metadata: authors: 0: author: Emran, M. Shabe
1: author: Otsuka, Misuzu
2: author: Shilpi, Forhad
docna: 0: docna: Gender, generations, and nonfarm participation
docty: Policy Research Working Paper
subtopic: Labor Markets,Educational Sciences,Gender and Development,Food Security,Rural Labor Markets
teratopic: Education
abstracts: cdata!: The authors present an empirical
            analysis of intergenerational links in nonfarm participation
            with a focus on gender effects. Using survey data from
            Nepal, the evidence shows that the mother exerts a strong
            influence on a daughter's employment choice. Having a
            mother in a nonfarm sector raises a daughter's
            probability of nonfarm participation by 200 percent. The
            effects are truly dramatic for skilled nonfarm jobs. Having
            a mother in a skilled job raises a daughter's
            probability by 1,200 percent. Having a father in a nonfarm
            sector, on the other hand, does not have any significant
            effect on a son's probability of nonfarm participation
            when the endogeneity of education and assets is corrected
            for by the two-stage conditional maximum likelihood
            approach. But a moderate positive intergenerational
            correlation between fathers and sons exists for skilled jobs.
repnme: repnme: Gender, generations, and nonfarm participation
display_title: Gender, generations, and nonfarm participation
listing_relative_url: /research/2003/06/2438511/gender-generations-nonfarm-participation
topicv3: Agriculture and Food Security m1326205 435,Labor Markets m1326488 855,Education m1327048 470,Gender m1327621 615,Jobs m1503931 2670
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Education
origu: Development Research Group (DECRG)
no_of_pages: 40
model_extractions: POLIcy RESEARlCH WORKING PAPElt 3087

# Abstract

Emran, Otsuka, and Shilpi present an empirical analysis of intergenerational links in nonfarm participation with a focus on gender effects. Using survey data from Nepal, the evidence shows that the motlher exerts a strong influence on a daughter's employment choice. Having a mother in a nonfarm sector raises a daughter's probability of nonfarm participation by 200 percent. The effects are truly dramatic for skilled nonfarm jobs. Having a mother in a skilled job raises a daughter's probability by 1,200 percent. Having a father in a nonfarm sector, on the other hand, does **not** have any significant effect on a son's probability of nonfarm participation when the endogeneity of education and assets is corrected for by the two-stage conditional maximum likelihood approach. But a moderate positive intergenerational correlation between fathers and sons exists for skilled jobs.

This paper-a product of Rural Development, Development Research Group-is part of a larger effort in the group to understand the process of development of the rural nonfarm sector. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washiingtoni, DC 20433. Please contact Pauline Kokila, room MC3-604, telephone 202-4733716, fax 202-522-1151, email address pkokila@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at semran@gwu.edu, motsuka@jhu.edu, or fshilpi@wvorldbank.org. June 2003. (33 pages)

> _The Policy Research Working Paper Series disseinitates the findings of work in progress to encourage the exchange of ideas about developnment issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers_ **_carry_** _the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._

Produced by Partnerships, Capacity Building, and Outreach
Gender, Generations, and Non-farm Participation M. Shahe Emran<sup>1</sup> George Washington University Misuzu Otsuka Johns Hopkins University Forhad Shilpi World Bank Key Words: Intergenerational Correlations, Occupational Mobility,<sup>Non-FarmParticipation.</sup> Gender effect, Two Stage Conditional Maximum Likelihood JEL **Classification :J62, 012**

> 1Emails for correspondence: semran@ggwu.edu, fshilpiSworldbank.org, motsuka©jhu.edu. We would<sup>liketo</sup> thank the participants at NEUDC, 2002 at Williams College, and<sup>BryanBoulier, JoseCanals-Cerda,Gershon</sup> Feder, Mary Hallman, Hanan Jacoby, Shabbir Jilany, Peter Lanjouw,<sup>MarkLong,Don Parsons,DavidRibarand</sup> Stephen Smith for useful discussions and/or comments on earlier drafts.
# Introduction

The evolution of income distribution, inequality and<sup>occupational structureacross generations</sup> has attracted increasing attention in recent economic<sup>literature.Thisrenewedinterestreflectsa</sup> widely shared consensus that the existence of intergenerational<sup>linkagesin socioeconomicstatus</sup> has profound implications for mobility (or immobility)<sup>ina society.Startingfromidenticalini-</sup> tial income distribution or occupational structure,<sup>theevolutionof two societiesmightdiverge</sup> dramatically if the strength of intergenerational<sup>linkagesdifferssignificantly.'A largebodyof</sup> empirical studies focusing mainly on developed<sup>countriesfindsthat intergenerationalcorrelations</sup> in earnings are positive, statistically significant and<sup>numericallylarge,rangingbetween 0.2to 0.5</sup> (for a survey, see Solon (1999)).2 There is also a<sup>(relatively)smallempiricalliteraturethatindi-</sup> cates significant positive correlations between parents<sup>andtheiroffspringin occupationalchoices</sup> (see Dunn and Holtz-Eakin (2000) on U.S, Sjogren<sup>(2000)onSweden,andChevalier(2001)on</sup> UK). In this paper, we focus on intergenerational<sup>correlationsinnon-farmemploymentin a</sup> developing country, Nepal, with a particular emphasis<sup>onthe gender dimensionof thesecorrela-</sup> tions.<sup>3</sup> Although there is a substantial literature<sup>onthedeterminantsof non-farmparticipation</sup> (see Lanjouw and Feder (2001) for a survey), the<sup>issueof intergenerationallinkageshas so far not</sup> received any attention. A vast literature also explores<sup>gendereffectsinintra-householdallocation</sup> of resources in developing countries (Kanbur and<sup>Haddad,1994;HaddinottandHaddad,1995,</sup> Thomas, 1997; Quisumbing and Maluccio, 1999).<sup>However,to ourknowledge,thegendereffects</sup> in intergenerational linkages in non-farm participation<sup>havenotbeenanalyzedbefore.</sup> Although so far neglected in the literature, the analysis<sup>of intergenerationallinkagesand gender</sup> effects in non-farm participation has important<sup>implicationsthat spana numberof policyareas.</sup> 'At one extreme, the socioeconomic opportunities available<sup>toanindividualinasocietymight beeffectively</sup> determined by birth as in a caste system. At the other<sup>extremeisa perfectlymobilesociety,in whichthesetof</sup> opportunities is same irrespective of the exogenous<sup>characteristicsof anindividual,likebirth,gender,ethnicity.It</sup> is often argued that mobility is much more restricted<sup>indevelopingcountries(see,for example,LamandSchoeni,</sup> 1993).

> 2As indicated in Solon (1999), most of the papers on intergenerational income correlations focused on the interlink between father and sons. Chadwick and Solon (2002) is one of the<sup>few</sup> exceptions<sup>lookingatthese</sup> correlations between mothers and daughters.

