Geographic Targeting and Normative Frames: Revisiting the Equity of Conditional Cash Transfer Program Distribution in Bolivia, Colombia, Ecuador, and Peru

Abstract

Background: Four Andean countries of Bolivia, Colombia, Ecuador, and Peru introduced national health-focused conditional cash transfer (CCT) programs in the 2000s. This study probes whether policymakers in these countries targeted CCT programs to subregions with the highest prevalence of ill-health or those with the lowest socioeconomic status (SES) to evaluate the equity of geographic targeting and means-testing, as well as the potential role of normative frames, bounded rationality, and clientelism as explanatory mechanisms for inequities in social spending. Methods: The distribution of vaccination coverage, underweight, stunting, and child deaths is established both within and between subnational regions and SES quintiles from 1998 to 2012 using every available nationally representative household survey. The equity of CCT program targeting and strength of association with subregional SES and health outcomes are measured using generalized entropy index decomposition and meta-regression. Finally, simple predictive models for CCT targeting are created using lagged subregional SES, health outcomes, and concentration indices. Results: Bolivia and Peru both effectively targeted at-risk subregions, but subregions in Peru with no CCT program coverage result in higher mistargeting rates for the country as a whole. Only Bolivia failed to attain CCT coverage concentration indices that are at least as large as the health inequalities they are targeting. Despite this insufficient progressivity, Bolivia has the most efficient subregional targeting, while the lowest rates of mistargeting for child deaths are found in Colombia and Ecuador. Finally, the simple predictive model performs as well or better than observed CCT coverage distribution for every country, year, and outcome. Conclusions: Both Peru and Ecuador have targeted programs to their poorest populations effectively, demonstrating that this is possible with both universal and geographic targeting. No clear evidence of clientelism was found, while the dominant normative frame underlying CCT program targeting decisions appears to be the relative SES of subregions, rather than absolute SES, prevalence of health outcomes, or health inequalities. To reduce the inequitable impacts of bounded rationality, policymakers can use simple predictive models to target CCT coverage effectively and without leaving behind the most vulnerable populations that happen to live in more affluent subregions.

Realizing the right to health in Latin America, equitably

“…In addition, many countries in Latin America and the Caribbean have also introduced reforms to their health systems in search of an expansion in health coverage by introducing demand-side interventions to improve access to care, over the last 30 years. Among those there are conditional cash transfer programs, subsidized health insurance, vouchers and other mechanisms to improve financial protection [ 18 ].…”

Cash Transfers and Contraceptive Use: A Regression Discontinuity Analysis

“…Using local polynomial density estimation (Cattaneo, Jansson, and Ma 2016), we find no evidence of discontinuity of the assignment variable at the threshold (t-statistic of 0.878 and p-value of 0.380). In a comparative study, Poirier confirms that there is no clear evidence of clientelism in Ecuador's BDH (Poirier 2020). Therefore, we assume that the program was locally randomized (Lee and Lemieux 2010).…”

The contribution of the private sector to poverty alleviation programs: exploring business engagement in conditional cash transfers

“… Sources: Size and scope (Row 2) was constructed by the authors based on the following:aMedellin and Prada (2015);bPoirier (2020);cOECD (2016);dDogherty (2014);ePoirier (2020);fParker and Todd (2017); supporting information on targeting system, benefit structure, payment frequency and duration is synthesized from the Celhay et al (2017), Dogherty (2014), McGuire (2013), Medellin and Prada (2015), OECD (2016), Parker and Todd (2017), Poirier (2020), TeleSUR (2020) and World Bank Report (2009);*Values in US$ are estimated based on exchange rates for local currencies as of December 2021…”

“…fParker and Todd (2017); supporting information on targeting system, benefit structure, payment frequency and duration is synthesized from the Celhay et al (2017), Dogherty (2014), McGuire (2013), Medellin and Prada (2015), OECD (2016), Parker and Todd (2017), Poirier (2020), TeleSUR (2020) and World Bank Report (2009);…”