An Empirical Analysis to Integrate Public Value and Child Labour Policy through an Evidence-based Approach for Sustainable Development.

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Fataraz Zahan, Mowshumi Sharmin, Shafin Haque Omlan, Md. Injamamul Haq Methun, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8937382/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Governments are currently prioritized to establish robust legal and policy adaption mechanisms to specify policy actions in order to address the public value and wide-ranging effects of child labor. However, one crucial question is the extent to which this policy is backed by evidence. The effective way to create a responsive and flexible policymaking process is to critically assess the state of evidence-based policymaking (EBP) and determine the circumstances that allow it to be used effectively throughout the policy cycle. This study examines the state of EBP within Bangladesh's public policy sphere, using the National Child Labor Elimination Policy 2010 (NCLEP) as a case study. Applying Structural Equation Modelling (SEM) and combining qualitative and quantitative methodologies, findings reveal that the Policy Implement Process has a positive impact on EBP in the setting of NCLEP, whereas the Public Policy Process and Institutional Framework have a negative impact. The findings are further supported by insights from focus group discussions (FGDs) and key informant interviews (KIIs). Effective policymaking is greatly influenced by strong governance structures and rigorously developed evidence. In this regard, prioritizing stakeholders’ involvement, evidence-based decisions for policy making are key to achieve the objective of the policy and SDG targets. Evidence-based policymaking (EBP) demands not only sound reasoning but also the integration of governance principles with empirical data, along with the capacity to translate complex findings into accessible narratives. Public Value Evidence-based policy Policy analysis Child labor elimination policy Structural Equation Model 1. Introduction Evidence-based policymaking (EBP) has gained momentum recently, and the presence of well-built evidence is crucial for policymaking. EBP is a crucial approach to creating the necessary policies to solve a country's issues with securing the effective and efficient use of public funds (Eden & Wagstaff, 2021 ; MacKillop & Downe, 2023 ). An EBP consequently focuses on topics like how evidence is to be located, categorized, utilized, co-created, and reinterpreted within particular policy-making contexts (Baron, 2018 ). A more accurate understanding of the problems that restrict and those that demand the use of information and empirical evidence in the process of decision-making is one of the most important circumstances for coordinated action and transformative solutions to any social change (Jabali et al., 2024 ). Globally, evidence-based policymaking has garnered increasing attention across a range of social policy domains, including health, education, and child protection (Ahad et al., 2024 ), underscoring its potential to improve policy outcomes and facilitate the efficient allocation of resources (Khan, 2010 ; Quattri & Watkins, 2019 ). The effectiveness of such policy initiatives, however, is contingent not only upon their formulation but also on the extent to which they are grounded in credible and systematic evidence (Françoise et al., 2022 ). In a scenario based on the theory of policy, Cairney, 2022 describes how choice and necessity result in a decentralized state. The centralization of policymaking is commonly rejected by their governments, but even if it were implemented, it would be a failure. An impact on policies can be made by using well-constructed evidence. A strong governance framework and improved policy foundation support the ability of policymakers to assist with and carry out standardized, uniform policies (Chriqui et al., 2023 ; Edler et al., 2022 ). However, child labor continues to pose a significant challenge in many developing countries, where it undermines fundamental human rights, restricts access to education, and sustains cycles of poverty (Heather, 2008 ; M. M. Rahman et al., 1999 ). In the context of Bangladesh, although we have made notable progress in poverty reduction and human development, eliminating child labor remains a complex and persistent issue. (Dodd et al., 2019 ; Nath & Hadi, 2000 ; S. Rahman et al., 2024 ). The NCLEP is closely linked with Sustainable Development Goals (SDGs), i.e., reduced inequalities, decent work and economic growth, peace justice which are pertinent for achieving vision 2041 of Bangladesh (UNICEF, 2021 ). Acknowledging the need for focused and structured interventions, the Government of Bangladesh adopted the National Child Labor Elimination Policy (NCLEP) in 2010, establishing a comprehensive framework to address child labor through legal, social, and institutional mechanisms (Ahad et al., 2024 ; Costa et al., 2020 ). Despite this, Bangladesh has a thorough legal system and ratified important international accords. Since the NCLEP mainly covers the official sector, most child workers involved in informal jobs like domestic work and agriculture are not protected by the law (Hoque, 2024 ). Furthermore, a crucial element in policy execution is the discrepancy between policy and actual monitoring (Howard et al., 2025 ; Pallett, 2020 ; S. S. Rahman, 2014 ). Furthermore, the widespread lack of birth registration complicates age verification, while existing laws fail to sufficiently address the issue of hazardous child labor. NCLEP is not fully effective or scientific in the sense of being evidence-based and outcome-driven; its success depends on context, enforcement, and integration with other interventions (From FGDs, Field Survey 2023). Despite these impediments, aligning robust research evidence remains essential for addressing the complex and deeply rooted challenges (Awaworyi Churchill et al., 2021 ) associated with child labor in Bangladesh. To give a thorough evaluation of the situation of EBP inside the NCLEP 2010, this study employs a combination of techniques that blends quantitative analysis using structural equation modelling (SEM) with qualitative observations from focus group discussions (FGDs) and key informant interviews (KIIs). However, there is much concern among policymakers and stakeholders about the evidence-based policy-making level across Bangladesh. There are several studies conducted to address child labor causes, consequences in general but yet no studies conducted to know the EBP process of NCLEP using a case study method employing SEM model. The study contributes to the existing literature both theoretically and methodologically. This study advances Leigh’s ( 2011 ) framework by aligning the stages of the policy cycle with Leigh’s evidence typology to identify at which phases evidence is most utilized within the policymaking process. On the other hand, many previous studies in this field that have predominantly employed qualitative methods, this research