Balanced Data Research Articles

Impacts and mechanisms of digital village construction on agricultural green total factor productivity

Green is the foundation of agricultural development. By leveraging modern information technology, digital village construction injects new momentum into the green growth of agriculture. Using balanced panel data from 31 provinces in China from 2011 to 2022, this paper employs the entropy approach and SBM-GML index to measure the indicators of digital village construction and agricultural green total factor productivity (AGTFP). The impacts and mechanisms of digital village construction on AGTFP are examined through fixed effect, mediating effect, and threshold models. The findings are as follows: (1) AGTFP in China shows an overall increasing trend from 2011 to 2022, with significant growth in the western region compared to the eastern and central regions. (2) Digital village construction significantly accelerates the improvement of AGTFP, a conclusion supported by robustness tests. (3) Mechanism analysis indicates that digital village construction advances AGTFP by fostering agricultural technology innovation, enhancing agricultural human capital, and improving agricultural productive services. (4) Digital village construction has a more significant impact on major grain-producing areas, economically underdeveloped areas, and northern region compared to other regions. (5) Environmental regulation acts as a threshold effect on the impact of digital village construction on AGTFP. Digital village construction substantially impacts AGTFP when environmental regulation is below the threshold, with the positive effect continuing beyond this point but at a reduced intensity.

Influence of Asset Quality on the Financial Performance of Insurance Companies in Kenya

The research aimed to investigate the influence of asset quality on the financial performance of insurance companies. The study was anchored on the efficiency structure theory. Positivism research philosophy was employed with a descriptive research design that targeted a population of 56 general insurance and life insurance firms in Kenya. Secondary balanced panel data spanning 15 years from 2008 to 2022 was also used in the study. The secondary data was sourced from the financial statements of the firms and industry publications from the Insurance Regulatory Authority (IRA). The quantitative data collected was analyzed using descriptive statistics inform of percentages, frequencies, mean, and standard deviation. Additionally, a balanced dynamic panel data regression model was performed. Prior to fitting the panel regression model, diagnostic tests were undertaken to evaluate linearity, stationarity, normality of residuals, serial correlation, and homoscedasticity. The research included 41 insurance firms that met the inclusion criteria. The study results determined that asset quality has a statistically significant and negative influence on the financial performance of insurance companies in Kenya (β = -0.1956, t = -2.93, p = 0.003). The study concluded that asset quality is a collective consideration of diversification, liquidity, risk and liability profiles and thus plays a crucial role in influencing profitability. The study recommends that additional regulations should be instituted by the IRA that entail developing a range of targets on each of the aspects of asset quality.

The PS4-likelihood ratio calculator: flexible allocation of evidence weighting for case-control data in variant classification

BackgroundThe 2015 American College of Medical Genetics/Association of Molecular Pathology (ACMG/AMP) variant classification framework specifies that case-control observations can be scored as ‘strong’ evidence (PS4) towards pathogenicity.MethodsWe developed the PS4-likelihood...

Meta-Hybrid: Integrate Meta-Learning to Enhance Class Imbalance Graph Learning

The class imbalance problem is a significant challenge in node classification tasks. Since majority class samples dominate imbalanced data, the model tends to favor the majority class, resulting in insufficient ability to identify minority classes. Evaluation indicators such as accuracy may not fully reflect the model’s performance. To solve these undesirable effects, we propose a framework for synthesizing minority class samples, GraphSHX, to balance the number of samples of different classes, and integrate the XGBoost model for node classification prediction during the training process. Conventional graph neural networks (GNNs) yielded unsatisfactory results, possibly due to the limited number of newly generated nodes. Therefore, we introduce a meta-mechanism to deal with small-sample problems, and employ the meta-learning approach to enhance performance on small-sample tasks by learning from a large number of tasks. An empirical evaluation of node classification on six publicly available datasets demonstrated that our balanced data set method outperforms existing optimal loss repair methods and synthetic node methods. The addition of the XGBoost model and meta-learning improves the accuracy by more than 5% to 10%, with the overall accuracy of the improved model being 15% higher than that of the baseline method.

