Population Policy: Abortion and Modern Contraception Are Substitutes

Because we study married women in a patriarchal society, the choice of contraception is likely to be the result of intrahousehold bargaining. We do not theoretically or empirically distinguish individual preferences from the choices that result from this bargaining process. However, our reduced-form estimates isolate important—and policy-relevant—parameters of interest. Recognizing this point, we refer to contraceptive decisions as women’s decisions for simplicity throughout the article.

A separate strand of economic theory, which studies the response of risk-taking behavior to perceived changes in the consequences of a bad outcome (e.g., increased automobile safety, availability of treatment for a medical condition), suggests an additional mechanism through which women who do not want to become pregnant may reduce contraceptive use when access to abortion improves. Indeed, evidence suggests that reducing the cost of a bad outcome may increase risk-taking in other health areas. Examples are Peltzman (1975) on the effect of automobile safety on dangerous driving, Dilley et al. (1997) on HIV treatment and risk-taking among men who have sex with men, and Peltzman (2011) on the effect of medical breakthroughs (e.g., new treatments for heart disease) on offsetting behavior (e.g., obesity).

The theoretical discussion of the tradeoff between contraception and abortion among demographers has tended to focus on the effect of changing contraceptive prevalence on the abortion rate (e.g., Bongaarts and Westoff 2000). In the economics literature, the tradeoff is understood as going both ways (for a discussion of the effect of a change in the cost of abortion on contraceptive use, see Kane and Staiger 1996). The difference stems from the economic modeling of contraceptive choices as depending on the cost of contraception relative to the cost of not using contraception, which in turn depends on the cost of abortion (see the Conceptual Framework section).

Changes in supply include both changes in availability and changes in the full price of contraception (monetary, social, and emotional price). For brevity, we refer to these changes collectively as “supply changes” throughout.

First announced in Mexico City in 1984 by President Ronald Reagan’s administration, the Mexico City Policy requires all non-governmental organizations (NGOs) operating abroad to refrain from performing or counseling women about abortion as a means of fertility control as a condition for receiving U.S. federal funding.

Menstrual regulation refers to manual vacuum aspiration procedures conducted after a missed menstrual period but before pregnancy is clinically confirmed.

In 1920, the Soviet Union was the first country to legalize abortion.

Although imprisonment was not a common outcome among women who had an abortion, among the small population of female inmates in Nepalese prisons (405 in 1997), a substantial proportion are believed to have been convicted on abortion-related charges (Ramaseshan 1997).

A total of 291 abortion centers in 2010 relative to a total Nepal population of 26.49 million reported in the 2011 Nepalese population census implies one center per 91,031 inhabitants. In the United States, there were 1,793 abortion providers in 2008 (Guttmacher Institute 2013) relative to a total population of 305 million (Population Reference Bureau 2008), implying one provider per 170,106 inhabitants.

This figure is based on a binary definition of proximity to a legal abortion center corresponding to the median distance to the nearest legal abortion center (28.6 kilometers). Various robustness checks for different definitions of access to a legal abortion center are presented in Valente (2014).

A survey of Comprehensive Abortion Care (CAC) providers conducted in 2009 revealed that only 8 of 139 surveyed providers introduced new contraceptive services or maternal and child health services at approximately the same time as they started providing abortion services (Valente 2014).

Of course, most abortion clients receive post-abortion contraceptive counseling (MOHP and CREHPA 2006). This could lead us to underestimate the extent of the substitution away from contraception if women who use abortion services are more likely to use contraception after having had an abortion. However, the results of our statistical analysis are virtually unchanged when excluding women who report having had an abortion within one year of the survey, thus suggesting that post-abortion changes in contraceptive use are not influencing our findings (full results are available on request).

The only difference when assuming a probability of becoming pregnant in the absence of contraception inferior to one is that Inequality (1) becomes c _i ^c  < p _p (1 − p _f )min(c _i ^a , c _i ^u ), where p _p is the probability of becoming pregnant in the absence of effective contraception, and p _f is the probability of becoming pregnant despite using contraception when contraceptive protection is needed (which is the case with probability p _p ). Allowing women to hold erroneous beliefs in terms of p _p and p _f changes the ranges of costs over which women decide to use contraception or not, but it does not alter the qualitative conclusions of the model about the effect of a decrease in the cost of abortion relative to contraception.

When c _i ^a  < c _i ^u , the expected cost of using contraception is p _f (c _i ^c  + c _i ^a ) + (1 − p _f )c _i ^c  = p _f c _i ^a  + c _i ^c , and the cost of not using contraception is c _i ^a . When c _i ^a  ≥ c _i ^u , the expected cost of using contraception is p _f (c _i ^c  + c _i ^u ) + (1 − p _f )c _i ^c  = p _f c _i ^u  + c _i ^c , and the cost of not using contraception is c _i ^u .

For example, we also restrict our sample to women ages 25 and older, among whom marriage is nearly universal: 97.3 % of respondents ages 25 and above in the 2006 and 2011 surveys were married at the time of interview. Focusing on married women is consistent with the composition of legal abortion service clients in Nepal. A survey conducted by CREHPA in 2006 indicated that nearly 98 % of clients were or had been married, close to 60 % were aged 20–29 (only 5 % were under 20), and less than 7 % had no living child (23 %, 40 %, and 31 % had one, two, and three or more living children, respectively) (MOHP and CREHPA 2007). An international comparison of legal abortion service client characteristics suggests that the characteristics of Nepalese clients are generally comparable with those observed in other less-developed countries (Bankole et al. 1999, MOHP and CREHPA 2007).