> 3 There are a couple of advantages in focusing on occupational correlation instead of income correlation. First, it is not fraught with the almost insurmountable measurement<sup>problemsin permanentincome,the variableof interest</sup> in income mobility studies. Second, as Coldberger<sup>(1989)pointedoutearlyon,intergenerationallinkagesmight be</sup> stronger for occupation choice (relative to income), and<sup>focusingonincomecorrelations"could leadan economist</sup> to understate the influence of family background<sup>oninequality"(P.513).</sup>
The available evidence shows that poverty in developing countries has a gender dimension; women axe among the poorest and chronically deprived segment of the population (World Bank, 2001). At the early stages of development, access to non-farm employment can be a way out for the poor and landless people in general, and women in particular, as has been demonstrated by the micro finance programs like Grameen Bank in Bangladesh. A related but more important point is that the bargaining power of a woman is likely to be positively influenced by her participation in non-farm sector since much of women's work in agriculture remain unpaid. Access to nonfarm employment bears special significance as a way to ensure control over income by women, which in turn has desirable effects on the intra-household allocations.<sup>4</sup> Notwithstanding the benefits of non-farm participation by women, there is a strong gender bias against women in non-farm participation in many developing countries (for instance, in our Nepal data set, the participation rates in the non-farm sector are 45 percent for men and 22 percent for women).5 It is important, especially from a policy perspective, to understand how much of this gender bias is due to occupational immobility caused by gender-specific intergenerational linkages. If there are strong intergenerational linkages in women's non-farm participation as the evidence discussed later suggests to be the case in Nepal, this implies that the long run benefits from women's participation in non-farm sector are likely to be much higher due to the intergenerational multiplier effect. Thus the standard cost-benefit analysis that ignores this multiplier effect is likely to underestimate the value of programs that simultaneously target poor women and encourage their participation in non-farm activities. 6 Our focus in this paper is on the more 'intangible' determinants of intergenerational linkages 4 A large number of empirical studies in the context of developing countries finds that greater resource control by women within the household leads to higher expenditure on family welfare (e.g. on food), especially higher expenditure on children (education, health etc.) and woman herself (for a recent survey, see World Bank, 2001). 5For similar evidence from India and Malaysia, see Feder and Lanjouw (2001). It should be noted that while this pattern holds in many African countries as well, in Latin American countries, participation rates in the non-farm sector are significantly higher for women compared with men.

> 6A different argument that underscores the importance of understanding the intergenerational linkages in nonfarm participation relates to the role of non-farm entrepreneurship in the structural transformation of an economy. A dynamic non-farm sector can be the seedbed for experimentation and development of an entrepreneurial class that eventually graduates to industrial activities, as was the case in Japan's rise to a modern industrial state from late Tokugawa to Meiji period (See Smith, 1988). The existence of strong intergenerational linkages in non-farm participation means that the initial conditions assume paramount importance, and the emergence and development of an industrial entrepreneurial class might be severely constrained when an economy starts with a tiny non-farm sector and a large agricultural sector.

2
in non-farm participation like role model<sup>effects,learning externalities,and transferof reputation</sup> capital. We explicitly control for education,<sup>assets,andnetwork variables intheregressions,and</sup> the parental occupation variables thus<sup>capturethe 'intergenerationallinkages'overandabove</sup> these more tangible (relatively easily<sup>measurable)factors.To separate outtheeffectsof parents</sup> as role models, and of learning externalities<sup>andtransferof reputationcapital,wealsoneedto</sup> control for genetic ability transmission<sup>whichis presumablyan importantintangible factorin any</sup> type of intergenerational link. While it is very difficult, if not impossible,<sup>tofindreasonably</sup> good controls for ability, we use parental<sup>andspousal educationto control forability correlations.</sup> The inclusion of spousal education is predicated<sup>onthe largeempiricalliteratureonassortative</sup> matching in marriage market.<sup>7</sup> The econometric<sup>results,usinghouseholdlevelsurveydata from</sup> Nepal, indicate strong intergenerational<sup>linkagesin non-farmparticipationrunning alonggender</sup> lines (mother-daughter, father-son) if, following<sup>the extantliterature,weignorethesimultaneity</sup> between education and occupation decisions.<sup>Theintergenerationalcorrelationsare pretty robust</sup> with respect to the inclusion of an array<sup>of controlvariables.However,theevidenceunambigu-</sup> ously rejects the null hypothesis of exogeneity,<sup>bothforeducationandassets,inthenon-farm</sup> participation decisions of sons. The estimated intergenerational occupational<sup>linkagesbecome</sup> numerically small and statistically insignificant<sup>for sonswhenendogeneity of education andassets</sup> is corrected by utilizing the Two-Stage<sup>ConditionalMaximumLikelihood(henceforthTSCMLE)</sup> approach (Blundell and Smith, 1986; Rivers<sup>andVoung,1988).Whendisaggregatedaccording</sup> to skill levels, there are evidence of a<sup>positivecorrelationbetweenfatherandson(s)onlyin the</sup> case of skilled jobs, thus indicating that<sup>theaggregateresults arepartlydrivenbythelackof</sup> intergenerational correlations in unskilled<sup>jobs.Fordaughters,thereis convincingevidencethat</sup> the endogeneity problems are not important,<sup>andthattherearestrongeffectsofparents',in</sup> particular mother's, non-farm participation<sup>regardlessoflevelsof skill.Havinga motherinthe</sup> non-farm sector raises a daughter's probability<sup>of non-farmparticipationby 200percentwhenthe</sup> sample consists of both skilled and unskilled<sup>groups.The effectsaretrulydramaticin thecaseof</sup> skilled jobs; a daughter's probability of being<sup>in skilled job increasesby 1200percent if her mother</sup>

> *<sup>7</sup> We do not have any data on cognitive tests,<sup>likeRaven'stest.</sup> However, it is not clear to what extent the results of a very rudimentary test like Raven's<sup>cancapturethe typeof abilitycorrelations relevantfor occupational</sup> choice.

3
is employed in skilled job. The cross gender intergenerational links (mother-son, father-daughter) in non-farm participation appear to be unimportant.

The remainder of the paper is organized as follows. Section II provides a conceptual framework that underpins the empirical work presented in the subsequent sections. It presents a systematic analysis of possible sources of intergenerational linkages and gender effects in employment and occupational choice. The Section III describes the empirical specification, while the next section discusses the data and construction of variables. Section V, arranged in a number of sub-sections, presents the empirical results. The first sub-section discusses the preliminary evidence and thus provides a first impression of the importance of the intergenerational dependence in the non-farm participation. The next subsection presents the results of the empirical analysis that takes into account the potential endogeneity problems. The following sub-section focuses on the role of skill differences in non-farm jobs. Section VI concludes the paper with a summary of the main findings.

# II. The Conceptual FRamework

To discuss the many different channels of intergenerational linkages in a coherent conceptual framework, we use a simple model of non-farm participation that is based on the standard occupational choice model but is augmented to capture the essentials of the intergenerational linkages.<sup>8</sup> There are two sectors in the economy: agriculture (a) and non-farm sector (n). There are overlapping generations of people, each with a life span of two periods; in the first period (to) they live with the parents, and build up human capital (schooling and/or in-house learning externalities), and at the beginning of the second period (t1) every person in the economy decides which sector to work for. Each individual is endowed with an innate ability Oi E [0, 1] that captures the genetic transmissions and idiosyncratic talents that are relevant for non-farm sector. So the higher is 6i the better suited an individual is for non-farm employment. The intergenerational linkages may arise from the fact that the genetic endowments of a child ( Oi) are likely to be correlated with those of parents. The other major sources of intergenerational occupational correlations include

> 8The model utilized here can be viewed as an extension of the celebrated contributions of Becker and Tome (1979 and 1986) and the recent extensions proposed in Sjogren (2000).

4
learning externalities (learning by watching<sup>onesparentsandlearningbydoingthroughinfor-</sup> mal apprenticeship), role model effects (both<sup>throughinducedpreferencechangeandrevelation</sup> of information about the unknown innate<sup>abilityparameterOi),relaxationof creditconstraints</sup> through financial transfer, inheritance<sup>of reputationcapitalandbetteraccessto agivenoccupa-</sup> tion due to the network created and<sup>cultivatedby the parentsworkinginthesameoccupation</sup> (For a more complete discussion see<sup>BeckerandTomes(1979,1986),Mulligan(1997),Lentzand</sup> Laband (1983)).

