metadata: authors: 0: author: Schultz,Theodoro Paul
docna: 0: docna: Women and development : objectives,
            frameworks, and policy interventions
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Labor Markets,Educational Sciences,Economics and Gender,Gender and Poverty,Gender and Economic Policy,Gender and Economics,Rural Labor Markets
teratopic: Health, Nutrition and Population
abstracts: cdata!: The contribution of women to an economy
            is in principle no different from that of men. But in
            practice, the problems of valuation, measurement, and policy
            inference are more complex and the implications for policy
            may be more controversial and culturally sensitive. This
            paper finds that the private and social returns are high on
            investments to improve women's economic productivity -
            particularly education. Where women recieve less education
            than men, efforts to redress that imbalance deserve
            priority. Measures to open women's access to
            information, technology, productive resources, and credit
            should be tested far more extensively.
repnme: repnme: Women and development : objectives,
            frameworks, and policy interventions
display_title: Women and development : objectives,
            frameworks, and policy interventions
listing_relative_url: /research/1989/04/437022/women-development-objectives-frameworks-policy-interventions
topicv3: Female-Headed Households m1326228 916,Labor Markets m1326488 855,Education m1327048 470,Jobs m1503931 2670,Health m1504661 3480
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Health, Nutrition and Population
origu: Policy Plan & Prog Review Dept
no_of_pages: 145
author_keys: 0: author_key: 638601
model_extractions: The contribution of women to ar economy is in principle no different from that of men. But in practice, the problems of valuation, measurement, and policy inference are more complexand the implications for policy and programs may be more controversial and culturally sensitive.

# childbearing and childcare.

* For similar reasons, extension or similar outreach. activities can increase the productivity of women in home-based production, including agriculture and the provision and use of housenold water.

Reviewing and integrating several lines of economic research on how women affect economic and social development, Shultz concludes that:

* Private retums to investmen **t** hat enance women's market and productivity are high, notably in primary and secondary education, especially in regions where women now receive less education than men.

> * Extension or outreach programs can correct factor market distortions, provide women with snore equal agricultural and household technologies, and expand the effective supply of credit where women produce for maiket.

To give us more firm evidence of how public policies affect women's productivity and family consumption pattems, Schultz recommends pilot programs to:

* These investments shift the aBocation of women's time toward market work and away from home-based work.

* rhese investements benefit the health and nutrition of the women's children and immediate family members.

* Increase school enrollment and graduation rate for girls.

A assess the effect of equalizing access to agricultural extension services by male and female farmers in Africa, Southeast Asia, and Lain America.

* TMese investments reduce desired ferfility and increase women's ability to obtain and use family planning services more effectively.

* Explore altemative configurations of child and maternal health programs, varying the mix of staff by level and gender.

* These investments make women more efficient managers of family resources of land, fuelwood, water, and food.

* Evaluate program innovations in the delivery of credit, such as the Grameen Bank in Bangladesh.

* Family planning and family health "outreach" progrgn-s especially help women, because women's mobilty is particularly constrained by culture and the practical realities of

All these pilot programs should be designed to monitor the families' choices that affect whether they produce healthy and educated sons and daughters.

This paper is a product of the Women in Development Division, Population and HumanResources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Johanna Klous, room S9-121, extension 33745.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Researcn Complex. An objective of the series is to get these fndings out quickly, even if presentations are less than fully polished. The findings, interpretaions, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
# Women and Development: Objectives, Framework,

# and Policy Interventions

|1.|Introduction|1|
|---|---|---|
||Economics of the Family and Development<br>Concepts<br>of**the**Family Guiding Empirical<br>Ahalysis|3<br>4|
|2.|Consumption<br>and**the**Family|7|
||Models of Family Behavior<br>and DistributLon|7|
||Mode<br>ls of Marriage<br>and Family Behavior|9|
||Empirical<br>Evidence on**the**Nash Bargaining<br>Model<br>||
||oftheFamily|12|
||Measurement of Intrafamily<br>Consumption Patterns<br>|17|
||The Composition of Families|21<br>|
||Households Headed by Women: Multiple<br>Causes|23|
|3.|Production<br>and the Family|27|
||Occupational<br>Segregation<br>and Discrimination<br>|28|
||Comparisons<br>of Productivity<br>intheMarket and Hom|30|
||Time Allocation<br>and Labor Productivity|32<br>|
||Fertility<br>and Investment<br>in Children|35|
|4.|Investments<br>in Human Capital and Rates of Return|38|
||Labor Supply and Rates of Return to Schooling|42|
||Social and Nonmarket<br>Returns to Schooling<br> <br>|60<br>|
||Determi-iunts of Child Nutrition<br>and Mortality<br> <br>|62<br>|
||Determinants<br>of Child Achievements in School<br> <br>|69<br>|
||Determinants<br>of Fertility|70|
||Policy Implications<br>of the Empirical<br>Evidence<br> <br> <br>|72<br>|
||Regional Patterns<br>of Investment<br>in Women and Men|75|
|5.|Factor Markets,<br>Information<br>Diffusion,<br>and Equipping Interventions|83|
||Home Production<br>and New Technologies<br>|84<br>|
||Agricultural<br>Extension<br>and Research<br> <br> <br>|85<br>|
||Land Owrership,<br>Credit,<br>and Labor Markets|88|
||Famili Planning and Haternal<br>Health|92|

93 6. Conclusions
# 1. Introduction

This paper examines from **an** economic perspective women who, **as** producers and consumers, are attached at varioui stages in their lifetimes to families and households. Women's contribution to the economy is in principle no different than men's. In practice, however, the problens of valuation, measurement, and policy inference are **more** complex. Moreover, the design and evaluation of development policies to increase women's productivity and advance other social objectives are culturally sonsitive, because these policies may change customary patterns of work, specialization, and the evolution of the family and other basic social institutions.

All societies coordinate, in one way or another, production and consumption of goods, child bearing, and investment in human capital, such **as** ectucation, and nonhuman capital. This occurs because such cooperation generates important economies, including increased ssale of production, specialization, and shared consumption of public goods. Many activities that produce goods and services and allocace them among persons are coordinatad by semi-permanent family groups that are not restricted to one sex or age. These family groups may also *hare a residence and join in reproduction and extended periods of child rearing. A comon group is a nucleaz family, perhaps with older members, but of course families take diverse forms around the modern world. It is difficult and probably inappropricte to analyze **women's** contribution to the development process without roference to the family or household.

Changes in women's earning power compared to men's and children's can affect what the family does better than othir institutions in society, such as 1
2 firms and government. Functions where the family retains a comparative advantage ;- also be performed with a different **mix** of labor and capital as the economic capabilities of women approach those of men.

No matter how overall economic coordination is achieved, governments are limited in their ability to redistribute resources among persons within families. This is because **families** can compensate for most government transfers, readjusting **the** allocation of private resources within the family until the distribution of total resources among persons **again** reflects the incentives and values of the feamily (or its more poverful members), To propose sbnsible policies that will afflect womn, it is essential to urderstand how family incentives affect women, children, and men; how governmeut activities penetrate the family and modify women's economic capabl4. _.es;_ and how the costs and results of such government activities compare to .rhtr s*cial investments. The family's consumption patterns, &avinGs, sind investments in family members may all change when women's and men's prc4uctive capabilities change. To maximize production and growth, irnterrv* rates of return to investments should be equalized across the formation of human capital, the increase of home-based production, and the increase of firm based-production. A divergence in the estimated rates of return to investmenas in home, firm, and human capital suggests an inefficiency, and calls for an analysis to isolate what institutions, information or incentives could be ksponslble for the seeming misallocation of resources.

Empirical evidence on the responsiveness of persons and families to different external influences and constraints show what the limits are on the decision-making units responsible for particular forms of behavior, ranging from a lone individual to an extended family. Such specific knowledge about
3 family coordinatLon and pooling of economic resources **slao** helps to specify general models of the family that better reflect the conditLons that constrain family choice in various cultures. More accurate predictions of the impact of socioeconomi **c** hange and polLcies should then be feasible insofar **as** they affect families **and** particular members of **these famiLies.** Economics of the **Family** and Development In more traditional agricultural societies and In **the** early stages of the development process, **the** fauily and **the** simple firm blend together. As trade and lndustrial specialization develop, technical economies in the scale of production emerge, and firms gradually replace families **as** the main source of output, except perhaps for agriculture (Kuznet;, 1957; Schultz, 1988a). Thls shift in production from family enterprises to non-family firms is associated with greater participation of women's in the work force on a more equal footing with men (Boserup, 1970; Schultz, 1988a). Initially, women are often confined to a broad set of home production activities. Later they move to participate more fully in wage and salary jobs outside of the family, to reap the full benefits of more specialized vocational training.

It is often said that by obtaining a job (for wages) outside of the family, a woman increases her control over the productive returns to her labor and hence gains relative influence over how the family uses its economic resources. Women may be motivated to enter the **wage** labor force, either by domestic poverty, or by the appeal of eploymnt opportunities outside the home. The level of a women's productlvity is of primary importance to her welfare and that of her children, but the conditions under which she works may also matter. Changes in women's labor force participation may be a good or ill event, depending on its causes. To dlstinguish between such income and
4 wage effecti on a women's marketed supply of labor, many factors must be evaluated together, as specified by a family model of decisionmaking Moreover, the distinctive role of women in managing<sup>investments in</sup> children may be a strong reason for incrtasing women's capabilities **and** their control over !amily resources. Improving **the** productivity of women is a social objective to advance economic development, and **also** possibly to channel that development toward socially desired<sup>investments</sup> in the food, health care, **and** schooling of children. Raising **women's** productivity often also leads to reduced child mortality and, at approximately the **same** time, to a reduction in fertility. If the latter more than offsets the former,<sup>the</sup> rate of population growth may be reduced and the proportion of the population in the labor force may be slowly increased, **as** the demographic transition progresses. Quantitative confirmation of these apparent linkages, however, requires such study and replication to build sound generalizations from oepirical regularities and to identify the special features in society that affect these behavioral tendencies. If these empiricai studies are<sup>guided bya</sup> coherent modeling framework, the accumulation of useful knowledge can be expected to occur more rapidly.

# Concepts of the Family Guiding Empirical **Analysis**

A conceptual and statistical framework is essential to draw inferences from notiaxperimental data about how family production, consumption, and behavior respond to changes. This applies to experimental pilot programs as well. It is hard to forecast how social institutions, such **as** families, firms, and government social programs will evolve over time. The first step is to work out the rules for decision-making in the family that account for observed producer and consumer behavior. This requires<sup>analysis of how individual,</sup>
5 social, and environmental constraints interact to determine the productivity and role of women in a particular society. Such constraints must be clearly defined, adequately measured, and beyond the family's direct control or exogenous to the family. Where the line is drawn between family choice variables and'exogenous constraints depends on tli question addressed. The analysis may encompass the broader extended family, it its members pool resources and share obligations. This coordinatin **g** roup **is the** elusive starting point for empirical analysis. Two questions are paramount: How extensively do individual and family decisions interact? What are the economic limits to the family? Ultimately, the family has a life cycle perspective. Moreover, there is an element of inter-generation **al** truism as parents spend part of their earnings to help their children, and grandchildrern, ad infinitum. However, past decisions. in the life cycle of the family should not be viewed thereafter as exogenous constraints on later behavior. Women who worked before marriage are certainly more likely to work after marriage than other women, for example, and they may also have fewer births. But do the work decisions influence the fertility decisions, or do both instead depend on other exogenous influences .or unobserved variables, such as preferences, operating on the basic sequence of lifetime choices?' The latter is more nearly correct. Marital status, work patterns and fertility are thus considered all endogenous and in need of explanation. According to many studies, the likelihood of being married, or living irn a family devrends on the distribution of endowments and capabilities of men and women (e.g., Becker, 1974, 1981; Friedan, 1974; Grossbard, 1976; King et al., 1986; Montgomery and Sulak, 1988). If the aim is to estimate the longer-run tendencies of families, and hence society, to adjust to changing conditions and constraints, a longer sequence of these interdfpendencies
6 should be **viewed as** *ndogonous. **The** modeling framework should thus specify the timeframe and the appropriate class of *ndogenous outcomes and exogenous conditions 2 To determine what family group **decides** on any particular production, consumption, investment, or behavioral decision in each society, it may be necessary teo design nev sampling procedures to collect data from soveral potentially relevant groups of family members. In some **societies,** fox *xample, men might **be** expected to help pay the school fies of their sister's children, in which case the schooling decision will be imperfectly understood unless the wage rate and u-iearned income of uncles are included among the constraints on child enrollmnt. A **simple** empirical test of the limits to such a family decisionmaking process'would establish whether an uncle's endowments affect significantly a child's schooling behavior. Conversely, if financial transfers are observed from the child's uncle to the child's mother, they should be treated as *ndogenous variables, potentially identified, perhaps, by information on the uncle's exogenous endowments.

M8ny disciplines contribute to the empirical literature on linkages within the family and offer evidence on how women's capacities are increased and how they in turn affect the productivity, welfare, and behavior of family members. These studies employ disparate standards for statistical evidence, and do not share the same modeling methodologies. Categorizing constraint-like variables into exogenous conditions and endogenous restraints that evolve over time because of past decisions is therefore an important step in formalizing an analytical framework that can potentially illuminate empirically women and development.
7

# 2. Conssniptioa **n** d the Family

"Who deserves consumption" involves the values of society and the family, and is not necessarily clarified with **the** tools of welfaro economics. Econo- **mists** can postulate o social-welfare function that will permit **welfare** comparisons to be drawn botween persons and thus derive 'optimal" distributive policy. But such economic formulations are rarely responsiblo for **the** trans- _fer_ sche_es that societios adopt. Nor **are** they often a persuasive basis for designing policies to intervene in the economy. Yet it may be possibl* to describe **the** tradeoffs made by society and by the family and thus identify factors affecting intrahousehold consumption patterns. This section discusses the neoclassical model of family behavior, suggests its limitations, and explores a more flexible "bargaining nodel* of family deciiommaking. This lattor model recognizes **the different** interests in, and control over, resources that individual family mombors **have.**

# Nodels of Family Bebhwior and Distribution

What is responsible for the inequality between men and women and why should it vary in different countries? Undoubtedly a tension exists in the family between efficiency and equity, an it does in society **as** a whole. From the point of view of efficiency, a family membe'rs food, consumption, health, and long term formation of skills, such as education, should be supported only **so** long **as** these investments are **more** profitable than alternatives. Conversely, pure parental altruism could lead **parents** to consider **each** child's satisfaction as important **as** their own. This would enco-arage parents to invest in their childrun's endowments until they are similar to that of the parents. Egalitarian parents may also derive satisfaction from leaving their children equally 'well off." By contrast, the criterion of efficiency is
d likely to encourage parents to invest more human capital Ln the children vho ,re more able, aqsuming that theme natural abilities **enhance** the returns on parental investment. Parental bequests to less able children would then include a greater proportion of property who.a market rates of return **are** not affected by the child's ibility. In this mnner, Becker and Tomes (1979) show how parents might distribute their humn and physical imvestmnts among their children of differing ability. If **the** marginal returns on humn capital **exceed** those on physical capital for any child, **the** parents' pursult of efficiency would cone. into conflict vith their ideal of consumption equal' - ln the family (Becker, 1981). Available *vidence suggests that on average the private rates of return to investmnts in education are larger than the returns on nonhuman capital today in low income countries (Psacharapolos, 1973). In **these** circumstnces, intergenerational mobility ls accomplished by parents more efficiently if they invest In the education of their children rather than transfer to then property durirg their lives or through bequests. EmpirLcal estimates of intrafamily distribution of consumption and human capital investments provide clues **as** to how familios trade-off efficiency and equity. Does this trade-off differ at different levels of family income? Are such differences comparable in different societies? Fmilies may balance the objectives of efficiency and equity differently when average incom falls toward subsistence and the family's very survival is at risk. Sex differentials in child survival, nutritlon, and schooling offer a baxis for analysis of welfare differences between girls a. boys. These in turn affect the economic capabilities of adult womn and men--and thus influence their social mobility and inequality, as well **as** on their productivity. **These** gender differentials within the family are analyzed here, first
9 across households within societies. Interpreting these differentials and estimating how policy might affect them requires a framework that accommodates the salient constraints on the family and accounts for critical, features of the personal distribut.. **o** fn family consumption and human capital investment. A more complete model nust also describe who is found in a family or, more precisely, family composition. Adults that **aro** observed to **tsside** in families **are** not a random **sample;** they **aro selacted** according to criteria that are likely to also influence what they expect to receive from the family's resources. A sample selection bias is thereforo potentially present in any analysis restricted to families. The decision rules that govern family formation and marriage should be combined with an analyst.s of the constraints doetrmining intrafamily resource allocations. Though elements of such a theor7 are at hand (McElroy, 1988), empirical applications are limited and largely confined to U.S. data. But the theoretical approach is wedl suited to the study of woaen's productivity in low income countries. 3 Models of Marriage and Family Behavior Adults presumably maintain their attachment to their existing family, or marry, or go their separate way, because there **are** net gains in doing so as compared to other foregone opportunities In marriages, so long as both partners share the net gains in excess of the opportunity costs given up from alternative arrangements, the union has at least an economic **basis** for continuation (Backer, 1974). The division of output botween partners uight be studied **as** it responds to market forces (Becker, 1981:42). But it has not **received** attention **because** it **is** hard to attribute consumption to a particular family member, and traditional models of the family do not prescribe distributional rules.
10

**The** classical unified family decision-making framework assumes that the family behaves **as** if it is trying to allocate the time of its members and other endowments to satisfy a comon set of "family" preferences (Becker, 1965). This process involves pooling resources and agreeing on the form of the family's preferences. Becker (1974) describes conditions under which this process could take place. The simplest assumption is for couples to share the same preferences. Given **the** limitations of this working assumption, Becker describes a dominant family decisionmaker, **the** husband, who allocates the gains from marriage to roward the other individual with more than she expects to receive as a single person or in another union. Incentives are also *stablished to encourage the non-dominant miember to allocate **her** time and other family resources to accomplish the solution chosen by **the** dominant individual. If the gains from the union are insufficient to motivate coordinated family behavior, the family may dissolve. This second model, though it is more realistic than the first, still does not imply any testable predictions about intrafamily consumption. One way to proceed further would be to specify the structure of household production and then estimate the magnitud.'of the gains from marriage based on the endowments of the partners, market prices, and production technology (Wallace, 1974).

How are such marital gains actually distributed? The family demand system does not help in predicting which lntrafadily allocations are more likely to occur. It does imply strong restrictions, but these may not fit data because the demand model also depends on auxilliary assumptions, such **as** the functional form of relationships, that are not central to the conception of a unified family decisionmaker. Extending the family demand model to accommodate the conflicting inter-
11

*sts of family members is **a** reasonable next step These conflicts of interest must be resolved by a specific bargaining mechanism to be tractable and testable. Many game theoretic models do not imply unique solutions, nor do they lend themselves easily to empirical testing. **The** cooperative Nash-bargained framework, as stated by McElroy **and** Horney (1981), is an interesting possibility. It **uests** within it **as** a special case the unified family deoand system. Statistical tests can **be** readily implemented, therefore, to determine whether data on observable family behavior satisfy **the** restrictions implied by the unified family demand model, or instead, suggest that this systematic form of bargaining is consistent with the data. **'These** tests are also intuitive: They simply imply that nonearned income of the husband or wife may influence consumption differently. When the woman controlls physical assets or streams of nonearned income, her bargaining power in the family to distribute the family's resources is increased. If these tests support the bargaining model versus the unified family demand model, additional more complex tests of the extended demand system can be worked out (Horney and McElroy, 1988).

Other approaches to bargaining behavior in marriage involve making specific assumptions about asymmetric information available to spouses, assume differences in transaction costs within and outside of the family, or specify the form of the utility function for the family (e.g., Fabella, 1982). For many reasons, few empirical tests **have been** implemented to shov that any of these bargaining approaches **are** helpful in explaining family behavior. Acceptance of bargaining models of family decision-making will depend on comparisons of the model;s capacity to account for related forms of family behavior that are not adequately explained by the unified family demand model (Manser and Brown, 1980; McElroy and Horney, 1981; Pollak, 1985; Jones, 1986;
12 Peters, 1986; Carlin, 1988; Schultz, 1988c).

Empirical Rvidence an **the Bash** Bargaining Model of tho Family Evidence is slowly accumulating that is consistent with the cooperative Nash-bargaiLned model of family decisLon-makLng but not with **the** strict formulation of the neoclassical unified famLly domand model. Other models of bargaining sty be developed that are less **restrictive** and **more** general than the cooperative Nash-bargained solutlon, and they may permLt the partners to know different infornation and choose **among a wLder range** of Pareto allocations (Chiappori, 1968). But the goal here is to describe the initial modeling effort3 that have added flexibility to the neoclassical faumly demand model by dealing with the dlstinct lnterests and resources of fauily mewbers. The model should also allow eventually for a partial pooling of resources, rather than the ccmplete pooling assumed ln the family demand model.

