A few days ago the World Health Statistics 2012 Report released its annual compendium of statistics. No doubt, it was a lot of work to compile—to verify every number in every cell, for each country and indicator. The WHO should be commended for providing this invaluable global public good. A sincere request: the Report would be more user-friendly and useful if the Report came with spreadsheets in downloadable tables (much like the World Malaria Report), and if the Report’s tables were consistent with their main database, the Global Health Observatory (GHO). For example, the coverage measures of oral rehydration therapy (ORT) which were included in the Report are absent from the GHO, as far as we can tell. (On an unrelated note, we did notice that the GHO recently added hand-washing as an indicator, perhaps in response to a recent blog—kudos to WHO!)

The Report also offers a glimpse into what is of current interest and priority among donors and countries. Global health donors generally have pet priorities and interests. These are reflected in part by how many countries report for a given indicator. For example, if one turns to the chapter on “Health Service Coverage” (beginning on page 98), we see that “immunization coverage” for measles and for diphtheria, pertussis and tetanus is available for the reporting 193 Member States. This is not surprising given the recent occurrence of measles epidemics in high-income countries as well as GAVI’s success and use of coverage levels as conditions of both eligibility and future funding. Similarly, when we consider the “big three” of AIDS, tuberculosis, and malaria, we see relatively high coverage of the relevant indicators.  For the indicator “antiretroviral therapy coverage among people with advanced HIV infection”, 44 of the 47 of sub-Saharan countries reported. When considering the “case-detection rate… of tuberculosis” indicator, 40 of the 47 countries reported the statistic. For the indicator on under-5 children with fever treated with anti-malarials, 38 of the 47 countries reported. These indicators seem to have higher-than-average coverage.

In contrast, when one considers the indicators for under-5 children (1) with diarrhea who received oral rehydration therapy (ORT), and (2) with pneumonia who receive antibiotics, the picture is bleaker. For the ORT indicator, 36 of the 47 reported. For the pneumonia indicator, less than half (23 of the 47) reported.  Moreover, under-5 children with diarrhea or with pneumonia who take zinc does not appear in the Report (or the GHO), even though zinc is in the WHO’s Model List of Essential Medicines and is widely recognized as an important intervention for preventing child mortality.

What explains the differences in coverage of these indicators? The differences in reporting reflect in part different global health priorities and with it, the money disbursed by donors. Where there is interest and money, there is an indicator, and the coverage of indicators is higher (the correlation is not perfect). Not surprisingly, the single disease category with the highest development aid in 2009 was AIDS (see here), and the ART indicator in the Report also has fairly high coverage. Malaria and tuberculosis are the other two leading single disease categories for development assistance, with the associated malaria indicator having slightly worse coverage than the tuberculosis indicator. However, when considering diarrhea and pneumonia (which are not listed as separate diseases in the IHME report despite causing 10% and 18% of all under-five deaths), less international aid is devoted to these diseases and not surprisingly the coverage of these indicators is lower, at least compared to the big three. While some have noted the tremendous dearth and low coverage of cause-of-death statistics, the phenomenon of low coverage is also true for health service indicators which are arguably easier to measure than cause of death.

Do these indicators matter? In her first address as director of the WHO, Dr. Margaret Chan quoted the axiom, “What gets measured gets done.” If what is measured gets done, then better measurement and reporting is urgently needed. However, if only what is of interest is what is measured — and only what is measured gets done — the question remains: For diseases which are of less interest, can we (the global health community and perhaps the WHO in particular) create the incentives for better measurement and reporting for those diseases?

So, will West Africa be able to capture the benefits of a demographic dividend?  The answer to this question hinges on how fast West Africa can bring down its high fertility levels.

A new study by Jean-Pierre Guengant for the French Development Agency (AFD) provides a detailed analysis of long-term population and economic trends for 12 Western African countries, i.e., the eight countries of the West African Economic and Monetary Union (WAEMU), plus Ghana, Guinea, Mauritania and Nigeria.

