Essays on Public Economics and Public Policy Evaluation – Methods and Applications

[eng] Economic policies and institutional design and decision-making vary greatly accross countries. Germany, the US and Canada, are federal states, where decision-making and economic policies are highly decentralized, while France and Greece are highly unitary countries. Belgium has had the two largest government formation deadlocks in Europe in the last 20 years, while other countries such as Portugal or Ireland have experienced none. During the COVID-19 crisis, some countries, like New Zealand, applied lockdowns with an incidence rate of 20 cases per milion inhabitants, while others like Spain, delayer their response until the incidence rate was higher than 130 cases per million Do differences in institutional design lead to differences in economic policies? Can these differences be explained? Is the agility of government decision-making influenced by common patterns across countries? The aim of my thesis is to contribute to the existing literature on public policy evaluation, with a particular focus on the role of institutions, providing new methodological, theoretical, and empirical results, to provide answers to questions such as the ones stated before. Five studies are presented in the thesis. In the first study, I analyze one of the most seminals questions that could be asked about governments and economic outcomes: Do government formation deadlocks affect the economy in the short term? From the methodological point of view, I develop a proposal to improve current methodologies to evaluate causal effects on quasi-experimental designs; concretely, the Synthetic Control Method. I illustrate the main advantages of the proposal evaluating the causal economic effects of the ten-month-long government formation impasse in Spain, after the December 2015 elections, as well as reproducing two previous studies: the impact of German reunification (analyzed in Abadie et al. 2015) and the effect of tobacco control programs in California (Abadie et al. 2010). In line with the results obtained by Albalate and Bel (2020) for the 18-month government formation deadlock in Belgium, my estimates indicate that the growth rate in Spain was not affected by the government deadlock, ruling out any damage to the economy attributable to the institutional impasse. The second and third studies focus on how governments decide in a context of high uncertainty and different degrees of information. Concretely, I build a theoretical model to assess the agility of government response to the COVID pandemic and evaluate the model empirically using data from OCDE and European countries. I find solid evidence that during the first outbreak, in a context of incomplete information, the agility of policy response was highly conditioned by a cost-benefit analysis where the perceived healthcare capacity to deal with the outbreak, and the associated economic costs of lockdown measures, significantly delayed the response. Institution design also played a role: federal states reacted faster than unitary ones. Higher competition in multilevel systems with collaborative governance between different levels of government and non-state institutions - (Scavo, Kearne, and Kilroy, 2008; Schwartz and Yen, 2017; Downey and Myers, 2020; Huang, 2020) provided incentives for more agile and effective responses. However, federal states could be dysfunctional in terms of internal coordination and suffer from high inequality in terms of agility within themselves. For the concrete case of the US, I find that Republican-controlled states reacted later and implemented softer contingency measures, which were associated with higher growth in the number of COVID-19 cases (Hallas et al., 2020; Shvetsova et al., 2022). The highly polarized context of the US provided incentives for Republican governors to align with President Trump’s preferred policy, which was to avoid lockdowns. These incentives vanished during the vaccination process, when information about the severity of COVID-19 was complete, and governors, no matter whether Republicans or Democrats, implemented the roll-out of the vaccination program with a similar level of agility. In the fourth paper, I suggest a new approach to assess the effect of institutional and policy developments (i.e. capital city) on economic growth that distort the natural equilibrium of the geographical distribution of the labor market. I propose a theoretical model of the way in which features of geography and nature can account for population density and distribution within a country. The model is empirically examined using data from comparable European regions. This allows us to detect deviations produced by the forces of human action, led mainly by institutions, and to evaluate the consequences in terms of relative economic performance. The results suggest that deviating from nature’s outcomes has a significant negative effect on economic growth and regional convergence. Hence, societies that choose to exploit the opportunities of the best locations, according to the natural endowment, rather than promoting a different distribution of the population across regions by means of institutional intervention, achieve better economic performance. In the last study, we focus on the most relevant government expenditure until the twentieth century: military expenditure. We examine the effects of military and trade alliances in military expenditure. We develop a theoretical model to understand why these alliances could influence military expenditure. In short, when countries build military and trade alliances with military leaders such as the US, they make themselves more valuable to the leader, and hence increase the likelihood of the leader providing military aid in case of an agression. This increases the military costs of a potential agresor, reduces the probability of war and let the non-leader country reduce its military expenditure. To empirically test the hypothesis derived from the model we employ data of 138 countries for the period 1996-2020. Our results show that trade relation with a military leader is a highly significant driver of military expenditure. For each percentage point in US GDP in trade between a certain country and the US, the military expenditure of the country reduces 0.5 percentage points. Moreover, when the trade balance is particularly beneficial for the US, the effect is even larger

