Purpose This paper examines whether female leadership captured by having a female chief executive officer (CEO) and greater women’s participation in the boardroom is associated with higher firm productivity in Ecuador, a context without mandatory private-sector board gender quotas. Design/methodology/approach We estimate a production function using the Gandhi et al.’s (2020) estimator. In this framework, CEO gender and female board participation are included as lagged firm-level leadership variables that may be associated with the evolution of future productivity rather than as simple contemporaneous controls. We also use matching techniques as complementary diagnostic exercises to assess whether the estimated associations are driven by observable differences across firms or by short-run productivity changes around CEO-gender transitions. Findings Our estimates show a positive association between female CEOs and firm productivity. This association is stronger in the services sector. By firm size, the results suggest that small, medium and large firms led by female CEOs display higher productivity, except in the natural resources sector. Similar patterns are found when female participation in the boardroom is analyzed. We also show that CEO tenure is more strongly associated with productivity among female CEOs than among male CEOs. However, the difference in difference matching (DDM) analysis does not provide evidence of short-run productivity changes following simultaneous CEO and CEO-gender transitions. Practical implications The findings suggest that women’s access to CEO and board positions is relevant for discussions on corporate governance, leadership pipelines and talent retention. However, the results should be interpreted as conditional associations rather than causal evidence that increasing female leadership automatically improves productivity. Since the estimated relationships vary across sectors and firm sizes, managerial and policy implications should focus on reducing barriers to women’s access to leadership positions and improving organizational conditions for retention and career progression, rather than on uniform productivity-based incentives. Originality/value The findings suggest that women’s access to CEO and board positions is relevant for discussions on corporate governance, leadership pipelines and talent retention. However, the results should be interpreted as conditional associations rather than causal evidence that increasing female leadership automatically improves productivity. Since the estimated relationships vary across sectors and firm sizes, managerial and policy implications should focus on reducing barriers to women’s access to leadership positions and improving organizational conditions for retention and career progression, rather than on uniform productivity-based incentives.
Purpose This study investigates whether reducing gender gaps in education and labor markets contributes to regional economic growth in Chile. It provides empirical evidence on the economic costs of gender inequality across the country's diverse sixteen regions, highlighting how tailor-made inclusive growth strategies can yield substantial growth dividends. Design/methodology/approach The research employs a dynamic panel-data model (system GMM). The framework identifies STEM graduation and labor force participation rates as the primary gender gaps drivers associated with regional growth. The specification incorporates macroeconomic controls and a mining-region interaction to capture structural heterogeneities. Findings The results reveal a statistically significant and negative association between gender gaps and regional GDP growth. A counterfactual “Minding the Gap” scenario estimates that a 25% reduction in these disparities yields a national growth dividend of up to 0.5% points. The impact is highly heterogeneous; the largest dividends are found in relatively poorer, non-mining regions, supporting regional convergence. While macro-level data limitations prevent capturing non-economic dimensions of inequality, the consistency across GMM specifications provides a robust basis for tailored policy interventions. Originality/value This paper provides novel subnational evidence on the economic impacts of gender inclusiveness within an emerging economy context. It suggests that persistent disparities restrict human capital efficiency, undermining regional productivity and long-run growth potential, thereby framing gender equality as a strategic lever for territorial development.
