Dummy Variable Model Research Articles

Nexus between digital financial inclusion and economic growth: a panel data investigation of Asian economies

PurposeThis study aims to investigate the relationship between digital financial inclusion and economic growth in specific Asian countries, emphasizing the exploration of how digital financial inclusion dynamics impact gross domestic per capita income.Design/methodology/approachThe study creates a digital financial inclusion composite index (DFII) by incorporating essential metrics from the Global Findex report. Economic growth is measured using Gross Domestic Product per capita income in its natural logarithmic form (LnPCI), with three control variables– employment-to-population ratio; population growth and inflation. The analysis utilizes a fixed-effect dummy variable model to examine the relationship, considering unobserved country-specific heterogeneity. 30 Asian countries have been selected for the study for the periods 2014, 2017 and 2021 based on their availability, as outlined in Table 4.FindingsThe research revealed a robust positive correlation between the Digital Financial Inclusion Index (DFII) and logarithmic GDP per capita income (LnPCI), indicating higher per capita income with enhanced digital financial inclusion. Employment and population exhibited minimal influence, whereas inflation had a notable negative effect on per capita income. Population growth showed a limited impact. The model demonstrated a high explanatory power for the dependent variable (high R-squared), and the residuals displayed low autocorrelation (Durbin–Watson of 1.96).Originality/valueThis study adds to the existing literature by examining the intricate connection between digital financial inclusion (DFI) and economic growth in 30 Asian countries, employing a comprehensive composite index for analysis.

Green accounting practices from the perspectives of legitimacy theory and stakeholders in the food and beverage industry

Implementing green accounting is a way for companies to improve environmentally friendly operations where there is a relationship between operating funds and the company's environmental budget. The impact of green accounting in companies influences environmental performance and environmental disclosure, which is a new variable from the dependent variable in this research. The independent variable is a financial performance variable measured using the Return on Asset (ROA) ratio of registered food and beverage sector manufacturing companies on the Indonesian Stock Exchange from 2019 to 2022. This research aims to determine how green accounting implementation impacts the company's financial performance. In this research, 44 sample companies were selected purposively. Using a dummy variable regression analysis model, the test results show that environmental disclosure variables influence financial performance, but environmental performance variables do not. The limitation of this research lies in the need for more samples used so that future research is expected to be able to add samples by expanding the research population.

An empiric on geopolitical risk and the tourism–economic growth nexus

Purpose This study empirically examines the moderating role of geopolitical risk on the tourism–economic growth nexus by applying a recent geopolitical risk indicator developed by Caldara and Iacoviello (2022) in a cross-country panel data growth model context for a sample of 24 countries.Design/methodology/approach A Dummy Variable Least Squares panel data model, nonparametric covariance matrix estimator and SYS-GMM estimation techniques are employed for the analysis. The authors capture the GPR moderating effect by disaggregating the cross-country sample according to low versus high country GPR score and through a GPR interaction coefficient. Several controls are included in the models such as gross fixed capital formation and—consistent with Barro (1990)—government consumption. Trade openness is used to account for the export-led growth effect. In line with neoclassical growth theory (e.g. Barro, 1991), the authors also include the real interest rate, to account for policy makers' commitment to macroeconomic stability, financial depth, as a proxy for financial development, population growth and the level of secondary school education. The authors also control for unobserved country-specific and time-invariant effects.Findings The research finds that the interaction term of geopolitical risk significantly contributes to the predictive ability of the regression and provides empirical evidence that confirms that only in low geopolitical risk countries international tourism positively and significantly contributes to economic growth. Important theoretical and policy implications flow from these findings.Originality/value The study not only contributes to advancing academic knowledge on the tourism–growth nexus, it also has impact beyond academia. Many countries have in the past pursued and many continue to pursue, tourism specialization and/or tourism-led growth strategies based on the theoretically well-established and empirically validated positive link between inbound tourism and economic growth. The findings alert policy makers in such countries to the significant moderating role that geopolitical risk plays in affecting the above-mentioned relationship and to the importance of prioritizing geopolitical stability as a policy precursor for the successful implementation of such strategies.

Construction of universal equations for knot attributes of three coniferous species.

