Time-series Cross-sectional Models Research Articles

Political Determinants of Health: Healthcare Privatization and Population Health in Europe.

The extent to which healthcare reforms affect health remains understudied. Healthcare reforms result in policy outputs that determine provision of medical services, which have consequences for the health of the population. We scrutinize this relationship between health policy outputs and population health, using an original dataset of healthcare reforms passed in 36 European countries from 1989 to 2019. Focusing more specifically on legislative changes implying privatization of healthcare delivery and finance, we ask the following question: What is the relationship between reforms that privatize healthcare provision and population health in terms of health outcomes and inequalities? We answer this question by relying on fixed effects time-series cross-section models. Health outcomes are operationalized using measures of subjective health status, unmet health needs and resulting health inequalities. Our results show that privatization of healthcare is associated with higher rates of bad subjective health and unmet health needs several years after the passing of reforms. These effects are stronger for individuals in the lower tiers of income and education, resulting in higher socio-economic inequalities. The article contributes to conceptualization of the political determinants of health as health policy outputs and a better understanding of the relationship between policy outputs and population health outcomes.

Demystifying hospital size distribution: A geographical approach

Hospital size distribution, i.e., the uneven allocation of healthcare resources between hospitals of different size classes, significantly impacts healthcare equality and efficiency and challenges healthcare governance. Yet, its variation is inadequately explained by two prevalent approaches to size distribution – the statistical approach merely works on statistical patterns, while the economic approach has difficulties in calculating hospital cost-returns. This study provides an alternative – a geographical approach – to demystify hospital size distribution. Incorporating the distinctiveness of healthcare into George Stigler's (1958) classic survivor analysis of firm size distribution, we conceptualize hospital size distribution as the result of hospitals' interactions with their socio-economic-technical environment in a defined area, and thus its pattern and variation could be understood through its coevolution with geographical contexts. We substantiate this idea with a study on China's provincial hospital size distributions between 2002 and 2019. During this period, hospitals with 0–99 beds maintained a stable proportion, while the size class of 800+ beds grew significantly, in contrast to the reduction in the size class of 100–799 beds. Cluster analysis identifies four clusters of provinces dominated by different hospital size classes after healthcare delivery reform in 2012. Cross-sectional time-series regression models find evolving impacts from market power, government intervention, and advancement of medical technology. We also offer a novel approach to understanding healthcare resource distribution by examining the structure rather than merely the number of healthcare resources.

Factor price convergence in OECD economies: the case of three profit rates indicators

Abstract This paper examines profit rate (rates of return to capital) convergence in OECD economies in the periods of 1960-2016 and relevant sub-periods. It also performs comparison of profit rates convergence in selected developed and developing economies, using the data for 1973-2003 period. Economy-wide, productive economy and manufacturing rates are estimated and three convergence concepts are considered (beta, sigma, and stochastic convergence). We use a combination of cross-sectional and univariate time series models and density distribution analysis. For each profit rate measure, a strong evidence of beta convergence is provided. In contrast, sigma convergence is indicated only in the case of productive economy profit rate; while in other cases, sigma divergence or the absence of either convergence or divergence are likely. Stochastic convergence is present in a smaller number of economies and is confined to productive economy and manufacturing. The comparison of convergence dynamics in developed versus developing economies confirmed beta convergence in both groups and for their aggregate, but did not establish sigma convergence, given the significant diversity of economies and their different economic trajectories.

Transnational Backlash and the Deinstitutionalization of Liberal Norms: LGBT+ Rights in a Contested World

Integration into the international community is typically used to explain liberal outcomes. However, is it possible that such integration can also explain rising illiberalism? Using the case of LGBT+ rights, I argue that backlash to liberal norms is increasingly organized transnationally and that exposure to global norms via integration explains both liberal and illiberal outcomes. I test this argument through extensive original data collection and by using time-series cross-section, multinomial, and cross-lagged panel models. Robust findings reveal how exposure to global norms spurs policy backlashes—not just expansions—depending on how countries are situated within pro- and anti-LGBT+ transnational networks. This study contributes to our understanding of the changing international system by revealing how illiberal actors use mechanisms built by the liberal international community to transnationally organize and advance illiberal norms—ultimately fueling the deinstitutionalization of once-dominant liberal models.

