metadata: authors: 0: author: Neelsen,Sven
1: author: De Walque,Damien B. C. M.
2: author: Friedman,Jed
3: author: Wagstaff, Adam
count: World
docna: 0: docna: Financial Incentives to Increase Utilization
            of Reproductive, Maternal, and Child Health Services in Low-
            and Middle-Income Countries : A Systematic Review and Meta-Analysis
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Health Service Management and Delivery,Taxation & Subsidies,Reproductive Health,Early Child and Children's Health
teratopic: Industry,Macroeconomics and Economic Growth,Health, Nutrition and Population
abstracts: cdata!: Financial incentives for health
            providers and households are increasingly used to improve
            reproductive, maternal, and child health service coverage in
            low- and middle-income countries. This study provides a
            quantitative synthesis of their effectiveness. A systematic
            review was conducted of the effects of performance-based
            financing, voucher, and conditional cash transfer programs
            on six reproductive, maternal, and child health service
            indicators, with eligible evidence coming from randomized
            controlled trials and studies using double-difference,
            instrumental variables, and regression discontinuity
            designs. Four literature searches were conducted between
            September 2016 and March 2021 using seven academic
            databases, Google Scholar, development agency and think tank
            websites, and previous systematic reviews. Random effects
            meta-analysis was used to obtain mean effect sizes. From 58
            eligible references 212 impact estimates were extracted,
            which were synthesized into 130 program-specific effect
            sizes. Financial incentives increase coverage of all
            considered reproductive, maternal, and child health
            indicators, but mean effects sizes are of modest magnitude.
            Effect size heterogeneity is typically low to moderate, and
            there is no indication that study bias risk, baseline
            indicator levels, or a combination of provider- and
            household-level incentives impact effect sizes. There is,
            however, weak evidence that mean effect sizes are somewhat
            smaller for performance-based financing than for voucher and
            conditional cash transfer programs, and that the increase in
            income, rather than the incentive itself, drives coverage
            improvements. Financial incentives improve reproductive,
            maternal, and child health service coverage. If future
            research confirms the preliminary finding that
            performance-based financing has smaller effects, voucher and
            conditional cash transfer programs are the preferred policy
            option among incentive interventions to achieve higher
            reproductive, maternal, and child health service coverage.
            The relative effectiveness and efficiency of incentives
            compared with unconditional increases of provider and
            household incomes, however, need to be studied further.
repnme: repnme: Financial Incentives to Increase
            Utilization of Reproductive, Maternal, and Child Health
            Services in Low- and Middle-Income Countries : A Systematic
            Review and Meta-Analysis
display_title: Financial Incentives to Increase
            Utilization of Reproductive, Maternal, and Child Health
            Services in Low- and Middle-Income Countries : A Systematic
            Review and Meta-Analysis
listing_relative_url: /research/2021/10/33464253/financial-incentives-increase-utilization-reproductive-maternal-child-health-services-low--middle-income-countries-systematic-review-meta-analysis
topicv3: Infant and Child Health m1326775 642,Sexual Reproductive, Maternal and Child
            Health m1327262 639,Health Services Delivery m1327294 1305,Health m1504661 3480,Vouchers m1504820 3553
publishtoextweb_dt: 2021-10-04T00:00:00Z
dois: 10.1596/1813-9450-9793
historic_topic: Industry,Macroeconomics and Economic Growth,Health, Nutrition and Population
origu: DECRG: Human Development (DECHD)
no_of_pages: 71
author_keys: 0: author_key: 1435450
1: author_key: 687876
2: author_key: 669320
3: author_key: 142270
model_extractions: Policy Research Working Paper 9793 Financial Incentives to Increase Utilization of Reproductive, Maternal, and Child Health Services in Low- and Middle-Income Countries A Systematic Review and Meta-Analysis _Sven Neelsen Damien de Walque Jed Friedman Adam Wagstaff_ Development Economics Development Research Group October 2021
Policy Research Working Paper 9793

# **Abstract**

Financial incentives for health providers and households are increasingly used to improve reproductive, maternal, and child health service coverage in low- and middle-income countries. This study provides a quantitative synthesis of their effectiveness. A systematic review was conducted of the effects of performance-based financing, voucher, and conditional cash transfer programs on six reproductive, maternal, and child health service indicators, with eligible evidence coming from randomized controlled trials and studies using double-difference, instrumental variables, and regression discontinuity designs. Four literature searches were conducted between September 2016 and March 2021 using seven academic databases, Google Scholar, development agency and think tank websites, and previous systematic reviews. Random effects meta-analysis was used to obtain mean effect sizes. From 58 eligible references 212 impact estimates were extracted, which were synthesized into 130 program-specific effect sizes. Financial incentives increase coverage of all considered reproductive, maternal, and child health indicators, but mean effects sizes are of modest magnitude. Effect size heterogeneity is typically low to moderate, and there is no indication that study bias risk, baseline indicator levels, or a combination of provider- and household-level incentives impact effect sizes. There is, however, weak evidence that mean effect sizes are somewhat smaller for performance-based financing than for voucher and conditional cash transfer programs, and that the increase in income, rather than the incentive itself, drives coverage improvements. Financial incentives improve reproductive, maternal, and child health service coverage. If future research confirms the preliminary finding that performance-based financing has smaller effects, voucher and conditional cash transfer programs are the preferred policy option among incentive interventions to achieve higher reproductive, maternal, and child health service coverage. The relative effectiveness and efficiency of incentives compared with unconditional increases of provider and household incomes, however, need to be studied further.

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 sneelsen@worldbank.org; ddewalque@worldbank.org; and jfriedman@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
# **Financial Incentives to Increase Utilization of Reproductive, Maternal, and Child Health Services in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis**

Sven Neelsen<sup>a</sup> , Damien de Walque<sup>b</sup> , Jed Friedman<sup>b</sup> , Adam Wagstaff<sup>b</sup> _aHealth, Nutrition, and Population Unit, The World Bank, Washington DC, USA_ _bDevelopment Research Group, The World Bank, Washington DC, USA_ <u>sneelsen@worldbank.org; ddewalque@worldbank.org; jfriedman@worldbank.org</u> JEL Codes: I15, I12, I11 Keywords: Financial Incentives; Performance-Based Financing; Conditional Cash Transfers; Vouchers; Reproductive, Maternal and Child Health, Systematic Review; Meta-Analysis
# **Acknowledgments**

This research was funded through the Health Results Innovation Trust Fund (HRITF) and the Knowledge for Change Program (KCP). We are grateful to Amanda Kerr, Peijing Li, Paula Mian, Jie Ren Gesabel Villar, and Shabab Wahid for providing outstanding research assistance. The funding sources had no involvement in the conduct of the research and the preparation of the manuscript. We also thank participants in the Fourth Global Symposium on Health Systems Research in Vancouver 2016, the iHEA World Congress 2017 in Boston, the Joep Lange Institute Mini-Symposium on Combining Expertise to Improve Healthcare 2017, the iHEA World Congress 2019 in Basel, and several internal seminars at the World Bank for their valuable comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **Introduction**

Aiming to increase the coverage and quality of health care services, many health systems are shifting their health financing strategy away from exclusively low-powered incentives such as salaries and budgets towards higher-powered incentives involving a mix of salaries/budgets and bonuses linked to performance. This push toward performance-based financing (PBF) or performance-related pay (P4P) has proceeded apace despite a relative lack of generalized evidence on its impacts, especially in low- and middle-income countries (LMICs). There is also a growing use of health service vouchers, which introduce incentives on both the demand and supply sides by entitling users to free or highly subsidized care while guaranteeing providers reimbursement for services rendered, and of conditional cash transfers (CCTs), which focus on the demand side and financially reward households for compliance with health and other service utilization requirements. These disparate financing approaches, categorized in Figure 1, have the common aim to increase health service uptake by lowering the price, relative to income, of accessing or providing care.

A burgeoning evaluative literature has explored the effectiveness of individual financial incentive interventions on health service coverage, and an increasing number of reviews are available that synthesize this growing evidence base. For PBF, the most recent comprehensive such review, for which literature searches were conducted in 2018, finds the evidence on reproductive, maternal and child health (RMCH) service coverage to be inconsistent and of low overall certainty.<sup>1</sup> By comparison, the latest reviews of voucher and CCT programs – for which literature searches date back five years or longer – find more consistent positive impacts, in particular on family planning (vouchers) and maternity care, whereas effects on childhood vaccination were inconclusive.<sup>2-5</sup> With the exception of a small number of reviews of CCT programs,<sup>6-9</sup> which form a literature that emerged earlier than that on PBF, and one review of voucher impacts on family planning,<sup>10</sup> the existing reviews are narrative in nature. Due to this absence of quantitative syntheses, the average magnitude and heterogeneity of effect sizes of financial incentive interventions, which form important parameters for policy decisions, remain unknown to date. Harnessing a substantial number of new studies – for instance, there is now evidence from PBF interventions in five countries which were not yet included in the previous most recent systematic review – we attempt to address this knowledge gap. Specifically, we 1
provide the first meta-analysis of financial incentive impacts on RMCH service coverage across PBF, voucher, and CCT programs, and estimate mean effect sizes for each intervention type separately. Moreover, while subgroup comparisons remain challenging given statistical power limitations, we also undertake a first quantitative analysis of possible sources of effect size heterogeneity, investigating, for instance, the role of baseline service coverage levels as well as whether programs that combine supply- with demand-side incentives are associated with larger effects on RMCH service coverage.

# **Methods**

# **_Search strategy and selection criteria_**

We conduct a systematic review and meta-analysis of the impacts of health financing interventions on RMCH service utilization in low- and middle-income countries (LMICs). A study protocol was published on the PROSPERO website in November 2016 <u>(https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=49272).</u> We use the following criteria to identify relevant studies. In terms of _publication format_ , we include studies in English that were published either in peer-reviewed scientific journals, as part of a working papers series, in books (with ISBN numbers), as doctoral dissertations, or official research or project reports. This excludes unofficial draft reports, conference abstracts, comments, op-eds, media briefings, and bachelor’s or master’s theses.

Regarding _interventions_ , we include those taking place in countries classified as low- or middleincome by the World Bank in at least one year in the 1987-2019 period and falling into one of three categories of financial incentivization: PBF of providers of primary or secondary care that reward RMCH service provision, vouchers which give beneficiaries free or subsidized access to RMCH services for which providers are reimbursed on a fee-for-service basis, and CCTs designed to financially reward enrollees for compliance with RMCH service use conditions. Because they are based on a different theory of change, this review will not consider interventions which affect the monetary price of providing or using MCH services only indirectly or implicitly. On the supply-side, omitted interventions include health worker training, provider performance tournaments, and the introduction of mobile health units or health worker home visits. On the demand side, we exclude interventions like information campaigns, 2
unconditional cash transfers and conditional cash transfer schemes which do not condition on MCH service use or employ soft conditions or co-responsibilities.

In terms of _outcomes_ , this review focuses on six indicators that either represent the official and supplemental RMCH indicators of the Millennium Development Goals (MDGs)<sup>11</sup> or are intermediate indicators critical to their achievement, namely the shares of (1) women of fertile age who use modern contraceptives, (2) pregnancies with four or more antenatal care checks, (3) pregnant women receiving tetanus vaccination, (4) births occurring in health facilities, (5) births with postnatal care, and (6) children receiving the full course of vaccinations recommended for the first year of life. Importantly, we only include impact estimates of outcomes whose provision is financially incentivized by the interventions under study. This incentivization may be direct, e.g. a fee the health facility receives for each birth taking place in it, or it may be indirect – e.g. maternal tetanus vaccination being incentivized in a CCT which rewards pregnant women for antenatal care visits during which maternal tetanus vaccinations are carried out. By contrast, we exclude impact estimates of outcomes without financial incentivization, e.g. those measuring an intervention’s unintended consequences. Finally, we only include impact estimates from samples representing the entire population of program beneficiaries – for instance, impacts on antenatal care content are included if based on a sample of all pregnancies and excluded if they come from the subsample of antenatal care users.

_Methodologically_ , we only include evidence from household survey data because of sample selectivity and reporting bias concerns in health facility and administrative data sets from LMICs.<sup>12-15</sup> Regarding study design, we include randomized controlled trials (RCTs) as well as evaluations of non-randomized interventions which identify impacts using regression discontinuity (RD), instrumental variables (IV), or double difference (DD) and triple difference (DDD) models. The parameter of interest in this review is a program’s intention-to-treat (ITT) effect – the impact on its full target population which consists of both compliers and noncompliers. We thus exclude effects estimated only for compliers, e.g. for enrollees of a CCT scheme, as opposed to its entire target group. As a requirement for meta-analysis, we only include impact estimates if they are presented with a measure of statistical uncertainty.

To identify relevant references, in September 2016, we searched the Cinhal, Cochrane Library, Econlit, Embase, Medline, Popline, and Scopus databases as well as Google Scholar for 3
references in English published from January 1, 1987. Appendix 2 provides our Medline search strings as an example. We also searched think tank and development agency websites, and previous systematic reviews identified in our scientific database searches or via the Cochrane Library, the EPPI-center, and the 3ie systematic review repository. Finally, we posted calls for relevant references on social media and in blogs. We repeated our search using the same sources in August 2017, September 2018, October 2019, and March 2021.