We constructed base model, dummy variable model, and mixture model with three variables including knot diameter, loose knot length, and sound knot length with three typical coniferous species, Pinus koraiensis, Larix olgensis, and Pinus sylvestris var. mongolica, from the Linkou Forestry Bureau and Mengjiagang forest farm in Heilongjiang Province in 2020. We analyzed the differences in knot properties among different tree species and simplified the modeling work. Firstly, we collected relevant knot property data through the sectioning method based on relevant literature, transformation of the model form and substitution of related variables to conduct a base model. We transformed the species into dummy variables as qualitative factors, and introduced the dummy variable model of the relevant attributes into the base model. We introduced the random effects of sample trees and sample plots when constructing the mixture model. By comparing evaluation indicators, such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), the mixture model with the best fitting effect was selected. We selected the optimal universal equation by comparing the fitting accuracy of the base model, dummy variable model and mixture model. The fitting accuracy of the dummy variable model and mixture model was higher than that of the basic model. The evaluation indicators (AIC and BIC) showed that the mixture model had a better fitting effect on knot properties than the dummy variable model. In the model comparison results, R2 of mixture models for sound knot length, the loose knot length, and knot diameter increased by 13.2%, 84.8% and 40.3%, respectively. The predictive accuracy of the three base models for different tree species' knot attributes was above 90%, and both the prediction accuracy of the dummy variable model and mixture model were above 94%, indicating that the constructed models could well predict knot-related properties. From the perspective of tree species, the sound knot length, knot diameter, and loose knot length was in order of P. sylvestris var. mongolica > P. koraiensis > L. olgensis. Fitted results of the dummy variable model and the mixture model were superior to the basic model, with higher accuracy.

Spatial variation in livelihood security among livestock-based agricultural farming systems in climatically vulnerable Indian Sundarbans.

Farm households in the UNESCO World Heritage site Sundarbans face serious problems, including increased soil salinity, frequent extreme weather events, seawater intrusion and flood damage, all of which cause distress to the livelihoods of the farm families. Policymakers commonly acknowledge livestock as a crucial resource for mitigating economic losses caused by crop failures due to extreme weather events. Despite Sundarbans' vulnerability to extreme weather events, smallholder farmers' livelihoods vary across the region. Identifying spatial livelihood variations aids in targeted strategies to address climate extremes. We chose the highest cow- and buffalo-populated blocks among the 19 blocks in the Sundarbans to assess variations in livelihood dimensions, including nutritional, economic, social and infrastructural security. We used dummy variable regression models to examine the differences in livelihood security dimensions among households living in different locations. The study found that Namkhana had the highest livelihood security score among the blocks studied, while Gosaba had the lowest score because it's in a remote area with limited infrastructure. The study found a significant difference in the overall livelihood security score among the blocks we examined, indicating the need for a location-specific, cluster-based approach for the overall development of the Sundarbans. The study can shape a policy framework for socio-economic development in the Indian Sundarbans through its findings on location-specific livelihood security. For securing smallholder farmers' livelihoods in the vulnerable Sundarbans region, policymakers must give priority to improving infrastructure, viz., roads, marketing facilities and animal healthcare centers.

New urbanization and high-quality urban and rural development: Based on the interactive coupling analysis of industrial green transformation

High-quality urban and rural development is an essential topic of China’s development and has clarified the new direction of urbanization development. Meanwhile, industrial green transformation is an essential industrial support for promoting new urbanization. This paper primarily discusses the economic and environmental effects of the new durbanization on the high-quality development in urban and rural areas, and further evaluates its structural effects from the perspective of green industry-driven growth. This paper conducts econometric research on sample data through breakpoint regression, impulse response, and dummy variable Tobit models. The main conclusions are: (1) In the long run, the new urbanization is not conducive to income balance and pollutant emission reduction between urban and rural area. In the explosive growth stage, new urbanization began to play a “diffusion effect.” In the steady deepening stage, new urbanization significantly promoted the reduction of solid waste emissions. (2) Driven by the industrial green transformation, the steady state of new urbanization shows a marginal increase in urban–rural income balance. In contrast, the explosion stage shows the characteristics of a marginal decrease in carbon emissions and wastewater discharge. (3) Based on the diversity of policy transmission, in the transmission path of industrial green transformation, new urbanization has the most prominent impact on the high-quality development of urban and rural. (4) Based on the regional perspective of policy implementation, the “polarization” effect of new urbanization and industrial green transformation on urban and rural income in the eastern and northern coasts is prominent.

