the determinants of teenage schooling in jamaica: rich vs. poor, females vs. males

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The determinants ofteenage schooling inJamaica: Rich vs. poor,females vs. malesSudhanshu Handa aa Department of Economics , University of theWest Indies ‐ Mona , Kingston, Jamaica, W.I.Published online: 23 Nov 2007.

To cite this article: Sudhanshu Handa (1996) The determinants of teenageschooling in Jamaica: Rich vs. poor, females vs. males, The Journal ofDevelopment Studies, 32:4, 554-580, DOI: 10.1080/00220389608422428

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The Determinants of Teenage Schooling inJamaica: Rich vs. Poor, Females vs. Males

SUDHANSHU HANDA

The belief that schooling is an important way to reduce poverty andincrease social mobility has lead to large government-sponsoredinvestment in education in developing countries. Jamaica has animpressive literacy and primary enrolment rate, yet the ability of itssecondary school system to enhance social mobility and reduceinequality is limited. Regression results from a nationallyrepresentative household survey show that family backgroundvariables (parental education and income) are important determi-nants of secondary school enrolment, and income is the single mostimportant determinant of enrolment in an 'elite' high school, withthe impact being twice as large for females. Part of the incomeeffect is shown to represent unobserved community heterogeneity.One conclusion is that the recent 'cost-sharing' education policy ofthe Jamaican government, if applied selectively to the eliteacademic high schools, will fall disproportionately upon richhouseholds.

1. INTRODUCTION

The belief that schooling is an important tool for reducing inequality andequalising adult socio-economic status is one of the primary motivations forthe impressive amount of research and expenditure on education policies.1

Education is often one of the largest expenditure categories of governmentbudgets in less developed countries (LDCs). For example, in a group of 44low and middle income countries, the average government budget share

Sudhanshu Handa, Department of Economics, University of the West Indies - Mona, Kingston,Jamaica, W.I. The author thanks the Planning Institute of Jamaica for kind permission to use thedata, David Macpherson and Brian Erard for valuable discussion, and Christopher Colclough andan anonymous referee of this journal for insightful comments. Responsibility for all errors lieswith the author.

The Journal of Development Studies, Vol.32, No.4, April 1996, pp.554-58OPUBLISHED BY FRANK CASS, LONDON

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DETERMINANTS OF TEENAGE SCHOOLING IN JAMAICA 555

devoted to education in 1990 was 13 per cent [World Bank, 1992: Table 11].Given this emphasis and the vast resources devoted to schools in developingcountries, it is important to know who actually is schooled in these countriesand what determines the demand for schooling, in order to assess theeffectiveness of education as a way of increasing social mobility andreducing the intergenerational transmission of poverty.

There is a large literature on education in LDCs which seeks to quantifythe rate of return to an individual's investment in education.2 This humancapital approach to studying education focuses on the market determinedvalue of education as the principle regulator of individual demand foreducation. However, there is an equally important non-market component tothe demand for education, especially for children and young adults. Thesenon-market factors manifest themselves through household characteristicsthat affect the time and opportunity cost of schooling for householdmembers, and there have been several recent papers which look at thehousehold demand for schooling [Deolalikar, 1993; Tansel, 1993; Satherand Lloyd, 1993; Singh, 1992], due in part to the increasing number ofhousehold surveys available in developing countries.

This study contributes to our knowledge about who is schooled indeveloping countries by analysing the household determinants of secondaryeducation in Jamaica.3 Three main questions are addressed: (1) what is therole of family background variables such as income and parental educationin the demand for children's education? (2) is the impact of familybackground robust to controls for community level heterogeneity? (3) arethe household factors affecting education different for males and females?These questions are motivated by the particular social and economicenvironment in the country.

Jamaica has an admirable record in human resource developmentcompared to other countries at the same income level. Expenditures oneducation are the single highest component of the government budget afterdebt service; the average share of government expenditure to education was16 per cent in the 1970s and 13 per cent in the 1980s [Planning Institute ofJamaica, various years]. Yet income distribution remains one of the mosthighly skewed in the world [Londono, 1995], and 30 per cent of householdsare below the poverty line [World Bank, 1994a]. This persistent inequalityin the face of the historic commitment to public education raises theimportant question of the impact of education in enhancing social mobilityin Jamaica. If public education is predominantly consumed by the rich, thenthe social policy of subsidised education may be regressive, and requires aserious appraisal of the existing school system. Moreover, the Jamaicangovernment has recently introduced 'cost-sharing' in its secondaryeducation policy which has received tremendous public criticism, but if

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556 THE JOURNAL OF DEVELOPMENT STUDIES

only the rich benefit from secondary and higher education in Jamaica, thispolicy may actually be progressive.

Finally, Jamaica is unique among LDCs in that the educationalachievement of girls is significantly higher than boys, so much so that theminimum cut-off level on the national exam to determine high schooladmission is lower for boys than for girls. Figures one and two showenrolment rates for all individuals, and average grade attainment for thosenot currently enroled in school, for teenagers (ages 13-19 years) in thesample used in this study - there is a clear gap between male and femaleoutcomes which increases by age group. It is therefore of special interest toseparately investigate and compare the determinants of schooling demandfor girls and boys.

FIQURE 1

ENROLMENT RATES BY AGE AND SEX: JAMAICA 1989A females

£ . 5 -

14 15 16 17age

FIQURE 2

GRADE ATTAINMENT BY AGE AND SEX: JAMAICA 1989o males o females

—i 1 1 r~16 17 18 19aae

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II. THE JAMAICAN EDUCATIONAL SYSTEM

Jamaica has a history of educational achievement, and its schooling indicatorssuch as gross enrolment, adult literacy and female enrolment are consistentlyhigher than other countries with similar levels of per capita GNP. Primaryenrolment is universal, and the challenges facing the schooling system relateto poor quality at all levels, and access to secondary and higher education.