SN carried out the electronic database searches and SN and a graduate research assistant (RA) both independently searched websites and previous reviews. All identified references were subsequently title-abstract screened in a mutually blinded process by SN and the RA, and screening decision conflicts were resolved by DDW, JF, and AW. All studies deemed eligible in the title-abstract screening were then full-text scanned by two RAs – again mutually blinded – for compliance with the review’s inclusion criteria. Full-text scanning conflicts were resolved by SN.

After reviewing all references deemed eligible after the full-text scan, we applied an additional selection criterion not stipulated in the PROSPERO protocol by excluding evidence from a few reports which use continuous treatment variables (e.g. the population share of program beneficiaries). Impact estimates from such reports are not comparable to those from binary treatment variables which are used by a great majority of the studies we identified.

# **_Data analysis_**

# _Data extraction_

For data extraction, as for eligibility screening, we used an independent, mutually blinded process with two reviewers and subsequent resolution of coding conflicts by a third reviewer. Qualitative data on program features and context were extracted by two RAs. Quantitative data – impact estimates, measures of statistical uncertainty, sample size, and the type of econometric model – were extracted by SN and an RA. When multiple impact estimates for the same indicator were available for a program, we took the following approach: (1) In the case of multiple impact estimates in the same report (e.g. across different statistical models or model specifications) we extracted authors’ _preferred_ impact estimates if these were identified by the authors explicitly or through mention in the abstract. If no preferred estimates were identified, 4
we extracted all available impact estimates; (2) In the case of multiple impact estimates across different versions of a report (e.g. a working paper and a journal article), we extracted the impact estimates from the latest version; (3) In the case of multiple impact estimates across different reports of the same program, we extracted the impact estimates from each available report.

# _Effect size standardization and aggregation_

Because this review shows effect sizes in percentage points, we convert impact estimates reported in other units – log odds ratios, odds ratios or risk ratios – to percentage points using formulas<sup>_16_</sup> we provide in Appendix 3. The appendix also describes how we obtain 95% confidence intervals and standard errors from other reported measures of statistical uncertainty. After obtaining percentage point effect sizes for all impact estimates, we aggregate to a single effect size per indicator and program if needed – like in the case of the multiple estimates of the impact of Rwanda’s P4P scheme on facility delivery. Following Borenstein et al.,<sup>17</sup> the aggregation method depends on the overlap of the samples from which the impact estimates were obtained: If they come from independent, non-overlapping samples, we aggregate using random effects meta-analysis; If there is partial or full sample overlap, we aggregate by forming the unweighted means of the impact estimates and standard errors. Additional details of the aggregation method are provided in Appendix 4.

# _Mean effect size computation_

The financial incentive interventions in this review do not only take one of three forms, but also differ by implementation context, e.g. country and start year, by design features like their scope (pilot vs at-scale), the intensity of incentives relative to baseline financing, whether they include a complementary supply or demand side component, as well as by the methods they were evaluated with (Tables 1 and 2). Because of this heterogeneity, we estimate overall financial incentive and PBF, voucher and CCT mean effect sizes and confidence intervals using random effects models which take into account the possibility that differences between impact estimates across financial incentive interventions may not only result from sampling error but also genuine difference in program effectiveness.<sup>17</sup> The usefulness of such mean effect sizes computation depends not on the number of included studies but on the degree of heterogeneity in outcome and intervention designs between them – 5
in fact, if outcomes and interventions are similar enough, meta-analysis is feasible as soon as two studies are available.<sup>18,16,19</sup> As we discuss further below, while outcome variable definitions are very similar across the studies in our review, differences in intervention design and contexts can be substantial even within the three intervention types. While cognizant of this limitation, we deem a quantitative synthesis of individual studies useful and timely, as financial incentive interventions as a whole, and each of our three intervention groups individually, have well defined common characteristics (Figure 1). Because of these commonalities, policy discussions often feature ‘financial incentives in health’, PBF, vouchers, and CCT programs as intervention groups. Obtaining mean effect sizes through meta-analysis of all available evidence is therefore preferable over the ad-hoc, implicit aggregation of often selective study results, which is frequently undertaken in its absence.

# _Effect size heterogeneity_

To assess the degree of ‘true’ effect size heterogeneity across financial incentive interventions and within intervention groups, we report _I_<sup>_2_</sup> -statistics and their _p_ -values. The _I_<sup>_2_</sup> - statistic represents the share of the variation around mean effect size that is explained by genuine differences in program-specific effect sizes (‘between-study variance’), as opposed to mere sampling error (‘within-study variance’).<sup>17</sup> Following Cochrane collaboration guidelines, we consider _I_<sup>_2_</sup> -statistics of 0-40, 40-60, and 60+ percent to indicate low, moderate, and substantial heterogeneity, respectively.<sup>17</sup> To further investigate the distribution of effect sizes around their meta-analytical mean, our forest plots include their prediction intervals as horizontal lines on both sides of the _diamonds_ that show the mean effect size confidence intervals. Prediction intervals form an important policy parameter, as they show the estimated range that 95 percent of program effect sizes – and therefore future interventions’ impacts – fall into.<sup>20</sup> They are distinct from the confidence intervals which, by contrast, show the estimated 95 percent range of the effect size means.

# _Subgroup analysis_

Our analysis includes estimating differences between the mean effect sizes of different subgroups of financial incentive interventions. The most important such subgroup analysis investigates mean effect size differences between the PBF, voucher, and CCT intervention groups. The intervention-group-specific mean effect size point estimates in our forest plots 6
inform about the relative effectiveness of PBF, voucher and CCT programs. Comparisons of the statistical significance of mean effect sizes across groups should, however, be avoided, as variation in the number of underlying program specific effect sizes (and, in turn, the number of observations underlying them) can make such comparisons highly misleading. Instead, we obtain the statistical significance of mean effect size differences between subgroups from bivariate, random effects meta-regressions, assuming similar between-study variances across subgroups.<sup>16</sup> Aside from differences across intervention types, we use this approach to test for differences between interventions introducing supply and demand side incentives simultaneously as opposed to intervening only on the facility or care user side, between interventions using control groups which receive an unconditional income increase equal to the average incentive payout in the treatment group as opposed to those where the control group maintains the pre-intervention health financing status quo, between interventions with varying levels of baseline service coverage, and, to examine the role of publication and methodological bias, between interventions for which effect sizes are based on studies with high as opposed to low or moderate bias risk. Because we carry out this relatively large number of meta-regression subgroup analyses, there is a risk of Type I error (false positives) from multiple hypotheses testing. Following Borenstein et al.,<sup>17</sup> we address this risk by using the 99 instead of the 95 percent threshold to determine statistical significance.

For all our outcomes, the number of effect sizes in each of the subgroups we analyze meets or exceeds the minimum thresholds of four for categorical and ten for continuous grouping variables that have been suggested for subgroup analysis based on studies with moderate or large sample size like those in our review.<sup>21</sup> Our analysis, however, remains insufficiently powered to precisely estimate a mean effect size difference of a small magnitude, so that the risk of Type II error (false negatives) must be kept in mind when interpreting the statistical significance of our meta-regression results. A simple, ex-post power calculation using our estimated mean effect sizes and standard errors, however, suggests that all but four of our meta-regressions are powered to estimate differences in the 3-9 percentage point range at the 1 percent significance level – a range narrow enough to ensure our analysis detects most differences of economically significant magnitude.

7
Our meta-regression, like all other quantitative analyses in this review, are conducted with Stata version 16.0, primarily relying on the metan and metareg commands.

# _Risk of bias assessment_

We grade studies as having low, medium and high risk of bias with a tool developed for reviews in social science<sup>22</sup> based on suggestions by the Cochrane Effective Practice Organisation of Care,<sup>23</sup> the Coalition for Evidence-Based Policy,<sup>24</sup> and the Cochrane Handbook for Systematic Reviews of Interventions<sup>16</sup> which classifies studies into high, medium or low bias risk groups. The tool is presented in Appendix 5, and the bias assessment results for each included study is shown in Table 1, alongside information on the studies’ evaluation design.

# **Results**

# **_Search and data extraction results_**

The PRISMA chart shown in Figure 2 depicts the results of our search and eligibility screening process. In the initial 2016 search round, we extracted a total of 6,289 references. After deduplication, title-abstract screening identified 470 potentially eligible references, of which 30 proved compliant with our inclusion criteria after full-text screening. Reasons for exclusion varied across interventions but methodological grounds such as the use of non-compliant identification strategies or the lack of statistical uncertainty estimates were common across all. In repeated searches in 2017, 2018, and 2019, and 2021, we found 28 additional references meeting our inclusion criteria, bringing the total number of included references to 58, with 24 reporting on PBF programs, one on vouchers and PBF, eight on vouchers alone, and 25 on CCT programs alone. From the 58 references, we extracted a total of 212 impact estimates across our six outcomes of interest. When aggregating to the program level, there are 130 program-specific effect sizes, with 75 effect sizes from 22 PBF programs, 31 effect sizes from ten voucher programs, and 34 from 20 CCT programs (for studies with multiple treatment arms we consider as separate programs treatment arms which differ in terms of having status quo as opposed to income equalized control groups, or in terms of introducing complementary demand- or supplyside financial incentives). Appendix 6 provides a breakdown of references, impact estimates, and program-specific effect sizes per outcome.

# **_Study characteristics_**

8
Tables 1 and 2 summarize key characteristics of the 52 programs and their evaluations in our review. Study designs, program characteristics, and implementation contexts vary both across and within the three intervention groups. About 55 percent of the studies in our review have randomized designs, which are most common for CCT programs, and only three studies rely on IV and RDD models to identify program impacts. Due to our strict methodological inclusion criteria, the share of studies with high bias risk is only 19 percent, while we classify 53 percent of studies as low bias risk.

In terms of implementation context, 82 percent of PBF programs in our review are in SubSaharan Africa, compared to 40 percent of voucher and 35 percent of CCT programs. The distribution is more balanced regarding country income groups, where 55 percent of PBF, 70 percent of voucher, and 50 percent of CCT programs are in low-income countries. With a median first implementation year of 2011, PBF programs are somewhat younger than vouchers and CCT schemes where the median year is 2009.

Regarding program characteristics, most programs were in various stages of piloting during evaluation, with only two PBF programs (Burundi’s PBF and Rwanda’s P4P scheme) and four CCT programs (India’s JSY, Mexico’s Progresa, Peru’s Juntos and Turkey’s Social Risk Mitigation programs) having nationwide or near nationwide scope. A small number of programs (India’s JSY, Kenya’s M-SIMU CCT pilot, and the Suraj and Chakwal vouchers in Pakistan) incentivize only a single health service, while the other programs typically target a broad range of family planning and maternal and child health indicators. Information on the magnitude of incentives relative to baseline facility, health worker, or household incomes is often lacking or difficult to compare across programs, but where available indicates substantial variation. In the Tajik PBF pilot, for instance, incentive payments amount to 70 percent of base health worker salaries – more than twice the rate as in the Afghanistan PBF pilot. Three PBF, five CCT, and, by definition, all ten voucher programs combine demand and supply side financial incentives instead of incentivizing either the supply or demand side alone. Finally, in five PBF schemes and one CCT, control observations, instead of remaining untreated, received lump sum payments equivalent to the average size of the treatment group incentive. For these programs, the estimated effect sizes identify the impact of the incentive alone, instead of the combined impact of incentives and increased financial means, as is the case for all other effect sizes in this review.

9
We use relatively narrow service coverage variable definitions to identify effect sizes eligible for our review in order to minimize the risk of outcome variable heterogeneity as a confounding factor. Nevertheless, variation in – and uncertainty about – coverage variable definitions remains (Table 3). For instance, reports estimating impacts on modern family planning sometimes do not list the specific contraceptive types they include, and among studies with explicit reporting of contraceptive types, some differences, e.g. whether condoms are included, can exist. However, examining the robustness of overall and subgroup specific mean effect sizes to the omission of studies with diverging indicator definitions in Appendix 7, we do not find meaningful differences from the main estimates reported.

# **_Mean effect sizes_**

# _Modern family planning_

The forest plot for modern family planning in Figure 3 shows a statistically significant mean effect size of 3.7 percentage points and a moderate level of effect size heterogeneity ( _I_<sup>_2_</sup> = 48.2 percent) across 16 financial incentive interventions. The PBF mean effect size amounts to a statistically significant 2.4 percentage points, with low heterogeneity across underlying program specific effect sizes. Consequently, the prediction interval indicates that at least 95 percent of PBF programs will yield positive impacts on modern family planning. For the four voucher programs, the mean effect size is 6.2 percentage points, but it is statistically indistinguishable from zero and unevenly distributed across underlying programs with an _I_<sup>_2_</sup> -statistic of 77 percent indicating substantial effect size heterogeneity. The difference in mean effect sizes between PBF and voucher schemes we obtain through random effects meta-regression is sizable, but its _p_ - value lies above the 1 percent significance threshold we use in order to account for Type I error (Table 4). Moreover, the large discrepancy in effect size heterogeneity between PBF and voucher programs limits the reliability of comparisons of effect sizes across the two intervention types.