An Economic Analysis on The Impact of the FRBM Act on Central Government Deficits in India

The major purpose of this paper is to analyze the deficits of the Central Government in India since the 1990s, that isduring the pre (1990-1991 to 2002-2003) and the post-FRBMAct period (2003-2004-2022-2023). It looks into the extent to whether the execution of the Fiscal Responsibility and Budget Management (FRBM) Act has played a positive role in bringing downthe overall deficits of the Government or not.The study is based on secondary data and employs Dummy Variable Model for analysis. It is found that the FRBMActwas not successful in minimizing the deficits of the Central Government in India. Thus, it is suggested that it would be advantageous if the Government complies with the targets put down by the FRBM Act as this in turn would help in achieving fiscal discipline, consolidation, growth and stability in the country.

Height-diameter models of regenerated saplings of Larix gmelinii based on dummy variable and quantile regression.

Based on data collected from 2054 saplings of Larix gmelinii forest in 55 fixed plots in 2018-2019 in Cuigang Forestry Station, Daxing'anling area, we classified the stand density index (SDI) into four classes, i.e., Class Ⅰ (SDI1<1863 plants·hm-2), Class Ⅱ (1863 plants·hm-2≤SDI2<2155 plants·hm-2), Class Ⅲ (2155 plants·hm-2≤SDI3<2459 plants·hm-2) and Class Ⅳ (SDI4≥2459 plants·hm-2) by using the quartile method. We constructed a dummy variable model and quantile regression model for the height-breast diameter of saplings of L. gmelinii with dummy variable method introduced SDI. The results showed that among the five selected representative non-linear tree height curve models, the Richards model fitted the best, with Ra2, RMSE and MAE of 0.7637, 0.8250 m and 0.5696 m. The dummy variable model including the SDI constructed based on the Richards model showed a 1.3% increase in Ra2 compared with the base model, while RMSE, MAE, and AIC decreased by 2.1%, 1.5%, and 11.2%, respectively. When the quantile τ was 0.5, Ra2 of quantile regression model was the maximum, and RMSE, MAE, AIC was the minimum, being 0.7612, 0.8294 m, 0.5657 m, and -767.19, respectively. Compared with SDI1, sapling height in SDI2-SDI4 was increased by 5.6%, 5.6%, and 11.3%, suggesting reasonable that regulation of stand density was conducive to increase the height growth of saplings in regeneration.

The Influence of the Gross Regional Domestic Product of the Trade and Manufacturing Industry and Services Sector on the Amount of Labor Absorption in the City of Samarinda and the City of Balikpapan

The purpose of this study was to determine the effect of the Gross Regional Domestic Product of the Trade and Processing Industry Sectors and Services on the Amount of Labor Absorption in Samarinda City and Balikpapan City. This study uses panel data from 2013 – 2022 in the two cities of Samarinda and Balikapapan using secondary data. Data analysis used a dummy variable multiple linear regression model using SPSS (Statistical Program Social Science) version 23. The results of this study indicate that the GRDP variables in the trade, manufacturing and service sectors together have a significant effect on the amount of labor absorption. The Trade Sector Variable partially has a significant positive effect on Total Labor Absorption. The Processing Industry Sector Variable partially has no significant effect on Total Labor Absorption. And the Service Sector Variable partially has a significant negative effect on Total Labor Absorption.

Role of institution in combating crime in India: an empirical study

PurposeThis paper examines the role of institution in the combating crime in India. This study also assesses institutions for controlling property crime in India in the post-reform era.Design/methodology/approachCrime and socio-economic data are taken from National Crime Record Bureau and the Reserve Bank of India, respectively. Twenty major Indian states are selected for the study purpose for the period of 1994–2019. Fixed effect panel data technique is used for analysis purpose.FindingsProperty crime rate declines with economic growth, while it increases with financial development. Findings of fiscal policy instruments are different. Own tax is positively associated with property crime in India, while non-tax fiscal instruments such as fine, penalty, and so on, are inversely related to it. Property crime rate is inversely related to institutional factors like charge sheet and conviction rate.Research limitations/implicationsFurther research is needed for other crimes in India. State-level data are used here for analysis purpose; however, spatial or cluster analysis techniques might provide more insights for combating crimes in India.Practical implicationsThis study suggests that economic growth and fiscal instrument along with institutional development are essential to control property crime in India.Social implicationsGovernment should take steps to improve the law-and-order system to control property crime across states.Originality/valueImpact of non-tax fiscal instrument reduces property crime while that of own tax is increases it in India. These findings are unique and added certain insight in the study. Institutional roles are captured its performances like charge sheet and convict rate, which are significantly reduce property crime in Indian states. Least square dummy variable model is applied to capture individual state effects.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0063