Exploring the path to promote energy revolution: Assessing the impact of new energy demonstration city construction on urban energy transition in China

Cities serve as the epicenter of energy consumption, and urban energy transition (UET) is a pivotal prerequisite for achieving carbon peaking and neutrality goals. Leveraging the quasi-natural experiment of China's New Energy Demonstration City Construction (NEDCC), this paper evaluates its impact on UET based on balanced panel data spanning 272 cities from 2006 to 2022. Employing the difference-in-differences model, our estimates underscore NEDCC's facilitative role in promoting UET, a finding corroborated by a series of robustness tests. Mechanism examinations reveal that NEDCC advances UET by enhancing government strategic guidance, fostering green technological innovation, promoting industrial structural upgrading, and optimizing resource allocation efficiency. Furthermore, in cities with higher administrative tiers and more advanced digital finance, NEDCC exerts a more pronounced effect on UET. Conversely, cities endowed with richer resources and pursuing more ambitious economic growth targets exhibit weaker responses to NEDCC's UET-boosting influence. Additionally, NEDCC's demonstration effect surpasses its diffusion effect on neighboring cities, generating a positive spatial spillover that propels their energy transitions. This study offers a novel policy lens for advancing UET.

Trend analysis of cotton production and trade

The aim of this study is to analyse the cultivation areas, production amounts and foreign trade data of cotton plant in the world and Turkey from an economic point of view. The data on Turkey's cotton cultivation area, cotton production and fiber cotton import and export data for the 20-year period 2003-2022 were obtained from the Food Agricultural Organization (FAO). The data set was transferred to MS Excel spreadsheets and the trend analysis technique was used in MS Excel programme to obtain the trends in cotton cultivation area, cotton production amount and near future forecast values for eight years between 2023-2030. In addition, for the foreign trade balance data obtained from fiber cotton import and export data for the period 2000-2022, the change trends of the 23-year period and the eight-year near future forecast values between 2023-2030 were calculated. Cotton cultivation area has a decreasing trend between 2023-2030. While the forecast value of cotton cultivation area for 2023 is 417 thousand hectares, this value is predicted to decrease to 373 thousand 722 hectares in 2030. The trend in the amount of cotton production has a decreasing trend after 2022 and it is predicted that there will be no break in the amount of production between 2023-2030. The estimated foreign trade deficit in fiber cotton for 2023 is 964,516 tonnes and for 2030 is 1,106,672 tonnes. The main reasons for the decrease in cotton cultivation areas are the increases in input prices such as fertilisers, pesticides and seeds. In order to eliminate the foreign trade deficit in cotton, studies should be carried out to enter new markets and increase the share in existing markets. In addition, it should be aimed to create a sustainable cotton sector by investing in R&D activities and entering new markets.

Income and Transport Cost: Multiple Responses to Tourists Arrivals in Zanzibar

The tourism sector plays an important role in the Zanzibar economy and continues to be a leading sector and the main contributor to the national GDP but this sector is faced unprecedented challenges and an existential threat from the impact of the COVID-19 virus. In this regard, this study aimed to analyse the determinants of international tourism demand from the top ten tourist’s origin countries to Zanzibar from 2005 to 2021 and to quantify their influence. In this study Gray’s travel motivation theory, Crompton’s motivations for pleasure vacation theory and Dann’s theory of push and pull factors were used to analyse behaviour of tourists and choice of destination. The study adopted panel data analysis and used long, strong balanced secondary panel data extracted from various sources to estimate the impact of GDP per capita as proxy of income, exchange rates, tourism infrastructure, price of crude oil as proxy of transport cost, consumer price indices adjusted by exchange rates as proxy of relative price and dummy variables to capture effects of Covid-19 and economic recession of 2008 on number of tourist arrivals as proxy for tourism demand in Zanzibar. Regression results of fixed effect model suggested that; at the 0.05 significance level, a one unit increase in GDP per capita results into 1.999 units increase in number of tourist arrivals and one unit increase in transport cost would decrease tourist arrivals by 64.991 units. Coefficients value of dummy variable COVID-19 implies that, on average, the number of tourist arrivals decreased on the year of occurrence of COVID-19 pandemic by 9,539 units. The coefficients of other independent variables exchange rates, tourism infrastructure, relative price and economic recession of 2008 bear the right sign even though they were not statistically significant at 0.05 levels. This study recommends that, the government and other stakeholders to work on marketing Zanzibar tourism product to the high-income countries and to work on reducing transport cost from origin countries to Zanzibar.

Asymptotic Confidence Interval, Sample Size Formulas and Comparison Test for the Agreement Intra-Class Correlation Coefficient in Inter-Rater Reliability Studies.