Purpose The study investigates the effect of investor fear on cryptocurrency crash risk, with emphasis on overall market sentiment and COVID-19-related fear. It also evaluates the relative performance of Google search-based measures compared to the economic policy uncertainty (EPU) index and the volatility indexes (VIX) as benchmark indicators of uncertainty. Design/methodology/approach This study employs a quantitative empirical approach to examine the impact of investor fear on cryptocurrency price crash risk. Investor sentiment is proxied using the FEARS index derived from Google search volumes and the coronavirus fear index. Crash risk is measured using negative conditional skewness of weekly returns and down-to-up volatility. The analysis is based on weekly data for the top 10 cryptocurrencies from August 2010 to October 2021. Regression models are used to examine the relationship between investor fear and crash risk and to compare the explanatory power of Google-based fear indicators with traditional uncertainty measures. Findings The results show that investor fear significantly increases the risk, while COVID-19-related fear further intensifies this effect, highlighting the vulnerability of crypto markets during periods of heightened uncertainty. Moreover, Google-based fear indicators outperform the EPU index and the VIX in explaining and predicting crash risk. Overall, the findings suggest that investor attention and sentiment are more powerful drivers of cryptocurrency crash risk than traditional volatility-based measures. Originality/value This study links investor fear, including COVID-19 sentiment, to cryptocurrency crash risk and finds that Google-based fear indicators outperform traditional measures like the EPU index and the VIX in predicting market downturns.
Purpose This paper aims to examine whether inflation expectations in Germany became more persistent and adjusted asymmetrically following the recent inflation shock faced by the European Central Bank (ECB). The analysis investigates whether expectations respond symmetrically to rising and falling inflation or whether they exhibit downward stickiness during disinflation phases. Design/methodology/approach The paper combines a theoretical extension of the Barro–Gordon framework with an empirical analysis based on German monthly data from 2020 to 2026. Inflation expectation dynamics are estimated using an autoregressive distributed lag (ADL) model with interaction terms capturing disinflation phases. Robustness checks include alternative lag structures, alternative definitions of disinflation and alternative model specifications. Findings The empirical results indicate persistence in inflation expectations and evidence of asymmetric adjustment. Inflation expectations react strongly when inflation rises but adjust more slowly when inflation declines. The interaction effect between disinflation phases and lagged inflation remains negative across alternative specifications, supporting the interpretation of downward stickiness in inflation expectations. Research limitations/implications The findings should be interpreted in the context of the exceptional macroeconomic environment of the 2020–2026 period, including the pandemic, the energy crisis and heightened geopolitical uncertainty. While the analysis focuses primarily on Germany, additional robustness checks using euro area survey data suggest qualitatively similar patterns. Originality/value The paper contributes to the literature by combining the concept of hysteresis in inflation expectations with the Barro–Gordon framework and by providing new empirical evidence on asymmetric expectation adjustment during the recent ECB inflation episode.
Purpose This paper investigates the relationship between occupational exposure to artificial intelligence (AI) and wage structures in the United States. While much of the literature focuses on the displacement effects of AI, less attention has been given to its implications for wage outcomes across occupations. Design/methodology/approach The study uses occupation-level data for 671 occupations, combining wage information with an AI exposure index that captures the extent to which occupations are affected by AI technologies. Cross-sectional regression models with robust standard errors are employed, controlling for employment size and occupational characteristics. Quantile regression is also used to examine variation across the wage distribution. Findings The results indicate a positive and statistically significant association between AI exposure and wages. Occupations with higher exposure tend to have higher wage levels. This pattern is consistent across model specifications and across the wage distribution. The findings are broadly consistent when using an instrumental variable approach. The association is stronger in occupations with higher cognitive skill intensity. Research limitations/implications This study is based on occupation-level data rather than individual-level observations, which limits the ability to capture within-occupation wage heterogeneity. In addition, the AI exposure index reflects potential exposure rather than actual adoption at the firm level. Future research could extend this analysis using firm-level or longitudinal data. Practical implications The findings suggest that occupations with higher exposure to artificial intelligence tend to exhibit higher wages, highlighting the importance of skill upgrading and targeted workforce policies. Policymakers and organizations should focus on enhancing digital skills and supporting workforce transition to maximize the benefits of AI. Social implications The results indicate that artificial intelligence may contribute to wage differences across occupations by enhancing productivity in certain roles. This highlights the need to ensure equal access to skills and training opportunities so that the benefits of AI are distributed more evenly across the labor market. Originality/value This study contributes by providing occupation-level evidence on the relationship between AI exposure and wages, shifting attention from employment effects to wage structures. It also highlights that this relationship is partly explained by occupational skill composition, while a residual association remains.
