# V. Discussion and conclusion

In this note we have analyzed the evolution of domestic government spending in health, education and social protection over the last 15 years. Creating a measure of social spending that covers the three sectors has proven to be a challenging task. While for the health sector only current spending is deemed reliable, in education there is no rigorous way to separate current from capital spending. Only for health spending is there high data coverage both over time and across regions. Education and especially social protection display large gaps. It is particularly worrying that in education there is not a consolidated upward trend in data availability. Low data density does not only plague LICs: in many middle-income countries (such as those in LAC), there are large gaps in data availability. Addressing data sparsity represents a clear area of opportunity for the Human Capital Project to leverage its data collection efforts in a manner that encourages more comprehensive data gathering for spending.

Increases in social sector spending have generally been slow and not steady. In fact, while education spending in LICs has seen a stable and steady increase, spending in health has been remarkably flat. Given the strong complementarities between health and education interventions, especially in the first years of life, coordinated investments might help maximize the returns. Some regional trends are also worrying: both EAP and South Asia have seen a reduction in education spending as a share of GDP.

Taking advantage of the newly-designed Human Capital Index (HCI), we go beyond existing analyses that look at the relationship between a single outcome sector-specific spending, and we assess whether a multidimensional measure of human capital is correlated with a multidimensional measure of spending. While this data curation effort was unique, the relationships identified here are challenging to interpret. The (unstratified) relationship between spending and the HCI often plateaus above a certain level of spending and is noisy throughout. While the relationship between spending and HCI appears stronger for HICs than for countries at other income levels, these relationships are not statistically significant.

Our conclusions do not change when we examine the relationship between specific inputs into the HCI and their associated sector’s spending. Among the three sectors, health spending seems to display a slightly clearer relationship with HCI than other sectors considered, but this might be partly explained by the higher density of the health data. The clearer relationship between health spending and HCI might also be related to a few factors that could contribute to less noisy data: the health data do not include capital spending (which can be lumpy), and also the WHO-GHED is the result of a long-standing international effort to collect and validate health spending data, possibly resulting in higher quality.

Our findings are in line with other studies that look at the relationship between spending and human capital outcomes, which find that increased spending does not necessarily translate to improved outcomes (AlSamarrai, Cerdan-Infantes, and Lehe 2018; World Bank Group 2018b). There are many possible reasons behind the weak correlation that we observe at the country level. As shown in Al-Samarai (2018), countries display dramatic differences in the ability to transform resources into outcomes, so poor outcomes may be reflective of spending inefficiencies (Al-Samarrai, Cerdan-Infantes, and Lehe 2018). The inefficiencies might either be the result of inability to prioritize resources towards the interventions that are most effective at improving outcomes, or to scale them up in a cost-effective way.

At the heart of our interest in assessing the relationship between social sector spending and outcomes is an interest in providing guidance on how much and what type of investment is needed to achieve high levels 23