The agreement intra-class correlation coefficient (ICCa) is a suitable statistical index for inter-rater reliability studies. With balanced Gaussian data, we prove the explicit form of ICCa asymptotic normality (ASN), valid both with analysis of variance (ANOVA), maximum likelihood (ML), or restricted ML (REML) estimates. An asymptotic confidence interval is then derived and its performances are examined by simulation compared to the most commonly used methods, under small, moderate and large sample size designs. Then, we deduce sample size calculation formulas, for the number of subjects and observers needed, to achieve a desired confidence interval width or an acceptable ICCa value test power and give concrete examples of their use. Finally, we propose a likelihood ratio test (LRT) to compare two ICCa's from two distinct subpopulations of patients (or raters) and study by simulation its first order risk and power properties. These methods are illustrated using data from two inter-rater reliability studies, one in physiotherapy with 42 patients and 10 raters and the second in neonatology with 80 subjects and 14 raters. In conclusion, we made recommendations to employ the proposed confidence interval for medium to large samples combined with the quantification of the minimal required sample size at the planning step, or the posterior-power at the analysis step, using simple dedicated formulas. Furthermore, with sufficient sizes, the proposed LRT seems suitable to compare inter-rater reliability between two patient subpopulations. Used wisely, this proposed methods toolbox can remedy common current issues in inter-rater reliability studies.

Islamic Banks Stability in Indonesia: Assesing the Role of Islamicity Performance Amidst the Pandemic Challenges

Research Originality: Islamicity performance reflects adherence to Sharia principles, which are fundamental to Islamic banking operations. Research that emphasizes the contribution of Islamicity performance to the stability of Islamic bank requires additional studies.Research Objectives: This research examines the influence of banks’ internal factors, Islamicity performance, and macroeconomic factors on the Islamic bank stability amidst the pandemic challenges using financial intermediation theory.Research Methods:A random effects test was performed on a balanced panel data covering the period of 2017.1-2023.3 on 10 Islamic banks continuously publishing quarterly financial reports.Empirical Results: Credit risk and eficiency affect stability. Higher expenses enhance stability during pandemic. Reserves for uncollectible debt reduce stability. Islamicity performance in profit-sharing financing may reduce stability. Economic growth and inflation negatively affect stability pre-pandemic. Interest rate and dummy variables have positive effects.Implications: These findings imply the significance of preemptive risk management, cost-effective operations, prudent reserves allocation, and regulatory compliance to enhance stability in Islamic bank across varying economic circumstances.JEL Classification: E4, G21, B22How to Cite:Amaroh, S., Masykuroh, E., Husnurrosyidah., & Ali, M., (2024). Islamic Banks Stability in Indonesia: Assesing the Role of Islamicity Performance Amidst the Pandemic Challenges. Etikonomi, 23(2), 317 – 332. https://doi.org/10.15408/etk.v23i2.37269.

Rethinking energy poverty alleviation through financial inclusion: Do institutional quality and climate change risk matter?

The main aim of this paper is to determine the impact of financial inclusion on energy poverty alleviation. It also interrogates whether institutional quality and climate change risk significantly influence the financial inclusion-energy poverty alleviation link using balanced panel data from 34 Sub-Saharan African countries from 2004 to 2021. Evidence from Discroll-Kraay Fixed Effects and Two-Step Instrumental Variable Generalized Method of Moments (2SIV-GMM) depicts heterogeneous energy poverty-alleviating impact of financial inclusion, demonstrating that financial inclusion is more instrumental in lower-income than lower-middle-income countries. Also, the results indicate a significant positive moderating role of institutional quality and a detrimental effect of climate change risk on financial inclusion-energy poverty alleviation nexus. Nevertheless, Dynamic Panel Threshold Regression results reveal threshold effects of financial inclusion, institutional quality, and climate change risk on energy poverty alleviation. The paper professes that financial regulations in allocating green resources would aid in alleviating energy poverty in Sub-Saharan Africa.

Analysis of the Status Quo and Influencing Factors of Revisiting Patients After Pituitary Tumor Resection: Based on a Chinese Patient Population.

