Shedding Light on the Socio-economic Status of Women Construction Workers in India

Shedding Light on the Socio-economic Status of Women Construction Workers in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Shedding Light on the Socio-economic Status of Women Construction Workers in India Dr. Akanksha Singh, Prof. Girish Mohan Dubey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7367980/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study emphasizes the importance of rising socio-economic status and active involvement in home decision-making, which are contingent upon women's participation in the workforce. Considering that worker, especially women, engage in various business and non-business endeavors, health significantly influences their participation in the construction industry. This study explores the socio-economic conditions of women employed in construction in Lucknow, India, with particular attention to the difficulties they encounter in their workplace. Using a random sampling method, the study surveyed 110 female respondents from Aashiyana and Alambagh localities in Lucknow. Data interpretation was conducted using AMOS software with SEM, and SPSS software with regression analysis and ANOVA. The study found that financial difficulties affect 66.4% of these workers, while 79.1% struggle with debt. The majority of workers (66.4%) have a monthly income between Rs 4000 and Rs 6000. Additionally, the study highlights significant health and income-related issues that impact the well-being of women working in construction. JEL Code: J16, J71, J01, N37, J08 Gender Studies Construction women workers Lucknow socio-economic condition health issues women empowerment AMOS Figures Figure 1 Introduction In the bustling landscape of construction sites, where towering cranes and ceaseless machinery dominate, an often overlooked yet indispensable force perseveres—women construction workers. These women, integral to the backbone of urban development, face a unique confluence of socio-economic challenges. This research delves into the lives of these resilient workers, shedding light on their financial struggles, health issues, and the broader societal implications of their labor. This research seeks to examine the socio-economic realities of women working in the construction sector, shedding light on their everyday struggles and the structural challenges they face. In many developing nations, the informal sector significantly contributes to economic growth and provides employment to a large segment of the population. Thus, the primary source of income in their economies is informal employment. In 1970 research on Ghana's metropolitan areas, Keith Hart coined the phrase "informal sector", he identifies the segment of the urban workforce that falls outside the organized labor market as the informal sector. A mission of the International Labour Organisation (ILO) (1972), which examined Kenya's employment situation within the framework of the World Employment Programme, helped to further clarify the idea. India's economy and employment are indeed significantly influenced by the building industry. According to a report by the Confederation of Indian Industry (CII), the construction industry in India is expected to grow at a Compound Annual Growth Rate (CAGR) of 11% during the period from 2020 to 2025. The sector is broadly divided into two key segments: the organised and the unorganised (informal) sectors (Confederation of Indian Industry [CII], 2020). The organised sector includes things like government-funded projects and big construction companies, whereas the unorganised sector includes things like independent contractors and unlawful labour. The informal sector holds a significant presence in India's construction industry, employing a substantial portion of the workforce. The construction sector is estimated to engage nearly 30 million workers, with women comprising about 51% of this workforce. It contributes nearly 5% to India’s Gross Domestic Product (GDP) and accounts for 8% of the nation's capital formation (SEWA Academy, 2000 ). Although they make up 48 per cent of the population in India, women have even less influence over the country's development. The fundamental way of life of the majority of women has not changed despite reforms, special legislation, the establishment of numerous institutions ostensibly to meet their needs, and increases in budgetary allocations over time. Despite women's advancements in various fields, the past three decades of progress have not uniformly benefited women in the workforce, particularly those in unregulated sectors (Manohar et al, 1980). Many workers in the construction sector do not hold official employment status and are therefore exempt from benefits like social health and welfare insurance. Workers may find it challenging to gain legal rights and protections due to their lack of formal employment. The construction industry is dominated by men on a worldwide scale; in the West, women who want to work in this profession often need to complete vocational training in related subjects like engineering, management, etc. (Tanzina Choudhury, 2013 ). Participating in the barriers and measures needed to enhance women in skilled trades and apprenticeships is one of the few studies however that has been undertaken in Canada (Bourdy, 2020). Seasonality is noticeably distinct in India's construction industry as most works are performed during the dry season. On the other hand, concerning its workforce, the construction industry in some ways is different from the majority of industries. As such, most construction sites are temporary since most buildings do not need a lot of maintenance once construction is completed. However, occupational mobility in the construction sector is faced with special circumstances in construction sites where temporary welfare facilities are required. The informal sector includes people working in the form of construction labor. In this case, they may be famine-stricken as most of such labour is seasonal, and such a high volume of labour will not be available during the monsoon season. To enhance the construction sector in India, the government has introduced several initiatives. One such reform is the Real Estate (Regulation and Development) Act, 2016, which seeks to safeguard consumer interests while ensuring greater transparency and accountability in real estate dealings. Additionally, the Pradhan Mantri Awas Yojana (PMAY), launched in 2015, aims to provide affordable housing to economically weaker sections and low-income groups. In conclusion, the construction sector in India is a large, potentially growing sector, but one that is also challenged by informality and seasonality, all of which could impact the livelihoods of the workers. The nature of the work in such sectors tends to be seasonal and depends on the quantity of work, which is vastly different depending on the project. Surprisingly, even though the industry contributes to jobs for a significant segment of India's urban population, construction workers remain among the most marginalized groups in society, with limited efforts to improve their situation. “Women involved in construction work primarily work as head-loaders, transporting materials like water, sand, cement, and bricks. Alternately, they break stones, clean, dig, and mix mortar”. On Indian construction sites, it's not uncommon to find groups of women lugging 90-pound brick bundles. Opportunities for women to train in more profitable but predominately male trades, such as carpentry, masonry, plumbing, and electrical work, are incredibly scarce in India. (Baruah, 2014). In addition to highlighting the potential benefits of women's paid employment, several researchers have also drawn attention to the drawbacks of women's involvement in the workforce. While paid work can reduce certain forms of women's subordination at home, these scholars argue that it can expose women to more exploitative and precarious situations in the public sphere. The research builds upon existing literature by focusing on the work experiences of female construction workers in Lucknow, specifically in the Aashiyana and Alambagh localities, where a high concentration of such workers is observed. By evaluating their expenditures and the health consequences of working on construction sites, which is the main source of sickness for women, Explore the specific health challenges faced by women in construction, considering factors such as occupational hazards, access to healthcare, and the impact of working conditions on their physical and mental well-being. the study will also examine their economic situation and delve deeper into the forms of discrimination experienced by women on construction sites, including both overt and subtle biases, and unequal opportunities for advancement. This study examines the economic status of women employed in construction and investigates workplace health risks through the formulation of two key hypotheses. The socio-economic challenges faced by female construction workers in Lucknow represent a critical concern, carrying significant consequences not only for the affected individuals but also for the wider society. This research is motivated by the need to highlight and address the socio-economic challenges these women face, including low wages, lack of social security, and health risks. Understanding these conditions is crucial for developing policies that can improve their quality of life and promote gender equality in the labor market. The primary research question is: "What are the socio-economic conditions and challenges faced by female construction workers in Lucknow, and how do these impact their overall well-being?" This question aims to provide a detailed analysis of the economic and social hardships encountered by these women, offering insights for policymakers and stakeholders to devise effective interventions. By identifying these issues, the research provides a basis for advocating for policy changes, interventions, and support systems that can enhance the socio-economic well-being of women in the construction sector. Therefore, this study adds value by highlighting the socio-economic challenges faced by women in the construction sector, promoting the cause of gender equity, and offering meaningful insights that can guide policy and interventions to enhance the well-being of women working in this field. Literature Review The socio-economic realities faced by women in the construction sector have drawn considerable scholarly attention, revealing a multifaceted set of factors contributing to their marginalization. Numerous studies underscore the prominent role of women in the informal construction workforce, especially in developing countries, where they make notable contributions to employment and household income (Mehta, 1995; Rani & Unni, 2000 ). Mehta (1995), for example, documented a rising dependency on the informal sector in Ahmedabad, with its employment share increasing from 46.5% in 1971 to 65% by 1981—an indication of expanding informal labor, often marked by insecure and unstable working conditions. Rani and Unni's (2000) study further emphasized the informal sector's contribution to Ahmedabad's economy, noting that it employed approximately 1,504,033 workers and generated an income of around $ 60,130 million in 1997-98. This demonstrates the sector's economic importance, even while acknowledging the often precarious nature of informal employment. Despite their significant contribution, women in construction often face numerous challenges. Jayalakshmi ( 2016 ) found that among female construction workers surveyed, only 36% possessed basic literacy skills, and a large proportion (48%) were migrant labourers, often travelling from various districts in search of work. This vulnerability is further compounded by low earnings, with 45% of respondents earning between INR 9,000 and INR 11,000 annually. Jayalakshmi's study concluded that construction workers, particularly women, represent a highly marginalized segment of society, often living in poverty. This aligns with broader observations about the construction industry, where workers are frequently among the most economically disadvantaged. The study also suggests a need for government intervention to improve the living standards of these workers, possibly through direct employment or by enforcing better labour practices within the private sector. Adding to these challenges, Banu ( 2017 ) highlighted a range of issues faced by women in construction, including a lack of social security, poor wages, sexual harassment, gender discrimination, illiteracy, and the dispersed nature of work sites. These factors combine to create a challenging and often exploitative environment for women. The lack of social security leaves them vulnerable to illness, injury, and old age without any safety net. Poor wages perpetuate their economic hardship, while sexual harassment and gender discrimination create hostile work environments. Illiteracy further limits their ability to access information and advocate for their rights. The dispersed nature of construction sites can make it difficult to organize workers and ensure compliance with labour laws. The vulnerabilities faced by women in construction are not limited to specific geographical contexts. Jubany and Castellanos (2021) explored the temporary migration of Mexican women workers to the US and Canada, examining how gender, race, and class intersect to shape their experiences. Their research delved