metadata: authors: 0: author: Portner, Claus C
1: author: Beegle, Kathleen
2: author: Christiaensen, Luc
count: World
docna: 0: docna: Family planning and fertility : estimating
            program effects using cross-sectional data
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Reproductive Health,Early Child and Children's Health,Educational Sciences,Family Planning Research
teratopic: Health, Nutrition and Population,Rural Development
abstracts: cdata!: Although reproductive health advocates
            consider family planning programs the intervention of choice
            to reduce fertility, there remains a great deal of
            skepticism among economists as to their effectiveness,
            despite little rigorous evidence to support either position.
            This study explores the effects of family planning in
            Ethiopia using a novel set of instruments to control for
            potential non-random program placement. The instruments are
            based on ordinal rankings of area characteristics, motivated
            by competition between areas for resources. Access to family
            planning is found to reduce completed fertility by more than
            one child among women without education. No effect is found
            among women with some formal schooling, suggesting that
            family planning and formal education act as substitutes, at
            least in this low-income, low-growth setting. This provides
            support to the notion that increasing access to family
            planning can provide an important, complementary entry point
            to kick-start the process of fertility reduction.
repnme: repnme: Family planning and fertility : estimating
            program effects using cross-sectional data
display_title: Family planning and fertility :
            estimating program effects using cross-sectional data
listing_relative_url: /research/2011/09/15151354/family-planning-fertility-estimating-program-effects-using-cross-sectional-data
topicv3: Infant and Child Mortality m1326544 1446,Education m1327048 470,Family Planning m1327501 1452,Contraceptives m1327726 1453,Health m1504661 3480
publishtoextweb_dt: 2011-09-01T00:00:00Z
historic_topic: Health, Nutrition and Population,Rural Development
no_of_pages: 33
model_extractions: WPS5812 Policy Research Working Paper 5812 Family Planning and Fertility Estimating Program Effects Using Cross-Sectional Data _Claus C Pörtner Kathleen Beegle Luc Christiaensen_ The World Bank Development Research Group Poverty and Inequality Team September 2011
Policy Research Working Paper 5812

# **Abstract**

Although reproductive health advocates consider family planning programs the intervention of choice to reduce fertility, there remains a great deal of skepticism among economists as to their effectiveness, despite little rigorous evidence to support either position. This study explores the effects of family planning in Ethiopia using a novel set of instruments to control for potential nonrandom program placement. The instruments are based on ordinal rankings of area characteristics, motivated by competition between areas for resources. Access to family planning is found to reduce completed fertility by more than one child among women without education. No effect is found among women with some formal schooling, suggesting that family planning and formal education act as substitutes, at least in this low-income, low-growth setting. This provides support to the notion that increasing access to family planning can provide an important, complementary entry point to kick-start the process of fertility reduction.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kbeegle@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 5812

# **Abstract**

Although reproductive health advocates consider family planning programs the intervention of choice to reduce fertility, there remains a great deal of skepticism among economists as to their effectiveness, despite little rigorous evidence to support either position. This study explores the effects of family planning in Ethiopia using a novel set of instruments to control for potential nonrandom program placement. The instruments are based on ordinal rankings of area characteristics, motivated by competition between areas for resources. Access to family planning is found to reduce completed fertility by more than one child among women without education. No effect is found among women with some formal schooling, suggesting that family planning and formal education act as substitutes, at least in this low-income, low-growth setting. This provides support to the notion that increasing access to family planning can provide an important, complementary entry point to kick-start the process of fertility reduction.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kbeegle@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **1 The Challenge of Measuring the Effects of Family Planning**

Many countries, especially in Africa, continue to have high fertility rates and most of the predicted increase in the world’s population until 2100 comes from these high-fertility countries (United Nations 2011). High fertility has potentially significant implications for women’s and children’s health as well as for economic development more broadly. Motivated by these concerns, policy discussions often focus on the role of family planning programs in helping individuals manage their fertility. Standard economic models of fertility decisions suggest, however, that many people in developing countries have little incentive to reduce the number of children. The cost of women’s time is low and children are potentially productive on the family farm or can serve as old age security. Furthermore, there is a lack of empirical evidence that family planning programs are effective. As a result, rather than focusing on the supply of family planning, economists instead emphasise factors that influence fertility demand such as household poverty and girls’ schooling (Pritchett 1994; Das Gupta, Bongaarts, Cleland and Joshi 2011).

The lack of convincing empirical evidence showing that family planning programs reduce fertility may be attributed to the challenge of measuring their impact. First, studies of family planning programs have often covered periods of rapid economic development and fertility decline, making it difficult to isolate the effects of family planning programs. Second, existing studies have largely ignored heterogeneous impacts, especially with regard to how family planning affects women with different education levels. Evidence from the US shows that although better-educated women are not more efficient users of modern contraceptives than less-educated women, better-educated women are more efficient at using “ineffective” contraceptive methods such as withdrawal or rhythm (Rosenzweig and Schultz 1989). The effect of family planning is therefore conceivably stronger the lower the education levels and family planning may thus substitute for education in reducing fertility at lower education levels. Finally, rigorous study is hampered by the challenge of non-random program placement (Rosenzweig and Wolpin 1986; Pitt, Rosenzweig and Gibbons 1993). If the government places programs in areas that are more “receptive” to reducing fertility, simply comparing fertility in areas with and without family planning will overestimate the impact of expanding the program. If the government places programs in high fertility areas and information on prior fertility is not available, comparing fertility across areas will underestimate the effectiveness of the program. Without information on the placement process it is difficult to assess the direction of the potential bias.
Technically, the non-random program placement problem could be overcome by randomising the allocation of programs and comparing the outcomes of interest between treatment and control areas. Although theoretically superior, such experiments have a number of drawbacks in practice. First, there are conceptual concerns about the external validity of experiments, which are often small in scale. Second, because of the cumulative nature of fertility, an experiment has to run for a substantial period of time before one can assess the effect on fertility. Short-run effects may simply reflect changes in spacing-patterns rather than changes in the overall number of children. When run for too short a period, experiments may also be prone to short-term health scares such as the one experienced by an experiment in Zambia (Ashraf, Fink and Weil 2010). Probably the best-known example of a family planning program experiment comes from Matlab, Bangladesh. It began in 1978, and by 1984, fertility was 24 percent lower in the villages that received the intensive family planning program compared to the villages that received only the standard family planning program (Phillips, Simmons, Koenig and Chakraborty 1988). More recent work using the same villages with data until 1996 finds a decline in fertility of about 15 percent in the program villages compared with the control villages (Sinha 2005; Joshi and Schultz 2007). These results reflect, however, a level of program intervention and intensity unlikely to be sustainable (Pritchett 1994). Per woman reached, the program cost 35 times more than the standard government family planning program and each averted birth cost USD 180 in 1987, 1.2 times GDP per capita at the time.

In short, although potentially superior from an analytical point of view, it is difficult to run family planning program experiments for a sufficiently long period and on a sufficiently large scale to generate the necessary external validity. At the same time, non-randomized family planning programs have been in place for a substantial period of time in many areas and it is cost-effective to make optimal use of the information that can be derived from these programs. If longitudinal data have been collected in parallel with the introduction of the program, program effects can be estimated using fixed effects, provided there are a sufficient number of areas that receive a program between the (minimum) two data points and provided the period between the surveys is long enough. Examples include studies of the family planning programs in Indonesia that found a negative (but not statistically significant) effect on fertility, responsible for only 4 to 8 percent of the decline in fertility from 1982 to 1987 (Pitt et al. 1993; Gertler and Molyneaux 1994).

