Contraceptive Use and the Quality, Price, and Availability ... · Contraceptive Use and the...

Contraceptive Use and the Quality, Price, andAvailability of Family Planning in Nigeria

Bamikale J. Feyisetan and Martha Ainsworth

Nigeria has experienced high fertility and rapid population growth for at least thepast thirty years. Only recently have public authorities launched efforts to promotecontraceptive use. In this article, individual women are linked to the characteristicsof the nearest health facility, pharmacy, and source of family planning to assess therelative importance of women's socioeconomic background and the characteristicsof nearby services on contraceptive use. The results suggest that the limited levels offemale schooling (and probably other factors affecting women's opportunity cost oftime) are constraining contraceptive use, especially in rural areas. Another majorconstraint to increased contraceptive use is the low availability of family planningservices in Nigeria. Broader availability of the pill and other methods in pharmaciesand of injectables and intrauterine devices (lUDs) in health facilities is likely to raisecontraceptive use. Outpatient or consultation fees at nearby health facilities do notappear to be constraining demand for modern contraceptive methods.

With a total population of nearly 100 million inhabitants, Nigeria is home toabout one in every five Sub-Saharan Africans (World Bank 1992). Althoughthere are indications of a fertility decline in southwest Nigeria (Caldwell,Orubuloye, and Caldwell 1992), the country as a whole has experienced highfertility and rapid population growth for at least the past thirty years. Onlyrelatively recently have public authorities become interested in affecting thesetrends and promoted contraceptive use. Among those involved in the delivery offamily planning services, there is a broadly held conviction that improved avail-ability of contraceptives and higher quality of services will result in greater con-traceptive use. At the same time, even the most recent surveys indicate thatNigerians often prefer large families. In the 1990 Nigeria Demographic andHealth Survey (NDHS), for example, the mean desired family size among the 40

Bamikale J. Feyisetan is with the Department of Demography and Social Statistics at Obafemi AwolowoUniversity, Ile-Ife, Nigeria; Martha Ainsworth is with the Policy Research Department at the WorldBank. This article was written as background for the research project on "The Economic and PolicyDeterminants of Fertility in Sub-Saharan Africa," financed by the World Bank Research Committee (RPO67691) and sponsored by the Africa Technical Department and the Policy Research Department of theWorld Bank. The authors gratefully acknowledge the late Esther Boohene, Trevor Croft of the Demographicand Health Surveys, and Kathleen Beegle and Susmita Ghosh. The authors appreciate comments fromJohn Caldwell, Susan Cochrane, J. A. Ebigbola, Andrew Foster, Elizabeth Frankenberg, S. K. Kwafo,Samson Lamlenn, Paulina Makinwa-Adebusoye, Lewis Ndhlovu, David Radel, Fred Sai, and Baba Traore.

© 1996 The International Bank for Reconstruction and Development /THE WORLD BANK

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160 THE WORLD BANK ECONOMIC REVIEW, VOL. 10, NO. 1

percent of women who gave numerical answers was 5.8 children (Federal Officeof Statistics and IRD/Macro International 1992). About 60 percent of the re-spondents replied that their family size was "up to God," indicating that theyhave no preferences, they are reluctant to state their preferences, or they wouldlike as many children as possible.

The success of efforts to lower fertility and promote greater contraceptive usewill depend on an understanding of the importance of factors affecting the de-mand for children and the demand for contraception—including individual char-acteristics and the availability, price, and quality of services. Public policy po-tentially can influence outcomes through all of these channels. However, up tonow, an assessment of the impact of the availability, price, and quality of ser-vices on the demand for contraception in Nigeria has been hampered by theunavailability of adequate data. Demographic surveys have concentrated on thecollection of data on household and individual characteristics that are hypothe-sized to influence fertility or family planning decisions. Community data, wherecollected, are often not linked with individual data because they are usuallycollected by different agencies or researchers for different purposes. The ServiceAvailability Module attached to the 1990 NDHS has now made such an analysispossible.

In this article, we link individual women with the characteristics of the near-est pharmacy, health facility, and source of family planning to assess the relativeimportance of socioeconomic background and service characteristics on contra-ceptive use in Nigeria. Section I presents background information on Nigeria'sfertility trends, population policy, and economy. Section II describes the modelthat motivates the choice of variables in the empirical analysis. Section III de-scribes the women and facilities in our sample. Empirical results for contracep-tive use are presented in section IV. Section V summarizes salient findings andoffers tentative conclusions.

I. BACKGROUND

Nigeria has been characterized by high, yet stable, birth rates. The 1965/66National Rural Demographic Sample Survey gave a crude birth rate of 50 per1,000 persons and an average completed family size of 5.6 children (FederalOffice of Statistics 1968). United Nations' estimates put the total fertility rate(TFR) at close to 7 and the crude birth rate at 50 between 1960 and 1980, and didnot indicate significant fertility declines over that period (United Nations 1985).'More recently, the 1981/82 Nigeria Fertility Survey (NFS) found a TFR for Nige-ria of 5.94 in 1980-82 and the 1990 NDHS put the TFR at 6.01 in the period1988-90 (National Population Bureau (Nigeria) and World Fertility Survey 1984;Federal Office of Statistics and IRD/Macro International 1992).

1. These are estimates from the "medium variant" projection. Other scattered surveys, which vary inscope and content, also indicate average completed family size of between five and six and crude birthrates around fifty (for more details, see Arowolo 1984).

Feyisetan and Ainsworth 161

Regional variations in fertility, especially between the southern and northernregions, have become more pronounced since 1980. The NFS found total fertilityto be highest in the northwest (6.38) and southwest (6.25), lower in the north-east (5.95), and lowest in the southeast (5.72). By 1988-90, the TFRs in thesoutheast and southwest (5.46 and 5.57, respectively) were both lower by aboutone child than those in the northeast and northwest (6.53 and 6.64, respec-tively). There is evidence, therefore, of significant decline in fertility in south-western Nigeria in the 1980s. As Caldwell, Orubuloye, and Caldwell (1992)point out, this decline is important on a continental scale because the southwest-ern region of Nigeria is more populous than all but three Sub-Saharan countries.Note also that the difference between the lowest and highest TFRS has increasedfrom 0.66 to 1.18 during the 1980s. Fertility differentials were also found bylevels of education, place of residence (urban or rural), marital status, employ-ment status, and use of contraception, among others. With respect to education,the NDHS found that total fertility was lower among women who had completedsecondary schooling (4.2) than among those who had completed primary school-ing (5.6) or those with no schooling (6.5). Women with incomplete primaryschooling had the highest total fertility (7.2).

The relatively stable high birth rates in Nigeria have been accompanied bysteady declines in death rates. The crude death rate was estimated to declinefrom 25 in 1960 to 17 in 1983; during the same period, the infant mortality ratedeclined from 195 to 115 and the under-five mortality rate from about 325 to190 per 1,000 live births (UNICEF 1985). The 1990 NDHS estimated an even lowerinfant mortality rate of 91; the highest rate was in the northwest (110), whileinfant mortality in the other three regions ranged from 83 to 88 (Federal Officeof Statistics and lRD/Macro International 1992). The balance between the stablehigh birth rates and the steadily declining death rates (in the absence of a majorcontribution by net migration) has led to an annual population growth rate inexcess of 3 percent.

Economy

Before the 1970s, Nigeria depended mainly on agriculture for its domesticand foreign earnings. Five major cash crops were exported—cocoa, rubber, cot-ton, groundnuts, and palm products. However, oil was discovered in the late1960s and went into large-scale production in the early 1970s. It became themajor revenue earner and contributed immensely to the growth of Nigeria'seconomy during the early 1970s, as the country benefited from the high price ofoil in the world market. This brought about a rapid increase in the number ofeducational institutions and health facilities and in the provision of roads, elec-tricity, and piped water. In addition, wages increased in the nonagriculturalsector.

