or overall employment.

Tobitmodel’sresults,an increasingof a v e r ag e pe rcapitamonthlyincomeswith1 ,000v nd theworkinghoursofmarriedw omenwouldreduce0.0086hoursinourestimationwiththe significantstatisticsat3.7%. decreasetheirworkinghoursoutsideandbetter- offtakecare herfamily’smemberbyhouseworkthaneconomicactivities.

In contrast, total household income positively influences the working hours of married women, but it does not affect their participation in economic activities within our subsample Conversely, in the full sample, higher household income is negatively correlated with both women's working hours and their occupational opportunities in the labor market This suggests that as total household income increases, female members may be less inclined to work outside the home; however, this higher income often stems from the increased working hours of their mothers, who tend to work harder than other female members in similar households in this study.

Husband’sCharacteristics:husband’s wage,husband’semploymentstatusandhusband’seducationdegree

The characteristics of a husband can significantly influence a wife's labor force participation, with previous research indicating that a husband's wages and employment status negatively impact this decision (Sultana et al., 1994; Blau and Kahn, 2005) Khan and Khan (2009) found that wives in low-income households with unemployed husbands are more likely to engage in the labor market However, our study reveals that higher wages and educational levels of the husband inversely affect a wife's likelihood of participating in economic activities, with an increase in the husband's wage leading to a 1% reduction in the wife's participation probability.

18 B l a u andKahn(2005)havefoundthatadecreasingofhusband’ssalariesthewife’slaborforceparticipationwouldhavea n increa singw i t h highspeed.Sultanae t al.

( 1994)havea l s o concludedtheirsimilarresultsasa decreasingo f wife’sworkinghoursa n increasingo f thehusband’ssalariesratio.Inanotherw a y , thewifecertainlyhast o sacrificetheiropportunitiesofemploymentifherhusbandshastomoveanotherworkingpl ace.

19 By ourTobitresults,t h e husband’swagehavestrongpositivei m p a c t o n themarriedwomen’sworkinghours.I t m a y provethatt h e wifewhohashighersalarieshusbandw o u l d l e a v e theeconomicactivities.Herworkinghourswouldb e transferredfromeconomi cactivitiesintohouseworkactivities. statusa n d a l m o s t hise d u c a ti o n d e g r e e s h a v e noa n y impactsonbotha m a r r i e d woman’se m p l o y m e n t s t a t u s 20a n d h e r w o r k i n g hours.W e c a n c o n c l u d e t h a t t h e othercharacteristics ofhusbandalmosthavenopositivesupportsfortheirwi vesinourresultsinVietnamcase in

2014.Excepting,thehigherwagehusbandscanreducet h e pressuresofhousehold’sfina nceburdenfortheirwivesandthesemarriedwomenarelikelystayathometodohouse workalsotakecaretheirchildrenrathert h a n workinthelaborforce(ofcauseinthesubs ampleinourstudy).

Previous studies have not estimated the impact of the Vietnam Women’s Union on married women's employment Despite limitations in methodology, our analysis incorporated the Vietnam Women’s Union variable, revealing significant positive effects on women's labor force participation, with a notable 0% significance in both the full sample and the subsample of married women The Vietnam Women’s Union provides strong support for women in their societal and economic lives, aligning with the findings reported by the Vietnam Women’s Union in 2014 This evidence underscores the Union's contribution to Vietnam's economic development and its role in enhancing women's quality of life.

Theauthorsofseveralpreviousstudiesarguedthatmothers- headedhouseholdarem o r e likelytoinvolveineconomicactivitiesthanthe others (KhanandKhan,2009;N a q v i andShahnaz,2002,Lokshinet.al.2000).Theotherauthor salsoarguedthatifnotcause ofdi s i nt e gr ati o n oft he i rf am i l i e s ( s e pa ra t e d, divor ced,wi do w e d status),

20 B y ourProbitresults,thehusband’semploymentstatusmayhavelightpositiveimpactonthewife’semploymentstatuswithsignifi cantat11.9%.

The Vietnam Women Union's 2014 report highlights significant achievements in vocational training and support for women A total of 42,321 women were trained in vocational skills, with 98% of them being female, of which 20% come from poor or near-poor households Additionally, the Union provided vocational consultations to 280,273 female laborers and introduced job opportunities for 189,626 women in various enterprises and factories Furthermore, the organization supported over 1.1 million poor women, including 519,154 who are heads of their households.

