Panel Data Model Research Articles

The Relationship Between Firm Formation and Unemployment: Evidence from Türkiye

This paper aims to explore the relationship between firm formation and unemployment using regional and city-level Turkish data. We focus on the post-2008 financial crisis period and analyze the effects of unemployment and other control variables on firm formation at NUTS-II and NUTS-III regions of Türkiye. We estimate a fixed-effects panel data model and control for GDP per capita, urbanization, population density, patents, high school education, and higher education. Our results suggest that there is a significant positive effect of unemployment on new firm formation. The analysis concerning the converse relation, the impact of firm formation on unemployment, reveals no significant results at the NUTS-II level; however, a significant negative relationship is found at the city level with 4- and 5-year lags.

Contribution of Economic Variables to Sustainable Development Goals (SDG 16): The Case of G20 Countries

Objective: The aim of this study is to examine the effects of selected macroeconomic variables on the global peace index. Theoretical Framework: This research provides a comprehensive theoretical framework for understanding the relationship between economic factors and peace by highlighting key concepts such as sustainable development goals (SDGs), peace economics and development theories. Method: Using data from G20 countries covering the period 2008-2022, the effects of inflation, GDP per capita, foreign direct investments, growth rate, exchange rate and consumption expenditures on the peace index were examined with panel data models. Results and Discussion: According to the analysis results; the inflation rate, foreign direct investments (fdi) and growth rate among the variables in the established model were statistically insignificant, while per capita income, exchange rate and final consumption expenditures were statistically significant. Research Implications: This research contributes to understanding of the relationship between economic development and peace. Theoretically, it reveals how economic factors shape peace and the interaction between sustainable development and peace; while in practical terms, it emphasizes the integration of peace strategies with economic growth and inequality for governments and international organizations. The results can be applied in areas such as peace economics and development policies. Originality/Value: This study brings an innovative approach to the literature by investigating economic factors’ effects on peace and offers contributions to the integration of sustainable development and peace strategies. The findings can help governments and international organizations shape peacebuilding and development policies more effectively.

On Omitted Variables, Proxies, and Unobserved Effects in Empirical Regression Analysis

Any result from regression analysis may be subject to omitted variable bias of unknown magnitude and direction as, in practice, no dataset contains all the variables of the population model. At the same time, many variables are irrelevant and don’t contribute to the analysis. This paper explores which combination of data sources or structures will produce the best results and should be made available to the research community. We present a unified statistical framework that nests and comparable sets of constraints that characterize the effectiveness of these approaches in reducing omitted variable bias. We demonstrate our framework by estimating a wage and labor market transition model using German administrative data with a large set of linked survey variables. Overall, we find that unobserved effects panel data models with a restricted set of regressors are preferable to cross-sectional analysis with an extended set of variables. Consequently, we recommend that data providers supply administrative panel data for key variables instead of conducting extensive cross-sectional surveys.

A Panel Data Model for Implementation of a Competitive Real Exchange Rate for the Promotion of Economic Growth in MENA Countries

The objective of this study is to determine whether maintaining a competitive real exchange rate has been beneficial for the economies in MENA countries, in the period 1965 to 2020. To confirm the research hypothesis, we use two econometric models, the first is quantitative which is a balanced panel data model and the second is a qualitative "Logit" response model on panel data, in this case unbalanced, that identifies the variables that promote and control currency devaluations, considered the theory proposed by Berg and Miao (2010). Our result maintains that a competitive real exchange rate has boosted the growth of MENA economies. The existence of the “Penn” effect in developed MENA economies is demonstrated, these countries have developed policies that allow good management of exchange rate misalignments and thus contain the shocks suffered by their currencies.

Banking concentration and its determinants at the regional level during the Spanish developmentalism

Abstract In 1962, Spain implemented significant banking law changes to boost competition. This study investigates their impact on provincial banking concentration from 1964 to 1975, utilising novel provincial-level private bank balance sheet data and including savings banks. Results show a substantial decline in concentration across most provinces. Panel data models identify the determinants of banking concentration: larger populations and higher gross domestic product per capita correlate with lower concentration, while agrarian-focused provinces exhibit higher concentration. The provincial financial sector's structure also matters, with a higher number of branches and headquarters per capita associated with reduced banking concentration. These findings refine existing literature and provide new insights into the intricate relationship between banking concentration and regional economies in Spain.