The optimization problem faced<sup>byan individualiof generation0atthe beginningof first</sup> period (to) is to choose an optimal level<sup>of education(ei)given the information set Q9°summarizing</sup> all relevant information including the estimated<sup>innateability,parental occupation(dP'),9and costs</sup> of education or acquired ability Ki(ei,<sup>dPi).l°Ahighereducationlevelincreasestheprobability</sup> of getting a better paid non-farm job<sup>inthe next period. Atthe beginningof secondperiod(tl),</sup> he/she takes the education level _e,_<sup>asgiven,andsolvestheoptimaloccupationchoiceproblem</sup> utilizing the relevant information set<sup>Q1l.</sup> Note that due to information revelation<sup>inthefirst</sup> period,<sup>1</sup> 1 the information set at the<sup>beginningoftheoccupationalchoiceisricher,implying</sup> Q° C 1i4.

# The **optimal Schooling Decision:**

Let the possible education level ei E<sup>[0,31,ViwhereSismaximum numberof yearsof</sup> schooling possible. Each level of education<sup>inducesaconditionaldistributionof incomeYithat</sup> incorporates the optimal choice of occupation<sup>inthefollowingperiodgiventheinformationset</sup> Q9°. Let _F(Yil I_ ei; 09?) be the conditional<sup>distributionof secondperiodincomewhenindividual</sup> i chooses education level ei given the<sup>informationset129.</sup> The associated probability density function is denoted as _P(Yil I_ ei;<sup>Q12).The optimalschoolingchoiceis asfollows:</sup>

> 9We denote the parental variables with the superscript p.

> '<sup>0</sup> The cost of acquired ability depends<sup>onparentaloccupation duetoin-houselearning externalities.</sup>

> "There are many types of information<sup>revelationthatmightoccurduringthe first period,likesucess/failurein</sup> formal education might reveal information<sup>aboutonesinnate ability</sup><sup>_Oi,_parentalnetwork mightexpandor contract</sup> etc.

5
where Y4°p is the first period income transfer from the parents and **Ji** is the discount factor, and Ui(.) is a concave utility function.'<sup>2</sup> Among other things, the optimal schooling decision highlights the importance of first period parental transfer as a way of relaxing the credit constraint for educational investment which is important for non-farm jobs. Since the non-farm income is usually higher than that from agriculture, the financing of children's education gives rise to intergenerational linkages in non-farm participation if children's education is not an inferior good for the parents.

The above model of human capital choice is built on the assumption that the value of education derives from the stream of higher earnings it implies in the future labor market. However, there is a substantial empirical literature in the context of developing countries which demonstrates that, for girls, education is geared more to the marriage market considerations rather than labor market opportunities (see, for example, Behrman et. al., 1999 on India). If this is also the case in Nepal, then education is not likely to be endogenous in the occupational choice of daughters.

# **The Optimal Occupational Choice**

At the beginning of second period, individual i takes the accumulation of human capital and the consequent estimate of ability as given, and optimally chooses the occupation di E {a, n. Now given the information set 0,1, a choice of occupation induces a probability distribution of income. Let _F(Yil. I a;_ Q!) denote the conditional distribution of income when individual chooses agriculture and the information set is Q2l with the associated probability density function P(YI _a;_

> We define the expected utility from choosing agriculture as:

> Analogously the expected utility from choosing non-farm sector is:

> "<sup>2</sup> Genetic preference correlations means that, on an average, the children of parents who are risk takers may themselves be more inclined to take risk and thus become non-farm entrepreneurs.

6
(4) The individual chooses non-farm employment<sup>iff thefollowingholds:13</sup>

# Vi(n, PIl) -Vi(a, Q!l) > 0

The probability that an arbitrary individual<sup>drawnfromthepopulationwilldecidetowork</sup> in the non-farm sector is Pr(Vi(n, PI) - Vi(a, Q,)<sup>>0).Attheheartoftheoccupationselection</sup> process is the formation of expectation about<sup>pay-offsfromdifferentoptionsusingtheinforma-</sup> tion set Ql. A critical element of the information<sup>setistheoccupationalchoicesof theparents.</sup> The parental occupation reveals two types of<sup>relevantinformation:(i)informationaboutones</sup> own genetic endowment (or innate ability),<sup>(ii)informationabout thecharacteristicsofacer-</sup> tain occupation. For example, if parents (either<sup>orboth)aresuccessful(unsuccessful)non-farm</sup> entrepreneurs, the estimate of children's ability to<sup>besuccessfulinsimilaroccupationwillbe re-</sup> vised upward (downward). Another important channel is that revelation<sup>ofinformationmight</sup> reduce the uncertainty about the parental occupation,<sup>andthusinducerisk-aversechildrento</sup> prefer the parental occupation to other alternatives.<sup>Thus,theinformationrevealedby parental</sup> choices (and their outcomes) can influence children's<sup>occupationdecisionthroughtheir effectson</sup> the conditional distribution function of income<sup>Yilgiving risetorole modeleffects(Manski1993;</sup> Streufert, 2000).'4 For example, consider a child's<sup>participationdecisionin non-farmsector.The</sup> parental role model effects due to information<sup>revelationimplythattheconditionaldistribution</sup> of income when parents are in non-farm F(Yil I<sup>n;</sup><sup>_nP,_Q!)is stochastically dominant(first or sec-</sup> ond order) over the conditional distribution<sup>ofincomewithneitherof the parentsisin non-farm</sup> _F(Yi'_ I n; _aP, Qi)._ 15 Similar effects on the conditional distribution<sup>of incomecanalsobe dueto</sup> learning and network externalities and financial<sup>and reputation capital transfersfromparentswho</sup> work in the better paid non-farm jobs. On the other hand, the role model effects<sup>alaDurlauf</sup> that mould the preferences of children are captured<sup>in theutilityfunction.</sup> The model presented above identifies a number<sup>of differentsourcesof intergenerationallink-</sup>

> 3 Assurning that the tie is broken in favor of non-farm sector.

> 14 The definition of role model adopted so far in economic literature is not uniform. For example, while Durlauf (2000) defines role model as the influence of "characteristics<sup>of oldermembers"onthe"preferencesof younger</sup> members", Manski (1993) and Streufert (2000)<sup>defineitasobservationson oldermemberswhosechoices reveal</sup> information relevant for the choice of younger members.

> 1<sup>5</sup> Again, the superscript p denotes a parental variable.<sup>For example,</sup><sup>**aP**impliesthatthe parents work in agricul-</sup> ture. Similarly, superscripts m and f denote mother<sup>andfatherrespectively.</sup>

7
> ages, but it leaves unexplored the sources of any gender effects in intergenerational linkages in

> employment and occupational choice. Why would one expect the correlations to be stronger along gender lines (mother-daughter and father-son)? Since the transfer of financial and reputation capital, and network effects can be reasonably argued to be largely gender neutral, we

> need to look for the answers in the other sources of intergenerational linkages discussed above.

> First, the genetic transmissions might have a gender dimension. For example, the preference of a daughter (son) is likely to be more aligned with that of her (his) mother (father) compared to that of her (his) father (mother). Second, and probably the most important factor behind gender

> effects in intergenerational linkages in occupational choices, is the gender dimension in role model

> effects. The information revealed by the choices (and consequent outcomes) of an older member of a society will be more informative for the choices of a given younger member the closer he/she

> is to the younger person in an appropriately defined socioeconomic space. The individuals can be

> grouped together by partitioning the socioeconomic space according to different exogenous (like ethnicity, gender) or endogenous (like schooling) characteristics. The finer the partitioning the

> more informative is the information revealed by the choices of a member of a given group for the other members of that same group. It immediately follows that, given the membership in a

> family, gender creates a finer partitioning, and the mother becomes the natural role model for the daughter, and the father for the son. This has also implications for learning by doing and observing as the daughter (son) 'sees' and 'hears' primarily what her (his) mother (father) does and says.