Consider, for example, how the indivLdual supplies labor. It ls generally assumed that increases in nonearned income (not contingent on the individual's allocation of time) increase deoand for leisure and nonoarket time and reduce tioe tiupplied to the labor market. As this framework was adapted to analyze the labor supply behavior of wives and then other family members (Mincer, 1963; Koster. 1966; Heckman, 1971), the leisure of each family nmeber was added to the family utillty function, but the family's nonearned income was pooled. This approach to famlly demands and labor supply consequently assumes that the effect of nonearned lncom would be identical regardless of the individual's status in the family or the source of the nonearned incom. Situations say arise where this neoclassical assumption appears realistic and others where it does not conform to what we know about resource pooling of family members or coordination of decision-making.
13 The cooperative Nash-bargained model **assumoes** the partners cooperatively maximize a product of the individuals' marital gains in their utility compared to their utility available outside of the union. This utility of the alternative state represents a *threat point" beyond vhich the partner would leave. It thus limits the consumption allocations wlthin the family that are acceptable to both spouses. Nonearned income of **the** husband or of the wife will, thus, influence the "threat point" of that **spouse:** it leaves the spouse less dependent on marital gains. It thus strengthens the spouse's bargaining power, and potentially changes the distribution of consumption in the family. 4 This would be most obvious in its effect on the consumption of leisu,^, since this is thought of as a private good. The effect on other forms of consumption may be more ambiguous, such as expenditures on-tobacco, alcohol, toys or apparel, for there is nothing to prevent wealthier women or men from deriving satisfaction from varied consumption activities.

Consequently, nonearned income (or its sources) should be divided into those elements brought to the marriage or accumulated during the marriage through distinct individual activities, the receipt of bequests or transfers, or other personal connections. A wife's nonearned income, such as she might have inherited or brought to the marriage as a dowry, might be expected to reduce her market labor supply more than the same amount of nonearned income 5 brought to the marriage by her husband. Conversely, the payment of a brideprice in many areas of subSaharan Africa by **the** groom to the bride's parents may be associated with the bride increasing her supply of time to the labor force. 6 This prediction of the individualistic bargaining model received only modest support from its first empirical test against U.S. household data (Horney and McElroy, 1988). Subsequent study of the allocation of time of
14 U.S. husbands and wives to housework provided more support for **the** bargaining approach (Carlin,.1988). But data from Thailand strongly confirm its usefulness (Schultz, 1988c).

In the 1981 Socioeconomic Survey of Ttailand, participation by women aged 25 to 54 in the labor force is reduced by three **times as** much for a given flow of nonearned incom (from rentals, interest or dividends) if that nonearned income **is** owned by the woman as compared to the effect of that income if it is owned by her husband. Conversely, men agegd 25 to 54 reduce their participation by three times the amount when **the** family's nonearned income is owned by the man rather than his wife (See Appenlix Tables A-3 **and** A-4). In Thailand women are frequently in the labor force, and while marriage has been nearly universal, they often divorce and remarry. It **might** be more difficult in some other societies to collect meaningful data on the ownership of nonearned income for each individual in a family. For example, in a survey of rural Northeast Brazil, few women report nonearned income, though the proportion increases in urban areas.

At a more anecdotal level, there is evidence that as women's education and marketable skills improve, women are treated better and consume more of their family's resources. But there reomin relatively few studies of household surveys from which to draw such generalizations. The neoclassical family demand model emphasizes that the human capital embodied in women affects their value of time and influences the allocation of time and investments within the family (Mincer. 1963; Becker, 1965). Consequently, evidence that time allocations, consumption, and investment patterns within the family respond to differences in male, female and child wages does not discriminate between the family demnd and bargaining models. But **the** cooperative Nash-
15 bargained model of household behavior also predicts differential consumption effects of nonearned income depending on who controls it. The bargaining framework suggests why **women may** engage in separate jobs from their husbands to enhance their control over resources. **Indeed,** this pattern **is** particularly notable in subSaharan Africa and South East Asia (Sct.ultz, 1988a), although women **are** still mostly wrrking **as** unpaid family workers.

In parts of Africa husband and wife often cooperate in the joint production of scue crops, while other crops or parts of the production process--e.g. marketing--ar **e** ntirely the responsibility of one meber. The neoclassical model of the family leads to the expectation that the wife allocates her time between the joint crops and her own crops to equalize the value of her marginal product across all activities. The bargaining model, however, predicts **that** s e would work more on her own fields, because **the** value of her marginal product there is more under her control. Jones (1986) confirmed these predictions of the bargainLg -model with survey data collected from North Cameroon. Allocatl.ve incentives within these Cameroon families, therefore, may not achieve an overall efficient use of labor but advance other individual interests of family members,7 Another implication of the deomnd model is that the income-compensated cross-substitution effects should be symetric or equal. This restriction of the unified family demand model implies in allocating labor supplies that the husband and wife agree on the value of each other's nonmarket time. It is possible to imagine, **as** an alternative hypothesis, that a husband would assign a higher value to his nonmarket time than does his wife to his time. In the case of their valuations of the wife's nonmarket tiwe, the wife might also value her own tim more highly than does her spouse. An individualistic
16 bargaining model should allow for the possibility that the wife and husband might value some "goods* differently, such **as** their own 1Ieisures." Hence, a test of the fauily demand model is the strong restriction that the incomecompensated effect of the husband's **wage** on **the** wife s nonmarket time is equal to the income-compensated effect of the wife's wage on the husband's nonmarket tim. Heckun (1971) tested this statistieally and rejected it with U.S. data, although this finding wa not emphasized in a subsequent paper (Ashenfelter and Heckman, 1974).8 But the test is conditional on many other aspects of the demand uodel, including functional-form approximations. To *valuate the partial effect of the husband's or wife's nonearned incom on household expenditure patterns, the wage rates of both partners must be hel1 contant. If expenditure shares are analyzed, then an instrumental variable estimate of **the** family's total expenditures might **be** included **as** a proxy for the effect of permanent income. It **is** expected that increases in the wife's nonearned income share of total expenditures will have the partial effect of increasing expenditure shares on children's nutrition, health, and ed,ocation as well as on her leisure. This assumos, however, that the wife's nonearned income has no effect on fertility, and the above variables representirg Lnvestments in children are defined on a per child basis. Unfortunately, most expenditure **surveys** do not distinguish these child investment **expenditures,** e.g. child and adult health care **are** coubined in a single coded expenditure category (Schultz and Thomas, 1988), and tests of this form **are** not yet reported.

The neoclassical family demand model, nonetheless, **has** the appeal of simplicity and widespread applicability, and some successful empirical application (Smith, 1980). How much realism should **be** sacrificed by a theoretical
17 paradigm to gain tractability **and** testable predictions is debatable. Perhaps in societies where nearly all women marry by age 30 and there is little dissolution of marriage, as was true until recently in Korea, China or Taiwan, the neoclassical model of the integrated family might prove satisfactory. But in much of Subsaharan Africa and South East Asia where mon and women often have different sources of income and distinct responsibilities for the suppoxt of family consumption, individual interests **may** not be submerged in a *unified" family. The cooperative Nash-bargained model of McElroy and Horney (1981) appears to be a suitable model to structure research on family and individual behavior across societies, because it generalizes the neoclassical family demand model. It directs particular attention to who controls what assets and streams of income in the family, and may lead to new insights about how women's status influences the development process.

# Measurement of Tntrafamily Consumption Patterns

It is hard to evaluate systematically and comprehensively what individual family members consume. Many household goods benefit all members: one person's consumption does not diminish how much of such a *public good" is available to others. This property of public goods can be used to explain family formation (Lam, 1988). Children are often referred to as a marriagespecific investment or public consumption good, though the analogy has its limitations (Becker, et a. 1977). Economies in the scale of home production and. public consumption are also difficult to disentangle empirically from the implications of public goods in the family. Both phenomena contribute to the gains from marriage.

Nonmarket production is also hard to track down in order to evaluate who in the family consumes what. Child rearing is the primary example of a family
18 nonmarket good that has some of the attributes for parents of a publlc good. For this reason most empirical analyses of intrafamily distribution of resources have focused on human capital investments in children, because such investments are largely produced by **the** family, **are** embodied **In** the children, and hence are subject to their future control, and are quantifiable in a survey. A family's investmnts in children account for a larg" part of a fasily's savings and intergenerational transfers. Three indicators of human capital investment in chil-iren **are** most frequently studied: mortality, anthropometric measures of child nutrition and health, and schooling. However, the number of studies examining gender differences in child mortality, health, and education in low income countries is still quite limited. A **few** econodic studies must suffice to illustrate how gender differences can **be** interpreted within fasilies to **measure** regularities in behavior that should inform policymaking.

Assume that survival rates of girls and boys, or those of women and men, are related directly to the relative contributions of women and men to their familiea' cash and kind incone. In India, where female mortality exceeds male mortality (Visaria, 1971), as more women participate in ethe local labor market and are more productive relative to adult men, the survival of girls compared to boys is higher. This pattern can be observed at the distrLct level in the 1970 **Census** of India among rural populations and **also at** the household level in parallel studies of representative rural **sample** surveys (Rosenzweig and Schultz, 1982a). A study in the Punjab (Anin and Pebley, 1987) suggests, moreover, that public policies that increase access to health services, without affec Ing the relative productivity of sen and women, reduce general mortality, but increase the mortality rate of girls compared to boys after the
19 first month of life. This is when the health of children responds primarily to the family's allocation of food and hbalth care. Similar patterns are noted in Bangladesh, Nepal and elsewhere in India (Chen eSt &I. 1980, 1981; Martorell et al., 1984; Bardhan, 1984; Sen, 1988).

In China, where social pressures have recently been focuse., on achieving a one-child family, the frequency of infant mortality appears to **have** incriossed disproportionately for girls (Aird, 1983; Zeng Yi, 1988). Perhaps *us a consequence, rural families in China are allowed increasingly to have a second child if the first is a girl--the goal apparently being to reduce feuale infant mortality. The productivity and education of women has traditionally been far below that of men in China and continues to be in interior rural areas (Freedman at al., 1988). Indirect findings such as these suggest that economic productivity of persons in the family has a bearing on their share within the family of consumption and humAn capital investments. But the correspohdence appears to weaken as the overall level of family income increases. Gender differences in nutritional status are smaller in studies of Nicaragua, Brazil, Philippines, Sri Lanka, and Ivory Coast (Blau, 1984; Popkin, 1980, S$nauer lt al.. 1986, 1988; Strauss, 1986b; Thomas et al., 1987; Thomas, 1989) and may reverse in some regions of subSaharan Africa where women take an active role in the labor force (Svedberg, 1988). With increased wealth families appear to exhibit a preference for greater equality in nutritional investments within the family, at least between children by gendA'r.

There is on analogous pattern across countries in the Investments families make in the schooling of girls compared to boys. At low income levels investments in boy's schooling exceeds that in girl's. As real income per adult increases, public expenditures on schools per child tend to increase as
20 do enrollment rates. But the income related increase in enrollsmnt rates among girls is significantly larger than it is among boys (Schultz, 1987) A catching up for girls is evident in both cross country comparisons at different **stages** of development and within countries **as** income increases. Equal educational treatment of boys and girls appears to be a "normal good" within **the** family, and as income per capita **increases** and reproductive goals are freely chosen, **a** variety of indicators of consumption and investmnt become more equally distributed between sale and female family members. Public policies sometimes **are** limited in their ability to influence the family's distribution of consumption because the family can, if it wants, compensate for them. A free school lunch program in Brazil or India, for example, leads to decreases in the family's supply of food to those children who benefit from the school feeding program. Part of the family's food that would have been supplied to the children in the program is apparently reallocated within the family to advance the family's own objectives. Nutritional intervention programs have had to take this into account (Chernichovsky and Zangwill, 1988).

There axe more efficient ways to target resources transferred by public programs to specific uses in the family. The **state** could train individuals to accomplish their goal rather than contribute to the general pool of family resources or give the family incone in-kind (i;a. food or health care) or transfer selected items for specific individuals to consume, such as school lunches. If public programs train women for vocational opportunities where they earn cash incomes directly, rather than through integrated (i.e. unpaid) family activities, the gain in women's productivity may shift consumption patterns toward women's priorities. Recent **analyses** of Brazilian data confirm
21 that nonearned income that is controlled by women has **a** larger effect on child health and nutrition than does nonearned income reported by men (Thomas, 1989). Empirical analysis of programs that encourage investments in women is needed to document what kind of changes occur in consumption patterns within families **as** the productivity of women is increased (Kennedy **and** Cogill, 1986; Blu.berg, 1988).

**The Composition** of **Families**

One way that people express their **demands for** consumption patterns is in the form of the families they create. An increase in the proportion of households headed by women has recently been observed in many countries. It is frequently linked to the change in the jobs that **women** take, at least in the industrially developed countries and urban Latin America (Youseff and Hefler, 1983; Kniesner et al., 1987). One explanation is the increasing productivity of women compared to men in the labor market. Cross sectional patterns imply that increasing the level of male and female productivity and wages at the same rate is sufficient to increase women's participation in the labor market, reduce the frequency and onset of marriage, and diminish lifetime fertility (Layard and Mincer, 1985). These developments ar! hypothesized to have reduced the net gains from specializatton of husbanl and wife in market and nonmarket production, respectively, within lifetim marriages (Becker, 1981). Though it **has** not been replicated in other U.S. sociul experiments, women who were given independent financial support for their children in the Seattle negative income tax experiment opted more often to separate from their husbands (U.S. Department of Health and Human Services, 1983). In those societies where women **earn** nearly **as** much as men, there **are fewer** marriages and more female headed households are enumerated.<sup>9</sup>
22 Individual data have also been analyzed to estimate the determinants of age-at-marriage among women. More educated women marry later, even in cases where marriage is sufficiently delayed in the overall society to avoid overlapping with school, **as** in gast and much of South-East Asia (Montgomery and Sulak, 1988; Anderson and Hill, 1980; King at &I., 1986). The growing tendency of young educated women to take paying jobs before marriage, financially encourages both them and their parents to delay their entry into marriage. Few studies have yet examined how local market demands for female workers affect the timing and length of marriage for womn. Evidence from Thailand suggests that **the** fmily bargaining model may help to understand variation in the prevalence of marriage in low income countries. Demographic and anthropological studies of Thai society document that marriage was until recently nearly universal. About 95 percent of men and women reported themselves **as** having been married (once) by **age** 35 (in the 1960 Census cited by Knodel et al., 1987; Table 5.1). An informal process of divorce has also been traditionally common with frequent remarriage (Smith, 1981). In the 1981 Socioeconomic Survey of Thailand 75 and 85 percent of the women and men, respectively, between the ages of 25 and 54 are living in the same household with their spouse. To explain who is currently married, the specialization argument as well as the bargaining model would suggest that marital gains would decrease with an increase in women's predicted wages and increase with an increase in men's predicted **wages,** other things equal. This pattern is confirmed for women's **wages** but not men's (see **Table** A-4). The bargaining model of the family suggests that **the** same pattern holds for nonearned income. If Thai women have unearned income this has a negative impcct on their living with a husband. The ownership of nonearned income is associated among Thai
23 men with a greater proportion residing with a wife. But the estimated elfect is nine times bigger for women than for men at similar levels of nonearned income (Appendix Table A-4): Marriage, it would appear, is not a "normal" good for womn.

Other hypotheses could also account for these patterns of marriage and residence in the Thai data, and the **available** survey data do not distinguish among them. The death of a spouse could **increae** an individua 's wealth through inheritance, and also transfer **the** individual to the *single" category. About half of **the** female headed households in Latin America are widows (e.g. Mohan, 1986; Rosenhouse, 1988). Alternatively, women might be more inclined than men, upon divorce, to move back into the household of their parents, other relatives, or children. Marital and residential histories with the timing of inheritance and transfers are needed to dXscriminate more adequately among these competing explanations for family formation patterns. Undoubtedly they will differ greatly as does the family in different societies.

Households Headed by Vomen: ultiple Causes Comparisons of income and welfare of female and-male headed households are not very informative. Most nale headed households tend to include wives, while customarily few female headed households include husbands 10 In some surveys the husband is treated as the household head even when he is not recently resident in the household. Which women find themselves in families that are called *male headed' or in female headed households will be influenced by their resources and other opportunities, as in Thailand. Several studies have found an associatiqn between wealth of individuals and decreased frequency of divorce, separation, and death of spouse (Becker et &I., 1977;
24 Peters, 1986). But the tendency noted in several parts of the world for the share of households headed by vomen to increase may be traced to a variety of sources, not all of which imply the **same** consequences. First, the increased longevity that occurs with development generally favors womon compared to men. Improved health is associated, therefore, with an increasing fraction of women at **the** older ages. Households headed by **the** older women who **have become** widows and not remrried is a large proportion of all femle headed households in Latin America and elsewhere. This group may not have children to support, and though their consumption, housing, and health needs are iaportant issues, these groups also may benefit from accumulating inheritances and ol&-age support schemes.

Another source of the increase in female headed households in low income countries is migration, which affects women differently fron one region to another. In Latio America, migration out of agricultuze to **the** cities **was** led by women, as it was in Europe and North America. Urban job prospects for women were better than for mGn, and the ratio of women to men in some metropolitan areas of Latin America was as high as 1.2 in the 19609 (e.g. Gregory, 1986; Mohan, 1986). As a result, many urban women did not marry, but they are not necessarily disadvantaged compared to the conditions they left behind in the countryside. The prospects for women advancing in Latin America from urban domestic jobs--given their education--to ones in industry, co merce, and other services, appears favorable compared with **men. The** overall productive status of women relative to mon is higher in **the** cities than in the countryside.

Unlike Latin America, migration flows in Africa were dominated by sen, drawn (or driven) to the **mines** and plantations, domestic services and con-
25 merce, state enterprises, **and** government bureaucracies. Women remained on the land, often continuing to produce traditional food crops largely without the **aid** of modern agricultural inputs or technologies. African women suffered from **lower levels** of *ducation than sen, explaining in part why men **were the** first to migrate from the rural sector and were more successful in setting themselves up in urban **businesses** (Caldwell, 1968). In Africa, t-herefore, the high proportion of famale headed households (da facto) is not associated with offsetting economic **benefits** for womn. In both Africa and Latin America, however, the divergence of male and female migration streams has probably contributed to the relative decline in the two parent household, **and** to the growth of associated social problens.

There is widespread evidence that women have lncreased their educational attainment compared to mn in most low income countries in recent decades (Schultz, 1986). Associated with these educational gains some data also confirm that wage rates and productivity of women have increased relative to that of men. Gains in the market productivity of women compared to men reduces the traditional spheres of specialization by women and men, and erodes the economic advantages cf lifetime marriage (Becker, 1981). This development along with the increasing participation of women in the market labor force is another factor contributing to **the** increase in the proportion of femle headed households.

Households headed by women **gensra2ly** report **lower** per capita income than those headed by **men.** Market income differencss between **male** and **female** headed households may overstate the gap in **welfare** unless consideration is given to a broader concept of *full, income vhich also includes nonmarket production and time allocated to hom production and even leisure. Even so, differences in
26 "full" income between male and female headed households warrant more study. There may be more children to support per adult in younger households headed by women than those headed by men (Youseff and Hefler, 1983; Preston, 1984). Changes in family structure are viewed **as** the choices of consenting adults, but society may be involved in the impact on third parties--in this case, children dependent on their **others.** If the physical **and mental** development of children is adversely affected by this **shift** in fmily structure, then society may wish to intervene to **reverse** the trend or compensate for its adverse consequences on children. If governimets cannot **ensure** that fathers pay child support, whether or not they live with their child's mother, generalized social transfers to poor children may be an attractive alternative. Such transfers might alleviate depressed consumption lovels in these families headed by women and thus advance **the** society's goal of equity. But direct aid of this form also encourages two-parent poor families to separate in order to better provide for their members. Targeted transfers to equalize consumption opportunities may weaken, therefore, **the** incentive to create or maintain the family which is8a long standing institutional response to the consumption and production disparities among persons of different ages and sex. Policy to reverse trends in family composition can be adequately designed only with an understanding of the fundamental forces that have motivated **more women and** men to live apart. Little is now known about **the** magnitude, source or severity of this trend. Treating **the** syIptom of the **disease** rather than dealing with the underlying **cause** within the family economic production-consumption system may be ineffective and possibly counterproductive.
27

# 3. Production and the Family

For the purposes of this survey, it **is** assumed that factor markets clear sufficiently regularly to ensure that market prices provide a satisfactorv approximation for the shadow value of factors, adjusted for transactions costs (Singh, Squire, and Strauss, 1986). Many exceptions, of course, can be imagined, such as slack agricultural **seasons** when **the** labor market is thin, or interlinked factor markets where collateral or bullock power may permit their owners differential access to tenancies (Bell end Srinivasan, 1985). Labor markets, particularly for women and children, may not be perfect in all societies, and the demographi **c** omposition of **the** family's labor supply would then be expected to influence household production decisions. But the current evidence does not confirm that this market imperfectlon, is important (Lopez, 1986; Seavey, 1987; Benjamin, 1988).

