metadata: authors: 0: author: Athey,Susan
1: author: Bergstrom,Katy Ann
2: author: Hadad,Vitor
3: author: Jamison,Julian C
4: author: Ozler,Berk
5: author: Parisotto,Luca
6: author: Sama,Julius Dohbit
count: Cameroon
docna: 0: docna: Shared Decision-Making : Can Improved
            Counseling Increase Willingness to Pay for Modern
            Contraceptives ?
docty: Policy Research Working Paper
subtopic: Family Planning Research,Reproductive Health,Educational Sciences,Health Care Services Industry,Health Service Management and Delivery
teratopic: Education,Industry,Health, Nutrition and Population
abstracts: cdata!: Long-acting reversible contraceptives
            are highly effective in preventing unintended pregnancies,
            but take-up remains low. This paper analyzes a randomized
            controlled trial of interventions addressing two barriers to
            long-acting reversible contraceptive adoption, credit, and
            informational constraints. The study offered discounts to
            the clients of a women’s hospital in Yaoundé, Cameroon, and
            cross-randomized a counseling strategy that encourages
            shared decision-making using a tablet-based app that ranks
            modern methods.   Discounts increased uptake by 50 percent,
            with larger effects for adolescents. Shared decision-making
            tripled the share of clients adopting a long-acting
            reversible contraceptive at full price, from 11 to 35
            percent, and discounts had no incremental impact in this group.
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repnme: repnme: Shared Decision-Making : Can Improved
            Counseling Increase Willingness to Pay for Modern
            Contraceptives ?
display_title: Shared Decision-Making : Can
            Improved Counseling Increase Willingness to Pay for Modern Contraceptives
listing_relative_url: /research/2021/09/33427864/shared-decision-making-can-improved-counseling-increase-willingness-pay-modern-contraceptives
topicv3: Education m1327048 470,Family Planning m1327501 1452,Contraceptives m1327726 1453,Health m1504661 3480,Gynecology m1504683 3610
publishtoextweb_dt: 2021-09-20T00:00:00Z
dois: 10.1596/1813-9450-9777
historic_topic: Education,Industry,Health, Nutrition and Population
origu: DECRG: Poverty & Inequality (DECPI)
no_of_pages: 38
author_keys: 0: author_key: 1811674
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model_extractions: Policy Research Working Paper 9777

# Shared Decision-Making

Can Improved Counseling Increase Willingness to Pay for Modern Contraceptives?

_Susan Athey Katy Bergstrom Vitor Hadad Julian C. Jamison Berk Özler Luca Parisotto Julius Dohbit Sama_ Development Economics Development Research Group September 2021
Policy Research Working Paper 9777

# **Abstract**

Long-acting reversible contraceptives are highly effective in preventing unintended pregnancies, but take-up remains low. This paper analyzes a randomized controlled trial of interventions addressing two barriers to long-acting reversible contraceptive adoption, credit, and informational constraints. The study offered discounts to the clients of a women’s hospital in Yaoundé, Cameroon, and cross-randomized a counseling strategy that encourages shared decision-making using a tablet-based app that ranks modern methods. Discounts increased uptake by 50 percent, with larger effects for adolescents. Shared decision-making tripled the share of clients adopting a long-acting reversible contraceptive at full price, from 11 to 35 percent, and discounts had no incremental impact in this group.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at bozler@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
# Shared Decision-Making: Can Improved Counseling Increase Willingness to Pay for Modern Contraceptives?

Susan Athey Katy Bergstrom Vitor Hadad Julian C. Jamison Berk Özler Luca Parisotto Julius Dohbit Sama<sup>∗</sup>

## JEL Codes: I12; I15; J13; O12; C13; C93; D91

Keywords: Family Planning; Fertility; Long-Acting Reversible Contraceptives; Heterogenous Treatment Effects;

> ∗Athey: athey@susanathey.com, Stanford University. Bergstrom, kbergstrom@worldbank.org, The World Bank. Hadad, vitorh@stanford.edu, Stanford University. Jamison, J.Jamison@exeter.ac.uk, University of Exeter. Özler (corresponding author): bozler@worldbank.org, The World Bank. Parisotto: lparisotto@worldbank.org, The World Bank. Sama, dohbit@yahoo.com, Hôpital Gynéco-Obstétrique et Pédiatrique de Yaoundé (HGOPY), Université de Yaoundé I. The study team is grateful to the administration of HGOPY, which is the site for this experiment. In particular, we thank Ewolo Esther, the team of nurse counselors at HGOPY, led by Hildegarde Ebenye, Juliette Kegne, Denise Noupele, who are too numerous to list here. We are also grateful to Prof. Mbu Robinson Enow, Prof. Fru Buzshi Angwafo III, Prof. Pascal Foumane, Dr. Emma Woks, and Mr. Julien Ondoua, all at HGOPY, for their support in facilitating this study. The tablet-based job support tool (the _app_ ) used by nurse counselors was developed in collaboration with the Cameroon Ministry of Health’s Department of Family Health (Direction de la Santé Familiale, MinSanté): special thanks go to Prof. Mbu Robinson Enow and Dr. Jeanette Afounde for their support. Drs. Anne E. Burke (Johns Hopkins University) and Maria Steenland (Brown University) provided valuable comments throughout the development of the _app_ and Dr. Burke kindly tested early versions. Jake Robyn and Jean Claude Taptue Fotso from the World Bank were instrumental in providing initial support for this study, and Yohana Dukhan provides continued support. We thank HEREG and Dr. Njoumemi Zakariaou for assisting with project management and data collection. We are grateful for comments we received from seminar participants at UCLA, ASSA, The World Bank, FHI360 and CEGA. Funding from the World Bank’s Strategic Impact Evaluation Fund, Knowledge for Change Program, Global Financing Facility, and Research Support Budget is gratefully acknowledged. The study is registered at the AEA RCT registry (AEARCTR-0003514) and clinicaltrials.gov (NCT03733678).
# **1 Introduction**

Globally, the maternal mortality ratio (MMR) remains high at 211 maternal deaths per 100,000 live births in 2019, despite significant reductions since 2000 (WHO, 2019). In sub-Saharan Africa the MMR is 534, implying that one in 38 women aged 15 will eventually die from a maternal cause. As many of the causes are preventable or treatable, substantial reductions in MMR can be achieved on the intensive margin – by expanding coverage and improving the quality of routine reproductive health care for pregnant women (Kassebaum et al., 2016).