Married group -sub sample (4,077 obs with husband’s factors)

Without husband’s factors) obs) (493 obs)

Union (3.48)(6.05)(3.48)(4.50)(2.41) thefemale- headedhouseholdphenomenonis rarelyacceptedintheircountries byp a r ti c u l a r socialprejudiceandstigma(Jesmin,2000;Quisumbinget.al.2001).KhanandKh an(2009)discussedthatthephenomenonoffemaleasaheadofthehouseholdi s m o r e p o p u l a r i n Africa,t h e C a r i b b e a n a n d LatinA m e r i c a t h a n i n t h e PacificandAsianc ountries.

Table10:Compare Strongest ImpactsonWomen’s Employmentstatus

In our study, summary statistics reveal that 71.75% of heads of households are women in the non-husband group, which includes separated, divorced, and widowed women In the full sample, 11.32% of heads of households are women, while this figure drops to 10.35% among married women The Probit model results indicate that the head of household variable has a significant inverse impact on women's employment status, particularly in the married women sub-sample, where it negatively affects their occupation with a 0% significance level Specifically, an increase in mother-headed households reduces the likelihood of these mothers participating in the labor force Conversely, the head variable positively influences the occupation probability of women in the full sample, with significance below 1% This suggests that married women, as heads of households, may prioritize household responsibilities over external employment, while those without such responsibilities experience increased labor force participation opportunities.

Conclusions

Inourresearch,householdsize doesnothaveanyimpactonfemalegroup(inthefullsample– n o t c o m p u t e w o m e n ’ s m a r r i a g e status)b u t i t hass t r o n g l y i m p a c t e d o n m a r r i e d womengroup(inthesub- sampleofmarriedwomen).Theinverseaffectionsofhouseholds i z e a n d n u m b e r o f c h i l d r e n o n m a r r i e d w o m e n ’ s o c c u p a ti o n opportunitiesprovedthatbigg erhouseholdsizeincreasingtheopportunitiesofm a r r i e d w o m e n ’ s l a b o r f o r c e p a r ti c i p a ti o n , w h i l e t h e h i g h e r n u m b e r o f c h i l d r e n reducing thisopportunitiesdownwithhighspeed.

Ther u r a l m a r r i e d womenh a v e m u c h m o r e o c c u p a ti o n opportuniti est h a n u r b a n marriedwomen’swhiletheurbanmarriedwomenhavetowork withhigherworkinghours.Althoughincreasinghusband’swagewasdriventhelab orforceparti cipation ofmarriedwomendownbutitcannotsupporttoimprovethewor kingh o u r s ofthiswomengroup.Themarriedwoman’soccupationopportunity notonlyw a s notimpactedbyherhusband’seducation butalsowasnotpositiveaff ectedbyt h e i r educationinourstudy.

This study highlights that the employment of married women in Vietnam is significantly influenced by individual characteristics such as age, household head status, and membership in the Vietnam Women Union While age and being the head of the household negatively affect job opportunities for married women, membership in the Vietnam Women Union has a strongly positive impact The findings indicate that women who are members of the Vietnam Women Union enjoy 55.79% more employment opportunities, and even those without a husband in the household still benefit from a 55.27% increase in opportunities This underscores the vital support the Vietnam Women Union provides for women's employment through its policy initiatives.

Withw i t h o u t husbandw o m e n group,t h e y r e a l l y n e e d m o r e s u p p o r t s fr omfamilya n d societyo ffi c i a l s w h e n t h e a l l off a c t o r s a s n u m b e r ofc h i l d r e n , l o c a l i t y , l i v i n g areastatus,agealsohavenegativeimpactontheiroccupationopportuni tiesinourresearch’sresults.

Ther a t e o f w o m e n ’ s e m p l o y m e n t a n d w o r k i n g h o u r s a r e a l s o h i g h a r e n o t goodforwomentobalancebetweentheireconomicactivities andhousewor ka c ti v i ti e s aswellasimprovethewomen’smentalandphysicalhealth.Itreally isanimportantdutyofwomeninconstructingandkeepinghappyfamily.

Policy implications

 Thegovernmentshouldhavemoredetailregulationsaboutthewomen’sworking hoursto s u p p o r t t he m bal ance orcontrolwe l l t he i r economica c ti v i ti e s and houseworkactivities.

 Thegovernmentandpolicymakersshouldpayattention tothewithouthusban dwomengrouptogivemoreusefulsupportregulationsforthisgroupw h e n thesi nglemotherphenomenonismorepopularinVietnamnowadays.

 Theeducationist,sociologistandrelatedformalofficialsmustresearchdeeplyt o s upportg o v e r n m e n t e s t a b l i s h a usefule d u c a ti o n systemt o improvewomen

’soccupationskillsandtrainingvocationalinsteadofgraduatedhigherl e v e l edu cation degree.Oureducation systemcangiveoursocietybackpositiveeffects wheneducationcertifications givemoregoodoccupationopportunitiesforhum an,furthermoreproductivitiesforsociety.