The famlly is engaged in many interdependent production and consumption decisions. The simplification that is widely adopted involves treating these two areAs of decisioh-making as sequential, in order to analyze first the production problem as a profit maximizing stage, and then the consumption problem of what is consumed and by whom. Having explored in the previous section some aspects of the consumption and human capital investment decisions made within the family, the production problem facing the fmily is now analyzed. How does the family allocate its resources and time between (i) Self-managed production **at** home or in **the** family farm or firm, or (ii) external factor markets, such as renting out family land or hiring out family labor for wages. If the family both sells some of its factors to **the** market and also uses **some** in its own production, the market price of the factor should be equated to the factor's return in self-managed production. For example, if a mother's time
28 is spent producing food and clothing for her family, that task is construed by the family as of at least the value to what she might have earned in the labor market, minus the transaction costs in terms of time and goods of her entering the labor force and any taxes that would be deducted from market earnings. If the value of the factor to the family's self-managed activity exceeds the market price, the profit motive ought to induce the family to hire in more of the factor than it initially owns, until diinaishing returns **set** in, and **the** factor's return to the family is driven down to the market supply price. Thus, by clearing factor markets across families, market prices are expected to signal appropriately the social value of factors to all families. This value at the margin is also assumed to be a satisfactory masure of the contribution of each factor to the family's income or welfare.

# Occupational Segregation and Discrimination

In some societies, there may be an additional cost to employment of women outside of the family household: customary restrictions on what is 'suitable" work for women or the circumstances under which they work with persons outside the family. These barriers to the allocation of women's (and mon's) time may at some stages of development serve some social purpose that outweighs the economic inefficiency that such rules impose. But when the workplace and 12 people's work are changing rapidly, . the new productive opportunities, both in the home and marketplace, require changes in old rules of employment Segregation of men and women. Otherwise, people may find themselves employed where they are not most; productive. As these customary assignment rules becom more costly to suctain in terms of their inefficiency or resulting lost family production, they may be retained only if preferences for them arm inflexible or barriers restrain the development of alternative institutional arrangements.
29 For **example,** purdah roquiring women to work in only sexually segregated settings, become increasingly inefficient when male and female labor are complementary in particular **lines** of production for which demand is growing. Inertia in occupational segregation **may** prevail in the short run, and as these traditional division-of-labor arrangements become increasingly counterproductive, **the** costs lover **the welfare** of families. If they penalize disproportionately womn, it **may be** useful to ask at what point does segrogation become discrimination? The answer may **be** important but very hard to clarify because it involves both cultural values and economic efficiency of entire institutional systems.

However, the mre existence of **sex** segrogation in labor markets does not necessarily liply that **these** patterns are an important source of ifefficiency or discrimination, though it presumably increasos the costs of production during modern economic growth. Intervention **s** hould be based on sound empirical evidence that these arrangements result in a major misallocation of labor. For example, it might be shown that womsn's productivity in nontraditional work exceeds their output in traditional work (e.g. in families) where there is no joint production and the women in both sets of activities possess the same endowments and capabilities. Evtdence of this form is hard to obtain, however, for two reasons. The product of women's traditional nonmarket activity is generally consumed directly within the household, and does not enter the marketplace, and is thus not valued, in monetary terms that are comparable to market wage rates. There are also cases where women jointly produce several goods which **are** difficult to monitor and value. For example, the need to look after children may lead women to accept lower-paying jobs so that they have flexible hours and informal work routines that can be located
30 in the home or occasionally combined with home duties. Women might choose a better paying job in **the** formal **wage** labor market, If they **were** not also productively engaged in nontarket family activities. This argument does not imply that women are biologically pre-disposed to look after children or even that nursing of infants cannot, **at a** cost, **be** rearranged to fit work routines of women in the formal sector (Blumberg. 1984; Priedl, 1975; Nerlove, 1974). The conclusion of many observers is, nonetheless, that customary forms of **sex** segrogation in the labor market in developing countries lead to a sisallocation of woman's labor. The reduction of sex segregatioB in the labor market should raise current output and encourage investment by families in women (Rosenzweig and Schultz, 1982a). Devising mechanism that encourage poor families to invest more heavily in the market oriented skills of girls is a ehallenge to policy In certain parts of the world. Improving the operation of factor markets that discriminate bttwven men and women **as** productive agents can also involve social costs, for -interventions use economic and administrative resources that are very scarce in most low income countries.

Comparisons of ProductLivty In **the** Marke; and Hose Because of the difficulty of measuring and valuing nonmarket production, economists have focussed on the marketable conpon6nt of income of families and Lndividuals, includlng the market value of products or factors sold or exchanged, not of purchased inputs. Within thls limited context, household surveys have found it notoriously hard to **estimate** the net return to selfemployed entrepreneurs. Even surveys such **as the** Current Population Survey in the United States account for only about half of the National Income Account estimates of the value added by self-employed producers such as farmers. With far less sophisticated accounting procedures available in low income coun-
31 tries, **extensive** farm or enterprise **management surveys** are needed to measure current incomo comprehensively, along with changes in **net** worth.

Beyond these limits, economists strive to document the market value of goods produced and conumed **at** home, such **as** food, fuel, and housing, for which there **are** market priced equivalents. But nobody, to my knowledge, ascribes **a** market value to **the** fetching of water for family use, though conceptually it is no different from the collection of fuel, or the home production of food, all of which tend to be women's work. In high income countries, **as** such forms of income in-kind have become an progressively smaller share of income they have been neglected. When this omission is replicated in low income countries, where income in-kind can be a large share of personal income, a serious distortion is introduced. This systemat!cally affects one's perception of woman's contribution to the economy. It distorts the entire quantitative record of modern economic growth. Economists understate sources of income that are proportionately larger for poor families, and within families for women's earnings relative to men's (Kusnic and DaVanzo, 1980; Evenson, 1983). This partial accounting of income reinforces the view that women who engage in traditional household activities are less useful than they would be if they worked in the market labor .force. From another perspective, per capita national income will appear to grow more tapidly than it should, if women allocate **more** of their time to the market labor force, because we **make** no offsetting debit to national income for the time women retract from nonmarket production that would otherwise benefit then and their families. Thus, statistics interject a subtle but pervasive slant into our study of modern economic growth and affect the choice of development policies. Assessments of technical change can be distorted. If it boosts the pro-
32 ductivity of workers in the labor market it will be encouraged. If it promises only to raise **the** productivity of workers **engaged** in undocumented home production it may be overlooked. Public policies have probably shifted the gains arising from technical change toward workers in the market labor force rather than nonsarket workers, and toward mon rather than women (Boserup, 1970; Blumberg, 1984; Ngwira, 1987). **This** oversight and distortion are corrected only when **the** quantitative importance of nonsarket and in-kind income is estimated, and its bearing on public sector priorities explicitly evaluated. Economic-demographLc household surveys in low income countries that account for the productive use of time in both market and nonmarket spheres are beginning to change how we view vomen **as** a productive and innovative agent in the development process (e.g. Binswanger at l.. 1980; Birdsall and McGreevy, 1983; Evenson, 1983; Gertler and Newman. 1988). Much work remains to be undertaken if the assessment of policies is to be neutral in its encouragement of nonmarket and market productivity.

Time Allocation and Labor Productivity Hicroeconomic analysis of family behavior assumes that people allocate physical resources and time in response to the family's nonhuman endowments, the opportunity value of the time of- each family member, and the relative prices of the family's potential market inputs and outputs. The market place sets **the wage** rate offered for various types of labor and thereby<sup>determines</sup> the marginal value of time for those working in the market or hiring<sup>in simi-</sup> lar labor. This framework **is** readily extended to account for differences in fertility **and human** capital investmnts that **add** to the productive capabilities of family members. First the implications of the neoclassical unified family allocation model are r^stated, and then relaxed to allow for
33 differences in individual preferences within a more general bargaining framework. Extensions of this approach appear to have promise also for understanding women's changing roles in the economy.

As described earlier, the simplest representation of the family is **as a** unified decisionaker, like **an** individual, but with several blocks of time to allocate, associated with different family members. This generalization of the neoclassical model of consumer demard end labor supply to a multiperson household has evolved in the contributions of Mincer (1963), Becker (1965), and Heckman (1971). It has become a suggestive tool for analyzing family economic and demographic behavior and has helped to account for numerous empirical regularities in both high and low income countries (Schultz, 1981, 1988d): This framework helps to explain why **the** market labor supply of married men is relatively inelastic or even backward bending. Men, who are on average already working nearly full time, weigh more heavily than do<sup>women the nega-</sup> tive "income effect' on labor supply due to any increase in<sup>their market wage</sup> rate. This may be only partially offset by the positive *substitution effect" associated with the increase in men's wages. For women, on the other<sup>hand,</sup> many of who are not yet in the market labor force, there<sup>are relatively</sup> few inframarginal hours to weight up the negative income effect. Consequently, the positive substitution effect of an increase in women's waged, is more likely to prevail. Implicit in this distinction **between a** negative income and a positive substitution effect on labor supply is the expectation that if most jobs come with substantial time co uitmnts, then the participation rate of women may positLvely respond to market **wage** offers, while<sup>women who already</sup> hold full-time jobs behave more like mon and possibly even exhibit a<sup>backward</sup>
34 bending supply of labor, on average, in respdnse to further increases in thir wag. rate (Schultz, 1980b).

There are good reasons, therefore, to analyze separately the decision to enter the market labor force and the choice of how many hours to work when participating. The analysis of both decisLons together **as** though they were both generated by a single truncated process (iLe. Tobit moedl) **may** supprxss distinctive features of the two decisLomuaking processes 13 Svidence of this pattern can be seen for Thailand in Tables A-3 and A-4.

To this individual market nonmarket time-allocation model of participation and hours, Mincer (1963) was the flrst to note that the **earnings** of a husband should increase the reservation wage of a married woman and thereby reduce her time allocated to the labor market. Symsetric treatment of both husband and wife implies that the wage rates of both **are** appropriate detersinants of the labor supply function of husband and wif (Heckuan, .1971). 14 Empirical studies of the labor supply of married couples have found that the labor supply of men is relatively inelastic with respect to their own-wage rate and relatively unresponsive to variation in their wivesl wage rate (actual or predicted). In the case of married women, market labor supply is elastic with respect to their own-wage and inversely related to **the** wage of their husbands. Because the own-wage elasticity is generally larger than that associated with her husband's cross-wage eiasticity (in absolute terms), the net effect of a proportionate increase in the level of wages paid to women and men is to increase women's market particLpation. **These** response patterns **are** replicated in cross sectional **data** from many industrially advanced countries and from a substantial number of low income countries. 5 A general increase in the l-vel of both men's and women's **wages is** sufficien **t** o induce an
35 increase in women's labor force participation. According to these estimates of female labor supply, it is not necessary for the ratio of female to male **wages** to increase in order to induce more woaen to enter the labor force, thouSh such a relative advance in female wages would of course accelerate the process.

nertility and Investment in Cblldren In addition to family labor supply behavior, the pattern of consumer demands may be sensitive to variation in market wage rates available to men and women, particularly in the cases of consumption goods that require a disproportionate amount of the time of either the husband or wife to produce. The care of children in many cultures consuoms uch of their mother's time. An increasein the earnings potential of women's time relative to men's is likely to increase **the** money **perceived** to be lost by childbearing and child care, and thus to depress fertility, even though at the same time the woman's earning potential increases the family's income opportunities. Increases in wages of women relative to men, **ar generally** associaead not only with a reduction in fertility but also a reallocation of vromen's time from nonmarket to market work. On **the** other hand, increases in the labor productivity and wage rites of men **and** particularly childron can enhance **the** attractions of a large family and **are** often **associated** with increased **levels** of fertility, at **least** in low incom agricultural countries (Schultz, 1981; Levy, 1985). **The** labor supply and fertility patterns predicted from this economic model of the family are **based** on **the** assumption that **the** nuclear family pools **resources.** Consequently, **a** marriod **wommn is** assumed to rely on the earnings of her husband for **the** purchso of airket goods consumed by her children and hersolf. Whero **wives engage** intensively in economic activities oriented
36 toward market exchange as well **as** family subsistence needs, such **as** in rural subSaharan Africa, Thailand, and Malaysia, it **is** less clear whether the productivity and wealth of the husband are pooled with that of the wife to support equally all members of the famLly.

The family decLsionmaking framework also addresses how the reproductive choice ls linked to the amount of resources **parents** lntend to invest In each chld. EmpLrical evidence **suggests** that lnvestmnts In chlld qualitym are an alternative for the numbers of chlldren parents have, or more specifically, child quality is a substitute for the quantity of chlldren. This conclusion does not follow from any general aspects of economlc theory, **as** shown by Rosenzweig and Wolpin (1980). Economic theory does not prescrLbe whether any two goods are substitutes or complements; this relationship depends on production technology and people's preferences. But thls limitation of theory does not diminish the usefulness of investigating how polLcies and price changes affect both child quantlty and aquality" decisions of parents. 16 Most models of family behavior call attention to the potentially distinct effects of the wage rates of the wlfe, the husband, and children on their economic behavior and on how many children the parents are likely to have (Mincer, 1963; Bocker, 1965). Consumption that takes a large asount of the women's time is said to be an intensive user of her time and will be consumed in lesser amounts as the labor market opportunities for women increase in relation to those for men. If the rearlng of children is such a femal timeintensive household Ocommodity,* fertility and **the** market productivity of women **are** likely to be inversely related across families and across societies and within societies over time. What we know from many societies about the allocation of time within famliLes and the relatlonship between market wage
37 rates of adt and women and fertility levels is consistent with these generalfzations.

Increases in a couple's wealth adds to its economic opportunities, permitting it to consum more goods that do not b."e superior substitutes. Children **seem** to fall in this category. honearned income that is derived from nonhuman capital such **as** land and physical property tends, therefore, to be positively related to fertility, at least In traditional rural societies (Mueller, 1984; Schultz, 1988).

Land and physical assets that can be employed in home production may also enhance the productivity of child labor for parents, and encourage landowners in particular to have additional children. At **the** frontiers of settlement in North and South America, Scandinavia, and Africa, people with more land and less labor have tonded to be relatively fartile, **as** noted by Malthus two centuries ago and reconfirosd in studies of low income countries today (e.g. Nerrick, 1978). But these tendencies may be offset if wealthier parents are inclined to spend more on the human capital and property of their offspring, which may deter them from indulging in a larger family.

A critical, but largely untested, hypothesis is- that the private returns on human capital increased in the last century throughout the world, and this development has encouraged parents to invest more in the 'quality' of their children and less in the quantity of them (T. W. Schultz, 1967, 1974, 1975, 1981; Becker, 1981). Contamporary evidence does not contradict this hypothesis. But sarious gaps in our knowledge prevent us from assessing how =ich of the observed decline in fertility can **be** attributad to this factor. Rarely is there documentation of how private rates of return on schooling have changed over time during the full course of the demographic transition. Nor is it
38 clear what caused the productivity of educated workers to increase initially, pushing up the private returns to schooling. Until such a forcing variable is isolated and measured, it is hard to confirm how it might have shifted the aggrogate demnd for educated labor **and thereby** affected fertility. But recent surveys provide a sounder basis for masuring contemporary private returns to human capital for girls and **boys. These measures** of returns for females and males may help to explain the patterns of human capital investmnts of parents in their children, and to forecast hov the pivotal differences in wage rates between women and men will evolve in the future in response to changes in human capital lnvestments.

**Wage** dlfferentials between groups with different amounts of schooling are used to approximate private returns to sche*ling. Schooling may operate as a screen to ldentify innately **more** productive individuals, or it may, as assumd in the himan capital literature, inpart disciplined work habits and productive skills. The private returns associated with schooling, plus any consumer benefits, signal the rewards that individuals and families can expe4t to receive by investing in schooling, regardless of whether the wage differentials are due to screening or acquired sklls (Schultz, 1988.).

# 4. Investments in Humn Capital and **Rates** of Return

Labor productivity can be approximted by **the wage** rate people are paid. According to this measure, women **are** on **average** less productive than mn in many labor mrkets (e.g. Schultz, 1986). **However,** comparisons of **wonmn's** dad sen's wages may not be representativo of the productive value of the time of all men and women for several reasons: 1. Many men and two-thirds of women do not report **a** wage because they do
39 not work for one (Tables A-5 and A-6). How the sample of wage earners is nonrandomly selected may bias intergroup comparisons, particularly if available wage data pertain only to a modern subsector of the economy, such as large manufacturing firms.

A first step toward a more accurate comparison of male and female wages is to identify the productive abilities of workers, as valued by what employers are willing to pay them. This information should help to assess how much of the gender gap in wages is accounted for by differences in abilities, innate or acquired, and how much remains unexplained. A large literature on wage and earnings differences has developed n the last 25 years to explore this question.

Estimates are obtained separately for men and for women of how productive characteristics of workers are partially associatad with their wage rates. If the regression estimates of his hedonic wage function for men are cross multiplied by the average characteristics of women workers, a prediction of the average wage for women is obtained based on the male wage function. The observed gender gap in wages can then be split up into components related to the differences between men and women in each of the measured productive characteristics (Oxaca, 1973; Birdsall and Fox, 1985; Gannicott, 1986). There is no obvious reason to prefer the wage function coefficients for one gender as the basis for such calculations. Consequently, this form of regression decomposition of group wage differences yields two, equally tenable, answers, and hence a range of uncertainty. 17 Also, no matter how finely one distinguishes worker capacities, unobserv ed productive characteristics of workers and amenities of jobs in addition to statistical ambiguities will always inject substantial uncertainty into such adjusted intergroup wage comparisons.
40 2. Individuals who decide to work entirely in nonmarket (or nonwage) employment are likely to do this because they are more productive than the average person at it or less productive in market (or wage) work. Thus it would overstate the gender gap in labor productivity to attribute market-wage opportunities to women not in wage employment. The assessment mlght better focus on **the** shadow value of their time in nontage work (Griffin, 1987).

3. Many individuals specialize during their lives in either market or nonuarket work, and marriage tends to encourage women to specialize in nonmarket activities (Becker, 1981). This increases the gap **at** later stages in the life cycle between the job opportunities available to married women and men in wage employment, if on-the-job experience is imperfectly substitutabl-e between home and market jobs. Differences in market job qualifications between men and women are therefore narrower among young people than among the middle aged, although this tendency may vary from country to country as does the persistence of women's attachmen **t** o the labor market (e.g. Goldin, 1989). Although the unobservables that might contribute to the first general form of sample selection bias could either widen or narrow the observed wage gap between man and women, the second and third sources of static and dynamic bias are likely to overstate the lifetime productivity gap between men and women if only market wages are analyzed. Moreover, these problem of comparing the productivity of women and men will not be easily resolved. The only direct solution is to assign comensurate value to nonmarket work in **the** houe. Since some forms of nonmarket production are untraded, such as children, each family arrives at its own valuation, and thxs adds to **the** difficulty of comparing the labor productivity across groups of persons who do not work **at** all in the market labor force.
41 The major part of th. private investment cost incurred in the formation of human capital is the time that the individual cannot be occupied in Imeudiately productive work during the investment period, whether as a student, trainee, migrant, etc. To rank alternative investments in terms of their economic efficiency, the internal rate of return is a suitable criterion. For young persons. with many expected **years** of adult work tim **ahead** of **them,** the percentage increase in **average** lifetime **wages** caused by an additional full year of human capital investment is **a** first approximation of the private internal rate of return to that investment of ioregone earnings (Becker, 1969; Nincer, 1974). The opportunity cost of women's time to attend school **may** be lower than that of men's time, while the wage of female graduates may still be lower than sale graduates. But the percentaga gains in average wages associated with a specific year of schooling **are** iot .systematically lower for women than for smn, **as** documented later. Second-order adjustments for other factors may raise or lower the private returns, from this wage differential without necessarily altering the **coupa-ison** of private rates of return to schooling for men and women. For example, if students work part time ;at hoe or in jobs when they are attending school or during vacations, this relative wage differential associated with a year of schooling can oerstate the student's time input to schooling and thus understate the private **rate** of **return.** Conversely, if school **attendance** imposes **added co'sts** on _the_ child's household that **exceed the value** of his or her production outside of **school,** such **as** for the purchase of school clothes, books, and tuition **_fes_ or** to rpeat **grades, the apparent relative wage gain** associated with a **year** of **cospleted** schooling **can overstate the private rate** of return. Shcr ***re,** of courc8, any **Othe2** limitations to intergroup comparisons
42 for estimating returns to schooling that are beyond the scope of this paper; they are discussed extensively elsewhfre (e.g. Griliches, 1977; Schultz, 1988.). One is the inadequacy of staistical controls to take account of individual ability (i.e. omitted input bias or selection by comparative advantage) (Willis and Rosen, 1979; Heckman and Sedlacheck, 1985). Another is the neglect of school quality in **the wage** fLunction, which **because** quality tends to be directly related to **the** quantity of schooling individuals receive, may overstate the returns to additional years of constant-quality schooling (Welch, 1966; Behrman and Birdsall, 1985). There is no consensus on precisely which variables should enter the wage function or how to statistically identify individuals who invest in schooling or work for **wages** in the labor force. Different strategies for constructing **the** comparison groups **may** imply private rates of return to schooling that are subtantially lower, or sometimes higher, than the simplest logarithmic wage function that includes only years of schooling in linear form and a quadratic in years of post schooling experience (Nincer, 1974). Controversies involving the methods' for estimating returns to schooling are, hopefully, less germane to my objective here, which is to compare the rates of return for women relative to those for men, rather than to assess precisely **the** absolute level of the returns to private and public resources invested in **the** education of women. 18 Labor Supply and **Rates** of Return to Schooling Several studies on returns to education by school level for smn and women are suimarized in Table 1. They **are** not entirely comparable. Several methodological problems **and** inconsistencies **arise** because of the different ways nalysts have handled the effects of time allocation on the computation of returns, and the potential bias due to sample selection because analysts can
43 Table I leturns to Zducation by **lea** and Sehoel LOvel: 195-1385

|Country<br>(year otdata)|lstimation<br>Hetbod e|Pri<br>**male**|mary<br>YJoalc|Jeenad<br>hale|ergy|1gb<br> <br>Male|:r<br>_tooaL*_<br>**Female**|
|---|---|---|---|---|---|---|---|
|Uaited<br>States<br>(1949)|S|17.9|5.6|14.0|13.0|3.7|4.2|
|Colobia,<br>Desoto<br>(1965)|IS|10.2|*11|$4.4|**1.39**|**4.5**|5.3<br>|
|memany<br>(aa 1330)|b|***0o**||***.**||4.6|5.0|
|Crecee<br>(ea<br>1390)|b|*****|*|3*0|5.0|*||
|Renya<br>(ea1030)|I|21.7|7.1|23.0|13.5|*** ***|*****|
|Malaysia<br>**(Ca**1360)|b|*.4|3.3|12.3|11.4|10.7<br>'|.*|
|NewZealand<br>**(Ca**1960)|b|*****|*****|10.4|25.3|13.4|S3.5|
|IrasiL<br>(1950)|b|17.3|33.6||||.|
|Australia<br>(ca 1060)||-|.|..|.|21.1|21.2|
|France<br>(1976)|b|***1**|*****|14.|16.2|20.0|12.7|
|United Kingdom<br>(1971)|b|**..**|..|10.0|<br>0.0|3.0|12.0|
|**Japan**<br>(1330)|I|*****|..|||5.7|5.6|
|South Korea<br>(1971)|b|*****|*****|13.7|16.9|15.7|22.9|
|Taiwan<br>(1032)|b|0.4|16.0|*****||*****||
|Puerto<br>Rica<br>(1050)|b|29.5|18.4|27.3|40.4|21.9|0.0|
|Andra Pradesh,<br>India (1977)|it<br>I|O.9<br>7.2|1l.3<br>.3|3.7<br>6.3|11.9<br> <br>2.41|6.2<br>5.5|5.9<br>5.5|
|IvoryCoast<br>(1365)|SS|10.3|51|17.0|33.7|21.1|13.6|

ts8tieation Hothods: I - Include partitipatiom rate io la.r torgc te d*flate returns, depressing tcmal **returns** diaproportsioately; SS - estimate **wage** rate relationahip wtthout labor totsc ad*uatmnt. **lee** tezt **for d4o9selsois.** blctimation method not known.