# _Four or more antenatal care checks_

We estimate a small but statistically significant mean effect size of financial incentives on pregnant women completing four or more antenatal care checks of 1.4 percentage points (Figure 4). Breaking programs down by intervention type, the mean effect size for PBF is close to zero, 10
with low heterogeneity across PBF schemes. The voucher mean effect size amounts to a nonsignificant 2.7 percentage points and the CCT effect size is a significant 4.4 percentage points. Like for PBF programs, the degree of heterogeneity in underlying effect sizes is low for both intervention types. Using meta-regression, we find the difference between the relatively large CCT and near zero PBF mean effect sizes to be statistically significant (Table 4). Neither for all interventions combined, nor for a specific intervention type does the prediction interval exclude zero.

# _Maternal tetanus vaccination_

The overall mean effect size of financial incentives on maternal tetanus vaccination is a significant 2.7 percentage points, with a moderate-to-substantial degree of heterogeneity in underlying program specific effect sizes (Figure 5). For PBF programs, the mean effect size is 3 percentage points, with a _p_ -value just above the 5 percent level and moderate heterogeneity. For CCTs, the mean effect size is similar, at 2.4 percentage points, but there is substantial heterogeneity that is driven by significant negative impact of Indonesia’s _Program Keluarga Harapan_ which contrasts with the positive effect sizes of the four other CCT programs. The small difference in mean effect size magnitude between PBF and CCT programs is not statistically significant (Table 4).

# _Facility delivery_

For facility deliveries, the overall mean effect size of financial incentive interventions is a statistically significant 5.3 percentage points, with moderate heterogeneity across programs (Figure 6). All intervention-group specific mean effect sizes are statistically significant as well, with the PBF mean effect size being smallest, at 4.4 percentage points, followed by the voucher mean effect size of 6.4 percentage points, and the CCT mean effect size that amounts to 7.3 percentage points. Unlike for PBF and CCT programs, which show moderate heterogeneity levels, heterogeneity for the voucher mean effect size is low, and its prediction interval is the only one in the significantly positive range. The mean effect size differences across intervention types, while somewhat substantive in magnitude, are not statistically significant (Table 4).

# _Postnatal care checks_

11
The mean effect size across all financial incentives interventions for postnatal care checks is a modest but statistically significant 2.7 percentage points (Figure 7). A low degree effect size heterogeneity across programs is mirrored in intervention type specific mean effect sizes of similar magnitude – a non-significant 2.2 percentage points for PBF, 3.2 percentage points – and significant – for vouchers, and 3.1 percentage points and not significant for CCTs. None of the small differences in effect size magnitudes are statistically significant (Table 4). Interventiongroup specific effect size heterogeneity is low for PBF and vouchers and moderate for CCTs.

# _Full childhood vaccination_

The overall mean effects size across financial incentive interventions amounts to a statistically significant 4.4 percentage points, with a low-to-moderate degree of underlying effect size heterogeneity (Figure 8). For PBF schemes, the mean effect size is a significant 3.9 percentage points, with low effect size heterogeneity, and for CCTs it is a significant 5 percentage points with low heterogeneity and a prediction interval above zero. The small difference in mean effect size between PBF and CCT schemes is not statistically significant (Table 4).

# **_Subgroup analysis_**

_Combining supply and demand side incentives_ To test the hypothesis that complementarities exist between demand- and supply-side interventions, we examine if effect sizes of schemes which combine supply- and demand-side financial incentives are larger than those of schemes which only incentivize either the demandor the supply-side. The meta-regression results in column 1 of Table 5 provide little evidence for such systematic complementarities. In no case do we find statistically significant differences between combined and single-side interventions, and meaningful differences in effect size magnitudes arise for just two service coverage outcomes: Modern family planning is the only indicator where the mean effect size of the four voucher and one PBF scheme which incentivize both the supply- and demand-side is substantively larger, at 4.3 percentage points, than for single-side interventions. By contrast, for maternal tetanus vaccination, the mean effect size across single-side interventions is, somewhat counterintuitively, 6.6 percentage points larger than that across the combined supply- and demand-side interventions.

_Control groups with budget equalization_ 12
Column 2 of Table 5 tests whether interventions where the control group receives an income increase equivalent to the mean incentive payout in the treatment group – i.e. an unconditional cash transfer in case of demand-side programs and a block grant in case of supply-side programs – have smaller effects than interventions where the control group continues to operate under the financial status quo. Meaningfully smaller mean effect sizes among programs with control group income equalization would indicate that the provision of additional funds, perhaps more so than the incentive itself, contributes to the positive impacts of financial incentive interventions on maternal and child health service coverage. Only one of the mean effect size differences we estimate is significant at the 1 percent level, but for five of the six outcomes, the relationship is negative, and for four, the differences are larger than minus two percentage points, which is substantive compared to the modest mean effect sizes we find above.

# _Baseline outcome values_

Table 5 column 3 shows the association of effect sizes with baseline outcome values, as a measure for pre-intervention health system effectiveness in reaching mothers and children with health services – a possible proxy for country income levels and overall implementation context. Contrasting hypotheses for this regressor predict differing impacts. A negative relationship would result if, for instance, low baseline outcome levels indicated low capacity to successfully implement financial incentives. A positive relationship would, by contrast, arise, if, for example, a low baseline outcome level indicated larger populations within reach of marginal changes in financial incentives. Our meta-regressions, however, find no meaningful relationships between baseline outcome levels and program effect sizes, indicating that neither effect is relevant or that the two hypothesized effects cancel each other out: None of the coefficients are statistically significant and the largest – estimated for 4+ antenatal care checks – indicates that a ten percentage point difference in baseline outcomes is associated with a mere 0.7 percentage point higher financial incentive effect.

# _Risk of bias_

To investigate possible impacts of study bias on our results, we regress program effect sizes on a dummy variable indicating if we classified the underlying evidence as having high, as opposed to medium or low, bias risk. Results in column 4 of Table 5 indicate no systematic relationship of study bias risk with the magnitude of effect sizes, as none of the estimated coefficients is 13
remotely statistically significant, and the largest point estimate is a two percentage point higher mean effect size among high bias risk studies for maternal tetanus vaccination.

# **Discussion**

Before reviewing and contextualizing the main findings, we discuss several limitations. Our methodological inclusion criteria are demanding, which we believe to be a strength of our analysis. The exclusion of studies with less rigorous empirical methods, however, lowers our statistical power, which, despite a growing evidence base, remains insufficient to carry out a more fine-grained analysis of the role of intervention design features and implementation contexts. This limitation applies to the comparisons of PBF, vouchers, and CCT effect sizes – given the inability to control for all confounding factors related to program design and setting, the differences we estimate across program types are strictly interpreted as associational rather than causal. To enable more detailed subgroup analysis, future studies of financial incentive interventions should use rigorous impact evaluation methods, minimize avoidable heterogeneity by using standard outcome variable definitions, and provide detailed accounts of program design features.

We also restrict our systematic review and meta-analysis to financial incentives designed with explicit conditions on MCH provision or use. On the demand side, this leads us to not consider the broad spectrum of unconditional cash transfers as well as cash transfers with soft conditions or co-responsibilities. On the one hand, we acknowledge that this narrows the scope of our comparisons, but, on the other hand, it allows a more direct comparison of financial incentives, on the supply and the demand sides, relying on a conditionality mechanism. We nevertheless analyze the role of conditionality when we differentiate between effects of financial incentives when they are compared with a pure control group vs. with a control group with equalized budgets (see results in column 2 of table 5).

A further limitation is that the scope of indicators targeted by financial incentive interventions typically goes beyond the narrow set of outcomes in our review. Most interventions incentivize additional health coverage indicators in and outside the maternal and child health domain. For instance, CCT programs often also include education and job training conditionalities and, like vouchers, can have additional effects on household consumption and welfare. In a similar sense, incentives to improve the quality of facility equipment and cleanliness, as well as of 14
administrative processes, are almost always built into PBF programs, whose impacts on transparency, provider accountability, and data usage are often hoped to have a transformational effect on health systems as a whole.<sup>25,26</sup> For these reasons, the evidence in this review warrants conclusions only about the effects of financial incentives on the six included indicators, and not about the overall (cost-) effectiveness of specific programs or entire intervention types.

With these caveats in mind, a number of insights emerge from our analysis. While on average, financial incentives increase coverage of all included maternal and child health service indicators, mean effect sizes are of modest magnitude, with the largest for facility delivery and full childhood vaccination where they amount to about five percentage points. We find effect size heterogeneity across financial incentive programs to be low to moderate for all indicators except maternal tetanus vaccination. Despite this finding, due to the still limited evidence base, mean effect size prediction intervals indicate less than 95 percent certainty for a positive impact of future programs for all indicators other than postnatal care – policy makers hence still face substantial uncertainty in deciding on interventions to improve service coverage.

The low to moderate levels of effect size heterogeneity across financial incentive interventions is mirrored in typically small differences in mean effect sizes across PBF, voucher, and CCT programs. Our analysis is not sufficiently powered to precisely determine the magnitude of these differences, but the totality of our results suggests that PBF is slightly less effective in improving maternal and child health coverage than voucher and CCT schemes. The one exception where the PBF mean effect point estimate is larger than that of vouchers and CCTs is maternal tetanus vaccination, a plausible result, as indicators with a strong content-of-care dimension like maternal tetanus vaccination are under more direct control of providers incentivized with PBF. In contrast, the five other coverage indicators are likely more easily influenced with demand-side incentivization, as they mainly depend on patient care-seeking decisions.

Testing for other possible drivers of effect size heterogeneity across financial incentive programs, we neither find systematic evidence for complementarities between supply- and demand-side incentives, nor for an influence of baseline indicator levels. However, there is some indication that income equalization in the control group substantively diminishes the impact of financial incentives schemes, suggesting that unconditional increases in health care provider and household incomes can lead to similar health service coverage increases as incentive 15
interventions – a finding in line with the conclusions of a recent narrative review by Diaconu et al.<sup>1</sup> This conclusion, similar to the result of smaller mean service coverage effects of PBF than of voucher and CCT programs, requires confirmation through future, better-powered meta-analysis.