 VietnamWomenUnionshouldhavemorepracticalactivitiestocontinuee x p a n d i n g t h e n u m b e r o f W o m e n Union’sm e m b erst h a t t h e y c a n supportal mostwomeninourcountryinsteadoflimitednumbersasatpresentperiod.

Policymakers and labor lawmakers should pay special attention to the labor conditions of married women, particularly those without spousal support These women play a crucial role in caring for their children, especially in nurturing their mental and physical well-being The current six-month maternity leave granted to mothers is insufficient, as formal pre-primary schools only accept children who are at least 12 months old Additionally, the one-hour early leave for mothers with children under 12 months is inadequate, given that they are responsible for their children's care until they are at least 15 years old Despite these challenges, these women continue to participate in the labor force alongside their peers.

Limitationsofthestudy

 Weh a v e noth a d a d e e p l y distinguisha m o n g singlew o m e n g r o u p , m a r r i e d womengroup,femaleand without husbandwomengroupwitht hei r overallf a c t o r s togetmorestronglyevidencesforourstudy’sconclusions.

 Wejustexaminetheoccupation opportunitiesofwomeninVietnamin2014onl y.Ithasnotbeencombinedyetwithdataoftheotheryearstohavemorestrongevid encestoproveourconclusion.

Theselimitationsi n t h e studym a y bei m p r o v e d a n d d e t a i l l y analyzedi n o urfurtherstudiesinthefuture.

Angrist,JoshuaD.,andWilliamN.Evans.“Childrenandtheirparents'laborsupply:Eviden cefromexogenousvariationinfamilysize”.No.w5778.Nationalbureauo f economi cresearch,1996.

Apps,PatriciaF , andR a y R e e s "Taxationa n d theHousehold."J o u r n a l o f PublicEc onomics35,no.3(1988):355-369.

Apps,Patricia,andRayR e e s “Publiceconomicsandthehousehold”.CambridgeUniversit yPress,2009.

Atal,Navin,andK.L.Bedi."Bioenhancers:Revolutionaryconcepttomarket."Journalo f Ayur vedaandintegrativemedicine1,no.2(2010):96.

Azid,Toseef,MuhammadAslam,andMuhammadOmerChaudhary."Poverty,f e m a l e la bourf o r c e participation,andcottageindustry:a casestudyofclothembroideryi n r u r a l Multan."ThePakistanDevelopmentReview( 2 0 0 1 ) :1 1 05 - 1118.

Azid,Toseef,RanaEjazAliKhan,andAdnanMSAlamasi."Laborforceparticipationofmarrie dwomeninPunjab(Pakistan)."InternationalJournalo f SocialEconomics37,no.8(20 10):592-612.

Basu,Kaushik."Genderandsay:Amodelofhouseholdbehaviourwithendogenouslydeterm inedbalanceofpower."TheEconomicJournal116,n o 5 1 1 (2006):558-580.

Berulava,George,andGeorgeChikava.“Thedeterminantsofhouseholdlaborsupply:a com paratives t u d y ”.N o 11/13e.E E R C R e s e a r c h N e t w o r k,Russiaa n d CIS, 2011.

Beninger,Denis,andFranỗoisLaisney."Comparisonbetweenunitaryandcollectivemodel sofhouseholdlaborsupplywithtaxation." (2002).

Berulava,George,andGeorgeChikava."Thedeterminantsofhouseholdlabors u p p l y i nGeorgia,FranceandRomania:Acomparativestudy."E u r a s i a n J o u r n a l ofBusi nessandEconomics5, no.9(2012):141-164.

Blau,FrancineD.,andLawrenceM.Kahn."DocognitivetestscoresexplainhigherU S wag einequality?."TheReviewofEconomicsandStatistics87,no 1(2005):184-193.

Blau,FrancineD , andLawrenceM.Kahn."TheU S genderpaygapi n t h e 1990s:Slowing convergence."ILRReview60,no.1(2006):45-66.

Blau,FrancineD.,andLawrenceM.Kahn."Changesinthelaborsupplybehaviorofmarried women:1980–2000."Journalo f LaborEconomics25,no.3 (2007):

Bloemen,H a n s Gerald."Anempiricalmodelo f collectivehouseholdlaboursupplywithnon participation."(2004).

