Unidirectional Causal Relationship Research Articles

The Impact of Climate Change on Life Expectancy in the UK: Empirical Evidence from Fourier Bootstrap ARDL Procedure

This study aims to determine the impact of surface temperature change, considered a proxy for climate change in the United Kingdom, on life expectancy using annual data from 1990–2021. Government expenditure is used as a control variable. The Fourier function-based bootstrap autoregressive distributed lag model is utilized to examine the cointegration among variables. The analysis outcomes reveal a long-term relationship between life expectancy, surface temperature change, and government expenditure. Furthermore, the findings reveal that surface temperature change significantly reduces life expectancy in the long run, while there is no significant relationship between the two variables in the short term. The Toda-Yamamoto causality analysis results show a unidirectional causality relationship between surface temperature change and life expectancy. Additionally, a significant bidirectional causality relationship is found between life expectancy and government expenditure.

RETHINKING THE PHILLIPS CURVE IN TÜRKİYE: NEW EVIDENCE FROM THE FREQUENCY DOMAIN

This study aims to investigate the short- and long-run relationship between the inflation rate, which has come to the fore after a long break all over the world, and the unemployment rate for the Turkish economy. For this purpose, the relationship between the two variables is analyzed using a causality test in the frequency domain, with monthly data for the period 2005:01-2023:03. The symmetric findings of the test indicate the existence of a unidirectional and short-run causal relationship from unemployment to inflation. The asymmetric findings of the test provide two important pieces of evidence. The first of these is the existence of a unidirectional and long-run causal relationship from the positive component of unemployment to the negative component of inflation. The second is the existence of a unidirectional, short- and long-run causal relationship from the negative component of unemployment to the positive component of inflation. These findings confirm the trade-off predicted by the Phillips Curve. It is also important for policymakers to try to increase productivity by making structural and institutional reforms in the labor market, as far as settling for a moderate inflation rate while struggling with inflation and unemployment problems.

Impact of public-private partnerships investment and FDI on CO2 emissions: A study of six global investment countries

This study investigates the impact of public-private partnerships investment in energy and FDI on environmental quality in global investment countries during 1995–2018. Economic growth, technological innovations and consumption of clean energy are also considered as additional determinants of environmental quality. The study applied advanced panel econometric models. Our empirical results affirm the evidence of a long-run association between environmental quality and its determinants. Specifically, economic growth as well as clean energy use improves quality of environment by lowering carbon emissions. Public-private partnerships investment in energy, FDI and technological innovations decrease carbon emissions. Energy consumption (generated from fossil fuel) increases carbon emissions. Heterogeneous causality evidence indicates the presence of a unidirectional causality relation from carbon emissions to public-private partnerships investment in energy and a feedback causality occurs between consumption of clean energy and CO2 emissions. This empirical evidence provides new insights for both policymakers and governments to support public-private partnership investments in energy for the improvement of quality of environment in global investment countries.

Exchange rate movement and stock returns in most capitalised economies in Sub-Saharan Africa

In line with the extant literature that financial markets do mutually cooperate, this study investigates the nexus between exchange rate movement and stock returns in the six most capitalised economies in Sub-Saharan Africa (SSA). The study ascertains the relevance of the traditional or portfolio adjustment theory to sub-Saharan Africa’s financial markets. The study accounted for heterogeneity and dependence among the cross-sections and applied the Driscoll and Kraay and the Feasible Generalised Least Square econometric procedures on Bloomberg (2023) monthly datasets spanning between 2000:1 and 2022:12. The study findings reveal the nexus between the variables flows from stock returns to the exchange rate in SSA and some consistency with the argument of the portfolio adjustment theory. Estimates of Dumitrescu-Hurlin's (2012) Granger non-causal test also reveal a unidirectional causal relationship between both variables in the most capitalised economies in SSA. Macroeconomic policies that vitiate the limitations of both variables in SSA are recommended.

Exploring the nexus between FDI inflows and economic growth: A sectoral level analysis.