# III. The Empirical Specification

> For the econometric estimation, we employ a standard probit model taking inequality (4) as the basis for our empirical specification. Specifically, we consider the binary response model

8
For estimation we impose linearity<sup>and assumethatthe latentvariableyiisgeneratedfroma</sup> model of the form Where Xig Q,! is a vector of explanatory<sup>variablesandei istheidiosyncraticrandom distur-</sup> bance term. For convenience, we partition<sup>Xiinto four subsets:(i)</sup><sup>**_Xi,_**the elements of which are</sup> individual specific characteristics<sup>(likeeducationeX,age,genderandmarital status)thatinflu-</sup> ence the productivity and preference,<sup>_(ii)_Xip,a vectorof parentalcharacteristics(mainlyparent's</sup> occupation (dip) and parental education),<sup>_(iii)_Xh,a vectorof householdcharacteristics(house-</sup> hold size and composition, spousal<sup>education),and household'sassetownershipthatincludes</sup> any transfers of financial capital<sup>fromthe parents,and(iv)</sup><sup>_Xi,_a vectorrepresentingnetwork</sup> variables like ethnicity, and measures<sup>of non-farmopportunitiesavailablein a village.Whenwe</sup> explicitly control for education<sup>andassetsalongwithcontrolsforintergenerationalabilitycor-</sup> relations ( parental and spousal<sup>education),the parentaloccupationvariable(s)thencaptures</sup> any intergenerational correlation<sup>resultingfromsimilaritiesof preferences,transferof intangible</sup> human and other types of capital<sup>(learning- by-watching,reputationcapital etc.)and role model</sup> effects due to induced preference<sup>changesandinformationrevelation.</sup> There are two salient econometric<sup>issues thatneed to be dealt within order to identify the effect</sup> of parent's occupation on children's<sup>occupationalchoices.First and perhapsthe mostimportant</sup> concern is that the intergenerational<sup>correlationinoccupationmayresult,</sup><sup>_spuriously,_fromthe</sup> fact that parents and children may<sup>facesimilarlabor market opportunities.Forinstance,if both</sup> parents and children live in an<sup>areawithbetternon-farmopportunities,thenintergenerational</sup> correlation in non-farm participation<sup>may be anartifact of not adequatelycontrolling for non-farm</sup> opportunities in the regression.<sup>Weusevillagelevelfixed effectsinthe estimationto controlfor</sup> non-farm opportunities.<sup>16</sup> Second,<sup>accordingto the theoreticalmodel,investmentin educationin</sup> first period is dependent on the<sup>expectedoccupationin the second period,and thus is endogenous</sup>

> "<sup>6</sup> Since non-farm opportunities are<sup>oftenclusteredaroundurbanareas,onecanusedistancetonearesturban</sup> center (or a non-linear function<sup>of it) asa control for unobservedheterogeneity in non-farm opportunities.Other</sup> candidate variables include the<sup>observedlevel of employmentdiversificationandaverageincomeina village.How-</sup> ever, even with a wide range<sup>of villagelevelcontrols,therecouldstillbe unobservedheterogeneityacrossvillages</sup> in terms of non-farm employment<sup>opportunities.</sup>

9
> in the non-farm participation decision.1<sup>7</sup> Moreover, current assets of the household are also likely to be endogenous to the occupations of the household members. To deal with the endogeneity problem, we employ the Two-Stage Conditional Maximum Likelihood (TSCMLE) approach.

# IV. The Data

The data for our analysis come from the Nepal Living Standard Survey (NLSS) 1995/96. The NLSS consists of a nationally representative sample of 274 primary sampling unit (PSUs) selected with probability proportionate to population size, covering 73 of the 75 districts in Nepal. In each of the PSUs, 12 households were also selected randomly (16 households in the Mountain regions) providing a total sample size of 3373 households. With an average household size of about 5.6, the survey collected detail information for 18855 individuals. The NLSS is unique in the sense that it contained an entire section of questionnaire on parental information, including level of education, sector of employment and place of birth. The survey contains detail information on employment by sectors and by occupations at individual levels. However, for those parents who do not live in the household, or who are deceased, only four types of employment status were recorded; whether they were employed as wage labor in agriculture or non-agriculture, or self employed either in agriculture or non-agriculture. Hence the sample does not allow finer analysis of occupational followings among children. As an individual is allowed to record more than one types of employment at the same time, the employment status data can be taken as if indicating whether the person was ever employed in non-farm sector. However, data on time spent on different activities are not available, precluding any attempt to distinguish between primary and secondary occupations. As regression analysis reported in the subsequent section indicate, the basic results on intergenerational correlations in occupation remain unchanged whether we focus on the sample that includes observations reporting multiple occupations or on the sample which focuses only on those reporting a single occupation.

> Of the total individual level sample of 18347 for whom parents can be identified from the data, nearly 71 percent reported participation in the labor force, but about 20 percent did not report

> "<sup>7</sup> Observe that even though the education level is predetermined when the occupational choice is made, it can not be treated as exogenous, assuming that the value of education derives from future labor market. Of course, as discussed earlier, in case of girls, the endogeneity might not be important if the value of education derives from assortative matching in marriage market.

10
any occupation.1<sup>8</sup> For the rest of the sample<sup>(9417observations),10percentareeitherchild</sup> labor (less than 14 years of<sup>age,9percent) ortoo old(morethan70yearsof age)andthusare</sup> dropped.1<sup>9</sup> But some of the parents of<sup>these individualsdid noteitherparticipateinthelabor</sup> force or report their labor<sup>forceparticipation, furtherreducing the sizeof thesample.20Moreover,</sup> a number of the PSUs showed<sup>no employment diversificationwhicharedroppedto avoid perfect</sup> fits in the regression analysis.<sup>Splittingthesamplebetweenmalesandfemales,weendupwith</sup> a final sample of 2090 observations<sup>(in152PSUs)fordaughtersand2948observations(in242</sup> PSUs) for sons.

The NLSS 1995/96 also contains<sup>awide rangeof variableson householdstructure,education,</sup> income and asset ownership<sup>whichcan beusedas controls inthe regression.Theyincludehouse-</sup> hold size, composition of household<sup>(share of femaleadults,shareof children,share of youngand</sup> share of old in total household<sup>size)aswellasindividual'smaritalstatusand gender.The human</sup> capital of an individual is<sup>measuredbyher/hislevelofeducation andage.21The educationvari-</sup> able codes different levels of<sup>education(e.g.0if illiterate,1 if literatebutno schooling,andso</sup> on). We also define a set of dummies<sup>(15 to be exact)depictingthe ethnicityof the individual.Fi-</sup> nally, we use two alternative<sup>representationsof themostcriticalvariablein ouranalysis:parental</sup> occupation. First, we define a<sup>dummy indicatingif atleastone of theparentswasemployedin</sup> non-agriculture (ne). Second, to<sup>allowfordifferentialimpacts,wedisaggregateanddefinethree</sup> different dummy variables: (i)<sup>whetherboth parentsareemployedinnon-agriculture</sup><sup>_(nP),(ii)_if</sup> only father was employed in<sup>non-agriculture</sup><sup>_(nf)_and</sup><sup>_(iii)_ifonlymotherwasemployedinnon-</sup> agriculture (n<sup>m</sup> ). The summary<sup>statisticsof theexplanatoryvariables arepresentedin appendix</sup> Table A.1.

