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Bulletin of Indonesian Economic Studies

ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20

Resolving contradictions in Indonesian fertility

estimates

Terence H. Hull & Wendy Hartanto

To cite this article: Terence H. Hull & Wendy Hartanto (2009) Resolving contradictions in Indonesian fertility estimates, Bulletin of Indonesian Economic Studies, 45:1, 61-71, DOI: 10.1080/00074910902836197

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Published online: 26 Mar 2009.

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RESOLVING CONTRADICTIONS

IN INDONESIAN FERTILITY ESTIMATES

Terence H. Hull* Wendy Hartanto*

Australian National University BPS–Statistics Indonesia, Jakarta

Over the last half-century Indonesia has been concerned about excessive popula-tion growth. To reduce the levels of fertility the government in 1968 established a national family planning program. In the following three decades fertility levels fell from over ﬁ_{ve to under three children per woman. In the past decade}

ques-tions have arisen about the validity and reliability of fertility estimates. Increasingly Indonesia’s Demographic and Health Surveys (DHS) have reported higher fertility than is recorded in the censuses and other large social sample surveys. In 2007 the discrepancy reached more than half a child on average. This paper reveals one of the major causes of the discrepancy and shows ways to adjust the DHS results to gain more reliable measures. At present Indonesian national fertility is very close to the policy target of replacement levels by the year 2010.

FERTILITY LEVELS AND TRENDS IN INDONESIA

Over the last four decades Indonesia, like most countries in Asia, has undergone a major transition from high to low fertility. Where women up to the 1970s had long borne an average of over ﬁ_{ve children, thereafter the pace of child-bearing slowed}

through a combination of delayed marriage and greater use of contraception. Indonesian women increasingly sought to prolong the time between births and end child-bearing with fewer children. Encouraging and supporting this major behavioural change was a national family planning program that provided free contraceptives, promoted small-family values and enhanced family welfare. This government service program, along with rapid increases in school enrolments and rises in formal workforce participation, meant that women could entertain ambi-tions beyond motherhood and families. Without the burden of frequent births, women found they had more resources to invest in the quality of their children. (Hull 1980, 1994, 2007; Hull and Dasvarma 1988; Hull and Hull 1997).

The transformation in behaviours surrounding family formation is reﬂ_ected

in the trend line representing the changing levels of fertility calculated from

* This research was supported by the UN Population Fund (UNFPA), Indonesia’s National Family Planning Coordinating Board (BKKBN) and the National Development Planning Agency (Bappenas) through a consultancy titled ‘Revitalizing Family Planning in Indo-nesia’. The authors would like to thank Dr Adrian Hayes, Dr Meimanat Hossain-Chavoshi, Professor Peter McDonald and Professor Henry Mosley for criticisms and suggestions that helped shape the presentation of this method of fertility analysis. Thanks also to Mr Arizal Ahnaf, Deputy for Social Statistics at Statistics Indonesia (BPS, the central statistics agency), for providing encouragement to this critical review of a key social measure in Indonesia.

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census-type surveys in ﬁ_{gure 1.}1_{This gives us a long-term ‘macroscopic’ view of}

trends from the beginning of the family planning program through to the most recent inter-censal survey (the 2005 Supas). Each of the points in the ‘census-type own-child estimate’ series is an estimate of fertility spanning a three- to ﬁ_ve-year

period centred on the point.

Statistics Indonesia calculates recent fertility levels from census and large-scale household surveys by comparing the number of young children (aged from birth to 3 or 4 years old) listed in the households with the number of women in the child-bearing age groups, including the mothers of the children. This is the ‘own-child method’ of fertility estimation. By taking into account the impact of infant mortality, researchers can estimate the total number of children born each year prior to the enumeration, and attribute this number to the estimated number of women living at that time, grouped in the standard ﬁ_{ve-year age groups between}

ages 15 and 49 that are referred to as the reproductive age groups.

These calculations form the basis for an estimation of total fertility rates (TFRs), which can be interpreted roughly as the average family size. The trend line of the TFR after 1970 was not strictly linear but fell continuously through the turn of the century, after which point the decline slowed as the rate neared the so-called replacement level of 2.1 children per woman.

Since the 1980s, an alternative set of fertility estimates has been developed to give more detailed insights into the social and biological processes behind fertil-ity. These surveys took a different approach to fertility analysis, relying on the collection of personal marriage and childbirth histories of a sample of ever-mar-ried women. The resulting pregnancy history database allowed analysts to recon-struct the marriage, fertility and mortality experienced by the women for some

1 The census-type surveys referred to in this note are the Inter-censal Survey (Supas), the National Socio-Economic Survey (Susenas) and the decennial population census.

[ [ [ [

[ [ [ [ [ [

[ [

[ [ [

[ _F

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Census-type own-child estimates

DHS pregnancy history estimates

Estimate for 1995 from DHS 2002–03

Estimate for 2000 from DHS 2007 FIGURE 1 Indonesian Fertility Trends, 1965–2007

(average number of children per woman)

Source: Estimates calculated by BPS–Statistics Indonesia.