Quantitative assessment of the response of seawater environmental quality to marine protection policies under regional economic development --A case study of Xiamen Bay, China

Xiamen is the epitome of having steady economic growth and non-negligible environmental stress over decades. Several restoration programs have been applied to address the conflicts between heavy environmental pressures and human activities, but the response of current coastal protection policies to the marine environment remains to be assessed. Therefore, to assess the effectiveness and efficiency of marine conservation policies under regional economic growth in Xiamen, quantitative techniques including elasticity analysis and dummy variable regression models were applied. Here we show the potential relationship between seawater quality (pH, COD, DIN and DRP) and economic growth including Gross Domestic Product (GDP) and Gross Ocean Product (GOP), to evaluate the ongoing related policies by using over 10 years of data (2007–2018). According to our estimates, a GDP growth rate of 8.5% represents a stable economic climate that is favorable for the overall rehabilitation of the local coastal environment. The results of the quantitative research indicate a strong relationship between economic development and seawater quality, with marine protection regulations serving as the direct cause. As GDP growth and pH are significantly positively correlated (coef. = 0.8139, p = 0.012), ocean acidification has decreased over the last decade. With an inversely proportional correlation with GDP (coef. = 0.8456, p = 0.002) and GOP (coef. = 0.8046, p = 0.005), the trend in COD concentrations effectively meets the targets of current pollution control legislation. By using a dummy variable regression model, we found that legislation is the most effective way in seawater recovery in the GOP section, and positive externalities of marine protection frameworks are also estimated. Meanwhile, it is predicted that the negative effects from the non-GOP section will gradually affect the coastal environmental quality gradually. An overall framework for controlling marine pollutant discharges, giving equal attention to maritime and non-maritime anthropogenic activities should be promoted and updated.

The Relationship Between Board Structure and Human Resource Disclosure Practices: A Study of Indian Companies

The objective of the present study is to examine the effect of board structure on human resource disclosure index (HRDI) in Indian listed companies. The present study is based on companies listed on NSE-200 Index. The final sample includes 126 companies for the period of ranging from F.Y. 2012–2013 to 2018–2019. The data of variables are collected from annual reports of the sample companies. In the analysis part, the descriptive statistics are used to check the level of human resource disclosures. After that, Pearson’s correlation matrix is applied for checking the correlation between board variables and HRDI. At last, to check the effect of board structure on HRDI, the one-way least square dummy variable (LSDV) regression model is applied. The outcomes of descriptive statistics show that the mean percentage of HRDI is 43.86. It lies on moderate side. Further, the outcomes of one-way LSDV regression model show that there is significant positive effect of board size, board meeting and audit committee on HRDI. In contrast, there is insignificant but positive effect of board independence, CEO duality on the level of human resource disclosure of the sample companies. Overall, it can be said that the human resource disclosure practice of the Indian companies is fairly good.

Empirical Evidence on the Relationship between Capital Structure and Organizational Life Cycle

The paper investigates the capital structure of companies listed on the Bucharest Stock Exchange (BSE) from an organizational life cycle perspective, seen as a determinant of the decisions to finance operations and growth. For this purpose, the life cycle is measured according to the cash flows patterns. Motivated by the pecking-order theory (POT) and carried out on a sample of 59 companies in the period 2010-2020, the study uses a Least Squares Dummy Variable (LSDV) panel data model and shows that listed Romanian companies resort more to bank financing in the initial stages of their life cycle. As they reach the maturity and shake-out stages, companies reorient towards internal financial resources and equity issuance, which is in line with the POT. Furthermore, the age of companies has a rather weak effect on the financial leverage, and its effect diminishes as firms age. The results also confirm the importance of having a high level of tangible fixed assets as collateral for bank loans, but also the significant role of the industry and geographical positioning on the degree of external financing.