Students write the Common Entrance Examination (CEE) at age 11(sixth grade) which determines their choice of high school. There are threebasic streams of secondary schools. The all-age schools terminate at gradenine, are the academically weakest of the three tiers, and accept the majorityof the students (40 per cent). The comprehensive, technical and newsecondary schools go up to grade eleven, provide a slightly better standard,but provide mainly technical and vocational curricula. The high schools arethe most academically prestigious, accept the best students, and offerpreparation for the CXC (Caribbean Examination Council) and 'O' Levelexaminations which determine entrance into grades 12 and 13 (sixth form).These in turn are required for entrance into tertiary schooling.

Secondary enrolment nation-wide is approximately 60 per cent, twicethe mean for middle-income countries, but this masks a highly dualisticsystem, with the majority of students entering the two lower tier secondaryschools and receiving low quality technical or vocational training. Thenumber of places available in the elite academic high schools, which offerthe best quality education, are limited and admission is extremelycompetitive. Thus most families that can afford it buy 'extra lessons' toraise the likelihood of their children gaining admittance to a high school.Not only is there an urban bias to the location of high schools, they alsoreceive the highest per pupil grant from the Ministry of Education comparedto the other types of secondary schools [World Bank, 1994a]. In the sampleused here, 50 per cent of 13-19 year olds in the poorest quintile arecurrently enrolled in a secondary school, but of these only 17 per cent are ina high school. In contrast, 70 per cent of the sample in the richest quintileare currently enrolled, and 70 per cent of these attend a high school. Theoverall high school enrolment rate in the poorest quintile is nine per cent;among those enrolled in a high school, eight per cent come from the bottomquintile, while 42 per cent come from the richest.

III. MODEL AND EMPIRICAL APPROACH

A. Model

The demand for children's education can be derived from a Becker-Lewismodel of household production where it is assumed that parents or elders

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make decisions regarding child schooling. A simple version of thehousehold's problem is to maximise

U(X,Z) (1)subject to px *X + w*L = w*T + Y (2)and Z = Z(X,t;Q) (3)

In this framework household utility (1) is maximised over market (X) andnon-market (Z) goods, subject to a full income constraint (2) and ahousehold production function for non-market goods (3). Inputs to theproduction function are time (t) and market goods, as well as an efficiencyparameter (Q) which depends on factors such as the ability or experience ofhousehold workers, access to complementary public inputs and so on.Market goods are purchased at price p«, household time endowment is T,unearned income is Y, the wage rate is w and leisure consumption L.

The outcomes measured in this study are Z-goods, and solving thehousehold problem yields demand functions that relate the optimal level oftheir consumption to the exogenous variables: prices, unearned income,household efficiency (Q) and preferences. I use this framework to guide thechoice of explanatory variables - thus the demand for schooling will be anincreasing function of the perceived benefits of schooling, and a decreasingfunction of its costs (both direct and opportunity costs). Householdcharacteristics that increase the costs (or decrease the benefits) of educatinga child will lower the household demand for education.

One characteristic of this model is that it ignores the potential forpreference conflicts and intra household bargaining which would influencethe household demand for schooling. From an empirical perspective, themain difference between a reduced form household demand functionderived from a household bargaining model [McElroy and Homey, 1981;Manser and Brown, 1980] and the common preference or neo-classicalmodel described above is that resources controlled by different members ofthe household will have different effects on demand patterns. Unfortunatelythe present data only provide this information at the household level, so Icannot accommodate the bargaining framework.4

B. Empirical Approach

Two outcomes representing the demand for education are considered in thisarticle. The first is the probability that a teenager is currently enrolled inschool. In addition, because educational achievement and thus socialmobility hinges so much on attending a high school in Jamaica, I analysealso the probability of attending a high school. Since the dependentvariables are dichotomous probit functions are used to fit the data.

There are two categories of dependent variables - individual specific

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and household specific. The former include age and its square, and parentaleducation, while the latter are gender of household head, two regionaldummies, miles to the nearest busstop which represents the time cost ofschool attendance, and (log of) total per capita household expenditure.Household expenditures are used because of the lack of income data, andare likely to be correlated with the error term in the model via the laboursupply decision.5 The potential endogeneity of expenditures is correctedusing the method of Rivers and Vuong [Rivers and Vuong, 1988] byincluding the residual from the first stage regression (predictingexpenditures) in the final probit model.6 The t-test on the coefficient of thisresidual term is actually a test for the exogeneity of total expenditures. Allregressions are estimated separately for males and females.

IV. DATA AND RESULTS

The data used in the estimation come from the second round of the 1989Jamaican Survey of Living Conditions (SLC), a nationally representativehousehold survey based on the Living Standards Measurement Surveys ofthe World Bank. There is detailed information on the educational status ofeach household member over three years old, which is merged with thehousehold roster, consumption, and community modules to get thenecessary information for the analysis. There are 1,413 individuals aged13-19 in the final sample who had complete information on parentaleducation and household characteristics.7 Variable means for the sample arereported in Table 1 - note in both instances females have higher rates ofenrolment and high school attendance than males.