To investigate the status of patients' post-pituitary tumor resection and analyze influencing factors, providing evidence for improved long-term management. The authors screened 1209 patients who underwent pituitary tumor resection at Zhejiang University's Second Affiliated Hospital from August 2020 to July 2022 using electronic medical records. Patients were classified into return visits (≥2 reviews/y or within 6mo) and missing visit groups. Demographic and disease-related data were extracted from inpatient records, whereas return visits and prognosis data were collected from outpatient records and phone inquiries. Propensity score matching (1:1) was used to balance the groups, followed by univariate and multivariate logistic regression analyses to identify influencing factors. Of the 1209 patients, 113 were unreachable. The study included 1095 patients, with 553 (50.5%) in the missing visit group and 542 (49.5%) in the return visit group. The authors matched 421 pairs, achieving balanced baseline data. Univariate analysis revealed significant differences in residence, unplanned readmission history, and current outcomes (P < 0.05). Multivariate analysis identified unplanned readmission history (odds ratio = 0.495, 95% CI: 0.307-0.799) as a protective factor. City residents had higher return visit rates than those from other provinces (odds ratio = 0.269, 95% CI: 0.610-1.579). Postdischarge return rates for pituitary tumor resection patients are low and influenced by various factors. Improving return visit policies and systems is essential for facilitating outpatient follow-ups.

Interplay of Foreign Direct Investment, Remittances, and Economic Growth: Insights from SAARC Countries

The current study focuses on finding and analyzing the nexus between remittances, foreign direct investment, and economic growth in SAARC countries. This study uses secondary balanced data of selected SAARC countries for the years 2000 to 2019. This study uses gross domestic product i.e., GDP, which serves as the proxy of economic growth, as the dependent variable. The core independent variables of this study include (FDI) foreign direct investment and remittances (RMTN). Additionally, gross capital formation (GCF) and carbon dioxide emissions (CO2) are the other two independent variables in this study. For the proxy for environmental degradation the study uses data of CO2 emissions. The data is collected from the world development indicators, WDI. The study focuses on how all the independent variables affect the growth of economy in selected SAARC countries which include Pakistan, India, Bangladesh, Sri Lanka, and Nepal. The study applies cross sectional dependence test, panel cointegration test, unit root tests, and PARDL (panel auto regressive distributed lag model), also called pooled mean group methodology, to obtain empirical estimates. The results of PARDL show that FDI, RMTN, and GCF are all positively and significantly related to GDP. This means that GDP or alternatively economic growth, is improving with rise in FDI, RMTN, and GCF in selected SAARC countries. The results show that CO2 emissions have a negative significant impact on GDP i.e., increase in pollution levels is harmful for the growth of the selected economies.

Evaluating impacts of ICT development on wages of workers

At the provincial level, there is a research gap in discussing the causality and internal mechanism between ICT development and wages of workers. The study utilizes the province-level balanced panel data over the period 2006–2021 in China, clarifies the impact and internal mechanism of ICT development on wages of workers, and uses the DID method to identify the causality between the two. This study found that there is a positive correlation between ICT development and workers’ wages, and skill level is a mediate transmission channel. Moreover, ICT development has a positive impact on workers’ wages in the central and western regions. Besides, compared to low-wage workers, high-wage workers gain more information dividends. The findings of this study have reference significance for policymakers. First, for the central and western provinces in China, it is necessary to actively develop the ICT industry, cultivate high-tech enterprises, and improve local ICT development levels. Second, we should improve the skill level of workers and enhance their competitive advantage in employment. Third, each province should continue to expand the enrollment scale of higher education institutions, and improve the quality of labor force.

SAGB: self-attention with gate and BiGRU network for intrusion detection

Network traffic intrusion detection technology plays an important role in host and platform security. At present, machine learning and deep learning methods are often used for network traffic intrusion detection. However, the imbalance of relevant data sets will cause the model algorithm to learn the features of the majority categories and ignore the features of the minority categories, which will seriously affect the precision of network intrusion detection models. The number of samples of the attacks is much less than the number of normal samples. The classification performance on unbalanced data sets is poor and can not identify the minority attack samples well. To solve these problems, this paper proposed the resampling mechanism, which used random undersampling for the majority categories samples and K-Smote oversampling for the minority categories samples, so as to generate a more balanced data set and improve the model's detection rate for the minority categories. This paper proposed the Self-Attention with Gate (SAG) and BiGRU network model for intrusion detection on the CICIDS2017 data set, which can fully extract high-dimensional information from data samples and improve the detection rate of network attacks. The Self-Attention with Gate mechanism (SAG) based on the Transformer performed the feature extraction, filtered the irrelevant noise information, then extracted the long-distance dependency temporal sequence features by the BiGRU network, and obtained the classification results through the SoftMax classifier. Compared to the experimental results of other algorithms, such as Random Forest (RF) and BiGRU, it can be found that the overall classification precision for the SAG-BiGRU model in this paper is much higher and also has a good effect on the NSL-KDD data set.

