Parallel_Session_2_Talk_6_Strobl

MotivationEmpirical analyses

Discussion and Conclusion

Community-based health insurance in Rwanda:An effective measure against child labour?

Renate Strobl

Department of Health Economics, University of Basel

13 September 2013Swiss Health Economic Workshop, Lucerne

Renate Strobl CBHI in Rwanda: Effective against child labour?



Outline

1 MotivationChildren as insurance assetFormal health insurance

2 Empirical analysesEmpirical strategyEstimation resultsRobustness check

3 Discussion and ConclusionDiscussion of resultsPolicy implicationsLimitations and Outlook




Children as insurance assetFormal health insurance

Outline








Informal risk coping strategies

Options:

1 Drawing on savings2 Selling assets3 Borrowing from the extended family4 Children as insurance asset:

School de-enrolment (→ economizing educationalexpenditures)Child labour (→ generating income; replacing family membersin household production)





Children as insurance asset

Ex post risk coping strategy:Income shock ⇒ consumption smoothing

agricultural crop shocks (Beegle et al., 2006, Jakoby andSkoufias, 1997)parental health shocks (Bazen and Salmon, 2010, de Janvry etal., 2006)economic crisis (Thomas et al., 2004)

Ex ante risk coping strategy:Income risk ⇒ precautionary savings

child time allocation is affected even if shocks do not realizeFitzsimons (2007), Kazianga (2012)





Children as insurance asset

Significant long-term costs:

Child labour is associated with

worsened health (O’Donnell et al., 2005)lower educational attainment (Beegle et al., 2008, Zabaleta,2011)lower cognitive achievements (Akabayashi & Psacharopoulos,1999, Heady, 2003)

Withdrawals from school reduce probability to return to school(de Janvry et al., 2006)

⇒ lower human capital accumulation⇒ lower future earnings, productivity, well-being;

perpetuated poverty





Outline








Literature

Formal health insurance ⇒ Informal risk coping strategies:

Dekker and Wilms (2009):

insured households sell assets less frequently and borrow lessmoney to finance medical treatment (Uganda)

Wagstaff and Pradhan (2005):

insured households have higher educational expenditures(Vietnam)

Guarcello et al. (2010):

children of insured households are less likely to work and morelikely to go to school (Guatemala)





Community-based health insurance in Rwanda

History:1999: pilot test in 3 districts, provided by so-called Mutuellesfrom 2003 on: progressively scaled up to a national systempopulation coverage rate: 7% (2003), 44% (2005), 73%(2006), 91% (2010)

Affiliation conditions (time of survey):enrolment at household levelannual premium: 2,500-11,500 RWF per household(3.80-17.40 USD)co-payments: 100-150 RWF (0.15-0.22 USD) per visit athealth center; 10% of hospital fee at district hospital

Benefit Packages:Minimum Package: all services provided at local health centerComplementary Package: limited services provided at districthospitals




Empirical strategyEstimation resultsRobustness check

Outline








Data

Data set:Household Living Conditions Survey (EICV2) 2005/06cross-sectional, nationally representativesocio-economic data of 6,900 households and 34,785individualscommunity-level data for 440 communities (only for rural partof Rwanda)

Final sample:restriction on rural regions of Rwandaexclusion of households insured in health insurance schemesother than Mutuelles (RAMA, MMI, private schemes)households with children from 7-15 yearshousehold head older than 15 years

⇒ 5,811 children living in 3,154 households⇒ 43,4 % of households insured by Mutuelles





Two outcome variables

1 Hours worked:children age 7 - 15 (minimum working age in Rwanda: 16years)total hours worked in the last 7 days (employment andhousehold chores)

2 Education Gap:Education Gap =max{0, Expected Education−Actual Education}

with Expected Education ={

0 if age57age−7 if 85age515

captures any delays (e.g. due to late entry, class repetitions) aswell as premature drop-outs





Hours worked

Figure 1: Average weekly hours worked, by gender and health insurance status





Education Gap

Figure 2: Average Education Gap, by gender and health insurance status





Estimation equation

Estimation Equation:

Hijk = β0l +β1Xijk +β2Mutuelleijk +ηl + εijk

Hijk : Hours worked or Education Gap

Mutuelleijk : Mutuelle enrolment (dummy)

Xijk : Child, household, community characteristics

ηl : District dummies

⇒ Problem: possible endogeneity of Mutuelle enrolment





Test of endogeneity

Two instrumental variables (IV)1 Community Mutuelle enrolment rate = Ei−Di

Ni−1

Ei = Number of enrolled households in communityDi = Dummy if household is enrolledNi = Total number of households in community

2 Satisfaction of household with quality of services provided atdistrict hospital (dummy)

Test on relevance and overidentifying restrictions⇒ IV validated

Test of endogeneity⇒ Mutuelle enrolment can be treated as exogenous

⇒ Simple count data regression models





Outline








Estimation results: Mutuelle effect

Table 1: Count data regression estimates: Average marginal effect of Mutuelle enrolment

Hours worked Education Gap

(ZINB) (Poisson)

n AME (SE) AME (SE)

All 5,811 -1.058** (0.475) -0.120*** (0.035)

Boys 2,880 -1.491*** (0.573) -0.085* (0.049)

Girls 2,931 -0.311 (0.616) -0.168*** (0.045)

Standard errors corrected for clustering at the community level;*p < 0.10, **p < 0.05, ***p < 0.01;Control variables: child: sex, age, number of older children in household; household:sex, age, education of head, household size, land owned in acres, distance to primaryand secondary school; community: proportion of households with landownings less 2acres, dummies indicating presence of health center, primary school, market, publictelephone.