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decades prior to the survey. From these data we can calculate the average dura-tion of inter-birth intervals as well as estimates of the duradura-tion or incidence of behaviours related to breast-feeding, amenorrhea, contraception and abortion, all of which contribute to the level and trend of fertility.

The Demographic and Health Surveys (DHS, but the ﬁ_{rst of which was called}

the Contraceptive Prevalence Survey) of 1987, 1991, 1994, 1997, 2002–03 and 2007 produced the pregnancy history estimates shown in ﬁ_{gure 1. They diverge}

sub-stantially from the census-type survey trend lines. In early years they were below the own-child estimates. Since 1995 the DHS rates have increasingly exceeded the levels found in the census-type national surveys. From about the year 2000 the published DHS estimates of current fertility (that is, average fertility over the three-year period prior to the survey) have been stagnant at 2.6 children per woman, well above census estimates, and half a child higher than the replacement level of 2.1.

The contradictions between the two systems of data collection pose serious prob-lems for statisticians and policy makers alike. If on the one hand the census-type estimates are correct, then Indonesia would appear to be progressing well towards the achievement of the national target of replacement-level fertility in 2010. How-ever if the DHS is more reliable, then not only is the country unlikely to achieve that target, but also it would appear that the family planning program is failing to main-tain the momentum established under the years of New Order government rule.

In the context of political debate, such data contradictions have led to strongly contrasting ideas about demographic futures for the nation and for provinces and districts. This is important because the contrasting scenarios of census or DHS fertility imply different population projections, with great consequences for gov-ernmental structures and planning decisions about education and health services. However, until recently, there has been no clear basis for preferring one interpre-tation over the other. Often the discussions were reduced to matters of faith, with the DHS being called the ‘gold standard’ owing to the detail of its data collection, and the census being regarded as the ‘complete enumeration’ because interview-ers in 2000 were told to visit every household and record every citizen. While demographers might have academic discussions about the technical shortcomings of both types of estimate, politicians demanded to know which was ‘right’.

HINTS OF PROBLEMS WITH THE DHS

The scissors pattern of the series in ﬁ_{gure 1 raises a number of questions about}

the TFR estimates from the DHS, since they show a ﬂ_atﬁ_{gure of 2.6 for}

contem-porary fertility measured in both the 2002–03 and the 2007 surveys. The DHS enumerators collected detailed data from ever-married women between the ages of 15 and 49, including complete histories of pregnancies and births. The total number of births in two discrete ﬁ_{ve-year periods for the decade prior to the}

survey was obtained from these histories. This allows us to calculate historical trends. One indicator of historical trends is shown by points on average 7.5 years immediately prior to each of the survey dates (that is, the average fertility for the earlier ﬁ_{ve-year period of the pre-survey decade); these are shown in}ﬁ_gure

1 as the diamond and triangle above the DHS trend line. Essentially they imply that the retrospective measure from the individual DHS surveys in 2002–03 and 2007 showed declining fertility, but the trend between 2002 and 2007 did not. This

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poses a statistical contradiction. Was there a problem with the DHS attempt to record accurate pregnancy histories, or did the two surveys suffer from unreliable sampling? To answer this we need to look at the way the DHS selects the house-holds and individuals to be included in the survey.

In the DHS it is assumed that single women are not sexually active and have produced no children. However, to calculate standard demographic measures of fertility the survey needs to record all women in the population irrespective of their marital status. This is because the denominator of the fertility rate is the total number of all women of the relevant age group, while the numerator is the total number of births produced by mothers. If the DHS is justiﬁ_{ed in assuming that unmarried}

women do not have any births then no harm is done to the numerator by interview-ing only ever-married women. Nonetheless, it is still important to have an accurate enumeration of all women, including single women, for the denominator.

The DHS household listing is the tool used to collect the data for the estimate of the total population of women and men in the sample households. Table 1 reveals that the DHS household listings consistently show lower proportions of single women than do census, Supas and Susenas enumerations taken at around the same time, particularly for the ages from 20 through 34, the peak years of repro-duction for Indonesian women. What explains the apparent lack of single women in the DHS listings? In part it appears that there is a major difference in the type of household covered by DHS and census-type surveys. Why should this occur?

Essentially, both the Susenas and the DHS surveys are meant to include only regular households (rumah tangga biasa), and interviewers should not visit ‘insti-tutional housing’ (rumah tangga khusus) such as prisons, dormitories and barrack facilities or religious group housing such as convents or schools. This should have an equal effect on both surveys if interviewers for all national surveys follow the sampling instructions carefully.