However, there is also ample room for improvement on the extensive margin: nearly half of all pregnancies worldwide are considered unwanted or mistimed (Sully et al., 2020). In Cameroon, the setting for our experiment, the share of such births is twice as high among adolescents as adults (DHS, 2018), and the risk of complications in pregnancy and childbirth is also higher (WHO, 2019). Therefore, increasing access to contraception, especially among adolescents, can contribute to large reductions in MMR. Delayed fertility is associated with improved maternal and child health outcomes (see Baird et al. 2011, and citations within), while unintended pregnancies are strongly predictive of short inter-pregnancy intervals, which are in turn positively associated with babies being born prematurely, at low birth weight, or small for their gestational age (DeFranco et al., 2015; Frost et al., 2014). Lower fertility rates may also contribute to economic growth through increased female labor supply (Bloom et al., 2009, 2020).

Modern contraceptive technology is highly effective in preventing unintended pregnancies. However, adoption in many low- and middle-income countries (LMIC) remains low: among women in LMICs who report wanting to avoid a pregnancy, about a quarter of women and half of adolescent females also report not using a contraceptive method (see Sully et al. 2020). Our formative qualitative work in Cameroon shows that there are a multitude of demand-side factors impeding adoption: concerns over side effects, misconceptions about methods, financial cost, concerns over discretion, and opposition from parents, spouses, or partners. These barriers are further exacerbated by supply-side problems, such as stock-outs of contraceptives, lack of trained health providers, and provider bias against recommending certain contraceptive methods to adolescents and women without children.

We conducted a randomized experiment to tackle two of these barriers: cost and uncertainty about returns from adopting contraceptives. The experiment was conducted at a women and children’s hospital in Yaoundé, Cameroon, and it constituted the pilot phase for an adaptive experiment that was being planned at the hospital. We developed a tablet-based _app_ , which assists healthcare providers conducting family planning (FP) counseling sessions. During a structured discussion, the _app_ records the clients’ fertility plans, needs, and preferences regarding contraceptive methods. The main innovation that the _app_ brings to FP counseling is an internal algorithm to rank methods according to their suitability for the client’s context. In the _shared decision-making_ treatment (SDM), the _app_ reveals the most suitable method and the provider suggests to the client that they discuss this method first. This contrasts with the _individual decision-making_ treatment (IDM), in which the provider asks the client to decide which method they’d like to discuss first, choosing from the full, unranked set of modern contraceptives. SDM is thus aimed at providing the clients with better information tailored to their individual needs, while IDM is meant to resemble the _status quo_ in contraceptive counseling. We also cross-randomized a _discount_ treatment, in which the _app_ revealed randomly-assigned discounts for modern contraceptive methods. The _discount_ treatment 2
relaxes the credit constraints that prevent clients from adopting LARCs due to their high upfront costs.<sup>1</sup> The tablet-based _app_ collects detailed information on client characteristics and choices. We are interested in whether the client adopted (i) a LARC (the IUD or the implant), (ii) a short-acting method, or SARC (the injectable or the pill), or (iii) no modern contraceptive method. We find that discounts for LARCs had a large effect on their adoption: while 32% of all eligible clients adopted a LARC at full price (CFA 4,000 or approximately USD 7.25), this share increased to 45% at mid prices (USD 1.8 or 3.6), and 48% at low prices (free or USD 0.25). Adolescents were particularly price-sensitive: take-up increased from 12% at full price to 42% at mid and 55% at low prices. The increase in LARC adoption is accompanied by a similar reduction in the share of clients adopting no modern method, rather than a shift from SARCs.

SDM was also effective in increasing LARC uptake, with this effect significantly interacting with the _discount_ treatment. Under IDM, only 11.4% of the clients adopted a LARC at full price. This share more than tripled to 35.3% under SDM. SDM may have also made clients less price-sensitive: discounts, on average, increased LARC uptake by 17 percentage points (pp) under IDM but the point estimate is close to zero under SDM (although the difference between the two effects is only statistically significant at the 10% level). We present a model of individual utility maximization, where clients care about the price and the perceived returns from adopting a LARC, that is consistent with this finding (Appendix C). The results indicate that a simple behavioral nudge during counseling can increase the willingness-to-pay for LARCs to a level sufficiently high so as to make discounts ineffective in further increasing adoption.<sup>2</sup> Our paper is related to the literature on the effect of providing vouchers and other price changes on contraceptive use (McKelvey et al., 2012). Prior research has shown that providing vouchers that fully or partially subsidize FP services increase the use of modern contraceptives among women (Ashraf et al., 2014; Bellows et al., 2016; Anukriti et al., 2021). Shah et al. 2021 finds that free provision of modern contraceptives to adolescent females at girls’ clubs in Tanzania did not lead to increased take-up. Ashraf et al. (2014) find that provision of vouchers to married women in the presence of husbands reduces their redemption compared to them being offered when they are alone in the setting of a large clinic in Lusaka. Recent quasi-experimental studies also indicate that women, unmarried young women in particular, may be responsive to contraceptive prices (Rau et al., 2017), with adolescents in the U.S. selecting LARCs and continuing to use them longterm (Mestad et al., 2011) and programs encouraging clinics to stock and provide free LARCs to low-income women causing decreases in teen birth rates (Lindo and Packham, 2017).

Our paper contributes to this literature by demonstrating that offering FP counseling and discounts to female clients at healthcare facilities can achieve large reductions in the number of women not using a modern method but wanting to delay pregnancy. While we find that discounts are effective

> 1The high upfront costs of LARCs, along with high cost of removals, contrast with the per-month cost of protection from unintended pregnancies. The IUD can be used for up to 10 years while the implant lasts three-to-five years. In contrast, the injectable (Depo-Provera) provides protection for only three months, while the pill needs to be taken everyday.