into the dilemmas these women face within the context of post-Fordist labour markets and heightened social control. They examined the complexities of interagency relationships, the impact of neoliberal policies, the challenges posed by the crisis of multiculturalism, and the effects of social control on the lives of these women. By analyzing these factors, Jubany and Castellanos aimed to provide a more nuanced understanding of temporary labour migration, moving beyond traditional frameworks to address the systemic inequalities linked to the working conditions and lived experiences of these women. Their research underscores the importance of considering the intersectional nature of disadvantage, where gender, race, and class combine to create unique forms of vulnerability. More recently, Ramila and Amalanathan (2023) reiterated the social and economic factors impacting women in the Indian construction industry, focusing on vulnerabilities such as low wages, gender discrimination, and a lack of access to social security. Their work reinforces the findings of earlier studies, highlighting the persistent nature of these challenges. Similarly, Mishra ( 2023 ) argued for urgent government support to address the challenges faced by women in construction, emphasizing the need for improved working conditions, fair pay, and the elimination of gender discrimination. Mishra's work underscores the urgency of the situation and the need for concrete action to improve the socio-economic realities of women working in this vital sector. Collectively, these studies highlight the persistent and multifaceted challenges faced by women in the construction industry worldwide. They call for comprehensive and multi-pronged strategies to address these issues, including policy interventions, stricter enforcement of labour laws, and greater awareness of the contributions and vulnerabilities of women in this sector. Understanding the complex interplay between these factors from the above literature provides a more nuanced understanding of the challenges faced by women workers and informs targeted interventions and policies to improve their overall socio-economic status. Therefore, this study could be addressed in this context in the lack of in-depth exploration into the intersectionality of factors influencing women's socio-economic well-being in construction. Specifically, there is a need to delve deeper into how health issues, discrimination, and economic challenges interact and compound each other to create unique barriers for women in this industry. Objective of the Study To examine the socio-economic conditions of female workers in Lucknow. To investigate the workplace conditions and risks to women workers' health. Research Methodology Research Design and Area of the Study Both primary and secondary data were employed in the investigation. In Lucknow city, 110 female construction workers were chosen at random to provide primary data from the areas of Aashiyana and Alambagh. Two locations in Lucknow City had a field assessment in June–August of 2022. To acquire information on workers' health hazards, workplace challenges, and socio-economic status, a comprehensive timetable was utilized. Most of the time, during lunch, employees are informed directly by their employer, and working conditions are examined. Several references were made, including articles, journals, books, and government documents like the Economic Review, and the ILO, to obtain secondary data. Research Tool The research methodology employed in the study of women in construction, using one-way ANOVA and SEM, implemented via AMOS, stands out from the previous research methodology which has looked at women in construction or the construction industry. The statistical technique, ANOVA, or analysis of variance, for situations where we want to compare means across three or more groups, or whether the observed differences are statistically significant or not. If we are looking at women’s work in the construction industry, we may want to use ANOVA to compare wage levels, working conditions, or job satisfaction across different groups of women workers. Structural equation modeling (SEM) is an advanced statistical technique utilized to assess and estimate complex interrelationships between variables. By applying SEM using AMOS, a multiple-variable analysis can be performed, thus facilitating a thorough investigation of the relationships among variables that influence job income and the well-being of construction workers. This method enables researchers to model latent variables and test complex hypotheses, providing a deeper insight into the socio-economic conditions of women workers. Here's how this methodology differs from other studies: The current methodology, which incorporates one-way ANOVA and the Structural Equation Model (SEM), represents a significant advancement over past studies on the socio-economic conditions of women workers in the construction industry. Traditionally, research in this area often relied on qualitative analyses and basic statistical methods to assess the socio-economic status of female construction workers. These earlier studies primarily focused on descriptive statistics and simple correlations to identify key issues and trends. In contrast, the use of one-way ANOVA allows for more precise comparisons between different groups, providing a clearer understanding of how various factors affect women workers' socio-economic conditions. Furthermore, SEM enables a comprehensive examination of complex relationships among multiple variables, offering deeper insights into the structural and causal relationships that influence the socio-economic status of these workers. This combination of advanced statistical techniques ensures a more robust and nuanced analysis, enhancing the reliability and validity of the findings. Reliability testing for data involves assessing the consistency and trustworthiness of the data. Table 1.1 Reliability Statistics Cronbach's Alpha Cronbach's Alpha Based on Standardized Items No. of Items 0.672 0.616 23 Internal consistency of the scale was evaluated using Cronbach’s Alpha. “The obtained values were 0.672 and 0.616 (based on standardized items), reflecting moderate reliability. These coefficients suggest that the items within the respective scales exhibit a reasonable level of interrelatedness, thereby supporting the internal reliability of the instrument (Cronbach, 1951 ; Tavakol & Dennick, 2011 )”. Results and Discussion As per the 2011 Census, 2,817,105 people were living in Lucknow city, with 1,460,970 men and 1,356,135 women. At 1,815 people per square kilometer (4,700/sq mi), the district has a high population density. Over the years 2001 to 2011, its population grew at a rate of 25.79 per cent. There are 906 girls for every 1000 men in Lucknow, which has a 79.33% literacy rate. The largest capital city in the Indian state of Uttar Pradesh is Lucknow. Additionally, to creating employment opportunities, aids in the growth of the city's infrastructure and real estate. Cement, steel, and other building materials industries, among others, benefit from the industry's growth as well. Considering how important it is to Lucknow, the construction industry was the focus of the current study. Health, social security, and wage difficulties are among the many problems that construction workers face. Table 1.2: Demographic of the Respondents (n=110) Variables Frequency Percentage Mean S.D Age 25-30 2 1.8 4.51 1.30 30-35 4 3.6 35-40 21 19.1 40-45 25 22.7 45-50 24 21.8 50-55 34 30.9 Educational Illiterate 41 37.3 1.77 0.725 Primary 56 50.9 Secondary 10 9.1 Graduate 3 2.7 Income 2000-4000 9 8.2 2.23 0.70 4000-6000 73 66.4 6000-8000 22 20.0 8000-10,000 5 4.5 Above 10,000 1 0.9 Expenditure 1000-1500 10 9.1 3.23 1.04 1500-2000 20 18.2 2000-2500 14 12.7 2500 and above 66 60.0 Member of SHG Yes 38 34.5 1.65 0.477 Time Below 8 2 1.8 2.82 0.425 8 Hours 15 13.6 Above 8 Hours 93 84.5 Seasonality Yes 31 28.2 1.71 0.451 Savings Nothing 4 3.6 3.68 0.918 0-500 6 5.5 500-1000 27 24.5 1000-1500 57 51.8 Above 1500 16 14.5 Debts Taken 87 79.1 1.20 0.40 Problems Health 16 14.5 2.04 0.580 Financial 73 66.4 Others 21 19.1 Health Problem Muscle Pain 29 26.4 3.600 1.74 Allergy 5 4.5 Asthma & Breathing 6 7.3 Cough 8 6.4 All of the above 7 55.5 Hospitals Govt 80 72.7 1.68 1.18 Private 5 4.5 Primary Health 5 4.5 Both 20 18.2 Wage Discrimination Exists 110 100 1.00 0.00 Source: Field Survey The respondents provide sociodemographic and economic information. Six age groups for workers are distinguished in Table 1.2 Of the 110 samples, 30.9% of the workers are in the 50–55 age range, followed by 22.7% in the 40–45 age range, 21.8% in the 45–50 age range, and 19.1% in the 35–40 age range. 50.9 per cent of the respondents have only primary school qualifications, 37.3 per cent of the respondents are illiterate, and 9.1 per cent of the respondents have secondary school qualifications. The range of monthly income for 66.4 per cent of respondents is between Rs. 4000-Rs. 6000, followed by Rs. 6000-Rs. 8000 for 20% of respondents and Rs. 2000-Rs. 4000 for 8.2 percent of respondents. The research by Saka et.al (2022) also highlights the significant contributions of women to social and economic development while underscoring the lack of appreciation and support they receive. They also emphasize the risks and vulnerabilities faced by women labourers due to factors such as low education levels and limited skills, pointing to the need for empowerment through training and skill enhancement. Of the respondents, 60% spend more than Rs. 2,500 per month, while 18.2% spend between Rs. 1,500 and Rs. 2000 per month. SHGs have become increasingly important in recent years, and a large number of women are becoming members and actively taking part in a range of SHG events. Findings reveal that merely 34.5% of workers are SHG members. These workers are generally poor, earning daily wages barely sufficient to maintain their families (Tiwari et al., 2012). While an eight-hour workday is often considered standard, evidence from the informal sector challenges this notion. Data reveals three distinct work hour patterns: less than eight hours, eight hours exactly, and more than eight hours. Strikingly, 84.5% of respondents reported working beyond eight hours daily, while only 13.6% adhered to an eight-hour schedule and a mere 1.8% worked less. Over half of the respondents (51.8%) reported monthly savings between Rs. 1000-1500, while a significant portion (24.5%) saved Rs. 500-1000 per month. Notably, the findings reveal a relatively low propensity for female construction workers to save. Workers in the construction industry are forced to take out loans for a variety of reasons due to their poor income and inability to cover their basic demands with their meager wages. 79.1 per cent of the respondents have taken loans. The primary cause of their debt is the disparity between inadequate income and rising expenses. To better grasp the actual situation facing female construction workers, focus groups were held. Many people acknowledge that their lack of other options and financial difficulties forced them to work in the industry. A study in Kerala highlighted that women in construction face under-representation and significant socio-economic difficulties. Null Hypothesis (H 01 ): There are non-significant determinants of the expenditure of women construction workers. Alternate Hypothesis (H a1 ): There are significant determinants of the expenditure of women construction workers. Analysis of Determinants of Expenditure for Women Construction Workers The function applied to a cross-section of construction women workers to investigate what variables are affecting the expenditures of the respondents. The model is, Y = α + β 1 X 1 + β 2 X 2 + β 3 X 3 + β 4 X 4 + β 5 X 5 + e where, Y - Per capita Expenditure (in Rs.) X 1 – Age (in number) X 2 – Education X 3 – Income (in Rs.) X 4 – Time of work X 5 – Debt (in Rs.) e - Error term The estimated linear expenditure function is furnished in the following table Table 1.3: Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.747 a 0.559 0.537 0.71324 a. Predictors: (Constant), debt, income, workers, Time, Educational, Age b. Dependent Variable: Expenditure Table 1.4: ANOVA a Model Sum of Squares df Mean Square F Sig. 1 Regression 66.949 5 13.390 26.321 .000 b Residual 52.905 104 0.509 Total 119.855 109 a. Dependent Variable: Expenditure b. Predictors: (Constant), debt, income, Time, Educational, Age Table 1.5: Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) (β 0 ) -2.129 .533 -3.991 .000 Income (β 1 ) -.078 .072 -.097 -1.092 .277 Age (β 2 ) .071 .122 .049 .579 .564 Educational (β 3 ) -.003 .108 -.002 -.030 .976 Time (β 4 ) 1.817 .209 .737 8.707 .000 Debt (β 5 ) .384 .193 .149 1.986 .050 a. Dependent Variable: Expenditure From Table 1.3, According to the R-value for female construction workers, all of the explanatory variables in the model simultaneously comprised 74.7% of the variations in the per capita consumption variances. The annual income is statistically significant at a five percent threshold, and they had a positive correlation with spending on consumption. Debt is strongly correlated with consumer spending and is statistically significant at the 5% level. Age was the major component that had the most impact on construction workers' consumption expenditures. A 1% rise in each of these factors may, in the case of yearly income, result in a corresponding 1% increase in consumer spending. The model fitted was determined to be