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Record W7009698233

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

2023· article· en· W7009698233 on OpenAlexaboutno aff

Bibliographic record

VenueDipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Public policyUnitary stateControl (management)Point (geometry)Empirical researchPublic sector
DOInot available

Abstract

fetched live from OpenAlex

[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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.078
metaresearch head score (Gemma)0.182
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.078
Threshold uncertainty score0.413

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0780.182
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.010
Science and technology studies0.0020.013
Scholarly communication0.0110.009
Open science0.0030.005
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0230.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.275
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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