Blundell,Richard,andCostasMeghir."Selectioncriteriaf o r a microeconometricmodeloflaboursupply."JournalofAppliedEconometrics1,no.1(1986):

Blundell,Richard,andIanWalker."Alife- cycleconsistentempiricalmodeloffamilyl a b o u r supplyu s in g cross- sectiondata."TheR e v i e w o f EconomicS t u d i e s 53,n o 4(1986):539-558.

Blundell,Richard,andIanWalker,eds.“Unemployment,searchandlaboursupply”.CUPAr chive,1986.

Blundell,Richard,andIanWalker."Alife- cycleconsistentempiricalmodeloffamilyl a b o u r supplyu si n g cross- sectiondata."TheR e v i e w o f EconomicS t u d i e s 53,n o 4(1986):539-558.

AndreChiappori,ThierryMagnac,andCostasMeghir."Collectivelaboursupply:Hete rogeneityandnon-participation."TheReviewofEconomicStudies74,no.2(2007):417- 445.

AndreChiappori."Collectivemodelsofhouseholdbehavior:Anintroduction."Europea nEconomicReview36,no.2-3(1992):355-

Cura,Enriqueta."Transitionsi n women'sandchildren'sworkp a t t e r n s a n d implica tionsf o r thestudyoff a m i l y i n c o m e a n d householdstructure:A cases t u d y fro mtheCatalantextilesector(1850–

Andre,BernardFortin,andGuyLacroix."Marriagemarket,divorcelegislation,andhou seholdlaborsupply."JournalofpoliticalEconomy110,no 1( 2 0 0 2 ) : 37-72.

Chiappori,Pierre-Andre."Collectivelabor supplyandwelfare."JournalofpoliticalEconomy100,no.3(1992):437-467.

Craik,Christine,a n d JulieJ Alderman." W h a t attractsmaturestudentstooccupationalt herapy?."BritishJournalofOccupationalTherapy61,no.10( 1 9 9 8 ) : 473-477. DictionaryofNguyenDinhTanandLeLieuLai,1999,page18.

Donni,Olivier."Collectivehouseholdlaborsupply:nonparticipationandincometaxation."J ournalofPublicEconomics87,no.5(2003):1179-1198.

Dostie,Benoit,andLeneKromann."Laboursupplyandtaxes:Newestimatesoftherespon sesofwivestohusbands'wages."(2012).

Durkin,MaureenS.,LeslieL.Davidson,PatriciaDesai,Z.MeherHasan,NailaKhan,Patrick

E Shrout,MarigoldJ Thorburn,WeiW a n g , andSultanaS Zaman."Validityofthet enquestionsscreenforchildhooddisability:results frompopulation- basedstudiesinBangladesh,Jamaica,andPakistan."Epidemiology( 1 9 9 4 ) : 283 -289.

Estudillo,JonnaP.,AgnesR.Quisumbing,andKeijiroOtsuka."Genderdifferencesinl a n d i nheritanceandschoolinginvestmentsintheruralPhilippines."LandEconomics77,n o.1(2001):130-143.

Fleischer,Aliza,andLeviaApplebaum."Spatialdifferencesinthelabourforceparticipationo fmarriedwomen:thecaseofIsrael'speripheralareas."JournalofR u r a l Studies8,n o.3(1992):293-302.

Francois,Patrick."Genderdiscriminationwithoutgenderdifference:theoryandpolicyrespo nses."JournalofPublicEconomics68,no.1(1998):1-32.

Fortin,Bernard,andG u y Lacroix." A testoftheunitarya n d collectivemodelsofhouseholdl aboursupply."Theeconomicjournal107,no.443(1997):933-955.

Furuta,Marie,andSarahSalway."Women'spositionwithinthehouseholda s a determinant ofmaternalhealthcareuseinNepal."Internationalfamilyplanningperspectives(2006

HàNôi: NhàxuấtbảnThốngkê.

GeneralStatisticsOffice,2015.BáoCáoĐiềuTraMứcsốnghộgiađìnhViệtNamN ă m 201 4.HàNôi:NhàxuấtbảnThốngkê.

Gordon,Meg,MonicaCuskelly,andLindaRosenman."Influencesonmothers'employment whenchildrenhavedisabilities."Journalo f PolicyandP r a c t i c e i n IntellectualDisabil ities5,no.3(2008):203-210.

Hartog,Joop,andJulesTheeuwes."Participationandhoursofwork:twostagesint h e life- cycleofmarriedwomen."EuropeanEconomicReview30,no.4(1986):833-857.