statistically significant at a five percent level, as demonstrated by the F-value of 26.321. Research by Archana Saini and K. Sharma (2021) also emphasizes the prevalence of informal work arrangements and lack of social protection for about 93% of construction workers. Female workers, often seasonal migrants, face occupational hazards, inadequate healthcare access, and gendered health impacts due to poor working conditions and poverty-induced challenges. Similar issues were observed in Gurugram, where most female laborers deal with inadequate social security, poor wages, and gender bias (Archana and K sharma, 2021). Null hypothesis (H 02 ): Workplace factors do not significantly affect the well-being of women construction workers. Alternate hypothesis (H a2 ): Workplace factors significantly affect the well-being of women construction workers. Table 1.6: KMO and Bartlett's Test Cronbach’s Alpha 0.872 Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.821 Bartlett's Test of Sphericity Approx. Chi-Square 722.016 df 45 Sig. .000 Source: Computed Data The table summarizes the findings of two essential statistical procedures used to assess whether the dataset is appropriate for factor analysis: the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity. “A KMO score of 0.821 indicates a high degree of shared variance among the variables, which confirms the adequacy of the sample for conducting factor analysis (Kaiser, 1974). Bartlett’s Test of Sphericity yielded a chi-square value of 722.016 with 45 degrees of freedom and a significance level of p < 0.001. This significant result demonstrates that the correlation matrix is not an identity matrix, suggesting meaningful relationships exist among variables and supporting the suitability of the data for structure detection (Bartlett, 1954)”. In this analysis, two independent constructs were explored: one focused on job-related income and overall well-being among construction workers. The dependent variables under this construct were identified as follows: F2 representing financial savings, F3 relating to financial security for the future, F4 addressing decision-making power within the household, and F5 indicating satisfaction with life quality. A second independent dimension explored workplace-related concerns, with dependent variables including W2 (availability of a secure and safe work environment), W3 (experiences of discrimination at work), W4 (instances of being paid less than male counterparts), and W5 (access to workplace social security). Table 1.7: Descriptive Statistics N Mean Std. Deviation Skewness Kurtosis Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error F1 110 2.1727 .66165 .378 .230 .470 .457 F2 110 1.4818 .51991 .273 .230 -1.477 .457 F3 110 1.8455 .41056 -1.090 .230 1.457 .457 F4 110 1.5091 .55431 .457 .230 -.871 .457 F5 110 1.7545 .47287 -.656 .230 -.273 .457 W1 110 1.5909 .54681 .145 .230 -.988 .457 W2 110 1.4455 .51734 .425 .230 -1.362 .457 W3 110 1.3091 .48359 1.084 .230 -.218 .457 W4 110 1.2727 .46746 1.306 .230 .404 .457 W5 110 1.3000 .47987 1.137 .230 -.081 .457 Valid N (listwise) 110 Source: Computed Data F1, F3, and F5 generally have higher mean scores than F2, F4, and the W variables. Thus there could be a bias towards those variables having higher values. Variable: F1, F4 — W1 offer broader scores in between reactions. Shape of Distributions: Most distributions are symmetric (slightly skewed but skewed near 0). F3, not W3--W5: F3 is the only factor leaning toward higher scores (negative skewness). Most variables have a kurtosis close to zero i.e. they are normally distributing F3 and W4 are quite similar but deviate slightly with more peaked shapes (positive kurtosis). Structural Model Assessment To evaluate the relationships among the constructs, a structural equation modeling (SEM) approach was utilized using AMOS software. The model fit was assessed through several established indices. “According to Hair et al. (2010), an acceptable model fit is indicated when the chi-square to degrees of freedom ratio (CMIN/df) is below 5, the Goodness-of-Fit Index (GFI), Tucker-Lewis Index (TLI; Tucker & Lewis, 1973), and Comparative Fit Index (CFI; Bentler, 1990) each exceed 0.90. Furthermore, acceptable values for the Standardized Root Mean Square Residual (RMR) should be under 0.05, while the Root Mean Square Error of Approximation (RMSEA) should lie between 0.05 and 0.08 (Hair et al., 2010). The fit indices derived from the current model fall within these recommended thresholds: CMIN/df = 1.426, GFI = 0.953, TLI = 0.980, CFI = 0.989, and RMSEA = 0.063.” These values collectively suggest a satisfactory model fit, confirming the suitability of the proposed structural framework. Table 1.8. Values of model fit indices in SEM to the data. Indices Expected values Model values Chi-square Test Preferably low, with non-significant p-value 1.426 (p = 0.125) Root Mean Square Residual (RMR) below 0.05 0.010 Root Mean Square Error of Approximation (RMSEA) Should be below 0.05 for a close fit 0.063 Goodness-of-fit Index (GFI) Values above 0.90 indicate good fit 0.95 Adjusted Goodness-of-fit Index (AGFI) Acceptable when above 0.90 0.92 Incremental Fit Index (IFI) A value greater than 0.90 shows good model fit 0.99 Normed Fit Index (NFI) Should exceed 0.90 for acceptable fit 0.96 Relative Fit Index (RFI) Ranges from 0 (no fit) to 1 (perfect fit) 0.93 Comparative Fit Index (CFI) Above 0.90 0.98 Parsimonious Normed Fit Index (PNFI) Higher values are preferred, reflecting model parsimony 0.51 A PNFI score of 0.51 suggests a reasonably good level of model parsimony. The standardized path coefficients for each hypothesized structural relationship are examined to assess the strength, direction, and significance of the proposed links. Within the presented conceptual framework, all the paths demonstrate statistically significant relationships at the 5% significance level. The research above indicates that the amenities offered at their workplace have an impact on the well-being of female construction workers. Additionally, research from Tamil Nadu noted that women workers often work in unhygienic conditions and lack basic amenities (P Jayalakshmi, 2016). For years, studies in India have shown how women workers, specifically in the construction sector, bear the brunt of economic and health adversities. In terms of economics, women are working in this stream primarily as unskilled labourer with the issues of wage disparity, gender bias and lack of chances for skill development. While providing significant employment, women are often paid less, and face gender discrimination and a glass ceiling. Female construction workers in India also face several health problems such as work-related musculoskeletal disorders, occupational health problems, and poor access to healthcare services. The working and living conditions of the construction industry may adversely affect women's health, which in turn raises concerns about job stress, workplace injuries, and a lack of appropriate maternity protection. However, there are compelling reasons for newer workplaces that ease the burden on women who are also primary caregivers at their homes; measures that can draw from these trends to improve both working condition and gender equality balance along with better health and safety for the women construction labor in India. What are women's perceived job aspirations? In the unorganized sector, women's projected goals for their careers often centre on becoming financially secure, getting into official jobs, and becoming acknowledged for their contributions by society. Empirical research suggests that women employed in the informal sector strive to establish consistent sources of income to uplift their level of living and provide for their children. They also want formal employment security and access to health benefits, which emphasises their need for social safety and economic stability. In addition, women want the autonomy to select the kind of job they undertake, looking for positions that suit their interests and skill set. To improve their socio-economic standing and employability, they also want to have access to chances for skill development and job growth. All things considered, women working in the unorganised sector hope to overcome the obstacles of unstable employment, poor pay, and lack of social acceptance by taking advantage of increased chances for financial independence and individual development. Many respondents lacked a career trajectory due to their recent entry into the field or their intention to depart due to a perceived lack of prospects. While many expressed a desire to join construction companies, they acknowledged that for a sustainable career, including the ability to balance family responsibilities or enjoy flexibility, transitioning to construction consultancy would be necessary. Conclusion and Recommendations The role of women in influencing the construction sector is becoming progressively more significant. They are shattering stereotypes, promoting innovation, and encouraging the next generation to enter the construction industry as leaders, pioneers, and inventors. By promoting diversity and inclusion, we can help the construction sector as a whole grow and realize the full potential of its workforce. The research highlights that women employed in Lucknow’s construction sector often work under unsafe and unjust conditions. One of their major challenges is the absence of adequate safety measures and personal security at construction sites (Subhash & Satyapal Yadav, 2022). Their employment is largely irregular, particularly for unskilled labor, as much of the work is seasonal, resulting in low monthly earnings. These women also face various forms of gender-based discrimination, limited job satisfaction, lack of access to social protection, and are vulnerable to both sexual and gender-based harassment. Women in the construction business lacked skills and had a variety of health issues; as a result, government and non-governmental groups ought to create training programs (R.Jency Antony Ramila and J.Amalanathan, 2022). In the study, women are paid less than males and have to put in more physical labour to finish building projects, their income at work has an impact on their overall well-being. Women in the construction sector are largely under-represented, accounting for about 10% of the labour force, often facing obstacles such as sexual harassment, wage discrimination, and workplace injuries. Despite performing similar tasks, women workers continue to earn less than their male counterparts. In addition to wage inequality, they also report facing harassment at the workplace (Hazarika & Madhukullya, 2024). First, gender-based violence creates a culture that allows employers to abuse many female construction workers besides reinforcing the gendered hierarchy of workplaces. To address gender based violence and discrimination and in order to prevent it from happening in the construction business, there should be policies to follow, remedies to provide and complaints procedures to follow. It is therefore self-evident that the industry is still discriminating against women and workers of colour in terms of employment based on their sex, ethnicity and country of origin. A research based on Chennai pinpointed the general socio-economic frailty of these women while stressing the improvements in their places of work and accessibility to welfare services. (Abdulraheem, 2020). In addition to low and inconsistent wages, gender-based pay disparities, and minimal access to social security or insurance schemes, women in the construction sector are frequently subjected to social and economic exploitation and are typically engaged in informal employment. The construction industry is mainly dominated by male employees with women being subordinated and occupying inferior positions within the company and mostly they are employed in non-mechanical occupations. Women engaged in informal construction work are particularly at risk of undernutrition and exposed to various occupational health hazards. Women working in the construction sector face several difficulties, including the absence of social protection, inadequate pay, gender-based discrimination, and incidents of sexual harassment. Due to the contract labour system, the majority of employment in the industry is insecure, and the exploitation of women workers is often continued. Additionally, it is imperative to note that the workplace is unfair and hostile. As per the laws formulated for the labourers, the workers are forced to work in the hostile environments where they are deprived of all the basic facilities. The problem is that it is difficult to find consistent work and there are very low monthly wages because many positions are only available in certain seasons. The payment has been affected through mechanization and this has forced the workers to seek chances to develop themselves through self-training. When workers are provided with quality vocational training, it increases their daily pay rate besides increasing their level of satisfaction. Furthermore, the degree of satisfaction considerably depends on such factors as daily pay, safety at work, etc. This programme is not known to most construction workers and those who are members of the welfare board are also not aware of the benefits associated with the programme there exists a welfare board for construction workers. The workers should be protected against potential harm and gain the maximum possible value from their work to feed their