16
# **Tables**

## **Table 1: Characteristics of included reports**

|||Start<br>|Years post-<br>treatment<br>|Modern family<br>planning|Indi<br>4+ ANC checks|cators with<br>Maternal tetanus<br>vaccination|impact esti<br>Facility delivery|mates<br>1+ PNC checks|Full child<br>vaccination|Random<br>|i<br>Statistic<br>|al<br> <br>Risk of<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Country|Intervention name|year<br>Reference<br>Perform|data collected<br>ance-Based Fina|<br> <br>ncing||<br>|||<br>|zed|model|<br>bias|
|Afghanistan<br>|System Enhancement for Health Action in Transition<br>|2010<br>Engineer et al<sup>27</sup><br> <br>|2010-2012<br>|X<br>||||X<br>||Yes<br>|SD<br>|Medium<br>|
|Burkina Faso|Reproductive Health Project I|2013<br>De Allegri et al<sup>28</sup>|2015-2017|X|X|X|X|X|X|No|DID|High|
|Burkina Faso|Reproductive Health Project II|2013<br>De Allegri et al<sup>28</sup>|2015-2017|X|X|X|X|X|X|No|DID|High|
|Burundi|PBF Scheme|2006<br>Bonfrer et al<sup>29</sup><br>|2007-2010<br>|X||X<br>|X<br>|||No<br>|DID<br>|High<br>|
|||Bonfrer et al<sup>30</sup><br>Gage and Bauhoff<sup>31</sup>|2006-2011<br>2006-2017||X|X|X<br>X||X|No<br>No|DID<br>DID|Medium<br>Medium|
|||Rudasingwa et al<sup>32</sup><br>|2007-2008|||X|X|||No|DID|High<br>|
|Cambodia|Contracting-in|2004<br>Van de Poel et al<sup>33</sup>|2004-2010||||X||X|No|DID|Medium|
|Cambodia|Government Scheme|2004<br>Van de Poel et al<sup>33</sup>|2004-2010||||X||X|No|DID|Medium|
|Cameroon|Health Sector Support Investment Project I<br>|2012<br>de Walque et al<sup>34</sup><br>|2013-2015|X||X|||X|Yes|DID|Low|
|Cameroon|Health Sector Support Investment Project II|2012<br>de Walque et al<sup>34</sup>|2013-2015|X||X|||X|Yes|DID|Low|
|Congo,Dem. Rep.|Health Sector Rehabilitation and Support Project|2010<br>Huilleryand Seban<sup>35</sup>|2011-2012||||X|||Yes|SD|Medium|
|Congo,Rep.<br>|Health Sector Services Development Project<br>|2012<br>Zenget al<sup>36</sup><br> <br>|2012-2014<br>|X|||X<br>|X||No<br>|DID<br>|Medium<br>|
|Gambia,The|Maternal and Child Nutrition and Health Results Project|2014<br>Ferguson et al<sup>37</sup>|2014-2016||||X|||Yes|DID|Medium|
|Lesotho|Health System Performance Enhancement Project|2016<br>Gage and Bauhoff<sup>31</sup>|2016-2018||X||X|||No|DID|Medium|
|Nigeria<br>|State Health Investment Project I<br>|2014<br>Kandpal et al<sup>38</sup><br> <br>|2015-2017<br>|X<br>|X<br>||X<br>||X<br>|No<br>|DID<br>|Medium<br>|
|Nigeria|State Health Investment Project II|2014<br>Kandpal et al<sup>38</sup>|2015-2017|X|X||X||X|No|DID|Medium|
|Rwanda|P4P scheme|2006<br>Basinga et al<sup>39</sup>|2006-2008||X||X||X|No|DID|Medium|
|||Gertler and Vermeersch<sup>40</sup><br>|2006-2008<br>|X<br>|X<br>|||||No<br>|DID<br>|Medium<br>|
|||Lannes et al<sup>41</sup>|2006-2008|X|X||X|||No|DID|Medium|
|||Okeke and Chari<sup>42</sup>|2000-2008||X|X||||No|DID|Medium|
|||Priedeman Skiles et al<sup>43</sup><br> <sup>44</sup>|2006-2008<br>|X<br>|X<br>||X<br>|||No<br>|DID<br>|Medium<br>|
|||Sherryet al|2006-2008|X|X|X|X||X|No|DID|Medium|
|Rwanda|CommunityLivingStandards Grant|2009<br>Shapira et al<sup>45</sup>|2010-2014||X||X|||Yes|SD|Low|
|Senegal|Health and Nutrition FinancingProject|2012<br>Gage and Bauhoff<sup>31</sup>|2012-2017||X||X|||No|DID|Medium|
|Tajikistan|Health Services Improvement Project|2015<br>Ahmed et al<sup>46</sup>|2015-2018|X|X||||X|Yes|DID|Medium|
|Tanzania|Pwani Pilot|2011<br>Binyaruka et al<sup>47</sup>|2012-2013||X||X|X||No|DID|Medium|
|Zambia|Zambia Health Services Improvement Project I|2012<br>Gage and Bauhoff<sup>31</sup>a<br>|2012-2018<br>||X<br>||X<br>|||Yes<br>|DID<br>|Low<br>|
|||World Bank<sup>48</sup>|2012-2015||X|X|X|X|X|Yes|DID|Low|
|||Zenget al<sup>49</sup>|2012-2015||||X|X||Yes|DID|Low|
|Zambia|Zambia Health Services Improvement Project II|2012<br>World Bank<sup>48</sup>|2012-2015||X|X|X|X|X|Yes|DID|Low|
|||Zenget al<sup>49</sup>|2012-2015||||X|X||Yes|DID|Low|
|Zibb|Hlh S Dl S P|2011<br>Gage and Bauhoff<sup>31</sup>|2012-2015||X||X|||No|DID|Medium|
|mawe|eat ector eveopment upport roject|World Bank<sup>50</sup>|2012-2014|X||X|X|X|X|No|DID|Medium|
||||<br>Vh||||||||||
|Cambodia|Reproductive Health Voucher|2010<br>Bajracharya et al<sup>51</sup>|oucers<br>2012-2013|X||||||No|DID|Medium|
|Cambodia|Targeted Maternal and Child Health Voucher|2007<br>Van de Poel et al<sup>52</sup>|2007-2010||||X|X||No|DID|Medium|
|Cambodia|Universal Maternal and Child Health Voucher|2008<br>Van de Poel et al<sup>52</sup>|2008-2010||||X|X||No|DID|Medium|
|Kena|Reroductive Health Voucher|2006<br>Dennis et al<sup>53</sup>|2010-2013||X||X|X||No|DID|Medium|
|y <br>Kenya|p <br>Maternal Voucher Experiment|<br> <br>2013<br>Grépin et al<sup>54</sup>|<br>2013||<br>X||<br>X|||<br>Yes|<br>SD|<br>Low|
|Pakistan|JhangMaternal Health Voucher|2010<br>Agha<sup>55</sup>|2010-2011||||X|X||No|DID|High|
|Pakistan<br>|Marie Stopes Chakwal Voucher<br>|2012<br>Ali et al<sup>56</sup><br> <br>|2015<br>|X<br>||||||Yes<br>|DID<br>|Medium<br>|
|Pakistan|Suraj|2008<br>Azmat et al<sup>57</sup>|2013|X||||||No|DID|High|
|Tanzania|HelpingPoor Pregnant Women Access Better Health Care|2010<br>Kuwawenaruwa et al<sup>58</sup>|2013-2014|X|X||X|X||No|DID|Medium|
|Uganda|HealthyBaby|2008<br>Obare et al<sup>59</sup><br>Condit|2010-2011<br>ional Cash Tran|sfers|X||X|X||No|DID|High|

17
|Country|Intervention name|Start<br>year|Reference|Years post-<br>treatment<br>data collected<br>Modern family<br>planning|Indi<br>4+ ANC checks|cators with i<br>Maternal tetanus<br>vaccination|mpact esti<br>Facility delivery|mates<br>1+ PNC checks<br>Full child<br>vaccination|Random<br>zed|i<br>Statistica<br>model|l<br>Risk of<br>bias|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Afghanistan|Ministryof Public Health Scheme|2016|Edmond et al<sup>60</sup>|2016-2017|||X||Yes|DID|High|
|Bangladesh|Demand-Side Financing Program|2004|Keya et al<sup>61</sup>|2011-2012|||X||No|DID|High|
||||Nguyen et al<sup>62</sup>|2008-2009|||X||No|DID|High|
|China|CHIMACA|2007|Hemminki et al<sup>63</sup>|2007-2009||||X|Yes|SD|High|
|Honduras|Bono 10,000|2010|Benedetti et al<sup>64</sup>|2012-2013||X||X<br>X|Yes|SD|Low|
|Honduras|Programa de Asignación Familiar(PRAF II)|2000|Morris et al<sup>65</sup>|2001-2002||X||X|Yes|DID|Low|
|India|Indira Gandhi Motherhood Support Scheme|2011|von Haaren and Klonner<sup>66</sup>|2012-2016||||X<br>X|No|DID|Medium|
|India|Jananni Surkshya Yojana|2005|Debnath<sup>67</sup>|2005-2008|||X||No|DID|Medium|
||||Powell-Jackson et al<sup>68</sup>|2005-2008|||X||No|DID|High|
|Indonesia|Program Keluarga Harapan|2007-200|8 Alatas<sup>69</sup>|2008-2009|X||X|X|No|IV|High|
||||Cahyadi et al<sup>70</sup>|2007-2013|||X||Yes|IV|Medium|
||||Kusuma et al<sup>71</sup>|2008-2009|X||X||Yes|DID|Medium|
||||Kusuma et al<sup>72</sup>|2009||||X|Yes|DID|Low|
||||Triyana<sup>73</sup>|2008-2009||X|X||Yes|DID|Low|
|Kenya|M-SIMU RCT|2013|Gibson et al<sup>74</sup>|2014-2015||||X|Yes|SD|Medium|
|Kenya|Maternal Conditional Cash Transfer Experiment I|2013|Grépin et al<sup>54</sup>|2013|X||X||Yes|SD|Low|
|Kenya|Maternal Conditional Cash Transfer Experiment II|2013|Grépin et al<sup>54</sup>|2013|||X||Yes|SD|Low|
|Kenya|Maternal Conditional Cash Transfer and Voucher Experiment|2013|Grépin et al<sup>54</sup>|2013|||X||Yes|SD|Low|
|Mali|Cash for Nutritional Awareness|2014|Adubra et al<sup>75</sup>|2014-2016|||X|X<br>X|Yes|DID|Low|
|Mexico|Progresa|1997|Barber and Gertler<sup>76</sup>|1998-2003||X|||Yes|SD|Low|
|Nicaragua|Red de Protección Social|2001|Barham and Maluccio<sup>77</sup>|2001||||X|Yes|DID|Low|
||||Handa and Maluccio<sup>78</sup>|2001||||X|Yes|SD|Medium|
|Nigeria|Maternal Cash Transfer Experiment|2017|Okeke et al<sup>79</sup>|2017-2018|||X||Yes|SD|Medium|
|Peru|Juntos|2005|Díaz and Saldarriaga<sup>80</sup>|2006-2014|X||X|X|No|DDD|Medium|
||||Díaz et al<sup>81</sup>|2001-2011||X|||No|DID|Medium|
|Philippines|Pantawid Pamilya|2008|Kandpal et al<sup>82</sup>|2008-2011|X||X|X|Yes|SD|Low|
|Turkey|Social Risk Mitigation Project|2004|Ahmed et al<sup>83</sup>|2005-2006||||X|No|RD|High|
|Zimbabwe|Manicaland HIV/STD Project|2010|Robertson et al<sup>84</sup>|2011||||X|Yes|SD|Medium|

Notes: Statistical model: IV = instrumental variables, SD = single difference, DID = double different, DDD = triple difference.

18
**Table 2: Characteristics of included programs**

|Country|Intervention name|World Bank income group at time of<br>intervention<br>Scop<br>Geographic<br>extent|e<br>Donor<br>(co-)<br>financing<br> <br>Rural<br>Urban<br>Md Fil|Impact estimate available<br>and service incentivized<br>Not available or not incentivized<br>Available and directly incentivized<br>Available and indirectly incentivized<br>oern amy<br>Planning<br>4+ ANC checks<br>Maternal tetanus<br>vaccination<br>Facility delivery<br>1+ PNC checks<br>Full child vaccination<br>Supply side<br>Demand side<br>Performance-Based Fina|Financial incentive<br>Control group<br>income equalization<br>Incentive formula<br>ncing|
|---|---|---|---|---|---|
|Afghanistan|System Enhancement for Health<br>Action in Transition|Low 11 of 34<br>provinces|Yes Yes World Bank|Yes No|No Quarterly incentive payments directly to healthcare workers on a case-basis and annual payments according to equity of<br>service delivery, facility quality scorecards and uptake of contraceptives in the catchment area. Salary bonuses<br>component amounts to 6-28% of base salaries.|
|Burkina Faso|Reproductive Health Project I|Low 15 of 351<br>departments|Yes Yes World Bank|Yes No|No Monthly incentive payments on a case-basis and additional quarterly quality payments if facility achieves quality score<br>of at least 50% of maximum score. Facilities with full autonomyregardinguse of bonuses.|
|Burkina Faso|Reproductive Health Project II|Low 15 of 351<br>departments|Yes Yes World Bank|Yes Yes|No Monthly incentive payments on a case-basis and additional quarterly quality payments if facility achieves quality score<br>of at least 50% of maximum score. Facilities with full autonomy regarding use of bonuses. Three type of demand-side<br>co-interventions, namely user fee waivers for the poor, user fee waivers and additional financial incentives for<br>healthcare workers to provide care to them, and community-based health insurance with waived premiums for the<br>poor.|
|Burundi|PBF Scheme|Low Nationwide|Yes Yes Cordaid|Yes No|No Nationwide scheme. Quarterly incentive payments on a case-basis which are multiplied by a factor between 1 and 1.25<br>according to results of check of facility conditions and process quality. Incentive can amount to up to 40% of base<br>budgets. 50% of incentivepayment can be used for salarybonuses|
|Cambodia|Contracting-in|Low 11 of 81<br>districts|Yes Yes Yes, but<br>specific<br>donor<br>information|Yes No|No<br>NGO contracted to design and manage performance incentive payments ingovernment facilities, but without<br>autonomy over staffing and procurement decisions. Nature of incentive payments at discretion of NGO, leading to<br>heterogeneity.|
||||not available|||
|Cambodia|Government scheme|Low 8 of 81<br>districts|<br>Yes Yes Belgian<br>Technical<br>Cooperation|Yes<br>No|No<br>Government operated scheme supported by NGO. Case-based payments and bonusesfor reaching quantitative targets<br>and for abstention from illegitimate practices.|
|Cameroon|Health Sector Support Investment<br>Project I|Lower-<br>middle<br>26 of 189<br>health<br>districts|<br>Yes Yes World Bank|Yes No|No Monthly incentive payments on a case-basis subject to 25% reduction if excessive reporting errors detected during<br>monthly verification. Case-based payments are increased by a factor between 1 and 1.3 according to results of check of<br>facility conditions and process quality. Additional adjustments for facilities with large structural deficiencies. Facility<br>management committee determines use of funds. Controlgroupwithout budget equalization.|
|Cameroon|Health Sector Support Investment<br>Project II|Lower-<br>middle<br>26 of 189<br>health<br>districts|Yes Yes World Bank|Yes No|<br>Yes Monthly incentive payments on a case-basis subject to 25% reduction if excessive reporting errors detected during<br>monthly verification. Case-based payments are increased by a factor between 1 and 1.3 according to results of check of<br>facility conditions and process quality. Additional adjustments for facilities with large structural deficiencies. Facility<br>manaement committee determines use of funds Controlrouwith budet eualization|
|Congo, Dem.<br>Rep.|Health Sector Rehabilitation and<br>Support Project|Low 1 of 26<br>districts|Yes Yes World Bank|Yes No|g.gp g q.<br>Yes Monthly incentive payments on a case-basis subject to reduction if reporting errors detected during monthly<br>verification. No adjustment for quality. Facility with autonomy on how to use funds. Control group with budget<br>equalization.|
|Congo, Rep.|Health Sector Services Development<br>Project|Lower-<br>middle<br>2 of 12<br>departments,<br>home to 30%<br>of population|<br> <br> <br>Yes Yes World Bank|Yes No|<br>No Quarterly incentive payments on a case-basis which are multiplied by a factor between 1 and 1.5 according to results of<br>check of facility conditions and process quality and user satisfaction. Additional bonuses for facilities situated in remote<br>areas, those with very poor catchment area populations, and those of very poor structural quality. Quality bonus not<br>paid out if at least 10% of reported services cannot be verified. Facility determines what share of incentive payment is<br>used for salarybonus.|
|Gambia, The|Maternal and Child Nutrition and<br>Health Results Project|Low 3 of 5<br>regions,<br>home to one<br>third of the|<br>Yes Yes World Bank|Yes Yes|No Quarterly incentive payments on a case-basis which are multiplied by a factor between 1 and 2 according to results of<br>check of facility conditions. 40% of incentive payment can be used for salary bonuses. As co-intervention, women<br>invited to enroll in CCT scheme rewarding timeliness of first and completion of 4 ANC visits.|
|||population||||
|Lesotho|Health System Performance<br>Enhancement Project|Lower-<br>middle<br>6 of 10<br>districts|Yes Yes World Bank|Yes No|No Quarterly incentive payments on a case-basis which are multiplied by a factor between 1 and 1.25 according to results<br>of check of facilityconditions andprocessquality. 50% of incentivepayment can be used for salarybonuses.|