This study investigates the relationship between Foreign Direct Investment (FDI) inflows and economic growth at sectoral levels in Bangladesh, employing a panel study framework. Utilizing sectoral-level panel data spanning six sectors from 2007-08 to 2018-19, the analysis is conducted using Panel Vector Error Correction Model (Panel VECM). Results from panel unit root tests confirm that all variables are integrated of order one I (1), indicating stationarity. The Pedroni panel co-integration test further supports the presence of co-integration among the variables. Notably, the Panel VECM reveals evidence of a unidirectional causal relationship from Real Gross Domestic Product (RGDP) to Real Foreign Direct Investment (RFDI) across all six sectors of Bangladesh. The findings underscore the significance of formulating pragmatic policies and implementing them effectively to attract FDI across sectors, thereby contributing to the overall economic growth of Bangladesh.

Exploring the interconnected between type 2 diabetes mellitus and nonalcoholic fatty liver disease: Genetic correlation and Mendelian randomization analysis.

Epidemiological and clinical studies have indicated a higher risk of nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM), implying a potentially shared genetic etiology, which is still less explored. Genetic links between T2DM and NAFLD were assessed using linkage disequilibrium score regression and pleiotropic analysis under composite null hypothesis. European GWAS data have identified shared genes, whereas SNP-level pleiotropic analysis under composite null hypothesis has explored pleiotropic loci. generalized gene-set analysis of GWAS data determines pleiotropic pathways and tissue enrichment using eQTL mapping to identify associated genes. Mendelian randomization analysis was used to investigate the causal relationship between NAFLD and T2DM. Linkage disequilibrium score regression analysis revealed a strong genetic correlation between T2DM and NAFLD, and identified 24 pleiotropic loci. These single-nucleotide polymorphisms are primarily involved in biosynthetic regulation, RNA biosynthesis, and pancreatic development. generalized gene-set analysis of GWAS data analysis revealed significant enrichment in multiple brain tissues. Gene mapping using these 3 methods led to the identification of numerous pleiotropic genes, with differences observed in liver and kidney tissues. These genes were mainly enriched in pancreas, brain, and liver tissues. The Mendelian randomization method indicated a significantly positive unidirectional causal relationship between T2DM and NAFLD. Our study identified a shared genetic structure between NAFLD and T2DM, providing new insights into the genetic pathogenesis and mechanisms of NAFLD and T2DM comorbidities.

Does Internet Use Affect Air Pollution? Evidence from OECD Countries

This study aims to analyze the relationship between the use of the internet, which has an important place in information and communication, and air pollution (Carbon Dioxide Emissions-CO2). In this context, the relationship between the variables for the 1994-2019 period in 28 OECD countries selected was tested with the help of a panel data analysis with Fourier functions (Fourier unit root test, panel Fourier cointegration test and panel Fourier causality test). The results of the analysis show that internet use reduces air pollution, while economic growth increases air pollution. The results of the Panel Fourier Granger Causality test revealed a bidirectional causality relationship between internet use and air pollution and a unidirectional causality relationship from air pollution to economic growth throughout the panel. The analysis results present a policy proposal to the governments of OECD countries that they can reduce air pollution by investing in information and communication technologies (ICT) in their economic growth processes.

Causal effects of immune cells in glioblastoma: a Bayesian Mendelian Randomization study.

Glioblastoma (GBM) is a highly malignant brain tumor, and immune cells play a crucial role in its initiation and progression. The immune system's cellular components, including various types of lymphocytes, macrophages, and dendritic cells, among others, engage in intricate interactions with GBM. However, the precise nature of these interactions remains to be conclusively determined. In this study, a comprehensive two-sample Mendelian Randomization (MR) analysis was conducted to elucidate the causal relationship between immune cell features and the incidence of GBM. Utilizing publicly available genetic data, we investigated the causal associations between 731 immune cell signatures and the risk of GBM. Subsequently, we conducted a reverse Mendelian randomization analysis to rule out reverse causation. Finally, it was concluded that there is a unidirectional causal relationship between three subtypes of immune cells and GBM. Comprehensive sensitivity analyses were employed to validate the results robustness, heterogeneity, and presence of horizontal pleiotropy. To enhance the accuracy of our results, we concurrently subjected them to Bayesian analysis. After conducting MR analyses, we identified 10 immune phenotypes that counteract glioblastoma, with the most protective being FSC-A on Natural Killer T cells (OR = 0.688, CI = 0.515-0.918, P = 0.011). Additionally, we found 11 immune cell subtypes that promote GBM incidence, including CD62L- HLA DR++ monocyte % monocyte (OR = 1.522, CI = 1.004-2.307, P = 0.048), CD4+CD8+ T cell % leukocyte (OR = 1.387, CI = 1.031-1.866, P = 0.031). Following the implementation of reverse MR analysis, where glioblastoma served as the exposure variable and the outcomes included 21 target immune cell subtypes, we discerned that only three cell subtypes (CD45 on CD33+ HLA DR+ CD14dim, CD33+ HLA DR+ Absolute Count, and IgD+ CD24+ B cell Absolute Count) exhibited a unidirectional causal association with glioblastoma. Our study has genetically demonstrated the close relationship between immune cells and GBM, guiding future clinical research.