The study acknowledges that economic growth has returned to the region since the mid-1990s.  However, revenues per capita have not increased much.  Assuming a yearly population growth of 2.5 percent and an economic growth of 5 percent per annum, it will take almost 30 years to just double the income per capita, which is already very low to start with.  Worse, total poverty levels ($1.25/day) remain high, between 30 and 50 percent of the population in all countries.  Poverty reduction is all the more challenging for ever growing populations, and so is the formation of human capital.

The AFD analysis offers new population projections, which are made consistent with increases in contraceptive use.  This methodology was proposed by Guengant and myself in a 2011 United Nations Expert Paper.  The intermediary scenario (Medium variant) of the UN population projections assumes sharp fertility declines that are not warranted given the slow increases in contraceptive coverage.  These are currently progressing at the snail pace of 0.5 percentage point per year in West Africa, which is not enough even to achieve the high scenario (High variant) of the UN projections (assuming a slower decline in fertility).

So what are the policy implications?

First, rapid declines in fertility spearheaded by strong family planning programs appear to be one of the prerequisites for sub-Saharan Africa to be able to reap the benefits of a demographic dividend.  The emphasis here is on rapid.

Second, human capital investments and poverty reduction efforts will require huge increases in education and health budgets, to the tune of at least 7 percent per year for the next two decades, a figure well above the current economic growth.

Third, West Africa and Africa at large need to capitalize on the ‘golden moment”, i.e., the boost of family planning efforts that will be discussed at the upcoming family planning summit scheduled in London on July 11.  The time has come for African governments to reenergize their family planning programs.

Too often African leaders and their partners want to believe that the demographic dividend is just around the corner.  This is wishful thinking.  The demographic dividend is not a given, and its chief precondition – the sharp decline in fertility – has not even started in earnest in many West African countries.

For the demographic dividend to present itself, voluntary family planning programs that respect human rights are necessary to trigger much needed fertility declines.  These programs will be useful in their own right as they will serve the needs of millions of African women who want to space and limit their children, but do not have the means to do so.  As these programs will help decrease fertility, they will also facilitate female universal education, which in turn will accelerate the fertility decline.  In short, stronger family planning programs and a faster fertility decline will help usher one of the necessary (but not sufficient) conditions for the countries to reap the benefit of a demographic dividend.

The original paper estimated that for every dollar that in developing-countries governments received in health-focused aid, they cut health spending from their own resources, such as tax revenues, by $0.43–1.14. Ergo, donors who thought they were investing in health were substantially financing something else unknown—road building? jets for dictators? That conclusion went for the noted private philanthropy that paid for the research. This uncomfortable factoid about fungibility was widely cited in academia, and in the press (e.g., the New York Times, where it cameoed, garbled, on the home page).

To the casual reader, the reanalysis by Rajaie Batniji and Eran Bendavid seems damning:

We…demonstrate that prior conclusions drawn from these data are unstable and driven by outliers. While government spending may be displaced by development assistance for health in some settings, the evidence is not robust and is highly variable across countries.

My advice: don’t trust the conclusions of either side.

Of the two studies, the original is of higher quality, not least because it embodies the Herculean effort to collect the data that the new study is quick to describe as flawed. It is always harder to create than destroy. Still, as I blogged when the Lancet paper appeared, there are strong technical reasons to doubt its confident claims. Sure, maybe countries receiving more aid for health spend less on it themselves, but what is causing what is less clear. It’s that old correlation-is-not-causation bugaboo. The IHME team battled the bugaboo with a fancy mathematical technique, but in a way that I am qualified to doubt as the author of a popular program to implement it.

The Stanford docs’ new scrutiny of the IHME health data set is healthy. But their own data undermine their conclusion that the Lancet conclusion is undermined by bad data. (In 2009, my colleague Mead Over took apart another study by Bendavid as tautological.)

You don’t need a Ph.D. in statistics to see how. The key table from the new paper is below. It shows the results of four sets of statistical runs, listed from top to bottom. The first set was done on the full IHME database, the next three after omitting certain extreme groups of data points in turn. Within each set of runs, the left side of the table uses health spending data from IMF and the right from WHO; the two sources disagree enough to significantly affect results. Also within each set, the first row uses the statistical method favored in the Lancet (“Arellano-Bover/Blundell Bond”—Did I mention I’m an expert on that? Yes I’m a highly expert expert on that) while the second uses the simpler method favored by Batniji and Bendavid (“Linear, country clustered”). Scan the table, then read my exegesis underneath:

The “coefficient” columns show the central estimates of fungibility. So, e.g., “–0.40″ suggests that for every dollar of health aid, receiving governments spent an average 40 cents less of their own money on health.