Often longitudinal data are not available or cover too short a period, in practice limiting researchers 1
to using cross-sectional data for analysing the effects of family planning programs.<sup>1</sup> To address program placement challenges in such contexts, one approach is to use variables that influence program placement, but that are unrelated to individual fertility as done for Tanzania (Angeles et al. 1998).<sup>2</sup> A woman in Tanzania exposed to family planning throughout her fertile lifespan is found to have 4.13 children compared with 4.71 children in the absence of family planning programs.<sup>3</sup> Lingering concerns remain, however, that some of the variables used to identify placement (such as child mortality levels and the presence of other family planning services) may also be correlated with unobservable variables that influence both placement and fertility decisions.

To address non-random program placement when evaluating the effect of family planning programs in Ethiopia, this study exploits detailed information on area characteristics and the geographical allocation decisions of the family planning programs. The identification strategy is novel in that it draws on the insight that areas compete for limited resources and that ordinal rankings (as opposed to cardinal scores of the ranking criteria) are often used to discern between competing demand.<sup>4</sup> To fix ideas, assume that there are only three areas, A, B and C, that compete for resources from the government. Using the extent of urbanization as an example, we expect that the degree of urbanization of area A will affect fertility in area A, but that the degree of urbanization of areas B and C will have little or no effect on fertility in area A.<sup>5</sup> Because the three areas compete for resources the _relative_ degree of urbanization may, however, affect the program placement decision.<sup>6</sup> This opens up the opportunity to use rankings as identifying variables (as opposed to levels). Imagine that urbanization is highest in area A, followed by B and C, and that the more urbanised an area is, the more likely it is to receive a program. Identification is achieved because the _rank_ of an area primarily depends on other areas’ absolute value of the ranked variable. Specifically, assume that

> 1There are also additional problems with using fixed effects, such as measurement error bias. For a discussion of this and other problems in the study of family planning see, for example, Angeles, Guilkey and Mroz (1998). 2Also using cross-sectional information, Miller (2010) found that Columbia’s family planning program, Profamilia, reduced lifetime fertility by around half a child, equivalent to only 10 percent of the the sharp decline in fertility over the period the program was implemented. As the process of allocating programs were assumed to be arbitrary, these results may, however, be under- or over-estimates.

> 3For Indonesia, Angeles, Guilkey and Mroz (2005) report using the same approach, but found no evidence of non-random placement of family planning programs.

> 4The approach closest to ours is by Pitt and Menon (2010), who used average characteristics of other areas, such as education level, for their instruments. A potential issue with their approach is that if network effects are important these averages might not serve as valid instruments.

> 5The cost of children may be higher in more urbanized areas reducing fertility.

> 6It may, for example, be less costly for the government to place programs in areas that are more urban because of easier access.

2
the underlying value for area B increases. Unless it increases enough to surpass area A the ranking will not change even though the increase in the value of the ranked characteristics may directly affect fertility.

There are two major advantages to this approach. First, the instruments are easy to create from readily available secondary data like a census or, possibly, even from the primary data set itself. Secondly, the instruments are intuitive in that they mimic expectations about the underlying resource allocation process. In other words, ranks likely reflect what policy makers care about when distributing programs, but are not directly related to fertility.<sup>7</sup> Furthermore, the process is agnostic about which characteristics actually determine placement.

The paper makes three contributions to the literature. First, it uses a novel but widely available set of instruments to identify the effect of family planning on fertility. Second, it examines the effect of a family planning program of limited means in a very poor setting that experienced little economic growth during much of the period of study.<sup>8</sup> As seen above, the scant evidence in the literature so far comes from very ambitious, costly programs (Matlab) or dynamic macro-economics settings (Indonesia and Colombia). Yet, it is in more stagnant low-income settings that high fertility often poses the more important obstacle to accelerating development. Third, this study focuses on how the effect of access to family planning is critically dependent on the education level of women.

We find that access to family planning in Ethiopia has a statistically significant and economically large impact on fertility of women with no schooling, while there are no discernible effects of family planning on fertility for women who have ever attended school. The reduction in completed fertility at more than 1 child is large compared to other studies.<sup>9</sup> These insights have important policy implications as 65 percent of women 30 years or older never attended school in Ethiopia (Central Statistical Authority of Ethiopia 2007). The reduction in fertility is concentrated among the youngest women and the oldest women indicating that access to family planning leads to a postponement of birth among younger women and a reduction in completed fertility through earlier completion of child bearing. Further analyses provide support to the direct effect of access to family planning on reduced fertility, as opposed to the indirect effect through improved child health and survival resulting from availability of the health facilitites in which the family planning

> 7A potential issue is that the rankings chosen may also explain other the distribution of other programs that may themselves affect fertility. The most obvious case is the placement of schools.

> 8Ethiopia’s GNI per capita in PPP went from just over USD 300 in 1980 to USD 480 in 2003.

> 9Unlike here, the results from these other studies represent an average across women with and without schooling.

3
services are offered.

# **2 Ethiopia – A High Fertility Country**

In 2005 Ethiopia’s current total fertility rate (TFR), the predicted number of children a woman will have during her reproductive life, was estimated at 5.4, in effect adding about 2 million people a year to Ethiopia’s population of about 74 million in 2007.<sup>10</sup> Population growth resulting from such high fertility is believed to come at a high cost to living standards. Already in 1999, the average land holding per rural person was estimated at only 0.21 ha, down from 0.5 ha in the 1960s. This, coupled with lack of agricultural productivity growth, has contributed to a (rapidly growing) core group of five to seven million who are chronically food insecure. Spatial resettlement of about two million people from the highlands to the lowlands, adopted in 2003 as one of a series of policy measures to tackle the problem of chronic food insecurity in many highland weredas, is unlikely to provide a sustainable solution (World Bank 2007). The high fertility and population growth rates are not unique to Ethiopia. There are about 20 countries that have a TFR higher than 5, almost all very poor (World Bank 2010).

Cognizant of the challenge population growth posed, the government of Ethiopia adopted a population policy in 1993. The overall objective was to harmonize the country’s population growth rate with that of the economy, specifically to achieve a TFR of 4 by 2015. One of the major strategies to do so was to increase the contraceptive prevalence rate to 44 percent by 2015 by expanding access to family planning (Transitional Government of Ethiopia 1993). Ethiopia has historically had among the lowest contraceptive prevalence rates in Sub-Saharan Africa. According to the first-ever national survey on fertility and family planning in 1990 only 4 percent of women of reproductive age were using some family planning methods and less than 3 percent were using modern contraceptives (Transitional Government of Ethiopia 1993). Results from the 2005 Demographic and Health Survey (DHS) show that this increased to 15 percent of married women using some method of contraception in 2005, with the majority relying on a modern method (Central Statistical Authority of Ethiopia and ORC Macro 2006).<sup>11</sup> The most commonly used modern methods are injectable

> 10There are substantial differences between Addis Ababa and the rest of Ethiopia. In Addis Ababa, the estimated total fertility rate is below replacement (Gurmu and Mace 2008).