> 18A large fraction of those not reporting occupation are in fact child labor with age 14 years or less. 1<sup>9</sup> Note that the empirical results<sup>reportedinthefollowingsectionsremainunchangedeven if weuseanyother</sup> cut-off age (e.g. dropping those<sup>below20 yearsof ageandabove65yearsof ageandso on).</sup>

> 20For 8394 individuals left in the sample, both parents reported participation in the labor force in the case of 6874 individuals, and for rest of<sup>the observations, employmentstatus ofeitherfather(347observations)ormother</sup> (1173) are missing. Note that<sup>if weusedummiestocapturethemissingparentalinformation,the samplesizecan</sup> be increased but the qualitative<sup>resultsremainunaffected.</sup> 211n addition to human capital,<sup>agecaptures anycohorteffectalso.</sup>
# V. Empirical Results

# .V.i Preliminary Results

> To gain a feel for our data, simple statistics on employment status of daughters and sons are presented in Table 1. Overall, men have a higher probability of being employed in non-farm (45 percent) compared with women (22 percent). Comparison of sons and daughters' employment status conditional on father's and mother's employment status reveals that probability of being employed in the non-farm sector is markedly higher for both sons and daughters if father or mother were employed in non-agriculture as well. However, mother's participation in non-farm

> sector appears to have a larger effect, compared with father's non-farm participation, on both

> sons' and daughters' probability of participation in non-farm sector. Moreover, mothers seem to exert greater influence on daughters; daughter's probability of non-farm participation nearly doubles (from 33 to 59 percent) if mother was employed in the non-farm sector compared with the case where father was employed in the non-farm sector. In the case of sons, the effect is less dramatic as probability of participation increases from 59 to 68 percent.

## [See Table 1]

With some indication of positive intergenerational correlations between parents' and children's employment status, we turn to see if the results survive in regression analysis. Starting from a simple bivariate regression of sons' and daughters' occupations on parental occupations, we take a stepwise approach in presenting the results, introducing an array of control variables in subsequent steps. This helps to demonstrate the robustness (or non-robustness) of intergenerational linkages in non-farm participation. The results from daughters' sample are reported in the first panel (column la to 3b) of Table 2, and for sons' in the second panel (from column 4a-6b). The result from the simple bivariate regression in column la shows that parents' non-farm participation has significant positive influence on daughter's probability of participation in the same sector. Switching the indicator variable for either parent in non-farm employment from zero to one raises the probability of daughter's employment in non-farm sector from 0.15 to 0.26. This suggests a marginal effect<sup>22</sup> of about 0.11 which is quite large compared to daughter's average probability

> 22 The marginal effect is estimated by holding all other explanatory variables at their sample mean values while

> switching the relevant indicator variable from zero to one.

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of participation in non-agriculture<sup>of 0.22.Theresultsfor thesons'sample(column 4a)indicate</sup> equally significant and positive<sup>intergenerationaleffect.Switching the indicatorvariablefor either</sup> parent in non-farm employment<sup>from zeroto oneraisessons'probabilityof participationinnon-</sup> farm sector by 0.15 compared<sup>with sampleaverageprobabilityof0.45.</sup>

# [See Table 2]

Columns lb and 4b of Table<sup>2reportthe regressionresultsfor daughtersand sons respectively</sup> in the case when the indicator<sup>variabledepictingparentalemploymentstatus is decomposedinto</sup> three different indicator<sup>variablesto allowfordifferentialimpactof father'sand mother'semploy-</sup> ment status. The results<sup>revealsomeinteresting differencesin the intergenerationaleffects between</sup> sons and daughters. For<sup>sons,resultspresentedincolumn4b of Table2 suggestthat havingboth</sup> parents in non-agriculture<sup>(n6)has the strongesteffectwith an estimate of marginal effectof 0.25</sup> at sample mean value. Having<sup>only fatherin non-farmsector</sup><sup>_(nf)_also has statistically significant</sup> (p-value=0.00) and positive<sup>marginaleffect(0.12)onson's employmentchoice.Butonlymother</sup> in non-farm sector (nm)<sup>appearstoexertno significantinfluenceon son'snon-farmparticipation</sup> (t-statistic=0.48). These<sup>resultsstandin sharpcontrastwith resultsfromthedaughter's sample.</sup> For daughters, having<sup>onlymotherinnon-farm sector(nm)hasthestrongesteffect;switching</sup> the indicator variable<sup>**_Am_**from zero to oneraises daughter'sprobabilityof non-farmparticipation</sup> by 0.39 which is slightly<sup>largerthanthemarginaleffect(0.36)ofhavingbothparentsinnon-</sup> agriculture. In comparison,<sup>having onlyfatherin non-farmsectorhasno statisticallysignificant</sup> effect on daughter's employment<sup>choice(t-statistic=-0.43).These results(incolumnslb and4b)</sup> suggest that intergenerational<sup>linkagesmayrunalonggenderlineswith mother(father) having</sup> stronger influence on daughters<sup>(sons)andviceversa.</sup> Regressions la-lb and<sup>4a-4battributevariationsindaughters' andsons'employmentchoice</sup> entirely to parent's employment<sup>choice.Theestimatedpositive correlationsmaynot necessarily</sup> represent any genuine<sup>intergenerationalexternalitybutmaysimplypickupeffectsofomitted</sup> factors such as non-farm<sup>opportunitiesthatinfluenceboth parents'andchildren'semployment</sup> decisions in the same<sup>direction.Theregressionresultsusingvillagelevelfixedeffectstocontrol</sup> in non-farm<sup>opportunitiesare summarized in columns 2a-2b(for daughters)and</sup> for heterogeneity 13
5a-5b (for sons).23 The inclusion of village level dummies leads to a significant reduction in the magnitude and statistical significance of the estimated coefficients and marginal effects of parents' occupational status on son's choice of occupation. However, the effects of either parents _(nP)_ (in column 5a) and of both parents _(nb)_ (in column 5b) employed in non-farm sector are still positive and statistically significant. The results for the daughters are again quite different from that for the sons. While there is a slight decline in the magnitude of the estimated coefficients and their respective statistical significance, the overall results on intergenerational correlations remain nearly unchanged. The effect of only mother's employment in non-agriculture sector (nm) is still the strongest with a marginal effect of 0.36 followed by both parents employed in non-agriculture _(nb)_ (0.30). As before, having only father in non-farm sector does not have any significant effect on daughter's employment choice. The slight changes in daughter's results compared with the drastic weakening of the intergenerational correlations for sons when village level fixed effects are introduced in the regression appear to be puzzling at first sight. However, it is not entirely unexpected in a traditional society such as Nepal where married women leave their natal family to join the household of the spouse, and hence face labor market opportunities different from their own parents. In most cases, sons, on the other hand, tend to live in the same village as their parents, if not in the same dwelling.<sup>24</sup> The next set of results reported in columns 3a- 3b and 6a-6b of Table 2 include a large number of household and individual level variables in addition to parental employment status and village dummies as explanatory variables. Access to non-farm jobs frequently depends on personal networks that often run along ethnic group/caste (see, for example, Dreze, Lanjouw and Sharma, 1998). To capture the variation in access to non-farm jobs, we include a set of dummies depicting the ethnicity of the individual in the regression. We also include dummies showing if there is any short/long-term migrant in the household, as migration frequently occurs on the basis of personal networks. A set of household variables including household size and composition are also added to

> 231f we use travel time to nearest urban center, village level median per capita expenditure, and share of non-farm employment in total village level employment as proxies for non-farm opportunities, the regressions results imply

> larger and more significant positive impact of parental variables compared with those obtained from regressions with village level fixed effects.

> 24 Although the tendency to migrate to urban areas is relatively higher among men, the incidence of permanent migration is not significant. In our current sample about 3.5 percent of all households reported any permanent migration from rural to urban areas.