Notice that the p values in the rows for the IHME-favored “Arellano-Bover/Blundell Bond” method are almost all 0 or 0.001. “p” stands for probability, so these small numbers are saying that if the IHME authors are wrong, if health aid is not fungible, then it is highly improbable that we would see such high estimates of fungibility as are reported to the left of the p values. Turning that around, if you buy the IHME statistical approach, aid is probably fungible, and little in this table changes that conclusion. Contrary to the damning verdict I quote at the outset, the Lancet analysis is robust to the deletion of data points that the Stanford authors deem dubious.

What does change the numbers—producing lower fungibility estimates and higher p values—is the switch to the “Linear, country clustered” method that the authors of the new study prefer. So the big divide within this table is not between its top quarter the rest, but between the”Arellano-Bover/Blundell Bond” rows and the “Linear, country clustered” rows.

That is, what breaks the fungibility finding is not the changes to the data sample, but the change to statistical method. The authors have interpreted their results backwards.

To go slightly technical for a moment, there are other problems, which together persuade me that the Batniji and Bendavid have more to learn about short-panel econometrics than the IHME team did. The argument for fixed effects over random effects reads like a non sequitur, focusing on whether nations are diverse instead of whether their diversity fits a bell curve. The authors do not explain why their statistical approach is superior, thereby seeming to confuse issues of data, which they do discuss, with issues of method, which they essentially do not. Their preferred method is described in just one sentence, which contains a phrasing,”country fixed effects, clustered by country,” that I have never seen before, and I think reveals confusion. It appears to me that the estimator does not include fixed effects, but merely clusters standard errors by country. (A quick e-mail exchange with Batniji has strengthened my guess that Ordinary Least Squares was done without fixed effects, but I’m still not certain.)

Most importantly, if my educated guess is right, then the authors appear unaware that their favored method is known to be biased. Dynamic panel bias is exactly what motivated Manuel Arellano and Stephen Bond to design their famous alternative. (See page 4 of this paper by Bond.) In the case at hand, the naive method will overestimate the ability of last year’s domestic health spending to predict this year’s, which will leave less room for variables such as health aid receipts to explain that outcome. OLS without fixed effects can be expected to underestimate the negative relationship between health aid receipts and governments’ own health spending, which is consistent with what we see in the table above.

When I blogged two years ago, I reported that the Lancet authors had not responded to a request for their data and computer code. In effect, and as is still the norm in academia, they asked the world to trust their results—indeed, to use them to shape policy involving billions of dollars and millions of lives—while they kept the precise method behind those results secret.

This new article sits on the servers of the Public Library of Science, which aims to break the centuries-old lock of the journals on scientific research. Now, anyone can be a reviewer. Check out the dozen comments I’ve peppered throughout the web version of the article. (Look for the little “1″ icons. Did I go overboard?)

It appears to me that the old and new-style publishers have both let slip into their pages articles with problematic methods—or at least methods whose results are over-relied upon—probably for lack of reviewers expert in short-panel econometrics. But at least with the PLoS one, the crowd can flag the low quality, and even correct it at the margin (and in the margins).

In January, I gave a talk on my new microfinance book at the IHME. Toward the end, I described CGD’s new data and code transparency policy, which calls on our researchers to publicly post all the data sets and the lists of computer commands (code) needed to reproduce our statistical results. I argued that the IHME could learn from this policy. My hosts were extremely gracious, and soon sent me the data and code for the Lancet paper. Now, two years later, I am engaged in a constructive, private dialog with them, that I will say more about when the time is right.

Meanwhile PLoS, unlike the Lancet, has a policy that calls for data sharing in letter and code sharing in spirit. I mentioned that I’m not yet 100% certain about the methods in the new paper. But thanks to that policy, I’m optimistic that, much more quickly this time, I’ll get to the bottom of the latest research on health aid fungibility.