Return and Volatility Spillover in Equity and Commodity Market: Some Indian Evidence

This study empirically investigates the return and volatility spillover among equity markets, Agro and non-Agro commodity markets. The study has used daily return data of BSE SENSEX as equity index, MCX iCOMEDEX, including Gold, Silver, and Crude oil, as non-agro indices, and NCDEX (AGRIDEX) as agro commodity indices to analyze volatility transmission between assets and markets using Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) model. The study has used dummy variables in the variance equation to identify the structural breaks during the COVID 19 pandemic. Further, Vector Auto Regression (VAR) pair-wise Granger causality test has been used to understand returns’ causality. The study has found a bidirectional and unidirectional causal relationship between variables. The study indicates that NCDEX could influence all the variables except MCX Gold. In structural breaks with deterministic dummy variables during COVID-19, we have found mixed evidence of asymmetric impact on conditional volatility. The study also indicates a positive and significant asymmetric volatility effect in the case of MCX Gold and MCX Silver during the COVID-19 period. The study has specified that SENSEX dominates the inflow of information to all commodity markets. Also, the findings signpost that information flows from NCDEX to MCX Gold, MCX Silver, and MCX Crude Oil. JEL Codes: C32, E30, E44, F41, G15, Q43

Immigration, Growth and Unemployment: Panel VAR Evidence From Ε.U. Countries

AbstractMigration is primarily propelled by economic and security considerations. As of 2021, the European Union (EU) housed 23.7 million non-EU citizens, emphasizing the crucial need to evaluate the economic ramifications of migration within the EU. This research seeks to scrutinize the impact of migration on economic development and unemployment across the 27 EU nations from 1990 to 2020, utilizing a PVAR model. The Pesaran CIPS test (2007) was employed for second-generation unit root testing, while cointegration was examined using the ARDL panel model. The ARDL panel model and error correction model were employed to assess causal relationships and their directions. Initial tests indicated that the fixed effects model was the most suitable, and there existed cross-sectional dependency and heterogeneity among EU countries. Furthermore, second-generation unit root tests indicated that the variables were integrated at order I(0) or I(1). The study’s findings demonstrate a significant positive correlation between both GDP per capita and the unemployment rate with the net migration rate to EU countries. Causal effects revealed a bidirectional long-term causal relationship between migration and unemployment, as well as a unidirectional long-term causal relationship between growth and migration, and growth and unemployment. Short-term Granger causality indicated a bidirectional causal relationship among all the variables under examination.

Ugly produce and food waste management: An analysis based on a social cognitive perspective

Despite a proliferation of literature exploring and testing marketing measures to overcome consumer barriers towards ugly produce, acceptance of it and the adoption of sustainable behaviours remain far from unanimous. The current research explores the role of an #imperfect-produce Instagram community in fostering shared knowledge and beliefs that establish positive social norms towards ugly produce. We employed a non-random sampling methodology to analyse the #imperfect-produce community on Instagram, chosen for its active and abundant nature in documenting visually imperfect produce. Following a comprehensive inclusion/exclusion criterion, we collected a sample of posts spanning May 2021 to May 2023 and conducted a thematic approach to analyse the data through manual sifting and coding. Four themes of shared content emerged within the hashtag thread, highlighting distinct unidirectional causal relationships among the three factors (Behavioural, Personal and Environmental) of social cognitive theory (SCT). The findings indicate that online communities formed by like-minded individuals create supportive communities that normalise sustainable behaviours and encourage social modelling. This research offers managers and marketers valuable insights into leveraging online communities and provides a foundation for future research. It also addresses a critical gap in SCT literature, which has previously neglected the impact of contextual factors.