> 2In principle, there could be negative consequences of the counseling intervention for women’s autonomy and decision-making. Using follow-up surveys with a latter cohort of clients in the adaptive experiment, We found no differences between the two counseling arms in terms of client satisfaction or their willingness to return to the hospital for FP services in the future (see Section 4.2).

3
among women specifically seeking FP services, they are even more effective among clients who were seeking other services and were simply offered FP counseling before they left the hospital. Consistent with the literature, we find that such patients and adolescent females are particularly price-sensitive when it comes to adopting modern contraceptives.

Our study is also related to a growing literature in the U.S. on counseling interventions – such as peer counseling, a _waiting room app_ for contraceptive counseling, and motivational interviewing techniques allowing the client to articulate goals and discuss plans – that showed promise in leading to higher levels of knowledge of contraceptive effectiveness, increased interest in adopting the implant, and higher rates of LARC uptake (Gilliam et al., 2014; Church et al., 2017; Wilson et al., 2014). It is also related to the literature in economics on providing sexual and reproductive health information to adolescents, young women, and to their spouses/partners (Dupas, 2011; Dupas et al., 2018; Duflo et al., 2015; Ashraf et al., 2020).

We contribute to this literature by testing a counseling approach (SDM) that allows for shared decision-making and comparing it to a counseling approach that mimics the status quo informed choice model IDM), in which individuals are given extensive information to make their own independent choices (Holt et al., 2017). We show that SDM substantially increases clients’ willingness to pay for LARCs without reducing their level of satisfaction. If an individual is risk averse and adopting a LARC is perceived to be riskier, reducing uncertainty by suggesting that a LARC may be the most suitable method for the client might encourage its adoption (Cortés et al., 2021). In this sense, our paper is also related to a literature in behavioral science as applied to decision-making in health, in particular the key role of defaults and choice architecture (Johnson et al. 2005, among others). There is a general sense that agency (being involved in decisions, rather than being told what to do) is especially important for compliance in medical contexts (Donovan, 1995) – beyond the simple fact that it is also likely to produce better decisions in the first place – in the sense of matching personal preferences.

# **2 Experiment design**

## **2.1 Setting and target population**

The study was conducted at the Hôpital Gynéco-Obstétrique et Pédiatrique de Yaoundé (HGOPY), a women’s hospital in the capital of Cameroon. In preparation for an adaptive experiment aiming to reduce unintended pregnancies among its clients, the study team organized a pilot in December, 2019. This phase served to introduce the _app_ to nurse counselors; provide FP training to nurses in other units, such as maternity and gynecology; iron out the study protocols; and, perhaps most importantly, get all trained nurses to become comfortable with providing FP counseling under either approach – IDM and SDM – using the _app_ . Despite the declaration of the global pandemic by the WHO in March, 2020, which reduced the number of clients presenting at HGOPY, these goals were successfully achieved by June 9, 2020. However, partly due to the disruptions caused by the pandemic, the study team was not ready to launch the adaptive experiment at that time. Instead, the team decided to run a static experiment with fixed and equal probabilities of assignment to each of the two interventions that were cross-randomized: _discounts_ and _counseling_ , which are discussed 4
in detail later in this section. This experiment ran for nine months until the launch of the adaptive experiment on March 9, 2021 and provides the basis for this study.

Women aged 15-49 who received FP counseling at HGOPY by a trained provider using the _app_ during the pilot were included in the study. Exceptions were clients who: (i) wanted to become pregnant within the next 12 months, (ii) were pregnant at the time of their consultation and had not yet come back to give birth at HGOPY by the end of the pilot, and (iii) completed their consultation without being exposed to either intervention.<sup>3</sup> During the study period, 1,008 clients were counseled at HGOPY, 784 of whom were eligible. 57% of eligible clients presented at the FP unit – either seeking to simply receive information; adopt a new method; switch to another method; or renew, manage the side effects of, or discontinue their current method. The remaining clients mainly presented at the maternity and gynecology wards: some had just given birth; some were pregnant and receiving ante-natal services; some might have returned to the hospital postpartum for a check-up or for their infants to receive vaccinations; yet others might have presented with a gynecological problem. Table A1 shows the characteristics of the study sample. The average client is 29 years old and 10% of the sample is under the age of 20. The sample is more or less equally divided between women who are single, cohabiting with a partner, and married. Approximately a quarter of them are students. The average number of children is 2.75 and 25% of clients report wanting no more children. Few clients were using any modern contraceptives at the time of their counseling session.

# **2.2 Interventions**

The interventions are centered around the use of a newly developed tablet-based _app_ (see Appendix E for a detailed description of the _app_ and its main features). For the purposes of our study, the app serves as both the platform through which the counseling approach and prices of contraceptive methods can be randomly assigned to each client and the data collection tool to assess the impacts of these interventions.

## **2.2.1 Discounts for modern contraceptive methods**

Eligible clients who received a FP consultation by a trained provider using the _app_ were offered randomly varying discounts for modern contraceptives.<sup>4</sup> ). The four modern contraceptive methods available at the hospital were grouped into two categories: LARCs (i.e., the copper IUD and the implant) or SARCs (i.e., the pill and the injectable). LARCs were offered at five different price levels with equal probability, which we collapse into three bins for statistical power: High (CFA

> 3We could not yet observe the outcome (contraceptive uptake) for clients in the second category. Clients in the third group include, for example, women who wanted to address the side-effects of their current method: such clients would be consulted with the _app_ , but if they chose to continue using their method and did not need to renew it, they would not be exposed to any of the interventions.

> 4The typical use effectiveness of modern methods vary widely: while 0.05% of women using the implant experienced an unintended pregnancy within the first year of use, the same figures were 0.8% for the copper IUD, 6% for the injectable (Depo-Provera), 9% for the pill, and 18% for the male condom. The lactational amenorrhea method (LAM), when used correctly, is as effective as a short-acting method for up to six months after giving birth (see: Trussell and Guthrie 2011, Table 3.2.