> 11Other studies have found use rates in line with the DHS number or higher (Pathfinder International Ethiopia 2004; Essential Services for Health in Ethiopia 2005). The Essential Services for Health in Ethiopia (ESHE) conducted three region-wide surveys in SNNP, Oromia, and Amhara regions between 2003 and 2004. The studies showed prevalence

4
contraceptives at 10 percent and oral contraceptives at 3 percent. Use of other modern methods such as condoms, female sterilization, and IUD accounted for less than 1 percentage point each.

# **3 Empirical Methodology**

We use three data sources to evaluate the impact of the availability of contraception on fertility: first, a contraceptive use survey collected under the auspices of Pathfinder International – Ethiopia (Pathfinder International Ethiopia 2005); second, a health facility survey collected to augment the Pathfinder survey; third, the 1994 Census of Ethiopia. The Pathfinder survey was collected in September 2004 and covered Ethiopia’s four largest regions, which together are home to 86 percent of the population:<sup>12</sup> Amhara, Oromia, SNNPR, and Tigray. It provides information on the level of knowledge, attitude, and practice of family planning. The survey used a stratified multi-stage sampling design in four regions combined with urbanrural residence for each region. Weredas (districts) constituted the primary sampling units. In total 58 weredas were sampled and 176 communities (PA/kebeles) within these districts were surveyed, 113 rural and 63 urban.

To collect information on health facilities, family planning services and Community Based Reproductive Health (CBRH) programs available in the 58 Pathfinder survey districts a Wereda Health Facility and CBRH (WHFC) survey was conducted in July 2005. The information was provided by health departments or social sector departments. In each wereda, general questions were asked regarding the entire wereda and specific questions were asked about the communities covered by the Pathfinder Survey. Five communities that could not be accurately identified are dropped, leaving 171 communities. Furthermore, uncertainty arose about whether some of the urban communities surveyed in the WHFC survey were accurately linked to the Pathfinder survey (26 in total) and, to be cautious, these were also dropped. After merging with the rates for modern contraceptives to be 14 percent, 16 percent and 14 percent in the Amhara, Oromia, and SNNP regions. In September 2004, Pathfinder International Ethiopia conducted a survey on family planning and fertility in Amhara, Oromia, SNNP, and Tigray regions. The use of modern methods was the highest in Oromia (24 percent) followed by Tigray (20.4 percent), Amhara (20.5 percent) and SNNP region (17.1 percent) (Pathfinder International Ethiopia 2004).

12Ethiopia is divided into 9 regions, with each region further divided into zones; there were 68 zones in Ethiopia at the time of the Pathfinder survey. Each zone is divided into weredas (or woredas, which correspond to districts). Each wereda is divided into a combination of Kebeles in urban areas and Peasant Associations (PAs) in rural areas. Kebeles and PAs are the smallest administrative unit of local government.

5
Pathfinder survey<sup>13</sup> , the final sample consists of 109 communities (91 rural and 18 urban) and 2,700 women, of which just over 2,000 remain after excluding never married and never partnered women.

# **3.1 Estimation Strategy**

Our approach is to first estimate the determinants of the decision on whether to place a program _P_ in area _k_ and then to estimate the effect of the program on the individual outcome _yi_ (fertility). The equations are: where _X k_ is a vector of exogenous variables that are area specific, and _Zk_ is a vector of area specific exogenous variables that affect program placement but do not affect the individual fertility decision. Individual characteristics are captured by _X i_ . Whether a program is available in the area, _Pk_ , is the main variable of interest and _β_ 3 measures the program’s impact on the outcome of interest. The main outcome of interest is the number of children ever born. In addition, to probe into the channels through which family planning affects fertility and to distinguish its effect from the presence of the health facilities through which the family planning services are provided, we estimate the effects of family planning on various measures of child mortality, recent birth or pregnancy, and whether last birth or pregnancy was wanted. Unfortunately, lack of birth histories in the data means that we cannot examine how the timing of births responds to family planning.

Using a modified two-stage method, _β_ 3 can be estimated under relatively relaxed conditions (Wooldridge 2002, Chapter 18). The first stage estimates the determinants of the placement decision using a Probit model and the fitted probabilities of having a program are calculated. In the second stage, the individual decision equation is estimated by IV, using the fitted probabilities from the first stage for _Pk_ , **Xk** and **Xi** as instruments. Identification comes from _Zk_ in the first stage. An attractive feature of this approach is that the results are robust even if the placement equation is not correctly specified (Wooldridge 2002, p. 623).

In addition to the instrumental variable results, for comparison we also present OLS results, where

> 13Because of incompleteness in the WHFC survey and the census, another 25 communities could not be merged to

6
Equation (2) is estimated under the assumption that there is no correlation between program placement and unobserved area characteristics. All regressions take into account the multi-stage sampling design and apply sample weights. Access to family planning is measured for each of the 109 communities in which the women in the sample reside and standard errors would be biased downwards if no correction is applied to account for this clustering (Moulton 1990). Standard errors for both OLS and IV regressions are therefore clustered at the community level.

# **3.2 Family Planning Programs and Placement**

For sample communities we have information on whether a health facility is available, when the facility opened, whether family planning services are offered at the health facility and, if so, the year it began offering family planning services. There are health facilities that do not offer family planning, but family planning is never offered outside of health facilities during the period we study.

A community is considered to have access to family planning if there is either a facility with family planning in the community or the closest facility with family planning is less than 40 kilometers away. Although the distance may appear long, most women only visit the family planning program every three months, either to pick up more pills or renew the injection. Also, there is only one community that is 40 kilometers away from the closest family planning program; the second most remote community is 30 kilometers away. For urban communities the maximum distance to the closest facility is 3.5 kilometers. The average distance for communities without a health facility with family planning is around 10 kilometers. Women in rural communities are assumed to have access to family planning the year family planning services were first offered in that administrative area. For urban areas we use the year the closest health facility began offering family planning services whether or not the health facility is located in the urban area or a neighboring area.

The definition of access leads to two potential issues. First, it is not possible to estimate the extent to which distance to a family planning program is an important factor in use. Although our conversations with providers indicate that many of their clients do, indeed, travel substantial distances to receive family planning services, nonetheless, increasing distances must at some point lower use rates. If our definition leads to the inclusion of family planning facilities that are not actually used because they are too far away the result will be an underestimate of the effect of access. Secondly, we only have information on access to the closest family planning program. Some areas may be coded as only having had family planning services 7
for a relatively short period if a new health center recently opened in the area, even though the neighboring area already offered family planning services. Similarly, it is possible that changes in facility type might not be reflected in the start date, i.e. a change from clinic to centre that results in access to a wider set of services. These issues are also likely to result in a downward bias of the estimated effect of access.

<!-- Start of picture text --> Health Facilities<br>Family Planning<br>CBRH Programs<br>1965 1970 1975 1980 1985 1990 1995 2000 2005<br>Source: The World Bank Wereda Health Facility and CBRH survey.<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Figure 1: Percent Communities with access to Health Facilities, Family Planning or CBRHA Figure 1 shows the development in access to health facilities, family planning services, and CBRH programs over the 30 years prior to our household survey.<sup>14</sup> We focus on the effects of having access to family planning services in 1990, when approximately 25 percent of all communities in the sample had access to a family planning program.<sup>15</sup> The prevalence of programs was essentially constant the decade

> 14The introduction of CBRH programs is an interesting development, but happened too recently and in too many areas simultaneously to allow for an analysis of long-term effects on fertility. Access to health facilities and family planning services track each other closely making it impossible to estimate whether there is an independent effect of access to health facilities.