Turkish power contestation with the United Arab Emirates: an empirical assessment of official development assistance

Turkey's post-Arab Spring regional rivalry with the United Arab Emirates (UAE) influences the allocation of its Official Development Assistance (ODA). Our paper provides a comprehensive comparison of Turkish and UAE’s ODA on global and regional levels. To understand the effects of power struggle on Turkish ODA, we employ a time-series cross-sectional model, taking Turkey’s annual ODA allocations as our dependent variable, and the UAE's global and regional ODA levels as our independent variables. We observe that Africa emerges as the main region where the rivalry between Turkey and the UAE intensifies. Based on our regression analysis covering 2000–2020, our findings demonstrate the limits of religious-cultural explanations of foreign aid, suggesting donors' geopolitical interests playing a higher role. We find out that Turkey allocates more ODA to Muslim countries, yet its main motivation is not based on religious-cultural affinity. Our case studies on Egypt and Somalia demonstrate competition between Turkey and the UAE for regional influence. We contribute to the existing foreign aid literature in two critical ways: First, we provide a comprehensive regional analysis of Turkey's foreign aid behavior, as each geography has its own unique geopolitical dynamics. Second, we show that religious proximity is insufficient to explain Turkey's ODA distribution, based on the existence of competing donors throughout multiple geopolitical crises.

STADL Up! The Spatiotemporal Autoregressive Distributed Lag Model for TSCS Data Analysis

Time-series cross-section (TSCS) data are prevalent in political science, yet many distinct challenges presented by TSCS data remain underaddressed. We focus on how dependence in both space and time complicates estimating either spatial or temporal dependence, dynamics, and effects. Little is known about how modeling one of temporal or cross-sectional dependence well while neglecting the other affects results in TSCS analysis. We demonstrate analytically and through simulations how misspecification of either temporal or spatial dependence inflates estimates of the other dimension’s dependence and thereby induces biased estimates and tests of other covariate effects. Therefore, we recommend the spatiotemporal autoregressive distributed lag (STADL) model with distributed lags in both space and time as an effective general starting point for TSCS model specification. We illustrate with two example reanalyses and provide R code to facilitate researchers’ implementation—from automation of common spatial-weights matrices (W) through estimated spatiotemporal effects/response calculations—for their own TSCS analyses.

Do Knowledge Economy Indicators Affect Economic Growth? Evidence from Developing Countries

The competitiveness of national economies is increasingly dependent on their ability to produce and use knowledge, as knowledge, education, and innovation are the main indicators of economic growth in a globalizing world. Many countries have adopted policies related to the production of knowledge and its transformation into wealth that stimulates the growth and competitiveness of their economies. Through our study, we measured some knowledge economy (KE) variables for a sample of 20 developing countries. During the period (1996–2020), using panel data, the estimate was made using three models: the cumulative regression model, the fixed-effects model, and the random-effects model. The results of the statistical tests indicated that the fixed-effects model is the appropriate model, and that the estimates of the proposed model parameters do not contradict the assumptions of economic theory, nor do they contradict the practical reality. In addition, the results showed that 93% one of the changes that occur in economic growth in the developing countries under study is due to the dependence on the knowledge economy. It became clear through the study that the use of cross-sectional time-series models increases the accuracy of statistical forecasting, because it considers the information with a time dimension in the time series, as well as the cross-sectional dimension in different units. Our study found a positive impact on economic growth of the internet and mobile phone users, control of corruption, political stability, foreign direct investment, and the total value of international trade. Additionally, spending on education, patents for residents, and trade openness had a negative impact on economic growth. Accordingly, the knowledge economy in developing countries contributed greatly to their economic growth and had a prominent role in maintaining high rates of growth for decades. Our study contributes by expanding the scope of developing countries in deciding to focus on the important indicators in the knowledge economy (KE), so that they can increase the added value of their economic growth.