N Not teportod.

sourcoe: Psaohbopous. 1973 (TSble 4.5), 1395 (table A-2). except<sup>torIndia</sup> (Tilak. 1907: Table 6.5). Average return; Ivory Coast (Van doe easg and Vijvorberg 1037: Appondix 2); and Colombia (Schultz. 1366:<sup>Tablo7)hourLy</sup> Wase.
44 only observe differences in wages for a portion of<sup>the population</sup> receiving different amounts of education.<sup>In the first studies of</sup><sup>**the**association</sup> between education and labor earnings, private rates of return<sup>were calculated</sup> under the assumption that the more educated **benefited** froo schooling only to 1 9 the extent that they held<sup>a job in the labor force and reported earnings.</sup> Thus, if a year of additional schooling **raises the wage** rates of **mn** and women by the seae proportion and they both work full<sup>time in the labor force, then</sup> the life-cycle internal rate of return<sup>to the opportunity</sup> cost of attending school for that year is<sup>approximately</sup> equal to **the** proportional wage increment, and the returr is then<sup>identical</sup> for sen and women. But if<sup>women worked</sup> only half of the time in<sup>the labor force after completing</sup> their schooling while men worked all of<sup>the time, the conclusion</sup> was drawn that women earn only half of men's rate of<sup>return on their year of schooling.</sup> Becker (1964), in his seminal contribution on this subject, observes that the lower return to women's<sup>education</sup> due to their lower participation in the labor force is consistent<sup>with the smaller proportion of women than men</sup> attending college in the<sup>United States in the 1950s (Becker,</sup> 1964: 178). Subsequent empirical studies<sup>and surveys of the field that specifically</sup> addressed male-female differences in rates of return to education<sup>adopted</sup> Becker's methodology (Hines X al., 1970; Thais and Carnoy,<sup>1969;</sup> Psacharopoulos, 1973; Woodhell, 1973).<sup>The implicitassumption</sup> of **these** researchers **was** that schooling had no apprecLabl, effect on **the** productivity of persons working outsLid<sup>of the market labor</sup><sup>**force.**</sup> **20 But** thLs **assumption** has subsequently **been** challonged by **a** large **number** of **empirical**<sup>**studios**</sup> that indicate education increases **the** productivity of time in nonmarket production, particularly that of women (summarizod in Eichel, 1982; Haveman<sup>and Wolfe,</sup>
45 1984). Moreover, the opportunity cost of the time of females while attending school **are** not symmetrically discounted; girls are implicitly assumed to be giving up a full-time job in the labor force to attend r hool, which may not **be the** case. These working assumptions for estimating the rates of return bias down the rates of return to groups, such as women, who participate in the labor force less than **averfge** amounts or vork more often **as** an unpaid family worker.

A second possible working assumption is that education affects labor productivity in market and nonmarket work to the same extent, and consequently the allocation of an individual's time between markit and nonmarket activities is not biased by his or her education. According to this approach the returns to education are approximated by estimating a function where the logarithm ot the wage rate is the dependent variable. This approach differs from that adopted by Becker (1964) and Hincer (1974) which examined differences in annual earnings, and thus mix together the productivity enhancing effect of schooling with its possible effect on market labor supply. It is an empirical fact that unemployment rates tend to be lower among more educated workers once they have reached age 25, and a common pattern, though far from universal, is for hours worked when employed to be larger for more educated workers. Consequently, rates of return to schooling **are** sometimes higher if based on **the** logarithm of annual earnings as the dependent variable rather than on th -logarithm of the hourly wage rate.

Becker's neglect of nonmarket productivity effects of schooling introduces a downward bias to the estimated rate of return to education for groups who do not all participate in the market labor force. But the direction of potential bias due to the second approach based on the estimation of a sage
46 rate function is ambiguous; it could overstate or understate the appropriate rate of return for the entire population. Either approach should deal with potential sample selection bias (Heckman, 1979; 1987). Statistical techniques have been videly used to correct for this source of parameter bias. They can be employed here if an identifying variable, observed for all persons, influences **the** probability of entering **the** labor force but does not affect **at the** sam tim th market wvae rate **a person is** offered. In other words, Information must be known about some exogenous determinant of the person's productivity or value of time in nonmarket production that does not affect her or his labor productivity to a firm. This critical information permits one to correct the **estimates** of the market wage-rate function for **the** potential statistical bias introduced because market wage rates **are** only observed for the selected sample of persons for whom **the** offered market wage exceeds the value of their time in alternative nonarket activities. Theoretically, the variable needed to identify the nonmarket productivity (or reservation wage) function is a household fixed productive. factor that affects the individual's nonmarket productivity, but does not alter his or her labor productivity in the market. In the short run, children have been viewed as such a variable raising a woman's productivity only in the home (Gronau, 1974). But over the life cycle, this variable **is** also jointly determined and responsive to labor market **wage** rates, and thus, should be viewed **as** *ndogenous or determined within **the sam** framework. Land ownership and family busines* assets and the mrket productivity of one's spouse may be **more** satisfactory measures of fixed household *ndownents that **enhance the** value of an individual's tim in nonmarket activities. The choice of this identifying restriction determines txow **the estimates** of **the** market wage function are
47 interpreted and hence whether the implied private rate of return to schooling is a satisfactory estimate for the entire population or for only the nonrandom sample of wage earners.

If more than one selection process is used to screen the observed sample, and the selection processes have different determinants, multiple sample selection equations and corrections **may** be appropriate **(e.g.** Catsiapis **and** Robinson, 1982). For example, participation in the market labor force, and acceptance of wage employment may respond to different home and market constraints. If the marginal product of labor is measured with less error for wage and salary workers than for self employed workers, this scheme of double sample selection _way_ be appropriate, to reduce measurement error, despite the loss in final sample size that is implied by estimating the market productivity of all persons from only wage earners.

Most recent studies of the returr- a education tend to include only wage earners and thus to exclude the self-employed and unpaid family workers for whom a wage is difficult to infer or measure precisely. This omission represents a relatively small segment of the labor force in high income cc.ntries, but it is a major part in low income countries. Nonmarket workers, the selfemployed, and family unpaid workers together represent about half<sup>of the</sup> adult male population of the world, and two thirds of the adult female population (see Tables A-5 and A-6). Thus, any bias from sample<sup>selection</sup> could be important for the analysis of educational returns to men as well as to women. Table 2 sumarizes private rates of returns to years of schooling for men and women in several Latin American countries. A comion conceptual and statistical methodology is adopted in all of these studies<sup>(Schultz, 1980b).</sup> Although these estimates deal plausibly with the labor supply issue, by using
48

# tabl a

Rstimates of Private ltetutrs la t Peret pa **vejer** Whisk Hen and Woan **larn** en Investims their TUNe<sup>toSbhelim.</sup> As Selected Labor Mark.ts and leara

|Country<br>and/er<br>Metropolitan<br>Area|Yearot<br>Survey|Int<br> <br>Age|ernal<br>Ra<br>tn Poreo<br>2*-44|te of**Ret**<br>ntper Yter<br>Age|**urn**<br> <br>45-65|
|---|---|---|---|---|---|
|Argentina.<br>.ueoesAires<br>**(asjimatee similar**in 1976)|b<br>1360|0.3|6.6|10.|11.|
|Bolivia,<br>LaPas<br>(estimates<br>similar<br>in 1976)|Il0|0.3|11.|3.6|6.7|
|Brazil,<br>SaoPaulo<br>(estimates higher in 10g0)|1071|5.4|6.3|6.0|6.1|
|Colombia<br>(estimates lower in 1930)|1073|is.|19.|16.|14|
|Paraguay.<br>Asuncion<br>(estimates<br>similar<br>In 1977)|1079|11.|6.0|10.|11.|
|Peru|1074|14.|14.|11.|19.|

aPrivtet returns refer to the estimated coefficsfit (times 100) on **the** veriable years of completed education ln a logarithmic hourly **wage rats regression** which *lso includes pot-scebooling experienee. eperience equared. and some regional or migration origin variables. Smples vry ftrom 21 to 3470, but all estimates *ae statistioslly significantly diffecont from soro at the .001 confidence level (t > 2.63). The solection of<sup>the sample of only workers in the</sup> labor force tot whom wage rate could be<sup>calculated Is not treated as a specif-</sup> ic source of bies in these estimatce.

bThe available **ago** groups in Argentina ar* 35-49<sup>and rver 49.</sup> Source: Sehults (l19OS).
tho hourly **wale,** they do not attempt to correct for analyzing only wage *x*ors. **becouse the** aomples **are** small, aome of the<sup>estimates</sup> are imprecise. Ovorall, **howtver, there is** no clear ***vildence** that **returns** to schooling differ systeomtlcally by sex. They do differ **considerably across** countioes, bowever, p:esumebly because of differences in macro economic conditions and the lovel of past imnestmnts in schoollng (ichults, 19680). An anAlysis of the roturns to edusation in Andre Pradesh. India illustrates how sensitive the return calculatlons are to the treatment of the labor force participation **rate** of women (Tilak, 1987). Both the private and social retur,.s to schooling at virtually every level **are** greater for women than mn when the returns **are** adjusted only for uawsployment. But when nonparticipatiaa in the labor force is also factored into the calculation<sup>as Becker</sup> propasd, the private rate of return for women is less than it is for **men** (see vast to the last two rows for Indian study in Table 1). This study also documnmts the lower public cost of female than male education, and the lower *pportunity cost of tUim for **female** than r,ale students. However, the adjustsent for nonparticipation of vomen is not Introduced to deflate the opportunity cost of female student tim but only to **deflate** the stream of benefits. **A** study of **Sri Lanka** also confirmed that womn's retutn to schooling that ar not **deflated** by nonparticipstion rates **exceed those** of smn. **The** privfte rteo of return to completing the gennral certificate of **xams at the** #ad of tb aecoodtry school **are three** tie. as high for womn as for men in urb. areas (36 vermU 13 peteent), and tiies as high in **rural areas** (14 *tt* 7 #*teftt). At **the** ufivateaity levi, hwever, **the rates** of retur in _WF_ to** _d"*a*_ tae &wtsetp bet"CtW **sen** and wmen (ahn and Aldermn. 1968: **I*i** _i7v._
50 Fixed-effect estimation procedures ca-. eliminate bias that may **arise** from certain types of unobserved or omitted variables in a relationship. A cross section is drawn from a number ef distinct localities. for which price and quality for market goods **and** public services **may** differ. If these local market variables influence the productivity of schooling or its quality, then this local variable should be controlled to estimate **the** returns to varying only years of schooling. In this case introducing a fixed effect for every school district into the **wage** function removes any bias due to the omission of school quality that might be correlated with quantity of schooling received. Clearly, the effect of other local market variables cannot then be estimated when comunity fixed-effects are included. If the local school quality changes over time or individua's in a school district move or attend more than one school, the community fixed effect is _a_ less adequate control for school quality.

It is also reasonable to expect that family background influences average ability through genetic and environmental mechanisms, instilling motivation and habios, as well as influencing the quality of schooling that siblings receive. If these family background characteristics affect productivity and are correlated with years of schooling, their omission from the wage function would also bias upward (in all likelihood) the estimated rate of return to schooling. One strategy for dealing with these unobserved characteristics is to introduce fixed effects for each family and thus base the estimates of schooling returns on only within-family variation in worker productivity. However, this procedure may increase the relative importance of measurement error by eliminating all between-family variation. Exaggerated measurement error would bias the within household fixed-effect estimates of schooling
51 returns to zero (Griliches, 1977, 1979). Household fixed effects, therefore, **may** represent a lower bound on **the** estimates of **the** market productivity effects of schooling. It **has** not, to my knowledge, been examined how the restriction of the estimation **sample** to those residing with other **wage** earning family members distorts the comparison group **and** thus potentially biases the return **estimtes.** Private rates of return to schooling have **been** estimated for men and women from a 1986 survey of Indonesia that compare ordinary **estinates** of the wage function to those that include both community fixed-effects (proxy for school quality, etc.) and household fixed-effects (proxy for family background correlates) (Behrman and Deolalikar, 1988b). **Three** increments of ochooling are compared in Table 3: primary, general senior high school, and'university. Private rates of returns to schooling for women in all **six** ctmparisons **exceed** those for men, and **as** expected the estiuates that include community and household fixed-effv..ts are between 9 and 24 percent smaller than thise obtained from the simple regressions that include inter-family and inter-community variation. These estimates do not attempt to control for the potentially unrepresentative character of the sample of wage earners, nor are the two sets of estimates based on the same sample, since 16.percent of the wage earners included in the first set of estimates apparently did not reside in a household with another wage earner and is therefore excluded from the fixed-effect estimates (Behrman and Deolalikar, 1988b: Table 3). Differences in the private rates of return estimated for women and men in Indonesia change moderately as fixed effects are added for the community and household, with the difference decreasing at the primary school level and increasing at the university level.
52

*ahl _I_

Implied Impeet of *a Additiemel tea at * b..Lim In Indonesia ia 1666 by sea. With sad Withet Ce .1 for 3th CeBoth ty md _tmily_ lried 3fet

||Implied Priv<br>PRiMary|ete lessra by Ue<br>seinior<br>Nish|b.Lim<br>Uwe?<br>9mi.sity|
|---|---|---|---|
|Without fixed-efoect<br>controle|t. <sup>1 2</sup>b<br>(21.1)|11Sb<br>(43.4)|12 .1<br>(27.6)|
|Withcomunityand<br>familyfixed-Offects|S.Zf<br>(1.32)|.6Zb<br>(35.3)|10a0<br>(24.?)|
|Hales:<br>Without fixed-eftect<br>controls|.<br>7<br>b<br>(2.64)|*.2Xb<br>(10.6)|*.22b<br>(6.41)|
|With com_nity and<br>familytixod-effecte|6.12 b<br>(1.43)|.<sup>2 </sup>4b<br>(10.3)|*4.t b<br>(5.23)|

aA logaritbhic monthly ***aenin&c** function s *estimated pelns _im MA_ _ _ that includes dumy variables for nine levels of sehoolias md qyeetatle la **age.** Parameters are estimated for the differeace between **ual m* femole** coefficients for all varishbles inluding the iatereept. ith ee*- it% *A housebold tixed effecte are believed to eontrol fez pooeable sael qualty variation and the *ftfot of tfamiy background an earninas **toe** **_test)._ hft** - tanately the ordinary resrosalon esti&ates and these t eluiima the tfied effects are for diftereat samples as notod ln tbe test, raising the pesebility that the diffteences reported may be due to the different **sampes Mtd** not due to the introduction of the tixed-offtet sontsels.

bthe absolute value of t ratios are repoeted in Parentheses boma%th the coot ficients in the ease ot tamle returns, and fot th* ditlefe i*e beftwe Oh male and female a*grest"s coe ffcients beneath th maet Reurms. Thuas a fte* siticant t ratio undeg a "a*le &surn e.g. *eseadaey. suests that **Ut** *OItL of return on schoolin got **mno and** weme difest by a StetIUSU*AX& s&isaitlet amount in this pooled _earnings repression._ Source: sebhsea and *eoLalihas, lobbstable A.
53 Chiswick (1976) develop-d **a** technique to include self employed workers in the estimation of an annual earnings function along with wage earners, and thereby avoid the selection bias due to analyzing only wage earners. Her approach attributed a **share** of self e*ployed earnings to entrepreneurial capital or risk taking. Based on an analysis of Bangkok from the 1971 Socioeconomic Survey of Thailand, sale **wage** earners (not self employed at all) received a 10.4 percent return on their years of schooling, whereas females received a 14.5 percent return. Including partial and full-time self employed Ln the sample reduced slightly the returns to schooling to 9.1 percent for men and to 13.0 percent for women. **The** urban estimtion sample Lncreased by 39 percent for males and by 53 percent for **females** with the inclusion of the self employed. In both cases, women's returns exceed those for men, but those who are self employed report slightly lower returns on their schooling than do wage earners. This is broadly consistent with the pattern of more educated women (beyond primary school) working more frequently in wage jobs than the less educated. It is uncertain whether underreporting of incomes by self employed biases such estimates of return to schooling and by how much. There are few studies of the relationship between wage rates and schooling that assess how the selective sample of wage earners biases findings (e.g. Anderson, 1982; Mohan, 1986; Griffin, 1987; King, 1986; Schultz,. 1988c). Moreover, these studies often **deal** with mn alone or women alone and thus do not help assess whether the bias due to **sample** selection **modifies** systematically comparLsons of male-female estLiated returns to education, **as** reported in Tables 1 and 2. Here is an important **issue** for public policy that has received-surprisingly little empirical study. Griffin (1987) analyzes the earnlngs of marrled women ln the rmilipplnes
54 in 1980 to appraise estimates of schooling returns subject to alternative methods for dealing with sample selection bias. He estimates a nonmarket (reservation) wage function and a function for market wage offers. The reservation wage function determines the shadow **value** of nonmarket time of the individual, and hence what the individual requires to inducc him or her to enter the market labor force. **The** selection-corrected model of Heckman's is plausibly identified within **the** context of **the** family or bargaining labor supply model. A conventiona **l** og linear specification of the earnings function is estimated where returns to schooling **are** constant across schooling levels. 22 The selection-corrected maximum likelihood estimate of schooling returns is 16 percent, compared with **the** conventional estimate of 14 percent (based on only the third of the sample who earn wages). In this case the estimated returns to schooling increases for womn with the adoption of a sample selection procedure, and the selectivity term is statistically significantly different from zero, showing that the sample of wage earning women are not a random sample of the population with regard to their wage rates. King (1986) analyzed the earnings of women in the 1985/86 Peruvian Living Standard Survey. A probit equation for women participating in paid employment (i.e. wage earner or self-employed) is used to **estimate** an hourly earnings function with Heckean's (1979) two-stage procedure. When **the sample** selection correction is employed, the rate of return for women decreased trivially from 12. 2 to 12. 0 percent for primary schools, from 8. 0 to 7. 8 percent for secondary school, but from 6.8 to a -1.7 **percent** return **at the** university level (if a diploma is received after four **years** of study) (King, 1986; Table 10, 11 and 12). As in Griffin's study, the family's nonearned income and husband's characteristics **are** included only in **the** paid-participation equation, along
55 with the woman's marital status, plus a variety of more controversial identifying variables.