19
|Countr|y<br>Intervention name|World Bank income group at time of<br>intervention<br>Scop<br>Geographic<br>extent|e<br>Donor<br>(co-)<br>financing<br>Rural<br>Urban<br>Mdrn Fmil|Impact estimate available<br>and service incentivized<br>Not available or not incentivized<br>Available and directly incentivized<br>Available and indirectly incentivized<br>Financial incentive<br>oe ay<br>Planning<br>4+ ANC checks<br>Maternal tetanus<br>vaccination<br>Facility delivery<br>1+ PNC checks<br>Full child vaccination<br>Supply side<br>Demand side<br>Control group<br>income equalization<br>Incentive formula|
|---|---|---|---|---|
|Nigeria|State Health Investment Project I|<br>Lower-<br>middle<br>3 of 36 states<br>covering<br>about<br>400,000<br>pregnant<br>women and<br>1.8 million<br>hild|<br>Yes Yes World Bank|<br>Yes No<br>No Quarterly incentive payments on a case-basis which are multiplied by a factor between 1 and 1.25 according to results<br>of check of facility conditions and process quality. 50% of incentive payment can be used for salary bonuses. Control<br>groups with and without budget equalization.|
|Nigeria|State Health Investment Project II|cren<br>Lower-<br>middle<br>3 of 36 states<br>covering<br>about<br>400,000<br>pregnant<br>women and<br>1.8 million<br>children|<br>Yes Yes World Bank|Yes No Yes Quarterly incentive payments on a case-basis which are multiplied by a factor between 1 and 1.25 according to results<br>of check of facility conditions and process quality. 50% of incentive payment can be used for salary bonuses. Control<br>groups with and with budget equalization.|
|Rwanda|Community Living Standards Grant|<br>Low 50 of 416<br>sectors in<br>districts<br>covered by<br>Rwanda P4P|<br>Yes No World Bank|Yes No Yes Quarterly incentive payments to community health workers who provide promotional and referral services according<br>to service utilization in the community, namely growth monitoring of children 6–59 months old, antenatal care<br>provided to women in the first 4 months of their pregnancy, in-facility deliveries, and family planning consultations.<br>No direct incentivization of achieving 4+ ANC visits beyond the incentive for commencing ANC early. 70% of<br>performance bonus invested into the community health worker cooperatives’ income-generating activities, rest is salary<br>b Ctlith bdtliti|
|Rwanda|P4P Scheme|Low All rural<br>districts|Yes No Several, incl.<br>Belgian<br>Technical<br>Cooperation,<br>PEFPAR,<br>and World<br>|onus. onrogroupw uge equazaon.<br>Yes No Yes Quarterly incentive payments on a case-basis which are multiplied by a factor between 0 and 1 according to results of<br>check of facility conditions and process quality. Facility autonomy in use of funds, on average 77% used for salary<br>bonuses, resulting in 38% salary increase. Similar use of additional funds in control group which receives budget<br>equalization.|
|Senegal|Health and Nutrition Financing Projec|t Lower-<br>middle<br>6 of 14<br>regions|Bank<br>Yes Yes World Bank|Yes Yes No Quarterly incentive payments on a case-basis which are multiplied by a factor between 1 and 1.25 according to results<br>of check of facility conditions and process quality. 75% of incentive payment can be used for salary bonuses. As co-<br>interventionprenant women receive vouchers for 4 ANC visits and skilled deliver|
|Tajikistan|Health Services Improvement Project|Low 7 of 58<br>districts|Yes No World Bank|, g y.<br>Yes No<br>No Quarterly incentive payments on a case-basis which are multiplied by a factor between 1 and 2 according to results of<br>check of facility conditions and process quality. Facilities with a quality score of less than 55% of the maximum receive<br>noqualitybonus. 70% of incentivepayment can be used for salarybonuses|
|Tanzania|Pwani pilot|Low 1 of 30 states|Yes No Gov’t of<br>Norway|<br>Yes No<br>No Half-yearly incentive payments based on achievement of at least 75% of service delivery targets. Minimum of 75% of<br>bonus payments are distributed among health workers, which amounts to up to 10% of base salaries. Direct<br>incentivization of malaria and HIV treatment durinrenancbut no direct incentivization of antenatal care visits|
|Zambia|Health Services Improvement Project I|Lower-<br>middle<br>11 of 117<br>districts|Yes No World Bank|g pgy.<br>Yes No<br>No Quarterly incentive payments on a case-basis which are multiplied by a factor between 1 and 1.5 according to results of<br>check of facility conditions and process quality. Facilities with a quality score of less than 61% of the maximum receive<br>no quality bonus. Up to 60% of incentive payments can be used for salary bonuses. Control group without budget<br>eualization|
|Zambia|Health Services Improvement Project I|I Lower-<br>middle<br>11 of 117<br>districts|Yes No World Bank|q.<br>Yes No Yes Quarterly incentive payments on a case-basis which are multiplied by a factor between 1 and 1.5 according to results of<br>check of facility conditions and process quality. Facilities with a quality score of less than 61% of the maximum receive<br>no quality bonus. Up to 60% of incentive payments can be used for salary bonuses. Control group with budget<br>equalization.|

20
|Countr|y<br>Intervention name|World Bank income group at time of<br>intervention<br>Sco<br>Geographic<br>extent|pe<br>Donor<br>(co-)<br>financing<br> <br>Rural<br>Urban<br>Mdrn Fmil|Impact estimate available<br>and service incentivized<br>Not available or not incentivized<br>Available and directly incentivized<br>Available and indirectly incentivized<br>Financial incentive<br>oe ay<br>Planning<br>4+ ANC checks<br>Maternal tetanus<br>vaccination<br>Facility delivery<br>1+ PNC checks<br>Full child vaccination<br>Supply side<br>Demand side<br>Control group<br>income equalization<br>Incentive formula|
|---|---|---|---|---|
|Zimbabwe|Health Sector Development Support<br>Project|Low 16 of 62<br>districts,<br>home to<br>about 3.5<br>million<br>people|Yes No World Bank|Yes No<br>No Quarterly incentive payments on a case-basis which are multiplied by a factor between 1 and 1.3 according to facility<br>remoteness and by a factor between 1 and 1.25 according to results of check of facility conditions and process quality.<br>Facilities with a quality score of less than 51% of the maximum receive no quality bonus. Up to 25% of incentive<br>payments can be used for salary bonuses.<br>|
|||||Vouchers|
|Cambodia|Reproductive Health Voucher|Low 9 of 77<br>health<br>ditit|Yes Yes KfW|Yes Yes No Household poverty targeting. Vouchers cover family planning and maternal and child health services, incl.<br>transportation. Vouchers distributed free of charge.|
|Cambodia|Targeted Maternal and Child Health<br>Voucher|srcs<br>Low 8 of 77<br>health<br>districts|Yes Yes Unknown|Yes Yes No Household poverty targeting. Vouchers cover maternal and child health services, incl. transport. Vouchers distributed<br>free of charge.|
|Cambodia|Universal Maternal and Child Health<br>Voucher|<br>Low 14 of 77<br>health<br>districts|Yes Yes Unknown|Yes Yes No No poverty targeting. Vouchers cover maternal and child health services, incl. transport. Vouchers distributed free of<br>charge.|
|Kenya|Maternal Voucher Experiment|<br>Low 1 of 47<br>counties|Yes No Georgetown<br>University<br>and Grand<br>Challenges<br>Canada|Yes Yes No Vouchers cover antenatal care visits, delivery, and postnatal care visits, plus a small premium to compensate facilities<br>for the administrative burden of adopting the system and recording utilization. One group of women received a<br>voucher fully covering all services, and another group of women received a voucher covering free care antenatal and<br>postnatal services, but required a 100 KSh (about $1.20 at the time) copayment for facility delivery, which represented<br>about 10percent of the median reportedprice for a normal delivery paid bythe controlgroup.|
|Kenya|Reproductive Health Voucher|Low 4 of 47<br>counties,<br>about<br>100,000<br>vouchers|Yes Yes KfW|Yes Yes No Household poverty targeting. Voucher covers family planning and maternal and child health services including 4<br>antenatal care visits, facility delivery and postnatal care. Maternal and child health voucher sold for $US2.50 and family<br>planning voucher for $US1.25.|
|||ld|||
|Pakistan|Jhang Maternal Health Voucher|so<br>Lower-<br>middle<br>1 of 150<br>districts,<br>about 4,000<br>vouchers|Yes No Unknown|Yes Yes No Household poverty targeting. Voucher covers family planning visit, 3 antenatal care visits, facility delivery and a<br>postnatal care visits, as well as transportation. Vouchers sold for $US1.25.|
|||<br>sold|||
|Pakistan|Marie Stopes Chakwal Voucher|Lower-<br>middle<br>1 of 150<br>districts,<br>about 7,000<br>h|Yes Yes Marie Stopes|Yes Yes No Household poverty targeting. Voucher covers family planning services. Vouchers are free of charge.|
|||voucers<br>distributed|||
|Pakistan|Suraj|Lower-<br>middle<br>3 of 150<br>districts|Yes Yes Marie Stopes|Yes Yes No Household poverty targeting. Voucher covers family planning services. Vouchers are free of charge.|
|Tanzania|Helping Poor Pregnant Women Access<br>Better Health Care|<br> <br> <br>Low 2 of 31<br>regions,<br>about<br>120,000<br>women|Yes Yes KfW|Yes Yes No First household poverty targeting, later regional. Nominally an insurance scheme but effectively and planned as a<br>voucher that covers maternal and child health services. Vouchers are free of charge.|
|Uganda|HealthyBaby|<br>Low 20 of 130<br>districts|Yes Yes KfW|Yes Yes No Household poverty targeting. Voucher covers maternal and child health services. Voucher sold for $US1.5.<br>Conditional Cash Transfers|