Purpose This paper studies demand-side determinants of women's employment in T & uuml;rkiye's manufacturing sector and tests whether firm characteristics relate differently to low versus high female employment ratio firms.Design/methodology/approach Turkish Statistical Institute micro data for 2005-2015 are merged, yielding over 250,000 firm-year observations for manufacturing firms with at least 20 employees. Female employment ratio is analyzed with panel quantile regression to trace heterogeneous associations across the conditional distribution for firm size, foreign ownership, age, export ratio, capital intensity, part-time, and overtime work.Findings Larger firm size is linked to higher female employment mainly in low and middle quantiles, with weaker effects at the top. Foreign ownership is positive throughout and stronger in higher quantiles. Export intensity is consistently positive, rising across quantiles and leveling off near the upper tail. Age, capital intensity, and overtime are negatively associated, with larger magnitudes at higher quantiles. Part-time work is positive, peaking in upper-middle quantiles.Practical implications Policies can be targeted by firms' position in the female employment distribution, combining support for SMEs and low female-employment firms with gendersensitive export and investment incentives and enforceable work-life measures that reduce excessive overtime while improving access to high-quality flexible work arrangements.Originality/value Using a large firm-level panel, the study provides distributional evidence via quantile methods that reveal heterogeneity masked by mean-based models.
يهدف هذا البحث إلى تحديد أهم مصادر النمو الاقتصادي في المملكة العربية السعودية خلال الفترة (2000-2023)، كما سنحاول من خلال هذه الدراسة تحديد مدى استجابة النمو للتغيرات في هذه المصادر على المدى الطويل. وقد اعتمدنا على منهجية الانحدار الذاتي للفجوات الزمنية الموزعة المبطئة. وقد أظهرت النتائج عدم وجود سببية قرانجر من كل مصدر باتجاه نصيب الفرد من إجمالي الناتج المحلي الحقيقي وذلك في المدى القصير. وفي المدى الطويل أظهرت نتائج الدراسة عدم استجابة نصيب الفرد من إجمالي الناتج المحلي الحقيقي للتغيرات الحاصلة في مصادره الثلاثة، ممثلة في الإنفاق الاستهلاكي النهائي، وإجمالي الصادرات، وإجمالي الواردات؛ إذ بينت النتائج أن نمو الإنفاق الاستهلاكي النهائي بـ 1% يؤدي إلى نمو نصيب الفرد من الناتج المحلي الإجمالي الحقيقي بنسبة أقل (0.31%)، كما أن نمو إجمالي صادرات المملكة العربية السعودية من السلع والخدمات بـ 1% يؤدي أيضا إلى ارتفاع نصيب الفرد من الناتج المحلي الإجمالي الحقيقي بـنسبة أقل (0.15%)، في حين أن نمو إجمالي واردات المملكة من السلع والخدمات بـ 1% يؤدي إلى انكماش نصيب الفرد من الناتج المحلي الإجمالي الحقيقي بـ 0.09%.