A Two-way Crossed Effects Fuzzy Panel Linear Regression Model

Over the last two decades, the panel data model has become a focus of applied research. While there are numerous proposals for soft regression models in the literature, only a few linear regression models have been proposed based on fuzzy panel data. However, these models have serious limitations. This study is an attempt to propose a kind of two-way fuzzy panel regression model with crossed effects, fuzzy responses and crisp predictors to overcome the shortcomings of these models in real applications. The corresponding parameter estimation is provided based on a three-step procedure. For this purpose, the conventional least absolute error technique is employed. Two real data sets are analyzed to investigate the fitting and predictive capabilities of the proposed fuzzy panel regression model. These real data applications demonstrate that our proposed model has good fitting accuracy and predictive performance.

Institutional Ownership and Corporate Risk-taking: Indian Evidence

The study examines the impact of institutional ownership on corporate risk-taking in India. It further differentiates institutional ownership into domestic and foreign categories and examines their respective influences on corporate risk-taking, alongside assessing the moderating role of the COVID-19 pandemic. The present study is based on a panel data set of 100 non-financial Indian firms randomly selected from the top 500 firms listed on the Bombay Stock Exchange, covering the period from 2013 to 2022. The study employs two market-based risk measures—total and idiosyncratic risk—to capture corporate risk-taking. Fixed effects panel data model, along with generalized estimating equations and generalized method of moments, is used to ensure robustness of results. The study finds a significant negative impact of institutional ownership on corporate risk-taking. Specifically, domestic institutional ownership significantly reduces corporate risk-taking, whereas foreign institutional ownership shows no significant impact. Furthermore, the COVID-19 crisis does not moderate the relationship between institutional ownership and corporate risk-taking or domestic institutional ownership and corporate risk-taking. However, it positively moderates the relationship between foreign institutional ownership and corporate risk-taking. The insights hold immense value for policymakers, regulators and industry practitioners. To the best of the authors’ knowledge, the present study is a maiden attempt to investigate the impact of domestic and foreign institutional ownership on corporate risk-taking in the Indian context. Furthermore, this is the first study to examine the moderating role of COVID-19 on relationships. Additionally, the study incorporates idiosyncratic risk as an additional measure of corporate risk-taking, largely overlooked in the Indian context.

The Examination of the Effects of FDI on International Trade of the ASEAN Economic Community

The examination of the effects of foreign direct investment on international trade and trade balance was conducted within the member states of the ASEAN Economic Community, which include Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam, covering the period from 2001 to 2022. To derive empirical insights, various static panel data models were employed, specifically the Pooled Ordinary Least Squares, Fixed Effect, and Random Effect models. In addition to FDI, the analysis incorporated three key variables: the real GDP growth rate, inflation rate, and foreign exchange rate, to evaluate their influence on international trade. The findings of this research indicate a positive correlation between net FDI inflows and trade levels, suggesting that increased FDI contributes to enhanced trade activity. Furthermore, the results emphasize that a rise in the real GDP growth rate and a depreciation of the exchange rate can foster a favorable trade balance, while an increase in the inflation rate tends to have a detrimental effect. Consistency across all panel data models was observed, with the Hausman test indicating that the Random Effect model is the most suitable for this analysis.

The health effect of relaxing entry regulation for private hospitals and its nonlinear characteristics: evidence from China

IntroductionRelaxing entry regulation for private hospitals and fostering competition in the healthcare market are crucial prerequisites for addressing the diverse healthcare demands of the population and promoting the development of a Healthy China. This study aims to comprehensively evaluate the health effect of relaxing entry regulation for private hospitals and to examine its nonlinear characteristics.MethodsUsing panel data from 31 provinces in mainland China, this study employs a fixed effects panel data model to investigate the health effect of relaxing entry regulation for private hospitals. To examine the potential nonlinear relationship, a panel threshold model is also employed. This study also employs a dynamic panel model as the econometric method to perform regression analysis for robustness testing.ResultsThe empirical findings indicate that relaxing entry regulation for private hospitals significantly improves the residents’ health level. This conclusion remains robust when different explanatory variables, measurement models and modified samples are applied. The heterogeneity analysis reveals that the health benefits of private hospital market entry are more pronounced in Central-Western region, regions with high medical resource endowment, and regions with High-quality competition. Furthermore, the health effect of relaxing entry regulation for private hospitals exhibits nonlinear characteristics: it weakens as economic development level reaches a certain threshold and becomes more pronounced as educational attainment reaches a specific threshold.DiscussionBased on these empirical results, Chinese provinces can be classified into three distinct types to guide the development of differentiated policies tailored to local conditions. This study provides valuable theoretical insights into promoting a fair competition mechanism in the healthcare market, enhancing the quality of private hospitals, and improving the overall health level of residents in China and other developing countries facing similar challenges.

