Is the Relationship Between Aid and Economic Growth Nonlinear?

In this paper, we investigate the relationship between foreign aid and growth using recently developed sample splitting methods that allow us to uncover evidence for the existence of heterogeneity and nonlinearity simultaneously. We also implement a new methodology that allows us to deal with model uncertainty in the context of these methods. We find some evidence that aid may have heterogeneous effects on growth across two growth regimes defined by ethnic fractionalization. In particular, countries that belong to a growth regime characterized by levels of ethnic fractionalization above a threshold value experience a negative partial relationship between aid and growth, while those in the regime with ethnic fractionalization below the threshold experience no growth effects from aid at all. Nevertheless, there exists substantial model uncertainty so that attempts to pin down the typology of these growth regimes as being decisively characterized by ethnic fractionalization remain inconclusive. When we account for model uncertainty, we find no evidence to suggest that the relationship between aid and growth is nonlinear. Overall, our results suggest that the partial effect of aid on growth is very likely to be negative although we cannot reject the hypothesis that aid has no effect on growth. In this sense, our findings suggest that aid is potentially counterproductive to growth with outcomes not meeting the expectations of donors.

Is the relationship between aid and economic growth nonlinear?:

"There have been intensive debates on the role of aid in promoting economic development in developing countries by using cross-country analyses. Cross-country regression assuming linear relationship between aid and growth and without taking into heterogeneity of countries would produce biased estimates. To correct this, in this paper we investigate the relationship between foreign aid and growth using recently developed sample splitting methods that allow us to simultaneously uncover evidence for the existence of heterogeneity and nonlinearity. We also address model uncertainty in the context of these methods. We find some evidence that aid may have heterogeneous effects on growth across two growth regimes defined by ethnolinguistic fractionalization. However, when we account for model uncertainty, we find no evidence to suggest that the relationship between aid and growth is nonlinear. In fact, our results suggest that the partial effect of aid on growth is likely to be weakly negative. In this sense, our findings suggest that aid is potentially counterproductive to growth with outcomes not meeting the expectations of donors... The methodology developed in this paper can be used to identify typologies on other outcome variables, such as those included in the Millennium Development Goals." from Authors' AbstractEconomic development, Cross-country studies, Foreign aid, Public investment, Nonlinearity, Typology,

The Relations Between Pedagogical and Scientific Explanations of Algorithms: Case Studies from the French Administration

The opacity of some recent Machine Learning (ML) techniques have raised fundamental questions on their explainability, and created a whole domain dedicated to Explainable Artificial Intelligence (XAI). However, most of the literature has been dedicated to explainability as a scientific problem dealt with typical methods of computer science, from statistics to UX. In this paper, we focus on explainability as a pedagogical problem emerging from the interaction between lay users and complex technological systems. We defend an empirical methodology based on field work, which should go beyond the in-vitro analysis of UX to examine in-vivo problems emerging in the field. Our methodology is also comparative, as it chooses to steer away from the almost exclusive focus on ML to compare its challenges with those faced by more vintage algorithms. Finally, it is also philosophical, as we defend the relevance of the philosophical literature to define the epistemic desiderata of a good explanation. This study was conducted in collaboration with Etalab, a Task Force of the French Prime Minister in charge of Open Data & Open Government Policies, dealing in particular with the enforcement of the right to an explanation. In order to illustrate and refine our methodology before going up to scale, we conduct a preliminary work of case studies on the main different types of algorithms used by the French administration: computation, matching algorithms and ML. We study the merits and drawbacks of a recent approach to explanation, which we baptize input-output black box reasoning or BBR for short. We begin by presenting a conceptual framework including the distinctions necessary to a study of pedagogical explainability. We proceed to algorithmic case studies, and draw model-specific and model-agnostic lessons and conjectures