In a recent working paper, Jacob Hughes, Walter Gwenigale and I describe Liberia’s unique experience in pooling donor funds for health in a post-conflict setting, with good results. We also describe a new and complementary agreement between Liberia and USAID, called the Fixed Amount Reimbursement Agreement (FARA). It’s been heartening to see USAID take this step towards implementing results-based aid in Liberia, but the process has also highlighted the problems that such aid faces in the ‘real world’.

In September 2011, the Government of Liberia and USAID signed a FARA for $42 million over 4 years. According to the agreement, “a FARA is a U.S. Government assistance mechanism whereby the host government implementing agency is reimbursed a fixed amount for the successful completion of specified activities or outputs with previously agreed upon specifications or standards.”

In the agreement, USAID will reimburse the Ministry of Health and Social Welfare (MOHSW) for the cost of implementing specific activities from the National Health Plan, namely performance-based contracting of NGOs for health service delivery and monitoring and evaluation of service delivery.

Reimbursement to the MOHSW is based on pre-determined amounts, irrespective of actual cost, and is contingent upon USAID verification and approval of each agreed deliverable. USAID has the right to withhold reimbursement until it verifies that each deliverable has been produced as per the verification criteria, but commits to thereafter completing the reimbursement within 45 days. The MOHSW agrees to keep USAID apprised of implementation progress through quarterly reports as well as to manage and monitor FARA supported activities. USAID source-origin policies for procurement of goods and services are waived in the agreement and all goods from the “Free World” are considered eligible (restricted countries include Cuba, Iran, North Korea and Syria). A limited number of additional terms and conditions are included such as pre-approval for drug procurement and requests for proposals that will exceed $1.5 million in value, based upon recommendations made during a 2010 USAID Office of Acquisition and Assistance Procurement System Assessment of the MOHSW. However, USAID accepts that reimbursable expenditure will be based on the MOHSW’s systems for planning, procurement and financial management.

The FARA replaces the previous arrangement whereby USAID funds for service delivery were provided through a cooperative agreement with the U.S.-based company, John Snow Incorporated. This change in approach reflects the USAID FORWARD Implementation and Procurement Reform, which commits USAID to “Strengthen[ing] partner country capacity to improve aid effectiveness and sustainability by increasing use of reliable partner country systems and institutions to provide support to partner countries.”

This is all quite revolutionary – USAID disbursing funds on achievement of outputs, supporting government health plans on-budget. This is a good news story that makes Global Health Initiative rhetoric on country ownership and sustainability a reality. It is a financial instrument that would allow USAID to go in the direction of COD Aid, conditioning disbursements on health coverage and outcomes.

But what about the pool fund? Why didn’t USAID join the pool to further increase the harmonization of aid and multilateral collaboration? The answer to this question says a lot about the trouble with aid in general.

The MOHSW did originally propose that USAID make the FARA reimbursement payments into the Health Sector Pool Fund mechanism. However, other donors to the pool fund felt that “. . .the concept was based on the premise that other donors underwrote USAID’s risk. Donors will find it difficult to advance money from the pool fund.”

In other words, the existing pool fund donors didn’t want to spend money on inputs when USAID would only spend on results.  While these donors should be credited with supporting the pool fund these many years while the United States went its own way, their objection is all about appearances (who looks most innovative? who produces results and how?) and not at all about delivering health aid in the most effective way possible.  The FARA contribution to the pool could have created complementary, virtuous incentives to achieve the pool fund results as efficiently as possible, which would be a credit to the Government and all the participating donors, and would set a precedent for USAID to participate in pooling in other countries.

Should donors really care whether their dollar is used ex-ante or ex-post if the hoped-for results on health are achieved? If donors are worried about pre-financing another donor’s support, why don’t more of them shift to funding results? If donors won’t “front the money” for each other, can low-income country governments like Liberia afford to do it?

Note: This blog reflects only the views of the authors of the blog. For sources on cited material in this blog, see the paper here.