A. Regression Results

Table 2 presents the probit estimation results for enrolment and high schoolenrolment for females and males (in each case the likelihood ratio testrejects (one per cent) the hypothesis that the coefficients are the same acrossgender). The impact of parental education (in columns 1 and 3) varies bygender - father's education is significant for sons but not daughters, andmother's education is significant for daughters but not sons. When incomeis included in the specification (columns 2 and 4) the parental educationcoefficients all decrease in magnitude, and although this decrease is greaterfor father's the main conclusion is the same: father's education is significantfor males only and mother's education for females only. Hence forenrolment the impact of parental education does not simply represent an'income effect'. Moreover, the test statistics at the bottom of Table 2indicate the parental education effects are not statistically different fromeach other.

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TABLE 1

VARIABLE MEANS (STANDARD DEVIATION) BY SEX

Individual Characteristics

currently enrolled

enrolled in a high school

age in years

age squared

mother's highest grade completed

father's highest grade completed

Household Characteristics

log per capita expenditure

1 if live in rural area

1 if live in town other than Kingston

miles to nearest busstop

1 if household head female

# of residents 13-19 year olds

# of residents 0-5 year olds

Interactions

Mother's education*miles to bus stop

father's education*miles to bus stop

mother's education*father's education

Full Sample Females Males

0.586(0.49)0.212(0.41)15.903(1.92)

256.588(61.47)7.949(2.29)7.798(2.19)

8.234(0.70)0.639(0.48)0.270(0.44)1.042

(2.16)0.416(0.49)2.285(1.07)0.849(1.12)

0.629(0.48)0.259(0.44)15.839(1.93)

254.591(61.75)8.015(2.33)7.860(2.13)

8.235(0.69)0.630(0.48)0.283(0.45)1.000

(1.95)0.424(0.49)2.221(1.04)0.949(1.20)

7.598(15.56)7.060

(13.80)65.460(31.63)

0.548(0.50)0.170(0.38)15.960(1.91)

258.364(61.21)7.890(2.25)7.742(2.24)

8.234(0.70)0.647(0.48)0.259(0.44)1.080(2.33)0.409(0.49)2.341(1.09)0.761(1.03)

8.035(17.08)7.279

(15.34)63.743(30.33)

Observations 1413 665 748

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DETERMINANTS OF TEENAGE SCHOOLING IN JAMAICA

-TABLE 2PROBIT COEFFICIENTS FOR ENROLMENT BY SEX

Females Males

561

age

age2

rural

town

miles to bus-stop

mother's education

father's education

log per capitaexpenditure

residual'

household head(l=female)

Log likelihood

psuedo R2

tX2(family background)2

mother's ed=father's ed3

Observations

withoutincome

(1)

-0.951(1.20)

0.009(0.36)

-0.186(0.77)

0.003(0.01)

-0.081**(2.28)

0.091**(2.76)

0.044(1.19)

-0.174(1.31)

-242.90

44.63

391.62**

full(2)

-1.132(1.33)

0.012(0.46)

0.229(0.87)

0.136(0.51)

-0.072**(1.97)

0.078**(2.19)

0.003(0.09)

0.801**(5.98)

-0.268*(1.85)

-0.140(1.00)

-222.34

49.32

432.74**

48.57**

1.34

665

withoutincome

(3)

-0.100(0.11)

-0.022(0.75)

-0.668**(2.80)

-0.547**(2.14)

-0.053**(2.13)

0.040(1.10)

0.079**(2.17)

-0.038(0.28)

-245.95

52.24

538.11**

full(4)

-0.239(0.25)

-0.018(0.06)

-0.597**(2.42)

-0.517**(2.01)

-0.049*(1.94)

0.039(1.06)

0.073**(1.96)

0.074(0.62)

0.240(1.60)

-0.016(0.12)

-243.06

52.80

543.88**

12.66**

0.27

748

Notes: Sample is all children age 13-19. Dependent variable equals 1 if currently enrolled(columns 1 and 2) or enrolled in a high school (columns 3 and 4). Constant term includedin the estimation but not reported.1/ Residual from first stage regression predicting log per capita expenditure.2/ Tests whether parental education and income are jointly significant.3/ Tests whether parental education coefficients are significantly different from each

other.

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TABLE 2 (cont.)

PROBIT COEFFICIENTS FOR HIGH SCHOOL ENROLMENT BY SEX

Females Males

age

age2

rural

town

miles to bus-stop

mother's education

father's education


residual'


Log likelihood

psuedo R2

z2

^(family background)2

mother's ed=father's ed'

Observations

withoutincome

(1)

0.465(0.89)

-0.018(1.07)

-0.401**(2.08)

0.310(1.56)

-0.079*(1.90)

0.012(0.42)

0.113**(3.61)

-0.227**(1.99)

-335.74

11.68

88.79**

full(2)

0.605(1.11)

-0.022(1.31)

-0.101(0.49)

0.425**(2.06)

-0.060(1.47)

-0.006(0.21)

0.079**(2.39)

0.648**(6.04)

-0.158(1.20)

-0.170(1.45)

-314.54

17.26

131.20**

55.35**

2.51

665

withoutincome

(3)

0.789(1.41)

-0.024(1.38)

-0.614**(3.40)

-0.091(0.49)

-0.016(0.46)

0.020(0.66)

0.077**(2.45)

-0.005(0.04)

-318.14

6.63

45.21**

full(4)

0.665(1.13)

-0.020(1.11)

-0.345*(1.82)

0.008(0.04)

0.007(0.23)

0.012(0.39)

0.049(1.53)

0.582**(5.34)

-0.124(0.93)

-0.015(0.13)

-300.48

11.82

80.55**

39.84**

0.47

748

Notes: 1/ Residual from first stage regression predicting log per capita expenditure.2/ Tests whether parental education and income are jointly significant.3/ Tests whether parental education coefficients are significantly different from each

other.