Khandker (1989) has subsequently examined the returns to schooling for both men and women based also on these Peruvian LSS data. He restricts his analysis to wage earners and identifies the sample selection probit equation by the fauily's land holdings, unearned income, **as** well **as** possibly **the** individual's marital status and. the education of his or her mother and father. The return estimw"tes appear to be relatively robust to variations in this list of identifying variables includod only in the sample selection equation. For the country **as** a whole, women's returns increase when controls are introduced for sample selection, and returns are then marginally higher for women than for men at the secondary and higher schooling levels. At the primary school level in the metropolitan **area** of Lima, however, **the** returns are low, and lower for women than for mon, 2 percent compared to 5 percent. This low level of private returns to women at the primary level is perhaps related to the frequency of domestic servants in this group, preponderantly women, whose income in kind (food and lodging) is difficult to evaulate and is often omitted from survey reports of wages. The same pattern has been noted elsewhere in metropolitan Latin America (see Schultz, 1968) and has led some researchers to exclude domestic servants from samples in estimating **wage** functions (Mohan, 1986).

The 1981 Socioeconomic Survey of Thailand provides another basis to **oval-** ute the effect of **sample** selection bias on the estimates of private rates of return 4o schooling for both women and mon. In this **case** two selection correction terms are incorporated into the analysis representing **the** probability of being in the labor force and being a wage earner. These selection
56 equations include family nonearned income, hectares of land owned by the family that are irrigated or unirrigated, plus **the** standard market wage rate determinants, including years of schooling completed at the primary, secondary, and higher education level. 23 Land and nonearned income are assumed to raise the nonnarket reservation wage of workers and thereby reduce the likelihood that they will take a **wage** job or work at all in **the** market labor force. Education does not exert a monotonic effect on the labor force and wage earner status of Thai men or women (see Appendix Tables A-7 and A-8). An individual with primary schooling is less likely to be a landless laborer. The more years of secondary schooling an individual has, the higher are the chances that she or he is working for a wage. Each **year** of university education strongly increases the likelihood of working in a wage or **salary** job. Herein is the clue why the sample selection bias can also be nonoonotonic with respect to level of schooling. Many landowners are also wage earners. If they have enough land, however, they presumably withdraw from the wage market. The controversial issue is whether land is exogenous or merely a proxy for self-employment, in other words, is land a legitimate variable to use to identify the selection model.

A final problem in specifying a **wage** function to estimate returns to education is how to model regional labor markets. If there were no interregional migration, wage functions should be estimated separately for each region. The wage differences related to education within a region would then be the appropriate parameter determining private investment in schooling in that closed regional population. But interregional migration occurs **and** more educated persons migrate more frequently. On balance, they move from lower to hlgher wage markets, and from rural to urban areas (Schultz, 1982). In Latin
57 America, for example, **as** such **as** half of the lifetime returns to schooling for the children of rural residents is realized by **the** increased likelihood that the children will migrate from **the** rural to **the** urban labor market (Schultz, 1988e). One could analyze this process lf one **knew whore** poople migrated fron, the costs they incurred in moving and where they received their schooling. Alternatively, if **the** individualVs current residential regional labor **mnrket is** held statistically constant in estinating a wage function, this procedure purges from the **estimate** of **the** return to schoolLng that component due to migration, to the extent that interregional migration toward higher wage regions occurs with increasing frequency for the better educated, as it clearly does in Latin America and the United States (Schwartz, 1976). Finally, regional labor market nominal **wage** differences may reflect compensating variation in price levels and reinforclng variation in **the** quality of subsidized public services, and thus not measure accurately real wage differences. Urban high wage regions havo more and better schooling, and regional shift variables in a wage function may also reflect this difference in the quality of schooling across workers (Behrman and Birdsall, 1983). Other than school and health services, most other prices ar% generally higher in urban high wage reglons, particularly housing. On balance, nominal regional wage dlfferences may exceed real **wage** differences, if public services are unimportant. Estimating the participation and wage functions with and without regions **as** explanatory variables should at least help to assess the impdrtance of migration ir. the estimation of school returns. **The** lack of information on migration in the Thailand survey data does not permit any further analysis of this issue **here. The** selection-corrected private rate of return estimates are contrasted
58 Table 4

**Istimat*9** of **Private ates, ef Return** to Scheoling in Thailand in **l001** by **Sax.** With and Without Smple Selection **Cereetien**

||Private<br>Internal<br> <br>tt stet4s<br>**Primary**|Rateof**Returns**b<br>tiss beneatb coo<br>Seceodcry|y Sbeeooling LOevl<br>t.foeent)<br>ligher|
|---|---|---|---|
|**Females:**<br>without<br>Correction<br>for samPle selection<br>of**wage earners**(OLS)|<br>3.22<br> <br>(2.70)|34.22<br>(22.1)|1.62<br>(-55)|
|with correction for<br>sample selectionof<br>**wage**earners<br>(two *tage Beckman)|10.02<br>(5.00)|24.73<br>(6.77)|10.6l<br>(M.04|
|males:||||
|Without Correction<br>for sample selection<br>of wage earners (OLS)|<br>15.95)<br> <br>(5.56)|2.)(.6<br>20.3)|**5**23.**2**<br>.4<br>(2.16)|
|With correction for<br>sample selectionof<br>**wage *arneras**<br>(two stage Beckman)|14.3S<br>(8.05)|13.12<br>(9.04)|4.12<br>(1.66)|

&The privte trate of return is approximated by the coefficient estimotod on the years of schooling variable in a logarithmic wage rate regression that also includes **as** regressorc the individual's years of postecbooling experience and *xperience squared, and four regional dumy variables, The sample includes all persons 15 to 49 in the 1901 Socieoaonomic Survey of Thailand. The Beekman (1979) lambda correction terms **are** included from probit equations for labor force porticipation and for **wage** earner status. identified by the inclusion of own nonearned incme and irriasted and sonirrigatod land.

Source: See Appendix **Tables** A-7 end A-S.
59 in Table 4 with those based on **the** ordinary least squares (OLS) estimates for wage earners that ignore **the** potential bias due to the selection of the sample of only wage earners. The two-step estimation procedure proposed by Heckman (1979) for dealing with selection bias is identified by the inclusion of nonearned income and hectares of irrigated and unirrigated land owned in the two selection probit equations that predict **the** probability that **the** individual is in the labor force and is a wage earner. Both the **wage** function and the two selection equations differ in level across **five** regions of Thailand: the least developed Northeast region, the rural population, the suburban sanitary districts, the urban municipal areas, and finally Bangkok. The gains that accrue to education through the more frequent migration of more educated persons from regions of lower **wages** to ones with higher wages are, thus, excluded in these estimates of the private returns to education, be-ause regional shifters are specified in the **wage** function.

Thailand has for most of this century invested heavily in primary education. Still, it enrolls a smaller proportion of its population in secondary schools than do other countries at a similar stage in their development, such as South Korea, Taiwan, Philippines and Malaysia or the two city states of Hong Kong and Singapore (Sussangkarn, 1988). On the other hand, the proportion of the Thai population enrolled in higher educstion is relatively large for a country at its income level. The relative supply of workers by educational level would lead to the expectation that in Thailand the returns to education would b. relatively high at the secondary level and relatively low of the university levels, coopared to other countries at Thailand's stage of development (Sussangkarn, 1988).

Table 4 shows that if one ignores the potential problem of sample selec-
60 tion, because analysis of labor productivity relies on analyses of only wage earners, the private rate of return to primary education is 5 percent per year for women, 34 percent to attend secondary school, and 2 percent to university education. For men the OLS primary school returns are 16 percent, secondary school 24 percent, and university education 5 percent. The statistical correction for **the** two **sample selection** processes that **might** render these estimates biased modifies the **estimates** markedly in four out of the **six** cases. Three out of four selection terms **are** statistically highly significant (Appendix Tables A-7 and A-8). The returns to primary education for women doubles to 10 percent, whereas **the** returns to secondary school declinte for both women (to 25 percent) and men (to 13 percent). Higher education private returns **increase** to 11 **percent** for women and decline for men to 4 percent. Although much more work is **needed** to assess **the** effect of alternative methods to deal with **sample** selection bia as it impacts on the returns to education of women compared to men, these data from Thailand suggests the levels of returns to schooling may be sensitive to this source of bias. In this instance, returns to women's schooling, increaso compared to the returns to men's schooling.

In concluding this section on measuring labor productivity and **the re-** turns to investing in the economic capabilities of **either women** or **men,** I should stress the need first to understand what underlies **the** individual's allocation of tim. This is required to correct analyses of **wage earners** from which one seeks to infer **the** effect of **investmnts** in schooling on the productivity of all workers. A potential bias is introduced by the process. This sample selection correction procedure depends on **the** relevant family unit and how family behavior is **modeled. The** bargaining model implies **a few,** pos-
61 sibly useful, differences in the empirical specifLcation of the family labor supply model from that implied by the unified family demand framework. But on the whole, both models require similar variables for inclusion in the wage function and for identification of **the sample selection** rule. Conclusions drawn from this section are likely to be robust to changes in how we model the family decisiotsaking process.

**Social and Nonasrket Returns** to Schooling

Social returns to schooling are trAditionally calculated by including public expenditures on edacation in addition to the private opportunity costs of the time of students. Hence, social returns are lower than private returns, often by between one-third and one-fifth. This adjustment should not affect comparisons between returns to women and men, unless the public resources employed in teaching women and men differ. In Latin America, this may be the case. Much of the higher education received by women is in preparation to be school teachers in 'normal schools," for which student costs per year are less than in regular academic universities (e.g. Birdsall and Fox, 1985). This is also noted in India (Tilak, 1987). In this case, including public costs of education could decrease the social returns from schooling by a greater amount for men than for women.

If governments tax labor earnings **and** hence the returns to human capital, **som** of **the** public expenditures on education, health. ***tc** , will be recovered. Only **market** returns, however, **are** taxable, **and** of **them** only **the** returns to wage and salary workers **are** effectively taxed in most low-income countries. 2 4 The tendency for more men than women to work for a wage might appear to provide a differential social return via taxes that would favor social investments in the schooling of males over females (Tables A-5 and A-6). But a more
62 complete answer to this question requires an analysis of how labor supply to wage employment responds to a change in market wage offers (after taxes). Most empirical labor supply studies in high- and lo-income countries indicate that women respond positively to an increase in their own market wage, and negatively to a wage increase of their husband. Consequently, the taxable supply of women's labor increases with an investmnt in women's capabilities **and** decreases with a conparable investment in sen's capabilities. Moreuver, male labor supply estimates often reveal a tendency for men to reduce their labor supply as their own wages rise.<sup>25</sup> Thus, to the extent that governments recoup public expenditures on human capital investments, the social return to investing in women should increase relative to those from investing in men. 26 A more generally accepted rationale for public expenditures on education of both mon and women is that there **are** social benefits to an educated population that increase welfare in a manner that cannot be readily captured by the more educated individual or their families. This argument for public support of education suggests that a more educated society is more capable of managing a political system that protects individuals' rights and facilitates efficient and equitable growth.. Though these claims are difficult to substantiate, there are more concrete examples that have been empirically confirmed. Most of them favor the schooling of females.
63

# Determinants of Chlld Nutrition and NortalLty

Studies in the 1970s and 1980s in demography, ecotiomics, anthropology and sociology came to the conclusion that there **was a** strong, probably causal, relationship between **increases** in mother's schooling and decreases in the Incldence of mortality among her own children, particularly in low income countries **(e.g.** Basu, 1987). Many hypotheses **are** offered as to why thls correlation is observed, **some** of which Are discussed below (e.g. Ran and Schultz, 1979; Barrera, 1988). The patterns is widely replicated because many fertility surveys provide the minium data on women: their **age,** education, children ever born and children stlll living. The universality of the relationship is reminiscent of the discovery in the 1960s of relative (logarithmic) wage differentials associated with the **years** of sctooling. The human capital interpretation of **these** latter wage-education differentials **as** an approximation of the private rate of return to schooling has gradually gained acceptance (Schultz, 1988e).

In the case of child mortality, an added year of maternal education is associated with a 5 to 10 percent reduction in child mortality. Although the level of mortality tends to be higher in rural than in urban populations of low income countries, the proportionate reduction in child mortality associated with an additional year of mother's schoolin& Is of a roughly similar magnltude in both urban and rural **areas** of many countries (Cochran at al., 1980). The proportionate mortality reducing effect **is** tauller for the father's schooling than the mother's, especially in rural populations (Mensch et al., 1986). Studies in Latin America have also noted that the child mortality differentials associated with maternal education are more moderate in Costa Rica end Cuba. The hypothesis advanced to explain **these** exceptions is
64 that these country's strong egalitarian public-health programs could improve access **and use** of health care even among the least educated mother* (Bohn, 1980). Other economic hypotheses for **the differences** in the relative magnitude of schooling effects on child **mortality are analyzed** in Colombia (Rosenzweig **and** Schultz, 1982b), **and** discussod **generally elsewhere** (Schultz, 1984; Thomas at *1.. 1987).

Is **education** simply correlated with **the use** of **more** health inputs, or does education provide **a** mother with **the capacity** to cope with health risks and better manage her child's environment? In Colombia an analysis of the 1973 **census** indicated that controlling for household incom or husband's education or **the** marital status of the **mother** did not eliminate or **even** greatly **reduce the** indepondent role of mother's education **as** a partial explanation for her children's survival (Schultz, 1980a). Studios elsewhero showed that it **ws** possible to control for many lifetime events and changes in socioeconouic status in relation to child mortality **rates** and still mother's education had its effect (Farah and Preston, 1982). In Brazil a third of the mother's education effect seems to operate through family income (Thomaz sat al, 1987). However, in addition to influencing child mortality, mother'p education undoubtedly influences many of **these** intercorrelated variables, such as migration, labor market behavior, **use** of health **care, and** modern attitudes. Therefore, to control statistically for all **these** types of variables is likely to understate **the** net effcet of schooling **(Kensch** atAl., 1986).

The puzzle rnmains why mother's education explains more of **the** variation in child mortality than other variablos describing **the** individual 's access to health care, or prices, or even totoil family income that could be spent on health care? Three competing hypotheses **are** (1) that **the more educated mother**
65 uses a different mix of observable health inputs, or (2) that she uses these inputs more effectively, or (3) that her education **is** positively correlated with the use of unobserved health inputs and her education is credited with the effect of **these** unobserved inputs on child health (Schultz, 1984). Clearly, the mother is the most important health worker for her children. How well **she** performs this task appears to **depend** "on **her** schooling, which *quips her with **general** and specific knowledge, and **the** mans and confidence to seek new ideas' (Barrera, 1988b: p. 4). The challenge is to discover how education influences the use of health inputs to reduce **the** probability of child mortality. Because relatively few children die in middle and high income countries, a more widely distributed or continuous indicator of child health status is sought to extend and broaden the analysis. Weight of a child is often recorded at birth and provides a sensitive predictor of child mortality as well as lifetime health problem and mental and physical developmental handicaps. The self-selected health inputs of pregnant women Mst be analyzed as endogenous variables if one is to estimate the unbiased effect of these inputs on the production of child health (Schultz, 1984). Selection of some health inputs occurs in response to the mother's expectation of a goodl Or poor birth outcome, and consequently the simple correlation between these self-selected inputs and health outcomes may **be** misleading. Seeing a doctor early into the pregnancy **is** correlated with having a lower birthweight child, **whereas** administering the **same** health input to an average woman exerts a positive effect, holding constant the initial (but generally unobserved) health condition of **the** mother and child. In a U.S. study, the effect of mother's education on birthweight in 1967-69 and in 1980 appeared to be transmitted largely through the variation in the same four measured prenatal inputs
66 (Rosenzweig and Schultz, 1988).

It is probably a more difficult task to measure all or even the major inputs that play a role in producing<sup>good nutrition, good health, sound</sup> development, and survival in an older child. A statistical explanation must also be found for which mother use each<sup>of these health inputs,</sup> if the effect of these inputs on the production of child health is to be estimated without input self-selection bias. It is simpler to exasine directly<sup>the impact on</sup> child health of interactions between mother's education and her constraints in caring for her children's health. Estimates of these interaction effects show how maternal education exerts its elusive effect on child health. For example, Caldwell (1979) hypothesized that the education of the mother enables **her** to e- 1ploit local public health care more effectively. His view sugiv*sts that the interaction between mother's education and local public health infrastructure as it determines child health is complementary or positive. But Rosenzweig and Schultz (1982b) found the opposite<sup>to be true in 1973 in Colombia,</sup> where differences in maternal education have a smaller impact on child mortality in urban populations that receive more public and private hospital and clinic services per capita. Their findings are consistent with the aggrelipe patterns .reported elsewhere in Latin America by Behn (1980) and<sup>Palloni (1981) and in</sup> Sri Lanka by Reegama (1981). If the effectiveness of health care of children associated with the education of mothers is<sup>a substitute</sup> for local public health expenditures in the production of child health, **as these** studies imply, additional outlays on public health should have the greatest impact improving child health among the least educated segoents of the population. Although more research is needed on the personal<sup>distribution</sup> of the benefits of many forms of public expenditure programs, these initial investigation **s**<sup>uggest</sup>
67 that public health programs distribute their benefits progressively across low income populations, according to **levels** of adult education.

Other studies examine interactions of health input choices themselves and environmental constraints on child mortality. Where household water and sanitation facilities **are** poor in Malaysia, breast-feeding is associated with larger reductions in infant mortality (Butz, St al., 1984). Estrey and Habicht (1987) found that **safe** water supplies **reduced** child mortality by a greater amount for more educated mothers, whereas access to toilets in the household is less effective in reducing child rortality for more educated mothers. In Costa Rica, Haines and Avery (1982) found **the** effect of a year of education for the mother is to reduce her child mortality between 6 and 7 percent, holding constant for household sanitation and dwelling quality and local levels of child mortality and health care facilities. Haines and Avery confirm that the child hea,lth gains **related** to mother's education are smaller in urban areas, a result also noted by Schultz (1980a) in Colombia and Behm (1976) in several Latin American capital cities.

But the studies of Malaysia and Costa Rica treat the household's water and sanitation infrastructure **'as** well as the mother's breast-feeding as _mg&2SM_ or, in other words, as not affected by maternal education or unobserved variables that might **themselves** influence child health outcomes. Barrara's (1988b) study of household and community data from the Bicol Province of the Philippines extends these earlier studies. He assumes that water and sanitary facilities of the household are andooMus, choice variables and may be thus potentially correlated with unexplained variation in child health. Consequently, he first analyzes the relation between maternal education and child health conditional on comantiZ averagg levels of water and
68 sanitation, not the household's actual variables that are affected by the family's own choices. Barrera then finds m ther's schooling has a larger protective effect on child health in unsanitary u io jtias where there are visible signs of excreta and in comunities that **are** further (in time) from out-patient health care facilities. In a com_unity where piped water is the predominant source of supply, the impact of mother's education is diminished. Where water sealed toilets **are more** coi_on in **the** community, there **are** larger maternal education differences in child health. larrera's shift from household-level measures of water and toilet facilities to comunity-level variables representing the local availability of **these** facilities, reverses the conclusions drawn by Estrey and Habicht (1987). Comunity water supply appears to substitute for mother's education, but **the** modern toilet complements it **(Barrera,** 1988b: Table 16). At the ***ame** time, higher income and mother's education are shown by Barrera to increase the chance that a household has acquired piped water and water-sealed toilets.

Another important input to child health, the duration of breast-feeding, is inversely related to mother's education in many countries (e.g. Blau, 1984; Wolfe and Behrman, 1982; Sirilaksana, 1986).. Breastf eding is a beneficial input for child health primarily if it is supplemented by other foods before the end of the baby's first year. tn Barrera s rural Philipplne population, the mother's educatiou shortens only the duration of un laf mmntga breastfeeding (1988b: Table 23). Moreover, **he estimates** that the duration of unsupplemented breast-feeding is a Opositiveo child health input only up to six months of age. The more educated mother is more able to replace mother's milk with sanitary substi-tutes. For **the** less educated, **Barrera** hypothesizes, the supplementation of breastfe*ding at less than six months is more harmful.
69 Thus, th measurement of **the** optimal duration of breastf eding and time to introduce supplementary foods in the child's diet depend on the education of the *sther who must manage to provide sanitary substitutes for her own supply of ilk. In other words. the duration of unsupplemented breastfeeding and education appears to be substitutes in producing child health. This fihding may partially account for why more *ducated mothers breastfeed less (larrera, 1988b: Table 25).