Recently, the American Journal of Tropical Medicine & Hygiene published a paper by Shepard et al. evaluating the impact of HIV/AIDS funding on Rwanda’s health system. The headline of the press release was catchy and assertive: “Six-year Study in Rwanda Finds Influx of HIV/AIDS Funding Does Not Undermine Health Care Services for Other Diseases. Study Addresses Long-standing Debate about Funding Imbalances for Global Diseases.”

But after reading the report, we quickly assessed that a more accurate and appropriate press release headline for this paper would be “Some Differences Observed in General Healthcare Delivery between Facilities with and without HIV/AIDS Services in Rural Rwanda.” The study has serious limitations associated with its design and its generalizability that aren’t reflected in its catchy press release, and thus have unfortunately gone unrecognized. And because there is, in fact, an important and “long-standing debate about funding imbalances for global diseases” that this study does not sufficiently address, it’s important to examination the shortcomings of the study’s results:.

1. Internal Validity: Does the study do what it claims to do?

No. Treatment was not randomly assigned, while matching and control strategies do not mitigate the effects generated by non-random assignment. As a result, the study’s current comparisons between the treatment and comparison groups are problematic in validly testing the proposed hypothesis.

The paper analyzes a “randomly selected” intervention group of 25 health centers that provided HIV/AIDS services, which is then “perfectly matched” to a control group of 25 health centers that did not offer HIV/AIDS services. But in reality, the intervention group was “randomly” selected only in the sense that the authors chose to study them, not that the health centers in the intervention group were randomly assigned for treatment.

Indeed, why were these health centers chosen to receive HIV/AIDS funding in the first place, back in 2002 or whenever? It’s quite possible that the centers were assigned to have HIV/AIDS funding because the centers were already more likely to have better outcomes. For example, centers that received funding may have had more and better (or better paid) doctors, or perhaps they were located in areas with higher population density, or with higher HIV/AIDS prevalence rates. Similarly, the authors note that, unlike the rural areas that were the subject of the study, all urban health centers in Rwanda provide HIV/AIDS services; this fact alone suggests that treatment (HIV/AIDS funding) was initially assigned based on facility characteristics rather than a random assignment in a representative list of centers.

The authors attempt to address this issue by matching the 25 intervention health centers to 25 control health centers. But the authors match on just three characteristics – (1) health center ownership, (2) performance-based financing, and (3) district income in 2002; however, it is unclear that these were the criteria for initial assignment to treatment.

Further, the authors do not provide any information to reassure us that the intervention group and control group were comparable on a range of relevant characteristics prior to treatment that might otherwise explain differential performance.

2. External Validity: How generalizable are study’s claims?

Beyond the internal validity constraints, the generalizability of the study’s findings is very limited.

The study—and particularly the press release—claims to measure the effects of HIV/AIDS funding on non-HIV/AIDS health services. Such a claim, however, ignores the numerous channels by which HIV/AIDS funding can affect a health system besides funding HIV/AIDS treatment in existing facilities; for example, HIV/AIDS funding can lead to technical assistance at the national level, newly built facilities operated by international NGOs or other foreign organizations, as well as health promotion and preventive care at the community level. But the authors’ indicator for HIV/AIDS funding is simply a binary categorization of whether a facility offered HIV/AIDS treatment or not. Moreover, the paper does not discuss the magnitude of funding, the funding source (PEPFAR or Global Fund vs. Ministry of Health disbursements), or whether the facility received an earmarked funding stream specifically for HIV/AIDS rather than general funds which it then elected to spend on HIV service provision. The narrowly focused study does not consider the wide array of other system level effects created by HIV/AIDS funding that have been raised in the previous literature.

In particular, the study does not tell us anything about the effects of parallel NGO service delivery or the impact of new or dedicated facilities exclusively for HIV/AIDS, both of which are hot topics in the HIV/AIDS health systems debate. Indeed, in 2008, less than 5% of Rwanda’s PEPFAR funding was channeled through national institutions; the rest was delivered via a range of contractors, most of which were American NGOs or universities (Table 1). The paper makes no effort to address the consequences this funding arrangement and the presence of the 44 PEPFAR prime partners in Rwanda.