By the early 1980s, dwindling oil revenues provoked a downturn in theeconomy (National Research Council 1993). Living standards worsened, as goodsbecame unaffordable. The quality of health and education services deteriorated


and essential facilities became scarce. Primary enrollment rates peaked at 98percent in 1980 and declined thereafter to only 72 percent in 1990 (Scribner1995). The situation eventually led to the adoption of a structural adjustmentprogram in 1986. By that time it had become very apparent that uncheckedrapid population growth is undesirable, no matter the level of economic growth.With the assistance of several international agencies, the federal government setin motion a course of action to slow down the rate of population growth.

Population Policy

During the oil boom of the 1970s, rapid population growth was not perceivedas an obstacle to economic growth. The Third National Population Policy reads:"Although Nigeria has (by world standards) a large and rapidly growing popu-lation, these demographic factors do not appear as yet to constitute a significantor serious obstacle to economic progress. The country is fortunate in possessinga large land area endowed with natural resources, which if carefully exploitedshould provide a basis for building a viable economy which would ensure asteadily rising standard of living for the population within the foreseeable futureespecially during the current phase of the country's demographic transition whichis characterized by rapid growth . . ." (Federal Republic of Nigeria 1975: 293-94). There was, however, a plan to continue with the integration of the familyplanning information and services into an overall health and social welfare sys-tem for the country through the National Population Council of Nigeria.

As living standards worsened in the 1980s, official population policy changed.The period 1983-89 marked the beginning of a government-sponsored, nationalfamily planning program. The 1988 National Policy for Development, Unity,Progress and Self-Reliance acknowledged that the laissez-faire approach to popu-lation issues was not effective in lowering population growth and had adverseconsequences on the welfare of the citizens and the socioeconomic developmentof the country. The new policy adopted specific demographic objectives andadvocated extending coverage of family planning services to half of all womenof childbearing age by 1995, and to 80 percent by 2000 (Federal Republic ofNigeria 1988).

Since 1983, organized family planning services have received great encour-agement from two related developments. First, the recognition of family plan-ning as part of the state public health system led to the establishment, in 1987,of a family planning coordinator in each state. Second, a major effort by theFederal Ministry of Health with the technical and financial assistance of theUnited States Agency for International Development (USAID), the World Bank,and the United Nations Population Fund (UNFPA) resulted in the distribution ofcontraceptives such as pills, injectables, IUDS, vaginal foaming tablets, andcondoms to health facilities. Hospitals, health clinics, maternity centers or ma-ternity homes, family planning clinics, pharmacies, and patent medicine storesbecame involved in the distribution of these commodities. In addition to theorganized stationary delivery points, the Planned Parenthood Federation of


Nigeria has, in recent years, recruited individuals to distribute nonclinicalcontraceptives in rural communities.

By 1990, 7.5 percent of all women of reproductive age were current users ofcontraception and use of modern methods was only 3.8 percent. Modern meth-ods include sterilization, IUD, injectables, pills, condoms, spermicides, and dia-phragm. The rate of use of any contraceptive among married women (6 percent)was only half that of unmarried women (13 percent) (Federal Office of Statisticsand IRD/Macro International 1992). The probability of use of modern contra-ception rose with female education, from only 1.3 percent of women with noschooling, to 3.9 and 6.4 percent of women with incomplete and complete pri-mary schooling, respectively, to 16.7 percent of women who have completedsecondary schooling. Caldwell, Orubuloye, and Caldwell (1992) point out thatcontraceptive use has particularly expanded among younger single women: theNDHS results found that 38 percent of use was among single women, and in their1990 study of urban women in Ekiti (Ondo State), contraceptive use rates werethree to five times higher among unmarried than among married women agedfifteen to twenty-four. The other main demand for modern contraceptives comesfrom married women who want to replace traditional methods of birth spacing.

II. THE MODEL

The demand for family planning is conditional on the demand for children.The "demand for children" broadly includes decisions on the number of chil-dren desired and the timing of births.2 Economic models of fertility highlightseveral key factors affecting the demand for children (Becker 1960, 1981;Rosenzweig and Schultz 1985):

• The value of women's time, because childrearing is intensive in the use ofwomen's time.

• The price of other child inputs, such as food, clothing, schooling, and healthcare.

• The value of children's time in home production now and as an economicasset in the future.

• Household income.

These are in addition to more subjective factors, often referred to as inherent"tastes" for children. The implication of these models is that as the costs ofchildren rise, holding other factors constant, the number of children desired willdecline. Alternatively, as the economic benefits of children rise, holding costsconstant, fertility can be expected to rise. The effect of household (nonlabor)income is ambiguous, because higher income means that couplescan afford more children or can decide to invest more per child for any givennumber.

2. For a more formal exposition of the model, see annex 1 of Feyisetan and Ainsworth (1994).

1 64 THE WORLD BANK ECONOMIC REVIEW, VOL. 10, NO. 1

The demand for family planning services arises from their role as an inputinto producing and achieving a desired number of children. Therefore, familyplanning demand will depend on the price and quality of services, conditionedon the target number of children. The cost of family planning services includesthe cost of the contraceptives, the cost of transport to the facility providingfamily planning services, the value of the time it takes to reach the facility, andthe registration (or outpatient) fee. The quality of services can be proxied bysuch variables as the number and composition of staff, the number and types ofservices available, and the regularity of supplies of contraceptives. Other waysof preventing births—such as periodic abstinence, rhythm, and withdrawal—rely to an even greater extent than modern methods on behavior modification,and thus also entail costs to the user. The costs of different methods will deter-mine users' choices. When the cost of modern methods rises, we expect a shiftfrom modern to traditional methods or, if traditional methods are also too costlyto practice, to no contraception and possibly a higher family size.

The empirical model estimated in this article expresses the use of moderncontraception as a function of factors affecting both the demand for childrenand the costs of fertility regulation:

(1) FP = FP(Pfp,Q,w,Pc,I)

where FP is use of modern contraception, P^ is the price of family planningservices, Q is the quality of services, w is the opportunity cost of women's time,P'c is the price of other child inputs, and / is household nonlabor income.

In the empirical work, we have employed variables that proxy the argumentsin equation 1. We expect variables that measure the value of women's time,such as education, urban residence, and region of residence, to lead to lowerdemand for children and higher contraceptive use. Variables that reflect theprice of family planning, such as the distance to a family planning source, theprice of commodities, and registration, consultation, or outpatient fees, shouldlower the use of contraception. We expect that indicators of the quality of fam-ily planning services will raise contraceptive use. Examples of variables thatmight be perceived as reflecting quality include the type of family planning source(pharmacy, hospital, health clinic or maternity center, and health center), thenumber and composition of staff at the family planning source, the types offamily planning services available, and the number and types of family planningmethods available. We have no a priori expectations about the relation betweencontraceptive use and nonlabor income because it depends largely on the rela-tion between income and the demand for children. If, as incomes rise, womenwant more children, then we would expect a negative relation between incomeand the use of contraception. In economic terms, this is the case if children are"normal" goods. By contrast if, as incomes rise, women want fewer children(perhaps of higher "quality"), then we would expect contraceptive use to risewith rising income.

Feyisetan and Ainsworih 165

The left-hand side of equation 1 is measured by current use of a moderncontraceptive. The dependent variable is dichotomous, taking a value of one orzero depending on whether the woman possesses the attribute under consider-ation. Estimating the coefficients of equation 1 using ordinary least squares (OLS)will produce biased estimates. Thus, maximum likelihood logit has been used toestimate the parameters of the equation.

In order to interpret results of the relation between service characteristics andcontraceptive use as an "impact" of services, we must assume that these servicesare randomly placed. However, if individuals selectively migrate to areas in re-sponse to program characteristics or if the placement of services is based on thecharacteristics of the population, then program placement would not be exog-enous and the estimates of the effect of program characteristics may be biased(Pitt, Rosenzweig, and Gibbons 1993; Rosenzweig and Wolpin 1988). For ex-ample, if public family planning services are selectively placed in areas with highfertility and low contraceptive use, one might observe a negative correlation be-tween the availability and use of family planning. On the other hand, if servicesare targeted.to women in areas of high demand for contraception, then estimatesof the positive relation between availability and contraceptive use may be over-stated. Unfortunately, with only a single cross-section of data to work with, weare unable to correct for the potential endogeneity of service placement. Familyplanning services are not widely available in Nigeria, however, so we suspectthat services have not been targeted to areas with low demand.