Hartog,Joop,HesselOosterbeek,andCoenTeulings."Age,wagesandeducationint h e N etherlands."LabourMarketImplicationsofEuropeanAgeing(1993):182-2 1 1

Hartog,J o o p , andHesselOosterbeek."Education,allocationandearningsi n theNetherl ands:0verschooling?."EconomicsofEducationReview7,no.2(1988):185-194.

Haviland,WilliamA."Settlement,society,anddemographyatTikal."Tikal:Dynasties,foreign ers,andaffairsofstate(2003):111-142.

Heckman,JamesJ."Thecommonstructureofstatisticalmodelsoftruncation,sampleselect ionandl i m i t e d dependentvariablesanda simplee s t i m a t o r f o r s u c h models." InAnnalso f Economica n d S o c i a l Measurement,Volume5 , number4,pp.475- 492.NBER,1976.

Heckman,JamesJ.,andGeorgeJ.Borjas."Doesunemploymentcausefutureunemploym ent?Definitions,questionsandanswersfromacontinuoustime modelofheterogeneityandstatedependence."Economica47,no.187(1980):247- 283.

Heckman,James."ALife- cycleModelofFamilyLabourSupply."InHumanR e s o u r c e s , EmploymentandD evelopment,pp.213-230.PalgraveMacmillanU K , 1983.

Heim,B r a d l e y T "Thei n c r e d i b l e shrinkingelasticitiesmarriedfemalelaborsupply,197 8–2002."JournalofHumanresources42,no.4(2007):881-918.

Ismail,Rahmah,andNoorasiahSulaiman."MarriedwomenlaborsupplydecisioninMala ysia."AsianSocialScience10,no.3(2014):221.

Jamil,HasanM.,andLaksVSLakshmanan."Adeclarativesemanticsforbehavioralinherit anceandconflictresolution."I nProceedingso f the12thInternationalL o g i c Progr ammingSymposium.2001.

Jones,Christine,andM i c h a e l Roemer."Editors'introduction:Modellinga n d measurin gparallelmarketsindeveloping countries."WorldDevelopment17,no.1 2 (1989):1861-1870.

Khan,RanaEjazAli,andTasnimKhan."Laborforceparticipationofmarriedwomeni n Punja b(Pakistan)."JournalofEconomicandSocialresearch11,no.2( 2 0 0 9 ) : 77.

Killingsworth,MarkR , a n d JamesJ Heckman."Femalel a b o r supply:A survey."

Lokshin,M i c h a e l , KathleenMullanHarris,andBarryM.P o p k i n "Singlem o t h e r s i n R u s s i a : householdstrategiesf o r copingwithpoverty."W o r l d development28,n o 1 2(2000):2183-2198.

Lokshin,Michael,ElenaE.Glinskaya,andMaritoGarcia.“Theeffectofearlychildhooddeve lopmentprogramsonwomen'slaborforceparticipationandolderchildren'sschoolingin Kenya”.Vol.15.WorldBankPublications,2000.

Lundberg,Shelly."Laborsupplyofhusbandsa n d wives:A simultaneousequationsapproa ch."TheReviewofEconomicsandStatistics(1988):224-235.

Merz,Monika."Collectivefemalelaborsupply:EvidencefromGermany."Manuscript,Unive rsityofBonn(2006).

Merz,Monika,andEranYashiv."LaborandtheMarketValueoftheFirm."AmericanEconom icReview97,no.4(2007):1419-1431.

Mills,JohnP."Tableoftheratio:areatoboundingordinate,foranyportionofnormalcurve."Bi ometrika(1926):395-400.

Mincer,Jacob."Laborforceparticipationofmarriedwomen:Astudyoflaborsupply."I nAsp ectsoflaboreconomics,pp.63-105.PrincetonUniversityPress,1962.

Mumtaz,Zubia,SarahSalway,MuneebaWaseem,andNighatUmer."Gender- basedbarriersto primaryhealthc are provisionin Pakistan:t he experienceoffemal eproviders."Healthpolicyandplanning18,no.3(2003):261-269.

Naqvi,ZareenF.,LubnaShahnaz,andG.M.Arif."HowdowomendecidetoworkinPakistan

Ofer,Gur,andAaronVinokur."Thelabor- forceparticipationofmarriedwomenintheSovietUnion:Ahouseholdcross- sectionanalysis."Journalofcomparativeeconomics7, no.2(1983):158-176.

Pangestu,Mari,andMedelinaK.Hendytio."Surveyresponsesfromwomenworkersi n Ind onesia'stextile,garment,andfootwearindustries."(1997).

