Which factors are necessary and/or sufficient for accelerated reduction of maternal and child mortality in low and middle-income countries?

Transcription

1 Which factors are necessary and/or sufficient for accelerated reduction of maternal and child mortality in low and middle-income countries? Results from a Boolean, Qualitative Comparative Analysis (QCA) Daniele Caramani and Beatrice Eugster, University of St Gallen 29 April 2014

2 Outline The research question Introduction to QCA and Boolean algebra Software and practical applications of QCA Research design Results: The bivariate analysis of necessity and sufficiency Combinatorial analysis Calibration and cut-off points Further steps 1/20

3 Research question What combination of factors contributes to success, i.e. explaining why some countries are on track while others are not? Explanatory factors from X-factor dataset which grew over time: selected factors based on previous analysis with different methods (statistical and case-study). Focus on factors that are policy-employable (differently from general socio-economic, cultural, geographical factors on which policy intervention is difficult in the short term. MGD4 and MDG5 (child and mother mortality) Idea of progress toward (operationalization familiar to those working in the field). 2/20

4 QCA and Boolean Algebra Origin: small-n analysis, case-oriented analysis, dichotomous variables (e.g., presence/absence of factors). However: own logic based on specific explanatory approach based on Mill s Methods of Difference and Agreement: Necessary and sufficient conditions (next slide). Logic of multiple causation: same phenomenon can have different causes (in different countries). Logic of combinatorial analysis, configurations: how explanatory factors combine. Three types of QCA: crisp (0/1), fuzzy set, multi-value. 3/20

5 Necessary and sufficient conditions Necessary condition: Factor in whose absence the outcome never occurs. A factor without which an outcome cannot occur, i.e. there is never the outcome without the condition (but not always sufficient): Cov = countries on track that have condition X as % countries on track Sufficient condition: Factor in whose presence the outcome always occurs. Factor that always produces the outcome, i.e. there is always the outcome with the condition (but not the only cause): Cons = countries on track that have condition X as % countries that have condition X 4/20

6 Necessary and sufficient conditions X 1 X 2 X 3 X 2 and X 3 X 5 E Issues: Etc Not S Not S, not N Not S, not N S, not N 1) Cut-off point of 0/1 2) Degree of sufficiency / necessity : not deterministic (see XLS file) 5/20

7 Boolean algebra Notation (logical operators): Combinatorial logic: AND ( ) Multiple causation: OR (+) Presence/absence: NOT (~) capitalized (occurrence) vs. low caps (absence) E = (ABF + ABf) + (ACF + ACf) ~D E = (AB + AC) ~D E = A (B + C) ~D Procedures of minimization and factorization. 6/20

8 Software and applications Most used software: Charles Ragin: fsqca Lasse Cronqvist: Tosmana New: Stata, and two modules in R (open source) (Thiem and Duşa, Qualitative Comparative Analysis with R: A User s Guide). Application to an amazing variety of fields. See literature in E.g. Health research & policy 7/20

9 Research design Data: from file 100plusvar.dta including 172 variables for 144 countries (MDG4) and 116 countries (MDG5). Analysis performed on three files: variables: water, doctors, health_exp, measles, out_pocket, edu, san, tub, urb, corr 105 countries (39 not on track excluded because missing data) 29 on track MDG4, 8 on track MDG variables: + skilled 111 countries 28 on track MDG4 (Lebanon missing), 8 MDG variables: + contraceptive, breastfeeding 81 countries 19 on track MDG4, 8 MDG5 8/20

10 Research design Time dimension: Cases = country. Variables = variable by year (21 years recoded into 5 periods: , , , , , taken averages). Definition of on track : see Cohen et al. (2013) Tables 3a and 3b last column (i.e. by Best Policy Adjusted High Performance Target 2). Generally, analyses performed with following thresholds: Necessity =.90 (coverage). Sufficiency =.50 (consistency). 9/20