families and children. Future studies explore the conditions under which female construction workers may work for more in detail. In light of the survey's results, a set of suggestions has been made. Providing regular non-formal training programmes would aid in the skill development of construction workers, as the majority lack formal education. Regardless of gender, put regulations in place to guarantee equal compensation for equal labour. Implement equitable pay policies to mitigate wage inequality and ensure women receive sufficient remuneration for their work. The government must provide sufficient pay and an equivalent number of working days in comparison to their male workers. For a comprehensive health strategy, the public health care system should be fortified. Provide networking opportunities and support systems for female construction workers so they can converse with others who work in the field. Encourage women workers to save and give them financial stability by making financial services like banking and insurance easier to access. Provide financial literacy initiatives to augment their comprehension of financial management and investing prospects. Attempt to enhance the industry's culture and image so that women feel more accepted and comfortable in the construction sector. Create mentoring programmes and support systems for women employees to empower them and advance their careers. Women employed in the construction sector should be motivated to build networks and collaborate, enabling them to share their experiences, insights, and opportunities for advancement. Provide accommodative work conditions, such as flexible scheduling and family-friendly rules, that speak to the needs of women construction workers. Women are informed of the gendered obstacles they may encounter in the construction business to better equip early-career women with the skills necessary to succeed in this male-dominated field. Motivating elements and potential workplace difficulties might be balanced to enable women to make well-informed choices about their work paths and possibilities (R. P. Zhang et al., 2021). Forging the Future It can therefore be postulated that women’s contribution and participation shall remain instrumental in the future for the construction industry. Let the women make better careers in construction that will lead to improved jobs, better and well remunerated careers be provided to women and work hand in hand in fashioning the world a better and deserving place for all. This study has some limitations because its participants are the construction workers from Lucknow city of Uttar Pradesh and the study is confined to only those women who are working in the Lucknow region’s building construction sector. Consequently, if the woman is employed in a different construction Industries or different geographical region then she would not find the findings to be relevant. To some extent, these surveys only have 110 respondents taking part, which weakens the generality of the conclusions obtained. Perhaps, a larger sample size would enable elaborate research on female construction workers. These gaps can only be inferred and thus we can only have an indication of the applicability of the findings to other nations and/or sectors of the economy. However, the female nature of the risks inherent in participants’ occupations seems quite similar to what some other countries’ building tradeswomen experienced. The work is an addition to an area that has not been the subject of extensive empirical research despite these limitations. Further Study Directions Promoting legislation to mitigate the socio-economic challenges faced by women employed in construction may be a useful area of research in the future. This includes those in equal remuneration for work of equal value, social security, and anti-discrimination for women. Skill development programmes which have been tailored to focus on women in construction could help liberate them economically. Future research studies may then compare the effectiveness of these interventions in improving the employment opportunities and overall social and economic status of women employees. Subsequent research could be useful when analyzing the extent of technology implementation within construction projects as well as its impact on the social and economic status of women employees. This means looking at how women employed within the construction industry could stand to gain with regard to technology advancements in areas such as the safety of employees, efficiency of work done, and employment guarantees. The following might be the potential avenues for future research, which could eventually bring about a considerable change in the current status of women employed in the construction industry and contribute towards developing a more egalitarian employment market. Declarations This study was reviewed and approved by the Institutional Ethics Committee of Dr. Harisingh Gour Vishwavidyalaya, a Central University, Sagar, Madhya Pradesh, India. The research protocol and methodology adhered to the ethical standards laid down by the University’s internal review process. All participants in this study were informed about the purpose, scope, and voluntary nature of their participation. Verbal consent was obtained prior to data collection, and participants were assured of confidentiality and anonymity." Data availability statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to containing information that could compromise the privacy of research participants. Disclosure statement The authors declare they have no competing interests. Funding The author received no financial support for the research, authorship, and/or publication of this article. 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Resilience in a hostile workplace: the experience of women onsite in construction. Construction Management and Economics , 39 (10), 839–852. https://doi.org/10.1080/01446193.2021.1981958 Zhang, R. P., Holdsworth, S., Turner, M., & Andamon, M. M. (2021). Does gender really matter? A closer look at early career women in construction. Construction Management and Economics , 39 (8), 669–686. https://doi.org/10.1080/01446193.2021.1948087 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7367980","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500162735,"identity":"fb5fa049-4708-429e-8056-49a36e283a71","order_by":0,"name":"Dr. Akanksha Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACHjaGAwwGDAwG7A3pHz4ABdjYidbCc+AZ4wyQFmYitICBgYTjM2YeEIuQFv6eY4kHfxTcsdsuwZz22ObXNnk+ZgbGDx9zcGuRONt24DCPwbPknbPb0o1z+24btjEzMEvO3IbHmvPsDYcZDA4nG9w5kyCd23ObEaiFjZkXjxZ5oJaDP0BabuR/kLbsuW1PUIsB0GEHeAwO2xncSEiTZvhxO5GgFsMzxxKAfjmcYHDmQLJhb8Pt5DZmxma8fpE7k2b88cefw/YGxxsSH/z4c9t2fnvzwQ8f8XkfChIbQCRjG5hsIKweCOwh1B+iFI+CUTAKRsEIAwCik1sTohqOEwAAAABJRU5ErkJggg==","orcid":"","institution":"KCC Institute of Legal and Higher Education","correspondingAuthor":true,"prefix":"Dr.","firstName":"Akanksha","middleName":"","lastName":"Singh","suffix":""},{"id":500162736,"identity":"62fd1236-ae4a-42fd-b8af-909d3b8c04a0","order_by":1,"name":"Prof. Girish Mohan Dubey","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Prof.","middleName":"Girish Mohan","lastName":"Dubey","suffix":""}],"badges":[],"createdAt":"2025-08-13 20:37:33","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7367980/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7367980/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89461370,"identity":"f204fa70-ad40-4eae-9cd3-ea29716b67c6","added_by":"auto","created_at":"2025-08-20 08:03:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141440,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlt-text: Structural Equation Model parameters for the impact of job and income on female construction workers' well-being\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7367980/v1/d33681d35d09bf8357d2f248.png"},{"id":89464195,"identity":"db4f3fe5-0eca-4596-87c7-044d55ff4537","added_by":"auto","created_at":"2025-08-20 08:19:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1377359,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7367980/v1/762ffa57-31f5-42f7-9b85-a5d2b178c888.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eShedding Light on the Socio-economic Status of Women Construction Workers in India\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the bustling landscape of construction sites, where towering cranes and ceaseless machinery dominate, an often overlooked yet indispensable force perseveres\u0026mdash;women construction workers. These women, integral to the backbone of urban development, face a unique confluence of socio-economic challenges. This research delves into the lives of these resilient workers, shedding light on their financial struggles, health issues, and the broader societal implications of their labor. This research seeks to examine the socio-economic realities of women working in the construction sector, shedding light on their everyday struggles and the structural challenges they face.\u003c/p\u003e\u003cp\u003eIn many developing nations, the informal sector significantly contributes to economic growth and provides employment to a large segment of the population. Thus, the primary source of income in their economies is informal employment. In 1970 research on Ghana's metropolitan areas, Keith Hart coined the phrase \"informal sector\", he identifies the segment of the urban workforce that falls outside the organized labor market as the informal sector. A mission of the International Labour Organisation (ILO) (1972), which examined Kenya's employment situation within the framework of the World Employment Programme, helped to further clarify the idea. India's economy and employment are indeed significantly influenced by the building industry. According to a report by the Confederation of Indian Industry (CII), the construction industry in India is expected to grow at a Compound Annual Growth Rate (CAGR) of 11% during the period from 2020 to 2025. The sector is broadly divided into two key segments: the organised and the unorganised (informal) sectors (Confederation of Indian Industry [CII], 2020). The organised sector includes things like government-funded projects and big construction companies, whereas the unorganised sector includes things like independent contractors and unlawful labour. The informal sector holds a significant presence in India's construction industry, employing a substantial portion of the workforce. The construction sector is estimated to engage nearly 30\u0026nbsp;million workers, with women comprising about 51% of this workforce. It contributes nearly 5% to India\u0026rsquo;s Gross Domestic Product (GDP) and accounts for 8% of the nation's capital formation (SEWA Academy, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Although they make up 48 per cent of the population in India, women have even less influence over the country's development. The fundamental way of life of the majority of women has not changed despite reforms, special legislation, the establishment of numerous institutions ostensibly to meet their needs, and increases in budgetary allocations over time. Despite women's advancements in various fields, the past three decades of progress have not uniformly benefited women in the workforce, particularly those in unregulated sectors (Manohar et al, 1980). Many workers in the construction sector do not hold official employment status and are therefore exempt from benefits like social health and welfare insurance. Workers may find it challenging to gain legal rights and protections due to their lack of formal employment. The construction industry is dominated by men on a worldwide scale; in the West, women who want to work in this profession often need to complete vocational training in related subjects like engineering, management, etc. (Tanzina Choudhury, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Participating in the barriers and measures needed to enhance women in skilled trades and apprenticeships is one of the few studies however that has been undertaken in Canada (Bourdy, 2020).\u003c/p\u003e\u003cp\u003eSeasonality is noticeably distinct in India's construction industry as most works are performed during the dry season. On the other hand, concerning its workforce, the construction industry in some ways is different from the majority of industries. As such, most construction sites are temporary since most buildings do not need a lot of maintenance once construction is completed. However, occupational mobility in the construction sector is faced with special circumstances in construction sites where temporary welfare facilities are required. The informal sector includes people working in the form of construction labor. In this case, they may be famine-stricken as most of such labour is seasonal, and such a high volume of labour will not be available during the monsoon season. To enhance the construction sector in India, the government has introduced several initiatives. One such reform is the Real Estate (Regulation and Development) Act, 2016, which seeks to safeguard consumer interests while ensuring greater transparency and accountability in real estate dealings. Additionally, the Pradhan Mantri Awas Yojana (PMAY), launched in 2015, aims to provide affordable housing to economically weaker sections and low-income groups.