Peterman,Amber,JuliaA.Behrman,andAgnesR.Quisumbing."Areviewofempiricalevide nceongenderdifferencesi n nonlandagriculturali n p u t s , technology,andservicesi ndevelopingcountries."InGenderinAgriculture,pp.145-

Quisumbing,AgnesR , EllenPayongayong,J B Aidoo,andKeijiroOtsuka."Women’slan drightsin th e transitiontoindividualizedownership:Implicationsf o r tree- resourcemanagementinwesternGhana."EconomicDevelopmentandC u l t u r a l

Quisumbing,AgnesR , L a w renceHaddad,a n d ChristinePeủa."Arewomenoverrepres entedamongt h e poor?

A n analysisofpovertyi n 10developingcountries."JournalofDevelopmentEconomi cs66,no.1(2001):225-269.

Quisumbing,MariaAgnesR.,ed.“Land,trees,andwomen:Evolutionoflandtenureinstitutio nsinWesternGhanaandSumatra”.Vol.121.IntlFoodPolicyResInst,2001.

Salway,S a r a h , ShahanaRahman,andSoniaJesmin." A profileofwomen’sworkparticipa tionamongt h e urbanpoorofDhaka."WorldDevelopment31,n o 5 ( 2 0 0 3 ) : 881- 901.

Smith,J.Barry,andMortonStelcner."LaboursupplyofmarriedwomeninCanada,1980."C anadian JournalofEconomics(1988):857-870.

Stoll,BarbaraJ.,NellieI.Hansen,RosemaryD.Higgins,AvroyA.Fanaroff,ShahnazD u a r a , RonaldGoldberg,AbbotLaptook,MichelleWalsh,

WilliamOh,andEllenH a l e "Verylowbirthweightpreterminfantswithearlyonsetneo natalsepsis:thepredominanceofgram- negativeinfectionscontinuesintheNationalInstituteofC h i l d HealthandHuman DevelopmentNeonatalResearchNetwork,2002–

ThedecisionNo.03/2014/QĐ-UBNDofJanuary14,2014ofHoChiMinhcity,Vietnam

ThedecisionNo.01/2011/QĐ-UBND ofJanuary10,2011ofHa

ThedecisionNo.45/2010/QĐ-UBNDo f October11,2010ofBaRia–

UBNDo f November2 7 , 2012ofT a y Ninhprovince,Vietnam

ThedecisionNo.46/2012/QĐ-UBNDofOctober11,2012ofDaNangprovince,VietnamTheSave,CareandEducateChildLawofVietnam,No.25/2004/QH11,15/06/2004.

Thieken,AnnegretH.,TheresiaPetrow,HeidiKreibich,andBrunoMerz."Insurabilitya n d mitigationoffloodlossesinprivatehouseholdsinGermany."RiskAnalysis2 6 ,no.2(2 006):383-395.

Vermeulen,Frederic."Collectivehouseholdmodels:principlesandm a i n results."

Zhang,Yi."TakeMyMother-in-law…

=.88324198 variable dy/dx Std.Err z P>|z| [ 95%C.I ] X hhsize attend~h child~15 oldpp60 poverty* totalinca verag~clo cality* assets* illness areacode agem arriage* vwunion* headh*pri mar~h*sec ond~h*hig hsch*high er~h*

probitWEshhsizeattendsch children15oldpp60 poverty totalinc averagecapinclocality assets

WEs Coef Std Err z P>|z| [95%Conf.Interval] hhsize 0192574 0179355 1.07 0.283 -.0158956 0544105 attendsch -.1985236 0234665 -8.46 0.000 -.2445171 -.1525301 children15 2077297 0291811 7.12 0.000 1505359 2649235 oldpp60 0298604 0324522 0.92 0.358 -.0337447 0934654 poverty 2166055 0812157 2.67 0.008 0574256 3757853 totalinc -5.80e-06 2.43e-06 -2.39 0.017 -.0000106 -1.04e-06 averagecapinc 0000123 0000111 1.12 0.265 -9.35e-06 000034 locality -.4047695 0401948 -10.07 0.000 -.4835499 -.325989 assets -.1338872 0913626 -1.47 0.143 -.3129546 0451803 illness -.0915657 0305179 -3.00 0.003 -.1513797 -.0317516 areacode -.0681935 0109837 -6.21 0.000 -.0897212 -.0466658 age 0223378 0020758 10.76 0.000 0182693 0264063 marriage 7409928 0464659 15.95 0.000 6499213 8320642 vwunion 5579013 0523035 10.67 0.000 4553884 6604142 headh 1953511 0695445 2.81 0.005 0590465 3316557 primarysch 1228126 0652032 1.88 0.060 -.0049833 2506085 secondarysch -.2167966 0620268 -3.50 0.000 -.3383669 -.0952262 highsch -.4285304 0680424 -6.30 0.000 -.5618911 -.2951697 higherhsch 4016687 0759011 5.29 0.000 2529052 5504322