Mineral resources and intellectual capital: Asymmetrical impacts and sustainable development outcomes

The existence and use of natural resources, and the impact that they have on sustainable development is a topic that has received significant attention, with a particular focus on the “resource curse hypothesis.” However, there is limited research that explores the combined role of natural resources and human capital, specifically when it comes to the realm of endogenous sustainable development. This study, therefore, takes into account this research gap, and investigates the interrelationship between natural resources, human capital, and sustainable development, specifically in the context of China. By conducting an advanced econometric analysis, we have examined the influence of oil rents, forest rents, and mineral rents on China's economic growth from the years spanning from 1992 to 2022, while considering the control variables such as foreign direct investment (FDI) and research and development (R&D) expenditures. Our findings have revealed an asymmetric impact of mineral rents and oil rents on economic growth, while forest rents have shown a positive and considerable influence as well. This essentially indicates the presence of beneficial effects from forest resources. Moreover, we have also observed a positive association between human capital and economic expansion. in addition to this, expenditures such as those pertaining to FDI and R&D are shown to stimulate economic growth in China. Furthermore, this study also finds bi-directional causality between oil rents, FDI, and human capital, while forest rents, mineral rents, and R&D expenditures show an uni-directional causal relationship with economic growth in China. Our study contributes towards the understanding of the complex dynamics that exist between natural resources, human capital, and sustainable development in China. These findings have policy implications for sustainable development through resource management, human capital development, and technological innovation.

ICT penetration and life expectancy in emerging market economies: panel evidence from asymmetric causality analysis

BackgroundLife expectancy is a significant result indicator of public health and sustainable development. Therefore, one of the final objectives of all economic and social policies is to increase the life expectancy. In this context, a limited number of researchers have investigated the relationship between ICT penetration and life expectancy. However, multiple interaction channels exist between ICT penetration and life expectancy. Furthermore, the studies have usually focused on the effect of ICT penetration on life expectancy through regression and ignored the effect of life expectancy on ICT penetration to a large extent. Therefore, this study aims to contribute to the empirical literature by investigating the causal relationship between ICT indicators and life expectancy.MethodsThis study uses symmetric and asymmetric causality approaches to investigate the two-way interaction between ICT indicators and life expectancy in emerging market economies over the 1997–2020 period. Employment of the asymmetric causality test enables us to analyze the hidden relationships between ICT indicators and life expectancy, unlike the traditional causality test.ResultsThe results of the symmetric causality test uncover a bidirectional causal interaction between mobile subscriptions and life expectancy but a one-way causal relationship from life expectancy to internet usage. However, the asymmetric causality test results uncover a unidirectional causal relationship between mobile subscriptions and life expectancy in China, Colombia, Czechia, Egypt, Greece, India, Kuwait and Turkiye due to positive shocks from mobile subscriptions. On the other hand, a bidirectional causal interaction exists between internet usage and life expectancy in all countries due to negative shocks from internet usage and life expectancy. Last, a unidirectional causal relationship exists between internet usage and life expectancy in all countries due to positive shocks from internet usage.ConclusionICT indicators significantly influence life expectancy health in a sample of emerging market economies. Therefore, internet usage and mobile devices are significant tools to improve life expectancy.