AN EMPIRICAL ANALYSIS OF OPTION PRICING WITH SHORT SELL BANS

Short sell bans are often imposed during a financial crisis as a desperate measure to stabilize financial markets. Yet, the impact of short sell bans on option pricing and hedging is not well studied, at least quantitatively, until very recently when Guo & Zhu [(2017) Equal risk pricing under convex trading constraints, Journal of Economic Dynamics and Control 76, 136–151] and He & Zhu [(2020) A revised option pricing formula with the underlying being banned from short selling, Quantitative Finance 20 (6), 935–948] formulated a new pricing framework with the underlying being either completely or partially banned from short selling. However, no empirical results were provided to substantiate the usefulness of the formulae, as well as to deepen our understanding on the effects of short sell bans. This paper provides a comprehensive empirical study on the effects of short sell bans to the standard option pricing theory by carrying out both cross-sectional and options time series model calibration of the model devised by He & Zhu (2020) [A revised option pricing formula with the underlying being banned from short selling, Quantitative Finance 20 (6), 935–948]. Overall, our empirical results indicate that the alternative option pricing formula considering short sell restrictions has the ability to capture highly-quoted implied volatility, with an evident improvement of 39% out-of-sample performance compared to the benchmark Black–Scholes model during the period of short sell ban.

The Democratic Cost of Consecutive Re-election and Presidential Term-Limit Evasion in Latin America

AbstractDespite theoretical arguments suggesting the strong effects of presidential term limits and re-election on democracy, there is surprisingly little empirical evidence to evaluate them. We test both the effect on democracy of the existence of a consecutive re-election rule and of reforms introducing it for incumbent presidents. Using evidence from Latin American countries between 1945 and 2018, we test their relationship to both vertical and horizontal accountability. A synthetic control method is employed to account for the effect of term-limit reforms, and time-series cross-section models for modelling the association with the re-election rule. Both vertical and horizontal accountability as well as the quality of democracy are eroded by term-limit evasion reforms in most countries and strengthened in none between 1990 and 2018. Allowing presidents to run for re-election – relative to term-limited ones – is consistently associated with weak democratic outcomes.

The reregulation of capital flows in Latin America: assessing the impact of post-neoliberal governments

This article assesses the impact of post-neoliberal governments on the level of capital controls in 17 Latin American countries for the period between 1995 and 2017. Contrary to administrations led by other left-of-center parties, especially the ones affiliated to the Socialist International, I contend that post-neoliberal parties, affiliated to the São Paulo Forum, opted to reregulate capital flows for three main reasons: increasing macroeconomic policy autonomy, favoring their constituencies, and/or giving concreteness to the rhetoric against financial and foreign interests. After proposing a new capital controls index and estimating a time-series cross-section model, I find that post-neoliberalism has been associated with an increase in the level of controls. Besides this main conclusion, I also find that larger financial sectors contribute to counteracting the reregulation of capital flows by post-neoliberal governments.

Underlying push and pull factors in undocumented immigration in the United States

ABSTRACT There is continued empirical and theoretical debate about the push and pull factors of immigration and the effects that legislation has. This study contributes towards this debate by identifying the drivers within the United States between 2005 and 2010. This study also puts forth a new more refined definition of restrictive immigration legislation and examines its impact on immigration. Hypotheses are developed to test for the effects of different policies, which are tested using a cross-sectional time series model with fixed effects and Driscoll-Kraay. The results show that undocumented populations were not broadly deterred by state immigration policies, but an E-Verify scheme, which limits access to employment and is punitive against employers was successful in deterring immigration. These findings have important consequences because to date, state immigration policies seem to function as symbolic overtures with very little or no actual policy output.

Rumor has it: The role of social ties and misinformation in evacuation to nearby shelters after disaster

When crisis strikes, why do some communities utilize evacuation shelters more than others? This mixed methods study draws on a new dataset of almost-daily tallies of evacuees at 660 local shelters following Japan’s 2018 Eastern Iburi Earthquake in Hokkaido to create a large-N time-series cross sectional (TSCS) dataset of local, short-distance evacuation. We pair time-series cross-sectional data models with qualitative comparative analysis (QCA) of nine affected municipalities to examine why some shelters see higher evacuation rates than others. While past studies have used Facebook user data, post-hoc surveys, or ad-hoc roadside interviews to measure evacuation, this study uses meticulously recorded shelter attendance data to draw inferences about evacuation behavior. Controlling for types of shelters, damage levels, infrastructure quality, social vulnerability, governance capacity, and community resources, we find that in affected communities, stronger bridging social ties, especially when aided by linking ties, motivate greater evacuation to shelters. In unaffected communities, stronger bonding and bridging ties encourage potentially unnecessary evacuation, helping spread rumors during blackouts. These results highlight the necessity of clear, transparent communication with the public, and fostering trust in government during crises.