\u003c/p\u003e\u003cp\u003eIn conclusion, the construction sector in India is a large, potentially growing sector, but one that is also challenged by informality and seasonality, all of which could impact the livelihoods of the workers. The nature of the work in such sectors tends to be seasonal and depends on the quantity of work, which is vastly different depending on the project. Surprisingly, even though the industry contributes to jobs for a significant segment of India's urban population, construction workers remain among the most marginalized groups in society, with limited efforts to improve their situation. \u0026ldquo;Women involved in construction work primarily work as head-loaders, transporting materials like water, sand, cement, and bricks. Alternately, they break stones, clean, dig, and mix mortar\u0026rdquo;. On Indian construction sites, it's not uncommon to find groups of women lugging 90-pound brick bundles. Opportunities for women to train in more profitable but predominately male trades, such as carpentry, masonry, plumbing, and electrical work, are incredibly scarce in India. (Baruah, 2014). In addition to highlighting the potential benefits of women's paid employment, several researchers have also drawn attention to the drawbacks of women's involvement in the workforce. While paid work can reduce certain forms of women's subordination at home, these scholars argue that it can expose women to more exploitative and precarious situations in the public sphere.\u003c/p\u003e\u003cp\u003eThe research builds upon existing literature by focusing on the work experiences of female construction workers in Lucknow, specifically in the Aashiyana and Alambagh localities, where a high concentration of such workers is observed. By evaluating their expenditures and the health consequences of working on construction sites, which is the main source of sickness for women, Explore the specific health challenges faced by women in construction, considering factors such as occupational hazards, access to healthcare, and the impact of working conditions on their physical and mental well-being. the study will also examine their economic situation and delve deeper into the forms of discrimination experienced by women on construction sites, including both overt and subtle biases, and unequal opportunities for advancement.\u003c/p\u003e\u003cp\u003eThis study examines the economic status of women employed in construction and investigates workplace health risks through the formulation of two key hypotheses. The socio-economic challenges faced by female construction workers in Lucknow represent a critical concern, carrying significant consequences not only for the affected individuals but also for the wider society. This research is motivated by the need to highlight and address the socio-economic challenges these women face, including low wages, lack of social security, and health risks. Understanding these conditions is crucial for developing policies that can improve their quality of life and promote gender equality in the labor market.\u003c/p\u003e\u003cp\u003eThe primary research question is: \"What are the socio-economic conditions and challenges faced by female construction workers in Lucknow, and how do these impact their overall well-being?\" This question aims to provide a detailed analysis of the economic and social hardships encountered by these women, offering insights for policymakers and stakeholders to devise effective interventions. By identifying these issues, the research provides a basis for advocating for policy changes, interventions, and support systems that can enhance the socio-economic well-being of women in the construction sector. Therefore, this study adds value by highlighting the socio-economic challenges faced by women in the construction sector, promoting the cause of gender equity, and offering meaningful insights that can guide policy and interventions to enhance the well-being of women working in this field.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThe socio-economic realities faced by women in the construction sector have drawn considerable scholarly attention, revealing a multifaceted set of factors contributing to their marginalization. Numerous studies underscore the prominent role of women in the informal construction workforce, especially in developing countries, where they make notable contributions to employment and household income (Mehta, 1995; Rani \u0026amp; Unni, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Mehta (1995), for example, documented a rising dependency on the informal sector in Ahmedabad, with its employment share increasing from 46.5% in 1971 to 65% by 1981\u0026mdash;an indication of expanding informal labor, often marked by insecure and unstable working conditions. Rani and Unni's (2000) study further emphasized the informal sector's contribution to Ahmedabad's economy, noting that it employed approximately 1,504,033 workers and generated an income of around \u003cspan\u003e$\u003c/span\u003e60,130\u0026nbsp;million in 1997-98. This demonstrates the sector's economic importance, even while acknowledging the often precarious nature of informal employment.\u003c/p\u003e\u003cp\u003eDespite their significant contribution, women in construction often face numerous challenges. Jayalakshmi (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that among female construction workers surveyed, only 36% possessed basic literacy skills, and a large proportion (48%) were migrant labourers, often travelling from various districts in search of work. This vulnerability is further compounded by low earnings, with 45% of respondents earning between INR 9,000 and INR 11,000 annually. Jayalakshmi's study concluded that construction workers, particularly women, represent a highly marginalized segment of society, often living in poverty. This aligns with broader observations about the construction industry, where workers are frequently among the most economically disadvantaged. The study also suggests a need for government intervention to improve the living standards of these workers, possibly through direct employment or by enforcing better labour practices within the private sector. Adding to these challenges, Banu (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) highlighted a range of issues faced by women in construction, including a lack of social security, poor wages, sexual harassment, gender discrimination, illiteracy, and the dispersed nature of work sites. These factors combine to create a challenging and often exploitative environment for women. The lack of social security leaves them vulnerable to illness, injury, and old age without any safety net. Poor wages perpetuate their economic hardship, while sexual harassment and gender discrimination create hostile work environments. Illiteracy further limits their ability to access information and advocate for their rights. The dispersed nature of construction sites can make it difficult to organize workers and ensure compliance with labour laws.\u003c/p\u003e\u003cp\u003eThe vulnerabilities faced by women in construction are not limited to specific geographical contexts. Jubany and Castellanos (2021) explored the temporary migration of Mexican women workers to the US and Canada, examining how gender, race, and class intersect to shape their experiences. Their research delved into the dilemmas these women face within the context of post-Fordist labour markets and heightened social control. They examined the complexities of interagency relationships, the impact of neoliberal policies, the challenges posed by the crisis of multiculturalism, and the effects of social control on the lives of these women. By analyzing these factors, Jubany and Castellanos aimed to provide a more nuanced understanding of temporary labour migration, moving beyond traditional frameworks to address the systemic inequalities linked to the working conditions and lived experiences of these women. Their research underscores the importance of considering the intersectional nature of disadvantage, where gender, race, and class combine to create unique forms of vulnerability.\u003c/p\u003e\u003cp\u003eMore recently, Ramila and Amalanathan (2023) reiterated the social and economic factors impacting women in the Indian construction industry, focusing on vulnerabilities such as low wages, gender discrimination, and a lack of access to social security. Their work reinforces the findings of earlier studies, highlighting the persistent nature of these challenges. Similarly, Mishra (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) argued for urgent government support to address the challenges faced by women in construction, emphasizing the need for improved working conditions, fair pay, and the elimination of gender discrimination. Mishra's work underscores the urgency of the situation and the need for concrete action to improve the socio-economic realities of women working in this vital sector. Collectively, these studies highlight the persistent and multifaceted challenges faced by women in the construction industry worldwide. They call for comprehensive and multi-pronged strategies to address these issues, including policy interventions, stricter enforcement of labour laws, and greater awareness of the contributions and vulnerabilities of women in this sector.\u003c/p\u003e\u003cp\u003eUnderstanding the complex interplay between these factors from the above literature provides a more nuanced understanding of the challenges faced by women workers and informs targeted interventions and policies to improve their overall socio-economic status. Therefore, this study could be addressed in this context in the lack of in-depth exploration into the intersectionality of factors influencing women's socio-economic well-being in construction. Specifically, there is a need to delve deeper into how health issues, discrimination, and economic challenges interact and compound each other to create unique barriers for women in this industry.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eObjective of the Study\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTo examine the socio-economic conditions of female workers in Lucknow.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTo investigate the workplace conditions and risks to women workers' health.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Research Methodology","content":"\u003cp\u003e\u003cstrong\u003eResearch Design and Area of the Study\u003c/strong\u003e\u003cp\u003eBoth primary and secondary data were employed in the investigation. In Lucknow city, 110 female construction workers were chosen at random to provide primary data from the areas of Aashiyana and Alambagh. Two locations in Lucknow City had a field assessment in June\u0026ndash;August of 2022.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eTo acquire information on workers' health hazards, workplace challenges, and socio-economic status, a comprehensive timetable was utilized. Most of the time, during lunch, employees are informed directly by their employer, and working conditions are examined. Several references were made, including articles, journals, books, and government documents like the Economic Review, and the ILO, to obtain secondary data.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResearch Tool\u003c/strong\u003e\u003cp\u003eThe research methodology employed in the study of women in construction, using one-way ANOVA and SEM, implemented via AMOS, stands out from the previous research methodology which has looked at women in construction or the construction industry.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe statistical technique, ANOVA, or analysis of variance, for situations where we want to compare means across three or more groups, or whether the observed differences are statistically significant or not. If we are looking at women\u0026rsquo;s work in the construction industry, we may want to use ANOVA to compare wage levels, working conditions, or job satisfaction across different groups of women workers.\u003c/p\u003e\u003cp\u003eStructural equation modeling (SEM) is an advanced statistical technique utilized to assess and estimate complex interrelationships between variables. By applying SEM using AMOS, a multiple-variable analysis can be performed, thus facilitating a thorough investigation of the relationships among variables that influence job income and the well-being of construction workers. This method enables researchers to model latent variables and test complex hypotheses, providing a deeper insight into the socio-economic conditions of women workers.\u003c/p\u003e\u003cp\u003eHere's how this methodology differs from other studies: The current methodology, which incorporates one-way ANOVA and the Structural Equation Model (SEM), represents a significant advancement over past studies on the socio-economic conditions of women workers in the construction industry. Traditionally, research in this area often relied on qualitative analyses and basic statistical methods to assess the socio-economic status of female construction workers. These earlier studies primarily focused on descriptive statistics and simple correlations to identify key issues and trends. In contrast, the use of one-way ANOVA allows for more precise comparisons between different groups, providing a clearer understanding of how various factors affect women workers' socio-economic conditions. Furthermore, SEM enables a comprehensive examination of complex relationships among multiple variables, offering deeper insights into the structural and causal relationships that influence the socio-economic status of these workers. This combination of advanced statistical techniques ensures a more robust and nuanced analysis, enhancing the reliability and validity of the findings.