Analysis of the influencing factors of the trade potential between countries along the ‘Belt and Road’ and China: a study based on spatial panel data model

ABSTRACT In 2013, China launched a cooperative initiative to build the “Silk Road Economic Belt” and the “21st Century Maritime Silk Road”. Based on the data of import and export trade between 57 countries along the “Belt and Road” and China from 2010 to 2021, this paper conducts research using the spatial Durbin model. The results show that the double fixed-effects model is optimal. The 57 countries along the “Belt and Road” have significant positive spatial spillover effects on trade with China, among which economic development, educational development, and transportation accessibility have significantly promoted trade between the countries along the “Belt and Road” and China.

Trends and Drivers of Greenhouse Gas Emissions in India: A Decadal Analysis (2010-2020)

This paper analyses trends and determinants of greenhouse gas (GHG) emissions in India in 2010-2020, the decade with some of the fastest economic growth rates and the increased awareness of environmental issues. The research pays attention to India, the world's third-largest emitter of GHG, which will be analysed regarding emissions in four sectors: energy, industry, agriculture, and waste management. A time series analysis of data drawn from national and international agencies, such as the World Bank and India's Ministry of Environment, Forestry, and Climate Change, was applied in this study. Statistical analysis and data visualisation were performed using the R software. The key findings show an increase of 42% in the total emissions of GHG over the last decade, with the energy sector dominating at 68% in 2020. Other significant contributors were industry (20%), agriculture (9%), and waste (3%). Growth in population, along with accompanying urbanisation and industrial expansion, is cited as the main factor responsible for emission rise. However, there are also indications of decoupling between growth in the economy and emissions intensity. The study analyzes the greenhouse gas emission trends for major emitters between 2010 and 2020, in India. The dynamic panel data model analyses indicate signs of decoupling of emissions from economic growth in developed economies and the role of renewable energy and carbon pricing. Research findings further indicate that stronger climate policy and investment in clean energy is the necessity to decrease global emissions.The paper identifies policy issues that require strong activity in enforcing efficiency measures, fastening the shift toward krenewable sources of energy, and effective sustainable planning and waste management practices in urbanisation processes. In that respect, examination of policies like the PAT scheme and promotion of electric vehicles has shown where more aggressive implementation may be seen as necessary to ensure India's climate commitments. This elaborate decadal review finds most of its utility within the framework of policy designing for sustainable development and climate change mitigation by policymakers and stakeholders in the Indian scenario.

The Impact of Export Diversification on Economic Growth by Provinces in Türkiye

Beyond being a national measure of success, export diversification is closely linked to regional development, local employment, innovation capacity, and economic resilience. In the current study, we investigate the impact of export diversification on economic growth by provinces in the case of Türkiye for the period 2004-2018. For the purpose of the study, we employ static panel data models using Fixed Effects (FE) and Random Effects (RE) estimators, which account for unobserved heterogeneity. The findings of the study show that there is a significant negative relationship between per capita income and export intensity (HHI and GINI). In other words, as export diversification increases, per capita incomes increase. On the other hand, the coefficients obtained for the THEIL index are statistically insignificant. Therefore, it can be argued that indices measuring different dimensions of export diversification reflect different effects despite their conceptual similarities. The findings suggest that promoting export diversification can enhance regional economic growth and resilience, indicating that policymakers should prioritize strategies that encourage diversification of export portfolios, especially in underdeveloped regions, to foster balanced economic growth.

Relationship between the average annual temperature and the area of Amazonian humid forest in the departments of Peru, 2013-2021

The present study analyzed the relationship between the average annual temperature and the area of Amazonian forest in the departments of Peru during the period 2013-2021, using a panel data model with random effects. The data used come from the National Institute of Statistics and Informatics (INEI) and include the average annual temperature in degrees Celsius and the area of Amazonian rainforest in thousands of hectares, both disaggregated by department. Additionally, CO2 emissions resulting from the loss of tree cover, measured in megatons (Mt) of carbon dioxide equivalent (CO₂e), were considered as a control variable. The results revealed a positive and statistically significant relationship between the area of Amazonian forest and the average annual temperature, denoting that an increase of one thousand hectares in the extension of the forest corresponds to an increase of 0.0004 °C in temperature. In this sense, the finding contradicts the climate-regulating role played by forests, however, this is attributed to the influence of unobserved confounding variables that are linked to both forest area and temperature.