TABLE 1 Proportions Single for Women of Reproductive Ages in Successive National Surveys in Indonesiaa

Age Group Supas DHS Census Susenas DHS Supas DHS 1995 1997 2000 2002 2002–03 2005 2007

Percentage of women in the age group who are single

15–19 85.7 82.1 89.3 89.7 85.4 90.8 86.9 20–24 40.1 36.1 43.1 47.0 41.2 51.4 38.3 25–29 15.2 14.1 16.7 16.3 13.8 19.7 15.4

30–34 5.5 5.3 6.9 6.5 5.9 8.1 7.0

35–39 2.8 2.4 3.5 2.9 3.0 4.3 3.6

40–44 2.1 2.9 2.4 2.1 2.1 2.6 2.6

45–49 1.9 1.7 2.0 1.4 2.0 2.0 1.9

All WRA 27.7 25.3 28.7 27.6 25.0 28.8 23.7

a _{WRA = women of reproductive age. See footnote 1 for explanation of other terms.}

Source: Calculated from the Measure DHS STATCompiler at <http://www.statcompiler.com/> and unpublished data from BPS–Statistics Indonesia.

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Since the 1980s Indonesia has undergone a remarkable change in the roles young women perform in society. They are increasingly likely to pursue educa-tion to higher levels, to work in expanding industrial and service occupaeduca-tions or to join the over four million Indonesian workers who are employed overseas and sending remittances home (though this group is not included in any census or survey enumeration). Single women in Indonesia often live in institutional set-tings. These women would thus not be enumerated unless they are mistakenly listed in their parents’ households.

What is more important is that single women also form what might be regarded as ‘non-standard’ regular households even if they are not in recognised institu-tional settings. Thus young people may be listed in a household in the BPS cen-sus listing, but they may live under very crowded or unusual circumstances, and the household may possess none of the characteristics regarded as features of ‘normal’ family life. Perhaps the best known of these household types are the

indekos or boarding houses that proliferate throughout urban areas. Anecdotal evidence from interviewers indicates that these households are sometimes passed over in the DHS canvassing because ﬁ_{eld-workers concentrate on units that are}

more likely to yield eligible respondents, and they know that the clusters of stu-dents or young workers are unlikely to be ever-married. In contrast, the decennial census enumeration attempts to include all individuals and all households, and the Supas makes special efforts to cover both family and non-family households, often with a particular interest in workers and students.

DHS analysts sometimes justify the lack of attention to the conditions of single women by using the assumption that unmarried women do not generally have sexual relationships and, even if they do fall pregnant, this is likely to lead to mar-riage in a very short period of time. Sociological and anthropological research, as well as simple observation of social trends, indicates that such an assumption has always been naïve, and recently has become completely misleading. With an estimate of over one million induced abortions annually, of which some 20–60% occur among the unmarried (Utomo et al. 2001: 8–9, 23, 27), it is vital for the DHS to ensure that all eligible single women are included in the sample. Moreover, the rising age at marriage means that an increasing proportion of women aged 20–34 are single, and they are also moving out of their parental homes and shedding many of the strictures that apply to adolescents. It is crucial that these women be included in any analysis of reproductive and sexual health.

In short, it is evident that the DHS has missed many young single women dur-ing the household listdur-ing, with the result that the denominators used to calculate fertility are under-estimated and fertility is over-stated. The problem of listing single women (and men) began in earnest in the 1990s, as massive transforma-tions of education, occupation and living arrangements took place. This problem has grown steadily through to the present day. It is evident from comparison of census-type surveys with the DHS, but that does not mean that the census is abso-lutely correct. In fact it is likely that Indonesia, in common even with Australia and the US, has an under-count of young men and women because of the social and geographic mobility of this age group. Nonetheless, if we assume that the DHS should at least include those young people who were counted in the census-type surveys, then we have a chance of adjusting the fertility rates to approach a more realistic level.

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RETURNING THE MISSING WOMEN TO THE DHS SAMPLE POPULATION

The adjustment of DHS fertility rates is a two-step process. First, the data in table 1 can be used to estimate the number of single women missing from the DHS sample compared to the expected number if the DHS had the same propor-tion single as was found by recent census-type surveys. Second, once those single women are added to the total number of women in the DHS households, the fertility rates can be re-calculated with new denominators. The calculation of the number of missing single women is based on the following logic. The proportion single in the DHS (ds) in each age group can be represented as Ds/Dw (single women in the DHS divided by all women in the DHS) for each age group. Then we calculate the proportion single in the DHS population if all the missing single women (represented as M in the equations) are restored to both the numerator and the denominator to achieve the same proportion single as was found in the recent census enumeration (cs). In order to solve for M (the missing women), we derive the following equations:

cs = (Ds + M)/(Dw + M) Ds + M = (cs x Dw)+ (cs x M) M – (cs x M) = cs x Dw – Ds M(1 – cs) = cs x Dw – Ds M = [(cs * Dw) – Ds]/(1 – cs)