III. DATA AND VARIABLE DEFINITIONS

This study uses data from the 1990 NDHS. Data were collected from 8,781women on their socioeconomic characteristics, fertility histories, and use of con-traception, among others. (The sample design is described in detail in FederalOffice of Statistics and IRD/Macro International 1992.) In addition, informationon family planning services was collected from two sources: groups of four orfive knowledgeable informants in the selected community, and staff at facilitiesvisited by the interviewers. Informants for the community questionnaire wereasked to identify the hospital, maternity clinic, health center, family planningclinic, and pharmacy nearest to each cluster of households interviewed for theNDHS. All named facilities (one of each type per cluster) were then visited for theservice module if they were within six hours on foot from the cluster. At eachfacility, information was collected about the availability and costs of drugs andfamily planning methods, the types and number of health personnel, registra-tion or outpatient fees, and the types of maternal and child health services thatwere available. By linking the exogenous facility data to the individual data, wewere afforded an opportunity to examine the impact of the availability, price,and quality of certain services on women's demand for modern contraception.

The facility data have some limitations. First, the service availability sur-vey was conducted in only 185 of the 299 survey clusters, of which almost

1 66 THE WORLD BANK ECONOMIC REVIEW, VOL- 10, NO. 1

all (165) were in rural areas. The criteria for including 20 urban clusters inthe service survey were not documented, but roughly 10 to 15 percent ofurban women from each of the four regions could be linked to the servicesurvey. Because two-thirds of the women using contraceptives were living inclusters not surveyed by the facility module, we were not able to includethem in our assessment of the impact of the quality of services on the de-mand for contraception. Second, the identification of health facilities andthe estimation of distances were left entirely in the hands of the communityinformants. In situations where people want more from the government, thereis the possibility of underestimating the number of health facilities in a local-ity. Respondents may also have identified facilities more distant than thenearest ones. Many clusters of women could not be linked to facilities be-cause the respondents could not name a facility, not because one was notavailable. Third, even when a facility was named by community respondents,it was not always interviewed. As a result, although 8,781 women were suc-cessfully interviewed, only 5,714 were located in clusters covered by the com-munity-facility survey and, of these, only 4,681 (81.9 percent) could be linkedto the nearest health facility. Our main sample for analysis is the 4,589 womenwho could be linked to the nearest health facility and for whom all explana-tory variables at the individual and health-facility level were present.

Characteristics of the Women

Of the 4,589 women in the working sample, 32 percent reside in the north-east, 23 percent in the northwest, 29 percent in the southeast, and 16 percent inthe southwest. Eighty-nine percent reside in rural areas. The average age of thewomen is 28.6 years; for ever-married women, the average age is 30.4 years.Approximately 84 percent have been married. Over half of the women (56 per-cent) are Muslim, 26 percent are Protestant, and 13 percent are Catholic. Theaverage number of children ever born is 3.48. Approximately 23 percent of thewomen have no children, and 34 percent have 5 or more.

The level of literacy of these mostly rural women is generally low. Only 38percent have ever attended school, and the average woman in the sample hascompleted only 2.6 years of schooling. Among those who have ever attendedschool, the average years of schooling is 7. Among women who have husbandsor partners, the male partners have on average 1 year more of schooling thanthe women (2.7 compared with 1.7 years). Slightly more than a third of the malepartners had some schooling (35 percent) compared with only about a quarterof the women who had ever been married (28 percent).

With respect to contraceptive use, 5.6 percent of the women in the workingsample were currently using a traditional or modern method of contraception,and 2.9 percent were currently using a modern method. Of those currently usinga modern method, 39 percent were using the pill, 22 percent IUD, and 14 percentinjection (see table 1). The main sources of contraceptives were hospitals (41percent) and pharmacies (25 percent).


Table 1. Distribution of the Sample of Women Using Modern Contraceptionby Method and Source

Method and source

MethodPillIUD

InjectionCondomSterilizationSpermicide, diaphragm

Total

SourceHospitalPharmacy or patent medicine storeHealth center, maternity center, or health clinicPrivate clinicPlanned Parenthood clinicFriends or relativesMarketPrivate doctorHusband's place of workUnknown

Total

Percent

38.922.313.711.59.44.3

100.0

41.025.213.77.22.92.91.41.40.73.6

100.0

Note: The sample size is 139 women. Totals may not add to 100 because of rounding.Source: 1990 NDHS data.

Health Facilities

Community data were collected from 185 clusters of households (165 ruraland 20 urban). The nearest of four types of stationary health facilities are iden-tified on the community questionnaire: hospital; health clinic, maternity center,or family planning clinic (including the few family planning clinics that wereidentified, this group is henceforth referred to as health clinic); health center;and pharmacy (see table 2). At least one type of health facility was identified in184 clusters: all four different types were identified in 32 clusters, three types in71 clusters, two types in 64 clusters, and one type in 17 clusters. The number ofclusters identifying a facility is highest for the pharmacy (144), followed by thehospital (134), the health clinic (108), and the health center (102). The commu-nity informants reported that 52 percent of all the named facilities provide fam-ily planning services. The percentage providing family planning services variesamong the different types of health facilities: hospital (81 percent), health clinic(55 percent), health center (45 percent), and pharmacy (28 percent). However,comparison of these responses with what was reported by the health facilitiesreveals that the community informants were often incorrect. Thus, in the analy-sis below we use measures of family planning availability collected directly fromthe facilities.

The mean distance from the cluster to the nearest health facility varies bytype of health facility and by availability of family planning services in the health


Table 2. Distance and Time from Communities to the NearestHealth Facilities

Community-level variableNearest facilityNumber of communities that named

a facilityPercentage of all 185 clustersMean distance to the nearest facility

(miles)Mean travel time by the most common

means of transport (minutes)

Nearest facility with family planningNumber of facilities named

with family planningPercentage of all named facilitiesMean distance to the nearest named

facility (miles)Mean travel time by the most common

means of transport (minutes)

Hospital

13472.4

15.5

92.8

10981.3

14.9

92.8

FacilityHealth clinic

maternity center,or family

planning clinic

10858.4

6.7

68.4

5954.6

7.6

87.9

Healthcenter

10255.1

8.0

89.9

4645.1

7.6

84.7

Pharmacy

14477.8

4.9

67.2

4027.8

5.4

235.2

Note: The community data are from 185 clusters of households.Source: 1990 NDHS Service Availability Survey, community informants.

facility. The mean distance is highest for the hospital (16 miles) and lowest forthe pharmacy (5 miles). Hospitals and health centers with family planning ser-vices are slightly closer to the community than those without family planning,and health clinics and pharmacies with family planning clinics are slightly far-ther from the communities than those without family planning services. Theaverage time it takes to reach the nearest facility by the commonest means oftransportation is generally greater than one hour. It takes roughly an hour and ahalf to reach hospitals and health centers. This is not surprising because most ofthe sample is in rural areas.

To be eligible for an interview, a facility had to be within six hours' walkingdistance from the community. A health facility of each type was visited if it metthe eligibility requirement. Ninety-three hospitals, 91 health clinics, 88 healthcenters, and 127 pharmacies were thus visited (see table 3). In 165 clusters (ap-proximately 90 percent of the 185 clusters with community modules) at least 1health facility was visited. Ninety-three percent of hospitals, 58 percent of healthclinics, 61 percent of health centers, and 32 percent of pharmacies that werevisited had family planning services. The private sector controls between 3 per-cent (health centers) and 40 percent (health clinics) of the facilities. The hospi-tals have been in existence for a longer period than the other types of facilities,and although higher proportions of hospitals charge a registration or outpatientfee, the fees are highest in the health clinics.


