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A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 46563

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Special Issue Editor

Kajal Lahiri

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Guest Editor

Distinguished Professor of Economics, and Health Policy, Management & Behavior, State University of New York, 1400 Washington Avenue, Albany, NY 12222, USA
Interests: econometric theory; forecasting; applied econometrics; panel data analysis; health econometrics; limited dependent and qualitative variables; probability and density forecasts; forecast combination; Bayesian analysis

Special Issue Information

Dear Colleagues,

This Special Issue aims to publish a collection of papers that use cutting-edge econometric methods using time series, cross-section and panel data to address issues in economics of health, medical care, and health service research. We solicit papers on all aspects of health economics and policy, including demand and supply for healthcare, alternative financing arrangements, including health insurance, and socioeconomic determinants of health. Papers that develop new methods or evaluate existing methods with special reference to spatial dimensions of healthcare and its outcomes will be particularly suitable for the Special Issue. Specific topics of interest include but are not limited to the COVID-19 pandemic, aging, child and maternal health, addiction, behavioral health, social insurance, economics of disability, health inequality, pharmaceutical economics, and international health.

Prof. Dr. Kajal Lahiri
Guest Editor

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Keywords

health econometrics
population health
health disparity

Published Papers (13 papers)

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Research

Open AccessArticle

Socio-Economic and Demographic Factors Associated with COVID-19 Mortality in European Regions: Spatial Econometric Analysis

Mateusz Szysz

and

Andrzej Torój

Econometrics 2023, 11(2), 17; https://doi.org/10.3390/econometrics11020017 - 20 Jun 2023

Viewed by 1021

Abstract

In some NUTS 2 (Nomenclature of Territorial Units for Statistics) regions of Europe, the COVID-19 pandemic has triggered an increase in mortality by several dozen percent and only a few percent in others. Based on the data on 189 regions from 19 European countries, we identified factors responsible for these differences, both intra- and internationally. Due to the spatial nature of the virus diffusion and to account for unobservable country-level and sub-national characteristics, we used spatial econometric tools to estimate two types of models, explaining (i) the number of cases per 10,000 inhabitants and (ii) the percentage increase in the number of deaths compared to the 2016–2019 average in individual regions (mostly NUTS 2) in 2020. We used two weight matrices simultaneously, accounting for both types of spatial autocorrelation: linked to geographical proximity and adherence to the same country. For the feature selection, we used Bayesian Model Averaging. The number of reported cases is negatively correlated with the share of risk groups in the population (60+ years old, older people reporting chronic lower respiratory disease, and high blood pressure) and the level of society’s belief that the positive health effects of restrictions outweighed the economic losses. Furthermore, it positively correlated with GDP per capita (PPS) and the percentage of people employed in the industry. On the contrary, the mortality (per number of infections) has been limited through high-quality healthcare. Additionally, we noticed that the later the pandemic first hit a region, the lower the death toll there was, even controlling for the number of infections. Full article

(This article belongs to the Special Issue Health Econometrics)

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Open AccessFeature PaperArticle

Comparing the Conditional Logit Estimates and True Parameters under Preference Heterogeneity: A Simulated Discrete Choice Experiment

and

Econometrics 2023, 11(1), 4; https://doi.org/10.3390/econometrics11010004 - 25 Jan 2023

Cited by 1 | Viewed by 1584

Abstract

Health preference research (HPR) is the subfield of health economics dedicated to understanding the value of health and health-related objects using observational or experimental methods. In a discrete choice experiment (DCE), the utility of objects in a choice set may differ systematically between persons due to interpersonal heterogeneity (e.g., brand-name medication, generic medication, no medication). To allow for interpersonal heterogeneity, choice probabilities may be described using logit functions with fixed individual-specific parameters. However, in practice, a study team may ignore heterogeneity in health preferences and estimate a conditional logit (CL) model. In this simulation study, we examine the effects of omitted variance and correlations (i.e., omitted heterogeneity) in logit parameters on the estimation of the coefficients, willingness to pay (WTP), and choice predictions. The simulated DCE results show that CL estimates may have been biased depending on the structure of the heterogeneity that we used in the data generation process. We also found that these biases in the coefficients led to a substantial difference in the true and estimated WTP (i.e., up to 20%). We further found that CL and true choice probabilities were similar to each other (i.e., difference was less than 0.08) regardless of the underlying structure. The results imply that, under preference heterogeneity, CL estimates may differ from their true means, and these differences can have substantive effects on the WTP estimates. More specifically, CL WTP estimates may be underestimated due to interpersonal heterogeneity, and a failure to recognize this bias in HPR indirectly underestimates the value of treatment, substantially reducing quality of care. These findings have important implications in health economics because CL remains widely used in practice. Full article

(This article belongs to the Special Issue Health Econometrics)

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Open AccessArticle

Are Vaccinations Alone Enough to Curb the Dynamics of the COVID-19 Pandemic in the European Union?