14
the set of explanatory variables. As discussed in the theoretical model, human and<sup>financial capital</sup> variables are important links in the intergenerational transmissions of<sup>socioeconomicstatus.To</sup> isolate the effects of other 'intangible' factors that include the genetic<sup>transmissionsthrough</sup> endowment and preference and the role model effects, we introduce<sup>humanandfinancialcapital</sup> variables to control for the 'tangible' source of intergenerational<sup>correlations.Inadditiontothe</sup> level of education eX, we include age of an individual as a<sup>humancapitalvariablerepresenting</sup> the work experience.<sup>25</sup> Total asset owned by the household<sup>isalsointroducedasaregressorto</sup> control for access to capital. Transfer income (remittances)<sup>receivedbythe householdandtravel</sup> time to nearest commercial bank are also included as additional controls<sup>foraccesstocapital.</sup> We include individual's marital status to account for taste and/or<sup>genderrelateddifferences.</sup> In Table 2, columns 3a -3b and 6a-6b present the results for the daughters'<sup>andsons'samples</sup> respectively ignoring the existence of potential endogeneity of human<sup>capitalandassets.26While</sup> individual, household level and network variables have statistically significant<sup>effectson both son's</sup> and daughter's probability of non-farm participation, the results<sup>on intergenerationalcorrelations</sup> in occupation improve slightly for sons and remains nearly unchanged for daughters.<sup>For instance,</sup> having either parent in the non-farm sector raises son's probability of non-farm<sup>participationby</sup> 0.09 which is slightly higher than the marginal effect [0.07] reported in<sup>column5a of Table2.</sup> The inclusion of a large number of variables as additional controls<sup>does</sup> not<sup>affectthestrength</sup> of parental influence on son's and daughter's non-farm participation<sup>significantly.Thissuggests</sup> that parent's occupation choice variables did not act as proxies for individual's<sup>humancapitalor</sup> access to financial capital or household and network variables.

# [See Table 3]

> 25In addition, age and its squared term will capture any cohort effect also.

> 2"If parental occupation dummies were excluded from these regressions, 3a-3b and 6a-6b (Table 2) would correspond directly to the standard specification used in the available literature (Ferrira<sup>andLanjouw,2001;Lanjouw,</sup> 2001, Lanjouw and Shariff, 2000). Consistent with the findings of these studies,<sup>we findthat an individual'sproba-</sup> bility of non-farm participation varies significantly and positively<sup>withthelevelof education(marginaleffectequal</sup> to 0.07 and p-value=0.0 for daughters and to 0.03 with p-value=0.03 for sons).<sup>Apartfromthe ethnicitydummies,</sup> having a migrant in the household also significantly improves son's but not<sup>daughter'sprobabilityof participation</sup> in the non-farm sector suggesting strong network effects in securing a non-farm<sup>jobformen.On theotherhand,</sup> participation in non-farm sector varies negatively with household size<sup>in</sup> the<sup>caseof daughtersbut has no significant</sup> relationship with household size for sons. Participation in non-farm sector<sup>variesnegativelywithownershipof as-</sup> sets (mainly agricultural land) implying some occupational following<sup>inagriculture.Ethnicitydummiesare jointly</sup> significant for both sons and daughters. Most of the other variables<sup>haveexpectedsignsandimpactsonnon-farm</sup> participation.

15
The results so far indicate that observed intergenerational correlations between parents and children are not due to 'tangible' intergenerational linkages, as the tangible sources like individual's education and asset transfers from parents are already controlled for in the regression. Among many 'intangible' sources of these correlations, perhaps most common is the correlation in ability of parents and children due to genetic transmission. While it is difficult to get indicators that measures ability reasonably well, we introduce father's and mother's education to control for ability correlations. As evidence from marriage market suggest prevalence of assortative matching (Boulier and Rosenzweig, 1984), we also include spouse's education as an additional control for ability. For un-married individuals, household head's education is used in the place of spouse's education. The regression results are presented in column la-lb (daughters) and 3a-3b (sons) of Table 3. Among the variables representing ability (not reported in Table 3), spouse's education has positive and statistically significant (p-value= 0.05) impact on daughter's non-farm participation.<sup>27</sup> In contrast, the level of mother's education influences son's non-farm participation positively and significantly (p-value=0.03). However, inclusion of the variables representing ability leaves the results on intergenerational correlations unaffected as obvious from the results reported in Table 3. The results on the strength of the intergenerational correlations in occupation choice appear to be robust to inclusion of a large number of explanatory variables, and do not seem to spring from tangible sources of intergenerational correlations or from correlations in ability.

# V.ii: Endogeneity of Education and Assets

The theoretical model and subsequent discussion on the empirical specification point out clearly that the level of education observed in the second period (t1) can not be taken as exogenous in the non-farm participation regression due to the fact that the optimal education decision depends on the expected labor market opportunities. Likewise, current levels of assets of a household are determined by income and hence occupations of its members and can not be treated as exogenous. As mentioned before, to deal with the potential endogeneity problems, we utilize the Two Stage Conditional Maximum Likelihood Estimation (TSCMLE). At the first stage of the TSCMLE, the suspected endogenous variables are separately regressed on all exogenous variables

> 27 The detail regression results are not reported in Table 3, but can be obtained from the authors.

16
in the regression model and a set of instruments.<sup>Giventhatwehave twosuspectedendogenous</sup> variables (education, and assets), we also identify two<sup>sets</sup> of<sup>instruments.FollowingCard(2001),</sup> we instrumented education using viUage level average<sup>distanceto nearest school,distance to school</sup> interacted with ethnicity dummies and with inherited<sup>agriculturalland.28For assetownership by</sup> the household, inherited agricultural land and its squared term<sup>are used as identifying instruments.</sup> The details of the first stage regression results are presented<sup>in the appendixTable A.2.The first</sup> stage regressions explain a considerable amount of the<sup>variationsin educationand assetswhile</sup> avoiding over-fitting. The adjusted R<sup>2</sup> is estimated to<sup>be 0.47(fordaughter's)and 0.49(forson's)</sup> education equations respectively, 0.58 (for daughter's)<sup>and0.56(forson's), regressionsforassets.</sup> Consistent with the findings of Card(1995), Conneely and<sup>Uusitalo(1997)andMaluccio(1998),</sup> both son's and daughter's education vary inversely with distance<sup>to nearestschool (p-value=0.0).</sup> While distance to school interacted with inherited land<sup>appearstohaveno significantinfluence</sup> on the level of education of either sons or daughters, distance<sup>to schoolinteractedwith ethnicity</sup> is statistically significant in explaining son's education<sup>level.Consistentwiththe expectation,</sup> inherited land and its squared terms are highly statistically<sup>significantin explaining household's</sup> current level of assets in both son's and daughter's<sup>samples(p-value=0.0inbothcases).The</sup> estimated coefficients imply a concave relationship<sup>betweenassetownershipandinherited land</sup> for both sons and daughters. The two sets of instruments<sup>areseparatelyandjointlyhighly</sup> statistically significant in the relevant regressions. The parental<sup>employmentstatusvariables</sup> are not statistically significant in education (for both<sup>sons anddaughters)orasset equationsfor</sup> daughters. Parent's non-farm participation appears to<sup>havenegativeinfluenceonthelevelof</sup> assets owned by the household only in the son's sample.