Effects of public procurement on corporate total-factor carbon productivity: evidence from China

Using balanced panel data for listed Chinese industrial firms from 2016 to 2021, this study examines the effect of public procurement (PP) on corporate total-factor carbon productivity (CTFCP) and the related mechanisms. The results show that (1) there is a positive correlation between PP and CTFCP. (2) PP exerts a more pronounced incentivizing effect on CTFCP for state-owned enterprises, larger corporations, and those with weaker financing constraints. (3) We decompose CTFCP into technological progress (TECH), pure technical efficiency (PTE), and scale efficiency (SE) and find that PP improves TECH and PTE, and the effect differs according to firm ownership, scale, and financing constraints. (4) PP promotes CTFCP through labor productivity (LP), capital productivity (CP), and corporate green innovation (CGI) while the mediating role of environmental productivity (EP) is not significant. This study contributes to theoretical discourse on PP’s influence on CTFCP and has practical implications for PP agencies and enterprises.

ESG Integration and Financial Performance: Evidence from Malaysia’s Leading Companies

This study examines the impact of environmental, social, and governance (ESG) scores on the financial performance of the leading publicly listed companies in Malaysia. Using balanced panel data from Bloomberg spanning 2015-2022, the study employs multiple regression analysis (MRA) and the generalized method of moments (GMM) to explore these relationships. The findings reveal a significant positive effect of the overall ESG score on ROA, suggesting that comprehensive ESG practices can enhance financial performance. However, individual environmental (E) and social (S) scores negatively impact ROA at a 10% significance level, indicating potential short-term costs associated with these initiatives. Corporate governance (G) scores, total assets (TA), and total debt to total assets (TDTA) ratios demonstrate a significant adverse effect on ROA. The inclusion of the global reporting initiative (GRI) standard as a control variable also adds robustness to the analysis, highlighting the role of standardized sustainability reporting in evaluating corporate performance. These insights underscore the complex interplay between ESG and companies’ financial performance in Malaysia, emphasizing the importance of considering both the benefits and potential short-term costs associated with ESG initiatives. The findings of this study are crucial for policymakers, corporate managers, and investors, offering guidance on aligning sustainability goals with financial objectives to ensure long-term value creation and risk mitigation. This research contributes to the growing body of knowledge on ESG practices in emerging markets, highlighting the critical role of sustainability in today’s global business environment.

FNIRSNET: A multi-view spatio-temporal convolutional neural network fusion for functional near-infrared spectroscopy-based auditory event classification

Multi-view learning is a rapidly evolving research area focused on developing diverse learning representations. In neural data analysis, this approach holds immense potential by capturing spatial, temporal, and frequency features. Despite its promise, multi-view application to functional near-infrared spectroscopy (fNIRS) has remained largely unexplored. This study addresses this gap by introducing fNIRSNET, a novel framework that generates and fuses multi-view spatio-temporal representations using convolutional neural networks. It investigates the combined informational strength of oxygenated (HbO2) and deoxygenated (HbR) hemoglobin signals, further extending these capabilities by integrating with electroencephalography (EEG) networks to achieve robust multimodal classification. Experiments involved classifying neural responses to auditory stimuli with nine healthy participants. fNIRS signals were decomposed into HbO2/HbR concentration changes, resulting in Parallel and Merged input types. We evaluated four input types across three data compositions: balanced, subject, and complete datasets. Our fNIRSNET's performance was compared with eight baseline classification models and merged it with four common EEG networks to assess the efficacy of combined features for multimodal classification. Compared to baselines, fNIRSNET using the Merged input type achieved the highest accuracy of 83.22%, 81.18%, and 91.58% for balanced, subject, and complete datasets, respectively. In the complete set, the approach effectively mitigated class imbalance issues, achieving sensitivity of 83.58% and specificity of 95.42%. Multimodal fusion of EEG networks and fNIRSNET outperformed single-modality performance with the highest accuracy of 87.15% on balanced data. Overall, this study introduces an innovative fusion approach for decoding fNIRS data and illustrates its integration with established EEG networks to enhance performance.