11 Daniele Caramani and Beatrice Eugster 27 April 2014 Research design: The analysis aims to identify factors that are either necessary and/or sufficient for low and middle-income countries to make fast progress in the reduction of U5MR from 1990 to For the definition of fast-track progress countries see Cohen et al. (2013) Tables 3a and 3b last column (i.e. by Best Policy Adjusted High Performance Target 2). A necessary condition is a factor in whose absence countries cannot make fast-track progress. A sufficient condition is a factor in whose presence countries always make fasttrack progress. The table includes bivariate results. For necessary conditions, scores indicate how many countries making fast-track progress have improved levels in the condition. For sufficient conditions, scores indicate how many countries that have improved levels in the condition make fast-track progress in reducing mortality. Scores vary between 0.0 and 1.0 (perfect necessity and sufficiency). Data: 100plusvar.dta including 172 variables (over time) for 144 countries. Crisp-set analysis performed with the QCA module in R (Thiem and Duşa, Qualitative Comparative Analysis with R: A User s Guide). In crisp-set analysis factors are dichotomized into 0 and 1 scores for absence and presence of factor. Cut-off points for attributing 0 and 1 scores (calibration) are discussed in PowerPoint presentation (Washington DC, 30 January 2014). Results: Results are presented with a focus on the multi-sector approach based on 7 factors. Emblematic indicators have been chosen based on comparability with statistical analysis (Oxaca/Blinder). Factors / category Necessary conditions Sufficient conditions N Emblematic indicator Scores Fast-track countries with factor Scores Countries with factor that are fast-track Immunization measles (% children aged months) Total fertility rate (negative effect) Access to improved water source (% of population) GDP per capita 1. Well-functioning health systems 2. Demographics / population dynamics 3. Environmental management 4. Economic development 5. Ensuring Lag 10 years Ratio female to education male primary enrollment 6. Women s Female legislators, senior participation officials, managers (%) 7. Inequality Gini index (negative effect) Notes: N indicates number of countries for which information is available. Results for other immunization schemes are very similar to those for measles indicated in table. Results for other female education indicators are very similar to those appearing in the table.

12 Other factors considered with strong necessity scores (above.750): Relating to factor 1 (see table): Births attended by skilled staff (%). Relating to factor 1: Physicians (per 1,000 people). Relating to factor 1: Health expenditure per capita. Relating to factor 3: Improved sanitation facilities (% population with access). Relating to factor 3: Urban population (%). Lagged (10 years) effect found for: female ratio in education (relating to factors 5 and 6), all immunization schemes (factor 1), birth attendance by skilled staff (factor 1), total fertility rate (negative effect) (factor 2) and urban population (factor 3). None of the factors analyzed display high sufficiency scores. The highest sufficiency scores are found for the following factors: Appearing in table. Percentage of female legislators, senior officials and managers. Not appearing in table. Physicians per 1,000 people with a score of.500 (44 countries) for a total number of 113 countries for which data are available. QCA has also been performed to identify configurations of factors that would be sufficient for countries to make fast-track progress (combinatorial causation). However, QCA did not yield to any specific configuration of factors proving necessary or sufficient for countries to make fast-track progress in reducing U5MR. Results, on the contrary, point to a high and diverse number of configurations that in different countries are associated with fast-track progress (multiple causation) pointing that the outcome is reached through different configurations of factors. No single configuration stands out. The following discussion of results (see Table) is therefore limited to the bivariate scores of each factor. Discussion: With the exception of higher scores for female education and number of physicians, QCA has identified neither single factors nor combinations of factors that are sufficient in their association with fast-track progress toward the reduction of child mortality. No single factor approaches a sufficiency score of 1.0 which would mean that whenever a specific factor is present in a country, fast-track progress can always be observed. Also combinations of factors (scores not reproduced here) feature low scores of sufficiency. A number of factors display high levels of necessity, meaning that fast-track progress cannot occur without these factors. High scores appear in particular for factors relating to the quality of health systems (skilled staff, number of doctors, immunization coverage), low fertility rates, access to clean water and education (for both males and females). The analysis has not identified growth and GDP per capita as a necessary condition for countries to make fasttrack progress. Similarly, no single model of political and socio-economic governance has been found either sufficient or necessary for fast-track progress (as measured through indicators such as control of corruption, out-of-pocket expenditures, Gini index). These negative results point to the diversity of successful models and to the need of investments across a number of sectors.

