# 6. Conclusions

This study has looked at a hitherto understudied aspect of health care pricing in the Latin America and Caribbean region: procurement policies. Our novel analytical focus was enabled by a unique, high-granularity item-level public procurement database covering 7 countries and 2 territories throughout the 2012-2018 period. We analyzed the impact of a host of policy-relevant predictors on unit prices for pharmaceuticals, equipment, and medical supplies, using a range of regression and machine learning methods. Overall, our models perform exceptionally well, accounting for over 80% of price variance in unseen data. These models enabled us to draw on a set of realistic policy changes to produce expected price savings in the health sector across the region. Overall, the identified small-scale interventions are predicted to lower unit prices by about 14%.

Our approach is novel, but it has a range of strengths and weaknesses which could be taken up by future research. The strength of the analytical framework rests in its rich empirical basis, government-wide administrative data of great detail; the strong links between well-established economic theories and the relationships sought; and the diversity of analytical methods, both quantitative and qualitative, leading to similar conclusions across the board. Moreover, our experiences with applying the framework in 9 LAC countries and territories suggest that the methods are replicable and largely standard across diverse contexts. Even though regulatory, data, and market differences require a great deal of care and adaptation of the methodology.

However, the analytical framework is not without its weaknesses, some of which can be addressed in future iterations or with better data. Most fundamentally, missing and incorrect data can invalidate findings which is, luckily, a relatively limited problem in transactional e-procurement systems. Nevertheless, highly technical and seemingly minor problems can pose substantial challenges. For example, lack of sufficiently standardized units of measurement or dosage information can render unit price comparisons flawed. While the analyzed products are highly standardized across the region, important quality differences may remain in the products themselves or in the delivery schedules (e.g. delivering earlier or later can have large price implications for suppliers). A crucial aspect of quality differences, especially impacting on equipment, is that the level of the hierarchical product classification used in the analysis may average over a range of important quality differences. Furthermore, missing information on payments and contract implementation raises questions about the validity of findings given that payments may well deviate substantially from contracted values.

On a methodological level, even though our models have performed exceptionally well in predicting prices on unseen data, we cannot claim to have identified causal relationships. Without more careful identification of causal effects, by for example using quasi experimental methods, the methodology can only capture correlations and has to rely on theory and case study methodology for further support.

From a policy perspective, while savings scenarios and predictors are actionable and directly relate to policy decisions made by procurement agencies, ministries of finance or individual 33