The question asked in the DHS is, “Are you currently doing something or using any method to delay or avoid getting pregnant?”

Strickler et al. (1997) and Beckett et al. (2001) compared contraceptive use by the same woman for the same periods of time but reported at two different survey dates. They found substantial inconsistencies at the disaggregated level. Specifically, Strickler et al. (1997) found that among Moroccan women reporting at least one period of contraceptive use, only 45.1 % reported periods of use and non-use in the same order in two different surveys, and only 29.3 % reported the same sequence and length of contraceptive use. Similarly, when comparing individual reports of contraceptive use in contemporaneous and recall data for Malaysia, Beckett et al. (2001) obtained a kappa coefficient of only 0.38. In the presence of misclassification (e.g., reporting not using contraception when in fact using and vice versa), Hausman et al. (1998) showed that estimates are inconsistent and that their precision can be overstated.

Recall error for the variables Das et al. (2012) examined, which include visits to the doctor and self-medication, is very relevant to recall error in contraceptive use but less so for more salient events, such as the birth of a child.

By definition, an abortion center is legal only if it is listed with the Technical Committee for Implementation of Comprehensive Abortion Care (TCIC), and the data used here are based on the list of all facilities included on the TCIC list up to February 2010.

For each pregnancy in the pregnancy history of the woman, she is asked whether the baby [was] “born alive, born dead, or lost before birth”; when the pregnancy ended and how long it lasted; and then “did you [the respondent] or someone else do something to end this pregnancy?” The sequence of questions is the same for the four surveys, but the variables available changed in 2011. Before 2011, we count as an abortion any pregnancy not ending in live birth for which the woman either says that something was done to end the pregnancy or refuses to answer the last question. In the 2011 survey, the data set does not contain the necessary raw data to apply the same rule, but contrary to the previous surveys, provides a classification of pregnancies as live birth, stillbirth, miscarriage, or abortion based on the same survey questions.

Table 1, panel C, shows that our sample is predominantly rural, with low levels of education (especially among women) that increase rapidly across study waves.

Note that C _ds is coded using abortion facility data as of February 2010 for the 2011 DHS wave because the administrative records for which we had access end in February 2010.

This and the other point estimates of interest in Table 2 are nearly unchanged when the linear age variable is replaced with seven 5-year age categories. Results are available on request.

Marginal effects reported in this article are computed at the mode of all categorical covariates, the mean of maternal age, and the mean number of abortion centers per district in the two post-legalization surveys.

Traditional methods, such as withdrawal and the rhythm method, are used by only 4 % of women in our pooled sample; however, their use has increased over time, from 2 % in 1996 to 7 % in 2011.

Among reversible modern methods, condoms may require more negotiation with male partners. Repeating our estimation separately for condom use (OR = 0.989; 95 % CI = 0.979, 1.0004) and for other reversible methods (OR = 0.976; 95 % CI = 0.967, 0.984), we find a larger association for other reversible modern methods, although the difference between them is not statistically significant.

Odds ratios on the other covariates generally have the expected signs: the indicators for each DHS survey capture the overall trends in contraceptive use described in the section Data on Nepalese Women and Legal Abortion Centers and confirm that urban, better-educated, and older women are more likely to use contraception. Coefficients on religious affiliation variables are also reasonable (e.g., Muslims are significantly less likely to use contraception than Hindus). It is interesting to note that the education gradient is steep for traditional methods but much less so for use of any type of contraception.

We also estimate the robustness test shown in Table 4, panel A, including both the number of children born to a woman and whether a woman had a job in the past 12 months. The resulting estimates change very little (and insignificantly so) when we include these additional covariates. These results are available upon request.

In this restricted sample, by definition, there is no variation in sterilization status in the 1996 and 2001 DHS (because those sterilized by 1996 or 2001 are dropped from the sample), so that we can use only the 2006 and 2011 DHS surveys for the analysis of the sterilization outcomes. The findings on sterilization outcomes using only the last two surveys suggest a statistically insignificant decrease in female sterilization and a marginally significant increase in male sterilization, although the total effect on modern contraception is still significantly and consistently negative overall.

Although results are less precise, the number of abortion centers per inhabitant is very similar to our main measure of program treatment: the number of abortion centers. The correlation between these two variables is .71.

A region is defined as the interaction between an economic region (of which there are five in Nepal) and an ecological belt (Terai, hill, or mountain), with 13 regions defined in the DHS.

The only outcome for which the conclusions vary when individual DHS surveys are excluded is male sterilization, which significantly increases with an additional abortion center if we exclude the 1996 survey and significantly decreases if we exclude the 2011 survey, whereas it has a statistically insignificant effect if we include all four surveys or exclude the 2001 or 2006 surveys. We therefore conclude from the results in Table 6 that there is no robust evidence of a change in male sterilization, as in the main analysis.

We also repeated the analysis excluding the capital Kathmandu, which has the largest number of abortion facilities of all districts. The estimated odds ratios are very similar to those obtained with the whole sample, but estimates become much less precisely estimated due to the loss in variation in our abortion supply variable, and therefore most odds ratios become statistically insignificant. Full results are available on request.

The effect estimated here is based on the variation over time in local availability of abortion centers across districts. It may therefore be an underestimate of the true substitution effect because women may be able to travel to abortion centers located outside their district (e.g., in the capital Kathmandu).