Purpose This paper studies the effects of commuting and moving costs on job creation and job mobility within an extended Pissarides-style spatial search and matching model. The analysis connects three strands of literature: commuting and job search, commuting and home moving and the housing liquidity issue. The paper also examines how housing market frictions, commuting distance and relocation costs shape labor market outcomes and workers' spatial allocation across regions.Design/methodology/approach The paper develops a theoretical search and matching model & agrave; la Pissarides with two regional labor markets and a housing market characterized by search frictions. Workers can commute or relocate when receiving outside job offers. The housing market equilibrium is modeled through the interaction between house prices and time-on-the-market. The framework is further extended to account for heterogeneous workers, heterogeneous housing quality and the effects of transport policies and housing externalities.Findings The analysis shows that commuting costs increase reservation wages, reduce job creation and raise unemployment. Moreover, commuting costs distort the spatial allocation of workers across regions. Relocation can improve job mobility, but it depends on housing liquidity and housing market frictions. At the aggregate level, moving decisions generate congestion externalities that worsen housing liquidity and reduce future mobility opportunities. The model also predicts higher housing prices and trading frictions in areas with better job opportunities and higher-quality housing.Research limitations/implications The model is intentionally stylized and does not fully capture the complexity of real cities, such as detailed household sorting, heterogeneous transport networks, or institutional differences across housing systems. Nevertheless, the framework provides a tractable theoretical foundation for studying the interaction between labor mobility and housing market frictions. The paper also suggests several extensions that may improve the empirical realism of spatial search and matching models.Practical implications The paper highlights important trade-offs for policymakers. Policies that improve accessibility and neighborhood quality may increase housing prices and reduce affordability. At the same time, housing market frictions and limited housing liquidity can restrict labor mobility and reduce access to better employment opportunities. Therefore, labor market efficiency depends not only on job creation policies, but also on housing market conditions, transport infrastructure and urban policies affecting residential mobility.Originality/value The paper contributes to the literature by integrating commuting, relocation and housing liquidity into a unified Pissarides-style spatial framework. Unlike previous studies, it explicitly models the housing liquidity issue through a search and matching approach to the housing market and links time-on-the-market dynamics to labor mobility. The paper suggests a theoretical explanation of how moving congestion and housing market frictions may generate aggregate negative externalities affecting job mobility and unemployment.
تهدف الدراسة إلى فحص تأثير تطور الجهاز المصرفي على النمو الاقتصادي في مصر خلال الفترة (1990-2023) في الأجلين الطويل والقصير، وذلك من خلال تعيين نموذج الانحدار الذاتي ذي الفجوات الموزعة ARDL لتقدير علاقات الأجل الطويل، فضلًا عن استخدام نموذج تصحيح الخطأ ECM لقياس علاقات الأجل القصير، وتوصلت نتائج الدراسة إلى أن التطور المصرفي يؤثر إيجابيًا ومعنويًا على النمو الاقتصادي في الأجل الطويل، وجاءت نتائج الأجل القصير متوافقة بشكل كبير مع نتائج الأجل الطويل، وتؤكد هذه النتائج على ضرورة وضع مجموعة من السياسات الهادفة إلى تعظيم أثر التطور المصرفي على النمو الاقتصادي في مصر. وتشمل التوصيات تعزيز مشاركة القطاع الخاص، وتشجيع دخول البنوك الأجنبية، وتبني أفضل الممارسات الدولية في الإشراف وإدارة المخاطر، إلى جانب تفعيل أحكام قانون البنوك بشأن حماية حقوق العملاء وزيادة الوعي العام بالخدمات المصرفية. كما تؤكد على تنمية رأس المال البشري عبر التدريب والشهادات المهنية، مع تبني سياسات اقتصادية داعمة مثل تعزيز الانفتاح التجاري، وزيادة تنافسية الصادرات، والسيطرة على التضخم، بما يعزز التأثير الإيجابي للتطور المصرفي على النمو الاقتصادي.
تهدف هذه الدراسة إلى توضيح الدور الوسيط لمستوى الصحة العامة، والمقاس بمعدل الوفيات، في العلاقة بين العوامل البيئية (خدمة الصرف الصحي المدارة بسلامة، واستخدام الوقود النظيف، ونصيب الفرد من انبعاثات ثاني أكسيد الكربون) ومعدلات البطالة في المملكة العربية السعودية خلال الفترة الزمنية من 1995 حتى 2023. وقد جُمعت البيانات من قاعدة بيانات البنك الدولي، واستُخدم تحليل الوساطة السببية بوصفه منهجًا إحصائيًا حديثًا؛ حيث اعتُبر فيروس كورونا متغيرًا معالجًا (سببيًا) يؤثر في مستوى الصحة العامة. وقد أظهرت نتائج الدراسة وجود تأثير طردي معنوي لفيروس كورونا في كل من معدل الوفيات ومعدل البطالة. كما بينت أيضًا أن خدمات الصرف الصحي المدارة بسلامة تؤثر سلبًا بشكل معنوي في معدل الوفيات، وأن تحسن مستوى الصحة العامة يسهم في تقليل معدل البطالة. كذلك، أشارت النتائج إلى أن مستوى الصحة العامة يؤدي دورًا وسيطًا عكسيًا ومعنويًا في العلاقة بين العوامل البيئية والبطالة؛ مما يدل على أن تحسن النظام الصحي في المملكة ساعد على تخفيف الآثار السلبية للجائحة على سوق العمل. وتوصي الدراسة بدعم البنية التحتية البيئية والصحية لتعزيز القدرة على مواجهة الأزمات المستقبلية.