Do women commemorate women? How gender and ideology affect decisions on naming female streets

Street names are not neutral identifiers to navigate through cities but are charged with strong symbolic connotations and reflect power relations within society. A growing body of geographic scholarship documents a strong gender bias in the urban namespace, where women only represent a small fraction of streets named after people. This article investigates whether the lack of women in political decision-making roles contributes to explaining their marginalization in urban toponyms. More specifically, we study the impact of the gender and ideology of town mayors on their decisions to commemorate women in the street map. Focusing on the universe of Spanish towns during the period 2001–2023, we find through fixed effects panel data models and regression discontinuity design that the mayor's gender does not affect the percentage of female-named streets, while the ideology of the governing party does. Our findings thus indicate that it is ideology rather than gender what shapes politicians' preferences regarding the commemoration of women in the street map. We argue that this is because, on the one hand, strong political parties can impose their agenda on local leaders, making irrelevant differences in their gender and, on the other, the ideological cleavage is more relevant than the gender one to account for differences in attitudes towards symbolic gender policies. A natural implication of our results is that simply having more female politicians will hardly suffice to address the gender gap in street names and in other symbolically charged policies.

Capital structure specificity in knowledge-intensive industries: A comparative study of EU countries

PURPOSE: This paper aims to examine and compare the capital structure patterns and financial decision-making determinants of knowledge-based firms and traditional industries in selected EU countries over the period 2000–2023. The study seeks to uncover sector-specific features, focusing on the unique financial behaviors of knowledge-intensive enterprises compared to their traditional counterparts. It also explores how factors such as asset structure, profitability, and growth opportunities influence capital structure decisions within these industries, thus providing implications for financial management in innovation-driven sectors. METHODOLOGY: The methodology involves analyzing data from 12 EU countries, categorized by industry (sectors M and J for knowledge-based firms and other sectors for traditional firms) and firm size. The research applies panel data modeling to evaluate the differences and influences on capital structure within these groups. FINDINGS: The study reveals that knowledge-based firms exhibit distinct capital structure characteristics, including a higher reliance on intangible assets and lower ratios of long-term debt compared to traditional firms. Specifically, the findings indicate that intangible assets are positively correlated with total and long-term debt in knowledge-based firms, meaning that an increase in intangible asset value is associated with a corresponding increase in total and long-term debt levels. This relationship aligns with the modified pecking order theory, which posits that firms prioritize funding sources based on their perceived cost and risk. The determinants of capital structure – such as profitability and growth opportunities – also demonstrate differing impacts, revealing the unique financial strategies employed by knowledge-based firms. IMPLICATIONS: This research highlights the importance of adapting financial strategies to the specific needs of knowledge-based firms, which often face unique challenges due to their reliance on intangible assets. Policymakers can use these findings to design targeted financial policies that support the growth and sustainability of innovation-driven enterprises, such as by offering incentives for intangible asset financing or by reducing barriers to accessing long-term debt. For practitioners, the insights highlight the need to align financial decision-making with sector-specific characteristics to optimize capital structure and drive competitive advantage. ORIGINALITY AND VALUE: This research makes a unique contribution by providing one of the first comparative analyses of capital structure determinants across knowledge-based and traditional firms in multiple EU countries over an extended timeframe (2000–2023). Unlike prior studies, which often focus on individual sectors or countries, this study offers a comprehensive and cross-sectoral perspective, enriching the understanding of how financial theories operate in diverse economic and institutional contexts. By addressing the financial behaviors of knowledge-intensive firms, the research bridges a critical gap in the literature and informs both academia and practice.

Integrated modified harmonic mean method for spatial panel data models

Integrated modified harmonic mean method for spatial panel data models

Tests for spatial dependence and temporal heterogeneity in time-varying coefficient spatial autoregressive panel data model

Time-varying coefficient spatial autoregressive panel data model is a powerful tool to simultaneously deal with spatial dependence and temporal heterogeneity in spatial panel data analysis. However, little work has been devoted to related statistical inference issues, which largely limits the application scope of the model. In this paper, we develop two generalized-likelihood-ratio-statistic-based bootstrap tests to detect spatial dependence of the response variable and temporal heterogeneity of the regression relationship, respectively. The simulation studies show that both tests are of accurate size and satisfactory power, and are quite robust to non-normality of error distribution. A house price data set is finally analyzed to demonstrate the application of the proposed tests in detecting spatial dependence and temporal heterogeneity.