The analysis reveals a significant relationship between various socio-economic variables and their impact on household dynamics Key factors such as household size (hhsize), educational attainment (highsch and higher), and income levels (talinc) play a crucial role in determining outcomes Additionally, the presence of children under 15 (child~15) and elderly individuals over 60 (oldpp60i) further influences these dynamics The model also highlights the importance of local context (locality*) and asset ownership (assets*p) in understanding poverty levels (overyty*) Overall, these findings underscore the interconnectedness of demographic and economic factors in shaping household conditions.

use"E:\class 22\thesis\thesis2\W15-55data 28.11.2017\data-04.12.2017-MarriedWEsWORKHOU

probitWEshhsize attendschchildren15oldpp60illness totalinclocalityassets povertyaverag

>ecapinc husbandwages husbandwkhprimaryschhsecondsch hhighsch hhigherhschvwunionheadh agea

The analysis reveals several significant factors affecting the studied outcomes Household size (hhsize) positively influences the results with a coefficient of 0.1394 (p = 0.026), indicating a meaningful relationship Conversely, locality shows a strong negative impact with a coefficient of -0.3827 (p < 0.001), suggesting that geographic factors are critical Additionally, average capital income (averagecapinc) has a positive effect (0.0002, p = 0.024), while husband wages (husbandwages) significantly decrease the outcomes (-0.00005, p = 0.001) Education levels, particularly higher education (hhigherhsch) and union membership (vwunion), also show notable relationships, with coefficients of 0.4311 (p = 0.085) and 0.3480 (p < 0.001) respectively Age negatively correlates with the outcomes (-0.0495, p < 0.001), highlighting its importance Other variables such as illness, total income, and various education levels show no significant effects, emphasizing the need to focus on key determinants like locality, household size, and education.

Note:0failuresand1success completely determined

Variable Obs Mean Std.Dev Min Max wpi 4077 17085.81 10391.04 6 35907 hpi 0

WEs 4077 9690949 1730818 0 1 hhsize 4077 4.30439 1.263713 2 11 attendsch 4077 1.675742 1.227079 0 8 children15 4077 1.074319 9978487 0 6 oldpp60 4077 3134658 5946669 0 3 illness 4077 2607309 57675 0 5 totalinc 4077 10225.09 9991.499 463.25 334500 locality 4077 2842777 451125 0 1 assets 4077 9602649 1953601 0 1 poverty 4077 0814324 2735316 0 1 averagecap~c 4077 2522.651 2522.386 154.4167 83625 husbandwages 4077 1126.301 2526.411 0 31916.67 husbandwk 4077 6188374 485732 0 1 hprimarysch 4077 2575423 4373342 0 1 hsecondsch 4077 3070885 461343 0 1 hhighsch 4077 1697326 3754438 0 1 hhigherhsch 4077 0789796 2697401 0 1 vwunion 4077 4988962 5000601 0 1 headh 4077 1035075 3046579 0 1 age 4077 40.98651 8.430549 16 55 areacode 4077 3.181997 1.751836 1 6 primarysch 4077 2754476 4467944 0 1 secondarysch 4077 2862399 4520583 0 1 highsch 4077 0838852 2772496 0 1 higherhsch 4077 1334314 3400821 0 1