5
4,000), Mid (CFA 2,000 or 1,000), and Low (CFA 150 or Free). SARC prices were cross-randomized at two price levels with equal probability: Full price (CFA 1,250 for the injectable and 1,500 for the pill) or for free.<sup>5</sup> Both short-acting methods need to be renewed every three months and the clients can do so at the same price offered at the initial consultation, which is valid for a period of one year, representing three renewals.

Two details about implementation matter. First, the prices for all modern methods were revealed at once and _after_ the client had chosen a method they wanted to adopt, unless they inquired about prices earlier on in the counseling session.<sup>6</sup> This decision was made after careful deliberation with the healthcare providers at the hospital and based on established best-practices and ethical norms.<sup>7</sup> Second, the reader might worry about the clients (alone or in collaboration with the providers) trying to ‘game’ the system to receive higher discounts - for example, by asking the counselor to start a new _app_ session or by returning later. Carefully developed study protocols for return clients, monitoring the data from the tablets (including meta- and para-data), and monthly audits of HGOPY’s own administrative data allows us to rule out the possibility of clients redrawing a new set of prices.

# **2.2.2 Counseling approach**

The structure of the counseling session guided by the _app_ is not fundamentally different than the accepted best practices used around the world. The main innovation that it brings to FP counseling is a small but important paradigm shift with respect to shared decision-making: the app uses an algorithm to internally rank methods from most to least suitable for the client’s preferences. In the established, or _status quo_ , approach to counseling, the client is given information about all contraceptive methods and asked to choose the method she would like to discuss. The counselor is expected to provide no guidance or advice during this _individual decision-making_ process. In contrast, in our alternative approach, the _app_ encourages _shared decision-making_ by revealing the most suitable method for the client according to the _app_ ’s internal algorithm. In order to test the effectiveness of this approach against the status quo, clients were randomly assigned to one of two interventions – each mimicking one approach:<sup>8</sup>

1. **Individual Decision-Making (IDM):** The tablet displays all available modern contraceptive methods that have not been ruled out by the client or contraindicated due to medical eligibility. The provider gives basic information on each available method (in order of the methods displayed on the tablet screen, which is randomized) covering its use, duration, and typical use effectiveness. These quick descriptions are designed to take 1-2 minutes for each method, or 5-6 minutes overall. The counselor then asks the client which method she would like to discuss, and provides detailed information with the help of the relevant cue card (see

> 5Due to low take-up of SARCs among the study population and the lack of impact of offering discounts for SARCs, the study team discontinued the cross-randomization of SARC prices on January 20, 2021. This implies that SARCs were offered at full price to everyone for approximately the last two months of the pilot study period.

> 6Only 4% of clients asked to see the random discounts before choosing a method.

> 7The clients were free to choose a different method, or no method at all, once the discounts were revealed.

> 8This randomization occurs towards the end of the consultation when the clients are ready to choose a method, after the discussion necessary for the client to make an informed choice but before the random discounts are revealed.

6
Appendix section E.4 for an example of a cue card). The client can then either choose this method or discuss another method (of her choice from the same unranked list). This process is repeated until a decision is made.

2. **Shared Decision-Making (SDM):** The tablet _only_ displays the most suitable method for the client according to the ranking algorithm of the _app_ . The provider tells the client that “. . . while there are a number of suitable methods for her, based on their discussion, the displayed method is most suitable for her needs,” and asks her if she would like to discuss this method first. If the client answers ‘no,’ then the next highest ranked recommendation is displayed, and the process is repeated until the client decides to discuss the recommended method (or decline all of them). When the client answers ‘yes,’ the provider uses the appropriate cue card to describe the method in detail. The client can then decide whether to adopt this method or discuss the next method recommended by the _app_ . Again, this process is repeated until a decision is made.

Our experiment aims to isolate the effect of telling the client that the _app_ recommends one method as being most suitable for her needs, rather than simply asking her to decide which method she would like to discuss from a list of unranked options.<sup>9</sup>

# **2.3 Experimental design**

All eligible clients were randomly assigned to a set of contraceptive prices.<sup>10</sup> However, not everyone was assigned to a counseling condition: slightly over half of the eligible clients visited the hospital seeking a specific method, which they either wanted to adopt or renew.<sup>11</sup> The pilot phase of our study clearly revealed that such clients do not want, or need, to sit through a time-consuming session where all methods are described to them in detail. Figure 1 shows a simplified flowchart of the paths taken by first-time clients through the app.

> 9In the behavioral science literature, it is sometimes claimed that _joint_ evaluation of options ( _IDM_ in our case) encourages more reasoned decision-making than does _separate_ evaluation ( _SDM_ here), and hence superior outcomes, in part because it focuses attention on relevant dimensions rather than irrelevant biases such as, e.g., gender (Bohnet et al., 2016). In our setting, however, the ranking is specifically tailored to the client’s preferences and, therefore _SDM_ is designed to try to overcome biases directly. If anything, given that many clients walked away having adopted no modern method (as we will see in Section 4.2), the study team was more concerned about choice overload under _IDM_ .

> 10Contraceptive prices and the counseling style are fixed for each client for one year per the random allocation at their initial visit, meaning that return clients face the same set of prices if they want to adopt a new method or switch their current one. They are also counseled using the same style – should there be a need for another session. Removals of LARCs are free for all study participants.

> 11We tried to make sure that these clients were expressing a strong intention to adopt a specific method: the question in the _app_ reads "is there a specific method that you absolutely want to adopt?"