This calculation is shown for the two most recent DHS surveys in tables 2 and 3. TABLE 2 Estimation of Total Number of Women Missing

from the 2002–03 DHS Sample

Age Group 2002–03 DHS Numbers Recorded by Age

Group

2002–03 DHS Single Recorded

by Age Group

2002–03 DHS Proportion

Single in Age Group

2000 Census Proportion

Single in Age Group

Estimate of Missing Women

Adjusted Total 2002–03

DHS Women

Dw Ds ds cs M Dw’

15–19 6,715 5,735 0.8540 0.8927 2,423 9,138 20–24 6,738 2,776 0.4120 0.4312 227 6,965 25–29 6,302 870 0.1380 0.1667 217 6,519 30–34 5,844 345 0.0590 0.0695 66 5,910 35–39 5,349 160 0.0300 0.0349 27 5,376 40–44 4,704 99 0.0210 0.0241 15 4,719 45–49 4,170 83 0.0200 0.0198 –1 4,169

All WRA 39,822 10,068 0.2500 0.2870 2,974 42,796

Source: Authors’ calculations.

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Table 2 shows that the 2002–03 DHS failed to record about 23% of the single women who should have been in the sample (the 10,068 recorded plus the 2,974 missing mean the sample should have contained 13,042 single women). Thus 7% of the total sample was missed. In 2007 over one-third of single women were skipped, representing 11% of the total sample who should have been listed in the DHS households (table 3). In both surveys the bulk of the missing women were in the age groups related to senior high school and university. Coincidentally, this is the age at which many young women are working in manufacturing and service industries.

DHS main report for 2002–03 (BPS and ORC Macro 2003), the method used for calculating fertility rates indicates that:

Numerators of the ASFRs [age-speciﬁ_{c fertility rates] are calculated by summing}

the number of live births that occurred in the period 1 to 36 months preceding the survey (determined by the date of interview and the date of birth of the child) and classifying them by the age (in ﬁ_{ve-year groups) of the mother at the time of birth}

(determined by the mother’s date of birth). The denominators of the rates are the number of woman-years lived in each of the speciﬁ_edﬁ_{ve-year groups during the 1}

to 36 months preceding the survey. Since only women who had ever married were interviewed in the IDHS [Indonesian DHS], the numbers of women in the denominators of the rates were inﬂ ated by factors calculated from information in the Household Question-naire on populations ever married in order to produce a count of all women. Never-married women are presumed not to have given birth (BPS and ORC Macro 2003: 43 [emphasis added]).

TABLE 3 Estimation of Total Number of Women Missing from the 2007 DHS Sample

Age Group 2007 DHS Numbers Recorded by Age

Group

2007 DHS Single Recorded

by Age Group

2007 DHS Proportion Single in Age Group

2005 Supas Proportion

Single in Age Group

Estimate of Missing Women

Adjusted Total 2007 DHS

Women

Dw Ds ds cs M Dw’

15–19 6,849 5,949 0.8686 0.9080 2,936 9,786 20–24 7,040 2,693 0.3825 0.5142 1,908 8,948 25–29 7,156 1,099 0.1535 0.1974 391 7,548 30–34 6,730 468 0.0695 0.0810 84 6,814 35–39 6,473 235 0.0364 0.0431 45 6,518 40–44 5,722 148 0.0259 0.0255 –2 5,720

45–49 5,127 96 0.0188 0.0197 5 5,132

All WRA 45,098 10,689 0.2370 0.2879 5,368 50,466

Source: Authors’ calculations.

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In tables 4 and 5 the published ASFRs (age-speciﬁ_{c fertility rates) and the}

cal-culated numbers of women recorded in the DHS household questionnaire are used to estimate the annual number of births for all women in 2002, assuming no decline in fertility over the period 2000–02. Then the annual fertility rates are re-calculated using the adjusted numbers of women who should have been listed in the DHS household questionnaire if the 2000 census marriage patterns had prevailed for the 2002–03 DHS.

Where the 2002–03 DHS Main Report showed a TFR of 2.57, adjusting the fer-tility rate for missing single women produces a TFR of 2.35 for the three-year period 2000–02 (centred on the point estimate for 2001). This is slightly above the trend line for census-type own-child calculations of fertility. A similar adjust-ment applied to the 2007 DHS, using the marital status distribution from the 2005 Supas, produces a TFR of 2.33 for the period from 2005–07 centred on the point estimate for 2006.