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The other coefficient estimates indicate that males outside of Kingston(that is, that live in rural areas or other towns) are most at risk of droppingout of secondary school, while region is not significant for females; bothhowever are adversely affected by distance from a busstop. Finally, familyincome is significant for females but not males.

The second half of Table 2 presents the results for the high schoolenrolment decision and these underscore the importance of familybackground on schooling: here family income (and father's education forfemales) is the most consistently significant determinant of high schoolenrolment, with some regional effects, especially for males. And whenincome is excluded the coefficients for father's education increasedramatically and become significant for both groups while mother'seducation remains insignificant. Table Al in the Appendix providesestimates of a model for high school enrolment based only on thoseteenagers currently enrolled in any type of school. Even in this selectsample of teenagers who already have higher than average parentaleducation and income (since these are important factors determiningenrolment), household resources continue to be highly significant. Thus inJamaica, attendance at the elite academic high schools hinges crucially onfamily resources.

Figures 3 and 4 display the change in probability of enrolment and highschool enrolment given (a) a half-mile decrease in distance to a bus-stop, (b)a one standard deviation increase in (log of) total per capita expenditure,and (c) an additional year of parental schooling.8 Figure 3 shows that thesize of the maternal education effect is only slightly smaller for sons thandaughters, but father's education is much bigger for sons. However, theprobability of enrolment for females is twice as sensitive to householdincome (proxied by per capita expenditure) as it is for males, which makestheir higher schooling achievement that much more impressive. Figure 4displays the relationships for high school enrolment - a one standarddeviation increase in family income increases the probability of high schoolenrolment by 10.5 per cent versus only five and one half per cent for males,while the quantitative impact of the other variables is very small.

B. Decomposing the Schooling Gap

Table 1 shows the gap in enrolment rates between males and females to bearound eight per cent, but part of this difference may be due to differencesin household characteristics between groups which are importantdeterminants of enrolment. The gender gap in enrolment can be separatedinto an explained portion, which is due to this difference in the value of theexplanatory variables, and an unexplained portion, which representsdifferences in the way household characteristics affect males and females

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hc

FIGURE 3

CHANGE IN PROBABILITY OF ENROLMENT

I females

12 n

9 -

6 -

O males

Note: Probabilities are calculated for a half mile decrease in distance to a bus-stop (bus-stop),one standard deviation increase in income (income), and an additional year of parentaleducation (fath.ed, moth.ed).

Sa

ks

FIGURE 4

CHANGE IN HIGH SCHOOL ENROLMENT

Bies ta (taies12-1

DUSStOP

Note: See note to Figure 3.

1ath.ed lncoie moth.ed

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(that is, differences in coefficients). Following the methodology in [Evenand Macpherson, 1993], let Ei indicate whether an individual of sex i(i=m,f) is enrolled in school, X the vector of observed individual andhousehold characteristics for an individual of sex i, Pi the correspondingvector of coefficients, and 4> the standard normal cumulative densityfunction. Then the probit model is written as:

Pr(E.=l/X0 = $ (X h) (4)

Using the estimated probit coefficients, the predicted enrolment rate of sexi is:

Ni•X <HX$) (5)

Then the gender gap in enrolment predicted by (4) is:

EGAP = P(Xr, $0 - P (Xm, |L) (6)

This can be decomposed into an explained portion, due to the difference inX variables, by estimating the enrolment rate that males would have if theyhad the female sample X characteristics

EXP = P(Xr, #.) - P (Xm, |L) (7)

The unexplained portion of the enrolment gap is the change in enrolmentthat would occur if the probability of male enrolment is determined by thefemale probit coefficients

UNEXP = P(X-, $<) - P (X., $m) (8)

There remains a residual or interaction part to the total enrolment gap whichforces an adding up constraint:

GAP = EXP + UNEXP + INTERACTION (9)

The interaction term is a measure of how much the enrolment gap wouldchange if the coefficients of the other group were used in the calculation.For example, the method outlined above uses males as the reference group,so the interaction term tells us how much the estimated gap would changeif females were used as the reference group. In addition [Even andMacpherson, 1993] show that the interaction term is equal in size butopposite in sign when the reference group is switched.

The decomposition results for the two models (enrolment and highschool enrolment) are provided in the two columns labelled (a) of Table 3.9

For enrolment, the model predicts a gender gap of 8.1 per cent, but three percent of this is explained by differences in the X characteristics between

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TABLE 3

DECOMPOSITION OF GENDER GAP IN SCHOOLING1

Enrolment(a)

3.0%5.9

-0.88.1%

(b)

2.9%6.3

-1.18.1%

High School(a)

0.1%8.30.69.0%

(b)

0.6%8.20.29.0%

Due to characteristicsDue to coefficientsDue to interactionTotal gap

Note: 1. Males used as the reference group. Column (b) uses the probit results including the twodemographic variables, which are reported in Table 4.

groups, leaving five point nine per cent (or 73 per cent of the entire gap)'unexplained', or due to differences in the way households treat males andfemales. For high school enrolment nearly the entire gender gap is due todifferent treatment of males and females - 92 per cent of the entire gendergap is unexplained, or due to differences in coefficients..

The fact that most of gender gap in schooling is due to differences in theway household characteristics affect males and females, and not todifferences in the characteristics themselves should not be surprising. Afterall, the sex of one's child is an exogenous outcome, and there is no reasonto believe that poorer households have more sons, or that more educatedparents have more daughters. However, in the Caribbean where householdstructure tends to be quite fluid, households may foster in (or out) childrenas a response to economic forces. This is especially relevant for teenagegirls who are often responsible for domestic chores and child care. IndeedTable 1 shows that females have more preschool children (under the age offive) living with them than males.