21
|Country|Intervention name|World Bank income group at time of<br>intervention<br>Scop<br>Geographic<br>extent|e<br>Donor<br>(co-)<br>financing<br>Impact estimate available<br>and service incentivized<br>Not available or not incentivized<br>Available and directly incentivized<br>Available and indirectly incentivized<br>Financial incentive<br>Rural<br>Urban<br>Modern Family<br>Planning<br>4+ ANC checks<br>Maternal tetanus<br>vaccination<br>Facility delivery<br>1+ PNC checks<br>Full child vaccination<br>Supply side<br>Demand side<br>Control group<br>income equalization<br>Incentive formula|
|---|---|---|---|
|Afghanistan|Ministry of Public Health Scheme|<br>Low 6 of 399<br>districts|<br>Yes No UNICEF<br>Yes Yes No No poverty targeting. Conditions on facility delivery with women delivering in facility receive 1,000 Afghani (13$US)<br>and communityhealth workers 300 Afghani(4$US)for bringingthem to facility. No incentivization ofpostnatal care.|
|Bangladesh|Demand-Side Financing Program|Low 46 of 492<br>sub-districts|Yes No Yes, pooled<br>donor<br>funding<br>Yes Yes No Regional and household poverty targeting. Freely available vouchers cover family planning and maternity service costs,<br>women can receive routine and emergency transport reimbursement of 500 Taka (7.5$US) and conditional on use of<br>skilled birth attendance women receive in kind (worth 500 Taka/7.5$US) and cash (2,000 Taka/30$US) benefit for<br>skilled birth attendance. Facilities reimbursed if theydeliverpackage of services covered byvoucher.|
|China|CHIMACA|Lower-<br>middle<br>3 of 1,355<br>counties|<br>Yes No No<br>No Yes No Regional poverty targeting. Conditions on use of antenatal and postnatal care. Maximum transfer of 20 RMB (3$US)<br>perpregnancy.|
|Honduras|Bono 10,000|Lower-<br>middle<br>666 of about<br>3,7000<br>villages|Yes No World Bank,<br>Inter-<br>American<br>Development<br>Bank, Central<br>American<br>Bank for<br>Economic<br>Interation<br>No Yes No Regional poverty targeting. Health conditions include registration with health clinic, and presentation for antenatal and<br>postnatal care, and growth monitoring visits. Maternal tetanus vaccination and childhood vaccinations not directly<br>incentivized but part of incentivized antenatal care and growth monitoring visits. Problems with enforcement of<br>conditionalities reported. Health component is 500$US per household per year in three installments.|
|Honduras|Programa de Asignación Familiar<br>(PRAF II)|Lower-<br>middle<br>6 of 298<br>municipalities|g <br>Yes No Inter-<br>American<br>Development<br>Bank<br>No Yes No Regional poverty targeting. Health conditions include antenatal and postnatal care as well as growth monitoring visits.<br>No direct incentivization of maternal tetanus vaccination but part of incentivized antenatal care visits. Problems with<br>enforcement of conditionalities reported. Health component is 40$US per child or pregnant woman per year.|
|India|Indira Gandhi Motherhood Support<br>Scheme/Pradhan Mantri Matritva<br>Vandana Yojana|Lower-<br>middle<br>52 of 640<br>districts|<br>Yes Yes No<br>Yes Yes No No poverty targeting. Conditions on using any antenatal care, maternal tetanus vaccination, postnatal care, child<br>vaccinations, collection of nutritional supplements, and completion of growth checks and counseling sessions. Total<br>payout to women was initially 4,000 Rp. (65$US), later raised to 6,000 Rp (98$US). Health workers in primary care<br>centers receive incentive of between 100 and 200 R(16-32$US)er comleted case|
|India|Jananni Surkshya Yojana|Low Gradual<br>nationwide<br>rollout,<br>estimated to<br>cover 36% of<br>pregnancies<br>in the<br>|p .. p p.<br> <br>Yes Yes No<br>Yes Yes No Regional and household level poverty targeting depending on state. Conditions on public or accredited private facility<br>delivery. Cash transfer varies by state between 11$US and 31$US. Social health workers are offered a cash payment of<br>between $4 and $13 for each delivery.|
|||country in<br>2009/10||
|Indonesia|Program Keluarga Harapan|<br>Lower-<br>middle<br>362 of about<br>75,000 sub-<br>districts|Yes Yes World Bank<br>No Yes No Household poverty targeting. Conditions on maternal and child health service use and school attendance. Indirect<br>incentivization of maternal tetanus vaccination through conditioning on antenatal care use. Quarterly cash transfers<br>varbhousehold composition between US$60 and US$220|
|Kenya|M-SIMU RCT|<br>Low 2 of 70<br>districts,<br>1,062<br>caregivers of<br>|y y .<br>Yes No Bill and<br>Melinda<br>Gates<br>Foundation<br>No Yes No No household poverty targeting. Conditions on timely vaccinations visits. Cash transfer per timely vaccination varies<br>by treatment arm between $US 0.88 and $US2.35.|
|||young<br>children||
|Kenya|Maternal Conditional Cash Transfer<br>Experiment I|<br>Low 1 of 47<br>counties|Yes No Georgetown<br>University<br>and Grand<br>Challenges<br>Canada<br>No Yes No No household poverty targeting. The CCT was paid for up to four ANC visits, a facility delivery, and up to three PNC<br>visits. An eligible woman received a transfer of 250 KSh (about $3 USD) for each eligible ANC and PNC visit and a<br>500 KSh ($6 USD) transfer for her delivery.|

22
|Countr|y<br>Intervention name|World Bank income group at time of<br>intervention<br>Scope<br>Geographic<br>extent<br>Rl|<br>Donor<br>(co-)<br>financing<br>Impact estimate available<br>and service incentivized<br>Not available or not incentivized<br>Available and directly incentivized<br>Available and indirectly incentivized<br>Financial incentive<br>ura<br>Urban<br>Modern Family<br>Planning<br>4+ ANC checks<br>Maternal tetanus<br>vaccination<br>Facility delivery<br>1+ PNC checks<br>Full child vaccination<br>Supply side<br>Demand side<br>Control group<br>income equalization<br>Incentive formula|
|---|---|---|---|
|Kenya|Maternal Conditional Cash Transfer<br>Experiment II|Low 1 of 47<br>counties<br>Y|es No Georgetown<br>University<br>and Grand<br>Challenges<br>Canada<br>No Yes Yes No household poverty targeting. The CCT was paid for up to four ANC visits, a facility delivery, and up to three PNC<br>visits. An eligible woman received a transfer of 250 KSh (about $3 USD) for each eligible ANC and PNC visit and a<br>500 KSh ($6 USD) transfer for her delivery. Control group receives unconditional cash transfer.|
|Kenya|Maternal Conditional Cash Transfer<br>and Voucher Experiment|Low 1 of 47<br>counties<br>Y|<br>es No Georgetown<br>University<br>and Grand<br>Challenges<br>Canada<br>Yes Yes No No household poverty targeting. The CCT was paid for up to four ANC visits, a facility delivery, and up to three PNC<br>visits. An eligible woman received a transfer of 250 KSh (about $3 USD) for each eligible ANC and PNC visit and a<br>500 KSh ($6 USD) transfer for her delivery. Women in addition receive vouchers which cover antenatal care visits,<br>delivery, and postnatal care visits, plus a small premium to compensate facilities for the administrative burden of<br>adopting the system and recording utilization. One group of women received a voucher fully covering all services, and<br>another group of women received a voucher covering free care antenatal and postnatal services, but required a 100<br>KSh (about $1.20 at the time) copayment for facility delivery, which represented about 10 percent of the median<br>reportedprice for a normal deliverypaid bythe controlgroup.|
|Mali|Cash for Nutritional Awareness|Low 3 of 59<br>districts<br>Y|<br>es No World Food<br>Programme,<br>UNICEF<br>No Yes No No household poverty targeting. Conditions on maternal and child health service use. Cash transfers paid during<br>antenatal care visits, delivery, vaccination and growth monitoring visits until age two with full compliance leading to<br>payout of$US75.|
|Mexico|Progresa/Oportunidades|Upper-<br>middle<br>Initially only<br>poor rural<br>areas, later<br>extended to<br>urban areas<br>with 5<br>million<br>households<br>b2005<br>Y|es Yes World Bank<br>No Yes No Conditions of use of antenatal and postnatal care as well as growth monitoring visits, among other health services and<br>school attendance. Only indirect incentivization of maternal tetanus vaccination through antenatal care attendance<br>condition. Typically, households receive the equivalent of $32.5 to $41.3 per month, constituting 19-24% of mean<br>household consumption.|
|Nicaragua|Red de Protección Social|y <br>Low Pilot with<br>35,000<br>households,<br>later scaled to<br>all rural areas<br>Y|es No Inter-<br>American<br>Development<br>Bank, Central<br>American<br>Bank for<br>Economic<br>Integration<br>No Yes No Regional poverty targeting. Conditions on growth monitoring and vaccination visits and school attendance. Health<br>component amounts to US$224 per household per year.|
|Nigeria|Maternal Cash Transfer Experiment|Lower-<br>middle<br>180 primary<br>health facility<br>areas<br>covering<br>about 1.2<br>million<br>people<br>Y|es No Unknown<br>No Yes No Regional poverty targeting. Cash payments of 5,000 Naira (approximately US$14), conditional on the use of antenatal<br>care (3+ visits), delivery, and postnatal care by eligible pregnant women in the household.|
|Peru|Juntos|Lower-<br>middle<br>Gradual<br>scale-up to<br>national level<br>scheme,<br>1011/~1,800<br>districts by<br>2012<br>Y|es Few Inter-<br>American<br>Development<br>Bank<br>No Yes No Regional and household poverty targeting. Conditions on maternal and child health service use, namely antenatal and<br>postnatal care as well as growth monitoring visits, and school attendance. Only indirect incentivization of maternal<br>tetanus vaccination through antenatal care attendance condition. Monthly transfer is ~US$35 per household per<br>month.|

23
|Country|Intervention name|World Bank income group at time of<br>intervention<br>Sco<br>Geographic<br>extent|pe<br>Donor<br>(co-)<br>financing<br> <br> <br>Rural<br>Urban<br>Modern Family|Impact estimate available<br>and service incentivized<br>Not available or not incentivized<br>Available and directly incentivized<br>Available and indirectly incentivized<br> <br>Planning<br>4+ ANC checks<br>Maternal tetanus<br>vaccination<br>Facility delivery<br>1+ PNC checks<br>Full child vaccination<br>Supply side<br>Demand side<br>Cntrl r|Financial incentive<br>oo goup<br>income equalization<br>Incentive formula|
|---|---|---|---|---|---|
|Philippines|Pantawid Pamilya|Lower-<br>middle<br>376,000<br>households<br>in study<br>period,<br>scaled-up to<br>roughly 3<br>million in<br>2012|Yes No World Bank|No Yes|No Regional and household poverty targeting. Health conditions include antenatal and postnatal care, skilled birth<br>attendance, and growth monitoring. Facility delivery indirectly incentivized through skilled delivery condition. Health<br>component is ~US$10.80 per household per month, equivalent to about 8% of household income.|
|Turkey|Social Risk Mitigation Project|Upper-<br>middle<br>Nationwide<br>scheme, 6%<br>poorest<br>households|Yes Yes No|No Yes|No Household poverty targeting. Health conditions include namely antenatal and postnatal care, facility delivery and<br>regular growth check attendance. Indirect incentivization of childhood vaccination through conditioning on growth<br>check visits during which vaccines administered. Bimonthly payments of US$23 per pregnant women and child and<br>US$74 for facilitydelivery.|
|Zimbabwe|Manicaland HIV/STD Project|Low 1 of 10<br>provinces,<br>1,319<br>households|Yes No Wellcome<br>Trust, World<br>Bank,<br>UNICEF|No Yes|No Poor and vulnerable households targeted. Conditions on use of maternal and child health services (growth monitoring<br>and up-to-date vaccinations) and school enrollment. Bimonthly transfers of US$18 per household plus $4 per child.|

24
# **Table 3: Health Service Coverage Indicators**

|Indicator|Main definition|Divergences from main definition|# of<br>original<br>impact<br>estimates|# of<br>program<br>-specific<br>effect<br>sizes|
|---|---|---|---|---|
|Modern family<br>planning|Women of fertile age<br>currently using<br>modern<br>contraceptives|Studies often do not list the contraceptives included in their definition of<br>modern contraceptives. Where specified, they typically include condoms,<br>diaphragms, foams and jellies, intrauterine devices, injectables, implants, and<br>female and male sterilization. In five cases, the specified lists exclude<br>condoms, and male and female sterilization. In one case, instead of_currently_,<br>the recall period for the indicator is_ever_, and in one case it is the_last twelve_<br>_months_.|21|16|
|4+ antenatal<br>care checks|Woman received at<br>least four antenatal<br>care checks during<br>her lastpregnancy|None|31|22|
|Maternal tetanus<br>vaccination|Woman received at<br>least one tetanus<br>vaccination during<br>her lastpregnancy|In most cases, the requirement is that the woman received one tetanus<br>vaccination during pregnancy. In six cases, it the indicator requires two<br>tetanus vaccinations and in another three cases, it requires that the woman<br>received_all appropriate_tetanus vaccinations.|21|14|
|Facility delivery|Women giving birth<br>at formal health<br>facility|In six cases, only public facilities are included and in another five cases, the<br>indicator is explicitly limited to deliveries in health facilities where a skilled<br>provider waspresent.|63|35|
|1+ postnatal<br>care checks|Mother received at<br>least one postnatal<br>care check|The timing of the postnatal care checks is typically unspecified, as is the type<br>of healthcare workers carrying out the check and whether or not the check<br>takes place in a facility or the mother’s home. The timing is specified in 16<br>cases – in eight of them it is 14 days or less, and in eight it is 42 days or<br>more. In six cases, the indicator is explicitly limited to postnatal care<br>provided by skilled health workers, and in ten cases it is explicitly limited to<br>postnatal careprovided in health facilities.|35|21|
|Full childhood<br>vaccination|Children received<br>BCG, Polio3,<br>DTP3/<br>Pentavalent3, and<br>measles vaccinations|In three cases fewer vaccinations that in the main definition are included and<br>in 16 cases the indicator includes additional country-specific vaccinations like<br>yellow-fever or HBV. Sampled children are typically age 12-23 months of<br>age, but in 13 cases, children older than one are included. In six cases, the<br>indicator is based on information from vaccination cards only.|41|22|

25
**Table 4: Comparison of mean effect sizes between performance-based financing, voucher and conditional cash transfer programs**

|(1)<br>PBF vs vouchers|(2)<br>PBF vs CCT|(3)<br>Vouchers vs CCT|
|---|---|---|
|Modern family planning<br>-5.41 (0.022)|-|-|
|_N_<br>16|||
|4+ antenatal care checks<br>-2.84 (0.162)|-4.61 (0.000)|-1.76 (0.443)|
|_N_<br>18|18|8|
|Maternal tetanus vaccination<br>-|0.73 (0.797)|-|
|_N_|14||
|Facility delivery<br>-2.40 (0.258)|-2.67 (0.199)|-0.05 (0.983)|
|_N_<br>25|28|17|
|1+ postnatal care checks<br>-0.97 (0.657)|-0.61 (0.779)|0.23 (0.907)|
|_N_<br>14|15|13|
|Full child vaccination<br>-|-1.42 (0.478)|-|
|_N_|22||

Notes: Table shows results from bivariate random effects meta-regressions. Coefficients are the percentage point differences in mean effect sizes between the first and second-mentioned group in the column title. _p-values_ of the differences shown in parentheses.