7
<!-- Start of picture text --> 81% (n=358) p----------------------------------,<br>wantedT ARC a ee ' Method in mind<br>56% (n=442) ' (in=385,, 49%49%) '<br>Sought a specific: '\ Not randomized; into a counselling~ +<br>methodx 32%NOT (n=27) willing wereto ' style butpreferred instead counselled method.** on their |<br>discuss other \ H<br>19% (n=84) methods Torre<br>wanted a<br>SARC/Other 68% (n=57) were pon nn nanan enna nn nn enn n enn n nnn nns<br>784 eligible willing to discuss | Randomized counselling |<br>. other methods ' |<br>clients \ (n=399, 51%) '<br>44% (n=342) Did Side-effects | 1. Individual Decision Making |<br>not seek a specific ee Preferences ——* (n=202, 51%) H<br>method questions ' '2. Shared Decisionvs.iMaking.'1|<br>(n=197, 49%)<br><!-- End of picture text -->

Notes: * Or wanted to renew their current method without discussing other methods (approximately 10% of this group of clients). ** In rare cases where a client’s preferred method is contra-indicated they were counselled on the next most suitable method according to a default ranking.
the end of the consultation. For each method discussed during the counseling session and not chosen by the client, the _app_ records the reason why she did not want to adopt it. The result is a rich database containing adoption decisions, along with relevant client characteristics and preferences.<sup>12</sup>

## **3.2 Outcomes**

The primary outcomes considered in this study are the shares of clients who adopted (i) a LARC, (ii) a SARC, or (iii) neither. We denote the last group as having _adopted no modern method_ although it includes clients who adopted LAM, condoms, or other traditional methods. We mainly focus our attention on the adoption of LARCs due to low demand for SARCs among our study population (Table A4 provides a detailed breakdown of the method mix). The primary outcomes are constructed using data collected on the tablets, which are cross-checked with hospital administrative records that are further verified by a third-party independent auditor. A non-negligible number of clients do not adopt a method during their initial visit but return to HGOPY to adopt a method later – e.g., after taking some time to think about their decision; having discussed it with their partners, spouses, or parents; needing to collect the money necessary to adopt the method; etc. For these clients, the data across consultations are linked so that the most up to date outcome is considered.<sup>13</sup> Similarly, some clients may return to the hospital to switch or discontinue their adopted method, which are reflected in the primary outcomes if they happen within the study period.<sup>14</sup> Please see Appendix D for ethical considerations, which include IRB approvals, study registration, and protocols.

# **4 Results**

## **4.1 Estimation strategy**

We begin by presenting the impact of offering discounts for LARCs using: where _Yi_ denotes the outcome for client _i_ . _Li_ denotes binary LARC price indicators for whether clients were offered LARCs at _Low_ (CFA 150 or 0) or _Mid_ prices (CFA 2,000 or 1,000), respectively. _Xi_ denotes covariate adjustments, which, by design, only includes an indicator variable for SARCs

> 12We supplement these data with follow-up surveys of HGOPY clients, who were enrolled into the adaptive experiment that followed the pilot, within one to two weeks of their initial FP counseling session. We use these data to assess if service quality and client satisfaction differ by counseling approach.

> 13Survey evidence from the sample of clients enrolled in the ongoing adaptive experiment indicates that every client who adopted a method later, adopted it at HGOPY.

> 14Approximately 90% of clients, who have returned to HGOPY to adopt (or remove or switch) their chosen method, have done so within 100 days of their initial visit. Therefore, our data include clients whose initial visit was between 9 June 2020 and the 9 March 2021, who may have returned to HGOPY for a follow-up visit until 17 June 2021.

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being offered for _free_ . It is de-meaned and interacted with each treatment indicator, so that the estimated coefficients for LARC prices can be interpreted as the average treatment effects.<sup>15</sup> We then present the effects of counseling and their interaction with discounts for LARCs by running the same regression model separately for MiM, IDM, and SDM. Tests of equality of means across IDM and SDM for each level of discount yield the causal effect of IDM on contraceptive take-up, compared to SDM.

Table 1: Impacts of price discounts for LARCs on method adoption.

||(1)<br>**Adopted a LARC**|(2)<br>**Adopted a SARC**|(3)<br>**Adopted nothing**|
|---|---|---|---|
|LARC: Low|0.160|0.005|-0.165|
||(0.048)|(0.024)|(0.049)|
|LARC: Mid|0.132|-0.032|-0.100|
||(0.047)|(0.021)|(0.048)|
|High LARC price mean:|0.321|0.058|0.622|
|Diference: _β_<sup>_LARC_</sup><br>_Low_<br>_−β_<sup>_LARC_</sup><br>_Mid_|0.027|0.037|-0.065|
|p-value|0.495|0.031|0.110|
|Obs.|784|784|784|

Notes: Robust standard error in parentheses; Free/low price group were offered LARCs at CFA 0 or 150, Mid group at CFA 1,000 or 2,000, and the control group at CFA 4,000; column 1 shows the results of estimation on the likelihood of clients having adopted a LARC during the study period, column 2 a SARC, and column 3 neither a LARC nor a SARC; all columns show the base model with only the LARC and SARC price indicators centered and fully interacted so that coefficients on the main effects show average treatment effects.

# **4.2 Main results**

Tables A2 and A3 show that client characteristics are balanced across randomly assigned LARC prices and counseling interventions, respectively. Table 1 presents the overall impacts of offering discounts for LARCs on our three primary outcomes, averaged over counseling conditions and SARC prices: at the highest price in our study, i.e. CFA 4,000, 32% of the clients adopt a LARC (column 1). Offering LARCs for free or at a very small price (CFA 150) increases LARC adoption by 16 pp - a 50% increase. Smaller discounts (CFA 2,000 or 1,000) are almost equally effective in increasing LARC uptake.<sup>16</sup> Discounts for LARCs do not affect the uptake of SARCs (column 2), which implies that the large gains in LARC uptake are mainly obtained from a similar reduction in the share of clients who would have left HGOPY having adopted no modern method (column 3).

> 15We treat the SARC price indicator as a covariate rather than a treatment variable, because, as explained in footnote 5, we discontinued randomizing clients to _free_ or _full price_ SARCs towards the end of the pilot period. Table A5 shows, in the sub-sample that received random SARC discounts, that offering SARCs for free had no discernible effect on the take-up of SARCs or LARCs - either directly or in interaction with discounts for LARCs.

> 16Tables A6-A8 assess the sensitivity of these findings. Examining the granular impact at each price level, including additional controls using a double selection lasso estimator, or adding fully-interacted month fixed effects do not alter the conclusions. Contrasting Table A7 with Table 1 reveals that the impacts grow in the days following the initial visit, during which time some clients, who had initially adopted no method or a SARC, return to adopt a LARC.