Digital skills as an impetus for the acceleration of economic digitalization: EU perspective

Digitalization, which gained momentum peculiarly after the invention of the Internet, has had a profound impact on the shape of the economy. It has changed the way of doing business and the labor market structure (Peetz, 2019). This change has brought the rise of automatized business processes and public services which aim to eliminate human manual work but at the same time cannot be realized without humans as a main component of information and communication technology (ICT). In order to fully leverage the positive effects of human capital in digitization, individuals must possess essential digital skills. This paper highlights the importance of citizens’ ICT skills for the acceleration of the digital economy, therefore the empirical analysis conducted examines the impact of different levels of digital skills on the digitalization of the economy. Additionally, the paper investigates whether the way individuals acquire digital skills differs between highly digitalized European Union (EU) economies and other parts of the Union. In this paper, secondary data sourced from Eurostat was used. To address the research questions, the pooled ordinary least squares (OLS) and least-square dummy variable (LSDV) models were utilized as well as the t-test. The findings of the study reveal a positive impact of digital skills on the digitalization of the economy. Furthermore, significant differences in the ways citizens acquire digital skills in most digitalized economies of the EU are uncovered.

The impact of corporate attributes on voluntary HR disclosure practices: a study of Indian corporate sector

PurposeThe objective of the present study is to examine the impact of corporate characteristics on human resource disclosures in Indian corporate sector.Design/methodology/approachThe study investigates the annual reports of 336 Indian listed companies of NSE-500 Index. The data are collected for the latest time period which contains eight years (FY 2012–13 to 2019–2020). The data of independent variables (company characteristics) have collected from annual reports and CMIE ProwessIQ Database of the Indian listed companies. The data of human resource dissclosure index (HRDI) is collected form annual reports using content analysis approach. For analysis purpose, descriptive statistics, Pearson's correlation matrix, Two-way Least Square Dummy Variable (LSDV) regression model have been used.FindingsThe outcomes show that net sales, market capitalisation, ROTA, return on equity, quick ratio, PAR have significant positive and age, profit after tax, current ratio have significant negative effect on HRDI. On the contrary, debt-equity ratio, earnings per share, type of auditor, listing status have insignificant positive and net fixed assets, promoter's holding have insignificant negative effect on HR disclosures of the selected Indian listed companies.Originality/valueThe HRDI constructed in the present study helps the Institute of Chartered Accountants of India (ICAI) and other regulatory bodies to make some standards regarding voluntary HR disclosure practices in Indian corporate sector.

Agglomeration of the new energy industry and green innovation efficiency: Does the spatial mismatch of R&D resources matter?

Based on China's provincial panel data from 2011 to 2019, this paper examines the complex impact of new energy industry agglomeration on green innovation efficiency from the perspective of spatial mismatch of research and development (R&D) resources. The super slack-based measured directional distance function (super-SBM-DDF) is applied to measure the green innovation efficiency, while the least square dummy variable model (LSDV) is implemented to evaluate the mismatch of R&D resources in the new energy industry. Based on the generalized method of moment (GMM), the moderating effect model, and the dynamic threshold model are employed to investigate the threshold effect of new energy industry agglomeration on green innovation efficiency and the moderating effect of spatial mismatch of R&D resources. We come to the following findings. First, the agglomeration of China's new energy industry exhibits a “west-east-central” gradient distribution. In the meantime, there is a widespread insufficient mismatch of R&D resources in the majority of regions. Second, the agglomeration of the new energy industry can significantly boost the efficiency of green innovation. Moderate agglomeration can have a marginally enhancing effect, whereas excessive agglomeration will obscure the green innovation effect of new energy industry agglomeration. Third, the relationship between the new energy industry agglomeration and green innovation efficiency is negatively moderated by the mismatch of R&D resources, while the moderating effect is amplified by the mismatch of innovation financial resources. Fourth, there are two inflection points in the positive impact of new energy industry agglomeration on green innovation efficiency when the innovation resource mismatch is assigned as the threshold variable.

PENGARUH PENGANGGURAN, PENGELUARAN PEMERINTAH DAN INFLASI TERHADAP KEMISKINAN DI INDONESIA TAHUN 2016-2020

This study aims to determine the effect of unemployment, government spending and inflation on poverty in Indonesia in 2016-2020 This study uses a quantitative approach. The data used is Indonesian secondary data from 2016-2020. The data analysis technique in this study uses time series data analysis with the Least Square Dummy Variable (LSDV) model. The results of the study show that: (1) The unemployment variable has a positive and significant effect on poverty by 3.559197; (2) Government expenditure variable has a negative and significant effect on poverty by -2.062619; (3) The inflation variable has a negative and insignificant effect on poverty by -0.116989; and (4) The variables of unemployment and government spending simultaneously affect poverty while the inflation variable simultaneously does not affect poverty.