> 15Unfortunately, there is not enough power to predict years of community access as opposed to dichotomous access

8
before 1990. A majority of the women who had access in 1990 therefore have been exposed to the program for up to 25 years (depending on the woman’s age) at the time of the survey allowing sufficient time to identify long-term effects on fertility.

There was a substantial expansion in access to health facilities and family planning programs after 1990 with coverage going from 50 to 100 percent over from 1997 to 2005. That is, our “control” or untreated women with respect to family planning access did, in fact, almost all have access by 2004. On average, the communities of these women had 5 years of access by 2004 compared with 25 years for our “treated” communities. The effect of this increase in program coverage is to bias downward the estimated effect of the program.

Table 1 shows the descriptive statistics for the dependent variable and the explanatory variables used for estimating program placement. There are two categories of explanatory variables. First, variables that affect both placement and individual fertility decisions. Secondly, the set of instrumental variables that are assumed to only affect the program placement.

The first set includes the district level variables: the total area of the district, the average yearly rainfall and its square, and the elevation of the district and its square. At the community level the variables include a dummy for whether it is an urban area or, in other words, a kebele, and a dummy for whether there is a market in the area. The accessibility of the area is captured by two variables: whether the area can be reached by car all year or only during the dry season (the excluded category is no road access).

We use the relative rank of zonal and community variables as instruments in the placement decision estimation. Each variable is ranked with 1 assigned to the smallest value and ties are assigned the same value, so that the sum of the ranks is preserved. That is, for a given variable an observation’s rank is 1 plus the number of values that are lower than that observation’s value. Five variables are ranked at the zonal level for the 36 zones in the sample and one variable is ranked within zones. For zones, the ranked variables are the size of the population, the degree of urbanization (measured as the percent of the population who live in urban areas) and the percentage of adults with various levels of education (none, primary or 1-6 years, and 7-8 years). These ranks are all based on data from the 1994 Census. The means of the rankings are not all equal to 19 because not all zones have the same number of communities and because weights are applied to calculate the means. The communities are ranked within each zone by their population size. The maximum in 1990. We do test robustness of the results to other cut-off years.

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Table 1: Descriptive Statistics for Program Placement

||Mean|Standard<br>Error|Min|Max|
|---|---|---|---|---|
|**Dependent Variable**|||||
|Family planning program in 1990 (ratio)|0_._19|0_._39|0_._00|1_._00|
|**Zone characteristics**|||||
|Percent with no education in zone|79_._20|5_._94|30_._07|94_._83|
|Percent with 1-6 years of education in zone|11_._62|5_._24|2_._05|37_._45|
|Percent with 7-8 years of education in zone|2_._39|1_._24|0_._20|10_._70|
|**District characteristics**|||||
|Total area (square km/100)|14_._53|9_._57|0_._00|53_._81|
|Avg. yearly rainfall (mm/100)|11_._91|4_._05|4_._46|20_._48|
|Avg. yearly rainfall squared (mm/100)<sup>2</sup>/100|1_._58|1_._02|0_._20|4_._19|
|Elevation (m/100)|19_._68|4_._25|8_._65|29_._26|
|Elevation squared (m/100)<sup>2</sup>/100|4_._05|1_._65|0_._75|8_._56|
|**Community characteristics**|||||
|Urban (rate)|0_._04|0_._19|0_._00|1_._00|
|Market in area (rate)|0_._35|0_._48|0_._00|1_._00|
|Road access - all year (rate)|0_._41|0_._49|0_._00|1_._00|
|Road access - dry season (rate)|0_._39|0_._49|0_._00|1_._00|
|Population / 1000|3_._23|5_._28|0_._35|96_._94|
|**Ranking of Zones (Nationally)**|||||
|Zone population rank|21_._76|9_._37|1_._00|36_._00|
|Zone urbanisation rank|19_._08|8_._76|1_._00|36_._00|
|Zone percent with no education rank|18_._88|9_._31|1_._00|36_._00|
|Zone Percent with 1-6 years of education rank|17_._21|9_._73|1_._00|36_._00|
|Zone Percent with 7-8 years of education rank|18_._59|9_._46|1_._00|36_._00|
|**Ranking of Communites (Within Zones)**|||||
|Community population rank|2_._27|1_._40|1_._00|10_._00|
|Number of communities||109|||

**Notes.** Estimated means and standard errors based on sample frame and weights. The ranking of zones is based on the sample, with 1 assigned to the smallest value and ties are assigned the same value, so that the sum of the ranks is preserved. For communities the ranking is based on the sample within a zone.

number of communities within a zone is ten, while for five zones there is only one community in the survey.

Although it would be advantageous to have more information at the community level, the set of possible variables is limited by the lack of information available at that level from published census reports.

# **3.3 Individual Data**

As discussed earlier, we surmise that the effect of family planning on fertility is highly dependent on a woman’s schooling. The lower a woman’s education, the more likely she is to benefit from access to family 10
planning services (Rosenzweig and Schultz 1989). This is especially so in Ethiopia where injectable contraceptives are the main method. Injectable contraceptives are ideal for women without education because they do not require any user action except the visit to a family planning clinic every 3 months.<sup>16</sup> In addition to the expected larger effect of family planning for women with no education, the age profile of fertility and the effect of other factors on fertility are likely to be different across education groups. Rather than assuming the appropriate specification given such interactions across education groups, the main sample is restricted to women with no formal education who have ever been married or lived together with a man. Among the original sample, 65 percent of women never attended school. Table 2 shows the descriptive statistics for this sample.<sup>17</sup> The main dependent variable is the number of children a woman had given birth to at the time of the survey (children ever born) which averages just over 4. The large number of births reflects the high fertility rate in Ethiopia, especially considering that the average age of the women in the sample is just over 28 years.<sup>18</sup> Age is captured using five-year age groups with the excluded category being aged 15-19. With the high population growth rate in Ethiopia younger cohorts are larger than older cohorts, but the percentage that have married or lived with a partner is smaller for young women compared to older women explaining the lower percentages of the two youngest age groups (15-19 and 20-24) in the sample. Just over half of the women are Orthodox Christian, a quarter are Muslim, and the remaining women are mainly other Christian. The remaining variables are the same community characteristics as in Table 1 used for the first stage.

Because there is no information in our survey data on migration of women the definition of access to family planning implicitly assume that a woman has spent her entire life in the area where she was found during the survey. This does not seem to be a problematic assumption. Data from the 2005 National Labour Force Survey show that 70 percent of women 15 to 45 have always lived in their current location and that another 15 percent have resided there for 10 years or more, presumably a move associated with marriage and the onset of the women’s entrance into family formation.

> 16This also makes them attractive for women who do not want to reveal to their partner that they are using contraceptives (Ashraf et al. 2010).

> 17The descriptive statistics for the full sample is available on request.

> 18For comparison the equivalent number for Guatemala is 2.8 and Guatemala has one of the highest total fertility rates in Latin America (P ̈ortner 2008). See also World Bank (2010) for TFR for other countries.