Cybersecurity Threats and Organisational Response: Textual Analysis and Panel Regression

ABSTRACT This study examines the relationship between cybersecurity threats faced and cybersecurity response planned by organisations. Classifying cybersecurity threats into four types – physical threats, personnel threats, communication and data threats, and operational threats – this study examines organisational responses to such threats. Using textual data on cybersecurity threats and response gathered from the 10-K reports published by 87 organisations, topic modelling was conducted to assess the threats and response. A cross-sectional time-series regression model fitted on the topic weights showed that cybersecurity response was influenced by cybersecurity threats, beyond the time-invariant control and period variables. Specifically, physical threats and operational threats influenced the technical response; physical threats, communication and data threats, and operational threats influenced the non-technical response; and personnel threats influenced the overall response. Implications for research and practice are discussed.

Support for insider parties: The role of political trust in a longitudinal-comparative perspective

European democracies have experienced drastic changes in electoral competition. Voter support for insider parties that have traditionally governed has declined while support for radical and populist parties has increased. Simultaneously, citizens’ declining political trust has become a concern, as confidence in political institutions and actors is low across numerous countries. Interestingly, the linkage between political trust and support for insider parties has not been empirically established but deduced from the fact that outsider parties are often supported by dissatisfied citizens. We address this gap adopting both an institutional- and an actor-centered approach by investigating whether trust in parliaments and in parties is associated with the electoral performance of insider parties on the aggregate level. Combining different data sources in a novel way, we apply time-series cross-section models to a dataset containing 30 countries and 137 elections from 1998 to 2018. Our results show that when political trust is low, particularly institutional trust, insider parties receive less electoral support. Hence, we provide empirical evidence that decreasing levels of political trust are the downfall of insider parties, thereby opening a window of opportunity for challenging outsider parties.

Globalization, Democratic Institutions and The Fairness of the Elections

A central normative argument of liberal democracy is that elections as instruments of democracy need to be free, fair and neutral to reflect peoples’ opinions. In many parts of the world, particularly democracies in developing countries of South Asia and Africa, governments are formed by elections that are sometimes considered 'flawed' by the people and international observers. This raises a critical question with far-reaching implications for democracy: What affects the fairness of the elections? Is there any significant direct effect of globalization on elections? If not, under what circumstances does globalization influence the quality of elections? Do effective political institutions condition the effect of globalization on the fairness of the elections? I empirically assess these questions from 2006 to 2010 for 100 countries in a time-series cross-sectional statistical model using the 2015 Quality of Government (QoG) dataset. The results suggest that greater levels of globalization significantly increase the fairness of the elections in countries where effective political institutions exist. The results furthermore demonstrate that in the absence of viable democratic institutions, an increased level of globalization may not always correspond to free, fair and neutral elections.

Businesses, Places, and Homicide: A Preliminary Empirical Examination

ABSTRACT This paper empirically examines the relations between homicide rates by age (adults 18 to 24 and adults 25 or older), the tattoo shop density, on-site and off-site alcohol outlets, and disadvantage. It offers an additional explanation for the relation between alcohol outlets and homicide besides alcohol availability and consumption being facilitators of such lethal violence, suggesting that these businesses are a proxy for attracting those most prone to violent behavior. The analysis estimated linear pooled cross-sectional time series models to test the relations between age-specific homicide rates and tattoo shop density, on-site alcohol density, off-site alcohol density, and a multidimensional measure of disadvantage. These relations were examined among 88 of the largest cities in the United States for the years 1998 to 2006 (city-years = 792). The findings showed positive relations between homicides rates involving young adults 18 to 24 years of age, off-site alcohol outlets, tattoo shops, controlling for disadvantage. Findings suggest business establishments that attract young adults increase the likelihood of lethal violence not solely because of the availability and consumption of alcohol, but by increasing the convergence in space and time of motivated offenders and suitable targets, with limited or absent capable guardianship.