Paweł Miłobędzki

Econometrics 2022, 10(2), 25; https://doi.org/10.3390/econometrics10020025 - 26 May 2022

Cited by 1 | Viewed by 2816

Abstract

I use the data on the COVID-19 pandemic maintained by Our Word in Data to estimate a nonstationary dynamic panel exhibiting the dynamics of confirmed deaths, infections and vaccinations per million population in the European Union countries in the period of January–July 2021. Having the data aggregated on a weekly basis I demonstrate that a model which allows for heterogeneous short-run dynamics and common long-run marginal effects is superior to that allowing only for either homogeneous or heterogeneous responses. The analysis shows that the long-run marginal death effects with respect to confirmed infections and vaccinations are positive and negative, respectively, as expected. Since the estimate of the former effect compared to the latter one is about 71.67 times greater, only mass vaccinations can prevent the number of deaths from being large in the long-run. The success in achieving this is easier for countries with the estimated large negative individual death effect (Cyprus, Denmark, Ireland, Portugal, Estonia, Lithuania) than for those with the large but positive death effect (Bulgaria, Hungary, Slovakia). The speed of convergence to the long-run equilibrium relationship estimates for individual countries are all negative. For some countries (Bulgaria, Denmark, Estonia, Greece, Hungary, Slovakia) they differ in the magnitude from that averaged for the whole EU, while for others (Croatia, Ireland, Lithuania, Poland, Portugal, Romania, Spain), they do not. Full article

(This article belongs to the Special Issue Health Econometrics)

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Open AccessArticle

Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries

Gaetano Perone

Econometrics 2022, 10(2), 18; https://doi.org/10.3390/econometrics10020018 - 09 Apr 2022

Cited by 6 | Viewed by 4581

Abstract

The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world’s economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths from COVID-19 in 12 hard-hit big countries around the world as of 20 August 2021. The data used in the analysis were extracted from the Our World in Data COVID-19 dataset. Both non-seasonal and seasonal autoregressive integrated moving averages (ARIMA and SARIMA) were estimated. The analysis showed that: (i) ARIMA/SARIMA forecasts were sufficiently accurate in both the training and test set by always outperforming the simple alternative forecasting techniques chosen as benchmarks (Mean, Naïve, and Seasonal Naïve); (ii) SARIMA models outperformed ARIMA models in 46 out 48 metrics (in forecasting future values), i.e., on 95.8% of all the considered forecast accuracy measures (mean absolute error [MAE], mean absolute percentage error [MAPE], mean absolute scaled error [MASE], and the root mean squared error [RMSE]), suggesting a clear seasonal pattern in the data; and (iii) the forecasted values from SARIMA models fitted very well the observed (real-time) data for the period 21 August 2021–19 September 2021 for almost all the countries analyzed. This article shows that SARIMA can be safely used for both the short- and medium-term predictions of COVID-19 deaths. Thus, this approach can help government authorities to monitor and manage the huge pressure that COVID-19 is exerting on national healthcare systems. Full article

(This article belongs to the Special Issue Health Econometrics)

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Open AccessArticle

The Impact of COVID-19 on Airfares—A Machine Learning Counterfactual Analysis

Florian Wozny

Econometrics 2022, 10(1), 8; https://doi.org/10.3390/econometrics10010008 - 16 Feb 2022

Cited by 1 | Viewed by 3197

Abstract

This paper studies the performance of machine learning predictions for the counterfactual analysis of air transport. It is motivated by the dynamic and universally regulated international air transport market, where ex post policy evaluations usually lack counterfactual control scenarios. As an empirical example, this paper studies the impact of the COVID-19 pandemic on airfares in 2020 as the difference between predicted and actual airfares. Airfares are important from a policy makers’ perspective, as air transport is crucial for mobility. From a methodological point of view, airfares are also of particular interest given their dynamic character, which makes them challenging for prediction. This paper adopts a novel multi-step prediction technique with walk-forward validation to increase the transparency of the model’s predictive quality. For the analysis, the universe of worldwide airline bookings is combined with detailed airline information. The results show that machine learning with walk-forward validation is powerful for the counterfactual analysis of airfares. Full article