To see how the schooling gap is affected by household demographiccomposition I add two variables to the model: the number of children fiveyears old or younger, and the number of teenagers residing in the household.The coefficient on the latter will tell us to what degree households make a'quality-quantity' trade-off in the schooling decision. Naturally both thesevariables are endogenous10 (which is why they were not included in the firstplace), so their coefficient estimates should be interpreted as strict partialcorrelation coefficients without any implication of causality.

C. Results with Demographic Variables

Estimates including the demographic variables are presented in Table 4. Innone of the equations does the number of resident teenagers make adifference in schooling outcomes, but the correlation between preschoolersand these outcomes is significant. For enrolment, the relationship isnegative for females and positive for males, which is consistent with the

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TABLE 4

PROBIT COEFFICIENTS FOR ENROLMENT AND HIGH SCHOOL BY SEXWith Demographic Effects

Equation Enrolment High SchoolFemales Males Females Males

age

age1

rural

town

miles to busstop

mother's education

father's education


residual'

household head(I=female)

residents age 13-19

residents age 0-5

Log likelihood

psuedo R!

t^(demographies)2

Observations

-1.353(1.57)

0.018(0.69)

0.125(0.46)

0.092(0.33)

-0.077**(2.05)

0.085**(2.33)

0.004(0.10)

0.626**(3.51)

-0.090(0.46)

-0.121(0.86)

0.092(1.24)

-0.187**(2.33)

-218.16

50.27

441.10**

8.18**

665

-0.277(0.29)

-0.017(0.58)

-0.466*(1.85)

-0.465*(1.80)

-0.048*(1.89)

0.037(0.99)

0.061(1.60)

0.357(1.18)

-0.083(0.42)

-0.050(0.37)

0.052(0.77)

0.210**(2.56)

-239.51

53.49

551.00**

6.94**

748

0.811(1.46)

-0.029*(1.65)

-0.048(0.23)

0.446**(2.15)

-0.055(1.34)

-0.010(0.33)

0.077**(2.33)

0.861**(5.98)

-0.386**(2.33)

-0.169(1.43)

0.055(0.91)

0.152**(2.25)

-311.90

17.95

136.47**

5.31*

665

0.632(1.08)

-0.019(1.07)

-0.240(1.23)

0.044(0.23)

0.009(0.30)

0.007(0.24)

0.044(1.37)

0.848**(5.94)

-0.408*(2.45)

-0.011(0.09)

-0.008(0.15)

0.245**(3.40)

-294.71

13.51

92.09**

11.66**

748

Notes: See notes to Table 2.1/ Residual from first stage regression predicting log per capita expenditure.2/ Tests whether the coefficients on the demographic variables are zero jointly.

hypothesis that teenage girls must babysit for younger householdmembers." However in the high school regression, the correlation issignificantly positive for both males and females.12

Figures 5 and 6 display the quantitative magnitudes of the relationshipsestimated in Table 4. The difference in the income effect for males andfemales (which we saw in Figures 3 and 4) is reversed for enrolment, and

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FIGURE 5

CHANGE IN PROBABILITY OF ENROLMENT: WITH DEMOGRAPHICS

females

12 -I

9 -

Q males


5

&

Ift

FIGURE 6

CHANGE IN HIGH SCHOOL ENROLMENT: WITH DEMOGRAPHICS

males

buss top fath.ed income moth.ed


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substantially reduced for high school enrolment. The technical reason forreversal in the enrolment regression can be seen using the well-knownformula for omitted variable bias. While for both sexes the omitted variable(number of preschoolers) is negatively related to the included variable (percapita expenditure), for females the omitted variable is negatively related toenrolment, while for boys it is positively related. Hence, the incomecoefficient is overestimated for females and underestimated for males inTable 2. The magnitudes of the other effects remain the same as before, withpaternal education continuing to have a larger effect on boys and maternaleducation on girls (for enrolment). Finally, columns (b) of Table 3recalculate the gender gap in schooling outcomes using the probitcoefficients from Table 4, and the results do not change - the unexplainedportion of the gender gap accounts for 77 per cent of the total gap inenrolment and 91 per cent for high-school enrolment."

D. Regional and Community Effects

Does the impact of family background represent largely unobservedregional or community level heterogeneity? Households in the SLC arechosen from enumeration districts which are constructed geographically.Each enumeration district is wholly contained in one of Jamaica's 14parishes and is selected randomly for inclusion in the survey withprobability equal to the share of the dwellings contained in the district[World Bank, 1994b]. I control for unobserved environmental heterogeneityby allowing for both parish and district level fixed effects. Since thedependent variable is dichotomous, the fixed effect model is usuallyestimated via the conditional logit [Chamberlain, 1980]. However in thepresent case the number of fixed effects are small relative to the number ofobservations so I can estimate them directly using dummy variables. Theparishes effects are controlled for using 13 dummy variables, while thecurrent sample of households come from approximately 75 differentdistricts. Districts with less than five households were aggregated withneighbouring districts due to perfect predictions in the probit function,leaving 59 separate districts in all, which are controlled for with 58 dummyvariables.

Table 5 presents probit coefficients when the parish level dummyvariables are included in the estimation (the fixed effects are not shown).The statistical significance pattern is the same as in Table 2: for enrolmentmother's education is significant for females and father's education formales (although the latter is now significant at the ten per cent level), andincome is significant for females only. For high school, income and father'seducation matters for girls but only income matters for boys.