10
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LARC prices: w Full a Discounted
a discount, on average, increases this share to 29%. Under _SDM_ , however, the uptake of LARCs at full price jumps to 35% - an increase of over 300% over _IDM_ . Interestingly, _SDM_ seems to also cause clients to become less sensitive to LARC prices: offering them discounts makes no difference to the adoption of LARCs. The difference in the effect of _Discounted_ LARCs between _IDM_ and _SDM_ is 18.1 pp (p=0.097).

In Appendix Section C, we present a model of individual utility maximization, where the clients care about the price of LARCs and the perceived returns to adopting them. This model provides straightforward implications that is consistent with _SDM_ causing a decline in the price elasticity of demand for LARCs. Importantly, follow-up surveys with clients indicate that this counseling approach maintained respect for their autonomy in decision-making: we found no differences between the two randomized counseling arms in a checklist of quality of service indicators, clients’ level of satisfaction with their counseling session, or their willingness to return to the hospital for FP services in the future (Table A10).

# **4.3 Heterogeneity of impacts**

In this sub-section, we ask, “What characterizes the client segment that is more likely to be persuaded by price discounts?” To answer this question, we first estimate the conditional average treatment effect (CATE) on LARC adoption of switching from _High_ prices to _Low_ or _Mid_ prices (as in the previous section, we binarized the treatment by pooling all the discounted prices). Then, we say an individual is relatively “strongly” affected by discounts if their estimated CATE on LARC adoption is above the median estimated CATE in the data, and relatively “weakly” affected otherwise, where we hold out folds of data in estimating the CATE so that an individual’s own outcome does not influence its subgroup assignment. We calculate the average treatment effect (ATE) for each subgroup. Finally, we compare the average value of each covariate of interest across the two subgroups. See, e.g., Chernozhukov et al. (2018, Section 3.2) or Athey and Wager (2019, Section 2.2) for similar methods.

We estimate the CATE non-parametrically by fitting three causal forests<sup>18</sup> (Athey et al., 2019). As our analysis in the previous section strongly suggests that individuals react differently to treatment depending on counseling regimes, we split our data into three separate subsets by counseling regime ( _MiM_ , _IDM_ or _SDM_ ), and fit a separate causal forest for each subset. We then concatenated their predictions to compute the results in this section; this is essentially equivalent to forcing the trees to split by counseling regime. Details are provided in Appendix Section F.

The first row in Panel A of Table 2 presents the ATE of discounts on LARC adoption by subgroup. For the _strongly_ -affected subgroup, we estimate an ATE of about 27 percentage points (pp), significant at the 1% level. Among those _weakly_ -affected by discounts, we cannot distinguish the effect from zero. The difference in ATE between the two groups is significantly different than zero at the 1% level. The second row in Panel A estimates the subgroup-specific ATE using augmented inverse propensity scores (AIPW), cross-fitted so that an individual’s outcome is not used to estimate the conditional mean or propensity score for themselves, enabling estimates with lower variance. The results are very similar across the two methods.

> 18Using the function `causal_forest` function implemented in the `R` package `grf` .

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Table 2: Average treatment effect within each subgroup defined by ranking

|(1)<br>(2)<br>**“Strong” group**<br>**“Weak” group**|(3)<br>**Diference**|
|---|---|
|_Panel A: Average subgroup ATE_||
|Sample averages<br>0.268<br>0.021|0.246|
|(0.059)<br>(0.061)|(0.084)|
|AIPW<br>0.255<br>0.029<br>(0.056)<br>(0.057)|0.226<br>(0.080)|
|_Panel B: Average subgroup ATE by counselling style_||
|SDM<br>-<br>-0.009<br>(0.091)|-|
|MiM<br>0.269<br>0.043|0.226|
|(0.073)<br>(0.117)|(0.138)|
|IDM<br>0.246<br>0.086|0.160|
|(0.081)<br>(0.090)|(0.121)|

Notes: “Strong” and “weak” refer to subgroups whose conditional average treatment effect (CATE) of switching from _High_ to _Discounted_ (i.e., _Low_ or _Mid_ ) prices is above (strong) or below (weak) the median CATE estimate in the data; Panel A shows estimates for the entire sample and Panel B shows estimates per counselling style. The ATE estimates in Panel B are sample averages.

Panel B of Table 2 further subdivides the sample depending on counseling assignment. Consistent with the analysis in the previous subsection, we find that clients receiving _SDM_ are all classified by the causal forest as weakly affected. Meanwhile, the “strong” group contains clients from both the _MiM_ and _IDM_ groups, and each of these subgroups show a positive and significant effect of discounts.

Table 3 characterizes clients in each subgroup. Clients who are strongly affected by discounted LARC prices are substantially younger than weakly-affected clients. Consequently, they are more likely to be students, to want to delay pregnancy by at least three years, and to want more children in the future. They also have higher levels of education and are more likely to present at the FP department to seek counseling. Figure B1 in the appendix presents LARC adoption rates under _High_ and _Discounted_ prices for each counseling regime by individual client characteristics. The patterns are consistent with Table 3.

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Table 3: Average covariate per data-driven subgroup (grf-based model).