تدرس هذه البحث كيفية تأثير الصدمات الاقتصادية على سعر صرف الجنيه المصري (1977–2022)، مع التركيز على الديون الخارجية والاحتياطيات الأجنبية ومعدلات التضخم. باستخدام نماذج VAR وبيانات البنك الدولي، توصلت النتائج إلى: تتمتع الصدمات التي تتعرض لها سعر الصرف ومحدداته (الاحتياطيات، التضخم، الديون) بآثار ممتدة تَضعف تدريجيًا بمرور الوقت. يُظهِر سعر الصرف حساسية أكبر تجاه الصدمات الذاتية (الداخلية)، يليها الصدمات في الاحتياطيات (التأثير الخارجي الأبرز)، ثم صدمات التضخم المحلي، فالتضخم المستورد، وأخيرًا صدمات الديون الخارجية. تكون صدمات التضخم المحلي أكثر تدميرًا واستمرارية مقارنةً بصدمات التضخم المستورد. توصي الدراسة بتبني سياسات نقدية مستقرة للحد من التضخم، وتقليل الاعتماد على الديون الخارجية، وتعزيز الاحتياطيات الأجنبية لتعزيز الصمود أمام الصدمات. كما تُؤكد على ضرورة إجراء إصلاحات هيكلية لتحقيق استقرار سعر الصرف في ظل التحديات الاقتصادية.
Purpose This study empirically examines the impact of financial institutions (mutual funds, pension funds, banks and insurance firms) on the ecological footprint of six developed economies. Design/methodology/approach Using data from 2001 to 2020, we employed robust econometric techniques, including CS-ARDL, FMOLS, and the Dumitrescu and Hurlin (2012) panel causality test, to estimate the environmental impact of the development of both bank and non-bank financial institutions. Findings The findings report that banking development, mutual funds and pension funds reduce the ecological footprint. Whilst the insurance market's development enhances the ecological footprint, it suggests heterogeneous and diverse impacts of financial institutions on environmental quality. On the disintegration of insurance funds, we found that both non-life and life insurance market development increases the ecological footprint (depicted in the Graphical abstract). The findings suggest comprehensive integration of ecological footprint quality in the investment/lending practices of financial institutions to mitigate environmental degradation in developed economies. Originality/value This study is the first of its kind to explore the impact of both financial (banks) and non-financial institutions' development on environmental degradation.
