Juan Cruz Lopez Del Valle

Abstract

COVID-19 poses dire threats for low and middle-income countries (LMICs). Yet, there remains limited rigorous evidence describing the characteristics and outcomes of hospitalized patients for LMICs, and often the evidence was based on small samples and/or unicentric. The objective of this study was to examine risk factors of COVID-19 mortality in Argentina, a hard-hit middle-income Latin American country. We analyze data on 5,146 COVID-19 patients from 11 centers across 10 cities in Argentina, making this one of the largest multi-centric retrospective observational descriptive studies in the LMICs. Information on demographics and co-morbidities was extracted from medical records. Outcomes of relevance consisted of whether the patient was discharged or deceased (as established in medical records), along with date of each event. We use survival models that account for competing risks.

Median age was 60 years (IQR: 48–72), there were fewer women (40.8%) hospitalized than men (59.2%), and the most prevalent comorbidities were hypertension (40.9%), diabetes (20.0%) and obesity (19.1%). Patients were hospitalized for a median duration of 8 days (IQR: 5–13), and in-hospital mortality was 18.1%, though it varied substantially across health centers (95%CI: 17.1%–19.2%). Baseline characteristics most associated with in-hospital mortality were respiratory rate (adjusted HR = 3.6, 95%CI: 2.5–5.4 for ≥ 26 breathes/min), older age (adjusted HR = 2.5, 95%CI: 2.0–3.3 for the 80+ age group), and chronic kidney disease (adjusted HR = 2.2, 95%CI: 1.8–2.8). Associations were attenuated when survival models did not account for the competing risk of being discharged.

We document lower mortality rates than those in prior studies, likely due to a lower prevalence of comorbidities amongst patients in our sample. Compared with standard Cox models, we find that, when using competing risk models, risk factors have a larger role in explaining COVID-19 mortality. Overall, we provide rigorous evidence describing the characteristics and outcomes of hospitalized patients for LMICs. Thus, our findings are useful to conduct a more accurate in-hospital monitoring of patient subgroups who may be at greater risk. They also provide valuable guidance for public health and policy efforts in Argentina and other developing countries.

Abstract

We implement a fiscal incidence analysis for Argentina with data from the 2017 national household survey. We find that Argentina’s fiscal system reduces inequality and poverty more than it is the case in many other comparable countries. This result is driven more by the size of the state (as measured by social spending to GDP) than by the progressivity of the fiscal system. While there are spending items that are quite progressive and even pro-poor, taxes are unequalizing and a number of subsidies benefit disproportionately the rich.

Abstract

I study whether an economy’s response to aggregate shocks depends on its level of development. Empirically, I estimate responses to high-frequency monetary shocks at the macro and micro level and compute hand-to-mouth shares for high, middle and low income countries. An inverted U-shaped relationship between these two is found. Theoretically, I show that matching this relationship requires a heterogeneous agent model with one key feature: countercyclical inequality and income risk. This generates amplifying income effects by shifting towards constrained households the additional aggregate demand generated by unconstrained households’ substitution effects. Amplification peaks with heterogeneity, when there is an equal share of households on and off their Euler equations. These results have implications for aggregate demand stabilization policies and the welfare costs of business cycles.

Abstract

We study the role of foreign ideas in U.S. innovation by constructing a directed “genealogy” of technologies from the full text of 13 million patents filed between 1894 and 2024. By tracing which earlier technologies appear in the founding patents of new ones, we recover a large-scale input–output network of technological recombination that identifies the foreign technologies that serve as inputs into innovation in the United States. We report four main findings. First, foreign ideas are pervasive: 56% of U.S. technologies build on at least one foreign input, and the most fertile U.S. technologies rely on foreign inputs most heavily. Second, foreign ideas have large causal effects on U.S. innovation. Using the historical settlement of migrants as a source of exogenous variation in local ancestry, we show that U.S. counties more exposed to knowledge from a given foreign country absorb substantially more technologies from that country and, in turn, invent significantly more U.S. technologies building on them.

Third, foreign ideas shape not only the level but also the direction of technical change: U.S. counties are far more likely to invent the specific technologies whose foreign inputs are already in their local idea stock, and the changing origins of foreign ideas accompany the shift of American innovation from its historical heartland toward the coasts and the Sunbelt. Fourth, these effects leave a lasting imprint on local labor markets: decades later, counties that absorbed more of an origin’s ideas host more jobs working with those ideas, and more still working with the U.S. technologies descended from them.

Abstract

We document the diffusion of nebulized ibuprofen in Argentina as a treatment for COVID-19. As the pandemic spread, this clinically unsupported drug reached thousands of patients, even some seriously ill, despite warnings by the regulator and medical societies. Detailed daily data on deliveries for all towns in one of the largest provinces suggests a role for “rational” forces in the adoption of a miracle cure: towns adopt it when neighbors that adopt it are successful in containing deaths (a learning effect), even after controlling for the average adoption of peers.

Results from a survey are consistent with learning. They also reveal a large role of beliefs: subjects that are classified as “Right” are more likely adopt and to learn, while those that are “Skeptical” report an increase in their demand when primed with the regulator’s ban.

Abstract

Many medical decisions during the pandemic were made without the support of causal evidence obtained in clinical trials. We study the case of nebulized ibuprofen (NaIHS), a drug that was extensively used on COVID-19 patients in Argentina amidst wild claims about its effectiveness and without regulatory approval. We study data on 5,146 patients hospitalized in 11 health centers spread over 4 provinces, of which a total of 1,019 (19.8%) received the treatment. We find a large, negative and statistically significant correlation between NaIHS treatment and mortality using inverse probability weighting estimators.

We consider several threats to identification, including the selection of “low” risks into NaIHS, spillovers affecting patients in the control group, and differences in the quality of care in centers that use NaIHS. While the negative correlation appears to be, broadly, robust, our results are best interpreted as emphasizing the benefits of running a randomized controlled trial and the challenges of incorporating information produced in other, less rigorous circumstances.