Nutrition and labor productivity **are** positively related at the individual or household level. With the recent use of simultaneous equation estimation techniques (Strauss, 1986) it has been shown convincingly in three studies that nutrition raises labor productivity, at least at very low levels of income (Strauss (1986) in Sierra Leone; DeolaliLar (1988) in India; and Sahn and Alderman (1988) in Sri Lanka). Since the demand for nutrition is also influenced by prices and incoms, the standard assumption that production decisions and consumption decisions can be sequentially and separately solved by the farm household may need to be studied further (Singh, n; al 1986). .o analyze intrahousehold allocation of nutrition, data are needed either on individual food intakes or on measures of nutritional status. Most analysis has beon based on the latter anthropometric indicators of nutritional status, such as height for age, or weight for height, thickness of skinfolds, etc. that can be readily **measured** in a single **survey.** But these anthropometric indicators of nutrition reflect more than **the** adequacy of curront nutritional inputs. They also incorporate **the** past pattern of nutrition, as modified by individual differences in metabolism, absorption **rates** possibly related to infectious and' parasitic diseases, and activity lovels. Studies such as Barrera (1988), Struass (1987) and Thomas, Qt_al. (1987) analyze such
70 accumulative indicators of nutrition.

Much less economic analysis has focused on actual individual consumption patterns by gender within the family, because (1) they appear to be accurate only over a short recall period, of say 24 hours, and (2) they should be repeated at least several **times over a year** to reflect the variation associated with seasonal changes in food availability and activities, and (3) they appear to embody more measurement errors than do anthropometric indicators. In the available studies of individual nutrient intakes ib low income countries, small working samples also **weaken the** evidence. **One** suggestive study of Central India finds that calorie intakes **are** not significantly biased in favor of boys compared to girls, *xcept during **the lean** season, when households have lower overall consumption levels (Behrman, 1988). Further studies of actual nutrient intakes within the family should be encouraged along with the demand for health care and time allocation within the family (e.g. Pitt and Rosenzweig, 1985, 1988).

# Determinants of Child Achievements In School

There are surprisingly few studies of the household determinants of school enrollment and attendance rates by gender in low income countries, though this may be changing. In an analysis of district-level data from the Indian Census of 1961, Rosenzweig and Evenson (1977) found that where child wages were higher, children supplied more labor to the market and enrolled less in school. Male wage rates were negatively associated with child schooling levels, while female wages were positively associated with the enrollment rates. These partial effects of **wage** rates on schooling were implied by their model, but only after a number of cross-comoodity substitution patterns were imposed and price (wage) effects were assumed to outweigh income effects.
71 More generally, individual level data often show father's education is also a stimulus to the child's achievements in school.

King at **al.** (1986) estimated from several Asian surveys the determinants of parents' investing in the schooling for their children, and how they respond differently toward the schooling of girls and boys. Analysis of schooling decisions from a Peruvian survey permitted **the** analysis of whether the parents invest by sending their children away to continue their schooling, generally at a private versus local public school. Aialysis of the schooling decision suggests that community school access and family income both increase enrollments, particularly for girls (King and Bellew, 1988; Gertler and Gleewe, 1988).

In most studies of schooling of children in high income countries, the mother's schooling has a larger effect than **the** father's, **even** though the father's education implies a larger market income effect, because he tends to receive a higher wage and work more hours in the labor force (Leibowitz, 1974; King et al., 1986). The evidence that mothers' schooling exerts a greater effect on the schooling of their daughters than their sons is less well established (e.g. DeTray, 1988): Table 5). It is a hypothesis that needs further study.

# Determinants of Fertility

Fertility is generally observed to decrease as women's education increases. Occasionally, however, fertillity of imnschooled women is lower than that of women with a few years of primary education (Cochrane, 1979). A husband with more schooling (or income) in traditional agricultural societies is frequently observed to have a larger family as noted previously. But spouses of similar education tend to marry. Because of the general positive
72 association between education of spouses, the evaluation of the partial effect of women's schooling on fertility becomes **more negative and** more likely to be monotonic when the positive effect of men's<sup>education</sup> or incomo on fertillty **is** controlled. Economic models of fmily decision-making direct attention to the distinctive effects of mnu's and women's schooling on demands for children and resulting fertility. Simple correlations can be misleading in this **area.** Woen's education not only decreases desired fertility, it also permits couples to come closer to reaching their reproductive goals. Their increased reproductive control is partially<sup>achieved by delaying marriage, but more</sup> educated wives also have fever<sup>unwanted</sup> conceptions (and births). Though the husband's education also enhances contraceptive effectiveness, the wife's *ducation has a stronger effect on<sup>these forms of reproductive</sup> behavior, whother inferred from a respondent's own classification of conceptiorm as unplanned or from econometric analyses of the<sup>reproduction</sup> function and lts residual (Rosenzweig and Schultz, 1985, 1987). This<sup>may be yet another sphere of</sup> nonmarket production where the<sup>education of women generates an important</sup> spillover that may be valued by society--avoidance of unwanted births.

All of these routes by which women's education may benefit society-reducing child mortalLty, improving child nutrition, lncreasing child schooling, and reducing unwanted fertLlity--lnvolve benefits that **are** partly captured by the woman's own<sup>children.</sup> If parents are entirely Ialtruisticm toward their offspring, and view their children's utility **as** their own, these activities would be allocated investments and be produced in an efficient manner within the family. In other words, most of the mexternalities" from women's education that accrue<sup>from her nonmarket production</sup> activities are internalized in the altruistic nuclear family. Nevertheless, there are
73 numerous instances **where** society has strengthened the rights of children relative to the economic claims of parents, such as when the conditions of child labor are restricted or school attendance legally mandated. Improvemonts in a woman's education **are** linked in many societies to increases in the level of investments in the human capital of her children. A subsidy that favored women's schooling would help to shift private household resources toward investments in the quality of the younger generation. Empirical stuuies have not yet sought to appraise the responsiveness of family resources to practical interventions of this form.

Pollcy Implications of the Empirical Evidence The available evidence indicates that primary and secondary schooling for women can be defended on the grounds of efficiency (high market social returns), equity (increasee. capabilities of the poor), and intergenerational redistribution that improves the health and education of children. In contrast, direct transfers of consumption to women and children, for exsmple by food programs, are likely to be only marginally effective because these public transfers are readily redirected within the family to conform to its traditional goals. Invostments in women's traditional vocational skills may not help much, because they may limit their future employment opportunities. Restricting women's work to traditional sectors may compel them to compete in less profitable and possibly declining sectors of the economy. Antidiscrimination laws afe of uncertain value oven in high-income countries, while in low-income countries **these** policies appear to penalize primarily large firms that cannot avoid the resulting buroaucratic red **tape.** Innovative institution building to advance the rights and welfare of women **is** appealing in principle, but in practice requires a deeper understanding of social
73 (a) engineering than is commonly encountered.

It would be useful to develop alternative measures of women's status that can be quantified for comparative purposes across societies. This would be particularly valuable in those cases where there **is** insufficient data to fit sample selection-corrected wage functions, **as** in rural Bangladesh (Khandkar, 1988), or **as a** check on **these** indirect statistical procedures. **The** voman's age-at-marriage is often used to approximate **the** economic influence **she** conmands in the family (e.g. Acharya and Bennett, 1983). The bargaining model of the family could also be used to interpret the difference between her husband's cge and her own as a relative measure of their economic powers. But as the earlier discussion of marriage patterns implied, these outcomes are conditioned by the woman's family's resources, her parents' education, their **investments** in **her** schooling, **the** number of **her** siblings, and so on (King j1 Al. , 1986). We need to know how these parental constraints interact with conmunity infrastructure (e.g. schools), and with the local social norms of marriage. This should be high on the research agenda if public policy is to increase the economic capabilities of women and provide them with the-opportunity to manage more of what the family spends.

From another perspective, the difference between a wife's education and that of her husband can proxy for **the** balance of economic **capacities.** This line of analysis can be further refined by imputing to both a market wage rate that is corrected for sample selection bias or **the** shadow value of nonmarket time for nonparticipants. But for the **same** reason that **age** differences between spouses is best viewed **as an** endogenous indicator of relative female-male authority and economic influence in the family, the educational' difference between spouses is **an even** clearer reflection of the opportunities
74 provided by **the** parental families to girls and boys intermediated by the marriage matching institutions of a specific society. To treat these spousal differences in education or age as an exogenous constraint on the family's subsequent behavior is to truncate the analysis before it is possible to understand how the parent resources and investments in children along with community policy variables work their effect on **the** balance of economic capabilities acquired by young **men** and vomen. Th. potential leverage of policy on 'this process is thus suppressed from empirical analysis by including measures of spouse matching, as though they were exogenous variables. In the short run these spousal differences are given, but in the longer run they should adapt to policy interventions.

No studies have assessed, to my knowledge. the impact of legislation that **sets** a minimum **age** for marriage for women, prohibits dowries, or specifies women's rights in divorce and inheritance. If most members of society to not see these laws as in their interest, are the laws still treated as symbolic social goals but merely left unenforced? What are the real consequences for women and society of such changes in legal code in low income countries? The topic could benefit from research, but the task will not be easy.

There is a consensus among anthropologists, sociologists, and economists that women generally achieve a higher status in society if they are more productive economically in relation to men (Blumberg, 1984, 1988; Marini, 1987). Targeting consumption resources to women is likely to be an ineffective and transitory means to increase the productivity of women and improve their lifetime welfare. Legislating changes in labor markets, prescribing employment practices, and modifying sectoral priorities to expand and improve women's employment opportunities appear to be blunt and probably ineffective policies
75 in a low incone country. Investing in the human capital of women to perform market and nonmarket work **more** productively is a more promising way to increase women's economic capabilities. Formal education. is the most widely documented way to achieve this goal efficiently. With better education of women, household spending is likely to go increasingly for child health, nutrition, and schooling. Basic schooling appears to ba th **most** efficient and equitable investment option, and one that is often now allocated disproportionately to mn. This allocation of human capital requires more study; its justification on "cultural' grounds should be reckoned carefully in terms of economic costs reflected in shorter lifetimes, higher fertility, and lower per capita productivity.

Regional Patterns of Investment in Women and Men The largest investments in human capital are in schooling and health. Differences in education between women and men can be inferred from two sets of data: school attainment as reported by individuals in a census and enrollment rates as reported by schools to UNESCO. Although both types of data have their shortcomings, and probably overstate the human capital received by women in relation to men, **the** patterns across countries in both are similar. Today in the industrially advanced high income countries women receive nearly the same number of years of schooling as do men, and woen often report higher enrollment rates than men until secondary school. Latin America and East Asia are quite similar to the developed countries in this regard. At the other extreme are most of the countries of South and West Asia and North and subSaharan Africa where women receive between half and three-fourths the years of schooling as do oen. This regional pattorn is illuatrated in column 1 of Table 5 using estimates of years of completed schooling from a recent census,
76 Table S tests of SeboeliAS **Cmpleted and Ranted years** of BareLlnent Rntios by Ion. fot legions from 13s0 and 1360: Ielseted Available Countries

|T<br>Region<br>(number of<br>eountries<br>observed)|eare of Sbho<br>C _emyted<br>Vmeo to<br>Hale Ratio<br>(1)<br>.|oling 1S<br> Age 20-2<br>HaloLev<br>is<br>teats<br>(2)|60-19d0<br>4<br>el<br>|**xpsete**<br>remae<br>Hale<br>(3)|**d**<br>**Teats**<br> to<br>Ratio<br>(4)|**6ftno**<br>Halo<br>in<br>(5)|**llment**<br> Level<br>toars<br> <br>(6)|
|---|---|---|---|---|---|---|---|
|1.ighinsane<br>idus-<br>trially<br>advanced<br>countrioe<br>(including<br>Japan.**Israel.**and<br>South Africa)|1.00<br>(24)|**5.S**<br>(24)||.64|1.00|10.6|<br>12.6|
|2. Latin America|.64|5.5||.92|.67|5.6<br>*|<br>.3|
|3. last Asia<br>(excluding Japan)<br>4-. South Asia|.63<br>(5)<br>.45<br>(5)|64<br>**(0)**<br>3.<br>(5)|IAsia<br>(exclud-<br>Iin Arab)<br>J|.65|.75|7.5|<br>9.4|
|5. West Asia<br>(excluding Israel)<br>S. North Africa|.34<br>(2)<br>.39<br>(2)|4 7<br>(2)<br>3<br> <br>(2)|lArab<br>i<br>States<br> J|°|.73|3.5|<br>7.0|
|7. SubUhaaran Afriea<br>(excluding South<br>Africa)|*<br>(7)|(i3|IAfrica<br> <br>(erxlud-<br>ong<br>sAeb)|.56|.73|3.5|7.0|
|World Total|na|n.e.|.75||.62|7.7|9.6|

Source: Coluns 1 and 2 deriva.d by averaging of the available **sample** o? country estimates reported in Sable A-1 by the author. Columne 3-6 derived from UtRSCO estimates of enrollment rates by lovel, with different regionol Saoupings. I 27StettietieYearbook. Table 2.10. **Soe** tfotnoto 27 fto defaintion 6of xpoeted years ot*solln ent.
77 and in columns 3 and 4 based on official enrollment rates accumulated across 27 school levels **(see** Table A-1). The estimates of educational attainment are for persons age 20 to 24 in a simple of censuses in about 1970, and approximate the distribut 3n of educational investmnts **made** around 1960. The 1960 accumu'ated enrollment rates are estimated by UNESCO to represent averages for all countries in each region. The figures in column 3 **are** slightly higher than those in column 1 for Asia and Africa. From 1960 to 1980 enrollment ratas increased, slowly converging between **women and men.** But differences remain large. Are these differences in fmale and **male** educational investments explained by **the** diverse economic conditions in these countries? An analysis of educational systems from 80 countries for the period between 1960 and 1980 sought to account for differences in public expenditures on education and enrollment rates on **the** basis of household demand and production theory. More than 90 percent of **the** intercountry variation in public school expenditures per school aged child and 40 to 70 percent of the variation in primary and secondary school enrollment rates are explained by real income per adult, the relative price of teachers, the school-aged share of the population, and urbanization. The proportionate increase in girl's enrollment rates associated with a proportionate increase in income is significantly larger than the analogous incom elasticity for boy's enrollment. Price elasticities **are** also larger in absolute value (negative) for female enrollment rates than for male. Eponomic growth and long run expansion of the educational system that contributes to **the** decline in **the** relative price of teachers appear to increase women's schooling in comparison to men,s. Witain countries over time this' gap between the educational attainment of wonmn and men has also narrowed, but more slowly than forecasted from this relationship
78 acro"s countries at different levels of development (Schultz, 1987).

Unless special **measures** are adopted, female enrollments may decrease by a larger proportion than male enrollmonts in periods of declining income per adult. Public sector retrenchments since 1975 in Africa and Latin America have cut real expenditures on public education, while private schools are also contracting (Schultz, 1987). Research is nnoded to determine whether female enrollments are indeed falling relativ to **male** enrollmnts in those countries that are undergoing more difficult periods of macroeconomic adjustment, or whether the forward momentum in investments in women has offset **the** current decline in income in Latin America and Africa.

This regression model is then employed to hold constant for national income, prices, and demographic structure, while the reomining unexplained variation in enrollment rates and public school expenditures per child are summarized in Table 6 by region. The pattern in these residuals is somewhat different from that in Table 5. Africa is an over-achiever in scnooling, given its low income and high cost of teachers. These conditions in Africa also account for the tower female to male enrollments in that continent. South and West Asia, however, continues to invest less in primary and secondary schools than can be explained, and the shortfall is significantly larger for females than for males. Latin America reports lower than expected enrollment rates, but its shortfall is greater for males than for females. In the high incomo countries, there is _a_ small positive deviation in enrollment rates for those predicted by the model, and they tend to be slightly larger for women than for mon at the secondary school level. There are no analogous indicators of **the** gender differential in investments in health or use of health services. TherL is even little agreement on
79

# Table **6**

Percenatge Deviatione in National Sducetlenel. Invavotuota Adjusting ter the tffecta ot Iceae. Teacher Selaries. and Demographic Composition: 1960-1930

|**Region**|Pr<br>Numberof<br>CountryfYear<br>Observed|siary Se<br> <br>Zarel<br>Rate<br>Terele|bol**S**<br>Lent<br>s<br> **Hale**|**ystem**<br>srpenditures<br>**per**Scbool<br>Ag*d Child|**Second**<br> <br>**Numberf **<br>o<br>**Country/Tear**<br>Observed|**ary Sch**<br>**morel**<br>Rate<br>**emnal-**|**ool S**<br>**Lment**<br>s<br> **Hale**|**yatem**<br>**Izpenditures***<br>perSchool<br>**Aged**Child|
|---|---|---|---|---|---|---|---|---|
|lurop. Ocea3nic<br>and North America|<br>|+6<br>+|+<br>6|-|21<br>1+|+3|+7<br>7|<br>-t<br>--|
|LatinAmerica|43|-10|-15|-5|35|-30|-39|-16|
|last Aila|21|+22|+12|+9|16|+27|+19|+2|
|Sauthand|24|-49|-24|-21|16|-49|-7|-4|
|Atrics|62|+26|+16|+15|49|+43|+34|+17|

aDependent **variables are** t1) the logerithm of jhe number of children *nrolled in primary schools divided by tho number ot ehildren **age** 6 to 11, and (2) the logarithm of the secondary enrollSent divided by the number of ehildren **age** 12 to 17; and the local publie ezponditures on thb school level divided by the cbildren in the respective benetiting **age** clasa **erpreased** in real 1970 local currency and converted to 1970 U.S. dollare on the basis of 'M *achange rates prevailing on average for 1960 to 1971. See source for more detail.

Source: T. P. Sehults (1967: Table 13).
so measures of adult morbidity or what constitutes functional good health that could be applied to men and women comparatively across societies. Hortality by age is therefore used as the preferred measure of health for men and women. It is typically summarized **as** life expectancy **at** birth, but infancy is the most vulnerable period, and infant mortality by sex is particularly sensitive to differential care in the family, as noted earlier.

Life expectancy appears to be greater for **women** compared to mon in populations where schooling is more equally distributed by gender, **as** shown by WHO estimates by region shown in Table 7. biological evidence suggests that women are genetically more viable than oen. Given similar consumption and self-investment opportunities, women have- lower death rates than men at all ages, except perhaps during the years women bear children if they have not had any education. Death due to bearing a child is twenty times higher in South and West Asia and North and subSaharan Africa than in developed countries, and women in these regions also bear two to three times as many children as do their counterparts in high income countries (column 7, Table 7). Nonetheless, infant and childhood mortality is quantitatively a larger factor io the low life expectancy of females in these low income regions. Differences in child mortality by sex are rarely known with great precision in poor populations, and tho official figures as averaged in columns 5 and 6 of Table 7 are not necessarily reliable. Greater confidence can probably be placed in the infant and childhood mortality estinates derived retrospectively from the recent round of World Fertility Surveys (Rutstein, 1983; Hobcraft It Al. 1985). Only two out of 29 surveys, those for Jordan and Syria, revealed greater female than male infant mortality, while three reported higher female than male child mortality between the ages of 2 and 5 years--Bangladesh,
81 Table 7 Life Expectancy at Uittb *ad lafeat Nrtality. Ratie by **Sax** ed Maternal Mortality feo **Regimes** In **1360 ead 1350**

||Life<br>E<br>**Fem**<br>to<br>1960-65<br>(1)|xpectancy<br>**ale**<br>Mele<br>1960-a5<br>t2)|**at b**<br>**Male**<br>1960<br>(3)|**irth**<br>**Level**<br>-<br>1360<br>(4)|latest<br>**_?Male_**<br>to**Male**<br>a<br>1360<br>(5)|**Mortality**<br>ablo Level<br>ea 1960<br>(6)|Maternal<br>**mortality**<br>**pot**<br>100.000 Live Birtbs<br>ca 1963<br>(7)|
|---|---|---|---|---|---|---|---|
|High Income<br>Zurepo<br>Oceania<br>North America<br>Japan<br>Israel|1.09<br>1.05<br>1.07<br>1.10<br>1.00<br>1.04|1.11<br>1.09<br>1.06<br>1.12<br>1.07<br>1.05|66.6<br>67.0<br>61.7<br>66.9<br>66.5<br>66.1|**60.5**<br>69.7<br>64.6<br>69.4<br>73.6<br>71.0|.0 *<br>(22)|17.4<br>(22)|30<br>30|
|&outh Africa<br>Latin America|1.07<br>1.06|1.05<br>1.07|52.1<br>54.9|60.9<br>62.1|.03<br>|44.09<br>|270|
||||||11|Cli||
|Zast<br>**Asia**<br>|1.06|1.05|51.0|64S|<br>.79|34.2|167|
|(excluding<br>Japan)||||||||
|<br>South lEst Asic|1.06|1.06|43.2|533.||||
|Other Zest Asie||||||||
|(e.g.Hong_tons,_<br>Korea)|1.07|1.06|54.2|63.4||||
|China|1.06|1.04|53.4|66.5||||
|South Asia|.97|.96|43.3|50.7||||
|(middle)|||||.62|51.0|616|
||||||(4)|(4)||
|West Asia|1.05|1.06|43.2|56 2||||
|Africa|1.07|1.06|40.6|49.3|--|--||
|North Africa|1.05|1.05|45.5|54.7|90|58.1|500|
|SublSheran Africa<br>Cexculding S.Afric|<br>1.08<br>a)|1.07|39.1|47.7|.01<br>(4)|$6.4<br>(4)|676|
|World|1.05|1.04|50.9|57.9||||

*Nubier of countrio avered in parenthesoe: Not necessarily representative of region because of data limitations.