Figure 2 takes a ‘microscopic’ view of the fertility estimates. The adjusted DHS results are just above the census-type survey trend line. While they indicate a slow rate of decline, they are substantially below the unadjusted DHS levels and on track to reach replacement level fertility within a few years, assuming there is no reversal in mean age at marriage or levels of effective contraceptive use in the population. It is notable that the UN Population Division calculations used in the UNFPA’s State of the World’s Population 2008 tables (UNFPA 2008) are in line with the census and substantially below the published DHS results, because they have relied on the own-child trends in fertility. The UNFPA, like many analysts,

TABLE 4 Fertility Adjustments for the 2002–03 DHS

Adjustment of 2002-03 DHS ASFRsa_{and Total Fertility Rates (TFRs)}

Age of Mother

Current Fertility Rates

of 2002–03 DHS Final Report (2000–02)

Women Recorded in 2002–03

DHS

Annual Births Implied by Fertility Rates

and Number of Women in

2002–03

2002–03 DHS Women Adjusted for 2000 Census

Marital Status

Adjusted 2002–03 DHS Fertility Rates Using 2000 Census-based

Estimate of Women

15–19 51 6,845 349 9,138 38

20–24 131 6,422 841 6,965 121

25–29 143 6,134 877 6,519 135

30–34 99 5,484 543 5,910 92

35–39 66 5,127 338 5,376 63

40–44 19 4,361 83 4,719 18

45–49 4 3,500 14 4,169 3

Total 37,873 3,045 42,796

TFR 2.57 2.35

a _{ASFR = age-speci}ﬁ_{c fertility rate.} Source: Authors’ calculations.

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TABLE 5 Fertility Adjustments for the 2007 DHS

Adjustment of 2007 DHS ASFRsa_{and Total Fertility Rates (TFRs)}

Age of Mother

Current Fertility Rates of 2007

DHS Final Report (2005–07)

Women Recorded

in 2007 DHS

Annual Births Implied by

Fertility Rates and Number of

Women in 2007

2007 DHS Women Adjusted for

2005 Supas Marital

Status

Adjusted 2007 DHS Fertility Rates

Using 2005 Supas-based

Estimate of Women

15–19 51 6,849 349 9,786 36

20–24 135 7,040 950 8,948 106

25–29 134 7,156 959 7,548 127

30–34 108 6,730 727 6,814 107

35–39 65 6,473 421 6,518 65

40–44 19 5,722 109 5,720 19

45–49 6 5,127 31 5,132 6

Total 45,097 3,546 50,466

TFR 2.59 2.33

a _{ASFR = age-speci}ﬁ_{c fertility rate.} Source: Authors’ calculations.

F _J

1.5 2.0 2.5 3.0

1994 1996 1998 2000 2002 2004 2006 2008 2010

Unadjusted estimates from DHS 2002–03

Unadjusted estimates from DHS 2007 DHS 2002–03 adjusted

DHS 2007 adjusted

UNFPA 2008 estimate Census-type

own-child estimates

FIGURE 2 DHS Fertility Estimates Adjusted for Missing Single Women Compared with Other Published Estimatesa

a_{Unadjusted trend lines from 2002–03 and 2007 DHS and three census-type surveys are compared}

with fertility estimates for 2002–03 and 2007 DHS adjusted for missing single women, and with the latest published UNFPA estimates (UNFPA 2008).

Source: As for ﬁ_{gure 1.}

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have been puzzled by the increasing divergence of the trend lines of the different methodologies, but they have not been able to resolve the differences. The logic we have presented above allows a more complete understanding with which to resolve these contradictions.

DISCUSSION AND CONCLUSIONS

This is an important ﬁ_{nding for two reasons. First, the higher DHS results have}

been used to fuel criticisms of the Indonesian family planning program, and to raise alarm about the possibility of a baby boom or a population explosion related to the implementation of a decentralised system of health and family planning service delivery since 2000. The adjustments show that fertility is falling, and is on track to meet planning targets, and there is certainly no baby boom in prospect.

Second, the failure of the DHS to account for all women is not a problem unique to Indonesia. It has also been found in Bangladesh, where young women have

ﬂ_{ocked to jobs in textile factories and are staying in school for longer periods as a}

result of positive government policies. DHS-type surveys are likely to encounter difﬁ_{culties accounting for single women wherever the roles of women are subject}

to rapid change and households are in a state of ﬂ_{ux. This study serves as a}

wake-up call to national statisticians, who will need to develop new ways to ensure that the sample designs encompass non-standard households.

By their nature, the members of non-standard households are not likely to be correctly registered in national administrative listings. The Indonesian system of kartu keluarga (family cards) is the basis for the kartu tanda penduduk (KTP), which serves as a universal population card that every Indonesian adult citizen is required to carry. Young people attending academies or universities generally still carry a KTP based on their parents’ residence. This may be hundreds of kilo-metres away from the place where they are studying, and infrequently visited by the young people concerned, particularly as they establish independent work and residence. In surveys, many interviewers assume that the registration docu-mentation is correct, and they adopt notions of ‘legal residence’ rather than actual residence for identifying respondents. The result is a picture of populations that is much more static than the daily reality to be seen in the cities, industrial com-plexes and burgeoning tertiary education sector.