Going long, going short, issue or liquidate? Corporate debt maturity of Mexican public firms

PurposeThis paper studies the determinants of the debt maturity of Mexican-listed companies by analysing the effects on the extensive (issuing or liquidating debt) and the intensive (debt maturity renegotiation) margins.Design/methodology/approachThis study, using a Tobit model for panel data and measuring maturity as a time variable, shows that size, liquidity and leverage, among other firm characteristics, as well as the market interest rate, explain debt maturity. Additionally, the study employs the McDonald and Moffitt decomposition to determine whether the explanatory variables of maturity have a more significant effect on the decision to issue or liquidate debt or on debt maturity renegotiations.FindingsThe results obtained highlight that the market interest rate negatively affects debt maturity. On the other hand, variables like size, liquidity, collateral and leverage demonstrate a positive relationship with the dependent variable. In addition, the extensive margin has a higher impact on corporate debt than the intensive margin, suggesting that firms prefer to liquidate or issue new debt rather than renegotiate preexisting contracts.Research limitations/implicationsThe main limitation of this study is the use of an unbalanced panel. The lack of data limits the application of specific methodologies suggested by the literature as a way to test the robustness of the estimates.Originality/valueFirst of all, this study adds empirical evidence of debt maturity decisions by publicly traded firms in a middle-income country such as Mexico to the existing literature on maturity choice. Second, the study treats debt maturity as a time-censored, limited variable. Finally, the authors have used the McDonald and Moffitt (1980) methodology to decompose the effect of each independent variable into extensive and intensive margins.

Regional Market Concentration and Its Impact on Health Plan Quality in Brazil: Insights for Regulatory Policy

This study explores the regional concentration of health plan operators in Brazil from 2014 to 2019, analyzing its impacts on the well-being of users. While existing literature addresses health insurance market dynamics, regional-level insights remain scarce. Employing a random-effects panel data model, we assessed variations in service quality—measured through user complaints—in relation to market concentration and income levels across 138 relevant markets. Our findings reveal that increased market concentration correlates with deteriorating service quality, as evidenced by a rise in complaints. Moreover, declining income levels exacerbate this trend, reflecting disparities in access to higher-quality plans. These results underscore the negative implications of market concentration for consumer welfare and raise critical questions about regulatory policies. By emphasizing regional disparities, this study contributes to the ongoing debate on antitrust measures and healthcare regulation, offering actionable insights for policymakers.

Quality of Life and Bank Investments of the Population in the Regions of the Russian Federation

Improving the living standards of citizens and improving the investment climate are the key benchmarks of any state.The purpose of this study is to analyze the relationship between quality of life and the growth of bank deposits in the regions of Russia.Materials and methods. The integral index of the quality of life of the population of the Russian regions from 2011 to 2021 was assessed using the method of principal components based on blocks of variables reflecting not only material but also spiritual and moral spheres of society. The authors analyzed the presence of correlation between the integral index of the quality of life and the volume of bank deposits. Assuming that the integral index of the quality of life can have a significant impact on the growth of bank deposits, panel data models with fixed and random effects were constructed and analyzed. The data from the Federal State Statistics Service (Rosstat) and from open Russian Internet resources on banking analytics were used in the paper.Results. The paper shows that despite the presence of a stable correlation between the value of the integral index of the quality of life and the estimate of the volume of deposits, the quality of life does not have a significant impact on the growth of bank investments. Regional inflation is expected to have a negative impact on deposit growth.Conclusion. The increase in free cash stimulates the population to choose the most reliable ways of their preservation and multiplication, for example, bank deposits. However, improving the quality of their lives, the population may begin to consider other, albeit less reliable, but perhaps more profitable areas of investment, such as real estate or mutual funds. In addition, today the compensation for deposits in a bank, in respect of which an insured event has occurred, is paid to the depositor in the amount not exceeding 1.4 million rubles, which encourages agents to consider other investment instruments, if the available free cash exceeds the above amount. In conditions when the growth rates of consumer prices exceed the return on deposits, their real return becomes negative, which means that the population will look for alternative ways of investment. Analysis that takes into account different types of investment by regions of the country is currently a difficult task due to the aggregation and non-transparency of this type of data. We also note that the turbulent period starting from 2022, characterized by a sharp increase in interest rates, requires separate consideration.