The quantitative impact of family background is displayed in Figures 7

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5 7 0 THE JOURNAL OF DEVELOPMENT STUDIES

TABLE 5

PROBIT COEFFICIENTS FOR ENROLMENT AND HIGH SCHOOL BY SEXWith Parish Effects

Equation

age

age2

rural

town

miles to busstop

mother's education

father's education


residual'


Log likelihood

Psuedo R2

tX2(parish dummies)1

Observations

EnrolmentFemales Males

(1) (2)

-1.321(1.52)

0.016(0.62)

0.713(1.19)

0.762(1.20)

-0.041(0.91)

0.081**(2.13)

-0.019(0.44)

0.916**(6.29)

-0.285*(1.72)

-0.223(1.54)

-213.27

51.39

450.88**

17.39

665

-0.178(0.18)

-0.020(0.67)

-0.289(0.58)

-0.140(0.27)

-0.045*(1.67)

0.029(0.77)

0.069*(1.78)

0.044(0.34)

0.282*(1.80)

-0.054(0.39)

-237.72

53.84

554.56**

10.30

748

High SchoolFemales Males

(3) (4)

0.674(119)

-0.024(1.36)

0.225(0.58)

0.689*(1.65)

-0.059(1.20)

-0.006(0.20)

0.095**(2.70)

0.648**(5.66)

-0.247*(1.78)

-0.136(1.12)

-300.57

20.93

159.13**

26.77**

665

0.678(1.13)

-0.021(1.11)

-0.108(0.28)

0.259(0.63)

0.032(1.10)

0.023(0.73)

0.039(1.17)

0.562*(4.80)

-0.109(0.77)

0.011(0.09)

-288.41

12.46

82.11**

9.29

748

Notes: See notes to Table 2.1/ Residual from first stage regression predicting log per capita expenditure.2/ Likelihood ratio test for whether the 13 parish dummies are significant jointly.

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FIGURE 7CHANGE IN PROBABILITY OF ENROLMENT: PARISH EFFECTS

I females

12 1

9 -

6 -

0

txjsstoo


tH naies

motived

FIGURE 8

CHANGE IN HIGH SCHOOL ENROLMENT: PARISH EFFECTS

I teraeies

12 n

9 -

6 -

3 -

busstop


males

fath.ed income

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and 8. The only differences between these and Figures 2 and 3 (which donot control for parish level fixed effects) is the diminished impact of incomeon enrolment for both sexes and the larger impact of father's education onfemale high school enrolment. The magnitudes of the other effects are thesame, and of policy importance is the persistently large effect of income onhigh school enrolment, especially for females.

TABLE 6

PROBIT COEFFICIENTS FOR ENROLMENT AND HIGH SCHOOL BY SEXWith Community Effects

Equation

age

age2

rural

town

miles to busstop

mother's education

father's education


residual'


Log likelihood

Psuedo R2

tX2(community effects)2

Observations

EnrolmentFemales Males

(1) (2)

-1.445(1.44)

0.018(0.60)

0.372(1.09)

0.168(0.50)

-0.100(1.62)

0.079*(1.75)

0.060(1.12)

0.980**(6.02)

-0.450**(2.31)

-0.160(0.95)

193.14

55.98

491.15**3.88

(0.19)

-0.023(0.63)

-0.392(1.23)

-0.133(0.41)

-0.091**(2.81)

0.030(0.69)

0.099**(2.17)

0.061(0.41)

0.452**(2.42)

-0.127(0.78)

-207.80

59.65

614.40**6.82*


(3) (4)

0.746(1.19)

-0.028(1.42)

-0.120(0.44)

0.431(1.59)

-0.114*(1.76)

-0.020(0.57)

0.100**(2.55)

0.780**(6.09)

-0.233(1.47)

-0.043(0.31)

-278.89

26.63

202.49**1.38

0.835(1.30)

-0.025(1.26)

-0.007(0.03)

0.444(1.84)

-0.060(1.22)

0.019(0.57)

0.047(1.27)

0.797(5.84)

-0.174(1.05)

0.01(0.09)

-268.89

21.09

143.71*6.44*

665 748 665 748

Notes: See notes to Table 2.1/ Residual from first stage regression predicting log per capita expenditure.2/ Likelihood ratio test for whether the 58 parish dummies are jointly significant. In theHigh School equations, 4 districts for females and 6 districts for males were merged dueto perfect predictions.

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Estimates controlling for community level heterogeneity are presentedin Table 6. Once again the statistical significance patterns are the same asthose in Table 2 which do not control for these fixed effects, but there arechanges in the quantitative impact of family background displayed inFigures 9 and 10. For enrolment the impact of income and maternaleduction is reduced but that of father's education for males persists. Forhigh-school enrolment all the estimated effects are reduced including that ofincome. A one standard deviation increase in household income increasesthe probability of high school enrolment by 5.5 per cent for females andonly 1.5 per cent for males. Apparantly a large part of the income effect onhigh school enrolment is due to community level differences, although theincome constraint is still significantly larger for females than males.