Variations in the quantity and quality of medical personnel and inpatientservices across types of health facilities reflect the level of complexity of thetasks each type of facility performs. Hospitals generally perform the most com-plex tasks, followed by health clinics. Although almost all of the hospitals have

Table 3. Description of the Nearest Health Facilities

Facility-level variable

Number of facilities visited

Ownership (percent)PublicPrivateOther

Mean years in operation

PricesPercentage of facilities that charge an

outpatient feeMean registration fee (naira) among

facilities that charge*

Medical staffPercent with at least one doctorMean number of doctorsMean number of nursesMean number of midwives

InfrastructureMean number of bedsEquipment (percent of facilities)

Examination tableElectricityRefrigeratorRunning water

Services (percent of facilities)Family planningPostnatal careAntenatal careDelivery servicesAntimalarial drugsAntibioticsAny vaccine

Hospital

93

75.318.36.5

22.8

87.1

2.55

95.711.640.627.0

147.8

96.887.192.585.0

92.595.794.695.789.376.381.7

FacilityHealth clinic


planning clinic

91

58.740.2

1.1

13.3

82.4

4.00

52.21.13.33.3

9.5

79.464.144.053.3

58.289.075.886.876.951.750.6

Healthcenter

88

94.33.42.3

12.0

70.5

1.71

27.30.53.43.7

11.2

65.953.453.452.3

61.472.763.860.264.847.773.9

Pharmacy

127

———

5.7

—

—

————

—

—52.826.038.6

32.3—

—96.960.6

—

— Not available. This information was not included in the questionnaire for pharmacies,a. The average 1990 market exchange rate was 12.8 naira per U.S. dollar.Source: 1990 NDHS Service Availability Survey, facility respondents.

1 7 0 THE WORLD BANK ECONOMIC REVIEW, VOL 10, NO. 1

at least one doctor, only 52 percent of health clinics and 27 percent of healthcenters have one. On average, each hospital has about twelve doctors, healthclinics one, and health centers zero. Similarly, the average number of nursesvaries from three (health clinics and health centers) to forty-one (hospitals) andthe number of midwives from three (health centers) to twenty-seven (hospitals)(table 3).

With respect to infrastructure, the results show that examination tables, elec-tricity, running water, and refrigerators are more likely to be found in hospitalsthan in any other type of facility. The number of beds in each facility reflects theamount of inpatient service it provides. The hospitals have, on average, 148beds; the average numbers for the health clinics and the health centers are about10 and 11, respectively.

Information was also collected on the types of maternal and child health ser-vices that were available in each facility. Four types of services are identifiedhere: antenatal, delivery, postnatal, and immunization. Hospitals are more likelyto provide these services than any other type of facility. With respect to thesupply of drugs, the pharmacies and the hospitals are the most likely to supplyantimalarial drugs and antibiotics, respectively. It is not surprising that almostall the pharmacies had antimalarial drugs at the time of the survey. In Nigeriathe patient (or the patient's relative) finds it more convenient to visit a phar-macy for antimalarial drugs than to visit a hospital; hospitals are usually con-sulted for complicated cases.

The Health Facilities with Family Planning Services

Table 4 describes health facilities that offer family planning services withrespect to the family planning methods offered, the average price of such com-modities, and the regularity of their supply. The number of facilities with atleast one doctor or nurse capable of inserting the IUD exceeded the number withan IUD insertion kit at the time of the survey. This suggests that nonavailabilityof trained personnel is not the binding constraint for expanding availability ofIUDS. The results also show that the likelihood of having sterilization performedis highest in the hospitals: 58 percent of hospitals with family planning serviceshave at least one doctor trained in female sterilization. This figure contrastswith 32 percent for health clinics and 7 percent for health centers.

With respect to the availability of methods, the pill is the most available,followed by the condom; female sterilization is least available. No method wasavailable in all the facilities at the time of the survey. While the probability ofobtaining the pill is highest in the health clinics, the probability of obtaining anIUD, condoms, or spermicides is highest in the hospitals. The health center is thesecond-best place where all methods, except female sterilization, can be found.The price of obtaining a method is generally higher in the health clinics andpharmacies, which are preponderantly privately owned. Not all facilities thathad a method at the time of the survey always had the method. For example,although 87 percent of hospitals with family planning services had the pill at the


Table 4. Description of the Nearest Health Facilities with Family Planning

Facility-level variable

Number of facilities visited thatoffered family planning

Family planning methods offeredPillsInjection (e.g., Depo Provera)IUD

CondomFoaming tablets (spermicide)Female sterilization

Mean price of methods (naira)'PillInjection (e.g., Depo Provera)IUD

CondomFoaming tablets (spermicide)Female sterilization

Hospital

86

FacilityHealth clinic,


planning clinic

53

(percent of facilities)87.264.086.186.168.646.5

1.256.665.130.752.68

25.94

Percent of facilities that ran out of stock within thePillInjection (e.g., Depo Provera)IUD

CondomsFoaming tablets (spermicide)

38.721.817.616.211.9

94.37.7

64.273.650.917.0

2.9411.4127.94

1.594.02

86.22

Healthcenter

54

92.668.568.585.263.0

3.7

0.773.124.060.535.843.70

Pharmacy

41

68.331.7

—82.941.5

—

6.2513.19

—1.856.91

—

r six months preceding the survey16.622.2

3.116.216.0

36.037.818.923.911.8

35.746.2

—23.511.8

Percent with someone trained toinsert IUD 88.4 77.4 75.9 —

Percent with an IUD insertionkit in stock 86.1 75.5 57.4 —

Percent with a doctor trained toperform sterilizations 58.1 32.1 7.4 —

—Not available. This information was not included in the questionnaire for pharmacies.a. The average 1990 market exchange rate was 12.8 naira per U.S. dollar. Mean prices include zeros.Source: 1990 NDHS Service Availability Survey, facility respondents.

time of the survey, only 61 percent had the pill in stock during the six monthspreceding the survey.

Definitions of Variables

The dependent variable is current use of any modern method of contracep-tion.3 The explanatory variables include characteristics of the women and char-

3. For results for determinants of ever use of contraception, see Feyisetan and Ainsworth (1994).


acteristics of the pharmacies, health facilities, and sources of family planningthat are nearest to the women surveyed. The explanatory variables can be orga-nized into three groups: the individual woman's characteristics, variables re-flecting access to a health facility or the price of services, and those reflectingthe quality of the nearest health facility.

The individual woman's characteristics include age, education, place ofresidence, and religion. The choice of individual variables is based on theirtheoretical as well as practical relevance. Women's education is also inter-acted with urban residence and region of the country to see in which areaseducation has a greater effect. In the absence of a direct measure of wealth,the type of floor in the dwelling is adopted as an index on the assumptionthat women who live in dwellings with polished wood, vinyl, ceramic, orcement floors are likely to have higher income than those in dwellings withanimal dung or earth-sand floors.

Access or price variables include the distance to the nearest health facility,the availability of family planning services, and, conditional on the facility of-fering family planning, the availability and price of individual methods. Thequality of the nearest health facility is measured by whether it is privately owned,whether the facility has at least one doctor, and the number of contraceptivemethods offered.

IV. EMPIRICAL RESULTS

The determinants of contraceptive use are estimated as a function of threetypes of facilities—the nearest health facility, the nearest pharmacy, and thenearest health facility offering family planning. Results are presented in tables5, 6, and 7. Descriptive statistics for the variables in the regressions are in ap-pendix table A-l.

Logit estimation results for the determinants of current contraceptive use as afunction of the characteristics of the nearest health facility or nearest facilitywith family planning are presented in table 5. The nearest facility is defined asthe hospital, health clinic-maternity center, or health center that is closest to acommunity. The first three specifications are for the sample of 4,589 womenwho could be linked to the nearest health facility and for whom there were nomissing values on independent variables. The first specification shows the re-sults for individual characteristics and characteristics of the nearest health facil-ity that reflect the quality, availability, and price of contraceptive methods. Inthe second specification, we add the characteristics of the nearest pharmacy. Inthe third specification, we replace the characteristics of the nearest health facil-ity by the characteristics of the nearest source of family planning. The fourthspecification is the same as the third, but includes the entire national sample of8,761 women.4 Note, however, that although using the full sample adds obser-

4. The total sample was 8,781 women, but values on the independent variables were missing for 20women.

Feyisetan and A insworth 173

vations to the variables measured at the individual level, the number of observa-tions on service characteristics is unchanged from the third specification andstill reflects only the primarily rural areas where this information was collected.Thus, the fourth specification is shown primarily for purposes of comparing theresults on women's characteristics with the earlier specifications.