Do Sectoral Indices React Differently to Lockdowns Imposed Due to Covid-19? Lessons for Wealth Generation

This research paper is an attempt to study the impact of Covid-19 on the sectoral indices using Event Study Methodology (EVM) and regression models. It tries to analyze the differences in mean returns of one composite and ten sectoral indices on India’s premier National Stock Exchange during four periods-before lockdowns, during the lockdown, during unlocking and post unlock. The analysis is based on 15346 daily observations. Imposition of Lockdown is found to have a positive impact on the daily mean return of the eleven Nifty indices under study. The mean returns of sectoral indices are compared using non-parametric tests. The mean returns across four periods are compared using Friedman’s ANOVA and are found to be significantly different over the four periods. Post Hoc Analysis using Wilcoxon signed-rank test revealed that the daily mean returns during the lockdown were more than the daily mean returns during the period before lockdown, during unlock period or post unlock period. Kruskal Wallis test was used to investigate the equality of means of eleven indices found, mean returns of indices to be equal to each other during all the four alternate periods studied separately. GARCH (1,1) model is then used to estimate returns and variance of sectoral indices A significant portion of variances in sectoral index returns was explained by the variances in market proxy Nifty 50. The study highlights the emerging relevance of the Energy, FMCG, Healthcare, IT and Pharma sector during the lockdown as the abnormal positive returns have increased in these sectors. Infrastructure, Media and realty sectors have been severely affected due to the lockdown. The robustness of estimated parameters is checked by using a dummy variable regression model and it is found that stock markets were strengthening during the period of lockdown. The results of the dummy variable regression model are in line with the results of the Event Study Methodology (EVM) and GARCH (1,1). Overall, the imposition of lockdown as a policy initiative by the Government of India helped in mitigating the effect of Covid-19 on the stock market.

A Decision Support System to Estimate Green Sustainability from Environmental Protection and Debt Financing Indicators

In the social context of advocating a low-carbon economy, achieving sustainable growth in line with current social development requirements is an issue that agribusiness must face. In order to explore the mechanisms influencing the sustainable growth of Chinese agriculture and to optimize the quality of agribusiness decisions, this paper examines the relationship between environmental management, debt financing indicators, and financial sustainable growth of the company in Chinese agriculture. Specifically, a decision support system based on the least square dummy variable (LSDV) model, mediating effects model and threshold effects model was constructed by using annual financial reports and questionnaire data of the listed agricultural enterprises. After empirical analysis, the following results were obtained: first, both environmental management and debt financing management help Chinese agricultural firms achieve financially sustainable growth. Second, debt financing can transmit the effect of environmental management on financially sustainable growth. Third, there are significant differences in the effects of debt financing on financially sustainable growth under different environmental management conditions. Finally, in order to promote the development of Chinese agriculture, this paper suggests that agricultural enterprises should actively implement environmental management and that relevant Chinese authorities should lower the financing threshold of the agricultural industry, while ensuring risk regulation.

The Influence of a Firm’s Attributes on Human Resource Disclosure Level: Evidence from India

The aim of the present study is to examine the effect of a firm’s different attributes on human resources (HR) disclosure level of the Indian listed companies’. The study has been conducted on 126 companies listed on National Stock Exchange (NSE)-200 index for the time period of 7 years (F.Y. 2012–2013 to 2018–2019). The data were gathered from ‘annual reports and Centre for Monitoring Indian Economy (CMIE) ProwessIQ Database. For analysis, descriptive statistics, Pearson’s correlation matrix and Two-way Least Square Dummy Variable (LSDV) regression model’ have been used. The results of the study depicted that on an average, companies disclose 43.86% of HR information in their annual reports. Further, the results of the two-way LSDV regression model shows that there is a significant positive effect of net sales, market capitalization, pages of an annual report and significant negative of debt to equity ratio, promoter’s holding, profit after tax and listing status on HR disclosure level of the selected Indian listed companies. Overall, it can be said that The Institute of Chartered Accountants of India (ICAI) and other regulatory bodies should set standardized guidelines for enhancing the HR disclosure practices of the Indian companies, and in the near future, they should come up with different HR disclosure sheet.