It has been put to us that if it is accepted that the fertility rates obtained from DHS data are over-estimated because of under-counting of single women, then the

marital fertility rates (MFRs, which do not include single women in the denomina-tor) obtained from the DHS should be comparable with the total marital fertility rates (TMFRs) obtained from census-type surveys. But the ‘missing single women’ issue is not the only problem with the fertility estimates. The marital fertility rates are also questionable. The MFR calculated from the DHS series for 2007 was 4.9, compared with 4.7 in 2002–03, and the TMFR from Supas 2005 was 5.2, despite the fact that the overall fertility rate from Supas was lower than the recent DHS esti-mates. The underlying explanations for this are complex. Much more work needs to be done on this issue, but that would be the topic of another paper.

We can conclude that there are serious problems with the methods used to estimate fertility in Indonesia. The own-child method of fertility calculation used in the decennial census, the inter-censal Supas and the annual Susenas produces

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par-ticular, a failure to record a substantial number of single women. Once adjusted for these missing women, the fertility rates for the three years prior to the 2002–03 and 2007 DHS are around 2.3, slightly above the National Family Planning Coor-dinating Board’s long-term goal of replacement-level fertility, but well below the levels assumed by many government planners.

REFERENCES

BPS (Badan Pusat Statistik – Statistics Indonesia) and ORC Macro (2003) Indonesian Demo-graphic and Health Survey 2002–2003, BPS and ORC Macro, Calverton MA.

Hull, Terence H. (1980) ‘Fertility decline in Indonesia: a review of recent evidence’, Bulletin of Indonesian Economic Studies 16 (2): 104–12.

Hull, Terence H. (1994) ‘Fertility decline in the New Order period: the evolution of popula-tion policy, 1965–1990’, in Indonesia’s New Order: The Dynamics of Socio-Economic Trans-formation, ed. Hal Hill, Allen and Unwin, Sydney: 123–45.

Hull, Terence H. (2007) ‘Formative years of family planning in Indonesia’, in The Global Family Planning Revolution, eds Warren C. Robinson and John A. Ross, World Bank, Washington DC: 235–56.

Hull, Terence H. and Dasvarma, G.L. (1988) ‘Fertility trends in Indonesia, 1967–1985’, Bul-letin of Indonesian Economic Studies 24 (1): 115–21.

Hull, Terence H. and Hull, Valerie J. (1997) ‘Culture, politics and family planning in Indo-nesia’, in The Continuing Demographic Transition, eds Gavin W. Jones, Robert Douglas, John C. Caldwell and Rennie D’Souza, Oxford University Press, Oxford: 383–421. UNFPA (United Nations Population Fund) (2008) State of World Population 2008: Reaching

Common Ground: Culture, Gender and Human Rights, UNFPA, New York.

Utomo, Budi, Hakim, Vera, Habsyah, Attas Hendartini, Irwanto, Tampubolon, Lamtiur, Wirawan, Dewa Nyoman, Jatiputra, Sujana, Siregar, Kemal Nazarudin, Tarigan, Luk-man Hakim, Affandi, Biran and Tafal, Zarﬁ_{el (2001)}_{Incidence and Social–Psychological}

Aspects of Abortion in Indonesia: A Community-Based Survey in 10 Major Cities and 6 Dis-tricts, Year 2000, United Nations Population Fund, Jakarta.

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RETURNING THE MISSING WOMEN TO THE DHS SAMPLE POPULATION

cs = (Ds + M)/(Dw + M) Ds + M = (cs x Dw)+ (cs x M) M – (cs x M) = cs x Dw – Ds M(1 – cs) = cs x Dw – Ds M = [(cs * Dw) – Ds]/(1 – cs)

This calculation is shown for the two most recent DHS surveys in tables 2 and 3. TABLE 2 Estimation of Total Number of Women Missing

from the 2002–03 DHS Sample Age Group 2002–03

DHS Numbers Recorded by Age

Group

2002–03 DHS Single Recorded

by Age Group

2002–03 DHS Proportion

Single in Age Group

2000 Census Proportion

Single in Age Group

Estimate of Missing Women

Adjusted Total 2002–03

DHS Women

Dw Ds ds cs M Dw’

15–19 6,715 5,735 0.8540 0.8927 2,423 9,138

20–24 6,738 2,776 0.4120 0.4312 227 6,965

25–29 6,302 870 0.1380 0.1667 217 6,519

30–34 5,844 345 0.0590 0.0695 66 5,910

35–39 5,349 160 0.0300 0.0349 27 5,376

40–44 4,704 99 0.0210 0.0241 15 4,719

45–49 4,170 83 0.0200 0.0198 –1 4,169

All WRA 39,822 10,068 0.2500 0.2870 2,974 42,796

Source: Authors’ calculations.