Table 1: Top Planned Recipients of PEPFAR Funding for Rwanda (USD), FY2008

What’s more, this particular country (Rwanda) is likely to be an outlier among HIV/AIDS funding recipients due to its exceptional national healthcare system, high quality HIV/AIDS service delivery, and innovative health initiatives like community-based health insurance. According to the World Bank’s World Wide Governance Indicators for 2009, Rwanda ranked 7th out of 45 Sub-Saharan African countries for government effectiveness, scoring more than one standard deviation above the mean. Moreover, HIV/AIDS funding in Rwanda accounted for about a fifth of total health spending, a percentage higher than 30 other countries in sub-Saharan Africa.

We understand that the authors likely suffered from significant data constraints; likewise, we recognize the enormous empirical challenges in demonstrating system-wide effects at the national level. Still, it remains important to carefully state qualify results and recognize the limitations of one’s research.

Bottom line: The jury is still out on whether HIV/AIDS funding has displaced or improved efforts on other disease control priorities.  Let the debate about funding imbalances for global diseases continue…

Pregnancy poses a substantial health risk for adolescents. The World Health Organization reports that health problems associated with adolescent pregnancy include increased maternal and neonatal mortality and increased incidence of preterm or low birth weight, among many other adverse health conditions. A study from the British Medical Journal found that complications from pregnancy and childbirth were the leading causes of death for young women between the ages of 15-19 in developing countries.

Yet beyond the health and rights consequences of teenage fertility, little work has systematized the extent of the non-health adverse effects associated with adolescent fertility and the effectiveness of the interventions used to date to reduce teenage fertility in low- and middle-income countries.  In a new paper, Kate McQueston, Rachel Silverman and I aim to fill this gap.

First, we explore trends between adolescent childbearing and socioeconomic outcomes. While the review finds strong correlations between adolescent fertility and school drop-out, the question of causation remains far more ambiguous, as effect sizes decrease sharply with more rigorous research methods. Moreover, the study also finds that in some contexts, high numbers of women continue education after child birth—suggesting that childbirth and education (and other related outcomes) may not be incompatible.

Similarly, the review of interventions to reduce adolescent fertility finds variation across studies, but also notes some general findings. The evidence base for conditional cash transfers, though somewhat variable, is by far the most robust when compared to the other interventions. Additionally, programs that lowered barriers to attending school or increased the opportunity costs of not attending school were also found to be effective—suggesting that education may substitute for adolescent fertility. Notably, the most effective interventions appeared to be outside of the typical reproductive health sphere.

The paper is limited by the quality and scope of the studies available. Further, adolescent fertility and its causes are complex, nuanced issues that are affected by a range of motivations and external factors. Nonetheless, this research reframes the conversation about adolescent fertility and the policies and interventions that might be used to reduce its frequency.

Among other findings, we suggest that adolescent fertility is more consequence than cause of socioeconomic disadvantage. While fertility is often correlated with school dropout, other factors – current school enrollment, marital status, anticipated economic returns to education, family attitudes, and other related context — drive both school continuation and the likelihood of experiencing an adolescent pregnancy. This finding suggests that a more holistic (and possibly complex) approach may be needed to address the foundational causes of adolescent fertility. Creating economic opportunities for women, reducing adolescent marriage, and changing gender norms are likely to be more effective in the quest to accelerate economic development than merely reducing adolescent fertility.

While interventions that focused on increasing knowledge and changing attitudes about sexual and reproductive health appeared successful in the short term, there was little evidence of any long term impact. On the contrary, interventions that encouraged school attendance proved more effective in reducing overall adolescent fertility. This evidence suggests that policymakers should expand educational opportunities for girls and create incentives for school continuation, such as conditional cash payments or the expectation of a worthwhile job following graduation. Reproductive health services are important for many young women, but increasing contraceptive access and uptake may not, on its own, be sufficient to change fertility trends.

As the world prepares for the upcoming family planning summit, it is worth pausing to examine the findings of the growing experimental literature on programs that aim to reduce teen pregnancy and to consider a broader and synergistic approach.

