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Centre for Health Economics Have SWAPs influenced aid flows and aid effectiveness? Rohan Sweeney, Duncan Mortimer and David W. Johnston 14 th February.

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Presentation on theme: "Centre for Health Economics Have SWAPs influenced aid flows and aid effectiveness? Rohan Sweeney, Duncan Mortimer and David W. Johnston 14 th February."— Presentation transcript:

1 Centre for Health Economics Have SWAPs influenced aid flows and aid effectiveness? Rohan Sweeney, Duncan Mortimer and David W. Johnston 14 th February 2014

2 Centre for Health Economics “SWAp has truly become a popular and widespread means of coordinating and structuring development aid” (Sundewall and Sahlin-Andersson 2006).

3 Centre for Health Economics The SWAp is a “process rather than a fixed blueprint” (Walford 2003) Agreement Sector-wide health strategy Government-led Budgeted Share processes Government systems

4 Centre for Health Economics SWAp has desired implications for DAH funding flows. SWAp promotes increased general sector support. SWAp can enable redirection of DAH towards domestic priority areas. Important because…. Project-based DAH continues to dominate – only 7.7% sector support between (Piva and Dodd, 2009). MDGs and disease focused donors (eg. GFATM, GAVI, Clinton) have encouraged disease specific project-based DAH. Evaluations have found increases DAH support, however case study methods don’t enable contemporaneous control, so we can’t predict what would happen in absence of SWAp.

5 Centre for Health Economics Impact of SWAp on funding flows - research questions a)has DAH allocated to sector support increased as a result of SWAp implementation? b)have SWAps changed how DAH has been allocated across other key health funding areas?

6 Centre for Health Economics Methods Searched for countries with implemented health SWAps. Constructed a unique dataset of DAH recipient countries, which includes total levels of DAH and levels allocated between key health areas (IHME 2011): –HIV, TB, “maternal and child health (MNCH), malaria, sector support and NCDs. Using a linear probability model, comparable treatment and control countries were identified. Likelihood of SWAp implementation was predicted given: –GDP/capita, DAH levels, geographic region, no. of donors, life expectancy, and population levels. Fixed effect panel regression techniques employed.

7 Centre for Health Economics The sample Table 1SWAp implementing countries (n=21) Non-implementing countries (n=18) P value VariableMeanStd. Dev.MeanStd. Dev. Total DAH (millions $US) DAH/capita ($US) population (millions) GDP/capita ($US) a life expectancy (years) a t-test for difference in means with unequal variance.

8 Centre for Health Economics Table 2: Impact on levels of Sector Support Log of Levels of DAH as Sector Support (1)(2) “Sector” DAH SWAp t *** (0.90) - Malaria t-1 (prob death/1,000)-0.01 (0.01) HIV t-1 (% of 15-49yr olds)0.14 (0.39)0.13 (0.40) TB t-1 (incidence/100,000)0.00 (0.01) IMR t-1 (per 1,000 live births)0.03 (0.05)0.04 (0.05) Log(GDP/capita) t (1.13)1.42 (1.11) Gov – effectiveness t (2.30)2.49 (2.27) Control of corruption t (1.79)-1.61 (1.74) Pre – SWAp (1-2 years prior) 1.50 (1.44) Early-SWAp (years 1-2) 3.06** (1.46) Later-SWAp (years 3+) 4.84*** (1.26) N SWAp countries Control countries

9 Centre for Health Economics ^ Beta distributions Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 Table 3: Impact on allocations to other health areas Log of Levels of DAH to health areas (1)(2)(3)(4) MalariaHIVTBMNCH SWAp t (1.25)-1.60** (0.65)0.23 (1.36)-1.56** (0.72) Malaria t-1 (prob death/1,000)0.03*** (0.01)-0.00 (0.01)0.03** (0.01)0.00 (0.01) HIV t-1 (% of 15-49yr olds)0.38 (0.35)-0.00 (0.20)0.30 (0.35)-0.27** (0.13) TB t-1 (incidence/100,000)-0.00 (0.00)0.00 (0.00)-0.01 (0.01)0.00 (0.00) IMR t-1 (per 1,000 live births)0.05 (0.04)-0.02 (0.04)0.05 (0.05)-0.02 (0.04) Log(GDP/capita) t (0.98)1.31* (0.71)-0.81 (0.62)0.46 (0.80) Gov – effectiveness t (1.80)3.90*** (1.08)0.08 (1.65)1.73 (1.13) Control of corruption t (1.52)-0.77 (1.00)-0.31 (1.34)1.18 (1.01) N773