For the second stage regression, the set of explanatory<sup>variables(includingthe suspecteden-</sup> dogenous variables) is augmented with the residuals<sup>fromthe first stage and then probit regression</sup> of non-farm participation is run on this augmented<sup>setofexplanatoryvariables.Anicefeature</sup> of the TSCMLE is that the t-statistic on the residual<sup>is</sup> a<sup>validtest of the nullhypothesisthat</sup> the suspected endogenous variable is in fact exogenous.<sup>Theestimatedcoefficientsofthefirst</sup>

> 28While ethnicity dummies are already in the structural equation, if educational attainment varies with ethnicity, then ethnicity interacted with distance to school still qualify<sup>as stronginstruments.Fordetail,pleaseseeCard</sup> (1995).

17
stage residuals and their respective t-statistics are reported in the second panel of Table 3. In addition to the usual test based on t-statistics, we also carried out F tests of joint significance of the residuals from the first stage regressions, results of which are reported in the last panel of Table 3. An important advantage of TSCMLE is that even when the null hypothesis of exogeneity is rejected, the estimated coefficients are consistent, although the standard errors and t-statistics have to be corrected for the fact that first stage residuals are added as regressors in the second stage. All t-statistics TSCMLE estimates reported in Table 3 are corrected for two-stage nature of regressions as well as for intra-cluster correlations due to clustered sampling.

The results from second stage regressions for daughter's and son's samples are presented in columns 2a-2b and 4a-4b respectively (Table 3). In the son's sample, as speculated by the theoretical model, both education and household's level of asset ownership are endogenous to son's non-farm participation decision. The absolute t-statistics on first stage residuals are 2.48 and 3.16 respectively for education and asset residuals in column 4a and 2.56 (education) and 3.21 (assets) in column 4b. The F-tests on the joint significance of residuals from first stage regressions on education and assets also confirm that the null hypothesis of exogeneity of education and assets can be rejected resoundingly at p-value=0.004 or less in both 4a and 4b. However, we get strikingly different results for the daughter's sample as reported in columns 2a and 2b of Table 3. The t-statistics on residuals from education and asset equations indicate that none of the coefficients of residuals are statistically significant at even 10 percent level. Indeed, the null hypothesis of joint exogeneity of education and assets for daughters sample can be rejected only at p-value=0.32 or higher. The results suggest that problems of endogeneity of education and assets are not serious in the daughter's sample. As discussed earlier, the exogeneity of education is not entirely unexpected in a traditional society such as Nepal where investments in daughter's education depend largely on marriage market considerations instead of expected labor market returns.<sup>2 9</sup> The exogeneity of assets may reflect the low level of participation by women in better paid non-farm jobs, as it implies that women's income on average do not have significant impact on household's asset accumulation.

As to the regression results presented in the upper panel of Table 3, the strength of the

> 29For similar evidence from India, see Behrman, Foster, Rosenzweig and Vishishtha (1999).

18
intergenerational correlations remains undiminished in the daughter's sample even after assuming that education and assets are endogenous to non-farm participation decision (columns 2a & 2b). As before, having either (ne) or both _(nb)_ parents employed in the non-farm sector increases daughtet's probability of non-farm participation significantly. However, having only mother _(n_<sup>_m_</sup> _)_ in the non-farm sector has the strongest effect while only father _(nf)_ has no appreciable influence on daughter's probability of non-farm participation. The estimated marginal effects are only slightly smaller compared with the estimates in columns la and lb. Even if we focus on the most conservative estimates (columns 2a & 2b), the intergenerational correlations in the case of daughter remains quite substantial. For instance, switching the indicator variable representing either parents employed in non-farm sector from zero to one raises daughter's probability of participation in non-farm sector from 0.15 to 0.24, providing an estimated marginal effect of 0.09. This represents nearly 63 percent increase in daughter's probability of being employed in non-farm sector if either parent was employed in the same sector. The effect of mother's nonfarm participation (n<sup>m</sup> ) on daughter's probability is stronger still: it raises daughter's probability of being in non-farm employment by 203 percent. If both parents were employed in non-farm sector _(nb),_ it increases daughter's probability by 169 percent. These estimates suggest that intergenerational correlations in employment choice is rather dramatic for daughters. Indeed, only the level of education has an impact on non-farm participation stronger than those observed for either both parents in non-farm sector (nb) or only mother in non-farm sector (nm).<sup>3</sup> 0 In contrast with the results for daughters, the moderate positive correlations observed between parents and sons (columns 3a and 3b) turn out to be non-robust once the endogeneity of education and assets are recognized. When endogeneity of education and assets is properly accounted for, the TSCMLE results show that the estimated coefficients have expected positive signs (4a and 4b), but their magnitudes decline considerably and they are not statistically significant, separately or jointly.<sup>3</sup> 1 The results from exogeneity tests indicate that expected occupation plays an important

> 30The level of education has a marginal effect of 0.29 on daughter's probability of non-farm participation. This result might seem counterintuitive given that education for girls is not geared to labor markets. However, there is no contradiction here. The result only shows that once a daughter gets education the probability that she will choose employment and actually be employed in the non-farm sector is much higher, whatever the original purpose of education might be.

> 31Endogeneity correction greatly enhances the impact of education on non-farm participation; the marginal effect of education on non-farm participation probability increases from 0.03 (in 3a & 3b ignoring endogeneity problem) to 0.20 (in 4a & 4b with proper correction for endogeneity).

19
role in the decision to invest in education in the case<sup>ofsonsandson'sincomesfromnon-farm</sup> sources are also important in household's asset accumulation.<sup>Whenendogeneityof education</sup> and assets are accounted for, parent's non-farm participation<sup>doesnot appeartoinfluenceson's</sup> probability of participation in the non-farm sector<sup>significantly.3 2</sup> The results thus indicate strong intergenerational linkages<sup>betweenmother anddaughter,</sup> but the absence of any significant linkages between fathers<sup>andsonsappearstobepuzzling,</sup> especially given contrary evidence in the context of developed<sup>countries.However,our resultsare</sup> not inconsistent with the existing literature; we also<sup>findrelativelylargerandmoresignificant</sup> linkages for sons when education and assets are assumed<sup>tobeexogenous.</sup> Thus, at least a part of the weaker intergenerational correlations in occupational<sup>choiceforsonsseemstohave</sup> resulted from the correction of endogeneity. Another possibility is that the non-farm sector, as broadly defined as in this paper, consists of a myriad<sup>ofactivitiesfromunskilledtohighly</sup> skilled occupations, and predominance of lower end and temporary<sup>jobsrequiringlittle skillmay</sup> have blurred the intergenerational linkages often reported<sup>formoreskilled jobsin thecontextof</sup> developed countries. We explore the role of skill composition more<sup>thoroughlyinthefollowing</sup> section.

# V.iii: Skill and Intergenerational Linkages

We define an indicator variable depicting if an individual<sup>isemployedin jobsrequiring special-</sup> ized skills or not.<sup>33</sup> Similar indicator variables are also<sup>defineddepictingif parentswereemployed</sup> in skilled occupations. Since many of the parents did not<sup>reportfineroccupationaldetails,the</sup> regressions were run on a much smaller sample of sons (827<sup>observations)anddaughters(494ob-</sup> servations) for whom we have complete information.<sup>The results are reportedin Table4,columns</sup> la-2b for daughters and 3a-4b for sons. The regressions<sup>forthedaughters'sampleexcludedthe</sup> village dummies as only 23 villages have more than<sup>onewomen employedinskilled jobs(total</sup> observation: 67). Instead we use 'share of non-farm<sup>employmentintotalvillagelevelemploy-</sup>

> **32As** an additional robustness check, we drop those individuals<sup>who reported multiple occupations(both inagricul-</sup> ture and non-agriculture), and run regressions similar<sup>to 4a and 4b (Table 3), wefind results nearly indistinguishable</sup> from those reported in Table 3.