13 Additional Analysis MDG4: necessary factors Factor Countries on track with factor Score Improved water source (% pop. with access) Doctors per 1,000 people Immunization (% children of age group) Primary pupils (% female) Births attended by skilled worker (%) Improved sanitation (% pop. with access) Incidence of tubercolosis Urbanization (% residents) Contraceptive, breastfeeding, health_exp, gdp, out_pocket, corruption, Gini All with much lower scores Note: Immunization as average of BCG, DPT, measles, Pol3. 10/20

14 Additional Analysis MDG4: sufficient factors Factor Countries with factor Score Improved water source (% pop. with access) Doctors per 1,000 people Immunization (% children of age group) Primary pupils (% female) Births attended by skilled worker (%) Improved sanitation (% pop. with access) Incidence of tubercolosis Urbanization (% residents) Control of corruption Health expenditure per capita Contraceptive, breastfeeding, gdp, out_pocket, Gini All with much lower scores 11/20

15 Additional Analysis MDG5: necessary factors Factor Countries on track with factor Score Improved water source (% pop. with access) 6.75 Doctors per 1,000 people 6.75 Immunization (% children of age group) 6.75 Primary pupils (% female) 5.63 Births attended by skilled worker (%) 6.75 Improved sanitation (% pop. with access) 6.75 Incidence of tubercolosis 5.75 Prenatal care (% pregnant women) 6.75 Contraceptive prevalence (% women) 5.63 Gini, urbanization, breastfeeding, health_exp, gdp, out_pocket, corruption All with much lower scores 12/20

16 Additional Analysis MDG5: sufficient factors Factor Countries with factor Score All factors with extremely low scores. 13/20

17 QCA summary Configurational analysis: which combination of these factors stands out among on track countries? On track countries present a huge variety of configurations. Many factors appear as necessary but no single configuration stands out as sufficient to explain why some countries are on track. Multiple causality: Different configurations of factors characterize different countries (no pattern of groups of countries) BUT with small differences between complex, long (many factors) and very similar configurations. 14/20