This strong and long overdue pitch to bring back the ‘P’ word into the environmental debate is most welcome.  In recent decades, international attention has shifted from rapid population growth to other urgent issues, such as the HIV/AIDS epidemic, humanitarian crises, climate change, and good governance.  But reproductive health and voluntary family planning programs are still very much needed, especially in high fertility countries, and they require political leadership and long-term financial commitment.  Broader access to family planning services will be needed to accelerate the decline of high fertility rates, particularly in countries where unmet needs for contraception are high.

However, as the report highlights, policies to address population and the environment must go well beyond family planning.  They should stress, first and foremost, the importance of inclusive development.  Today, 1.3 billion people still live with only US$ 1.25 per day.  The international community needs to lift them out of absolute poverty, which will require focused efforts in economic development, education, and health including family planning.  For their part, developed and emerging countries must first stabilize and thereafter reduce their levels of material consumption.  This can be achieved through greater efficiency in the use of resources as well as an array of practical measures to reduce waste, invest in sustainable resources, technologies, and infrastructures, and systematically decouple economic activity from its environmental impact.

Other policy levers should be explored as well.  One should harness the potential for urbanization to reduce material consumption, remove barriers to achieve high-quality education for all at both primary and secondary levels, implement comprehensive wealth measures (i.e., reform the system of national accounts and improve natural assets accounting), and develop new socio-economic systems that are not dependent on continued material consumption growth.  More research is also needed into the interactions between demographic change, consumption, and environmental impact.

I hope the ideas presented in People and the Planet will usher an entirely new way of thinking about population issues and sustainable development and that it will be taken into account at the upcoming Rio+20 Conference.  Population policies and programs should no longer be viewed as necessary and relevant in their own right.  On the contrary, such policies should be integrated with broader and comprehensive interventions that address economic equality, health and education needs, and technological advances.  The ultimate goal should be to improve the life of all human beings as a necessary condition to protect our environment and safeguard the sustainability of our way of living.

This month, John releases a new book, World Population Policies, that examines the links between population and reproductive health policies and economic development efforts. Of the book, demography great John Bongaarts writes: “After more than a decade of neglect, population trends and their adverse social, economic, health, environmental, and political effects have returned to the global policy agenda. Government interventions aimed at minimizing unfavorable demographic developments have often been contentious. This timely and comprehensive book provides a wealth of valuable insight and thoughtful commentary on controversies and policy options.”

CGD has a history of working at the intersection of population and development, as demographic changes affect economic growth and poverty reduction in many parts of the world. CGD’s past work on this topic includes the Demographics and Development in the 21st Century initiative and the Population and Development working group, among others.

We are pleased to welcome John to our team—look for more from him here on our Global Health Policy Blog.

One of the most prominent issues underlying all these discussions was the growing disenchantment in Europe with general budget support programs. The main political argument being leveled against budget support is that it can’t demonstrate performance, and the basic response has been to move toward project-specific aid. If European aid agencies move in this direction it will be a shame – throwing out the baby with the bathwater. My hope is that they will see that they can preserve the good aspects of budget support – working through country systems and giving recipients greater ownership – by agreeing to disburse flexible funds in relation to progress on a few key high-level indicators, such as educational attainment, reduced child mortality, better security, less deforestation or cleaner energy.

The discussion of Results-Based Aid is also extremely useful for uncovering the dynamics of foreign aid politics. For example, a key advantage of results-based mechanisms is that they can reduce transaction costs associated with tracking inputs. Nevertheless, we heard several cases in which the measurement of outcome indicators was simply added on top of existing spending control mechanisms. In addition, results-based aid from one government to another should be an opportunity to keep attention on broad high-level goals and leave the recipient with flexibility on how they respond. Instead, it is extremely tempting for the discussion to fall into using the payments to get “them” to do what “we” think they should.

The entire conception of foreign aid is changing – with new actors, new constraints, and new ideas. I think the process of working out these new ideas in practice will show if the system can really be reformed or whether it will be increasingly marginalized.