> 33An individual is assumed to be employed in a skilled job if she/he reported any of the following occupations; professional and technical workers, administrative and<sup>managerialworkers,clerical workersandoperators,skilled sales</sup> and services workers, skilled workers in agriculture, production<sup>workersrequiringspecializedskills(e.g.machine</sup> operator, metal processors etc.).

20
ment' as a proxy for the common labor market opportunities faced by parents and daughters.<sup>34</sup> The qualitative results for daughters remain unchanged from those reported in Table 3. Parents, particularly mothers, exert great influence on daughters occupational choice. The marginal effects are similar in magnitude compared with those reported in Table 3 (la-2b). However, the sample probability of daughter's participation in skilled jobs is nearly half (0.13) of that in non-agriculture as a whole (0.22). When expressed in percentage terms, the impact of parent's participation in skilled jobs on that of daughters is simply spectacular. For instance, compared with the situation when none of the parents were employed on skilled jobs, either parent's participation in skilled jobs raises daughter's probability of having a skilled job by 650 percent. Having only mother in skilled jobs increases daughter's probability by 1200 percent. Having both parents in skilled jobs has yet stronger effect; it raises daughter's probability by 1400 percent. The intergenerational positive correlations are thus much stronger in the case of skilled jobs for daughters.<sup>35</sup>

# (See Table 4]

Consistent with our findings for daughters, the intergenerational correlations are stronger in skilled jobs in the case of sons too. The overall results for sons represent an improvement over that reported in Table 3 (columns 3a-4b). When endogeneity of education and assets are ignored, we find significant and positive impact of father's participation in skilled occupations on the probability of sons participation in similar jobs. Compared with the results for all non-farm jobs, the marginal effects are much larger in magnitude; having both parents or only father in skilled jobs improves son's probability by 0.28 and 0.21 respectively. Since the null hypothesis of exogeneity of education and assets can be rejected at 20 percent significance level, the appropriate TSCMLE estimates (4a and 4b) show that parental influence on sons' occupational choice is only slightly weaker. Both 'either parents in skilled jobs' (column 4a) and 'only father in skilled jobs'

> 34The comparison of results using share of non-farm employment in total village level employment as an explanatory variable with that of the case when village level dummies are included shows that the differences between these two sets of estimates are small in the overall daughter's sample (farm vs non-farm choice). For instance, the implied marginal effects of either parents in non-agriculture is 0.07 compared with 0.10 in column 2a of Table 3 (TSMLE estimates). Results for other specifications (2b, Table 3) are also similar. This suggests that the share of non-agriculture in total village level employment, is also a good proxy for labor market opportunities.

> 35In addition to the choice of skilled vs. unskilled jobs, we also experimented with different sub-samples of daughters to check robustness of the linkages. For instance, when we consider the sample of only married women, the intergenerational correlations are found to be slightly stronger. Given the tradition of women leaving their natal family upon marriage and joining spouse's households, the sample of married women can correct for any unobserved household level factors common to both parents and children residing in the same household.

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(column 4b) are statistically significant at<sup>5percentorless.Theestimatedcoefficientsbear</sup> correct signs and are also large in magnitude.<sup>Theimpliedmarginaleffectsarealso large;having</sup> a father in skilled job raises son's probability<sup>by 0.21,whichrepresents nearly a 97percent increase</sup> in probability over the case when father<sup>wasemployedinunskilledjob.Havingeitherparents</sup> employed in skilled jobs has effect similar<sup>inmagnitudeonson'sprobabilityof beingemployed</sup> in non-farm jobs. The results in Table<sup>4thussuggestthattherearepositiveintergenerational</sup> correlations in occupations even in the sons'<sup>sampleandthatthese correlationsrunalong gender</sup> lines as observed in the case of daughters'<sup>in non-farmparticipationdecisions.</sup> To summarize, intergenerational correlations<sup>inoccupationchoiceseemto bestrongerin the</sup> case of skilled jobs for both sons and<sup>daughters.Asinthecaseof non-farmoccupationsinclud-</sup> ing both skilled and unskilled jobs, the<sup>correlationsrunalonggenderlinebutaremuchstronger</sup> for daughters. Though the regressions<sup>controlforabilitycorrelationsusingfather, mother and</sup> spouse's education as explanatory variables,<sup>onemaystillarguethat these areimprecisemea-</sup> sures of ability. However, if the intergenerational<sup>correlationsuncoveredin theprevioussections</sup> were primarily driven by the correlations<sup>in abilityof theparentsandchildren,thenone would</sup> expect similar magnitudes of correlations<sup>forchildrenof differentgender.Thehugedifferences</sup> in the strength of these correlations<sup>between sonsanddaughterssuggestthatintergenerational</sup> correlations observed in our data are<sup>not primarilydueto correlationsofparentsandchildren's</sup> ability. The results point more to the<sup>'rolemodel'effectsandlearningexternalitiesasa sources</sup> of these correlations. Women in a traditional<sup>society,beingbroughtupwithintheconfinesof</sup> a household, have their mother as their<sup>primaryrole modelwiththeeffectthatmothershave</sup> disproportionate influence on daughter's<sup>occupationchoice.For sons,thesetof role modelscan</sup> extend well beyond the household, and<sup>whilefatherexerts someinfluence,father's impactonsons</sup> occupation choice becomes diluted as education<sup>opensupa richermenuof occupationalchoices</sup> to them.

# VI: Conclusions

Despite the recent surge in interests in the<sup>determinantsof non-farmparticipationin develop-</sup> ing countries, the issue of intergenerational<sup>linkagesin non-farmparticipationhas,toour knowl-</sup> edge, not been addressed in the literature.<sup>A burgeoningliteratureon socioeconomicmobility,on</sup> 22
the other hand, emphasizes these intergenerational correlations. However, the empirical research in this literature has focused mostly on intergenerational income correlations in the context of developed countries.<sup>36</sup> In this paper, we present some first empirical evidence on intergenerational occupational correlations in non-farm participation in a developing country, Nepal.

The empirical results show that there are strong intergenerational correlations, especially for daughters, in non-farm participation arising from 'intangible' factors like role model effects, learning externalities, and transfer of reputation capital. The results of Two Stage Conditional Maximum Likelihood analysis that correct for potential enodgeneity of education and assets in the regressions, suggest dramatic impact of a mother's employment status on daughters' nonfarm participation rate. The probability of a daughter's non-farm participation increases by 200 percent if mother is employed in the non-farm sector, even after controlling for a large number of relevant variables including education, assets, and non-farm opportunities. The linkage between mother and daughter is especially strong in case of skilled non-farm jobs, thus implying very restricted occupational mobility for women out of agriculture and low skilled non-farm activities. In contrast, the intergenerational link between father and son is found to be nonexistent, except for the case of skilled non-farm jobs. The difference in the strength of intergenerational correlations between son-father and daughter-mother implies that the observed correlations in case of mothers and daughters are not primarily due to intergenerational correlations in genetic ability or taste. Other intangible factors such as role model effects and learning externalities are perhaps more important in determining these correlations. The much stronger and positive intergenerational correlations between mothers and daughters also suggest the existence of a strong gender bias against women in occupational mobility. Such robust occupational correlations, on the other hand, provide a strong argument in favor of policy interventions for promoting non-farm participation of women due to the dynamic multiplier effect. It will be interesting to see if the pattern of intergenerational linkages found in case of Nepal is valid for other developing countries, especially in South Asia and Africa where the initial non-farm participation rates are strongly skewed against women.

> 36 Solon (1999) in his survey of studies on intergenerational mobility cited only one paper on income mobility in the context of developing countries; the paper cited being Lillard and Kilburn (1995) that uses data from Malaysia.

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