11
Table 2: Descriptive Statistics for Women Ages 15-45 With No Schooling

||Mean|Standard<br>Error|Min|Max|
|---|---|---|---|---|
|Children even born|4_._14|2_._73|0_._00|13_._00|
|Age 20-24|0_._17|0_._37|0_._00|1_._00|
|Age 25-29|0_._21|0_._41|0_._00|1_._00|
|Age 30-34|0_._20|0_._40|0_._00|1_._00|
|Age 35-39|0_._18|0_._38|0_._00|1_._00|
|Age 40-45|0_._17|0_._38|0_._00|1_._00|
|Orthodox|0_._54|0_._50|0_._00|1_._00|
|Muslim|0_._26|0_._44|0_._00|1_._00|
|**Community characteristics**|||||
|Total area (square km/100)|15_._51|10_._10|0_._00|53_._81|
|Avg. yearly rainfall (mm/100)|11_._97|4_._25|4_._46|20_._48|
|Avg. yearly rainfall squared (mm/100)<sup>2</sup>/100|1_._61|1_._05|0_._20|4_._19|
|Elevation (m/100)<br>|19_._36|4_._18|8_._65|29_._26|
|Elevation squared (m/100)<sup>2</sup>/100|3_._92|1_._62|0_._75|8_._56|
|Urban (rate)|0_._04|0_._20|0_._00|1_._00|
|Market in area (rate)|0_._35|0_._48|0_._00|1_._00|
|Road access - all year (rate)|0_._41|0_._49|0_._00|1_._00|
|Road access - dry season (rate)|0_._41|0_._49|0_._00|1_._00|
|Percent with no education in zone|79_._97|6_._40|30_._07|94_._83|
|Percent with 1-6 years of education in zone|11_._17|5_._42|2_._05|37_._45|
|Percent with 7-8 years of education in zone|2_._28|1_._27|0_._20|10_._70|
|Population / 1000|3_._08|4_._90|0_._35|96_._94|
|Access to family planning (rate)|0_._18|0_._38|0_._00|1_._00|
|Observations||1326|||

**Notes.** Estimated means and standard errors based on sample frame and weights.

# **4 Results**

We begin by examining results of our estimates for equation (1) on the placement of programs. Table 3 presents the results from the determinants of placement. The dependent variable is whether a given community was within 40 kilometers of the nearest family planning program in 1990. Most of the variables have the expected signs. Areas that have a market are statistically significantly more likely to also have access to family planning services. Furthermore, urban areas and areas with easier access, as measured by whether there is road access by car either all year or during the dry season, are more likely to have a program, although the effects are not statistically significant.

The main variables of interest are the rank variables that identify program placement. Almost all instru12
Table 3: First Stage Probit – Determinants of Family Planning Program Placement

|Variable|Program|
|---|---|
||available<br>in 1990|
|Total area of district|_−_0_._021<br>|
||(0_._027)|
|Rainfall (mm/100)|_−_0_._717<sup>_∗∗_</sup><br>|
||(0_._338)|
|Rainfall squared (mm/100)<sup>2</sup>/100|3_._715<sup>_∗∗_</sup><br>|
||(1_._478)|
|Elevation (m/100)|0_._999<sup>_∗∗_</sup><br>|
||(0_._425)|
|Elevation squared (m/100)<sup>2</sup>/100|_−_3_._050<sup>_∗∗∗_</sup><br>|
||(1_._134)|
|Urban area|0_._474<br>|
||(0_._989)|
|Market in PA/kebele|0_._994<sup>_∗∗∗_</sup><br>|
||(0_._368)|
|Road access - all year|0_._296<br>|
||(0_._558)|
|Road access - dry season|_−_0_._120<br>|
||(0_._540)|
|Percent with no education in zone|_−_0_._018<br>|
||(0_._165)|
|Percent with 1-6 years of education in zone|_−_1_._601<sup>_∗∗∗_</sup><br>|
||(0_._393)|
|Percent with 7-8 years of education in zone|3_._427<sup>_∗∗∗_</sup><br>|
||(1_._003)|
|PA/kebele population / 1000|0_._063|
||(0_._044)|
|Constant|0_._745<br>|
||(14_._332)|
|**Ranking of Zones**||
|Total population|0_._155<sup>_∗∗∗_</sup><br>|
||(0_._032)|
|Urbanisation|0_._085<sup>_∗∗∗_</sup><br>|
||(0_._026)|
|Percent with no education|_−_0_._126|
||(0_._078)|
|Percent with 1-6 years of education|0_._785<sup>_∗∗∗_</sup>|
||(0_._180)|
|Percent with 7-8 years of education|_−_0_._579<sup>_∗∗∗_</sup><br>(0_._139)|
|**Ranking of PA/kebeles within Zone**||
|Total population|_−_0_._511<sup>_∗∗∗_</sup>|
||(0_._163)|
|All ranks equal to zero F(6,96)|6_._14<sup>_∗∗∗_</sup>|
|<br>Observations|109|

**Notes.** * sign. at 10%; ** sign. at 5%; *** sign. at 1%. Weighted probit with robust clustered standard errors in parentheses estimated using Stata’s svy command. Dependent variable is whether family planning was available within 40 km of community in 1990.

ments are statistically significant. Relatively more populated and urbanized zones are more likely to have access to a family planning program. Within a zone, communities with relatively smaller population are more likely to have access to family planning programs. Policy-makers also appear to target zones with a relatively larger share of people with 1 to 6 years of education. The effects of the two other education rank13
ing variables, no education and 7 to 8 years of education, are negative. One interpretation of the education rank variables is that the government was actively trying to place family planning programs in areas where the population is less educated but not overwhelmingly lacking in education. Presumably those with more education are likely to live in areas where there are other means of obtaining family planning services or have lower desired fertility. The F-test for all instruments being jointly equal to zero is 6.14. Despite the low number of observations, the F-test indicates that the instruments perform well.

# **4.1 Effect on Fertility**

Table 4 presents the results for the effect of access to family planning in 1990 on the number of children ever born by 2004.<sup>19</sup> Models I and II assume that program placement is exogenous and estimate the effect of family planning using OLS. Models III and IV treat program placement as endogenous and use the predicted probability of access to a family planning program from Table 3.<sup>20</sup> Models I and III estimate the average effect of access to family planning services on children ever born across all women in the sample. Because the effect of access is likely to vary by age, Models II and IV include interactions between family planning access and the five year age group dummies.

The average effect of access to family planning on children ever born is negative and strongly statistically significant for both OLS and IV estimations. The OLS estimate indicates that providing family planning reduces the number of children ever born by 0.7 children. Taking account of program placement leads to an even larger estimated impact of access to family planning with fertility falling by 0.9 children. Given the sample’s average, the effect is equivalent to an approximately 20 percent reduction in the number of children born per woman.

That the IV estimates are larger than the OLS estimates is in line with the results of earlier studies using longitudinal data (Rosenzweig and Wolpin 1986; Pitt et al. 1993). These studies found that fixed effects estimates were larger than OLS estimates indicating a downward bias in OLS estimates. The larger IV effect can be taken as an indication of a compensatory approach to allocation of programs, where resources are provided to less-endowed areas with higher fertility.