Today Debrework Zewdie, the Deputy General Manager of the Global Fund, spoke on “The Global Fund at Ten Years: Reflecting on its Impact and Looking Forward to Challenges Ahead”, followed by extensive Q&A at the Council for Foreign Relations. Her brief remarks summarized the immense challenges of undertaking the Consolidated Transformation Plan (see Jaramillo’s slides here for more details). In particular, I am very encouraged about the prospect of two changes: (1) the creation of a new Division called ‘Strategic Investment and Impact Evaluation’ which will shape the optimal portfolio of investments by country and disease (rather than its prior ad-hoc, or more kindly, its one-size-fits-all, approach), and (2) the creation of new committees for each disease (AIDS, Tuberculosis, and Malaria) that will meet monthly to discuss the bigger picture of the ‘disease war’, and not individual grant/country ‘battles’. Both of these have great importance for achieving better ‘value for money’.

The new Division and committees will both help ensure that the Fund’s portfolio obtains greater health gains for the funding invested. Whereas in the past there had been consternation among observers that the Technical Review Panel failed to consider the costs of interventions proposed by a country and did not optimize disease control strategies relative to spending in its set of countries, these two changes will help gain better ‘value for money’. Nevertheless, care should be taken to not mechanically allocate funding by disease burden, as it may be optimal to control diseases based on the population at risk or other factors in disease spread (in the case of malaria or tuberculosis for example). Furthermore, getting the mix of interventions funded right is as important as getting the right level of funding by country. Most encouraging is that the positions in this new Division will be staffed by those who manage grants and are in the grant-making team, and not external reviewers. We look forward to seeing that this new Division has the needed leverage in staff to optimize their current portfolio.

Zewdie also noted an increased focus on impact evaluation of Global Fund grants. This could also be a potentially huge improvement for getting better value for money. Zewdie mentioned that while the Global Fund had done some impact evaluation in the past, it had not been systemized and integrated with grants nor had external experts been sought (it would be nice to know more about these past impact evaluations). I asked Zewdie on the potential uses of impact evaluation, in particular for learning general lessons on delivery strategies which may be applicable to multiple countries and whether impact evaluation might be integrated with the Global Fund’s current ‘performance’-based financing (PBF) model. One new delivery strategy that Zewdie mentioned, for example, was better coordination and collaboration of the Global Fund with PEPFAR, starting in Nigeria and 1 other country. An impact evaluation of this strategy would greatly benefit other countries as well. Learning into better delivery strategies and approaches, if looped back into grant management, will give more value for money.

As for ‘performance’-based financing (PBF) and impact evaluation, although the Fund pioneered PBF, the current PBF approach has many limitations; most indicators are related to ‘outputs’ e.g. number of bed nets distributed (not a true measure of performance in health and vulnerable to unintended consequences) and decisions on disbursements have not necessarily related to performance. But exactly how impact evaluation might be integrated with PBF will need careful thinking, so that such evaluation helps countries to learn and rewards them with successful, while avoiding bad incentives through harsh punishment (which is true for PBF in general). For the Global Fund to make appropriate connections between impact evaluation and PBF would be an important, if not forward-thinking, step among global health donors. Work at the Center for Global Development on Cash on Delivery, with its approach on minimal indicators, independent verification, and public dissemination of indicators, may be useful as the Fund moves forward. By further creating incentives for countries to reach certain levels of performance, the Fund can also gain better value for money.

The challenge of conducting impact evaluation will require able staff with expertise in a wide array of tools for impact evaluation – which are not necessarily onerous with high costs. The methodologies of impact evaluation, largely from the fields of economics, political science, statistics and perhaps to a lesser extent epidemiology, have advanced, such that an array of quasi-experimental tools (e.g. staggered phase-in) can be employed without the burden of ‘randomization’ or exclusive surveys per se. Moreover, this work on impact evaluation may be used to build national health information systems which have been suffering from neglect in many countries in recent years despite the growth of global health funding.

I’m very encouraged and hopeful that the Global Fund is on the right track. I look forward to seeing these new changes and we hope our Value for Money working group will give useful recommendations to the Global Fund and other global health funding agencies to save more lives for the money.

(Thanks to Amanda Glassman, Denizhan Duran, and Jenny Ottenhoff for their very helpful comments.)