||(1)|(2)|(3)|
|---|---|---|---|
||**“Strong”**<br>**group**|**“Weak”**<br>**group**|**Diference**|
|Age|27.025<br>|31.546<br>|-4.521<br>|
||(0.279)<br>|(0.386)<br>|[0.000]<br>|
|Cohabiting|0.378<br>|0.362<br>|0.017<br>|
||(0.024)|(0.024)|[0.699]|
|Married|0.302<br>|0.367<br>|-0.065<br>|
||(0.023)|(0.024)|[0.097]|
|Single|0.320|0.272|0.048|
||(0.024)|(0.023)|[0.228]|
|Family Planning|0.650|0.492|0.157|
||(0.024)|(0.025)|[0.000]|
|Education [Primary]|0.086<br>|0.174<br>|-0.088<br>|
||(0.014)|(0.019)|[0.001]|
|Education [Secondary]|0.492|0.405|0.087|
||(0.025)|(0.025)|[0.029]|
|Education [Tertiary]|0.421|0.421|0.001|
||(0.025)|(0.025)|[0.982]|
|Spacing [1-3 yrs or unsure]|0.353<br>|0.369<br>|-0.016<br>|
||(0.024)|(0.024)|[0.699]|
|Spacing [3+ yrs]|0.480|0.290|0.190|
||(0.025)|(0.023)|[0.000]|
|Spacing [No more children]|0.168|0.341|-0.174|
||(0.019)|(0.024)|[0.000]|
|Occupation [Salaried]|0.332<br>|0.344<br>|-0.011<br>|
||(0.024)|(0.024)|[0.780]|
|Occupation [Domestic]|0.206<br>|0.241<br>|-0.035<br>|
||(0.020)|(0.022)|[0.351]|
|Occupation [Self-employed]|0.195|0.221|-0.025|
||(0.020)|(0.021)|[0.479]|
|Occupation [Student]|0.266<br>|0.195<br>|0.072<br>|
||(0.022)|(0.020)|[0.033]|
|Children|2.421|3.079|-0.658|
||(0.079)|(0.100)|[0.000]|
|Modern contraceptive|0.061|0.079|-0.019|
||(0.012)|(0.014)|[0.406]|
|Ideal num. of children|2.558|1.956|0.602|
||(0.091)|(0.096)|[0.000]|
|Counselling style [MiM]|0.726|0.254|0.472|
||(0.023)|(0.022)|[0.000]|
|Counselling style [IDM]|0.274|0.241|0.033|
||(0.023)|(0.022)|[0.406]|
|Counselling style [SDM]|0.000|0.505|-0.505|
||(0.000)|(0.025)|[0.000]|

Notes: _Strong_ and _Weak_ refer to subgroups whose conditional average treatment effect (CATE) when switching from zero to positive discounts. A positive number in the “Difference” column indicates that the average covariate value for the “strong” subgroup is higher. Standard errors in parenthesis. P-values for the difference between groups shown in square brackets (using Benjamini-Hochberg correction for multiple hypothesis testing). 14
# **5 Concluding discussion**

We conducted a randomized experiment to tackle two main barriers to the uptake of contraceptives: credit constraints and information. Offering discounts was very effective in increasing LARC adoptions, particularly among younger, unmarried clients. Clients presenting in the maternity or the gynecology ward, most of whom had recently given birth, also responded strongly to low prices for LARCs (Table A11). While increasing demand for LARCs by reaching more women outside health care settings is surely important, current clients of clinics represent an important opportunity. Checklists for antenatal visits, childhood vaccinations, postpartum and post-abortion clients should all include an item to ask whether the client is using an effective contraceptive method and, if not, whether she would like a brief counseling session (accompanied with improved and discounted services). Our findings are consistent with recent evidence on the effect of price reductions in increasing the uptake of LARCs. They are also consistent with the use of incentives to increase the take-up of other preventive health measures, such as immunizations in India (Banerjee et al., 2021) or male circumcisions in South Africa (Friedman and Wilson, 2021).

Counseling clients with the shared decision-making approach ( _SDM_ ) substantially increased the adoption of LARCs at full price. In fact, _SDM_ was so effective in raising willingness-to-pay for LARCs that offering discounts to these clients did not further increase adoption. This is particularly striking given the relatively minor differences between _SDM_ and _IDM_ : in both cases the client can choose (or decline) to discuss one method in more detail first, followed by other methods until she decides. The only variation is in whether or not the _app_ suggests an initial order of discussion. This tweak caused increases in LARC uptake that were as large as the effect of providing these methods for _free_ under _IDM_ . As such, we not only show that LARC uptake can be substantially increased within a comprehensive framework for high-quality counseling (Holt et al., 2017), but also that such a shift might be cost-effective in preventing unintended pregnancies without causing any trade-offs in client satisfaction or autonomy.

Finally, the heterogeneity analysis suggests that unmarried adolescents may be highly motivated to avoid unintended pregnancies and are responsive to low prices. For example, clients who respond strongly to discounts for LARCs are more likely to be students than those who respond weakly. Campaigns to reach adolescent girls and young women might be successful if they can combine high-quality counseling with affordable prices and convenient (and discreet) opportunities to adopt. This is consistent with recent evidence, also from Cameroon, which showed that providing sexual and reproductive health information to adolescents at school can decrease the incidence of teenage pregnancies (Dupas et al., 2018).

# **References**

Anukriti, S., C. Herrera-Almanza, and M. Karra (2021). Women’s access to family planning: Experimental evidence on the role of peers and vouchers. Technical report.

- Ashraf, N., E. Field, and J. Lee (2014). Household bargaining and excess fertility: an experimental study in zambia. _American Economic Review 104_ (7), 2210–37.

15
- Ashraf, N., E. M. Field, A. Voena, and R. Ziparo (2020). Maternal mortality risk and spousal differences in the demand for children. Technical report, National Bureau of Economic Research.

- Athey, S., J. Tibshirani, S. Wager, et al. (2019). Generalized random forests. _Annals of Statistics 47_ (2), 1148–1178.

- Athey, S. and S. Wager (2019). Estimating treatment effects with causal forests: An application. _arXiv preprint arXiv:1902.07409_ .

- Baird, S., C. McIntosh, and B. Özler (2011). Cash or condition? evidence from a cash transfer experiment. _The Quarterly journal of economics 126_ (4), 1709–1753.

- Banerjee, A., A. G. Chandrasekhar, S. Dalpath, E. Duflo, J. Floretta, M. O. Jackson, H. Kannan, F. N. Loza, A. Sankar, A. Schrimpf, et al. (2021). Selecting the most effective nudge: Evidence from a large-scale experiment on immunization. Technical report, National Bureau of Economic Research.

- Belloni, A., V. Chernozhukov, and C. Hansen (2014). Inference on treatment effects after selection among high-dimensional controls. _The Review of Economic Studies 81_ (2), 608–650.