E. Interaction Effects

Several studies, primarily in the health literature, have explored theinteraction of maternal education with household characteristics andcommunity infrastructure to determine how mother's education influenceschildren's health outcomes [Handa, 1994; Thomas, Strauss and Henriques,1991; Barrera, 1990; Strauss, 1990; Rosenzweig and Schultz, 1982]. Iperform a similar analysis here by interacting mother's and father'seducation with each other, and with distance to the nearest bus-stop, to see

FIGURE 9

CHANGE IN PROBABILITY OF ENROLMENT: COMMUNITY EFFECTS

males

12 1

9 -

6 -

3 -

busstop


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FIGURE 10

CHANGE IN HIGH SCHOOL ENROLMENT: COMMUNITY EFFECTS

najes12-1

9 -

busstop fath.ed Income


whether there are important complementarities in the demand for children'sschooling. A positive coefficient on the parental education interaction termsignifies that mother's and father's education act as complements in childschooling, while positive coefficients on the distance to bus-stop/parentaleducation variables indicate these inputs act as substitutes in the demand forschooling.14

The estimation results with interactions are presented in Table 7. Formale enrolment father's education acts as an important complement for thetime cost of schooling, meaning the effect of father's education is enhancedthe closer one lives to a busstop (hence the smaller the time cost ofschooling). The other significant variables are the parental educationinteractions in the high school regressions. The positive signs here implythat mother's and father's education have a significant complementaryimpact in the determination of high-school enrolment.

V. CONCLUSIONS AND POLICY IMPLICATIONS

Socioeconomic background is an important determinant of the demand forsecondary schooling in Jamaica, and an even more important determinant ofenrolment in high school. School supply also plays a role in influencing

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TABLE 7

PROBIT COEFFICIENTS FOR ENROLMENT AND HIGH SCHOOL BY SEXWith Interactions

Equation

age

age2

rural

town

miles to bus-stop

mother's education

father's education


residual 1


miles to bus-stopx mother's ed

miles to bus-stopx father's ed

mother's edx father's ed

Log likelihood

Psuedo R2

t^(interactions)2

Observations

Notes: See notes to Table 2.1/ Residual from first stage regression predicting log per capita expenditure.2/ Tests whether the coefficients on the interaction terms are zero jointly.

Females(1)

-1.098(1.28)

0.011(0.41)

0.277(1.05)

0.165(0.61)

-0.156(0.98)

-0.036(0.29)

-0.050(0.38)

0.806*"(5.97)

-0.291*(1.86)

-0.138(0.99)

0.025(1.38)

-0.018(1.52)

0.011(0.71)

220.05

49.84

437.32**

3.97

665

EnrolmentMales

(2)

-0.504(0.51)

-0.010(0.33)

-0.557**(2.25)

-0.491**(1.90)

0.212*(1.69)

0.060(0.77)

0.121(1.62)

• 0.073(0.61)

0.257*(1.70)

-0.016(0.12)

-0.006(0.38)

-0.033**(2.34)

-0.002(0.18)

-239.67

53.46

550.67**

7.15*

748


(3) (4)

0.564(103)

-0.021(1.23)

-0.103(0.50)

0.441**(2.13)

-0.171(0.90)

-0.213**(2.03)

-0.121(1.25)

0.639**(5.93)

-0.181(1.36)

-0.154(1.30)

0.022(1.00)

-0.008(0.60)

0.024**(2.26)

-311.60

18.03

137.07**

5.76

665

0.565(0.96)

-0.017(0.94)

-0.302(1.57)

0.035(0.18)

0.177(1.60)

-0.126(1.63)

-0.077(1.06)

0.544**(4.93)

-0.097(0.71)

0.021(0.17)

-0.009(0.51)

-0.020(1.28)

0.019**(2.26)

-294.75

13.50

92.00**

11.37**

748

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enrolment - teenagers from rural regions or those who live farther awayfrom a busstop are less likely to be enrolled.

There are important differences in the demand for schooling by gender.For enrolment, mother's education is important for females, but father'seducation is only significant for males,15 and the effect of father's educationis enhanced the smaller the time cost of schooling. For high-schoolenrolment on the other hand, father's education is significant for girls butonly marginally significant for males, while maternal education isinsignificant for both, and parental education is an important complementfor high-school enrolment for both sexes.

Household income has a much bigger effect on the probability ofenrolment, especially high school enrolment, for females than for malesindicating that income constraints are more binding for females. However,the impact of income on secondary enrolment is significantly reduced whenaccount is taken of community level heterogeneity, indicating that the effectof income partially captures variations in community services and facilities.

The most important policy conclusion from this study is the presentlimited impact of the secondary education system in reducing inequality inJamaican society, due to the dualistic secondary system which placeschildren from higher socioeconomic backgrounds in the academically elitehigh-schools and those from poor backgrounds in lower level all-age ortechnical and vocational schools. Among high-school pupils, 42 per centcome from the richest consumption quintile compared to only eight per centfrom the poorest quintile. The multivariate analysis also shows income andparental education to be important determinants of teenage secondaryschool enrolment, while for the academically elite high schools, income isthe single most influential household characteristic affecting enrolment.

The direct impact of income is due to the limited spaces available in theacademic high schools, which makes performance on the CommonEntrance Examination in grade six extremely crucial. Because the stakes areso high, better-off families send their children for extra lessons to increasetheir performance on the CEE, and ensure admission into one of the highschools. Ironically the reason the CEE was adopted in 1957 was to ensuresecondary education for all, that is, based on merit rather than class oreconomic status, but the CEE has accomplished exactly the opposite, sinceonly the relatively well-off households can afford extra lessons as well asprovide the home environment necessary for adequate preparation. Indeednearly 60 per cent of elementary school children, mostly from publicprimary schools, do not even attempt the CEE [Butrick, 1992].Consequently one policy suggestion is to expand the number of placesavailable in high schools by converting some of the all age or compre-hensive schools and ensuring they receive adequate resources to increase

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their academic standing.16

The income effect on high school enrolment is also manifested indirectlythrough access to services and facilities at the community level. Hence thepolicy option is to not just expand the number of high-school places, but toexpand the number of high-schools, especially in rural areas where accessto a bus is especially difficult.