\u003c/p\u003e\u003cp\u003eReliability testing for data involves assessing the consistency and trustworthiness of the data.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1.1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReliability Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCronbach's Alpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCronbach's Alpha Based on Standardized Items\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo. of Items\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eInternal consistency of the scale was evaluated using Cronbach\u0026rsquo;s Alpha. \u0026ldquo;The obtained values were 0.672 and 0.616 (based on standardized items), reflecting moderate reliability. These coefficients suggest that the items within the respective scales exhibit a reasonable level of interrelatedness, thereby supporting the internal reliability of the instrument (Cronbach, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1951\u003c/span\u003e; Tavakol \u0026amp; Dennick, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u0026rdquo;.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eAs per the 2011 Census, 2,817,105 people were living in Lucknow city, with 1,460,970 men and 1,356,135 women. At 1,815 people per square kilometer (4,700/sq mi), the district has a high population density. Over the years 2001 to 2011, its population grew at a rate of 25.79 per cent. There are 906 girls for every 1000 men in Lucknow, which has a 79.33% literacy rate. The largest capital city in the Indian state of Uttar Pradesh is Lucknow. Additionally, to creating employment opportunities, aids in the growth of the city\u0026apos;s infrastructure and real estate. Cement, steel, and other building materials industries, among others, benefit from the industry\u0026apos;s growth as well. Considering how important it is to Lucknow, the construction industry was the focus of the current study. Health, social security, and wage difficulties are among the many problems that construction workers face.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.2: Demographic of the Respondents \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(n=110)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"566\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e25-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e30-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e35-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e40-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e45-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e50-55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e30.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e37.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eGraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2000-4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4000-6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e66.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e6000-8000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e8000-10,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAbove 10,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExpenditure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1000-1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1500-2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2000-2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2500 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMember of SHG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e34.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eBelow 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e8 Hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAbove 8 Hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e84.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeasonality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSavings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNothing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0-500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e500-1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1000-1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAbove 1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDebts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eTaken\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e79.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProblems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eHealth\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eFinancial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e66.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth Problem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMuscle Pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAllergy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAsthma \u0026amp; Breathing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eCough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAll of the above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e55.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospitals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eGovt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePrimary Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWage Discrimination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eExists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSource: Field Survey\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe respondents provide sociodemographic and economic information. Six age groups for workers are distinguished in Table 1.2 Of the 110 samples, 30.9% of the workers are in the 50\u0026ndash;55 age range, followed by 22.7% in the 40\u0026ndash;45 age range, 21.8% in the 45\u0026ndash;50 age range, and 19.1% in the 35\u0026ndash;40 age range. 50.9 per cent of the respondents have only primary school qualifications, 37.3 per cent of the respondents are illiterate, and 9.1 per cent of the respondents have secondary school qualifications. The range of monthly income for 66.4 per cent of respondents is between Rs. 4000-Rs. 6000, followed by Rs. 6000-Rs. 8000 for 20% of respondents and Rs. 2000-Rs. 4000 for 8.2 percent of respondents. The research by Saka et.al (2022) also highlights the significant contributions of women to social and economic development while underscoring the lack of appreciation and support they receive. They also emphasize the risks and vulnerabilities faced by women labourers due to factors such as low education levels and limited skills, pointing to the need for empowerment through training and skill enhancement.\u003c/p\u003e\n\u003cp\u003eOf the respondents, 60% spend more than Rs. 2,500 per month, while 18.2% spend between Rs. 1,500 and Rs. 2000 per month. SHGs have become increasingly important in recent years, and a large number of women are becoming members and actively taking part in a range of SHG events. \u0026nbsp;Findings reveal that merely 34.5% of workers are SHG members. These workers are generally poor, earning daily wages barely sufficient to maintain their families (Tiwari et al., 2012).\u003c/p\u003e\n\u003cp\u003eWhile an eight-hour workday is often considered standard, evidence from the informal sector challenges this notion. Data reveals three distinct work hour patterns: less than eight hours, eight hours exactly, and more than eight hours. Strikingly, 84.5% of respondents reported working beyond eight hours daily, while only 13.6% adhered to an eight-hour schedule and a mere 1.8% worked less.\u003c/p\u003e\n\u003cp\u003eOver half of the respondents (51.8%) reported monthly savings between Rs. 1000-1500, while a significant portion (24.5%) saved Rs. 500-1000 per month. Notably, the findings reveal a relatively low propensity for female construction workers to save.\u003c/p\u003e\n\u003cp\u003eWorkers in the construction industry are forced to take out loans for a variety of reasons due to their poor income and inability to cover their basic demands with their meager wages. 79.1 per cent of the respondents have taken loans. The primary cause of their debt is the disparity between inadequate income and rising expenses. To better grasp the actual situation facing female construction workers, focus groups were held. Many people acknowledge that their lack of other options and financial difficulties forced them to work in the industry. \u0026nbsp;A study in Kerala highlighted that women in construction face under-representation and significant socio-economic difficulties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNull Hypothesis (H\u003csub\u003e01\u003c/sub\u003e):\u003c/strong\u003e There are non-significant determinants of the expenditure of women construction workers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlternate Hypothesis (H\u003csub\u003ea1\u003c/sub\u003e):\u003c/strong\u003e There are significant determinants of the expenditure of women construction workers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of Determinants of Expenditure for Women Construction Workers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe function applied to a cross-section of construction women workers to investigate what variables are affecting the expenditures of the respondents. The model is,\u003c/p\u003e\n\u003cp\u003eY = \u0026alpha; + \u0026beta;\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e1\u003c/sub\u003e + \u0026beta;\u003csub\u003e2\u003c/sub\u003e X\u003csub\u003e2\u003c/sub\u003e + \u0026beta;\u003csub\u003e3\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e + \u0026beta;\u003csub\u003e4\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e + \u0026beta;\u003csub\u003e5\u003c/sub\u003eX\u003csub\u003e5\u003c/sub\u003e + e\u003c/p\u003e\n\u003cp\u003ewhere, Y - Per capita Expenditure (in Rs.)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;X\u003csub\u003e1\u003c/sub\u003e \u0026ndash; Age (in number)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;X\u003csub\u003e2\u003c/sub\u003e \u0026ndash; Education\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;X\u003csub\u003e3\u003c/sub\u003e \u0026ndash; Income (in Rs.)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;X\u003csub\u003e4\u003c/sub\u003e \u0026ndash; Time of work\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;X\u003csub\u003e5\u003c/sub\u003e \u0026ndash; Debt (in Rs.)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;e - Error term\u003c/p\u003e\n\u003cp\u003eThe estimated linear expenditure function is furnished in the following table\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"414\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 414px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.3: Model Summary\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eR Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eAdjusted R Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.747\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.71324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 414px;\"\u003e\n \u003cp\u003ea. Predictors: (Constant), debt, income, workers, Time, Educational, Age\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 414px;\"\u003e\n \u003cp\u003eb. Dependent Variable: Expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"526\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 526px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.4: ANOVA\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e66.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e13.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e26.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.000\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e52.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e119.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 526px;\"\u003e\n \u003cp\u003ea. Dependent Variable: Expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 526px;\"\u003e\n \u003cp\u003eb. Predictors: (Constant), debt, income, Time, Educational, Age\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"622\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 622px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.5: Coefficients\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized Coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized Coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(Constant)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u0026beta;\u003csub\u003e0\u003c/sub\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-2.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-3.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u0026beta;\u003csub\u003e1\u003c/sub\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-1.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u0026beta;\u003csub\u003e2\u003c/sub\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u0026beta;\u003csub\u003e3\u003c/sub\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u0026beta;\u003csub\u003e4\u003c/sub\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e8.