(This article belongs to the Special Issue Health Econometrics)

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Open AccessArticle

Modeling Hospital Resource Management during the COVID-19 Pandemic: An Experimental Validation

and

Econometrics 2021, 9(4), 38; https://doi.org/10.3390/econometrics9040038 - 14 Oct 2021

Cited by 3 | Viewed by 2654

Abstract

One of the main challenges posed by the healthcare crisis generated by COVID-19 is to avoid hospital collapse. The occupation of hospital beds by patients diagnosed by COVID-19 implies the diversion or suspension of their use for other specialities. Therefore, it is useful to have information that allows efficient management of future hospital occupancy. This article presents a robust and simple model to show certain characteristics of the evolution of the dynamic process of bed occupancy by patients with COVID-19 in a hospital by means of an adaptation of Kaplan-Meier survival curves. To check this model, the evolution of the COVID-19 hospitalization process of two hospitals between 11 March and 15 June 2020 is analyzed. The information provided by the Kaplan-Meier curves allows forecasts of hospital occupancy in subsequent periods. The results shows an average deviation of 2.45 patients between predictions and actual occupancy in the period analyzed. Full article

(This article belongs to the Special Issue Health Econometrics)

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Open AccessArticle

Air Pollution and Mobility, What Carries COVID-19?

C. Vladimir Rodríguez-Caballero

and

J. Eduardo Vera-Valdés

Econometrics 2021, 9(4), 37; https://doi.org/10.3390/econometrics9040037 - 11 Oct 2021

Viewed by 2673

Abstract

This paper tests if air pollution serves as a carrier for SARS-CoV-2 by measuring the effect of daily exposure to air pollution on its spread by panel data models that incorporates a possible commonality between municipalities. We show that the contemporary exposure to particle matter is not the main driver behind the increasing number of cases and deaths in the Mexico City Metropolitan Area. Remarkably, we also find that the cross-dependence between municipalities in the Mexican region is highly correlated to public mobility, which plays the leading role behind the rhythm of contagion. Our findings are particularly revealing given that the Mexico City Metropolitan Area did not experience a decrease in air pollution during COVID-19 induced lockdowns. Full article

(This article belongs to the Special Issue Health Econometrics)

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Open AccessFeature PaperArticle

An Empirical Model of Medicare Costs: The Role of Health Insurance, Employment, and Delays in Medicare Enrollment

Yuanyuan Deng

and

Hugo Benítez-Silva

Econometrics 2021, 9(2), 25; https://doi.org/10.3390/econometrics9020025 - 08 Jun 2021

Viewed by 3761

Abstract

Medicare is one of the largest federal social insurance programs in the United States and the secondary payer for Medicare beneficiaries covered by employer-provided health insurance (EPHI). However, an increasing number of individuals are delaying their Medicare enrollment when they first become eligible at age 65. Using administrative data from the Medicare Current Beneficiary Survey (MCBS), this paper estimates the effects of EPHI, employment, and delays in Medicare enrollment on Medicare costs. Given the administrative nature of the data, we are able to disentangle and estimate the Medicare as secondary payer (MSP) effect and the work effects on Medicare costs, as well as to construct delay enrollment indicators. Using Heckman’s sample selection model, we estimate that MSP and being employed are associated with a lower probability of observing positive Medicare spending and a lower level of Medicare spending. This paper quantifies annual savings of $5.37 billion from MSP and being employed. Delays in Medicare enrollment generate additional annual savings of $10.17 billion. Owing to the links between employment, health insurance coverage, and Medicare costs presented in this research, our findings may be of interest to policy makers who should take into account the consequences of reforms on the Medicare system. Full article

(This article belongs to the Special Issue Health Econometrics)

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Open AccessArticle

Racial/Ethnic Health Disparity in the U.S.: A Decomposition Analysis

Kajal Lahiri

and

Zulkarnain Pulungan

Econometrics 2021, 9(2), 22; https://doi.org/10.3390/econometrics9020022 - 06 May 2021

Cited by 2 | Viewed by 2885

Abstract

Following recent econometric developments, we use self-assessed general health on a Likert scale conditioned by several objective determinants to measure health disparity between non-Hispanic Whites and minority groups in the United States. A statistical decomposition analysis is conducted to determine the contributions of socio-demographic and neighborhood characteristics in generating disparities. Whereas, 72% of health disparity between Whites and Blacks is attributable to Blacks’ relatively worse socio-economic and demographic characteristics, it is only 50% for Hispanics and 65% for American Indian Alaska Natives. The role of a number of factors including per capita income and income inequality vary across the groups. Interestingly, “blackness” of a county is associated with better health for all minority groups, but it affects Whites negatively. Our findings suggest that public health initiatives to eliminate health disparity should be targeted differently for different racial/ethnic groups by focusing on the most vulnerable within each group. Full article

