Causality and Reverse Causality: Issues Arising from Studies of Poverty and HIV/AIDS

Causality and Reverse Causality: Issues Arising from Studies of Poverty and HIV/AIDS Murray Leibbrandt

We are not going to …. • Yesterday we put a huge range of issues on the table: • Macro perspectives, micro perspectives, macro-micro interface • Panel data and timing • The big C word came up a lot • I would not dare to talk technically about causality (This is a problem with having an all star cast). • Yet, for policy, causality is a huge issue. This is NOT statisticians on steroids, this has been completely integral to our discussions from the talk that Christine McCafferty gave us. • There have been massive advances in thinking about causality and pathways. DTs’ rapid fire hard sell aside, this framework does need to be in whatever we do even if it is a humbler version such as David Canning’s “Pushing the causality back one level”. • For the record, the AIDS  Poverty issue can be seen as a sub-set of the large Health  Income issue. There is a huge literature to guide us.

We are going to… • Try to focus some of the issues from yesterday through African empirical case studies. • This is not an ambitious agenda but… • There are many in this room that have done this work and they can add to what I say. • It should take us closer to where we are in Africa right now which is our base. • Raise some issues at the micro-level that resonate a lot with what David Canning and Robert Eastwood had to say at the macro level. • 3 sources of data were put into the pot yesterday (DHS and LSMS cross-sections, the DSS panels, some specialist panel data sets)

Where are we with the DHSs? • We use a study by Glick and Sahn (2005) as a guide • They use DHS data to look at risk behaviours and HIV/AIDS in 8 countries with 2 DHS surveys each about 5 years apart. • Each DHS contains questions on age at first sex, whether are sexually active, number of current sexual partners and whether used a condom at last sex. • They look for correlates of these behaviours and for evidence of behaviour change between the surveys. • They focus on the relationship between education and wealth and these risk behaviours. • Findings – for men education and wealth are positively associated with more partners but education is positively associated with condom use. This is not true for women (but don’t get a negative association on wealth either).

What has this got to do with causality? • On the one level, nothing. However, there are interesting issues that arise; e.g. • large differences across countries in each period and in the changes • separately estimate male, female, rural, urban • On another level the paper is saturated with linguistic tiptoeing around causality. The rules of the game have clearly changed. Higher standards of evidence are demanded. • The paper uses theory (a model of the demand for risky behaviour) to help to give structure to thinking. • The paper raises the issue of repeated cross-sections and micro-simulation work for our attention

But I thought we were doing the impact of HIV/AIDS on Poverty? • We are. I was supposed to raise the issue of reverse causality as so I thought that I would get it out of the way by tricking you into an analysis of education/wealth  HIV/AIDS • The point is that even with an explicit decision to focus on the pathway from reproductive health to socio-economic outcome, there are socio-economic variables driving that reproductive health behaviour. • There are many flow diagrams in many current papers to illustrate this. (Here’s one)

How to model the impact of household income on sexual behaviors? Xijt = individual observable characteristics a1 gj = household unobservable characteristic “type” Yijt=1 if risky behavior, 0 if not a2 Ijt = typical household income a3 a4 b1≠ 0 Sjt = past economic shock Sjt+1 = future economic shock b2≠ 0 ? b2≠ 0 ? • If estimate of a4 is 0, we are more confident that b2 = 0 and that a3 is a consistent estimate of the effect of economic shocks on behavior

Panels do not provide black box quick fixes for causality • So, access to panel data that enables one to include HIV/AIDS on the RHS and explain consequent “impacts” on well-being is not a miracle cure. • The variable on the right hand side is endogenous. It gets to the present with a history that may even depend on the LHS variable itself.

So what does one do? Be clear about the hypothesised pathways • Aids is multidimensional and so are its impacts. Are there any effectsare they brought about by: • the status of being affected • AIDS-related morbidity • AIDS-related mortality • What are the channels of impact?: • Labor Supply / Income Primary Impact (“Gross Impact”) • Family Composition • Grant / remittance income Coping Mechanisms • Saving / Sale of Assets / Borrowing • Expenditure  Final Impact (“Net Impact”)

More on what one does • One can build structural models that are the micro analogue of the larger macro models. These can be useful for pathways. • One thinks through the issue and uses that best panel skills that one as evidenced by the Chapoto and Jain papers on Zambia. • From May et al, pay real attention to the ex post timing of impact • This same dampening is found in the KHDS Tanzanian panel. They find that economic growth is lower for recent deaths but not for those further in the past. Suggests a bouncing back effect which again requires a panel to pick up. These interactions of stocks and flows and prices and quanities makes it very hard to deliver precise policy advice. • But we use the Tanzanian panel to flag migration …..

What about migration? The KHDS – Tanzanian panel • This study provides an example of novel use of old data. Built the panel off a 1991/94 LSMS • One response of a household to a death is for members to migrate. To explore this one needs BOTH successfully track the movers and keep in touch with the stayers. • Tracking strategies and panel attrition are serious research issues in their own right. • In the case of Kagera, those who moved are better off those who stayed and those who moved further are best off. • Average consumption is much higher when re-unite migrants and their base households. • Strong orphanhood effects. Let’s go to orphan studies as a case study of “the gold standard”.

Three Orphan Studies and the Gold Standard (1) • “Everyone agrees that after a parent death, child outcomes should not improve. The household loses a source of income, and children often move to new households where they may not be taken care of very well (not as well as their parents). But need evidence .. • Evans and Miguel use the famous worms data to look at the implications of parent deaths for growth? • Find: Parent deaths in rural Kenya have moderate negative impacts on school participation: • 5-6 percentage point average drop • Maternal deaths 9 percentage point drop • Heterogeneous impacts – particularly large adverse effects for worse students and large point estimates for effects on younger girls • Most recent edition of Demography has a Case and Ardington study

Three Orphan Studies and the Gold Standard (2) • C and A study uses the DSS data to make a convincing argument as to the impacts of parental death on school enrollment and achievement. • Controlling for household socio-economic status they find that children whose mothers have died are behind in school • relative to other children in the DSS at large • relative to other children who have moved since 2001 • relative to the non-orphaned children with whom they live • relative to the non-orphaned children within the same school • They are less likely to be enrolled than are other children in the DSS • Less is spent on their schooling • By going back to the first socio-economic module they look at children whose mothers are going to die in the future. • We find that these children are NOT significantly behind or less likely to be enrolled. • These results are consistent with a model in which mothers’ deaths have a causal effect on children’s educations • Even with this successful case the policy implications are muted. With these dynamics sorted out with the DSS data, the paper uses census data to show that these impacts are consistent with estimates at the provincial and national levels.

Three Orphan Studies and the Gold Standard (3) • These studies are the gold standard yet their policy implications are muted • Where is the third study? • A recent Timeaus et al study on the KIDS data puts fathers back on the map

Data Wrap Up • Do we build on existing data sets that allow these questions to be answered? Merrick-Tanzania as an examples of possibilities of being creative. One worked one didn’t. • Can see that these pathways are tricky to nail, so is a case of specialist data sets or sub-data sets. • David Ahlburg’s question: Do we know enough about what is out there? Dinner discussion about the DSS sites and whether they can’t do what another birth-cohort study could do? • Generalisability country by country. (The need for country work). Robust work from gold standard to generate some higher level indicators for other countries. • We are in a research programme that is advancing. More stringent rules now. What are the potential trade-offs between focused intervention studies and broader panel data studies? • Gold standard is evolving

Causality and Reverse Causality: Issues Arising from Studies of Poverty and HIV/AIDS