For ease of interpretation, the logit coefficients have been transformed intothe marginal effects of a change in the explanatory variables on the probabilityof contraceptive use, evaluated at the mean of each variable and multiplied by100. Thus, the coefficients can be interpreted as the increase (or decrease) incurrent contraceptive use (in percentage points) associated with a one-unit changein the variable, evaluated at its mean value.

Women's Characteristics

In the first three specifications (table 5), current contraceptive use increaseswith age but at a decreasing rate. This is expected: as women age, the longer istheir potential exposure to pregnancy and their fertility will approach desiredfamily size. However, they also become less fecund and thus are less likely toneed contraception to limit births. The two measures of the opportunity cost ofwomen's time—education and urban residence—exert great positive impact oncontraceptive use. Education and urban residence increase the opportunity costof women's time by enabling women to hold higher-paying jobs, makingchildrearing more costly. This leads to a decline in the demand for children andan increase in the demand for contraception. The negative marginal effect of theinteraction between schooling and urban residence indicates that the positiverelation between female education and contraceptive use is weaker in urbanthan in rural areas. For ease of interpretation of the relation between educationand contraceptive use by region, table 6 combines the schooling coefficientsinteracted with regional and urban dummy variables using the first specificationin table 5. The positive relation between female schooling and contraceptive useis greatest in the rural north and rural southwest.

Although the interaction between female schooling and the regions is jointlysignificant, the dummy variables for regions are not individually or jointly sig-nificant in the first specification of table 5. However, when characteristics of thenearest pharmacy and nearest source of family planning are included (as in thesecond and third specifications), they become weakly jointly significant andwomen in the northwest appear to have lower contraceptive use than in otherregions. In the first three specifications (table 5), women in Protestant house-holds are more likely to be current users of contraception.

Most of the sample in the first three specifications is from rural Nigeria,where the most distinguishing feature of wealth is the type of housing a familyhas. As such, women who reside in dwellings with polished wood, ceramic, orcement floors no doubt have access to higher income. The positive marginaleffect on type of floor, therefore, indicates an increase in the demand for contra-ception as income rises. This result might seem to imply that income would


Table 5. The Marginal Effect of the Characteristics of Women andthe Nearest Health Facility, Pharmacy, or Family Planning Sourceon Contraceptive Use

Explanatory variable

Individual womanAge (years)

Age squared

Urban residence*

Years of schooling

Interaction: urban x schooling

Northeast residence*

Northwest residence*

Southwest residence*

Interaction variablesNortheast x schooling

Northwest x schooling

Southwest x schooling

Type of floorb

Protestant*

Muslim*

Nearest health facilityDistance (miles)

Outpatient registration feec

Family planning offered*

Injection offered*

Injection priceCid

IUD offered*

IUD priced

Pill offered*

1

0.277(3.19)-0.003

(-2.21)1.252

(2.14)0.132

(3.17)-0.094

(-1.81)-0.196

(-0.41)-0.780

(-1.38)-0.177

(-0.32)

0.104(1.37)0.107

(1.61)0.088

(1.46)0.754

(2.19)0.585

(2.12)0.154

(0.39)

-0.088(-2.74)-0.038

(-1.17)-1.132

(-1.73)0.998

(2.29)-0.027

(-0.56)0.830

(1.69)-1.41x10^

(-0.01)0.105

(0.17)

Specification

2

0.231(3.45)-0.003

(-2.41)1.276

(2.79)0.127

(4.00)-0.093

(-2.40)-0.120

(-0.36)-0.743

(-1.61)-0.117

(-0.23)

0.060(0.93)0.067

(1.33)0.072

(1.55)0.511

(1.95)0.519

(2.45)0.152

(0.51)

-0.074(-2.65)-0.017

(-0.62)-1.212

(-2.62)0.896

(3.10)-0.049

(-1.92)0.414

(1.57)0.015

(1.84)0.578

(1.63)

3

0.242(3.48)-0.003

(-2.46)1.421

(3.12)0.138

(4.27)-0.118

(-2.72)-0.352

(-1.06)-0.999

(-2.23)-0.330

(-0.74)

0.060(0.83)0.094

(1.83)0.083

(1.69)0.618

(2.37)0.546

(2.41)0.147

(0.48)

-0.036(-1.65)

0.026(0.84)

0.967(3.69)-0.063

(-3.38)

-0.408(-1.04)

4

0.509(5.84)-0.006

(-4.42)2.298

(4.21)0.346

(5.47)-0.133

(-2.03)-0.971

(-1.44)-2.213

(-2.82)-0.721

(-1.20)

0.019(0.15)0.198

(2.20)0.034

(0.45)0.498

(2.04)0.375

(1.29)-0.105

(-0.30)

-0.081(-1.42)

0.041(0.54)

1.464(2.68)-0.072

(-1.91)

-1.057(-1.17)

Table 5. (continued)



Pill priced

Condom offered*

Condom priceCpd

At least one doctor*

Hospital*

Health clinic*

Privately owned facility*

1

0.028(0.47)0.045

(0.12)0.065

(0.85)-0.645

(-1.89)0.694

(1.92)0.222

(0.62)0.639

(1.66)Health facility with family planning

linked to the cluster*

Nearest pharmacyPharmacy interviewed*

Distance (miles)

Number of hours open per week

Family planning offered*

Number of family planningmethods available

Injection offered*

Injection priceCi<i

Pill offered'

Pill priced'1

Condom offered*

Condom pricec>d

Constant

LikelihoodPseudo R2

Sample size

-10.037(-7.73)

-486.750.1962

4,589

Specification

2

0.054(0.97)-0.057

(-0.22)0.102

(2.03)-0.743

(-2.83)0.910

(3.24)0.124

(0.45)0.516

(1.64)

-1.446(-3.64)

0.011(0.37)0.014

(3.62)0.333

(0.66)0.541

(1.68)-0.319

(-0.81)-0.034

(-3.08)1.041

(1.86)-0.254

(-2.05)-0.630

(-1.09)-0.382

(-1.65)

-10.441(-7.77)

-472.560.2196

4,589

3

0.154(3.24)0.422

(1.46)0.140

(3.36)-0.621

(-2.42)0.575

(2.29)0.014

(0.04)0.233

(0.87)-0.130

(-0.58)

-1.325(-2.97)-0.026

(-0.78)0.016

(3.61)-0.184

(-0.46)0.666

(2.81)-0.137

(-0.44)-0.024

(-2.84)1.588

(2.83)-0.321

(-5.09)-0.530

(-0.97)-0.417

(-1.81)

-10.944(-8.48)

-475.360.2150

4,589

4

0.251(2.45)0.794

(1.14)0.247

(2.42)-1.421

(-2.23)1.767

(2.67)0.497

(0.70)0.168

(0.25)0.094

(0.17)

-2.087(-1.88)-0.109

(-1.16)0.022

(1.88)-0.314

(-0.30)1.703

(3.12)-0.785

(-0.90)-0.063

(-2.78)3.325

(2.51)-0.698

(-3.66)-1.537

(-1.08)-1.074

(-1.77)

-9.265(-13.17)

-1465.450.1821

8,761

(Table continues on the following page.)