- Bellows, B., C. Bulaya, S. Inambwae, C. L. Lissner, M. Ali, and A. Bajracharya (2016). Family planning vouchers in low and middle income countries: a systematic review. _Studies in family planning 47_ (4), 357–370.

- Bloom, D. E., D. Canning, G. Fink, and J. E. Finlay (2009). Fertility, female labor force participation, and the demographic dividend. _Journal of Economic growth 14_ (2), 79–101.

- Bloom, D. E., M. Kuhn, and K. Prettner (2020, 05). The contribution of female health to economic development. _The Economic Journal 130_ (630), 1650–1677.

- Bohnet, I., A. Van Geen, and M. Bazerman (2016). When performance trumps gender bias: Joint vs. separate evaluation. _Management Science 62_ (5), 1225–1234.

- Chernozhukov, V., M. Demirer, E. Duflo, and I. Fernandez-Val (2018). Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immunization in india. Technical report, National Bureau of Economic Research.

- Church, K., C. E. Warren, I. Birdthistle, G. B. Ploubidis, K. Tomlin, W. Zhou, J. Kimani, T. Abuya, C. Ndwiga, S. Sweeney, et al. (2017). Impact of integrated services on hiv testing: a nonrandomized trial among kenyan family planning clients. _Studies in family planning 48_ (2), 201–218.

- Cortés, P., J. Pan, L. Pilossoph, and B. Zafar (2021). Gender differences in job search and the earnings gap: Evidence from business majors. Technical report, National Bureau of Economic Research.

- DeFranco, E. A., L. M. Seske, J. M. Greenberg, and L. J. Muglia (2015). Influence of interpregnancy interval on neonatal morbidity. _American Journal of Obstetrics and Gynecology 212_ (3), 386.e1 – 386.e9.

- DHS (2018). Republique du cameroun: Enquete demographique et de sante 2018. Technical report, Institut National de la Statistique du Cameroun (INS), and ICF, Yaounde, Cameroun.

16
- Donovan, J. L. (1995). Patient decision making: The missing ingredient in compliance research. _International Journal of Technology Assessment in Health Care 11_ (3), 443–455.

- Duflo, E., P. Dupas, and M. Kremer (2015). Education, hiv, and early fertility: Experimental evidence from kenya. _American Economic Review 105_ (9), 2757–97.

- Dupas, P. (2011). Do teenagers respond to hiv risk information? evidence from a field experiment in kenya. _American Economic Journal: Applied Economics 3_ (1), 1–34.

- Dupas, P., E. Huillery, and J. Seban (2018). Risk information, risk salience, and adolescent sexual behavior: Experimental evidence from cameroon. _Journal of Economic Behavior & Organization 145_ , 151–175.

- Friedman, W. and N. Wilson (2021). Can nudging overcome procrastinating on preventive health investments? _Economics Human Biology_ , 101040.

- Frost, J. J., A. Sonfield, M. R. Zolna, and L. B. Finer (2014). Return on investment: A fuller assessment of the benefits and cost savings of the us publicly funded family planning program. _The Milbank Quarterly 92_ (4), 696–749.

- Gilliam, M. L., S. L. Martins, E. Bartlett, S. Q. Mistretta, and J. L. Holl (2014). Development and testing of an ios waiting room “app” for contraceptive counseling in a title x family planning clinic. _American journal of obstetrics and gynecology 211_ (5), 481–e1.

- Holt, K., C. Dehlendorf, and A. Langer (2017). Defining quality in contraceptive counseling to improve measurement of individuals’ experiences and enable service delivery improvement. _Contraception 96_ (3), 133–137.

- Jain, A., K. Aruldas, A. Mozumdar, E. Tobey, and R. Acharya (2019). Validation of two quality of care measures: results from a longitudinal study of reversible contraceptive users in india. _Studies in family planning 50_ (2), 179–193.

- Johnson, E. J., M. Steffel, and D. G. Goldstein (2005). Making better decisions: from measuring to constructing preferences. _Health Psychology 24_ (4S), S17.

- Kassebaum, N. J., R. M. Barber, Z. A. Bhutta, L. Dandona, P. W. Gething, S. I. Hay, Y. Kinfu, H. J. Larson, X. Liang, S. S. Lim, et al. (2016). Global, regional, and national levels of maternal mortality, 1990–2015: a systematic analysis for the global burden of disease study 2015. _The Lancet 388_ (10053), 1775–1812.

- Lindo, J. M. and A. Packham (2017). How much can expanding access to long-acting reversible contraceptives reduce teen birth rates? _American Economic Journal: Economic Policy 9_ (3), 348–76.

- McKelvey, C., D. Thomas, and E. Frankenberg (2012). Fertility Regulation in an Economic Crisis. _Economic Development and Cultural Change 61_ (1), 7–38.

- Mestad, R., G. Secura, J. E. Allsworth, T. Madden, Q. Zhao, and J. F. Peipert (2011). Acceptance of long-acting reversible contraceptive methods by adolescent participants in the contraceptive choice project. _Contraception 84_ (5), 493–498.

17
- Rau, T., M. Sarzosa, and S. S. Urzúa (2017). The children of the missed pill. Technical report, National Bureau of Economic Research.

- Shah, Manishah and, S. J., J. Montalvao, and M. Goldstein (2021). Two sides of gender: Sex, power, and adolescence. Technical report.

- Sully, E., A. Biddlecom, J. E. Darroch, T. Riley, L. S. Ashford, N. Lince-Deroche, L. Firestein, and R. Murro (2020). Adding it up: investing in sexual and reproductive health 2019.

- Trussell, J. and K. Guthrie (2011). Contraceptive efficacy. _Contraceptive Technology. 20th ed. New York: Ardent Media_ .

- WHO (2019). Trends in maternal mortality 2000 to 2017: estimates by who, unicef, unfpa, world bank group and the united nations population division. Technical report.

- Wilson, E. K., K. E. Krieger, H. P. Koo, A. M. Minnis, and K. Treiman (2014). Feasibility and acceptability of a computer-based tool to improve contraceptive counseling. _Contraception 90_ (1), 72–78.

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