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ADJUSTING THE FERTILITY RATES FOR MISSING SINGLE WOMEN The census-based estimate of missing women allows the reconstruction of age-speciﬁ_{c and total fertility rates for the 2002–03 and 2007 DHS. According to the} DHS main report for 2002–03 (BPS and ORC Macro 2003), the method used for calculating fertility rates indicates that:

Numerators of the ASFRs [age-speciﬁ_{c fertility rates] are calculated by summing}

(determined by the mother’s date of birth). The denominators of the rates are the number of woman-years lived in each of the speciﬁ_edﬁ_{ve-year groups during the 1}

to 36 months preceding the survey. Since only women who had ever married were interviewed in the IDHS [Indonesian DHS], the numbers of women in the denominators of the rates were inﬂ ated by factors calculated from information in the Household Question-naire on populations ever married in order to produce a count of all women. Never-married women are presumed not to have given birth (BPS and ORC Macro 2003: 43 [emphasis added]).

TABLE 3 Estimation of Total Number of Women Missing from the 2007 DHS Sample

Age Group 2007 DHS Numbers Recorded by Age

Group

2007 DHS Single Recorded

by Age Group

2007 DHS Proportion Single in Age Group

2005 Supas Proportion

Single in Age Group

Estimate of Missing Women

Adjusted Total 2007 DHS

Women

Dw Ds ds cs M Dw’

15–19 6,849 5,949 0.8686 0.9080 2,936 9,786

20–24 7,040 2,693 0.3825 0.5142 1,908 8,948

25–29 7,156 1,099 0.1535 0.1974 391 7,548

30–34 6,730 468 0.0695 0.0810 84 6,814

35–39 6,473 235 0.0364 0.0431 45 6,518

40–44 5,722 148 0.0259 0.0255 –2 5,720

45–49 5,127 96 0.0188 0.0197 5 5,132

All WRA 45,098 10,689 0.2370 0.2879 5,368 50,466

Source: Authors’ calculations.

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In tables 4 and 5 the published ASFRs (age-speciﬁ_{c fertility rates) and the} cal-culated numbers of women recorded in the DHS household questionnaire are used to estimate the annual number of births for all women in 2002, assuming no decline in fertility over the period 2000–02. Then the annual fertility rates are re-calculated using the adjusted numbers of women who should have been listed in the DHS household questionnaire if the 2000 census marriage patterns had prevailed for the 2002–03 DHS.

TABLE 4 Fertility Adjustments for the 2002–03 DHS Adjustment of 2002-03 DHS ASFRsa_{and Total Fertility Rates (TFRs)} Age of

Mother

Current Fertility Rates

of 2002–03 DHS Final Report (2000–02)

Women Recorded in 2002–03

DHS

Annual Births Implied by Fertility Rates

and Number of Women in

2002–03

2002–03 DHS Women Adjusted for 2000 Census

Marital Status

Adjusted 2002–03 DHS Fertility Rates Using 2000 Census-based

Estimate of Women

15–19 51 6,845 349 9,138 38

20–24 131 6,422 841 6,965 121

25–29 143 6,134 877 6,519 135

30–34 99 5,484 543 5,910 92

35–39 66 5,127 338 5,376 63

40–44 19 4,361 83 4,719 18

45–49 4 3,500 14 4,169 3

Total 37,873 3,045 42,796

TFR 2.57 2.35

a _{ASFR = age-speci}ﬁ_{c fertility rate.}

Source: Authors’ calculations.

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TABLE 5 Fertility Adjustments for the 2007 DHS Adjustment of 2007 DHS ASFRsa_{and Total Fertility Rates (TFRs)} Age of

Mother

Current Fertility Rates of 2007

DHS Final Report (2005–07)

Women Recorded

in 2007 DHS

Annual Births Implied by

Fertility Rates and Number of

Women in 2007

2007 DHS Women Adjusted for

2005 Supas Marital

Status

Adjusted 2007 DHS Fertility Rates

Using 2005 Supas-based

Estimate of Women

15–19 51 6,849 349 9,786 36

20–24 135 7,040 950 8,948 106

25–29 134 7,156 959 7,548 127

30–34 108 6,730 727 6,814 107

35–39 65 6,473 421 6,518 65

40–44 19 5,722 109 5,720 19

45–49 6 5,127 31 5,132 6

Total 45,097 3,546 50,466

TFR 2.59 2.33

a _{ASFR = age-speci}ﬁ_{c fertility rate.}

Source: Authors’ calculations.

F _J

1.5 2.0 2.5 3.0

1994 1996 1998 2000 2002 2004 2006 2008 2010 Unadjusted

estimates from DHS 2002–03

Unadjusted estimates from DHS 2007 DHS 2002–03 adjusted

DHS 2007 adjusted

UNFPA 2008 estimate Census-type

own-child estimates

FIGURE 2 DHS Fertility Estimates Adjusted for Missing Single Women

Compared with Other Published Estimatesa

a_{Unadjusted trend lines from 2002–03 and 2007 DHS and three census-type surveys are compared} with fertility estimates for 2002–03 and 2007 DHS adjusted for missing single women, and with the latest published UNFPA estimates (UNFPA 2008).