Sour0ss: Fizst tour colunra weighted by populations. Zetimates fro United Netions. D-caarscje Indicators oas. New York. 1962; Colums 5 and 6 **are** unweiiibted **averages** of countries reporting in 1903iUDlls nh I Yo;rbook. Table 16; Colum 7 are *ftimates **weighted by** population of reoisons from **Rosec** alt., 1930. Toble 34. For country lovel *ptimates that are probably mrc reliable for **Attica** ead **Asia. see** Appendix Table S.
82 Pakistan, and Egypt. All of these countries fall in the South and West Auian and North African regions.

Analyses of mortality trends by sex confirm, as with education, that in most regions of the world the prospects for survival are improving more rapidly by women than for men (United Nations, 1982). Most countries are converging toward the sex differences in mortality schqdules that **are** observed in the industrially advanced countries, but **the pace** of progress differs markedly by region. Women's education is the **best** predictor of the level and sex differences in mortality schedules; female enrollment rates are used in 50-year forecasts of national mortality schedules (Zachariah and Vu, 1988: xv). Until life expectancy at birth for women exceeds 60 **years,** the rate of increase in longevity is greater in countries where more than 70 percent of girls are enrolled in primary school (as 1956-59).' Thus, studies of national mortality patterns confirm the critical role of women's education in the rate of health improvement of national populations, extending the evidence beyond the survival of a woman's own children that is documented from household studies (Cochrane gt &I., 1980; Mensch *al_., 1986).

In conclusion, women are especially poor in relation to men in their acquired capabilities, such as schooling, in the world's poorest countries, and. in these same countries women's low health status is reflected in their shorter lives in comparison to men's. This deficit is particularly pronounced in South and West &sia and North antd subSaharan Africa. Increasing the productive capabilities of women in **these** poor countries through investment in their schooling appears to be an efficient use of social and private individual investment resources. Beyond schooling, other public policies have been proposed to help women realize more efficiently their productive potentiai and
83 contribute more to the growth of their societies. Thoughp these policies have not been empirically evaluated as extensively as investment in education, some appear promising.

5. Factor Markets, Information. Diffusion, and Equipping Interventions There is much evidence that the long-run individual and social benefits from increased human capital investments in women are attractive compared to alternative uses of public resources. But are there other avenues for development programs that could also enhance women's productivity, given their current stocks of human capital. There are indications that women could be more productive in the short run, if they had greater access to new technical knowledge, had more instruction on how to use effectively new and existing productive inputs, had means to acquire credit to respond to economic opportunities, and had greater control over their own assets **and,** livelihood. The public sector subsidizes and/or manages many activities and services because the private market fails to produce them in sufficient amounts. The most commonly cited reason for the underproduction of these services is because market structure does not provide private firms with the means to charge the consumer or producer for what they produce. Alternatively, there may be internal economies of scale that encoulage natural monopolies uiith their exploitative pricing. Arguments of basic needs, social externalities and-equity are also made to justify these interventions of the public sector. Individuals may benefit as consumers or producers because **the** public sector reduces time and monetary prices of such goods and services.

The provision of public services frequently complements the capabilities of some people more than others, adding to their economic productivity. Some
84 public services **are** associated more with female than male productivity, and vice versa. But administrative records, and hence quantitative analys iS, rarely consider the gender of the recipient of a public service or for that matter the gender of the administrative staff who provides the service to the public It has been observed that public services that favor female productivity are given relatively low priority in m*Ot low income countries. This **may be** traced to our undervaluation of home production. For example, priority **is** often given to supporting cash crops, which are often managed by males, because of the commercial and governmental interests in these potential export crops that provide tax revenue. Regardless of why private markets fail to meet social needs, the economic issues of effectiveness and distribution of benefits tend to be hard to evaluate empirically. They involve measuring the substitutability between male and female labor in many production processes, the interaction of modern productive inputs and extension with male and female labor, and the functioning of interconnected factor markets as they impinge on the marginal product of both types of labor.

# Home Production and Nev Technologies

Women stand to gain from the development and spread of new technologies that improve their efficiency in home production. There is relatively little systematic investment by the public sector in low-income countries designed to improve labor productivity in the home, yet this is a large share of total production in these countries. Only a few international assistance agencies have encouraged research and development projects that seek small scale technologies that would enhance production possibilities in the home. Systematic evaluation of these undertakings **is** rarely attempted. Women in the lowest incone countries spend much tiLe fetching water and
85 fuel, preparing family food, and caring for children. Innovations developed to relieve these female tasks can also allow women to transfer some of their production from the home to the market, as in the case of rice milling in Indonesia (Timmer, 1973) or the production of boer in Africa (Haggblade, 1987). Reductions in consuuer prices should distribute widely the benefits from such new technology in the long run. If this process is monitored more frequently, policymakers will be alerted to **the** home and price mnvironm&nt in which technical change will permit women to capture more of the resulting economic opportunities. Technical change will undoubtedly continue to transfer production from the family to market-oriented firms. Instigation and guidance of this complex process will require empirical evaluation studies that distinguish between the repercussions for men, women, and children.

Agricultural **Exteusion** and Research Studies of the effectiveness of agricultural extension programs do not provide a stron3g foundation for policy although $5 billion a year is now spent on it, according to recent world estimates (Birkhaeuser et al. , -1988). Most studies are from India and the United States, and do not necessarily agree on why some programs are more effective than others. To determine the environment in which extension is either more or less cost effective requires more comparable studies in a broader range of countries, with different management structures, staff training, technological infrastructure, price policies and, most of all, the extent of the knowledge to be taught that is profitable for farmers to use.

A number of studies suggest that extension, by increasing farmer productivity, narrows the difference between **more** and less educated farmers. This parallels family plannlng, where contraceptive adoption and fertility
86 differentials between more and less educated women are narrowed by extension work (Schultz, 1988b). Agricultural and family planning extension appears to be a substitute for formal education. Extension particularly benefits the recipient who is less **wvel** trained to decipher and evaluate new technologies, adopt novel techniques, and obtain combinations of inputs that are likely to be more profitable or cost-effective. It has been argued for nearly two decades (Boserup, 1970) that some agricultural extension agents should be women, and that contacts with female as well **as** male farmers should be monitored in areas of the world where farms are often managed be women. Extension programs in the United States have generally included fenale agents to work with women in nutrition and homo health measures, as well as **farn** management. Still there are no strong evaluation studies that distinguish how the gender of extension staff or of the person contacted at the **farm** level affects the, productivity of farm labor. Farm-management and household-production studies have shown the levels of productivity of female farm managers and how it increases with education (Moock, 1976, 1981). But these approaches have not been used to evaluate extension or credit policies or to provide a basis for replication studies (Spring at I.,a 1983; Ngwira, 1987; Herz, 1988; Birkhaeuser, 1988).

Agricultural extension systems are often criticized as not serving women, particularly in subSaharan Africa, where women predominate in the farm workforce (Boserup, 1965; Lele, 1975, Ber,!e* rt1.A., 1984; MRI, 1985; Ngwira, 1987; Herz, 1988). 28 Women tend to concentrate on food staples eaten by the family and men on cash crops in Africa and in other parts of the developing world. But agricultural research to improve yields initially focused on export crops and consequently faised the productivity of men more rapidly than
87 that of wonen. ;n the last **decade, sowe** of this bias has been corrected; more research has been allocated to staple foods consmed by the poor, such as cassava, pulses, and sorghum (Judd a& al., 1986). Nonetheless, there is evidence that the direction agricultural technology has evolved in Africa **has** placed women at a growing disadvantage relative to oen. The recent relative decline in prices of **some** export crops in Africa compared to staple crops, may enhance vomen s productivity. But growing agricultural productivity is likely to continue the trend of slowly declining prices of rice, wheat, maize, and traded staples. If women's productivity in local foods does not keep pace, or they fail to diversify (e.g., into aquaculture or vegetables), their real income vill continue to be eroded (Kennedy and Cogill, 1986).

There is debate today on how to stimulate African food production (Gladwin and McMillan, 1989). Some would encourage larger scale production units that can introduce more sophisticated technologies with greater capital intensLty. Others would increase the productivity of existing small-scale farm,s though the effect on marketed surplus might be smaller. The former direction would lead rapidly to fewer female farmers, if the larger scale units proved to' be cost-effective. The latter course would entail programs to increase the productivity of peasant farms, most of which are currently uanaged in Africa by women. But perhaps ln the longer **sweep** of history **increasing** lntensification of African agriculture vill nonetheless displace women, as expected by Boserup (1965, 1970). But this form of displacemnt would occur ore gradually and at very different rates throughout subSaharan Africa, since some areas have not **yet** expanded to the linits of cultivation. Among the benefits and costs of these alternative forms of subSaharan agricultural development are the likely changes that they would imply for family
88 consuption and human capital investment patterns. The studies reviewed in Sections 3 and 4 suggest that changing the balance of productivity between women and men could have long-run consequences for the level of child mortality, nutrition, schooling and fertility in the continent, **as well** as rates of economic growth. The current policy debate should weigh these connections by evaluating carefully the consequences of **existing** programs that **have** been successful in promoting agricultural developmnt in either direction.

Land Ownership, Credit, and Labor Narkets Along with extension services, farmers also need credit to pay for new seeds (HYV), irrigation wells, fertilizers, pesticides, etc. Although there are few experimentally designed studies, casual evidence suggests strongly that women are at a serious disadvantage in getting credit. Land ownership is the usual collateral for farm credit, but women seldom own land. Even where religious- law refers to the equality of men and women, actual practices of inheritance and the control of property in marriage, strongly suggests that women do not have equal access to loan collateral and thus are denied credit (Davision, 1988; Moock, 1986; Hossain and Afsar, 1988). Women working on conmunal land have no marketable property rights to mortgage. If this form of market failure leads to an inefficient allocation of credit, institutional innovations may b. needed--perhaps by replacing loans based on collateral by loans based on closely knit groups which are identified with their comeunity. The community's future credit thereby depends on the repayment of current individual loans.

The most extensive and best documented program providing credit to the poor is that of the Grameen Bank of Bangladesh. As of August 1988, its 453 branches operated in about 9,000 villages, about ten percent of all villages
89 in Bangladesh. It had lent to some 413,000 "members" and 84 percent of these were womon, and they received 70 percent of the loans. Groups of members borrow with no collateral and complete their repayment within one year or lose the group's rights to borrow in the future. Membership **is** restricted to only persons with less than .5 acres of cultivatable land or assets not exceeding the valu, of one acre of medium quality land. **Repayment** rates **have** been favorable compared to other commercial banks, but the cost for loan placement and monitoring of assets is not easily derived from reports. Evaluation studies of the Bank are favorable but not sufficiently analytical. They do suggest, however, that here is an institutional innovation that mitigates the disadvantages of poor womon in expanding their means of production. Comparisons of the consequences of the Bank being in **a** village on average productivity and earnings of village women and men would strongthen future evaluation studies. Is economic progress more rapid in "bank villages" than control villages without access to Grameen Bank resources? (Hossain and Afsar, 1988; Hossain, 1987).

To promote the more efficient allocation of talent across the entire economy, women should be helped to compete in male-dominated professions and industries as men should be encouraged to enter female dominated segments of the economy. Women may work in declining sectors, where modern emerging technologies are likely to depress their incomes by the generation of new man-made substitutes. These wonen need opportunities to enter more favorably situated sectors. If they do not have as much education as men, women will need more specific training and assistance to redeploy their skills. Nonetheless, general education remains the most reliable means to improve the mobility of women in the long run. Women have fared poorly relative to men in
90 those regions -South and West Asia and Africa--where they have had the l.Bast education compared to mon. The jobs they cati compete for are limited by their schooling and vocational skills, and the hallmark of modern economic growth Is relatively rapid changes in the structure of employment that rewards mobility (Kuznets, 1957, 1966).

Educated women are **scarce** relat,lve to educated men in some countries, and they are in equal numbers to men in other countries. It might be expected that where educated women were relatively scarce their wage advantaf,e over less educated women would be greater than that for men. But private returns to schooling are roughly comparable for men and women across a wide range of countries. Perhaps the apparent rigidity of sex segregation by occupation and industries does not preclude the substitution of **male** for female labor of conparable education across a sufficient range of activities to equalize relative returns at each level of schooling for men and women.

There is little evidence that families in all regions of the world are guided in theie investments in female and male chlIdren by the private rates of return to schooling received by **the** chlld. The puzzle is why families are not efficiently allocating their investments among their offspring? Parental claims on the adult productivity of boys may be more secure, given some family-cultural systems, than their future claims on girls. If the parent's crlterion for allocating school investments between boys and girls does not lead to a socially optimal intergenerational pattern of imvestment in human capital in som countries, what should be the role of public policy? The inefficient allocation of resources, even if we do not know precisely the cause, can frequently be corrected by a judicious application of taxes and subsidies.
91 In the classic textbook example of the apple grower and bee keeper, the external social economies linking these two activities lead to an underproduction of both because of the market failure. A subsidy to both independent producers could maximize social product, or both activities could be undertaken by the same producer to internalize the e*onomy. In the case of the parents, unless they are appropriately rewarded by **the** groom for their *ducated daughter, the parents may underinvest in their daughter. Research should focus on why families invest different aounts in their girls and boys. It was noted above that this disparity in investment in mun nd women has a tendency to be smaller in high income industrially advanced countries and to diminish over tim **as** countries develop (Schultz, 1987). Public policy can probably speed up this process with incentives that would motivate **the** family to invest more in females. But the costs of and responses to such interventions are unknown. The family's schooling behavior by gender must be studied to identlfy promislng policies that could be justified.given the scarcity of administrative talent and severely strained social-welfare budgets. Analysis would then be needed of pilot programs to evaluate precisoly the capacity of such policies to produce the desired changes in intrafamily investment: and to assess whether the policies induced any undesirable side-effects.
92 Family Planning and Maternal lealth Family planning programs subsidize contraueptive supplies and employ field workers to **explain** birth control techniques to potentlal<sup>users</sup> and inform them how these **methods may be** obtained. Some programs do offer male contraceptlves, such **as** the condom or vasectomy. But the technological breakthroughs of **the** 1960's-1970's that **were disseminated** by **the** first wave of family planniLng programs **were female** methods, such **as** the IUD and oral steroid (pill). These and female sterilization **are** still the most widely used contraceptives. Experience in East Asia suggested that female field workers were more successful in recruiting contraceptive users **(e.g.** Rogers and Solomon, 1975; Repetto, 1977). Older married women or **midwives** with av educational level on **a** par with their community appeared to be most effective. Similar evidence has been collected in Mexico and elsewhere (Azcona atal.. 1980). It would be desirable to analyze actual fertility **declines** induced by the different types of field workers and not simply the acceptor rate (Schultz, 1988c). But it is reasonable to assume that where the mothods offered by the program are for women, female field workers should have an rAvantage.; The same reasoning has not been transferred, to my knowledge, to the delivery of prenatal, maternal, and child health care. These kinds of health care are also received by women who, if adsquately instructed, would use it at home. Women doctors and paramedic staff might prove better than men<sup>in</sup> delivering many forms of health **care** oriented toward women and infants. I could not find reports on the design of pilot or experimental programs to test such a plausible hypothesis. Just as in **the** case of agricultural extension programs, the predominance of male health program administrators may have contributed to the allocation of a large proportion of government jobs to
93 males, even though women might do some of them better and probably at less cost. 29 Entirely new public policies may bo appropriate to deal with the health problems of women and children. Nutritional supplementation in conjunction with child and maternal health **is** one such option (Winikoff, 1988). The large gap from region to region in maternal mortality rates Lllustrated in Table 7 **shows** that this **serious** health problem **is** not being resolved by traditional public policies (Herz and Measham, 1987). Some have even concluded that in **subSaharan** Africa, public health is unlikely to **make** any progress until the level of women's education improves (Anonymous editorial, Lancet, 1987).

# 6. ConclusLons

Reliance on traditional market-oriented national income accounts **has** introduced a systematic bias in development economics that has asxigned too low a value to the economic contribution of women in the development process. In reorganizing our approach to economic development, and refocusing our analysis of women and development, it is essential that a family economic framework incorporate both rsonmarket and market activities of the members of the household. In what **way** would such a reappraisal of women's contribution to society clarify aeglected opportunities for public policies? Women are usually enmeshed in a family that trades off the competing interests of its members and coordinates the uses of its members' tim and resources in market and nonmarket production. To affect women's status one must generally work through the family. Society can transfer resources to families to raise current consumption levels, but **the** family can reallocate its resources In a compensatory manner. Thus, this intervention vill not
94 necessarily raise the efficiency of the family **as** a production unit or even influence how it allocates its resources among ommbers. The effect o~` public transfers on intrahousehold consumption patterns is thus likely to be greatly reduced.

More promising are social interventions that work to change the constraints impinging on the family and thereby to motivate it to achieve its goals by investing more human capital resources in its female members. The private and social rates of return to wooen's education at the primary and secondary level are at least equal to those of oen. Thus, a more equal distribution of education between sen and women would not slow economic growth. In fact, because female education has greater effects on health, nutrition and fertility, it might actually accelerate development. This possibility warrants more study, particularly in South and West Asia and Africa where female education levels are especially low.

There is evidence to suggest that the economic bias of accounting only for market income has led to a misallazation of public development resources in favor of market vis-a-vis nonmarket sativities, and consequently men's rather than women's work. A reevaluation of women's nonmarket work has begun to show the pervasiveness of this bias. But this has not yet affected development planning. The rhetoric on "women and development" has been substantial in the last decade, but progress toward establishing an analytical framework free of the traditional market bias has been slow. This framework must be used to guide the collection of data from households and communities **tltat** will show where investments enhance women's productivity, broadly defined to include nonmarket or home production.

The traditional economic approach to modeling family decision-making as
95 the outcome of **a** unified demand system needs to be relaxed to accommodate bargaining over conflicting individual interests. This will not entail weakoning the emphasis it has assigned to the separate prices of women's, men's, and children's time **as** critical factors explaining the family's allocation of time, determination of fertility, and demand for market goods. But the bargaining model also emphasizes the ownership and control of nonearned income and the context in which individual members of the family work and are compensated for their labor. New data **are** needed to test and to exploit the distinctive features of this cooperative Nash-bargained framework, such **as** who owns and controls nonearned income in the householl. Labor supply studies will be needed to categorize "job types" in which women's "control" of their output varies, ruch **as** wage jobs, own account wora at home or outside of the hQme, and unpaid family work. This classification process may be aided by parallel studtes of family human capital investment in children and other intrahousehold resource allocations for which society has a vested interest. Research could then assess whether the intrafamily distribution of resources varies depending on whether women are engaged in different "job types.' A clearer distinction is needed as to what is the approprlate model for studying family behavior in various regions of the vorld. What are the m- pirical consequences of female control of resources for hor production and consumption decisions? Evaluation of public policies affecting households will then be more sharply focused and precise. This should lead to sound policies that increase she output of market and nonmarket goods while raising the productive capabillties of women. Anthropological studies of gender differencesi in family production and
96 consumption should be integrated with economic studies of household p.oductin, labor supply, and expenditure patterns. The family bargaining framework for evaluating public policies in low income countries should lead to new policy insights. Sinc& not much work has yet **been** undertaken, one can only identify where the payoffs are likely to be 1arge and the direction in which they may move development policy. In thls tentative form, directions **are** offered for research **and** policy.

First, statistical methods have been outlined and the family bargaining framework **has been** mentioned **as a useful** basis for ostimat'ng private and social rates of return to schooling for women and men. Labor force participation and choice of wage employmnt both influence the selection of samples on which market **wage** functions are estimated. Procedures should correct for **these** sources of sample selection bias. Few studies have used comparable identification restrictions. What evidence is available suggests that primary and secondary schooling is a high return activity, and no **less** so for women than for men. It would **seem** inefficient to maintain much lower schooling investments in females than in males, as evident now in South and West Asia and in North and subSaharan Africa. Development agencies have a clear mandate to emphasize this investment gap and encourage cour.trios to raise female schooling rates toward those of males.

How **thls is** to be accomplished at **least** cost has not **yet been addressed** by practical policy **makers** nor **studied extensively** by **researchers.** How do **the** constraints of institutions, household resource endowments, prices and **wages** determine school enrollmnt rates for boys and girls in various cultural regions of the world? The family controls this decision and mUst be motivated to adopt the view that Sirts are as good an investmnt of parent resources as
97 are boys. This is apparently not now the case, **and** consequently social interests would appear to diverge from parental private interests Persuasion and propaganda are probably not the answer. Regulations such as quotas that require schools to increase the fraction of girls enrolled, are also readily circumvented, if the regulations are not viewed as in the parents' own interests.

Variable user fees for boys and girls is one possibility that destrves study as more public sector activities are financed through user fees. Lower school fees for girls than for boys might be effective. One planning minister of Nigeria proposed to do just this. Until maternal mortality rates of Asia and Africa are brought down to **the** current levels in Latin America (Table 7), an argument cQuld also be made to waive fees for prenatal and maternal health care at least for poorer rural populations.