Table 5. (continued)


Joint tests'Schooling: regions x schooling

Method availability(health facility)

Method availabilty (pharmacy)

1

35.94[0.000]15.50[0.009]

Specification

2

41.54[0.000]16.94[0.005]29.53[0.000]

3

54.43[0.000]16.44[0.003]38.55[0.000]

4

73.34[0.000]8.03

[0.090]19.06[0.002]

Note: Values represent the marginal change in current contraceptive use (percent) for a one-unitchange in the explanatory variable, ^-statistics are in parentheses and are from the original logit regressions.Logit coefficients have been transformed into marginal changes using the formula dpldx^ = /?t • exp (x'fi)l[1 + exp (x'fi)]2, and the result has been multiplied by 100. Standard errors have been estimated usingHuber's technique, which is robust to heteroskedasticity and cluster effects.

a. Dummy variable: the value is 1 if the condition is true; 0 otherwise.b. Dummy variable: the value is 1 if the floor is parquet, vinyl, ceramic, or cement; 0 otherwise.c. Fees and prices are in naira. The average 1990 market exchange rate was 12.8 naira per U.S. dollar.d. The variable is availability of the method (1 or 0) times the price.e. The value reported is the x2 statistic for the joint test; p-values are in brackets.Source: 1990 NDHS data.

exert a negative effect on fertility. However, this seems not to be the case inNigeria (see Ainsworth, Beegle, and Nyamete 1995).

The results for individual variables in the fourth specification reveal that therelationship between contraceptive use and female schooling, area, and regionof residence is stronger in the sample of all Nigerian women than in the sampleof women for which the characteristics of health facilities were available. Themarginal effect of an additional year of female schooling, for example, increasesfrom roughly 0.13 in the first three specifications to 0.35 in the fourth (table 5).The relation with urban residence also increases dramatically from about 1.3 inthe first three specifications to 2.3 in the fourth. These "stronger" results arisefrom the fact that the marginal effects are computed at the mean of the indepen-dent variable, and the mean levels of schooling and urban residence are higherfor the sample of all Nigerian women. For example, the mean years of femaleschooling for the first three regressions is 2.6 years, while for all women it is 3.7years. Eleven percent of the women in the first three regressions lived in urbanareas, compared with 40 percent of those Nigeria-wide (see table A-l).

Nearest Health Facility and Nearest Facility with Family Planning

With respect to the characteristics of the nearest health facility, both the firstand second specifications in table 5 show that greater distance is associated witha lower probability of contraceptive use, irrespective of whether family plan-ning is actually offered at the facility. When in the third specification we con-sider the distance to the nearest health facility with family planning, the relationis also negative, but only half as strong and very weakly statistically significant.

Feyisetan and A insworth 177

Table 6. The Marginal Effect of a One-Year Increase in Female Schooling onCurrent Contraceptive Use

Region

SoutheastSouthwestNortheastNorthwest

Urban

0.040.130.140.15

Rural

0.130.220.240.24

Note: The data show the marginal change in current contraceptive use for a one-unit change in theexplanatory variable. Logit coefficients have been transformed into marginal changes using the formuladp/dxk = Pk • exp (x'p)l[\ + exp (x'p)\l, and the result has been multiplied by 100.

Source: 1990 NDHS data.

In none of the specifications is current use of contraception related to the levelof outpatient or registration fees.

Conditional on the availability of any method of family planning, womenwith access to injections and the IUD at the nearest health facility are more likelyto be current users (the first specification).5 The coefficients on availability ofother specific methods are not individually significant, but are jointly significantin determining current contraceptive use. The results for prices of specific meth-ods are generally insignificant, but are sensitive to inclusion of the characteris-tics of the nearest pharmacy in the second and third specifications. In the lattercase, strong positive price effects on contraceptive use appear for the IUD, pill,and condom, while the relation with injection prices is always negative (table 5).

Controlling for the other characteristics of health facilities, women forwhom the nearest health facility or source of family planning is a hospitalare more likely to be current users of contraception, compared with thosefor whom the nearest facility is a health clinic or dispensary. This may haveto do with the quality of the services provided at a hospital, which we havebeen unable to completely control for, or may reflect some characteristic ofwomen who live near hospitals. Women for whom the nearest health facilityis private are also more likely to be using modern contraception, althoughwhether or not the nearest facility with family planning is private is not re-lated to contraceptive use. Curiously, the presence of a medical doctor at thenearest facility is associated with lower contraceptive use and almost exactlyoffsets the positive coefficient on hospital. A similar result has been found inZimbabwe (see Thomas and Maluccio 1995). One plausible explanation isthat most Nigerian doctors are men; women may prefer to receive contra-ceptive services from female providers. In other specifications, the distanceto a primary school and the number of years that the facility had been oper-ating were not significant determinants of current use.

5. When the availability of various individual methods and their prices are included in the regression,the coefficient on the availability of any family planning is negative (see table 5). However, this coefficientmust be used in combination with those on specific methods. When only the availability of family planningis entered (without dummy variables for specific methods and their prices), the coefficient is positive,although of borderline significance (see Feyisetan and Ainsworth 1994, table 6A, specification 2).


Nearest Pharmacy

Because of the recency in the integration of family planning programs intothe primary health care scheme, the pharmacy has, for a long time, been themajor distributor of nonsurgical contraceptives in Nigeria. It is not uncommonin some areas to find pharmacies stocked with contraceptives that are lacking inthe hospitals, especially in government-owned hospitals. But because pharma-cies do not perform several of the functions of the three other health facilities(hospitals, health clinics, and health centers), we present the results for phar-macy characteristics in addition to whichever of the three other stationary fa-cilities is closest to the community.

In the second and third specifications in table 5, we add to the regressions thecharacteristics of the nearest pharmacy. The nearest pharmacy was not inter-viewed for 27 percent of the women for whom the characteristics of the nearesthealth facility were obtained. A dummy variable for whether a pharmacy wasinterviewed has thus been introduced. The distance to the nearest health facility(and, marginally, the nearest source of family planning) remains a significantdeterminant of contraceptive use, but the distance to the pharmacy is not sig-nificant. However, the results for other characteristics of the nearest pharmacyare more highly and consistently significant than those for the nearest healthfacility or nearest facility with family planning. Increased hours of operation atthe nearest pharmacy are associated with higher contraceptive use, as is thenumber of methods offered. Among specific methods, availability of the pill atthe nearest pharmacy is associated with significantly higher contraceptive use,but the prices of injections, the pill, and condoms are associated with lowercontraceptive use. Recall that only 3 percent of women overall were currentusers of a modern method. Only 22 percent of the women who could be linkedto a pharmacy lived nearest to a pharmacy that supplies the pill. This suggeststhat increased availability of the pill at private pharmacies may have an impor-tant impact on raising contraceptive use in Nigeria.

In the fourth column of table 5 the full set of methods and quality character-istics is entered for the nearest family planning source and pharmacy for allwomen in the NDHS, irrespective of whether the women could be linked to thesefacilities. The major difference between the results of the third model and thoseof the fourth is that the levels of significance of some of the characteristics of thenearest family planning source and pharmacy decrease in the fourth model, whilethe size of the marginal effects increases. Note that the pseudo R2 declines forthe full sample of women as well; this is not surprising in light of the fact that 70percent of them had no values for the characteristics of the nearest source offamily planning and 56 percent had no pharmacy characteristics.

Regional Differences

A chi-squared test confirmed that there are structural differences in the determi-nants of contraceptive use by region. The sixth and seventh specifications in table 7

Feyisetan and Ainswortb 179

show the relationship between the characteristics of the nearest health facility andcurrent use of contraception among women in the sample living in the north andsouth. Because the models were estimated separately for the two regions, the inter-actions between urban residence, women's education, and regions have been dropped.The results of the fifth specification, on the pooled women and without these re-gional and schooling interactions, are provided for comparison.

A strong relation between urban residence and contraceptive use is observedamong northern women, but not among women in the south. The marginal effect ofa one-year increase in women's schooling is also much larger in the north (.402)than in the south (.016). In the north, women's schooling has a positive relation withcontraceptive use in both urban and rural areas, but in the south it shows a netpositive relation in rural areas only. The proxy for income is insignificant amongwomen in both regions, but it is significant for the entire sample in the fifth specifi-cation. Protestant women in the north are more likely to use modern contraceptionthan women from other religious groups; both the significance of the coefficient andthe size of the marginal effect of religion are smaller in the south.