The results for family planning access interacted with age groups suggest that access to family plan-

> 19Table A-1 shows the full results.

> 20 Choosing a different cut-off year does not substantially change the results for years immediately around 1990. The results for other years are available on request.

14
Table 4: Effect of Family Planning Access on Number of Children Ever Born for Women Without Schooling

||Children|Ever Born||
|---|---|---|---|
||OLS|IV Model<br>Plac|of Predicted<br>ement<sup>a</sup>|
||Model I<br>Model II|Model III|Model IV|
|Family planning|_−_0_._687<sup>_∗∗∗_</sup>|_−_0_._892<sup>_∗∗∗_</sup>||
||(0_._215)|(0_._323)||
|Family planning_×_age 15-19|_−_0_._656<sup>_∗∗_</sup>||_−_1_._052<sup>_∗∗_</sup>|
||(0_._288)||(0_._412)|
|Family planning_×_age 20-24|_−_0_._219||_−_0_._281|
||(0_._254)||(0_._465)|
|Family planning_×_age 25-29|_−_0_._302||_−_0_._899<sup>_∗∗_</sup>|
||(0_._236)||(0_._448)|
|Family planning_×_age 30-34|_−_0_._919<sup>_∗∗_</sup>||_−_0_._925|
||(0_._395)||(0_._590)|
|Family planning_×_age 35-39|_−_0_._928<sup>_∗∗∗_</sup>||_−_0_._700<sup>_∗_</sup>|
||(0_._339)||(0_._418)|
|Family planning_×_age 40-45|_−_0_._932<sup>_∗_</sup>||_−_1_._269<sup>_∗∗_</sup>|
||(0_._487)||(0_._604)|

**Notes.** * sign. at 10%; ** sign. at 5%; *** sign. at 1%. Robust standard errors clustered at community level in parentheses. Family planning indicates whether there was family planning available within 40 km in 1990. Additional variables not shown are region dummies, ethnic group dummies, five year age group dummies, dummies for religion, area of wereda, rainfall and rainfall squared of wereda, dummy for urban area, dummy for market in area, and dummies for road access all year and road access only during dry season. Number of observations for all models is 1326. Results for including other explanatory variables are in Table A-1. a Weighted IV estimation using Stata’s svy command with family planning access treated as endogenous.

ning reduces fertility more among younger and older women. In particular, the OLS results show that the reduction in number of children is 0.6 for the youngest age group, aged 15-19, smaller and not statistically significant for women between 20 to 29 and then large and statistically significant at just below 1 for women aged 30 to 45. Except for women aged 35 to 39, the IV effects are larger than the OLS effects. For women less than 20 years old taking account of program placement almost doubles the effect of family planning on number of children; the IV result indicates that family planning access decreases the number of children born by 1 for the youngest women. In other words, young women substantially delay their child bearing when they have access to family planning.

Because few women give birth after age 45, the estimated effect for the oldest age group is an indicator of the impact of family planning access over most of a woman’s reproductive years on completed fertility. For the oldest age group, women aged 40 to 45, the IV results are also larger than the OLS results. According to the IV results access to family planning decreases completed fertility by 1.3 children among 40-45 old women without education. More precisely, women who received access to family planning later in their 15
reproductive years are predicted to have approximately 6.7 children by the time they end child bearing, whereas women with access for 15 or more years will have approximately 5.5 children.<sup>21</sup> The differences in effects across age groups suggest that long-term access to family planning services leads to a substantial compression in the timing of births. One might a priori expect a uniform reduction in births across age or that the cumulative effect would become larger with increasing age. Instead there are large reductions for women younger than 20 and women older than 30. Because the outcome is cumulative births, women with access to family planning must have had more children in their early twenties than those without access to family planning. Women aged 15 to 19 with access are estimated to have 1 birth less than those without access; women aged 20 to 24 with access have only 0.3 cummulative births less than those without access. In other words, compared to those without access to family planning, women with access have, on average, given birth to 0.7 children _more_ between the ages of 20 and 25.<sup>22</sup> This compression is possible because the average birth spacing in Africa is substantially longer than in other areas with women relying on extended periods of breastfeeding and abstinence as methods for controlling fertility (Bongaarts, Frank and Lesthaeghe 1984). With access to family planning a woman can directly control both timing of births and when to stop having children and this allows for a compression of fertility.

Table 4 is restricted to the sample of women with no schooling. Including women with schooling, we find that for women who have passed first grade or above, there is no discernible impact of access to family planning on fertility.<sup>23</sup> OLS results show that for women with 1 to 5 years of education access to family planning increases fertility by approximately 0.1, whereas access decreases fertility by around 0.1 for women with 6 to 12 years of education. Using the IV results, the effect for women with 1 to 5 years of education is still an approximately 0.1 increase and for women with 6 to 12 years of education the effect is a reduction of 0.2. None of these results are statistically significant. Using the same age groups as above for the two education groups leads to no consistent results.

> 21Predictions based on average values of all variables except for age and access to family planning.

> 22For example, a woman without family planning has 1 child between 15 and 19 and an additional 1.3 child between

> 20 and 25, then a woman with access will have 0 children between 15 and 19 and 2 children between 20 and 25.

> 23Table A-2 presents results for the sample of all women.

16
# **4.2 Family Planning or Health Facilities?**

An important question is whether the effects on fertility arise from access to family planning services or from the concurrent health services offered by health facilities. Both can reduce fertility, either directly through control of conception or indirectly through lowering mortality of offspring. In Ethiopia government family planning programs are offered only at health facilities and not as stand-alone clinics. As Figure 1 shows there is a close correspondence between the presence of health facilities and family planning programs; in 1990, 18 percent of women had access to a health facility with family planning whereas an additional 6 percent had access to a health facility with no family planning services. The low number of women with access to health facilities with no family planning makes it impossible to estimate the effects of access to such health facilities with any degree of confidence. As an alternative, substituting any health facility (regardless of the availability of family planning service) in the models above lead to smaller and less statistically significant effects on fertility using OLS.<sup>24</sup> The smaller estimate is an indication that the effect on fertility is mainly due to access to family planning at health facilities and not access to health services as such. This interpretation is supported by the observed compression in fertility with access to family planning. It is only optimal for a women to diverge from the standard African birth spacing pattern if there is sufficient access to modern contraceptives to control both the timing and number of births and health facilities on their own will not provide that.

Another approach to examining which service is most important is to look at child mortality along with the results on fertility. On the one hand, access to health facilities should directly reduce child mortality and that in turn allows parents to achieve a desired number of surviving children with fewer births (Sah 1991; Schultz 1997; Wolpin 1997). On the other hand, the effect of family planning on child mortality is indirect: better ability to control spacing of births and more resources available per child because of reductions in fertility should lead to lower child mortality. Hence, although reductions in child mortality could in principle be the result of either family planning or health services, we would expect the effect of access to health services on child mortality to be larger than the effect of access to family planning. Similarly, we would expect the effect of family planning on fertility to be larger than that of health services, where the effect is more indirect. As a result, if we find little effect from our measure of access (which

> 24The first stage for health facility access performs worse than for family planning access with the F-statistics for the instruments jointly equal to zero close to 3. Results for both OLS and IV are available on request.