The Impact of Fiscal Decentralisation on Local Economic Development in Serbia

Serbia is a transition country that has experienced strong negative spillover effects from the global financial crisis and the subsequent eurozone debt crisis. At the same time it is a candidate for accession to the European Union. Success in local economic development is likely to affect prospects for economic recovery and a successful EU integration process and the paper analyses the contribution of fiscal decentralisation to successful local economic development. The analysis is based upon a cross-section time-series regression model that reveals a positive impact of local public expenditure on economic development. Expenditure on education has a particularly strong effect on local economic development, as does investment per capita from both public and private sources. The conclusion of the paper is that local economic development has been enhanced by the fiscal decentralisation that has taken place in Serbia.

Rumor Has it: The Role of Social Ties in Evacuation to Nearby Shelters after the 2018 Hokkaido Earthquake

When crisis strikes, why do some communities utilize evacuation shelters more than others? This mixed methods study draws on a new dataset of almost-daily tallies of evacuees at 660 local shelters following Japan’s 2018 Eastern Iburi Earthquake in Hokkaido to create a large-N time-series cross sectional (TSCS) dataset of local, short-distance evacuation. We pair time-series cross-sectional data models with qualitative comparative analysis (QCA) of nine affected municipalities to examine why some shelters see higher evacuation rates than others. While past studies have used Facebook user data, post-hoc surveys, or ad-hoc roadside interviews to measure evacuation, this study uses meticulously recorded shelter attendance data to draw inferences about evacuation behavior. Controlling for types of shelters, damage levels, infrastructure quality, social vulnerability, governance capacity, and community resources, we find that in affected communities, stronger bridging social ties, especially when aided by linking ties, motivate greater evacuation to shelters. In unaffected communities, stronger bonding and bridging ties encourage potentially unnecessary evacuation, helping spread rumors during blackouts. These results highlight the necessity of clear, transparent communication with the public, and fostering trust in government during crises.

An Empirical Study of the Option Pricing Formula with the Underlying Banned from Short Sell

Short sell bans are often imposed during a financial crisis as a desperate measure to stabilize financial markets. Yet, the impact of short sell bans on option pricing and hedging is not well quantitatively studied until very recently when Guo and Zhu (2017) and He and Zhu (2018) formulated a new pricing framework with the underlying being either completely or partially banned from short selling. However, no empirical results were provided to substantiate the usefulness of the formulae, as well as to deepen our understanding on the effects of short sell bans. This paper provides a comprehensive empirical study on the effects of short sell bans to the standard option pricing theory by carrying out both cross-sectional and options time series model calibration of the model devised by He and Zhu (2018). Overall, our empirical results indicate that the alternative option pricing formula considering short sell restrictions has the ability to capture highly-quoted implied volatility, with an evident improvement of 39% out-of-sample performance compared to the benchmark Black-Scholes model during the period of short sell ban.

DSRIP’s innovation and collaboration in population health management: A cross-sectional segmented time series model

Texas Medicaid Section 1115 waiver approved Delivery System Reform Incentive Payment (DSRIP) program has four categories, namely infrastructure development, program innovation and redesign, reporting of quality improvement outcomes, and population health improvement. A metric of the fourth category, preventable hospitalization rate, was analyzed for a set of eight diagnostic conditions to assess the impact of DSRIP on participating- and non-participating hospitals over two time periods, pre-DSRIP and post-DSRIP, with the help of a cross-sectional segmented time series regression model. Texas Healthcare Information Collection database was leveraged to obtain preventable hospitalization rate data. The dependent variables were preventable hospitalization rates of eight program-specified conditions and the independent variables were time, intervention, and post-implementation intervention. The overall combined preventable hospitalization rate for DSRIP hospitals was observed to decrease by 25.73%, whereas the overall combined preventable hospitalization rate for non-DSRIP hospitals was observed to increase by 37.57%. DSRIP hospitals had invested in coordinating healthcare projects and were subsequently reimbursed by the state for healthcare improvements. The implementation of DSRIP may have had the capacity to decrease preventable hospitalization rates in regions wherein its adoption may have improved the health of the population.