A Predictive Model and Risk Factors for Case Fatality of COVID-19

This study aimed to create an individualized analysis model of the risk of intensive care unit (ICU) admission or death for coronavirus disease 2019 (COVID-19) patients as a tool for the rapid clinical management of hospitalized patients in order to achieve a resilience of medical resources. This is an observational, analytical, retrospective cohort study with longitudinal follow-up. Data were collected from the medical records of 3489 patients diagnosed with COVID-19 using RT-qPCR in the period of highest community transmission recorded in Europe to date: February-June 2020. The study was carried out in in two health areas of hospital care in the Madrid region: the central area of the Madrid capital (Hospitales de Madrid del Grupo HM Hospitales (CH-HM), n = 1931) and the metropolitan area of Madrid (Hospital Universitario Príncipe de Asturias (MH-HUPA) n = 1558). By using a regression model, we observed how the different patient variables had unequal importance. Among all the analyzed variables, basal oxygen saturation was found to have the highest relative importance with a value of 20.3%, followed by age (17.7%), lymphocyte/leukocyte ratio (14.4%), CRP value (12.5%), comorbidities (12.5%), and leukocyte count (8.9%). Three levels of risk of ICU/death were established: low-risk level (20%). At the high-risk level, 13% needed ICU admission, 29% died, and 37% had an ICU-death outcome. This predictive model allowed us to individualize the risk for worse outcome for hospitalized patients affected by COVID-19

Role of Innate and Adaptive Cytokines in the Survival of COVID-19 Patients

SARS-CoV-2 is a new coronavirus characterized by a high infection and transmission capacity. A significant number of patients develop inadequate immune responses that produce massive releases of cytokines that compromise their survival. Soluble factors are clinically and pathologically relevant in COVID-19 survival but remain only partially characterized. The objective of this work was to simultaneously study 62 circulating soluble factors, including innate and adaptive cytokines and their soluble receptors, chemokines and growth and wound-healing/repair factors, in severe COVID-19 patients who survived compared to those with fatal outcomes. Serum samples were obtained from 286 COVID-19 patients and 40 healthy controls. The 62 circulating soluble factors were quantified using a Luminex Milliplex assay. Results. The patients who survived had decreased levels of the following 30 soluble factors of the 62 studied compared to those with fatal outcomes, therefore, these decreases were observed for cytokines and receptors predominantly produced by the innate immune system-IL-1 alpha, IL-1 alpha, IL-18, IL-15, IL-12p40, IL-6, IL-27, IL-1Ra, IL-1RI, IL-1RII, TNF alpha, TGF alpha, IL-10, sRAGE, sTNF-RI and sTNF-RII-for the chemokines IL-8, IP-10, MCP-1, MCP-3, MIG and fractalkine; for the growth factors M-CSF and the soluble receptor sIL2Ra; for the cytokines involved in the adaptive immune system IFN gamma, IL-17 and sIL-4R; and for the wound-repair factor FGF2. On the other hand, the patients who survived had elevated levels of the soluble factors TNF beta, sCD40L, MDC, RANTES, G-CSF, GM-CSF, EGF, PDGFAA and PDGFABBB compared to those who died. Conclusions. Increases in the circulating levels of the sCD40L cytokine; MDC and RANTES chemokines; the G-CSF and GM-CSF growth factors, EGF, PDGFAA and PDGFABBB; and tissue-repair factors are strongly associated with survival. By contrast, large increases in IL-15, IL-6, IL-18, IL-27 and IL-10; the sIL-1RI, sIL1RII and sTNF-RII receptors; the MCP3, IL-8, MIG and IP-10 chemokines; the M-CSF and sIL-2Ra growth factors; and the wound-healing factor FGF2 favor fatal outcomes of the disease

Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence

Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19's effects on patients' lung health.Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU).Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians' diagnosis, and test for improvements on physicians' performance when using the prediction algorithm.Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%

Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence

Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19’s effects on patients’ lung health.Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU).Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians’ diagnosis, and test for improvements on physicians’ performance when using the prediction algorithm.Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%

Impact of the Innate Inflammatory Response on ICU Admission and Death in Hospitalized Patients with COVID-19

Objective: To describe the capacity of a broad spectrum of cytokines and growth factors to predict ICU admission and/or death in patients with severe COVID-19. Design: An observational, analytical, retrospective cohort study with longitudinal follow-up. Setting: Hospital Universitario Príncipe de Asturias (HUPA). Participants: 287 patients diagnosed with COVID-19 admitted to our hospital from 24 March to 8 May 2020, followed until 31 August 2020. Main outcome measures: Profiles of immune response (IR) mediators were determined using the Luminex Multiplex technique in hospitalized patients within six days of admission by examining serum levels of 62 soluble molecules classified into the three groups: adaptive IR-related cytokines (n = 19), innate inflammatory IR-related cytokines (n = 27), and growth factors (n = 16). Results: A statistically robust link with ICU admission and/or death was detected for increased serum levels of interleukin (IL)-6, IL-15, soluble (s) RAGE, IP10, MCP3, sIL1RII, IL-8, GCSF and MCSF and IL-10. The greatest prognostic value was observed for the marker combination IL-10, IL-6 and GCSF. Conclusions: When severe COVID-19 progresses to ICU admission and/or death there is a marked increase in serum levels of several cytokines and chemokines, mainly related to the patient's inflammatory IR. Serum levels of IL-10, IL-6 and GCSF were most prognostic of the outcome measure

Table1_Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence.docx

Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19’s effects on patients’ lung health.Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU).Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians’ diagnosis, and test for improvements on physicians’ performance when using the prediction algorithm.Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%.</p