26
**Table 5: Possible explanators of effect sizes differences from meta-regressions**

||(1)<br>Combination of<br>supply and<br>demand side<br>financial<br>incentives|(2)<br>Control group<br>with budget<br>equalization|(3)<br>Baseline outcome<br>value|(4)<br>High bias risk<br>study|_N_<sup></sup>|
|---|---|---|---|---|---|
|Modern family planning|4.27 (0.068)|-2.29 (0.505)|-0.01 (0.900)|-1.21 (0.663)|16|
|4+ antenatal care checks|1.15 (0.592)|-2.35 (0.033)|0.07 (0.090)|0.98 (0.590)|22|
|Maternal tetanus vaccination|-6.55 (0.593)|0.88 (0.797)|0.01 (0.907)|2.00 (0.583)|14|
|Facility delivery|-0.30 (0.853)|-0.48 (0.810)|-0.03 (0.344)|1.79 (0.352)|35|
|1+ postnatal care checks|-1.42 (0.333)|-5.87 (0.242)|0.04 (0.395)|-1.11 (0.574)|21|
|Full child vaccination|0.09 (0.976)|-3.33 (0.275)|-0.03 (0.565)|0.33 (0.885)|22|

Notes: Table shows results from bivariate random effects meta-regressions. Coefficients in columns 1, 2 and 4 are the percentage point differences in mean effect sizes between the group described in the column title and the respective reference group, _p-_ values of the differences shown in parentheses. For column 1 reference group are interventions with either supply or demand side financial incentives, for column 2 it is interventions where the control group receives no treatment, and for column (4) it is interventions where the bias risk is classified as low or medium. Column 3 gives the percentage point change associated with a one percentage point increase in the baseline outcome value.<sup></sup> Because no baseline outcome values are available for two facility delivery and two postnatal care check effect sizes, the number of observations underlying the results in column 3 are 33 and 19, respectively, instead of 35 and 21 for the meta-regressions.

27
<!-- Start of picture text --> Financial incentive interventions<br><!-- End of picture text -->
# **Figure 2: PRISMA chart**

||**PBF**|**Vouchers**|**CCT**|**TOTAL**|
|---|---|---|---|---|
|**References identified**|**2,267**|**1,583**|**2,439**|**6,289**|
|Academic databases|825|334|964|2,123|
|Google Scholar|944|898|800|2,642|
|Literature reviews|484|345|649|1,478|
|Other intervention searches|9|4|13|26|
|Think tank & donor websites|1|1|6|8|
|Happenstance discovery|1|0|4|5|
|Social media calls|2|1|1|4|
|Search for latest official version|1|0|2|3|
|**Duplicates removed**|**624**|**474**|**967**|**2,065**|
|**Title-abstract screened**|**1,643**|**1,109**|**1,472**|**4,224**|
|Non-compliers|1,505|1,038|1,211|3,754|
|**Full-text screened**|**138**|**71**|**261**|**470**|
|<br>Excluded|128|66|246|440|
|**2016**<br>Missingfull text|8|5|4|17|
|<br>Duplicates|6|3|22|31|
|Publication format|13|7|34|54|
|No impact evaluation|30|10|23|63|
|Not low- or middle-income country|3|0|0|3|
|Intervention|18|13|45|76|
|Outcome|7|1|56|61|
|Method|34|20|37|91|
|Identification|20|13|24|57|
|Standard errors|6|5|0|11|
|Population level estimates|8|2|3|13|
|Dummytreatment variable|0|0|6|6|
|Intention-to-treat|0|0|4|4|
|No official version available|6|1|10|17|
|Later official versions available|3|4|15|22|
|Duplicates across interventions|0|2|0|2|
|**Compliers**|**10**|**5**|**15**|**30**|
|**2017-2021**<br>**Search update compliers**|**14**|**4**|**11**|**28**|
|<br>**Total included references**|**24**|**9**|**26**|**58**|
|**16-2021**<br>**Impact estimates**|**129**|**23**|**60**|**212**|
|**20**<br>**Program-specific effect sizes**|**75**|**21**|**34**|**130**|

29
# **Figure 3: Impacts of financial incentives on modern contraceptive use of women of fertile age**

|Intervention and Program<br>points (95% CI)<br>Percentage|Weight<br>%|
|---|---|
|Performance-Based Financing||
|Tajikistan: Health Services Improvement Project<br>-4.70 (-171.66, 162.2|6)<br>0.01|
|Cameroon: Health Sector Support Investment Project I<br>-3.70 (-14.28, 6.88)|2.68|
|Afghanistan: System Enhancement for Health Action in Transition<br>-0.50 (-8.30, 7.30)|4.94|
|Burkina Faso: Reproductive Health Project I<br>1.00 (-2.28, 4.28)|27.93|
|Cameroon: Health Sector Support Investment Project II<br>1.70 (-7.99, 11.39)|3.20|
|Rwanda: P4P scheme<br>1.76 (-1.83, 5.34)|23.41|
|Nigeria: State Health Investment Project II<br>2.10 (-22.11, 26.31)|0.51|
|Burkina Faso: Reproductive Health Project II<br>2.43 (-5.55, 10.41)|4.72|
|Congo, Rep.: Health Sector Services Development Project<br>3.50 (-4.73, 11.73)|4.43|
|Zimbabwe: Health Sector Development Support Project<br>4.90 (-2.81, 12.61)|5.05|
|Burundi: PBF scheme<br>5.00 (-0.00, 10.00)|12.01|
|Nigeria: State Health Investment Project I<br>5.70 (0.50, 10.90)|11.12|
|Subgroup, DL (I<sup>2</sup>= 0.0%, p = 0.891)<br>2.38 (0.65, 4.12)|100.00|
|with estimated 95% predictive interval<br>(0.41, 4.35)||
|Voucher||
|Tanzania: Helping Poor Pregnant Women Access Better Health Care<br>-3.00 (-12.55, 6.55)|22.85|
|Pakistan: Suraj<br>4.00 (-9.12, 17.12)|17.56|
|Cambodia: Reproductive Health Voucher<br>5.64 (-0.00, 11.28)|29.34|
|Pakistan: Marie Stopes Chakwal Voucher Project<br>14.83 (9.78, 19.89)|30.25|
|Subgroup, DL (I<sup>2</sup>= 77.0%, p = 0.005)<br>6.16 (-1.67, 13.99)|100.00|
|with estimated 95% predictive interval<br>(-27.73, 40.05)||
|Overall, DL (I<sup>2</sup>= 48.2%, p = 0.016)<br>3.69 (1.27, 6.12)||
|with estimated 95% predictive interval<br>(-3.54, 10.92)||
|Heterogeneity between groups: p = 0.356||
|-30<br>-25<br>-20<br>-15<br>-10<br>-5<br>0<br>5<br>10<br>15<br>20<br>25<br>30||
|Program reduces prob.<br>Program increases prob.<br>NOTE: Weights and between-subgroup heterogeneity test are from random-effects model||

30
# **Figure 4: Impacts of financial incentives on pregnant women having four or more antenatal care visits**

|Intervention and Program<br>points (95% CI)<br>Percentage|Weight<br>%|
|---|---|
|<br>Performance-Based Financing<br>||
|Burundi: PBF scheme<br>-6.00 (-18.50, 6.50)|0.52|
|Nigeria: State Health Investment Project I<br>-3.80 (-10.57, 2.97)|1.79|
|Nigeria: State Health Investment Project II<br>-2.50 (-9.60, 4.60)|1.63|
|Burkina Faso: Reproductive Health Project II<br>-1.60 (-13.89, 10.69)|<br>0.54|
|Burkina Faso: Reproductive Health Project I<br>-1.10 (-4.71, 2.51)|6.30|
|Zambia: Health Services Improvement Project II<br>-0.40 (-1.49, 0.69)|69.33|
|Rwanda: P4P scheme<br>-0.33 (-4.77, 4.11)|4.17|
|Zimbabwe: Health Sector Development Support Project<br>0.10 (-12.40, 12.60)|0.52|
|Tajikistan: Health Services Improvement Project<br>0.70 (-2.31, 3.71)|9.08|
|Zambia: Health Services Improvement Project I<br>1.08 (-8.12, 10.28)|0.97|
|Senegal: Health and Nutrition Financing Project<br>2.00 (-10.50, 14.50)|0.52|
|Rwanda: Community Living Standards Grant<br>3.60 (-2.87, 10.07)|1.96|
|Tanzania: Pwani Pilot<br>4.10 (-2.44, 10.64)|1.92|
|Lesotho: Health Sector Performance Enhancement Project<br>12.00 (1.50, 22.50)|0.74|
|Subgroup, DL (I<sup>2</sup>= 0.0%, p = 0.573)<br> <br>-0.19 (-1.09, 0.72)<br>|100.00|
|with estimated 95% predictive interval<br>(-1.19, 0.82)||
|Voucher||
|Tanzania: Helping Poor Pregnant Women Access Better Health Care<br>0.00 (-14.33, 14.33)|7.33|
|Kenya: Reproductive Health Voucher<br>1.20 (-3.53, 5.93)|67.24|
|Kenya: Maternal Voucher Experiment<br>6.40 (-4.58, 17.38)|12.49|
|Uganda: Healthybaby<br>8.11 (-2.66, 18.89)|12.95|
|Subgroup, DL (I<sup>2</sup>= 0.0%, p = 0.587)<br>2.66 (-1.22, 6.53)|100.00|
|with estimated 95% predictive interval<br>(-5.86, 11.17)||
|Conditional Cash Transfer||
|Kenya: Maternal CCT Experiment I<br>2.60 (-7.00, 12.20)|5.66|
|Peru: Juntos<br>3.80 (0.86, 6.74)|60.44|
|Indonesia: Program Keluarga Harapan<br>5.55 (1.34, 9.76)|29.42|
|Philippines: Pantawid Pamilya<br>7.65 (-3.15, 18.44)|4.48|
|Subgroup, DL (I<sup>2</sup>= 0.0%, p = 0.819)<br>4.42 (2.13, 6.70)|100.00|
|with estimated 95% predictive interval<br>(-0.60, 9.44)||
|Overall, DL (I<sup>2</sup>= 27.6%, p = 0.114)<br>1.37 (0.00, 2.74)||
|with estimated 95% predictive interval<br>(-2.02, 4.76)||
|Heterogeneity between groups: p = 0.001||
|-30<br>-25<br>-20<br>-15<br>-10<br>-5<br>0<br>5<br>10<br>15<br>20<br>25<br>30||
|Program reduces prob.<br>Program increases prob.<br>NOTE: Weights and between-subgroup heterogeneity test are from random-effects model||

31
# **Figure 5: Impacts of financial incentives on pregnant women being vaccinated against tetanus**

|Intervention and Program|points (95% CI)<br>Percentage|Weight<br>%|
|---|---|---|
|Performance-Based Financing|||
|Zambia: Health Services Improvement Project I|-3.90 (-10.31, 2.51)|11.95|
|Burkina Faso: Reproductive Health Project II|-3.77 (-26.39, 18.86|)<br>1.78|
|Burkina Faso: Reproductive Health Project I|-2.50 (-10.70, 5.70)|9.07|
|Cameroon: Health Sector Support Investment Project II|2.10 (-2.48, 6.68)|15.81|
|Cameroon: Health Sector Support Investment Project I|2.30 (-2.21, 6.81)|15.99|
|Rwanda: P4P scheme|2.67 (-4.55, 9.89)|10.53|
|Zambia: Health Services Improvement Project II|6.00 (-1.62, 13.62)|9.89|
|Zimbabwe: Health Sector Development Support Project|7.50 (-0.19, 15.19)|9.79|
|Burundi: PBF scheme|9.80 (4.95, 14.65)|15.20|
|Subgroup, DL (I<sup>2</sup>= 50.5%, p = 0.040)|3.04 (-0.09, 6.17)|100.00|
|with estimated 95% predictive interval|(-5.57, 11.65)||
|Conditional Cash Transfer|||
|Indonesia: Program Keluarga Harapan|-4.70 (-8.23, -1.17)|25.29|
|Mexico: Progresa|3.70 (-0.84, 8.24)|23.32|
|Peru: Juntos|5.00 (0.10, 9.90)|22.58|
|Honduras: Bono 10,000|5.20 (-0.88, 11.28)|20.16|
|Honduras: Programa de Asignacion Familiar (PRAF II)|6.15 (-7.90, 20.20)|8.65|
|Subgroup, DL (I<sup>2</sup>= 75.4%, p = 0.003)|2.38 (-2.55, 7.31)|100.00|
|with estimated 95% predictive interval|(-14.49, 19.25)||
|Overall, DL (I<sup>2</sup>= 62.7%, p = 0.001)|2.69 (0.03, 5.36)||
|with estimated 95% predictive interval|(-6.12, 11.51)||
|Heterogeneity between groups: p = 0.826|||
|Program reduces prob.<br>-30<br>-25<br>-20<br>-15<br>-10<br>-5<br>NOTE: Weights and between-subgroup heterogeneity test are from random-effects model|Program increases prob.<br>0<br>5<br>10<br>15<br>20<br>25<br>30||