يلعب التغيير الهيكلي الاقتصادي دورًا حيويًا في دول مجلس التعاون الخليجي، حيث يؤثر بشكل مباشر على النمو الاقتصادي, التنمية الاقتصادية والتلوث من خلال انبعاثات ثاني أكسيد الكربون. وتهدف الجهود المبذولة من طرف دول مجلس التعاون الخليجي لتنويع الاقتصاد إلى التقليل من الاعتماد على النفط والغاز كمصادر رئيسية للطاقة، ويتضمن ذلك تعزيز الطاقة المتجددة، والاستثمار في البنية التحتية، وتطوير قطاعات الخدمات مثل التعليم والصحة والسياحة والتجارة وغيرها. غالبًا ما تؤدي مثل هذه التغييرات الهيكلية إلى تحسين كفاءة استعمال الطاقة، مما يسهم في تقليل التلوث وحماية البيئة. الهدف الأساسي لهذه الدراسة هو تحليل تأثير التغيير الهيكلي والمتغيرات الأخرى، بما في ذلك استهلاك الطاقة، والنمو الاقتصادي (الناتج المحلي الإجمالي)، والسكان، والعولمة، على انبعاثات ثاني أكسيد الكربون في دول مجلس التعاون الخليجي من عام 2003 إلى عام 2021. ولتحقيق هذا الهدف، قمنا باستخدام منهجية PMG-ARDL وبيانات زمنية/ مقعطعية. تكشف نتائج هذه الدراسة أنه في المدى الطويل، تتأثر انبعاثات ثاني أكسيد الكربون في هذه الدول بشكل كبير بالنمو الاقتصادي (الناتج المحلي الإجمالي)، والنمو السكاني، واستهلاك الطاقة، والعولمة. في المقابل، يسهم التغيير الهيكلي بشكل ملحوظ في تقليل هذه الانبعاثات. تترتب على هذه النتائج آثار مهمة على مستوى السياسات الاقتصادية في دول مجلس التعاون الخليجي، مثل ضرورة تركيز استراتيجيات التنمية المستدامة على دمج الاعتبارات البيئية حيث ينبغي أن تتضمن هذه الاستراتيجيات تعزيز التغييرات الهيكلية التي تساهم في تحسين كفاءة الطاقة وتقليل الانبعاثات.
تهدف هذه الدراسة إلى تحليل محددات دالة الطلب على النقود واختبار مدى استقرارها في اقتصادات دول منطقة الشرق الأوسط وشمال إفريقيا (MENA) خلال الفترة (1990–2023). اعتمدت الدراسة منهجية المربعات الصغرى الديناميكية (DOLS) لكل دولة على حدة، إضافةً إلى ذلك، طُبّقت اختبارات الاستقرار الهيكلي (CUSUM) و(CUSUMSQ) لقياس مدى ثبات دالة الطلب على النقود وتتبع تطوّرها عبر الزمن. أظهرت النتائج ارتباطًا إيجابيًا ذا دلالة إحصائية بين الناتج المحلي الإجمالي الحقيقي والطلب على النقود، مع تباين في حجم هذا الأثر بين الاقتصادات النفطية وغير النفطية. كما كشفت النتائج عن علاقة عكسية بين سعر الفائدة والطلب النقدي، إلا أن هذه العلاقة بدت ضعيفة في الدول التي تعاني من محدودية في تطور أنظمتها المصرفية. أما تأثير سعر الصرف فقد اتسم بالتباين؛ إذ كان إيجابيًا في الدول ذات الاستقرار النقدي، وسلبيًا في الدول التي شهدت تقلبات حادة في قيمة عملتها المحلية. كذلك، بينت اختبارات الاستقرار وجود تحولات هيكلية في دالة الطلب على النقود في عدد من الدول، بما يعكس تعرضها لصدمات اقتصادية وسياسية. وتؤكد هذه النتائج ضرورة تبني سياسات نقدية مرنة واستباقية قادرة على التكيف مع الصدمات الداخلية والخارجية، مع أهمية صياغة هذه السياسات وفق الخصائص المؤسسية والاقتصادية لكل دولة. ففي حين يمكن للدول ذات الأنظمة المصرفية المتطورة اعتماد الأدوات التقليدية للسياسة النقدية، يتعين على الدول ذات الأسواق المالية المحدودة التركيز على تعزيز الشمول المالي وتطوير أدوات بديلة لزيادة فاعلية السياسات النقدية.