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDebt\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u0026beta;\u003csub\u003e5\u003c/sub\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 622px;\"\u003e\n \u003cp\u003ea. Dependent Variable: Expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFrom Table 1.3, According to the R-value for female construction workers, all of the explanatory variables in the model simultaneously comprised 74.7% of the variations in the per capita consumption variances. The annual income is statistically significant at a five percent threshold, and they had a positive correlation with spending on consumption. Debt is strongly correlated with consumer spending and is statistically significant at the 5% level. Age was the major component that had the most impact on construction workers\u0026apos; consumption expenditures. A 1% rise in each of these factors may, in the case of yearly income, result in a corresponding 1% increase in consumer spending. The model fitted was determined to be statistically significant at a five percent level, as demonstrated by the F-value of 26.321. Research by Archana Saini and K. Sharma (2021) also emphasizes the prevalence of informal work arrangements and lack of social protection for about 93% of construction workers. Female workers, often seasonal migrants, face occupational hazards, inadequate healthcare access, and gendered health impacts due to poor working conditions and poverty-induced challenges. Similar issues were observed in Gurugram, where most female laborers deal with inadequate social security, poor wages, and gender bias (Archana and K sharma, 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNull hypothesis (H\u003csub\u003e02\u003c/sub\u003e):\u003c/strong\u003e Workplace factors do not significantly affect the well-being of women construction workers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlternate hypothesis (H\u003csub\u003ea2\u003c/sub\u003e):\u003c/strong\u003e Workplace factors significantly affect the well-being of women construction workers.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"501\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 501px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.6: KMO and Bartlett\u0026apos;s Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 414px;\"\u003e\n \u003cp\u003eCronbach\u0026rsquo;s Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 414px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Kaiser-Meyer-Olkin Measure of Sampling Adequacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBartlett\u0026apos;s Test of Sphericity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eApprox. Chi-Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e722.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Source: Computed Data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe table summarizes the findings of two essential statistical procedures used to assess whether the dataset is appropriate for factor analysis: the Kaiser-Meyer-Olkin (KMO) measure and Bartlett\u0026rsquo;s Test of Sphericity. \u0026ldquo;A KMO score of 0.821 indicates a high degree of shared variance among the variables, which confirms the adequacy of the sample for conducting factor analysis (Kaiser, 1974). Bartlett\u0026rsquo;s Test of Sphericity yielded a chi-square value of 722.016 with 45 degrees of freedom and a significance level of p \u0026lt; 0.001. This significant result demonstrates that the correlation matrix is not an identity matrix, suggesting meaningful relationships exist among variables and supporting the suitability of the data for structure detection (Bartlett, 1954)\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eIn this analysis, two independent constructs were explored: one focused on job-related income and overall well-being among construction workers. The dependent variables under this construct were identified as follows: F2 representing financial savings, F3 relating to financial security for the future, F4 addressing decision-making power within the household, and F5 indicating satisfaction with life quality. A second independent dimension explored workplace-related concerns, with dependent variables including W2 (availability of a secure and safe work environment), W3 (experiences of discrimination at work), W4 (instances of being paid less than male counterparts), and W5 (access to workplace social security).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 617px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.7: Descriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkewness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKurtosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e2.1727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.66165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.4818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.51991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-1.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.8455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.41056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-1.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.5091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.55431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eF5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.7545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.47287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.5909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.54681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.4455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.51734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-1.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.3091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.48359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eW4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.2727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.46746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eW5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.47987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eValid N (listwise)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSource: Computed Data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eF1, F3, and F5 generally have higher mean scores than F2, F4, and the W variables. Thus there could be a bias towards those variables having higher values.\u003c/p\u003e\n\u003cp\u003eVariable: F1, F4 \u0026mdash; W1 offer broader scores in between reactions.\u003c/p\u003e\n\u003cp\u003eShape of Distributions: Most distributions are symmetric (slightly skewed but skewed near 0). F3, not W3--W5: F3 is the only factor leaning toward higher scores (negative skewness).\u003c/p\u003e\n\u003cp\u003eMost variables have a kurtosis close to zero i.e. they are normally distributing F3 and W4 are quite similar but deviate slightly with more peaked shapes (positive kurtosis).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural Model Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the relationships among the constructs, a structural equation modeling (SEM) approach was utilized using AMOS software. The model fit was assessed through several established indices. \u0026ldquo;According to Hair et al. (2010), an acceptable model fit is indicated when the chi-square to degrees of freedom ratio (CMIN/df) is below 5, the Goodness-of-Fit Index (GFI), Tucker-Lewis Index (TLI; Tucker \u0026amp; Lewis, 1973), and Comparative Fit Index (CFI; Bentler, 1990) each exceed 0.90. Furthermore, acceptable values for the Standardized Root Mean Square Residual (RMR) should be under 0.05, while the Root Mean Square Error of Approximation (RMSEA) should lie between 0.05 and 0.08 (Hair et al., 2010). The fit indices derived from the current model fall within these recommended thresholds: CMIN/df = 1.426, GFI = 0.953, TLI = 0.980, CFI = 0.989, and RMSEA = 0.063.\u0026rdquo; These values collectively suggest a satisfactory model fit, confirming the suitability of the proposed structural framework.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.8. Values of model fit indices in SEM to the data.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExpected values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eChi-square Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePreferably low, with non-significant p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e1.426 (p = 0.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eRoot Mean Square Residual (RMR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003ebelow 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eRoot Mean Square Error of Approximation\u003c/p\u003e\n \u003cp\u003e(RMSEA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eShould be below 0.05 for a close fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eGoodness-of-fit Index (GFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eValues above 0.90 indicate good fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eAdjusted Goodness-of-fit Index (AGFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAcceptable when above 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eIncremental Fit Index (IFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eA value greater than 0.90 shows good model fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eNormed Fit Index (NFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eShould exceed 0.90 for acceptable fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eRelative Fit Index (RFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eRanges from 0 (no fit) to 1 (perfect fit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eComparative Fit Index (CFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAbove 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eParsimonious Normed Fit Index (PNFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eHigher values are preferred, reflecting model parsimony\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA PNFI score of 0.51 suggests a reasonably good level of model parsimony. The standardized path coefficients for each hypothesized structural relationship are examined to assess the strength, direction, and significance of the proposed links. Within the presented conceptual framework, all the paths demonstrate statistically significant relationships at the 5% significance level. The research above indicates that the amenities offered at their workplace have an impact on the well-being of female construction workers. Additionally, research from Tamil Nadu noted that women workers often work in unhygienic conditions and lack basic amenities (P Jayalakshmi, 2016).\u003c/p\u003e\n\u003cp\u003eFor years, studies in India have shown how women workers, specifically in the construction sector, bear the brunt of economic and health adversities. In terms of economics, women are working in this stream primarily as unskilled labourer with the issues of wage disparity, gender bias and lack of chances for skill development. While providing significant employment, women are often paid less, and face gender discrimination and a glass ceiling. Female construction workers in India also face several health problems such as work-related musculoskeletal disorders, occupational health problems, and poor access to healthcare services. The working and living conditions of the construction industry may adversely affect women\u0026apos;s health, which in turn raises concerns about job stress, workplace injuries, and a lack of appropriate maternity protection.\u003c/p\u003e\n\u003cp\u003eHowever, there are compelling reasons for newer workplaces that ease the burden on women who are also primary caregivers at their homes; measures that can draw from these trends to improve both working condition and gender equality balance along with better health and safety for the women construction labor in India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat are women\u0026apos;s perceived job aspirations?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the unorganized sector, women\u0026apos;s projected goals for their careers often centre on becoming financially secure, getting into official jobs, and becoming acknowledged for their contributions by society. Empirical research suggests that women employed in the informal sector strive to establish consistent sources of income to uplift their level of living and provide for their children. They also want formal employment security and access to health benefits, which emphasises their need for social safety and economic stability. In addition, women want the autonomy to select the kind of job they undertake, looking for positions that suit their interests and skill set. To improve their socio-economic standing and employability, they also want to have access to chances for skill development and job growth. \u0026nbsp;All things considered, women working in the unorganised sector hope to overcome the obstacles of unstable employment, poor pay, and lack of social acceptance by taking advantage of increased chances for financial independence and individual development. Many respondents lacked a career trajectory due to their recent entry into the field or their intention to depart due to a perceived lack of prospects. While many expressed a desire to join construction companies, they acknowledged that for a sustainable career, including the ability to balance family responsibilities or enjoy flexibility, transitioning to construction consultancy would be necessary.\u003c/p\u003e"},{"header":"Conclusion and Recommendations","content":"\u003cp\u003eThe role of women in influencing the construction sector is becoming progressively more significant. They are shattering stereotypes, promoting innovation, and encouraging the next generation to enter the construction industry as leaders, pioneers, and inventors. By promoting diversity and inclusion, we can help the construction sector as a whole grow and realize the full potential of its workforce.