ABNORMAL TRAFFIC DETECTION BASED ON ATTENTION AND BIG STEP CONVOLUTION

The identification of abnormal traffic is essential to network security and service quality. A big-step convolutional neural network traffic detection model based on the attention mechanism is provided as a solution to the significant challenges in abnormal traffic identification caused by feature similarity and the detection model's single dimension. First, the raw traffic is preprocessed and mapped into a two-dimensional grayscale picture after the network traffic characteristics are examined. After that, histogram equalization is used to create multi-channel grayscale pictures. An attention mechanism is then added to give traffic characteristics varying weights in order to improve local features. In order to improve the flaws in convolutional neural networks, including local feature omission and overfitting, pooling-free convolutional neural networks are finally integrated to extract traffic characteristics of various depths. Both a real data collection and a balanced public data set were used for the simulation experiment. The suggested model is contrasted with ANN, CNN, RF, Bayes, and the two most recent models using the widely used method SVM as a baseline. 99.5% accuracy percentage with several classes is achieved experimentally. The best anomaly detection is found in the suggested model. Additionally, the suggested technique performs better in F1, recall, and accuracy than existing models. It is shown that the model is not only effective in detecting things, but also resilient to a variety of complicated contexts.

Smallholder access to purchased seeds in the presence of pervasive market imperfections and rainfall shocks: panel data evidence from Malawi and Ethiopia

Seed purchasing enables farmers to respond to adverse events that may cause chronic and temporary seed insecurity by allowing them to exploit opportunities associated with accessing new seeds. However, as with other inputs, seed purchasing is complicated by pervasive market imperfections and climate risk common in Sub-Sahara Africa. This study uses balanced household panel data for Malawi (2010–2018) and Ethiopia (2012–2016) and applies dynamic random effects Probit and Tobit models to assess how seed purchase decisions are affected by earlier participation in the market, lagged rainfall shocks, and historical climate variables. Our findings show that there are nonlinear effects of lagged seed purchase decisions on subsequent decisions with strong initial effects (weakening over time). For instance, initial maize seed purchase decisions are associated with about 11 and 22% higher probability of purchase and 1 and 2% higher shares of seed volumes purchased in later rounds in Malawi and Ethiopia, respectively. Seed purchase decisions also respond to climate variability and shocks. For instance, lagged drought shocks enhance subsequent maize purchase decisions in both countries. Historical average rainfall and temperature enhance maize seed purchase decisions in both countries. Overall, results point to state dependency on the demand side of the seed market, leading to selective access to purchased seeds. Also, seed purchase in smallholder farming is a liquidity and risk-dependent input choice. Policy efforts need to continue targeting reducing transaction costs and other barriers to entry into seed markets to enhance access to off-farm seed and support adaptation to rainfall shocks.

Exploring the impact of renovation subsidies on housing markets – evidence from the Swedish property market

PurposeThe primary aim of this research is to examine the effects of the Renovation, Conversion, and Extension (ROT) tax deduction for renovations on the scope and quality of renovations and its subsequent impact on house prices across various Swedish municipalities.Design/methodology/approachThis study utilises a two-way fixed effect instrument variable (IV) spatial Manski approach, analysing balanced panel data from 2004 to 2020 at the municipal level (290 municipalities) in Sweden. The methodology is designed to assess the impact of the ROT subsidy on the housing market.FindingsThe study reveals that the ROT subsidy has significantly influenced house prices, with noticeable variations between municipalities. These differences are attributed to the varying amounts of tax reductions for renovations and the extent to which property owners utilise these subsidies.Research limitations/implicationsThe research is limited to the context of Sweden and may not be generalisable to other countries with different housing and subsidy policies. The findings are crucial for understanding the specific impacts of government subsidies on the housing market within this context.Practical implicationsFor policymakers and stakeholders in the housing market, this study highlights the tangible effects of renovation subsidies on property values. It provides insights into how such financial incentives can shape the housing market dynamics.Social implicationsThe research underscores the role of government policies in potentially influencing equitable access to housing. It suggests that subsidies like ROT can have broader social implications, including the distribution of housing benefits among different income groups and regions.Originality/valueThis study contributes original insights into the field of applied real estate economics by quantitatively analysing the impact of a specific government subsidy on the housing market. It offers a unique perspective on how fiscal policies can affect property values and renovation activities at the municipal level in Sweden.