PurposeThe present paper aims to systematically map the research landscape of artificial neural networks (ANNs) in forex rate forecasting by particularly (1) uncovering significant research trends, key players, scientific collaborations, hot topics, emerging themes, and primal knowledge dimensions; and (2) discovering potential areas for future research in the concerned field. Design/methodology/approachTo delve deeply into the field, the present study employed the fusion approach of bibliometric analysis (quantitative) and content analysis (qualitative) to analyse 487 articles published in Scopus-indexed journals during 1993–2024. The extracted data was analysed using RStudio (Biblioshiny) and VOSviewer software tools. FindingsThe analysis revealed the overall upward trend of the research with (1) the proliferation of publications since 2019; (2) China as the most productive country; (3) “Expert Systems with Applications” as the prominent journal; and (4) “exchange rate prediction” and “genetic algorithm” as the trendy areas, whereas “quantitative trading”, “hybrid models”, and “long short-term memory” are the emerging themes of the field. Additionally, model optimization, technical analysis, model hybridization, and modelling data complexity were discovered as the primal knowledge dimensions in the field. Originality/valueTo the best of the authors' knowledge, the current study is the first that systematically deconstructs the social, conceptual, and intellectual structure of ANN research in forex rate forecasting. Its main contribution lies in equipping (1) researchers with potential areas for future investigations and advancements in the field; and (2) practitioners with means of overcoming modelling challenges and improving the forecasting accuracy of ANNs, thereby enhancing their forecasting-based decision-making capacity.
PurposeThe Arab region's instability in attracting foreign direct investment (FDI) can be partly attributed to observed challenges in corruption and macroeconomic instability. To this end, this study aims to investigate the impact of the macroeconomic environment, considering corruption, on FDI inflows into the Arab region.Design/methodology/approachThis study utilizes panel data representing a sample of twelve Arab countries during the period 2003-2022, and applies the autoregressive distributed lag (ARDL) regression model and conducts a panel two-stage least squares with fixed effects (2SLS-FE) as a robustness check. Along with corruption, different dimensions, including market size, human development, energy efficiency, trade openness and property rights, measure the macroeconomic environment.FindingsEven though a positive tendency is observed in the macroeconomic environment in the long run, corruption's impact on FDI inflows is negative and statistically significant for the countries under study, whereas it has a positive significant impact on FDI in the short run. In contrast to the negative role of corruption, FDI inflows may be significantly boosted by the macroeconomic environment measured by location indicators such as market size, human development, energy efficiency and internationalization indicators like trade openness and property rights.Originality/valueThe study's novelty inherent in establishing a clear divergence in the corruption and FDI inflows nexus. The positive tendency of corruption's impact on FDI inflows in the short run might speed up the FDI entry process and support the greasing the wheels hypothesis. Instead, in the long run, the impact of Corruption Index on FDI will transition to be negative as an outcome of institutional reforms and increased global scrutiny, which becomes viewed as a sign of political instability, which supports the grabbing hand hypothesis for modern foreign investors. However, the macroeconomic indicators emerged as the proper significant drivers of FDI. This study recommends applying sound legal procedures and policies that support the macroeconomic environment. It also suggests focusing on maintaining macroeconomic stability while aggressively simplifying institutional reforms and regulations as the most effective anti-corruption tools.
Purpose This paper sheds light on the factors affecting household credit aspects in four European countries, such as Germany, Netherlands, Portugal and Italy, further extending the analysis of Xidonas et al. (2025) about France. It exploits the third wave of the Household Finance and Consumption Survey conducted by the European Central Bank, utilizing datasets from 4,919 German households, 2,344 Dutch households, 5,914 Portuguese households and 6,998 Italian households. Design/methodology/approach Focussing on a broad set of household variables, covering dimensions such as demographics, employment, income, wealth, assets and expenditures, we estimate four logistic regression models of strong significance that capture the behavioural factors of the households which apply for credit. Findings We identify three robust patterns. First, life-cycle effects dominate, i.e., younger households are consistently more likely to apply for credit. Second, wealth composition matters more than income, i.e., liquid wealth substitutes for credit everywhere, while total assets facilitate access. And third, institutional and cultural differences shape motives. Originality/value Our findings comply with the current literature and provide new micro- and macro-level insights into our core research question. Finally, we provide an elaborate policy implications discussion associated with the qualitative aspects of our results.