Stock Market Integration among Trading Partners

This study makes an attempt to analyse the dynamics of integrating relationship shared by Indian and Chinese stock market for the period ranging from January 01, 2002 to June 30, 2021. Prominent stock indices namely BSE SENSEX index of Bombay Stock Exchange Limited, India and Shanghai Stock Exchange Composite Index (SCI) of Shanghai Stock Exchange Limited, China were considered. Johansen Cointegration test was used to analyse the long term integration shared by the stock markets. Granger causality test was employed to know the causal relation shared by the stock markets. Presence of long term integration and unidirectional causal relation between the stock markets was found. It may be inferred that investment diversification among these markets may not provide fruitful returns to the investors in long run. Presence of causality from BSESENSEX to SCI and absence of causality from SCI to BSESENSEX suggests significant flow of information from Indian stock market to the Chinese stock market but not vice-versa. Findings of the study also suggests that extent of connectedness shared by the Indian and Chinese market does not vary too much with time. The findings revealed that although the financial markets of trading partners share significant integration but the intensity of trade does not impact the extent of financial linkages shared by the nations. These implications may provide significant insights to the investors, fund’s managers, policymakers and other market players.

Impact of Rural Infrastructure on Agricultural Growth in Tamil Nadu: Econometric Analysis by VECM

The present study examined the impact of rural infrastructure investment on the agriculture and economic growth of Tamil Nadu, applying the VECM. The study further examined the causal relationship between dependent and independent variables using the VECM Granger-causality test from 1990-91 to 2020-21. The study considered the influence of economic reforms on economic growth. The estimated results of co-integration showed that there was a long-run relationship among the variables. The VECM results confirmed a long-run relationship among the variables. The estimated results of the Granger-causality analysis indicated a unidirectional causal relationship between AC and GAP was significant statistically and no relationship between GSDP and AC. The decomposition results suggested that the economic growth variation was explained mainly by agriculture credit. The findings would be helpful to policymakers in developing appropriate strategies in the state.

The role of outward foreign direct investment as a filter for high energy intensity economies in the European Union

This study examines the effects of outward foreign direct investment (FDI) on energy intensity among the member countries of the European Union between 2000 and 2021. This relationship is deemed important in elucidating whether there is a process of filtering out energy-intensive companies where the use of FDI is employed to lead out production and improve energy intensity in the source country of the investment. To estimate this relationship, we employ feasible generalized least squares (FGLS) and panel corrected standard error (PCSE) for robustness purposes. Additionally, we complement our results with an estimation of the Granger causality relationships between our variables. Our findings reveal a highly significant trend wherein outward FDI from European Union countries tends to reduce energy intensity, thereby enhancing the productivity of the source countries of the investment. Conversely, only the technological productivity ratio exhibits a unidirectional causal relationship with energy intensity, while GDP per capita and natural resource rents serve as causes of energy intensity and vice versa. From this study, some policy recommendations can be derived whereby the European Union can continue to incentivize this filtering process to ensure better technological advancement in its production system, thereby improving the efficiency of the European industrial fabric.

Utilizing systematic Mendelian randomization to identify potential therapeutic targets for mania.

Mania has caused incalculable economic losses for patients, their families, and even society, but there is currently no effective treatment plan for this disease without side effects. Using bioinformatics and Mendelian randomization methods, potential drug target genes and key substances associated with mania were explored at the mRNA level. We used the chip expression profile from the GEO database to screen differential genes and used the eQTL and mania GWAS data from the IEU database for two-sample Mendelian randomization (MR) to determine core genes by colocalization. Next, we utilized bioinformatics analysis to identify key substances involved in the mechanism of action and determined related gene targets as drug targets. After differential expression analysis and MR, a causal relationship between the expression of 46 genes and mania was found. Colocalization analysis yielded six core genes. Five key substances were identified via enrichment analysis, immune-related analysis, and single-gene GSVA analysis of the core genes. MR revealed phenylalanine to be the only key substance that has a unidirectional causal relationship with mania. In the end, SBNO2, PBX2, RAMP3, and QPCT, which are significantly associated with the phenylalanine metabolism pathway, were identified as drug target genes. SBNO2, PBX2, RAMP3, and QPCT could serve as potential target genes for mania treatment and deserve further basic and clinical research. Medicinal target genes regulate the phenylalanine metabolism pathway to achieve the treatment of mania. Phenylalanine is an important intermediate substance in the treatment of mania that is regulated by drug target genes.