Outline








Propensity score matching

Table 2: Propensity score matching: Average treatment effect of Mutuelle enrolment

Hours worked per child Education Gap per child

n ATT (SE) Γ ATT (SE) Γ

Radius 3,263 -1.216** (0.437) 1.4 -0.148** (0.059) 1.2

Kernel 3,263 -1.751** (0.673) 1.0 -0.107 (0.070) -

Standard errors attained by bootstrapping (50 replications);*p < 0.10, **p < 0.05, ***p < 0.01;Caliper width for radius matching: 0.01; bandwidth for Kernel matching: 0.06;Γ: Sensitivity parameter of Rosenbaum bounds sensitivity analysis;Covariates: sex, age and education of the head, dummy indicating chronical health problemsof household members, household size, number of children under 7, number of children in age7-15, land owned in acres, dummy indicating if the household is houseowner, dummies forconsumption quintiles, distance to the nearest health center, district dummies.




Discussion of resultsPolicy implicationsLimitations and Outlook

Outline








Discussion of results

Substantial impact of mutual health insurance on schoolingoutcomes:

remarkably, as Mutuelles cover only a part of total economiccost of illness:

do not compensate for lost earningsco-payments have to be effected

compared to decrease of child working time of 1 hour per week:

suggests that impact on schooling not only via time-liberatingeffect





Possible impact channels

Formal health insurance ⇒ Child labour and schooling:

1 Mutuelles protect Rwandan households efficiently againsthigh OOP payments (Lu et al., 2012, Saksena et al. 2011,Shimeles, 2010)

ex ante: reduced need to build up a buffer stockex post: facilitated consumption smoothing

2 Mutuelle members use modern health care more likely(Lu et al., 2012, Saksena et al. 2011, Shimeles, 2010)

faster recovery / better overall health statusless income losses and treatment costsless time absent in school, better learning performance





Outline








Policy implications

Formal health insurance:

Indirect benefits so far unrecognized:

crowding out of inefficient risk coping strategies (child labourand school de-enrolment)higher human capital accumulationshould be taken into account in cost-efficiency analyses of suchpolicy interventions

Useful and necessary complement to explicit child labourcountermeasures (bans, compulsory schooling):

eliminating child labour without undermining household’sability to cope with risk





Outline








Limitations and Outlook

Cross-sectional data set:Unobserved heterogeneity and biasing effect on estimatescannot be definitely ruled out

however, solid evidence on robustness of results by using twodifferent evaluation methods

Impossible to disentangle impact channels of healthinsurance (ex post, ex ante, health-related effects)

might be important to choose optimal policy intervention(e.g. if ex post consumption smoothing is main driver of results→ could also be reached with provision of credits)





Thank you very much for your attention!


Appendix References

References I

Akabayashi, H., Psacharopoulos, G. (1999). The trade-offbetween child labour and human capital formation: ATanzanian case study. The Journal of Development Studies,35(5), 120-140.

Beegle, K., Dehejia, R., Gatti, R. (2006). Child labor andagricultural shocks. Journal of Development Economics, 81,80-96.

Beegle, K., Dehejia, R., Gatti, R., Krutikova, S. (2008). Theconsequences of child labor: Evidence from longitudinal data inrural Tanzania. Policy Research Working Paper No. 4677.Washington, DC.: World Bank.


Appendix References

References II

de Janvry, A., Financ, F., Sadoulet, E., Vakis, R. (2006). Canconditional cash transfer programs serve as safety nets inkeeping children at school and from working when exposed toshocks? Journal of Development Economics, 79, 349-373.

Dekker, M., Wilms, A. (2009). Health insurance and otherrisk-coping strategies in Uganda: The case of MicrocareInsurance Ltd. World Development, 38(3), 369-378.

Fitzsimons, E. (2007). The effects of risk on education inIndonesia. Economic Development and Cultural Change, 56(1),1-25.


Appendix References

References III

Guarcello, L., Mealli, F., Rosati, F.C. (2010). Householdvulnerability and child labor: The effect of shocks, creditrationing, and insurance. Journal of Population Economics, 23,169-198.

Heady, C. (2003). The effect of child labor on learningachievements. World Development, 31(2), 385-398.

Jacoby, H.G., Skoufias, E. (1997). Risk, financial markets andhuman capital in a developing country. Review of EconomicStudies, 64, 311-335.

Kazianga, H. (2012). Income risk and household schoolingdecisions in Burkina Faso. World Development, 40(8),1647-1662.


Appendix References

References IV

Lu, C., Chin,B., Lewandowski, J.L., Basinga, P., Hirschhorn,L.R., Hill, K., Murray, M., Binagwaho, A. (2012). Towardsuniversal health coverage: An evaluation of Rwanda Mutuellesin its first eight years. PloS ONE, 7(6), e39282.

O’Donnell, O., Rosati, F.C., van Doorslaer, E. (2005). Healtheffects of child work: Evidence from rural Vietnam. Journal ofPopulation Economics, 18, 437-467.

Saksena, P., Antunes, F.A., Xu, K.Musango, L., Carrin, G.(2011). Mutual health insurance in Rwanda: Evidence onaccess to care and financial risk protection. Health Policy, 99,203-209.


Appendix References

References V

Shimeles, A. (2010). Community based health insuranceschemes in Africa: The case of Rwanda. African DevelopmentBank Group Working Paper Series. Working Paper No. 120.Tunis: African Development Bank.

Wagstaff, A., Pradhan, M. (2005). Health insurance impactson health and nonmedical consumption in a developing country.World Bank Policy Research Working Paper 3563. Washington,DC.: World Bank.

Zabaleta, M.B. (2011). The impact of child labor on schoolingoutcomes in Nicaragua. Economics of Education Review, 30,1527-1539.


Parallel_Session_2_Talk_6_Strobl

Economy & Finance

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