10 Centre for Health Economics SWAps have facilitated increased levels of DAH directed as sector support. SWAps appear to have facilitated reallocations of DAH fund flows across key health areas, specifically away from HIV and MNCH. SWAp implementation has facilitated changes in funding flows consistent with SWAp aims to increase country ownership of DAH programmes. Key messages

11 Centre for Health Economics References Brown A, Foster M, Norton A and Naschold F. The status of sector wide approaches. Centre for Aid and Public Expenditure. Working Paper Overseas Development Institute. Institute for Health Metrics and Evaluation, I. (2011). Development Assistance for Health Country and Regional Recipient Level Database Seattle, Institute for Health Metrics and Evaluation OECD Development Co-operation Directorate. (2010). "Paris Declaration and Accra Agenda for Action." Piva, P. and R. Dodd (2009). "Where did all the aid go? An in depth analysis of increased aid flows over the past 10 years." Bulletin of the World Health Organization 87: Ravishankar N, Gubbins P, Cooley RJ, Leach-Kemon K, Michaud CM, Jamison DT, Murray CJL. Financing of global health: tracking development assistance for health from 1990 to The Lancet 2009;373; Sundewall J, Sahlin-Andersson K. Translations of health sector SWAps--a comparative study of health sector development cooperation in Uganda, Zambia and Bangladesh. Health Policy May;76(3): Walt G, Pavignani E, Gilson L, Buse K. Health sector development: from aid coordination to resource management. Health Policy Plan. 1999(a);14:207–18. Walford, V. (2003). Defining and evaluating SWAps: a paper for the Inter-Agency Group on SWAps and Development

12 Centre for Health Economics Appendix: model specifications Has sector DAH increased under SWAp? (1) sector_DAH it = α i + δSWAp it-1 + β 1 malaria it-1 + β 2 HIV it-1 + β 3 TB it-1 + β 4 IMR it-1 + β 5 log(GDP/capita) it-1 + β 6 gov_effect it-1 + β 7 corruption it-1 + μ t + ε it Note: the impact on both absolute levels of Sector_DAH and also the proportion of total DAH allocated to the sector is estimated. Has SWAp changed allocations between other key health areas? (2) tb_DAH it = α i + δSWAp it-1 + β 1 malaria it-1 + β 2 HIV it-1 + β 3 TB it-1 + β 4 IMR it-1 + β 5 log(GDP/capita) it-1 + β 6 gov_effect it-1 + β 7 corruption it-1 + μ t + ε it Comparable specifications estimated for DAH directed to HIV, malaria and MNCH. NCDs omitted due to lack of meaningful burden of disease control for full period.

13 Centre for Health Economics ^ Beta distributions Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 Appendix: Table 4 Sector Support displacement effect Log of Levels of DAH to other health areas (2)(1)(3)(4) malariaHIVTBMNCH Log(Sector DAH) t (0.068) (0.041)0.014 (0.051)0.021 (0.032) SWAp t (1.742) (0.608)1.836 (1.293)-1.257* (0.641) SWAp*log(Sector DAH)t (0.128)-0.130** (0.057)-0.235* (0.117) (0.038) Malaria t-1 (prob death/1,000)0.026*** (0.009) (0.008)0.025** (0.012)0.003 (0.009) HIV t-1 (% of 15-49yr olds)0.377 (0.356)0.009 (0.189)0.313 (0.336)-0.271** (0.131) TB t-1 (incidence/100,000) (0.003)0.004 (0.003) (0.005)0.003 (0.003) IMR t-1 (per 1,000 live births)0.046 (0.037) (0.035)0.040 (0.044) (0.037) Log(GDP/capita) t (0.966)1.395** (0.648) (0.624)0.450 (0.806) Gov – effectiveness t (1.770)4.196*** (1.053)0.230 (1.534)1.713 (1.141) Control of corruption t (1.500) (1.011) (1.355)1.144 (1.013) N773

14 Centre for Health Economics Appendix: Table 5 Impact of SWAp on Infant Mortality Rate Logged Infant Mortality Rate (IMR) SWAp t *** (0.029) Log (DAH/capita) t (0.013) Proportion of DAH to MNCH0.095** (0.046) Log(GDP/capita) t (0.044) Gov – effectiveness t * (0.039) TB (incidence/100,000)0.000 (0.000) Malaria (prob death/1,000)0.001* (0.000) HIV (% of 15-49yr olds)-0.021** (0.010) Log (health expenditure/capita) t N SWAp countries Control countries


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