Calculation and Cause Analysis of Hidden Unemployment—A Case Study of the Northeast State-Owned Forest Areas in China

The Northeast State-owned Forest Areas comprise the largest region of resources in China. The transformation of the Northeast State-owned Forest Areas is due to various stimuli, such as policies, systems, and markets. In the context of ecological construction and the reform of the state-owned forest system in Northeast China, these have undergone a transformation in terms of economy, society, and ecology. However, a mismatch persists between employment and industrial structures, leading to hidden unemployment. This study employs the framework of hidden unemployment theory, utilizing the Cobb–Douglas production function to quantify the hidden unemployment rate, revealing the relationship between transformation and hidden unemployment. Through this analysis, the underlying causes of hidden unemployment in Northeast China’s state-owned forest regions are scrutinized. There is a bidirectional causal relationship between ecological transformation and hidden unemployment, while there is a unidirectional causal relationship between economic transformation and hidden unemployment. Variance decomposition analysis highlights the substantial contribution of social transformation, followed by economic transformation, with ecological transformation playing a comparatively smaller role. Based on the above research, this article proposes expediting the reforms within the forest industry groups, facilitating the separation of governmental and corporate interests. Additionally, it advocates for strategic forestry development planning to effectively absorb surplus labor, and proposes three policy directives aimed at supporting forest area employees, including provisions for job transfers and reemployment opportunities.

Assessment of the Impact of Financial Intermediation on Gross Domestic Product in Nigeria from 1996 to 2022

This paper assesses financial intermediation in Nigeria and how it has impacted economic growth. The study sourced time series data between 1996 and 2022 from the Central Bank of Nigeria and utilized econometric techniques using Ordinary least square regression, Error correction model, and Pairwise Causality to assess the impact of the financial intermediation institutions on the growth of the nation's Gross Domestic Product. The paper focused on banks, capital markets, and the insurance industry being significant institutions in the financial system. The GDP is the dependent variable, while Credit to the Private Sector as a percentage of GDP (CPS/GDP) and Total Savings as a percentage of GDP (TS/GDP) were used as proxies for assessing banks' contributions to economic growth. Stock market capitalization was used as a proxy for the contribution of the capital market to economic growth, and Insurance industry assets were used as a proxy for the insurance industry's contribution to economic growth. The study revealed no co-integration and long-term relationship between economic growth and the financial intermediation variables. There was, however, a unidirectional causal relationship between economic growth and the banks and the capital markets. There was no causality between economic growth and insurance. We recommend that policymakers undertake periodic reforms to deepen the capacity of the institutions to support de-risking and funding small and medium enterprises and the agricultural value chain, which are vital channels for diversifying the economy. The regulatory authorities are pivotal in upscaling the institutions' contribution to economic development.

Political Business Cycles in Türkiye: A Chaotic Perspective

AbstractThis paper has the goal of analyzing the chaotic structure of political business cycles in Türkiye for the period from 1986 March to 2020 January. To investigate the existence of uncertainty, the chaotic dynamics of the analyzed variables was examined using the Lyapunov exponent, Shannon entropy, and Hurst entropy. The Fourier VAR model was applied to determine the effects of unemployment, inflation, economic growth, and the minimum wage on voting behavior in Türkiye. The Granger Causality approach was also used to assess the direction of the causal relationship between macroeconomic factors and political party groups. Granger causality analysis findings of this paper reveal a bi-directional Granger causality link between the center-right party and economic growth, inflation, unemployment, and the minimum wage. Furthermore, there is also a uni-directional causality relation from inflation, unemployment, and economic growth to the center-left party and a bi-directional causality relation between the minimum wage and the center-left party. In this regard, one of the most remarkable evaluations of this paper is that the sensitivity findings of party groups to macroeconomic factors in Türkiye do not align with the core assumptions of the Partisan Theory concerning the right-left party division. Therefore, contrary to the related theory, the analysis results indicate that it is not eligible to make a sharp and distinct distinction between right-wing and left-wing parties in Türkiye. Additionally, Lyapunov exponent, Shannon entropy, and Hurst entropy findings point to the existence of chaotic structure and uncertainty in political cyclical fluctuations in Türkiye. Accordingly, it is figured out that the existence of chaotic structure and uncertainty in party groups and analyzed variables allows only short-term and not long-term forecasting.