32
**Figure 6: Impacts of financial incentives on delivery in a health facility**

|Intervention and Program<br>points (95% CI)<br>Percentage|Weight<br>%|
|---|---|
|<br>Performance-Based Financing<br>||
|Zambia: Health Services Improvement Project II<br>-4.60 (-17.37, 8.17)|2.20|
|Congo, Dem. Rep.: Health Sector Rehabilitation and Support Project<br>-3.00 (-11.19, 5.19)|4.21|
|<br>Gambia: Maternal and Child Nutrition and Health Results Project<br> <br>-2.39 (-16.21, 11.42|)<br><br>1.93|
|<br>Congo, Rep.: Health Sector Services Development Project<br> <br>-1.80 (-7.09, 3.49)|6.71|
|Cambodia: Contracting-in<br>-0.20 (-4.51, 4.11)|7.82|
|Rwanda: Community Living Standards Grant<br>190 (-065 445)|995|
|Lesotho: Health Sector Performance Enhancement Project<br> <br>3.00 (-3.50, 9.50)<br>. ., .|5.52<br>.|
|Senegal: Health and Nutrition Financing Project<br> <br>3.00 (-2.50, 8.50)<br>|6.49|
|Burkina Faso: Reproductive Health Project II<br>3.33 (-0.86 7.52)|7.97|
|Burkina Faso: Reproductive Health Project I<br> <br>5.20 (0.50, 9.90)<br>,|7.37|
|Zimbabwe: Health Sector Development Support Project<br>5.70 (-2.72, 14.12)|4.06|
|Zambia: Health Services Improvement Project I<br>5.99 (-1.93, 13.91)|4.40|
|Nigeria: State Health Investment Project I<br> <br>8.60 (1.88, 15.32)<br>|5.33|
|Rwanda: P4P scheme<br>8.80 (3.71, 13.90)|6.93|
|Tanzania: Pwani Pilot<br>9.60 (4.76, 14.44)|7.21|
|Cambodia: Gov't scheme<br>10.60 (4.52, 16.68)|5.91|
|Nigeria: State Health Investment Project II<br>11.45 (4.71, 18.19)|5.31|
|Burundi: PBF scheme<br>16.69 (-8.06, 41.44)|<br>0.68|
|Subgroup, DL (I<sup>2</sup>= 55.0%, p = 0.003)<br>4.39 (2.29, 6.50)<br>|<br>100.00|
|with estimated 95% predictive interval<br>(-2.66, 11.44)||
|Voucher||
|Tanzania: Helping Poor Pregnant Women Access Better Health Care<br>2.50 (-5.52, 10.52)|12.20|
|Kenya: Maternal Voucher Experiment<br>4.59 (-5.12, 14.29)|8.33|
|Kenya: Reproductive Health Voucher<br>5.50 (1.26, 9.74)|43.62|
|Pakistan: Jhang Maternal Health Voucher<br>7.76 (0.75, 14.77)|15.96|
|Uganda: Healthybaby<br>8.87 (-1.84, 19.59)|6.83|
|Cambodia: Targeted Maternal and Child Health Voucher<br> <br>11.30 (0.72, 21.88)<br>|7.00|
|Cambodia: Universal Maternal and Child Health Voucher<br>11.80 (0.43, 23.17)|6.07|
|Subgroup, DL (I<sup>2</sup>= 0.0%, p = 0.776)<br>6.44 (3.64, 9.24)|100.00|
|with estimated 95% predictive interval<br> <br>(2.76, 10.11)<br>||
|Conditional Cash Transfer||
|Philippines: Pantawid Pamilya<br>1.76 (-7.80, 11.32)|9.15|
|Mali: Cash for Nutritional Awareness<br> <br>1.86 (-6.11, 9.83)<br>|11.11|
|India: Jananni Surkshya Yojana<br>2.60 (1.56, 3.64)<br>|21.43|
|Afghanistan: Ministry of Public Health Scheme<br> <br>3.30 (-12.64, 19.24)|<br>4.50|
|Kenya: Maternal CCT Experiment I<br>7.90 (-1.90, 17.70)|8.89|
|Bangladesh: Demand-Side Financing Program<br> <br>8.80 (-7.43, 25.04)<br>|4.37|
|Kenya: Maternal CCT Experiment II<br> <br>11.60 (2.77, 20.43)<br>|10.00|
|Indonesia: Program Keluarga Harapan<br>12.29 (5.23, 19.35)|12.43|
|Kenya: Maternal CCT & voucher experiment<br> <br>13.00 (3.59, 22.41)<br>|9.32|
|Subgroup DL (I<sup>2</sup>= 580% p = 0011)<br>Nigeria: Maternal Cash Transfer Experiment<br>729 (349 1109)<br>14.00 (4.12, 23.88)|10000<br>8.80|
|with estimated 95% predictive interval<br>,., .<br>(-3.27, 17.86)<br>. ., .|.|
|Overall, DL (I<sup>2</sup>= 49.5%, p = 0.001)<br>5.31 (3.84, 6.79)||
|with estimated 95% predictive interval<br> <br>(-0.25, 10.88)<br>||
|Heterogeneity between groups: p = 0.309<br> <br>||
|-45<br>-40<br>-35<br>-30<br>-25<br>-20<br>-15<br>-10<br>-5<br>0<br>5<br>10<br>15<br>20<br>25<br>30<br>35<br>40<br>45||
|Program reduces prob.<br>Program increases prob.<br>NOTE: Weights and between-subgroup heterogeneity test are from random-effects model||

33
# **Figure 7: Impacts of financial incentives on receiving postnatal care**

|Intervention and Program<br>points (95% CI)<br>Percentage<br>Weight<br>%|
|---|
|Performance-Based Financing|
|Zambia: Health Services Improvement Project II<br>-3.05 (-12.78, 6.68)<br>11.96|
|Tanzania: Pwani Pilot<br>0.20 (-5.79, 6.19)<br>31.57|
|Afghanistan: System Enhancement for Health Action in~~Transition~~<br>0.90 (-69.46, 71.26)<br>0.23|
|Burkina Faso: Reproductive Health Project I<br>2.40 (-5.47, 10.27)<br>18.27|
|Burkina Faso: Reproductive Health Project II<br>3.43 (-7.82, 14.69)<br>8.93|
|Congo, Rep.: Health Sector Services Development Project<br>4.20 (-3.84, 12.24)<br>17.52|
|Zimbabwe: Health Sector Development Support Project<br>8.12 (-8.69, 24.93)<br>4.00|
|Zambia: Health Services Improvement Project I<br>9.25 (-3.01, 21.51)<br>7.53|
|Subgroup, DL (I<sup>2</sup>= 0.0%, p = 0.826)<br>2.20 (-1.16, 5.57)<br>100.00|
|with estimated 95% predictive interval<br>(-2.00, 6.40)|
|Voucher|
|Tanzania: Helping Poor Pregnant Women Access Better Health Care<br>-3.00 (-15.61, 9.61)<br>4.32|
|Pakistan: Jhang Maternal Health Voucher<br>1.54 (-3.50, 6.57)<br>27.07|
|Uganda: Healthybaby<br>2.26 (-8.95, 13.48)<br>5.46|
|Kenya: Reproductive Health Voucher<br>3.80 (-0.50, 8.10)<br>37.23|
|Cambodia: Universal Maternal and Child Health Voucher<br>4.70 (-0.98, 10.38)<br>21.27|
|Cambodia: Targeted Maternal and Child Health Voucher<br>7.40 (-4.75, 19.55)<br>4.65|
|Subgroup, DL (I<sup>2</sup>= 0.0%, p = 0.824)<br>3.17 (0.55, 5.79)<br>100.00|
|with estimated 95% predictive interval<br>(-0.54, 6.88)|
|Conditional Cash Transfer|
|Honduras: Programa de Asignacion Familiar (PRAF II)<br>-5.65 (-15.82, 4.52)<br>8.07|
|China: CHIMACA<br>-0.48 (-11.48, 10.53)<br>7.11|
|India: Indira Gandhi Motherhood Support Scheme<br>0.97 (-1.68, 3.62)<br>30.45|
|Mali: Cash for Nutritional Awareness<br>1.73 (-9.75, 13.21)<br>6.64|
|Peru: Juntos<br>4.78 (0.90, 8.65)<br>24.77|
|Honduras: Bono 10,000<br>6.10 (-3.50, 15.70)<br>8.83|
|Philippines: Pantawid Pamilya<br>10.22 (3.35, 17.08)<br>14.14|
|Subgroup, DL (I<sup>2</sup>= 44.2%, p = 0.096)<br>3.09 (-0.17, 6.34)<br>100.00|
|with estimated 95% predictive interval<br>(-5.04, 11.21)|
|Overall, DL (I<sup>2</sup>= 0.0%, p = 0.670)<br>2.70 (1.28, 4.11)|
|with estimated 95% predictive interval<br>(1.19, 4.20)|
|Heterogeneity between groups: p = 0.897|
|-30<br>-25<br>-20<br>-15<br>-10<br>-5<br>0<br>5<br>10<br>15<br>20<br>25<br>30|
|Program reduces prob.<br>Program increases prob.<br>NOTE: Weights and between-subgroup heterogeneity test are from random-effects model|

34
# **Figure 8: Impacts of financial incentives on full child vaccination**

|Intervention and Program<br>points (95% CI)<br>Percentage|Weight<br>%|
|---|---|
|Performance-Based Financing||
|Nigeria: State Health Investment Project II<br>-3.80 (-10.57, 2.97)|11.42|
|Cambodia: Gov't scheme<br>-3.47 (-22.22, 15.28|)<br>2.54|
|Burkina Faso: Reproductive Health Project II<br>-3.20 (-22.44, 16.04|)<br>2.42|
|Rwanda: P4P scheme<br>-0.90 (-9.97, 8.16)|8.07|
|Zimbabwe: Health Sector Development Support Project<br>0.30 (-21.02, 21.62)|2.01|
|Burkina Faso: Reproductive Health Project I<br>1.30 (-2.96, 5.56)|16.73|
|Cambodia: Contracting-in<br>3.03 (-3.37, 9.44)|12.10|
|Burundi: PBF scheme<br>4.40 (-0.19, 8.99)|15.96|
|Zambia: Health Services Improvement Project I<br>5.20 (-6.68, 17.08)|5.46|
|Tajikistan: Health Services Improvement Project<br>7.40 (-11.02, 25.82)|2.62|
|Nigeria: State Health Investment Project I<br>10.50 (3.69, 17.31)|11.35|
|Zambia: Health Services Improvement Project II<br>11.60 (-9.05, 32.25)|2.13|
|Cameroon: Health Sector Support Investment Project I<br>16.70 (0.63, 32.77)|3.33|
|Cameroon: Health Sector Support Investment Project II<br>20.65 (5.96, 35.34)|3.88|
|Subgroup, DL (I<sup>2</sup>= 36.1%, p = 0.087)<br>3.89 (0.73, 7.05)|100.00|
|with estimated 95% predictive interval<br>(-4.08, 11.86)||
|Conditional Cash Transfer||
|Zimbabwe: Manicaland HIV/STD project<br>1.80 (-5.05, 8.65)|9.28|
|Honduras: Bono 10,000<br>2.70 (-1.81, 7.21)|17.73|
|Mali: Cash for Nutritional Awareness<br>2.76 (-5.73, 11.25)|6.40|
|Indonesia: Program Keluarga Harapan<br>3.47 (-1.72, 8.67)|14.44|
|India: Indira Gandhi Motherhood Support Scheme<br>5.31 (2.25, 8.37)|28.39|
|<br>Kenya: M-SIMU RCT<br>5.33 (-0.91, 11.57)|10.82|
|Nicaragua: Red de Proteccion Social<br>10.29 (0.62, 19.96)|5.06|
|Turkey: Social Risk Mitigation Project<br> <br>13.90 (6.35, 21.45)|7.87|
|Subgroup, DL (I<sup>2</sup>= 22.0%, p = 0.254)<br>5.02 (2.75, 7.30)|100.00|
|with estimated 95% predictive interval<br>(0.34, 9.71)||
|Overall, DL (I<sup>2</sup>= 31.1%, p = 0.083)<br>4.44 (2.52, 6.36)||
|with estimated 95% predictive interval<br>(-0.88, 9.76)||
|Heterogeneity between groups: p = 0.569<br> <br>||
|-40<br>-35<br>-30<br>-25<br>-20<br>-15<br>-10<br>-5<br>0<br>5<br>10<br>15<br>20<br>25<br>30<br>35<br>40||
|Program reduces prob.<br>Program increases prob.<br>NOTE: Weights and between-subgroup heterogeneity test are from random-effects model||

35