Second, budgetary allocations of the International Agricultural Research Centers should be reviertd according to where each project can be linked to a particular crop and region (e.g. IRRI, 1985). These expenditures on a crop and region should then be categorized as benefiting primarily either female or male agricultural productivity, or both. If expenditures are imbalanced according to this accounting exercise, the international centers could plan to change their priorities gradually or explore policies that would erode sex segregation of labor for speciflc high payoff crops.

Third, governments should publish data on the gender of staff and of recipients of their extension and service programs. If these data were available by region, program evaluation of the consequences of these variations could be undertaken. These data should cover agricultural extension programs, family planning extension and services, public health extension and
g8 services, academic and vocational education, home Iconomics and community health programs, informal and adult education, agricultural credit and small business loans, etc. Analysis of these data may perhaps show that employment of more women doctors in the public health clinics or more midwives in the family planning outreach program would be more effective than existing programs. Data **are** rarely collected now in a form that enable hypotheses such as these to be tested. **TLe** cost of collecting such dat&, **geared** to explore plausible trade-offs in the mix of staffing and recipients, should be quickly recovered fron improvements in the implementation of the programs. Althougn it would require more administratfv **e** ffort, a parallel goal should be to collect the data on public sector salary and fringa benefit costs for all male and fesale staff by professional level, not only to promote wage parity for those performing comparable jobs, but also to permit *valuation studies to estimate the cost-effectiveness of alcornative staffing and management structures.

Fourth, employment logislation and effective protection of some industries can generate large benefits (rents) for some workers and impose costs on others. Data on the distribution of these benefits and costs within families might show wvhch public policies place a disproportionate burden on nonmarket production, and which policies benefit better educatd workers, or those whc work ln cities or capital-intensive sectors. Ron and women are not necessarily helped equally by such policies. Evidence suggests, for example, that fewer women participate in the modern labor force in low lncome countries when minimum wages are relatively high (Schultz, 1988a). Minimum wages discoura&% employment of persons whose productivity is below the minimum vage. A larger proportion of female workers than male workers fall in this category.
99 Finally, uncertainty and risk havo led individuals to insure themselves against future variations in their income by risk pooling across the extended family (Rosenzweig, 1988; Rosenzweig and Stark, 1988). The public sector may try to provide alternative institutions to assist individuals when the family's mechanisms for coping with risk generate negative social externalities. What are the consequences for women's economic status if parent-arranged marriages for daughters are designed primAriiy to mitigate the family's risks from local crop failurw? Crop insurance programs might provide an alternative worth exploring, where 'he variation of weather can be easily monitored to simplify administration.

Social insurance is also relied on to meet the costs of illness and old age. These forms of insurance influence how the extended family behaves. Women and young girls tend to withdraw their time from the labor force when there is a family need to care for the sick or elderly (e. g. Pitt and Rosenzweig, 1988). Consequently, these fontw of social insurance undoubtedly affect women's market participation and productivity, and may thus influence the willingness of parents to invest in the market skills of their daughters. Can poor countries afford to provide parents with a public sector old-age support scheme that substitutes for having many surviving sons? Little empirical research is focussed on the family **as** an institution for mitigating risk in order to discover how fanily decision-makino adapts to government social policies.

Without much firm evidence of how public policLes affect women's productivity or family consumption patterns, it would be useful to design and implement a series of pilot programs based on current knowledge of more promislng inierventions. Accurately measured indicators of program objectives
100 must be selectod and the program and control groups should be iarge enough and diverse enough to confidently assess whether tho program was effective. Because there are .auy constraints on the family's decisionmaking that will also influence women's productivity, and these constraints may differ in unmeasured ways across communities, **a** dispersed sample of comunities for the pilot programs and for **the** control **cox unities** Is desirable. **A** theoretical nodel of family behavior should prescribe **the** primry factors systematically influencing the indicators of program success. **Tnese** control factors would be monitorod in the base-line and follow-up<sup>surveys ofthepilot and control</sup> community households. Evidence on the prograu's impact will be sore convincing if a rindom design of the counity sample **is** adopted.

Pilot programs might fir5t be designed around the objective of Increasing school enrollment and graduation rates for girls. A second series of monitored pilot programs could **assess** the effect of equalizing access to agricultural extension services by male and female farmers in Africa, South-East Asia and Latin America. Measures-of "farmer contacts"<sup>as an indicator</sup> of extension output has serious problems (Birkhaeuser i".jJ , 1988), and therefore measures of productivity of men and women across distinct program<sup>regions should be</sup> analyzed. A third set of pilot programs might explore alternative configurations of child and maternal health progrims, where the mix of staff by level and gender would vary. Finally, program innovations in the delivery of credit, such as the Grameen Bank in Bangladesh, should be evaluated. Not only in terms of the gender of the loar recipient, but also the program's consequences for community-wide productivity of womev, and men. Data should also be sought on investments of famil'es in the education and health of males and females in the program and control coomunities. An intergenerational consequence of
101 raising the productivity of women should be to sncourag parents to invest more in their daughters. All of the above pilot programs should therefore monitor the choices made by families in producing healthy and educated daughters and sons.
_10'_

# Footnotes

l/This unobserved variable enters into the statistical error in these outcome equations, as would idiosyncratic but individually persistent tastes.

2/Statistical specification tests of endogeneity are only feasible when there is agreement on model identification, which in **the** analysis of household behavior frequently rests on controversial distinctions that need more explicit discussion.

aAny mechanism that converts "compensating variationw in family composition and specific consumption patterns into measures of **the** "cost of a child" or adult equivalence scales are ambiguous, Intercorrelations among endogenous variables are tricky to interpret. The emphasis here on disaggregation of family nunearned income by who controls it adds another complication to this time honored, but unsatisfactory, practice of conditioning demands on family composition (see Deaton, 1986; Gronau, 1988).

i/Of course, the bargaining could occur at the outset, when the family is formed, which suggests that members use their initial resource endowments to agree on the weights for individual goals in the 'family's utility function'u If these resources change unexpectedly, because of a bequest or inheritance or alternative marriage proposition, the 'threat points" would shift and a new bargain and agreed-upon family utility function would be adopted as a guide to subsequent intrafamily allocations.
103 In principle, the measurement of nonearned incom is to capture an exogenous difference across persons in their budget constraint that does not also induce a change in money or time prices of various types of consumption or behavior. In practice, nonearned income (rents, dividends, interest, and capital gains) could arise from inheritances that are similar to schooling, in that they are largely financed by parents and family and can be **viewed as** *xogenous at **the** start of adult life. But nonearned income also represents returns on a person's life cycle accumulation of savings, and hence captures in part the person's behavior. It then becomes, for some purposes, an endogenous choice variable. Hence, it is desirable for survey questionnaireni to pursue the source of current nonearaed incom, current ssets, and the date of receipt of bequests that led to these current assets, and whether they came from the husband's or wife's side of the family. The Rand Malaysian family life survey comes closest to asking these questions, but I know of no analysis of these data from the perspective outlined in this raper (Butz and DaVanzo, 1978).

L/JEidence compiled by Svedberg (1988) indicates that female nutritional status and survival prospects in subSaharan Africa are superior overall to male, possibly because women are economically more productive in converting calories into work than oen. As a consequence, perhaps, parents are paid brideprices for their daughters and have a stronger incentive to invest in their health. The one region of subSaharan Africa vhere Svedberg's anthropometric indicators of nutrition and mortality do not indicate as strong a bias in favor of females is in Nigeria and perhaps Senegal. Both of these countries contain a significant Islamic element and women's productive roles are more circumscribed in these segments of the population (Caldwell and
104 Caldwell, 1987).

1/In principle there might be a superior Pareto efficient allocation of husband and wife labor that would yield **a** larger output for both **members** of the family. But in practice, thero are costs in monitoring labor ivputs over scattered plots and transaction costs In exchange of inputs and outputs that mig)ht be required to provide both persons with the incentivos needed to achieve Pareto efficiency. These transaction costs might absorb most of **the** output gain.

A/The overall determinant-condition of maximization theory in the fmily deimnd model is also rejected by Hecl=tan in the staiLc case (1971: Chapter 2, pp. 32-33). Both the static **and** 'life cycle' estimation approachos pursued by Heckman lead to rejection of the syoetry condition. Ultimateoly, however, he imposed the restriction to obtain his preferred estimates (Chapter 2, p. 37-38). One possible explanation for the rejection of the domand systeu parameter restrictions is the difference in spouse-specific nonearned income effects that may be used to infer individual compensated cross-wage effects.

2/Aggregate data were analyzed, for example, in Chilo (DaVanzo, 1972), relative education in Thailand (Maurer at &I.. 1973), the U.S. (Frieden, 1974; Becker at **al.,** 1977), and in Puerto Rico (Nerlove and Schultz, 1970).

/For example, Rosenhouse (1988) illustrates from the 1985 LSMS from Peru that 90 percent of the male headed households are currently married, while
105 only 5 percent of the fe2ale headed households are in such unions. Her data **also** show that in Peru half of the fwmale household heads **are** widowed, and they are older than the male heads. **These** groups **are** really quite incomparable and not particularly well structured to analyze particular sources of poverty in society. As discussed in **the** text, t **ere are** many possible causes for the increase in female headed households. **The** greater longevity of women then men is one possible source. Another source would be **_vhe_** lower frequency of remarriage by women then men. Female household heads also work fewer hour than do male heads, even ignoring the contribution of wives to their hous& holds, and _the_ higher average **wages** received by men than womon. Multiple earner households are also the rule, not the exception, in Peru, and the sources of behavioral and biological selection of individuals into bouseholds of very different compositions. It is simply difficult to infer anything from the widely reported characteristics of households with male end female heads.

:9/It is easy to fault definitions of 'head of household," when there is no consensus on the concept being measured or its use. There is a need to distinguish one individual around which to relate other household members, for the purposes of establishing kinship. There is alio the idea of dominant economic provider or family elder whose authorlty is respected. But in the LSNS in Cote d'Ivoire the customary approach is to count females in **the rural** sector as belonging to a male headed household even though the head resided in a distant clty, more or less permanently. the increasing documentation oa short-term seasonal or circulating migration in many low income countries underscores the need to measure household membership according to a variety of rules depending on how the data are to be used. For a list of some of the
106 problems with the current data collection practices **see** Rosenhouse (1988).

1 2 1Obvious changes involve **the** decline in child and adult mortality, the in- **crease** in age at first marriage, the decrease in marital fortility, and the redistribution of population toward urban areas. All of these changes might plausibly **lead women** to take on **more economic** activities outside **the** family/ household.

t<sup>3</sup> /This is empirically illustrated by Schultz (1980b). though the noted differences between the responsiveness of participation and hours can also be explained by other hypotheses, such **as** fixed costs of entering the labor force (Cogan, 1980b).

> 2 Cross-equation restrictions **are** also implied by th* family model of labor supply, because the income-compensated cross-wage effect of the husband's wage on the wife's labor supply should be the same as the income-compensated cross-wage effect of the wife's wage on the husband's labor supply. Heckman (1981) reported a test of this parameter restriction and rojectod it in the case of a sample of married U.S. couples, both of whom were working and reported a **wage rate.** This is one **way** to test **the** restrictive nature of the neoclassical family labor supply model, though lt is rarely attempted. Other estimatos for U.S. married couples by 'ag and race groups also confirm that the symmetry of **the** compensated cross-wage effects does nqt appear consistent with **the** unrestricted reduced form estimates (Schultz, 1981; Table 7.4).
107 L"Layard and M4incer (1985) compares **estimates** of female labor supply parameters from various industrially advanced countries. A comparative study of seven Latin American countriec (ECIEL, 1982) in the 1970s replicated the family labor supply model specification proposed by Schultz (1980b) and reported large positive own-wage labor supply elasticities for married women and smaller negative husband cross-wage elasticities. The estimates of labor supply behavior for women living without husbands **were** less elastic, **as one** would expect because they tend to already be ore fully in the labor market and more self-sufficient. Man without wives, particularly at young and old ages, exhibit more positive labor supply responses to their own market wage rate, as do single women. Cogan (1980a) shows evidence that the lack of adjustment for sample selection appears to change very little the parameter estimates of the labor supply model for U.S. married women, tho deographic group for which the bias might be thought a Lrgori to be most serious. Returns to education for this group is changed from 8.5 to 8.8 percent, when corrected for sample selection bias (Cogan, 1980a: Tablo 2.1).

i/For example, do subsidized birth control information, services and supplies reduce the lifetime fertilLty of couples and also increasa the "quality* of their offspring? **Are** more accessible and higher quality (i.e., lower priced) public schools an effective means of increasing school enrollments (quality) and also reducing fertility? The answer to both related questions appears to be a qualified 'yes' in the few studies we have to date (Roscnzweig and Uolpin, 1982). Both types of policies are also likely to be associated with women taking on more market production activities and gradually increasing the market wage opportunities of women relative to men (Rosenzweig, and Schultz,
108 1985; Rosenzweig, 1988).

/yHeck1an and Polachek (1974) confirmed that the logarithmic wage equation fit US male earnings data somewhat better than did the linear age equatlon. They also show statistically that Nincer's (1974) specification of the quadratic in post-schooling **ye#:s** of potential experience **explains** more of the variance in log-wages or log-earnings than does a quadratic in **age.** Rarely has a theoretically based empirical regularity such **as** this specification of the **vage** function **been** replicated so widely, in **so** many parts of the world, and had such an effect on economic thinking about a diverse range of socLal issues.

2WFor example, if more able students self-select theselves into the more educated comparison group, the returns to education could be overstated until a selection-corrected measure of the private returns to schooling is computed (Willis and Rosen, 1979). But this form of selection might not necessarily bias the comt-irison of the directly measured returns of men relative to women, if ability operates in an analogous way to influence who goes to school among both girls and boys. The next section examines evidence on this issue.

1/From the perspective of the early 1960s, this empirical strategy may have reflected a desire to not overstate the returns tc s1hooling, because the concept was novel and controversial. But as indicted below, the resulting differences in empirical methods practiced in the literature has led to reports of women's school returns being deflated for honparticipation in some cases by half or more, and in other reports the estimates are drawn directly
109

# from **wage** functionw without deflation or sample selection correction.

aQ/The analysis of educational returns **is** complicated if education influences the supply of labor (Schultz, 1968). When a worker's human capital is increased, it is expected to **exert** an income effect reducing market labor supply **and** a substitution effect increas ing sarket labor supply. If **the** costs of acquiring **the** humn capital **are** fully borne by the individual or family and they exactly offset the market discountod gaLn in earnings, then the income effect of the human capital is zero. This is the implicit assumption in **the** equilibrium investment model of Decker (1964) and MLncer (1974) and elaborated by Lindsay (1971). If there is no income effect associated with education, the more educated should work more hours, because the rmaining substitution effect on labor supply. must be positive. But empirical exceptions can **be** readily found, such as in Colombia where **the** university educated worked in Bogota less than those with only secondary schooling (Schultz, 1968) and Thailand where in 1981 the more educated worked fewer hours por month, or in Indonesia in 1986 where men with more unlversity training worked fewer hours (Behrman and Doolalikar, 1988b). Thus, in reality, both an income and substitution effect may be associated with **the** acquisition of schooling, and it can be argued that the return to schooling should include **the** incoe sffect but exclude the labor supply effect induced by the substitution effect. In practice, if the dependent variable of the earningp function is the logarithm of the hourly wage rate, the coefficient on years of schooling approximates the private rate of return, holding labor supply constant. This procedure is correct if the income effect is negligible, as assumed in **the** equilibrium investment model. It is an underestimate of **the private** rate of rpturn to
110

# schooling if the more educated'work fewer hours.

> 22 Becker (1964) used the average earnings (of those with earnings) from tabulations of the 1950 Census and apparently then deflated these benefit streams by participation rates. This approach ignored the sample selection of those who had positive earnings, **as** well as **the** problem of mixing productivity per hour and labor supply responses associated with education. Mincer (1974) examined the 1960 Census public use sample, and fit the individual's logarithm of annual earnings to education and other variables. He could only include individuals with positive earnings and no correction for the potential bias is discussed from his analysis of a selected sample. However, Hincer's and Becker's analysis is focused on males and not females.

A shortcoming of the sample selection correction methodology, as it is commonly applied, is that it can lead one to conclude incorrectly that there is no sample selection bias, because the sample selection rule is not vwell" identified. The only basis for identification can be the functional form assumed for the error in the selection rule equation, e.g. normally distribut,ed (Heckman, 1979). More confidence can be placed in the estimates if theory prescribes conditioning variables that enter significantly in the sample selection rule equation, but ars excluded a priori from the market wage equation. Thus, an insignificant sample selection term (i.e. Heckman's lambda) may become statistically significant when the exclusionary restrictions of the model are selected with more care. _As_ a consequence, the sample selection correction procedure may then change the estimated returns to schooling. Agreement on acceptable criteria for identification would accelerate advancement of empirical research on this topic.
111 The nonmarket reservation **wage** of the wife is affected by the fnmily's land, ownership of a business, assets, nonearned income, and the husband's educatioui and experience. These variables **are** added to the wage-status probit equation. The Heckaan (1979) two stage estimate that is less efficient yields an estimate of returns of .16 (Griffin, 1987; Table 3).

'/Alternative specifications can include **the age** and education of **the** spouse or the predicted wage of the spouse and a marital status duiy variable if these characteristics of the spouse are missing. These identifying variables are believed to be more likely to be endogenous than the land and nonearned income variables. But their inclusion in the model does not change appreciably the estimates of the private rates of return reported in Table 3 (Schultz, 1988c).

Taxes on personal income are a relatively L small portion of government revenues in low income countries. The exception may be Latin America if one includes 'social securityw taxes (World Bank, 1988: 84). In the case of Thailand in 1981, direct taxes on personal incomes amounted to less than 5 percent of household expenditures and only 10 percent of wage recipients in Bangkok (National Statistical Office, undated). Most of government revenues in low income countries are from taxes on companies and conmodities (excise and trade). This adjustment may therefore be of secondary importance in low income countries but could be quite important in industrially advanced countries.
112 'VThe marginal revenue generated by the expansion of education for women, Ew and men Em , **can be** expressed: dR/dEw -twalw(lw **_+_** 2) dR/dE m- twlu a(plm + P2w3 where 1i 0lw **are** the private rates of return to schooling of men and women which **ar*** Assumed equal, P 1w > 0 is the wife's uncompensated **own vage** effect, P2w < 0 in the wife's uncompensated husband's wage effect, 0 i 0 is the husband's uncompensate. own wage effect, and p 23 0 is the husband's uncompenaated wife's wage effect. The above indicated signs are those commonly obtained in static instrumented family labor supply models (Cogan, 1980a; Schultz, 1981).

A parallel public finance argument can be made for taxing more heavily inelastically supplied factors, if the goal is not to distort the optimal (i.e. untaxed) allocation of factors. This tax criterion implies that given the labor supply parameters outlined in footnote 25 that male labor incomes should be taxed more heavily than female labor incomes because they are inelastically supplied. I have not seen an endorsement of such an optimal tax policy.

SZThe expected years of education reported in Table 5 is a synthetic cohort measure, defined as the sun of the three conmonly reported age-specific enrollment rates--associated approximately with primary, secondary, and higher educational levels--weighted by the six year length of the **age** intervals used to compute each of the enrollment rates. Thus it represents the number of
113 years an individual would enroll in school if he or she enrolled at the average rates from age 7 to age 25 reported in a particular year. There are many reasons to suspect that these enrollment rates exceed attendance rates, ant the actual time and effort applied to schooling undoubtedly differs greater across countries- and over time within countries. There may also be differences in these *real" investmnt rates per year of enrollment between boys and girls, but they are not reflected ln any of these UNESCO statistics.

T/There are few quantitative studies that seek to test **the** efficacy of public policy that varies (1) the mix of women and men hired to extend new agricultural teb..ologies, (2) the sex of the agricultural worker these personnel have contacted, (3) the reported source of innovative practices or now productive inputs, and finally (4) the effects on the hourly productivity of male and female workers and managers, as well as their overall earnings. One description of the agricultural extension program in Malawi (Spring et al., 1083) shows the growing predominance of women full-time and part-time workers and managers. Female headed households and wives, however, benefit from relatively fewer extension visits than do oen. But women attribute to extension agents their sources of modern agricultural practices more than do men. One interpretation of these data from Lilongwe Rural Development Project is that women agricultural workers **are** more responsive to the extension inputs they received. But they received fever such inputs. Unfortunately, the education of the male and foeale farm managers and their size of holdings are not reported, and these characteristics of the farmers might help to explain the reported patterns. None of 50 extension evaluation studies reviewed by Evenson (1988) or Birkhaeuser et al. (1988) note the differential effective-
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**ness** of male and famale **_staff_** or the gender of farm contacts.

> 2 'VTh*re are also numerous more specialized tasks .n the delivery of health care where less highly trained (and less costly) parmedics who are dedicated to performing a fey functions can do these activities better than, or at least **as veil as,** doctors. In Thailand midwives **were trained** to _insert_ IUDs. They were then carefully evaluated against doctors providing **the same** sorvice. The midwives **vere** more successful in delivering this single service than were the doctors. Performance was measured in term of the incidence of a variety of objective complications and in terms of patient satisfaction and frequency of complaints (Population Council, 1972).