17
Table 5: Effect of Family Planning Access on Mortality of Children for Women Without Schooling

||O|LS|IV Model o<br>Place|f Predicted<br>ment<sup>a</sup>|
|---|---|---|---|---|
||Model I|Model II|Model III|Model IV|
|||Any Chil|dren Died||
|Family planning|0_._008<br>||_−_0_._008<br>||
||(0_._050)||(0_._079)||
|Family planning_×_age 15-19||_−_0_._094<br>||_−_0_._111<br>|
|||(0_._088)||(0_._138)|
|Family planning_×_age 20-24||0_._013<br>||0_._060<br>|
|||(0_._055)||(0_._097)|
|Family planning_×_age 25-29||_−_0_._000||0_._012|
|||(0_._075)||(0_._098)|
|Family planning_×_age 30-34||0_._007||_−_0_._073|
|||(0_._084)||(0_._117)|
|Family planning_×_age 35-39||0_._010||0_._019|
|||(0_._098)||(0_._141)|
|Family planning_×_age 40-45||0_._036||_−_0_._005|
|||(0_._086)||(0_._130)|
|||Number of D|ead Children||
|Family planning|_−_0_._094||_−_0_._110||
||(0_._097)||(0_._163)||
|Family planning_×_age 15-19||_−_0_._215||_−_0_._284|
|||(0_._168)||(0_._259)|
|Family planning_×_age 20-24||_−_0_._122||_−_0_._154|
|||(0_._097)||(0_._189)|
|Family planning_×_age 25-29||0_._023||_−_0_._053|
|||(0_._136)||(0_._171)|
|Family planning_×_age 30-34||_−_0_._274<sup>_∗_</sup>||_−_0_._090|
|||(0_._156)||(0_._330)|
|Family planning_×_age 35-39||_−_0_._264||_−_0_._269|
|||(0_._231)||(0_._315)|
|Family planning_×_age 40-45||0_._155||0_._024|
|||(0_._270)||(0_._367)|
|||Share of Chil|dren that Died||
|Family planning|_−_0_._002||0_._011||
||(0_._019)||(0_._031)||
|Family planning_×_age 15-19||_−_0_._072<sup>_∗_</sup>||_−_0_._096|
|||(0_._042)||(0_._074)|
|Family planning_×_age 20-24||_−_0_._015||_−_0_._010|
|||(0_._026)||(0_._050)|
|Family planning_×_age 25-29||_−_0_._002||0_._026|
|||(0_._026)||(0_._033)|
|Family planning_×_age 30-34||_−_0_._010||0_._003|
|||(0_._035)||(0_._057)|
|Family planning_×_age 35-39||_−_0_._018||_−_0_._003|
|||(0_._037)||(0_._049)|
|Family planning_×_age 40-45||0_._042||0_._046|
|||(0_._043)||(0_._061)|

**Notes.** * sign. at 10%; ** sign. at 5%; *** sign. at 1%. Robust standard errors clustered at community level in parentheses. Family planning indicates whether there was family planning available within 40 km in 1990. Additional variables not shown are region dummies, ethnic group dummies, five year age group dummies, dummies for religion, area of wereda, rainfall and rainfall squared of wereda, dummy for urban area, dummy for market in area, and dummies for road access all year and road access only during dry season. Number of observations for all models is 1242. Complete results including other explanatory variables are available on request. a Weighted IV estimation using Stata’s svy command with family planning access treated as endogenous.

captures both access to health services and family planning) on child mortality, the reduction in fertility is likely due to family planning rather than health services.

Table 5 presents the estimated effects of access to family planning on three measures of child mortality: 18
whether any of a woman’s children have died, the number of children who have died, and the share of children who have died.<sup>25</sup> For the sample of women who have had children, nearly 30 percent have had at least one child die, the average number of children who died is 0.57 and 10 percent of children born have died.<sup>26</sup> None of the average effects are statistically significant, although they are negative as expected, except for the OLS estimate of any children died and the IV estimate of share of children that died. The reductions in whether a woman has had at least one child die by age group are small, statistically insignificant, and many have a positive rather than negative sign. For the number of children that have died, there are statistically significant and negative effect of family planning for women 30-34 using OLS. Likewise, for the share of children who have died, the only statistically significant effect is for women younger than 20. For older women the effects are small and not statistically significant, although negative (except for women age 40 to 45). The small effects on child mortality and that the effects seem to be concentrated among the youngest women indicate that it is unlikely that the reduction in fertility comes from a reduction in mortality because of access to health facilities. A more convincing explanation is that family planning services reduced fertility and that lead to slightly lower child mortality.<sup>27</sup> Yet another indirect approach to determining whether it is access to health facility services in general or access to family planning services specifically that are responsible for the reduction in fertility is to examine two outcomes that are mainly influenced by family planning rather than health facilities: unwanted births or pregnancies, and recent birth or pregnancy. Even if lower child mortality leads to lower desired fertility, it is, for example, harder to avoid unwanted births or pregnancies unless one has regular access to family planning services.

Table 6 shows the effects of family planning on the last birth or current pregnancy being unwanted. The results should, however, be interpreted with caution because around 80 percent of women have had access 25The corresponding results using any health facility access are available on request, but lead to qualitatively similar results.

> 26It should be kept in mind that this includes mortality after age 5 and the sample consists solely of women with no schooling. For comparison the 2005 Ethiopian DHS show an under 5 mortality rate of 123 per 1000 live births for the 5 years preceeding the survey, 141 per 1000 live births for the period 5 to 9 years before the survey, and 165 per 1000 live births for the period 10 to 14 years before the survey. In addition, the under 5 mortality rate for women with schooling for the 10 years before the survey was 139 per 1000 live births (Central Statistical Authority of Ethiopia and ORC Macro 2006).

> 27The reduction in mortality among younger women is consistent with the delay in fertility among very young women. This delay would lead to higher birth weight and therefore higher survival probability for each birth.

19
Table 6: Effect of Family Planning Access on Unwanted Fertility for Women Without Schooling

||Last/Current Pre|gnancy Unwa|nted|
|---|---|---|---|
||OLS|IV Model<br>Plac|of Predicted<br>ement<sup>a</sup>|
||Model I<br>Model II|Model III|Model IV|
|Family planning|_−_0_._071|_−_0_._051||
||(0_._046)|(0_._071)||
|Family planning_×_age 15-19|_−_0_._073||_−_0_._104|
||(0_._104)||(0_._130)|
|Family planning_×_age 20-24|_−_0_._039<br>(0_._071)||_−_0_._001<br>(0_._130)|
|Family planning_×_age 25-29|_−_0_._079<br>(0_._071)||_−_0_._034<br>(0_._132)|
|Family planning_×_age 30-34|0_._034||0_._083|
||(0_._083)||(0_._120)|
|Family planning_×_age 35-39|_−_0_._142<sup>_∗∗_</sup>||_−_0_._124|
||(0_._061)||(0_._094)|
|Family planning_×_age 40-45|_−_0_._113||_−_0_._140|
||(0_._073)||(0_._107)|

**Notes.** * sign. at 10%; ** sign. at 5%; *** sign. at 1%. Linear probability model with robust standard errors clustered at community level in parentheses. Family planning indicates whether there was family planning available within 40 km in 1990. Additional variables not shown are region dummies, ethnic group dummies, five year age group dummies, dummies for religion, area of wereda, rainfall and rainfall squared of wereda, dummy for urban area, dummy for market in area, and dummies for road access all year and road access only during dry season. Number of observations for all models is 1340. Complete results including other explanatory variables are available on request. a Weighted IV estimation using Stata’s svy command with family planning access treated as endogenous.