(This article belongs to the Special Issue Health Econometrics)

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Open AccessArticle

Uncertainty Due to Infectious Diseases and Stock–Bond Correlation

Konstantinos Gkillas

Christoforos Konstantatos

and

Costas Siriopoulos

Econometrics 2021, 9(2), 17; https://doi.org/10.3390/econometrics9020017 - 19 Apr 2021

Cited by 12 | Viewed by 3366

Abstract

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock–bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of artificial neural networks so as to investigate the predictability of this type of uncertainty on realized stock–bond correlation and jumps. Our findings reveal that uncertainty-due-to-infectious-diseases has significant predictive value on the changes of the stock–bond relation. Full article

(This article belongs to the Special Issue Health Econometrics)

Open AccessFeature PaperArticle

Estimating Endogenous Treatment Effects Using Latent Factor Models with and without Instrumental Variables

Souvik Banerjee

and

Anirban Basu

Econometrics 2021, 9(1), 14; https://doi.org/10.3390/econometrics9010014 - 17 Mar 2021

Cited by 2 | Viewed by 3748

Abstract

We provide evidence on the least biased ways to identify causal effects in situations where there are multiple outcomes that all depend on the same endogenous regressor and a reasonable but potentially contaminated instrumental variable that is available. Simulations provide suggestive evidence on the complementarity of instrumental variable (IV) and latent factor methods and how this complementarity depends on the number of outcome variables and the degree of contamination in the IV. We apply the causal inference methods to assess the impact of mental illness on work absenteeism and disability, using the National Comorbidity Survey Replication. Full article

(This article belongs to the Special Issue Health Econometrics)

Open AccessArticle

Hospital Emergency Room Savings via Health Line S24 in Portugal

Paula Simões

Sérgio Gomes

and

Isabel Natário

Econometrics 2021, 9(1), 8; https://doi.org/10.3390/econometrics9010008 - 20 Feb 2021

Viewed by 2397

Abstract

Hospital emergency departments are often overused by patients that do not really need urgent care. These admissions are one of the major factors contributing to hospital costs, which should not be allowed to compromise the response and effectiveness of the National Health Services (SNS). The aim of this study is to perform a detailed spatial health econometrics analysis of the non-urgent emergency situations (classified by Manchester triage) by area, linking them with the efficient use of the national health line, the Saude24 line (S24 line). This is evaluated through the S24 savings calls, using a savings index and its spatial effectiveness in solving the non-urgent emergency situations. A savings call is a call by a user whose initial intention was to go to an urgency department, but who. after calling the S24 line. changed his/her mind. Given the spatial nature of the data, and resorting to INLA in a Bayesian paradigm, the number of non-urgent cases in the Portuguese urgency hospital departments is modeled in an autoregressive way. The spatial structure is accounted for by a set of random effects. The model additionally includes regular covariates and a spatially lagged covariate savings index, related with the S24 savings calls. Therefore, the response in a given area depends not only on the (weighted) values of the response in its neighborhood and of the considered covariates, but also on the (weighted) values of the covariate savings index measured in each neighbor, by means of a Bayesian Poisson spatial Durbin model. Full article

(This article belongs to the Special Issue Health Econometrics)

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Open AccessArticle

Long-Lasting Economic Effects of Pandemics:Evidence on Growth and Unemployment

C. Vladimir Rodríguez-Caballero

and

J. Eduardo Vera-Valdés

Econometrics 2020, 8(3), 37; https://doi.org/10.3390/econometrics8030037 - 17 Sep 2020

Cited by 16 | Viewed by 8100

Abstract

This paper studies long economic series to assess the long-lasting effects of pandemics. We analyze if periods that cover pandemics have a change in trend and persistence in growth, and in level and persistence in unemployment. We find that there is an upward trend in the persistence level of growth across centuries. In particular, shocks originated by pandemics in recent times seem to have a permanent effect on growth. Moreover, our results show that the unemployment rate increases and becomes more persistent after a pandemic. In this regard, our findings support the design and implementation of timely counter-cyclical policies to soften the shock of the pandemic. Full article

(This article belongs to the Special Issue Health Econometrics)

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