18 Example for MDG4 S1: CLEANWATER2005.9*DOCS1K2005.9*healthexppc2005.9*IMMDPTPERC2005.9*IMMMEASPERC2005.9*IMMPOL3PERC2005.9*OOPHEALTHEXPPERCTOT2005.9*primedfemperc2005.9*T BINC100K2005.9*urbanizationperc2005.9*contcorrupt CLEANWATER2005.9*DOCS1K2005.9*IMMDPTPERC2005.9*IMMMEASPERC2005.9*IMMPOL3PERC2005.9*OOPHEALTHEXPPERCTOT2005.9*PRIMEDFEMPERC2005.9*SANITATION2005.9*TB INC100K2005.9*URBANIZATIONPERC2005.9*contcorrupt cleanwater2005.9*docs1k2005.9*healthexppc2005.9*immdptperc2005.9*immmeasperc2005.9*immpol3perc2005.9*oophealthexpperctot2005.9*primedfemperc2005.9*s anitation2005.9*tbinc100k2005.9*urbanizationperc2005.9*contcorrupt cleanwater2005.9*docs1k2005.9*healthexppc2005.9*immdptperc2005.9*immmeasperc2005.9*immpol3perc2005.9*oophealthexpperctot2005.9*primedfemperc2005.9*s anitation2005.9*tbinc100k2005.9*urbanizationperc2005.9*contcorrupt CLEANWATER2005.9*docs1k2005.9*healthexppc2005.9*immdptperc2005.9*immmeasperc2005.9*immpol3perc2005.9*oophealthexpperctot2005.9*primedfemperc2005.9*s anitation2005.9*tbinc100k2005.9*urbanizationperc2005.9*contcorrupt CLEANWATER2005.9*docs1k2005.9*healthexppc2005.9*immdptperc2005.9*IMMMEASPERC2005.9*IMMPOL3PERC2005.9*oophealthexpperctot2005.9*PRIMEDFEMPERC2005.9*S ANITATION2005.9*TBINC100K2005.9*URBANIZATIONPERC2005.9*contcorrupt CLEANWATER2005.9*docs1k2005.9*HEALTHEXPPC2005.9*IMMDPTPERC2005.9*IMMMEASPERC2005.9*IMMPOL3PERC2005.9*oophealthexpperctot2005.9*PRIMEDFEMPERC2005.9*s anitation2005.9*tbinc100k2005.9*urbanizationperc2005.9*contcorrupt CLEANWATER2005.9*DOCS1K2005.9*HEALTHEXPPC2005.9*immdptperc2005.9*immmeasperc2005.9*immpol3perc2005.9*OOPHEALTHEXPPERCTOT2005.9*PRIMEDFEMPERC2005.9*S ANITATION2005.9*TBINC100K2005.9*URBANIZATIONPERC2005.9*contcorrupt CLEANWATER2005.9*DOCS1K2005.9*HEALTHEXPPC2005.9*IMMDPTPERC2005.9*IMMMEASPERC2005.9*IMMPOL3PERC2005.9*oophealthexpperctot2005.9*primedfemperc2005.9*S ANITATION2005.9*TBINC100K2005.9*URBANIZATIONPERC2005.9*contcorrupt CLEANWATER2005.9*DOCS1K2005.9*HEALTHEXPPC2005.9*IMMDPTPERC2005.9*IMMMEASPERC2005.9*IMMPOL3PERC2005.9*oophealthexpperctot2005.9*PRIMEDFEMPERC2005.9*S ANITATION2005.9*TBINC100K2005.9*urbanizationperc2005.9*contcorrupt /20

19 The problem of cut-off points Can be understood also as tipping points: From which level onwards (e.g., % of vaccinated population, health expenditure, etc.) is there an effect? Affects decision (calibration) which countries are coded 0 or 1 in the explanatory factors. Determines 0/1. Necessity: above cut-off point ALL countries are on track. Sufficiency: above cut-off point NO countries are not on track. If cut-off point is moved up and down a variable (e.g., % of vaccinated population) distribution of countries changes. Why important? Because needs knowledge from researchers and policy makers! 16/20

20 The problem of cut-off points Number of countries NOT ON TRACK ON TRACK % vaccinated % vaccinated Not sufficient: there are not on track countries above the cut-off point Necessary: no country is on track below the cut-off point 17/20

21 The problem of cut-off points Number of countries NOT ON TRACK ON TRACK % vaccinated % vaccinated Sufficient: no country is not on track countries above the cut-off point Not necessary: some countries are on track below the cut-off point 17/20

22 Example 1 of cut-off point Countries being on track to achieve MDG 4 not on track on track Frequency Community health workers (per 1,000 people) Comment: dotted line refers to cut-off value used for QCA analysis, here: average=.4. 18/20

23 Example 2 of cut-off point Frequency Countries being on track to achieve MDG 4 not on track on track Improved sanitation facilities (% of population with access) Comment: dotted line refers to cut-off value used for QCA analysis, here: average= /20

24 Conclusion In bivariate analysis a number of factors appear as necessary conditions to put countries on track (MDG4 and MDG5): No factor on its own can be considered a sufficient condition. Varying the cut-off point does not yield to different results. No impact of considering previous time points in explanatory factors. Multi-variate analysis: Multiple causation: countries can be on track under the impact of numerous, slightly different, complex configurations. Combinatorial/configurational analysis: there are many different combinations of factors but analysis does not find one or few specific combinations that stand out. Further steps: small N (MSSD), regional analysis. 20/20

25 Which factors are necessary and/or sufficient for accelerated reduction of maternal and child mortality in low and middle-income countries? Results from a Boolean, Qualitative Comparative Analysis (QCA) Daniele Caramani and Beatrice Eugster, University of St Gallen 29 April 2014