EQUITY PROFILES OF THREE SOCIAL FRANCHISE NETWORKS IN WEST AFRICA Nirali Chakraborty, Ph.D Research Advisor for Reproductive Health 9 th World Congress on Health Economics, Sydney, Australia 10 July 2013

 Background –Franchising –Study sites  Equity calculation methodology  Results –Benin –Democratic Republic of Congo (DRC) –Mali  Implications Outline PAGE 2

24 FRANCHISES IN 23 COUNTRIES SOCIAL FRANCHISING AT PSI +10,000 FRANCHISEES10 MILLION CLIENTS PER YEAR

+ Health Impact ✓ Quality $ Cost-Effectiveness Equity Market Expansion Improving population health Ensuring adherence to clinical standards for client care Providing services at equal or lower cost to alternatives Enabling the poorest to access services Delivering services that would not otherwise be provided Goals of Social Franchising

 Pilot equity measurement at franchises  Justify use of national or sub-national reference population, for program decision making Study objectives page 5

 Client exit interviews  Equity benchmarked to reference population  Franchises primarily urban and peri- urban Study context page 6

Benin page 7 IndicatorTotalUrbanRural CPR among married women Unmet need among married women Under 5 mortality Has electricity Urban residence41.4 Private Health Expenditure/THE 46.7 Out of Pocket/PHE91.2 Source: DHS 2006 and WHO Global Health Observatory 2011 data

 Offers Family Planning, SRH/HIV and MNCH services  185 clinic outlets  ~33% of providers are MDs  ~100,000 clinic visits recorded in 2012 Benin – ProFam franchise page 8 Source: 2013 Social Franchising Compendium,

Democratic Republic of Congo page 9 IndicatorTotalUrbanRural CPR among married women Unmet need among married women Under 5 mortality Has electricity Urban residence 45.4 Private Health Expenditure/THE 66.3 Out of Pocket/PHE65.7 Source: DHS 2007 and WHO Global Health Observatory 2011 data

 Offers Family Planning, MNCH and Water Purification services  138 clinic outlets  ~15% of providers are MDs  ~192,000 clinic visits recorded in 2012 DRC – Réseau Confiance page 10 Source: 2013 Social Franchising Compendium,

Mali page 11 IndicatorTotalUrbanRural CPR among married women Unmet need among married women Under 5 mortality Has electricity Urban residence 33.7 Private Health Expenditure/THE 54.9 Out of Pocket/PHE 99.6 Source: DHS 2006 and WHO Global Health Observatory 2011 data

 Offers Family Planning, SRH/HIV and MNCH services  71 clinic outlets  ~42% of providers are MDs  ~43,000 clinic visits recorded in 2012 Mali – ProFam franchise page 12 Source: 2013 Social Franchising Compendium,

Equity measurement methodology PAGE 13

Data collection

1.Principal Components Analysis on weighted DHS asset ownership data 2.Capture eigenvector from first principal component for each asset, and quintile cut-points from asset index 3.Standardize Client data to DHS data 4.Multiply each asset by eigenvector 5.Sum (Std value*eigenvector) for each client 6.Place clients within DHS quintiles Placing clients within reference population Calculation done twice: National population Urban only

 Let A i1 =Asset score for each household i in DHS  Let =standardized value of each asset for household i in DHS  Let  v = Value of eigenvector from first component for variable v  Let A i2 =Asset score for each client i sampled Mathematically speaking… page 16 DHS data Client data

 Wealth quintiles of franchising clients, within national reference population Results: Client wealth profile page 17 QuintileBeninDRCMali n=535n=242n=293 1 (Poorest) (Richest)

QuintileNationalUrban Poorest Quintile Quintile Quintile Richest Results: Client wealth profiles in context page 18 Benin – ProFam Franchise

QuintileNationalUrban Poorest00 Quintile Quintile Quintile Richest Results: Client wealth profiles in context page 19 DRC – Réseau Confiance

QuintileNationalUrban Poorest00.3 Quintile Quintile Quintile Richest Results: Client wealth profiles in context page 20 Mali – ProFam Franchise

 Social Franchise community of practice is recommending client equity to be benchmarked against national reference population  For program decision making, sub-national reference population may be more informative  In these 3 countries, franchises appear to serve a wealthy population segment  Do social franchises serve the poor? Should social franchises aim to serve the poor(est)? Implications page 21

Acknowledgements: I gratefully acknowledge the PSI research managers from the three countries where this data was collected: Cyprien Zinsou (Benin), Willy Onema (DRC), and Mamadou Bah (Mali). page 22

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