Source: As for ﬁ_{gure 1.}

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DISCUSSION AND CONCLUSIONS

This is an important ﬁ_{nding for two reasons. First, the higher DHS results have} been used to fuel criticisms of the Indonesian family planning program, and to raise alarm about the possibility of a baby boom or a population explosion related to the implementation of a decentralised system of health and family planning service delivery since 2000. The adjustments show that fertility is falling, and is on track to meet planning targets, and there is certainly no baby boom in prospect.

Second, the failure of the DHS to account for all women is not a problem unique to Indonesia. It has also been found in Bangladesh, where young women have fl_{ocked to jobs in textile factories and are staying in school for longer periods as a} result of positive government policies. DHS-type surveys are likely to encounter diffi_{culties accounting for single women wherever the roles of women are subject} to rapid change and households are in a state of fl_{ux. This study serves as a} wake-up call to national statisticians, who will need to develop new ways to ensure that the sample designs encompass non-standard households.

By their nature, the members of non-standard households are not likely to be correctly registered in national administrative listings. The Indonesian system

of kartu keluarga (family cards) is the basis for the kartu tanda penduduk (KTP),

which serves as a universal population card that every Indonesian adult citizen is required to carry. Young people attending academies or universities generally still carry a KTP based on their parents’ residence. This may be hundreds of kilo-metres away from the place where they are studying, and infrequently visited by the young people concerned, particularly as they establish independent work and residence. In surveys, many interviewers assume that the registration docu-mentation is correct, and they adopt notions of ‘legal residence’ rather than actual residence for identifying respondents. The result is a picture of populations that is much more static than the daily reality to be seen in the cities, industrial com-plexes and burgeoning tertiary education sector.

It has been put to us that if it is accepted that the fertility rates obtained from DHS data are over-estimated because of under-counting of single women, then the

marital fertility rates (MFRs, which do not include single women in the

denomina-tor) obtained from the DHS should be comparable with the total marital fertility rates (TMFRs) obtained from census-type surveys. But the ‘missing single women’ issue is not the only problem with the fertility estimates. The marital fertility rates are also questionable. The MFR calculated from the DHS series for 2007 was 4.9, compared with 4.7 in 2002–03, and the TMFR from Supas 2005 was 5.2, despite the fact that the overall fertility rate from Supas was lower than the recent DHS esti-mates. The underlying explanations for this are complex. Much more work needs to be done on this issue, but that would be the topic of another paper.

(6)

a steady downward trend of fertility from the 1970s through to the present day. In contrast, the pregnancy history techniques used by the DHS yielded lower fertil-ity than the census prior to 2000, and increasingly higher levels since then. The discrepancy appears to be caused by the failure of the DHS sample and interview methods to capture a true proﬁ_{le of all women of reproductive ages and, in} par-ticular, a failure to record a substantial number of single women. Once adjusted for these missing women, the fertility rates for the three years prior to the 2002–03 and 2007 DHS are around 2.3, slightly above the National Family Planning Coor-dinating Board’s long-term goal of replacement-level fertility, but well below the levels assumed by many government planners.

REFERENCES

BPS (Badan Pusat Statistik – Statistics Indonesia) and ORC Macro (2003) Indonesian Demo-graphic and Health Survey 2002–2003, BPS and ORC Macro, Calverton MA.

Hull, Terence H. (1980) ‘Fertility decline in Indonesia: a review of recent evidence’, Bulletin of Indonesian Economic Studies 16 (2): 104–12.

Hull, Terence H. (1994) ‘Fertility decline in the New Order period: the evolution of popula-tion policy, 1965–1990’, in Indonesia’s New Order: The Dynamics of Socio-Economic Trans-formation, ed. Hal Hill, Allen and Unwin, Sydney: 123–45.

Hull, Terence H. (2007) ‘Formative years of family planning in Indonesia’, in The Global Family Planning Revolution, eds Warren C. Robinson and John A. Ross, World Bank, Washington DC: 235–56.

Hull, Terence H. and Dasvarma, G.L. (1988) ‘Fertility trends in Indonesia, 1967–1985’, Bul-letin of Indonesian Economic Studies 24 (1): 115–21.

Hull, Terence H. and Hull, Valerie J. (1997) ‘Culture, politics and family planning in Indo-nesia’, in The Continuing Demographic Transition, eds Gavin W. Jones, Robert Douglas, John C. Caldwell and Rennie D’Souza, Oxford University Press, Oxford: 383–421. UNFPA (United Nations Population Fund) (2008) State of World Population 2008: Reaching

Common Ground: Culture, Gender and Human Rights, UNFPA, New York.

Aspects of Abortion in Indonesia: A Community-Based Survey in 10 Major Cities and 6 Dis-tricts, Year 2000, United Nations Population Fund, Jakarta.

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Terence H. Hull & Wendy Hartanto

RESOLVING CONTRADICTIONS

IN INDONESIAN FERTILITY ESTIMATES

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Manajemen | Fakultas Ekonomi Universitas Maritim Raja Ali Haji 0007491042000205187

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