to family planning services for at least two years at the time of the survey, whereas our family planning access measure reflects long-run access. In other words, the results capture the difference between having long-term exposure to family planning compared to relatively short-term or no exposure. To capture control over fertility, women without children are coded as not having had an unwanted birth or pregnancy; women who have not had any children have presumably been able to avoid a pregnancy at least in part because of access to family planning. The average effects indicate that longer exposure to family planning reduces the risk of an unwanted birth or pregnancy but the effects are not statistically significant. The results by age group show that mainly older women benefit from family planning in terms of avoiding unwanted fertility. For both women aged 35 to 39 and 40 to 45 there is a substantial reduction in the probability of last birth or current pregnancy being unwanted with the effects statistically significant for women 35 to 39 for the OLS results. That there is a reduction in unwanted fertility among the older women indicates that the reductions in fertility is likely due to family planning access and not health facilities.

Finally, Table 7 presents the estimated impact of long-term access to family planning on whether a 20
Table 7: Effect of Family Planning Access on Recent Birth or Pregnancy for Women Without Schooling

||Birth within last 12 mon<br>OLS|ths or current<br>IV Model<br>Plac|ly pregnant<br>of Predicted<br>ement<sup>a</sup>|
|---|---|---|---|
||Model I<br>Model II|Model III|Model IV|
|Family planning|_−_0_._063<sup>_∗_</sup>|_−_0_._071||
||(0_._034)|(0_._059)||
|Family planning_×_age 15-19|0_._095||0_._173|
||(0_._151)||(0_._170)|
|Family planning_×_age 20-24|0_._189<sup>_∗∗_</sup><br>(0_._074)||0_._304<sup>_∗_</sup><br>(0_._173)|
|Family planning_×_age 25-29|_−_0_._110<sup>_∗_</sup>||_−_0_._030|
||(0_._065)||(0_._156)|
|Family planning_×_age 30-34|_−_0_._114<br>(0_._075)||_−_0_._140<br>(0_._107)|
|Family planning_×_age 35-39|_−_0_._168<sup>_∗∗_</sup>||_−_0_._301<sup>_∗∗∗_</sup>|
||(0_._075)||(0_._086)|
|Family planning_×_age 40-45|_−_0_._029||_−_0_._056|
||(0_._056)||(0_._077)|

**Notes.** * sign. at 10%; ** sign. at 5%; *** sign. at 1%. Linear probability model with robust standard errors clustered at community level in parentheses. Family planning indicates whether there was family planning available within 40 km in 1990. Additional variables not shown are region dummies, ethnic group dummies, five year age group dummies, dummies for religion, area of wereda, rainfall and rainfall squared of wereda, dummy for urban area, dummy for market in area, and dummies for road access all year and road access only during dry season. Number of observations for all models is 1021; sample consists of women without education who are between 20 years and 39 years of age. Complete results including other explanatory variables are available on request. a Weighted IV estimation using Stata’s svy command with family planning access treated as endogenous.

woman has either had a birth within the last 12 months or is currently pregnant. As for Table 6, the results show the difference between having access to family planning for a substantial period of time compared with only having access for a relatively short period of time or not at all. In the OLS estimation, the average effect is negative and statistically significant. The IV results indicate that a woman with long-term access to family planning is around 7 percentage points less likely to have had a birth within the last 12 months or be currently pregnant compared to a woman with short-term or no access to family planning. The average effect masks substantial differences across age groups. For women younger than 25 access to family planning _increases_ the chance of a recent birth or pregnancy; the OLS and IV effects for women 20 to 24 are statically significant. For older women the effect of access is negative. The IV results show large reductions in the probability of a recent birth or pregnancy with women 30 to 34 are 14 percent less likely and women 35 to 39 are 30 percent less likely with access to family planning. These results are consistent with compression of fertility into a substantially shorter period of time with long-term access to family 21
planning as also found for the main results on cumulative births. Again, this evidence points to the direct role of family planning access on age differentiated fertility patterns as opposed to indirect effects of health services.

# **5 Conclusion**

Despite substantial interest among policy makers in family planning programs, especially in high fertility, low-income settings, the debate about their effectiveness has so far been lacking in reliable empirical evidence. This is partly due to the methodological challenges involved in controlling for the potential nonrandom program placement of family planning programs. Although experimental data provide a theoretically “clean” way to address these concerns, their application to fertility is complex given the time span over which fertility decisions are made and therefore, in practice, only survey data are usually available.

This paper studies the effects of family planning on fertility in Ethiopia and to address potential nonrandom program placement uses a set of novel instruments: the rankings of area characteristics (as opposed to the levels). We argue that such rankings of area characteristics are likely reflective of policy makers’ actual decision process when allocating family planning programs, while not affecting fertility directly. They are intuitive and easy to generate from readily available secondary data like a census or even from the primary data set itself, enabling easy replication of the methodology in other settings.

The results suggest that access to family planning reduces the total number of children born for Ethiopian women without education. The reduction in completed fertility is large at more than one child. This is more than twice the effect reported in other studies, typically from more developed settings. Moreover, the actual impact is likely larger as the results are arguably underestimates. As detailed in this paper, our approach is conservative in attributing no access to women who have some access later in the study period. This biases our impact estimates downward. No effect of access to family planning was found among women with some education, suggesting that family planning may act as a substitute for education in reducing fertility.

There are two likely explanations for the large estimated effect. First, as Ethiopia’s fertility is high compared to other study countries, such as Colombia, the marginal effects of increasing access to family planning are likely higher. Second, education levels are extremely low in Ethiopia and certainly lower than in the settings examined in other studies. If family planning acts as a substitute for education, previous 22
studies’ failure to distinguish the effects of family planning by education level would lead to a lower average estimated effect.

The available evidence further supports the conclusion that the reduction in fertility is attributable to the availability of family planning, and not the indirect result of the presence of health services per se. First, if health facilities were responsible, one would expect a larger effect on child mortality than we find. Second, the age pattern of the effects on fertility, with large reductions for women younger than 20 and women older than 30, is evidence of a strong compression of the timing of births. This is consistent with access to family planning, but not with the notion that the health facilities are primarily responsible. Without access to modern contraceptives the main African way to reduce fertility is to ensure that the space between births is as long as possible. Only with additional control over the timing of births and completed fertility would it be optimal for a woman to have children more closely spaced. The closer spacing of births also points to an important benefit of family planning: it allows women to spend more time in the labor market thereby increasing household income.

Despite the relatively large estimated effect of family planning, skeptics will rightly argue that it will by no means suffice to reduce fertility in Ethiopia to near replacement levels. Nevertheless, it does suggest a low cost and complementary entry point to reduce fertility and speed up the development process in such a setting. This is especially important in poor areas where low schooling level and high fertility rates prevail. As simulated in World Bank (2007), in addition to improving women’s health and overall empowerment, the long-term and self-reinforcing consequences of initiating such a process can be substantial. With the total fertility rate still exceeding 5 children per woman in more than 20 countries, the opportunities are clearly substantial.

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