Clustering and External Validity in Randomized Controlled Trials

In the literature studying randomized controlled trials (RCTs), it is often assumed that the potential outcomes of units participating in the experiment are deterministic. This assumption is unlikely to hold, as stochastic shocks may take place during the experiment. In this paper, we consider the case of an RCT with individual-level treatment assignment, and we allow for individual-level and cluster-level (e.g. village-level) shocks to affect the potential outcomes. We show that one can draw inference on two estimands: the ATE conditional on the realizations of the cluster-level shocks, using heteroskedasticity-robust standard errors; the ATE netted out of those shocks, using cluster-robust standard errors. By clustering, researchers can test if the treatment would still have had an effect, had the stochastic shocks that occurred during the experiment been different. Then, the decision to cluster or not depends on the level of external validity one would like to achieve

Multivariate epidemiologic analysis of type 2 diabetes mellitus risks in the Lebanese population

Background: The burden of diabetes in Lebanon requires well-targeted interventions for screening type 2 diabetes mellitus (T2DM) and prediabetes and prevention of risk factors. Newly recruited 998 Lebanese individuals, in addition to 7,292 already available, were studied to investigate the prevalence of diabetes, prediabetes and their associated risk factors. Methods: Participants had fasting blood sugar and glycohemoglobin tests in addition to a lipid profile. Clinical and demographic information were obtained from a detailed questionnaire. The relationship between T2DM, its risk factors, and its complications were tested. Comparisons of these risk factors among diabetics, healthy, and coronary artery disease (CAD) patients were performed. Results: The prevalence of T2DM significantly increased with increasing BMI (p < 0.0001). Exercise activity level negatively correlated with the disease (p = 0.002), whereas the prevalence of T2DM (p < 0.0001) and CAD family history (p = 0.006) positively correlated with the affection status. The mean levels of triglycerides and LDL-C were significantly higher in diabetics (1.87; 1.35) compared to individuals with prediabetes (1.63; 1.26) and unaffected controls (1.49; 1.19). People with T2DM showed a significant decrease in HDL-C levels. A strong correlation of overall hyperlipidemia with the diabetes affection status was shown (p < 0.0001). Other comorbid factors such as hypertension (p < 0.0001) and self-reported obesity (p < 0.0001) were highly associated with T2DM and prediabetes. Reproductive health of women showed a strong correlation between giving birth to a baby with a high weight and the occurrence of T2DM and prediabetes later in life (p < 0.0001). Retinopathy and peripheral neuropathy were significantly correlated with diabetes and prediabetes (p < 0.0001). Conclusions: The present study shows an alarming prevalence of diabetes and prediabetes in the studied subgroups representative of the Lebanese population. Electronic supplementary material The online version of this article (doi:10.1186/1758-5996-6-89) contains supplementary material, which is available to authorized users

Essays in Applied Econometrics

This dissertation consists of three essays that use and develop econometric methods to causally investigate topics in education and development economics. In the first chapter, I develop an econometric framework to correctly and efficiently draw inference in models where estimated value-added (VA) is an explanatory variable (and models where it appears as the dependent variable). Estimated VA measures have become increasingly popular metrics of worker and institutional quality, and they are now widely used in regressions by researchers seeking to establish links between worker quality and a broad range of outcomes. Although consistent standard errors are crucial to obtain correct confidence intervals and assess the validity of conclusions drawn by studies using VA measures in regressions, the literature has not yet tackled this issue. I contribute to this literature by setting up an econometric framework that allows me to show why naïve standard error estimators are inconsistent in such models, derive consistent standard error estimators, propose a more efficient estimator for models using VA measures as explanatory variables, and propose a testable condition under which naïve standard errors are consistent for models that use VA measures as dependent variables. Then, in an application using data from North Carolina public schools, I find that the increase in standard errors resulting from the required correction that I propose is larger than the impact of clustering standard errors. In the second chapter, based on joint work with Serena Canaan and Pierre Mouganie, we use VA measures to provide the first causal evidence on the impact of college advisor quality on student outcomes. To do so, we exploit a unique setting where students are randomly assigned to faculty advisors during their first year of college. We find that having a higher grade VA advisor reduces time to complete freshman year and increases four-year graduation rates by 2.5 percentage points. It also raises high-ability students' likelihood of enrolling and graduating with a STEM degree by 4 percentage points. The magnitudes of our estimated effects are comparable to those from successful financial aid programs and proactive coaching interventions. We also show that non-grade measures of advisor VA predict student success. In particular, advisors who are effective at improving students' persistence and major choice also boost other college outcomes. Our results indicate that allocating resources towards improving the quality of academic advising may play a key role in promoting college success. In the third chapter, based on joint work with Clement de Chaisemartin, we consider the case of a randomized controlled trial with individual-level treatment assignment, and we allow for individual-level and cluster-level (e.g. village-level) shocks to affect the units' potential outcomes. We show that one can draw inference on two estimands: the ATE conditional on the realizations of the cluster-level shocks, using heteroskedasticity-robust standard errors; the ATE netted out of those shocks, using cluster-robust standard errors. We show that by clustering, researchers can test if the treatment would still have had an effect, had the stochastic shocks that occurred during the experiment been different. Then, the decision to cluster or not depends on the level of external validity one would like to achieve

TWOWAYFEWEIGHTS: Stata module to estimate the weights and measure of robustness to treatment effect heterogeneity attached to two-way fixed effects regressions

Last revised November 24th, 2022.twowayfeweights estimates the weights and the measure of robustness to treatment effect heterogeneity attached to the two-way fixed effects regressions studied in Chaisemartin & D'Haultfoeuille (2018), it can also compute inference measures for the weights

Cartographie du champ proche de dimères de nanoparticules d'or par SNOM sans ouverture en mode exaltation

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Imported haemorrhagic fever with renal syndrome caused by Dobrava–Belgrade hantavirus in France

International audienceAcute kidney injury (AKI) caused by hantavirus infections is rare but should be suspected in any patient presenting with flu-like symptoms, signs of haemolytic–uraemic syndrome or presence of anti-glomerular basement membrane (anti-GBM) antibodies. We report the first case of Dobrava–Belgrade virus in France imported from southeastern Europe. The characteristic macroscopic appearance of the fresh renal biopsy specimen, displaying a haemorrhagic appearance of the medulla, suggested hantavirus infection. AKI caused by hantavirus infections remains a diagnostic challenge, especially outside endemic areas