SummaryofWEs headh Mean Std.Dev Freq

SummaryofWEs attendsch Mean Std.Dev Freq

SummaryofWEs hhsize Mean Std.Dev Freq

SummaryofWEs higherhsch Mean Std.Dev Freq

SummaryofWEs highsch Mean Std.Dev Freq

Total 96909492 17308178 4077 tabsecondarysch,sum(WEs) secondarysc h

SummaryofWEsMe an Std.Dev Freq

SummaryofWEs primarysch Mean Std.Dev Freq

SummaryofWEs hhigherhsch Mean Std.Dev Freq

SummaryofWEs hhighsch Mean Std.Dev Freq

SummaryofWEs hsecondsch Mean Std.Dev Freq

SummaryofWEs hprimarysch Mean Std.Dev Freq

SummaryofWEs husbandwk Mean Std.Dev Freq

SummaryofWEs assets Mean Std.Dev Freq

SummaryofWEs poverty Mean Std.Dev Freq

SummaryofWEs oldpp60 Mean Std.Dev Freq

SummaryofWEs children15 Mean Std.Dev Freq

SummaryofWEs locality Mean Std.Dev Freq

SummaryofWEs illness Mean Std.Dev Freq

SummaryofWEs areacode Mean Std.Dev Freq

SummaryofWEs age Mean Std Dev Freq

The analysis of work hours reveals several significant factors influencing them Household size (hhsize) shows a negative correlation with work hours, with a coefficient of -6.016, approaching significance (p=0.060) Attendance in school (attendsch) and the number of children under 15 (children15) do not significantly impact work hours, as indicated by p-values of 0.801 and 0.563, respectively The presence of elderly parents (oldpp60) and illness (illness) also exhibit negative effects, but neither is statistically significant (p=0.448 and p=0.158) Total income (totalinc) positively correlates with work hours (p=0.045), while locality significantly influences work hours with a coefficient of 13.715 (p=0.039) Asset ownership (assets) and poverty levels (poverty) do not show significant relationships Average capital income (averagecapinc) negatively impacts work hours (p=0.037) The husband's work hours (husbandwk) significantly increase work hours (p=0.000), while husband’s wages (husbandwages) do not Educational attainment at various levels shows mixed results, with primary school (hprimarysch) and secondary school (hsecondsch) having no significant effect Overall, the findings suggest that factors such as total income, locality, and the husband's work hours are crucial in determining work hours.

Obs.summary: 1555left-censoredobservationsatWORKHOURS|t| [95%Conf.Interval] hhsize 2.026888 1.804757 1.12 0.261 -1.51084 5.564615 attendsch -.1414306 2.444859 -0.06 0.954 -4.933901 4.65104 children15 -3.583468 2.899191 -1.24 0.216 -9.26653 2.099594 oldpp60 -1.867062 3.367946 -0.55 0.579 -8.468989 4.734866 poverty -5.568928 7.576577 -0.74 0.462 -20.42071 9.282857 totalinc -.000469 0002587 -1.81 0.070 -.0009761 0000382 averagecapinc 0016439 0009351 1.76 0.079 -.0001892 003477 locality 3.27789 4.244475 0.77 0.440 -5.042229 11.59801 assets 4.326641 9.2605 0.47 0.640 -13.82601 22.47929 illness 1.181128 3.192052 0.37 0.711 -5.076006 7.438263 areacode -2.759193 1.126907 -2.45 0.014 -4.968183 -.5502032 age -.5002472 2054215 -2.44 0.015 -.9029193 -.0975751 marriage 12.46126 5.066589 2.46 0.014 2.529612 22.39291 headh 669086 6.377775 0.10 0.916 -11.83278 13.17095 vwunion -2.270554 4.372262 -0.52 0.604 -10.84116 6.300057 primarysch 2238081 6.043942 0.04 0.970 -11.62367 12.07128 secondarysch -9.133192 6.010433 -1.52 0.129 -20.91498 2.648599 highsch -2.336579 7.108194 -0.33 0.742 -16.27023 11.59707 higherhsch -7.986141 7.217277 -1.11 0.269 -22.13362 6.161336 _cons 84.41126 14.99527 5.63 0.000 55.01719 113.8053

Obs.summary: 3530left-censoredobservationsatWORKHOURS|z| [95%Conf.Interval] hhsize -.11038 0232695 -4.74 0.000 -.1559873 -.0647727 attendsch 1703328 0386071 4.41 0.000 0946644 2460013 children15 -.0272997 0433144 -0.63 0.529 -.1121942 0575949 oldpp60 0627034 0461084 1.36 0.174 -.0276675 1530742 poverty -.0541106 1112151 -0.49 0.627 -.2720882 163867 totalinc -6.00e-07 2.94e-06 -0.20 0.838 -6.37e-06 5.17e-06 averagecapinc 0000102 0000118 0.86 0.388 -.0000129 0000333 locality -.4602912 0548257 -8.40 0.000 -.5677476 -.3528349 assets -.1348051 1259113 -1.07 0.284 -.3815868 1119766 illness -.113269 0389515 -2.91 0.004 -.1896125 -.0369254 areacode -.1372113 0153682 -8.93 0.000 -.1673324 -.1070903 age 0063157 0027501 2.30 0.022 0009257 0117057 vwunion 3655484 0604689 6.05 0.000 2470315 4840653 headh -.0636959 0832645 -0.76 0.444 -.2268913 0994995 primarysch 1253159 0766641 1.63 0.102 -.0249429 2755747 secondarysch 0092862 0802524 0.12 0.908 -.1480057 166578 highsch -.1724913 0957291 -1.80 0.072 -.3601168 0151342 higherhsch 1235664 0933859 1.32 0.186 -.0594665 3065993