There are important differences among northern and southern women withrespect to the relation between characteristics of services and contraceptive useas well. Among northern women, the distance to the nearest health facility, theprice of outpatient consultations, and the presence of a medical doctor are sig-nificantly associated with lower use of modern contraceptives. In addition, con-traceptive use is higher among women for whom the nearest facility is a hospi-tal. None of the variables measuring the availability or price of specific methodsare statistically significant.

In contrast, among southern women contraceptive use does not significantlyvary by distance, the presence of a doctor, or the type of facility, and higheroutpatient fees are associated with higher contraceptive use. The results for dis-tance and presence of a doctor reinforce the suspicion that the negative relationbetween doctor and contraceptive use may be caused by the gender of the doc-tors, because the majority of northern women are Muslim. The positive relationbetween outpatient fee and contraceptive use among southern women may becapturing some other aspect of service quality that could not be controlled forbut that is associated with higher fees. Finally, the availability of the pill is verystrongly correlated with higher contraceptive use among women in the south,and private ownership of the nearest health facility is associated with lower use.All of the contraceptive methods are jointly significant for the southern sample,but only at marginal levels for the northern sample. The independent variablesexplain a much higher proportion of the variation in contraceptive use amongsouthern women than among northern women (the pseudo R2 is 0.2718 and0.1457 for southern and northern women, respectively).

Partner's Characteristics

In the first seven specifications, we have included all women in the regres-sions, regardless of their marital status. There are several reasons for this. First,


Table 7. The Marginal Effect of the Characteristics of Women and the NearestHealth Services on Current Contraceptive Use, by Region and Marital Status


Individual womanAge (years)

Age squared

Urban residence'

Years of schooling

Interaction: urban x schooling

Northeast residence"

Northwest residence'

Southwest residence"

Type of floorb

Protestant*

Muslim*

Partner or husband*

Partner's years of schooling

Nearest health facilityDistance (miles)

Outpatient registration feec

Family planning offered'

Injection offered*

Injection priced

ruD offered*

IUD price*'d

All women5

0.281(3.16)-0.003

(-2.18)1.057

(1.98)0.187

(5.43)-0.075

(-1.66)0.278

(0.66)-0.214

(-0.47)0.380

(1.03)0.745

(2.10)0.607

(2.18)0.095

(0.24)

-0.102(-2.98)-0.036

(-1.01)-1.178

(-1.74)0.968

(2.24)-0.021

(-0.41)0.880

(1-74)-8.22X10-4

Womenin thenorth

6

1.013(4.07)-0.013

(-3.38)2.853

(2.27)0.402

(5.45)-0.119

(-1.00)

1.126(1.23)1.294

(1.82)1.000

(0.90)

-0.139(-1.72)-0.237

(-2.30)-2.550

(-1.07)1.095

(0.78)0.017

(0.11)2.190

(1.48)-0.015

Women inthe south

7

-1.03x10^(-0.01)

1.04x10^(2.46)0.033

(0.32)0.016

(2.24)-0.016

(-2.66)

0.059(1.30)0.115

(1.56)0.049

(0.61)

-0.008(-1.52)

0.028(3.08)-1.169

(-11.95)0.063

(0.94)0.015

(1.01)0.011

(0.13)0.003

Women whohave apartner

8

0.281(3.00)-0.003

(-2.07)0.503

(1.29)0.120

(3.01)

0.355(0.83)-0.133

(-0.29)0.376

(1.01)0.748

(2.03)0.585

(2.06)0.086

(0.22)-0.707

(-1.58)0.074

(2.32)

-0.105(-3.08)-0.043

(-1.18)-1.058

(-1.55)0.830

(2.07)-0.015

(-0.33)0.816

(1.69)-0.004

Fcyisetan and Ainsworth 181


Pill offered'

Pill priced

Condom offered"

Condom priceCid

At least one doctor*

Hospital*

Health clinic*

Privately owned facility*

Constant

LikelihoodPseudo Rz

Sample size

Joint tests'Regions

Method availability (health facility)

Woman's schooling, partner,partner's schooling

All womenS

(-0.04)0.238

(0.37)0.015

(0.25)0.060

(0.16)0,044

(0.58)-0.697

(-1.97)0.111

(1.85)0.241

(0.66)0.731

(1.88)

-10.304(-7.99)

^88.580.1931

4,589

2.63[0.452]16.63[0.005]

Womenin thenorth

6

(-0-27)0.717

(0.29)0.141

(0.97)-0.197

(-0.19)0.096

(0.44)-2.107

(-2.04)2.704

(2.19)1.361

(1.41)0.987

(1.02)

-11.839(-7.57)

-339.700.1457

2,071

9.26[0.099]

Women inthe south

7

(0.27)1.239

(14.19)-0.004

(-0.17)-0.116

(-1.64)-0.018

(-0.83)-0.047

(-0.61)0.013

(0.17)-0.050

(-0.98)-0.407

(-1.78)

-7.747(-3.57)

-134.280.2718

2,518

269.87[0.000]

Women whohave apartner

8

(-0.20)0.302

(0.47)0.008

(0.15)-0.007

(-0.02)0.041

(0.58)-0.676(-1.97)

0.772(2.05)0.249

(0.70)0.753

(1-93)

-9.726(-7.23)

^186.570.1965

4,589

2.59[0.459]13.53[0.019]41.40[0.000]

Note: Values represent the marginal change in current contraceptive use (percent) for a one-unitchange in the explanatory variable, r-statistics are in parentheses and are from the original logit regressions.Logit coefficients have been transformed into marginal changes using the formuladpldxk = /?t • exp (x'fi)l[1 + exp (x'fi)]1, and the result has been multiplied by 100. Standard errors have been estimated usingHuber's technique, which is robust to heteroskedasticity and cluster effects.

a. Dummy variable: the value is 1 if the condition is true; 0 otherwise.b. Dummy variable: the value is 1 if the floor is parquet, vinyl, ceramic, or cement; 0 otherwise.c. Fees and prices are in naira. The average 1990 market exchange rate was 12.8 naira per U.S. dollar.d. The variable is availability of the method (1 or 0) times the price.e. The value reported is the x2 statistic for the joint test; p-values are in brackets.Source: 1990 NDHS data.


as Caldwell, Orubuloye, and Caldwell (1992) have pointed out, the demand forcontraception appears to be higher among unmarried women than among mar-ried women. Dropping the unmarried women would therefore eliminate a ma-jor share of users. Second, the decision to marry is related to the decision to havechildren, and thus marital status is jointly endogenous with fertility and contra-ceptive use. Third, to the extent that more-educated women delay marriage andtherefore have higher contraceptive use before marriage, excluding unmarriedwomen will distort the relation between education and contraceptive use. How-ever, one advantage of conditioning on marital status is the ability to comparethe relative effects of male and female schooling on women's contraceptive use.Thus, in the last column of table 7, we control for whether the woman has everhad a husband or partner and for the education of the current or most recentpartner.

Having a partner is associated with lower contraceptive use, but the relationis not statistically significant at conventional levels. When the partner's orhusband's variables are included, the marginal effect of female schooling de-clines (from 0.19 in the fifth specification to 0.12 in the eighth specification),but is still significant and substantially greater than the marginal effect of maleschooling (0.07) (see table 7). Husband's schooling is often included as a proxyfor income, but its inclusion in the eighth specification does not detract from theproxy for wealth (type of flooring), which remains positive and significant. Theresults for other characteristics of health facilities are substantially the same asin the fifth specification.

V. SUMMARY AND CONCLUSIONS

This article has examined the impact of individual characteristics and theavailability, price, and quality of services at the nearest pharmacy and healthfacility on current use of contraception in Nigeria. Only 3 percent of the womenin this study were currently using a modern method of contraception. Amongthe background characteristics that had the strongest relation with contracep-tive use are factors thought to raise the opportunity cost of women's time—women's schooling and urban residence. Women's schooling in particular had aconsistently strong positive relation with contraceptive use in every specifica-tion; in rural areas, the relation between schooling and contraceptive use is morepronounced. In the sample of women analyzed, overall levels of schooling wereextremely low. Of all women aged fifteen to forty-nine in our sample, 62 per-cent had received no formal schooling whatsoever; in rural areas, this figure is67 percent, and in urban areas about a quarter of the women had no schooling.Our analysis strongly suggests that policies to raise female schooling will resultin greater contraceptive use, holding constant the characteristics of services.The relation between our proxy for income and contraception was also consis-tently positive, indicating that higher incomes will contribute to greater contra-ceptive use.