The results in this study also imply that the burden of recent 'costsharing' measures by the government, where families are required tocontribute towards the purchase of books and other school inputs, if appliedsystematically to the elite high schools, would actually fall dispro-portionately upon rich households. Not only are better-off households theprincipal consumers of these schools, but high schools presently receive thehighest public grants per full-time equivalent student [Butrick, 1992].

final version received September 1995

NOTES

1. Other reasons for the focus on education in development policy include the substantial non-market benefits attributable to education, especially female education, in the form of lowerfertility and infant mortality rates, and increased child nutritional and health status.

2. See T.P. Schultz [1988] for a review.3. Since primary education is universal in Jamaica, I focus on secondary schooling only. Also,

there has been no previous multivariate study on the determinants of secondary schooling inJamaica.

4. However, I do try and control for intra household bargaining by including the gender ofhousehold head in my equations.

5. Several other studies of children's outcomes have used expenditures as a proxy for income[Thomas and Strauss, 1992; Thomas, Strauss and Henriques, 1991].

6. Identifying instruments in the first stage included type of walls (brick, adobe, concrete andso on), domicile (detached, semi-detached, town-house and so on), whether the householdhad a telephone, rented its home, or received any property income, the type of toilet andwater facilities in the household, the value of durable goods owned by the household, and theage and education of the household head. This regression explained 56 per cent of thevariation in log of per capita expenditure.

7. Almost a third of the sample had missing values for parental education, a feature which isquite common in such data sets [e.g Tansel, 1993]. The main cause of the missing values isparents separated from their children ('fostering'), which in Jamaica can be for a variety ofreasons including education. In previous work [Handa, 1993] I have shown that excludingthese observations does not bias the estimates of the variables used here, since there is nosystematic reason why child fostering occurs, or at least not one that is related to theeducational outcomes considered.

8. The probabilities are calculated by evaluating the probit function at the means of all thevariables, changing the value of the variable of interest, and then re-evaluating the probit.

9. Only the results using males as reference group are provided - the size of the interaction termindicates the choice of reference group does not make a big difference.

10. I do not have any instruments to try and correct for this problem.11. Note the direction of causality is ambiguous: preschoolers may force teenage girls to stay

home, or idle teenage girls may induce fostering in of young children for child care.

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5 7 8 THE JOURNAL OF DEVELOPMENT STUDIES

12. Since fertility is a choice variable, the high school results indicate that tastes for children arepositively related to tastes (or other unobservable characteristics) for schooling achievement.

13. In earlier work I explored other ways demographic composition could influence schoolingoutcomes using variables such as total sibling size, rank, number of older brothers or sistersand so on but found no significant relationships.

14. This is because the greater the distance, the greater the cost of schooling, hence the worse offis the child.

15. Such gender differences in the impact of parental education on children's human capital isalso reported in Thomas [1994].

16. This has actually been done to several schools recently, but without the resource support toincrease their academic level.

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Behrman, J., Hrubec, Z., Taubman, P. and T. Wales, 1980, Socioeconomic Success; A Study of theEffects of Genetic Endowments, Family Environments, and Schooling, New York: North-Holland.

Behrman, J. and B. Wolfe, 1984, 'The Socioeconomic Impact of Schooling in a DevelopingCountry', Review of Economics & Statistics, Vol.66, No.2, pp.296-303.

Becker, G., 1981, A Treatise on the Family, Cambridge, MA: Harvard University Press.Birdsall, N., 1985, 'Public Inputs and Child Schooling in Brazil', Journal of Development

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Deolalikar, A., 1993, 'Gender Differences in the Returns to Schooling and School EnrollmentRates in Indonesia', Journal of Human Resources, Vol.28, No.4, pp.899-932.

de Tray, D., 1988, 'Government Policy, Household Behaviour, and the Distribution of Schooling:A Case Study of Malaysia', in T.P. Schultz (ed.), Research in Population Economics, Vol.6.

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Hanushek, E., 1986, 'The Economics of Schooling', Journal of Economic Literature, Vol.24,pp. 1141-77.

King, E. and L.Lillard, 1987, 'Education Policy and School Attainment in Malaysia and thePhilippines', Economics of Education Review, Vol.6, No.2, pp.167-81.

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DETERMINANTS OF TEENAGE SCHOOLING IN JAMAICA 5 7 9

pp.243-50.Leibowitz, A., 1977, 'Parental Inputs and Children's Achievement', Journal of Human

Resources, Vol.12, No.2, pp.242-50.Londono J.L., 1995, 'Inequality, Poverty, and Social Participation', paper presented at the World

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Policy Research Department.

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APPENDIXTABLE Al

PROBIT COEFFICIENTS FOR HIGH SCHOOL BY SEXSample of currently enrolled

age

age2

rural

town

miles to bus-stop

mother's education

father's education


residual1


Log likelihood

psuedo R2

tObservations

females(1)

0.168(0.21)

-0.006(0.24)

0.185(0.75)

0.700**(2.78)

-0.088(1.50)

0.014(0.63)

0.040*(1.74)

0.685**(4.97)

-0.161(0.98)

-0.186(1.32)

-223.93

15.76

83.80**

665

High Schoolmales

(2)

-0.525(0.48)

0.025(0.70)

0.038(0.16)

0.332(1.34)

0.025(0.57)

0.036(1.40)

0.014(0.47)

0.614**(4.46)

-0.120(0.71)

0.046(0.76)

-185.38

15.55

68.28**

748

Notes: 1/ Residual from first stage regression predicting log per capita expenditure.

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the determinants of teenage schooling in jamaica: rich vs. poor, females vs. males

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