\u003c/p\u003e\n\u003cp\u003eThe research highlights that women employed in Lucknow\u0026rsquo;s construction sector often work under unsafe and unjust conditions. One of their major challenges is the absence of adequate safety measures and personal security at construction sites (Subhash \u0026amp; Satyapal Yadav, 2022). Their employment is largely irregular, particularly for unskilled labor, as much of the work is seasonal, resulting in low monthly earnings. These women also face various forms of gender-based discrimination, limited job satisfaction, lack of access to social protection, and are vulnerable to both sexual and gender-based harassment. Women in the construction business lacked skills and had a variety of health issues; as a result, government and non-governmental groups ought to create training programs (R.Jency Antony Ramila and J.Amalanathan, 2022).\u0026nbsp;In the study, women are paid less than males and have to put in more physical labour to finish building projects, their income at work has an impact on their overall well-being.\u003c/p\u003e\n\u003cp\u003eWomen in the construction sector are largely under-represented, accounting for about 10% of the labour force, often facing obstacles such as sexual harassment, wage discrimination, and workplace injuries. Despite performing similar tasks, women workers continue to earn less than their male counterparts. In addition to wage inequality, they also report facing harassment at the workplace (Hazarika \u0026amp; Madhukullya, 2024). First, gender-based violence creates a culture that allows employers to abuse many female construction workers besides reinforcing the gendered hierarchy of workplaces. To address gender based violence and discrimination and in order to prevent it from happening in the construction business, there should be policies to follow, remedies to provide and complaints procedures to follow. It is therefore self-evident that the industry is still discriminating against women and workers of colour in terms of employment based on their sex, ethnicity and country of origin. A research based on Chennai pinpointed the general socio-economic frailty of these women while stressing the improvements in their places of work and accessibility to welfare services. (Abdulraheem, 2020).\u003c/p\u003e\n\u003cp\u003eIn addition to low and inconsistent wages, gender-based pay disparities, and minimal access to social security or insurance schemes, women in the construction sector are frequently subjected to social and economic exploitation and are typically engaged in informal employment. The construction industry is mainly dominated by male employees with women being subordinated and occupying inferior positions within the company and mostly they are employed in non-mechanical occupations. Women engaged in informal construction work are particularly at risk of undernutrition and exposed to various occupational health hazards. Women working in the construction sector face several difficulties, including the absence of social protection, inadequate pay, gender-based discrimination, and incidents of sexual harassment. \u0026nbsp;Due to the contract labour system, the majority of employment in the industry is insecure, and the exploitation of women workers is often continued.\u003c/p\u003e\n\u003cp\u003eAdditionally, it is imperative to note that the workplace is unfair and hostile. As per the laws formulated for the labourers, the workers are forced to work in the hostile environments where they are deprived of all the basic facilities. The problem is that it is difficult to find consistent work and there are very low monthly wages because many positions are only available in certain seasons. The payment has been affected through mechanization and this has forced the workers to seek chances to develop themselves through self-training. When workers are provided with quality vocational training, it increases their daily pay rate besides increasing their level of satisfaction. Furthermore, the degree of satisfaction considerably depends on such factors as daily pay, safety at work, etc. This programme is not known to most construction workers and those who are members of the welfare board are also not aware of the benefits associated with the programme there exists a welfare board for construction workers. The workers should be protected against potential harm and gain the maximum possible value from their work to feed their families and children. Future studies explore the conditions under which female construction workers may work for more in detail. In light of the survey\u0026apos;s results, a set of suggestions has been made.\u003c/p\u003e\n\u003cul start=\"5\"\u003e\n \u003cli\u003eProviding regular non-formal training programmes would aid in the skill development of construction workers, as the majority lack formal education.\u003c/li\u003e\n \u003cli\u003eRegardless of gender, put regulations in place to guarantee equal compensation for equal labour. Implement equitable pay policies to mitigate wage inequality and ensure women receive sufficient remuneration for their work.\u003c/li\u003e\n \u003cli\u003eThe government must provide sufficient pay and an equivalent number of working days in comparison to their male workers.\u003c/li\u003e\n \u003cli\u003eFor a comprehensive health strategy, the public health care system should be fortified. Provide networking opportunities and support systems for female construction workers so they can converse with others who work in the field.\u003c/li\u003e\n \u003cli\u003eEncourage women workers to save and give them financial stability by making financial services like banking and insurance easier to access. Provide financial literacy initiatives to augment their comprehension of financial management and investing prospects.\u003c/li\u003e\n \u003cli\u003eAttempt to enhance the industry\u0026apos;s culture and image so that women feel more accepted and comfortable in the construction sector.\u003c/li\u003e\n \u003cli\u003eCreate mentoring programmes and support systems for women employees to empower them and advance their careers. Women employed in the construction sector should be motivated to build networks and collaborate, enabling them to share their experiences, insights, and opportunities for advancement.\u003c/li\u003e\n \u003cli\u003eProvide accommodative work conditions, such as flexible scheduling and family-friendly rules, that speak to the needs of women construction workers.\u003c/li\u003e\n \u003cli\u003eWomen are informed of the gendered obstacles they may encounter in the construction business to better equip early-career women with the skills necessary to succeed in this male-dominated field. Motivating elements and potential workplace difficulties might be balanced to enable women to make well-informed choices about their work paths and possibilities (R. P. Zhang et al., 2021).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eForging the Future\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt can therefore be postulated that women\u0026rsquo;s contribution and participation shall remain instrumental in the future for the construction industry. Let the women make better careers in construction that will lead to improved jobs, better and well remunerated careers be provided to women and work hand in hand in fashioning the world a better and deserving place for all.\u003c/p\u003e\n\u003cp\u003eThis study has some \u003cstrong\u003elimitations\u003c/strong\u003e because its participants are the construction workers from Lucknow city of Uttar Pradesh and the study is confined to only those women who are working in the Lucknow region\u0026rsquo;s building construction sector. Consequently, if the woman is employed in a different construction Industries or different geographical region then she would not find the findings to be relevant. To some extent, these surveys only have 110 respondents taking part, which weakens the generality of the conclusions obtained. Perhaps, a larger sample size would enable elaborate research on female construction workers. These gaps can only be inferred and thus we can only have an indication of the applicability of the findings to other nations and/or sectors of the economy. However, the female nature of the risks inherent in participants\u0026rsquo; occupations seems quite similar to what some other countries\u0026rsquo; building tradeswomen experienced. The work is an addition to an area that has not been the subject of extensive empirical research despite these limitations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFurther Study Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePromoting legislation to mitigate the socio-economic challenges faced by women employed in construction may be a useful area of research in the future. This includes those in equal remuneration for work of equal value, social security, and anti-discrimination for women.\u003cbr\u003e\u0026nbsp;Skill development programmes which have been tailored to focus on women in construction could help liberate them economically. Future research studies may then compare the effectiveness of these interventions in improving the employment opportunities and overall social and economic status of women employees.\u003c/p\u003e\n\u003cp\u003eSubsequent research could be useful when analyzing the extent of technology implementation within construction projects as well as its impact on the social and economic status of women employees. This means looking at how women employed within the construction industry could stand to gain with regard to technology advancements in areas such as the safety of employees, efficiency of work done, and employment guarantees. The following might be the potential avenues for future research, which could eventually bring about a considerable change in the current status of women employed in the construction industry and contribute towards developing a more egalitarian employment market.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eThis study was reviewed and approved by the Institutional Ethics Committee of Dr. Harisingh Gour Vishwavidyalaya, a Central University, Sagar, Madhya Pradesh, India. The research protocol and methodology adhered to the ethical standards laid down by the University\u0026rsquo;s internal review process.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003eAll participants in this study were informed about the purpose, scope, and voluntary nature of their participation. Verbal consent was obtained prior to data collection, and participants were assured of confidentiality and anonymity.\u0026quot;\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to containing information that could compromise the privacy of research participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed equally to the conception and design of the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdulraheem, A. 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A closer look at early career women in construction. \u003cem\u003eConstruction Management and Economics\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(8), 669\u0026ndash;686. https://doi.org/10.1080/01446193.2021.1948087\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Construction women workers, Lucknow, socio-economic condition, health issues, women empowerment, AMOS","lastPublishedDoi":"10.21203/rs.3.rs-7367980/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7367980/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study emphasizes the importance of rising socio-economic status and active involvement in home decision-making, which are contingent upon women's participation in the workforce. Considering that worker, especially women, engage in various business and non-business endeavors, health significantly influences their participation in the construction industry. This study explores the socio-economic conditions of women employed in construction in Lucknow, India, with particular attention to the difficulties they encounter in their workplace. Using a random sampling method, the study surveyed 110 female respondents from Aashiyana and Alambagh localities in Lucknow. Data interpretation was conducted using AMOS software with SEM, and SPSS software with regression analysis and ANOVA. The study found that financial difficulties affect 66.4% of these workers, while 79.1% struggle with debt. The majority of workers (66.4%) have a monthly income between Rs 4000 and Rs 6000. Additionally, the study highlights significant health and income-related issues that impact the well-being of women working in construction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Code: \u003c/strong\u003eJ16, J71, J01, N37, J08\u003c/p\u003e","manuscriptTitle":"Shedding Light on the Socio-economic Status of Women Construction Workers in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 08:03:24","doi":"10.21203/rs.3.rs-7367980/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"93e0eeb6-d39e-4da2-a66c-171b33e4f278","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53428850,"name":"Gender Studies"}],"tags":[],"updatedAt":"2025-08-20T08:03:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-20 08:03:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7367980","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7367980","identity":"rs-7367980","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}