The low availability of family planning services in Nigeria is a constraint toincreased contraceptive use. Increased distance to the nearest health facility wasassociated with lower contraceptive use, but distance to the nearest facility withfamily planning was not. This suggests that women who seek health care aremore likely to consider using contraception, even if methods are not available atthe nearest facility. Women in the sample were generally closer to a pharmacy(4.3 miles) than to the nearest health facility (5.1 miles) or to the nearest facilitywith family planning (6.9 miles), making the pharmacy the least costly source inat least one sense. However, pharmacies in this predominantly rural samplewere also less likely to stock contraceptives than were health facilities: 32 per-cent of the nearest pharmacies offered family planning, but 55 percent of thenearest health facilities did so. Longer hours of operation at the nearest phar-macy were associated with higher use. Conditional on the availability of anyfamily planning method at the nearest health facility, the availability of injec-tions and sometimes the IUD is often significantly associated with higher currentuse of contraception. At pharmacies, the total number of methods and the avail-ability of the pill are associated with higher contraceptive use, and the price ofall methods is associated with lower use. Outpatient or consultation fees at nearbyfacilities do not appear to be constraining demand for modern methods.

The results of this study suggest that the limited levels of female schooling(and probably other factors affecting women's opportunity costs of time) areconstraining contraceptive use in both urban and rural areas, but more so inrural areas and in the north. Broader availability of the pill through pharmacyoutlets and of the injection and IUD in stationary health facilities is likely to raisecontraceptive use as well. Pharmacies are a particularly good outlet for nonsur-gical methods, as pharmacies are generally more accessible to rural women andunmarried men and women than are health facilities that offer family planning.Furthermore, they may require less waiting time for the client and may be lessconspicuous.

The results for the effect of service quality variables are not strong. Of course,the facility questionnaires are not able to capture all of the quality aspects of aservice. Interviews with clients and potential clients and observation of servicedelivery may yield more subjective insights. The Population Council recentlyconducted an in-depth situation analysis of 147 family planning service deliverypoints in six states of Nigeria, which included observation of client-providerinteractions and interviews with staff and clients (Mensch and others 1994).Multivariate analysis of the number of new family planning clients per 1,000women resident in the area found no statistically significant effect of the qualityof provider services. However, the study was not able to control for the socio-economic characteristics of the female clients or for the availability of services,as the present article has done. The final report for the situation analysis notedthat queuing is not common at health facilities and method stockouts are not "amajor problem" (Adewuyi and others n.d.). Despite improvements that couldbe made in sanitation, counseling, and availability of basic equipment, client


satisfaction was very high. However, insistence on spousal consent may be lim-iting access to services in many facilities. In a country such as Nigeria, whereavailability of services and female schooling are both very low, it is difficult tobelieve that improved service quality (and the implied higher unit costs) of pro-viding it will be the most cost-effective means of raising contraceptive use. Someof the most measurable aspects of quality and availability that public authoritiescan monitor are measured in the NDHS.

Although this study has shown some statistically significant marginal ef-fects of both individual and service factors on contraceptive use, it would beincorrect to infer that these marginal effects reflect the probable outcomesof large increases in the share of women who are educated or in the avail-ability of contraception. The experience of countries such as Zimbabwe showsthat substantially raising the schooling of large numbers of women and theiraccess to family planning services (as was done during the 1980s) can have amuch greater impact than these marginal effects would imply. Furthermore,we would expect that as contraceptive services become more available inNigeria, distance will cease to be a binding constraint to increased contra-ceptive use.

We have had to assume that the placement of health facilities and pharmaciesand the availability of family planning within them is independent of the women'sfertility desires or their demand for contraception. Since Nigeria is a countrywhere family planning services are not widely available, this may not be a goodassumption. Certainly in the case of private pharmacies, it is likely that theprobability of family planning being offered will be influenced by perceptions ofthe demand environment. We have no way of correcting for the potentialendogeneity of the supply of family planning within the existing data set. Moreaccurate estimates of the potential impact of improved availability or quality ofcontraceptive services could be obtained through a study in which facilities wererandomly assigned family planning services and contraceptive use could be ob-served over time. Since one objective of the population program in Nigeria is toimprove the availability of methods, it seems to us that careful data collectionfrom women and facilities, as they are sequentially provided these services, wouldyield even greater insight on the impact of availability and quality of services oncontraceptive use.

Feyisetan and Ainsworth 18S

Table A-l. Variable Means for Characteristics of Women, the Nearest HealthFacilities, and the Nearest Pharmacies by Various Specifications

Variable

Dependent variableCurrent use of contraception

Independent variableIndividual womanAge (years)Age squaredUrban residence1"Years of schoolingInteraction: urban x

schoolingNortheast residence1"Northwest residence1"Southwest residence1"Interaction variable

Northeast x schoolingNorthwest x schoolingSouthwest x schooling

Type of floor0

Protestant1"Muslim1"Partner or husband1"Partner's years of schooling

Nearest facility1

Distance (miles)Outpatient registration feec

Family planning offered6

Injection offered1"Injection priceCif

IUD offered1"IUD pricee>f

Pill offered6

Pill pricee'f

Condom offered1"Condom price'1'At least one doctor1"Hospital1"Health clinic1"Privately owned facility1"Health facility with family

planning linked to the cluster1"

Nearest pharmacyPharmacy interviewed1"Distance (miles)Number of hours open per weekFamily planning offered

1, 2, S, 8

0.029

28.6906

0.1072.63

0.7880.3170.2320.162

0.2780.2180.8860.5290.2630.5590.8372.27

5.142.330.6430.4192.720.4633.040.5881.030.5460.4880.5590.3370.3790.212

0.7333.03

57.40.227

3

0.029

28.6906

0.1072.63

0.7880.3170.2320.162

0.2780.2180.8860.5290.2630.559

5.731.80

0.5263.15

0.7350.7870.6820.6110.5730.4130.2240.143

0.581

0.7333.03

57.40.227

Specification'

4

0.052

28.2876

0.4023.72

2.430.2330.1930.310

0.2340.2452.080.5830.3350.487

3.180.993

0.2981.68

0.4110.4470.3810.3230.3160.2290.1270.080

0.304

0.4371.87

34.30.133

6

0.014

28.6902

0.0380.904

0.104

0.3980.0890.887

7.051.390.7470.4822.630.5192.690.6630.8260.7010.5770.5210.4010.2660.075

7

0.048

28.6-910

0.1914.72

1.35

0.6880.4750.161

2.833.460.5180.3422.820.3943.460.4961.290.3580.3800.6060.2580.5170.379

(Table continues on the following page.)


Table A-l. (continued)Specification'

Variable 1,2,5,8Number of family planning

methods offered 0.707 0.707 0.294Injection offered1" 0.061 0.061 0.040Injection price1-' 0.833 0.833 0.506Pillofferedb 0.159 0.159 0.091Pill price1' 1.12 1.12 0.616Condoms offered1" 0.194 0.194 0.108Condom price'-' 0.360 0.360 0.198

Sample size 4,589 4,589 8,761 2,071 2,518

a. The specifications correspond to those in tables 5 and 7.b. Dummy variable: the value is 1 if the condition is true; 0 otherwise.c. Dummy variable: the value is 1 if the floor is parquet, vinyl, ceramic, or cement; 0 otherwise.d. For specifications 3 and 4, this is the nearest source of family planning; for all of the other six

specifications, it is the nearest health facility.e. Fees and prices are in naira. The average 1990 market exchange rate was 12.8 naira per U.S. dollar.f. The variable is availability of the method (1 or 0) times the price.Source: 1990 NDHS data.

REFERENCES

The word "processed" describes informally reproduced works that may not be com-monly available through library systems.

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