adopts both qualitative and quantitative analyses to assess the state of EBP within Bangladesh’s public sector. Bangladesh has implemented numerous policies critical to its broader development trajectory. Within this context, the present case study focuses on the NCLEP 2010. Accordingly, a central aim of this study is to examine the current status of evidence-based policymaking in Bangladesh’s public policy domain, using the NCLEP2010 as a focal case to generate new insights that can contribute to strengthening the policymaking process in Bangladesh which is very pertinent to the current spectrum of socio-economic spectrum related to SDG. 1.1. Study Objectives To find the state of evidence-based policy making in the National Child Labor Elimination Policy To find out the challenges in employing evidence-based policymaking in Bangladesh Through the objectives we found out whether the policy-making process was evidence-based in the case of NCLEP 2010 and could compare how much it was evidence-based in Bangladesh. We could also determine the various types of evidence used to formulate this policy. 2. Literature review There is considerable potential to strengthen EBP, underpinned by expanded access to data and strategic investments in high-quality research (Frith, 2015 ; Phillips et al., 2020 ). Integrating robust evidence into policy decisions enhances the efficiency of resource allocation (Braun & Clarke, 2021 ; Head, 2010 ) by reducing expenditure on ineffective initiatives and directing investments toward interventions that demonstrate clear economic and social benefits (Behague et al., 2009 ; Wolffe et al., 2019 ). In democratic systems, it is seen as a means of accumulating knowledge that guides iterative policy learning and has its roots in the rationalist tradition (Dowding et al., 2025 ). Systematically using the best available data to guide policy choices and improve the effectiveness, efficiency, and accountability of policy initiatives (Bandelow et al., 2022 ; Wenzelburger & Jensen, 2022 ). The most important evidence includes data that is credible, valid, and directly connected to the policy’s goals and outcomes. This evidence can come from scientific research, program evaluations, expert analysis, and practical experience (Castanho et al., 2019 ; Listorti et al., 2020 ). Nonetheless, prioritize the indigenous knowledge and inclusive stakeholder participation alongside the intricacies of institutional dynamics (Bijlsma et al., 2011 ). Even though many countries have enacted laws and policies to prevent child labor, the methodical integration of trustworthy data into the planning and implementation of these programs is often essential to their effectiveness. According to research, methods based on comprehensive and well-organized data are usually more successful in reducing child labor (Heather, 2008 ; M. M. Rahman et al., 1999 ). The effective translation of evidence into policy action is frequently hindered by inadequate institutional capacities, limited use of data in decision-making, and insufficient coordination among key stakeholders (Delap, 2001 ; Head, 2010 ). Hoque, 2024 evaluate the facts on child labor trends and emphasizes on countries child labor from both the "demand" and "supply" sides in order to effectively combat it. On the basis of the lessons learned from the available data, they also examine policies to combat child labor. There is various study that reveals a number of supply and demand barriers exist that prevent better use of data and assessment in policy decisions on both the supply and demand sides. Despite their knowledge and potential contribution to the policy process, some academics find it challenging to engage in it effectively. Policies were frequently created in a way that prevented thorough review due to a lack of reliable usable data and the possibility that input would be biased (Braun & Clarke, 2021 ; Head, 2010 ). The prevalence of bonded child labor, its root causes, and its adverse effects on the physical, emotional, and educational well-being in South Asian perspective was studied by various research papers. The study of Ahad et al., 2024 emphasizes the urgent need for effective policy frameworks and interventions and contributes significantly which should be evidence base, providing valuable knowledge that can guide policy engagement efforts to combat bonded child labor in South Asia effectively. Child labor, which involves a number of parties, makes it challenging for the parties to work together effectively in order to harmonize laws, regulations, and law enforcement, increase access to social protection, expand and improve social protection (Howard et al., 2025 ). The implementation of EBP in Bangladesh remains limited due to institutional, political, and financial constraints. Although efforts have been made to strengthen evidence systems through improved data collection and enhanced research utilization, and the integration of evidence into policy decisions. To produce effective policies, policymakers must prioritize evidence-based and data-driven decision-making, identify the most frequent causes of policy failure, and adopt an evidence-based approach to policymaking (Kamruzzaman & Hakim, 2018 ). The 'National Child Labor Elimination Policy 2010' was formulated incorporating essential elements of national, regional, and global levels in socio-economic, cultural, and geopolitical arenas (UNICEF, 2021 ). In this regard, prioritizing stakeholders’ involvement, evidence-based decisions for policy making are key to achieving the objective of the policy and SDG targets. Therefore, a comprehensive study is required to develop a workable plan for ending child labor that is appropriate for Bangladesh's unique social and economic situation. Previous studies have underscored the need for aligning policy interventions with research evidence to effectively tackle the multifaceted nature of child labor ((Mckinney et al., 2017 ). More specifically, evidence is rarely used when making decisions about policy in sectors such as the economy (Amin & Dogan, 2021 ). Nevertheless, evidence is increasingly being used in our country's policymaking. However, little is known about the evidence's current status and usage patterns in the process of formulating labor policy. To try to address this gap, this work employs a mixed method approach. 3. Theoretical Framework Leigh, ( 2011 ) identifies six distinct categories of evidence that are particularly valuable in the formulation of social policies across sectors such as education, health, and social welfare. Leigh further suggests that the relevance and applicability of these evidence types may vary in other policy domains such as defense or fiscal policy (Leigh, 2011 ). Our study evaluates the applicability of Leigh’s evidence typology within the context of three selected policy areas in Bangladesh, offering insights into the nature and quality of evidence underpinning national policy decisions. In order to systematically assess the degree and mode of evidence use in policymaking processes, this study aims to investigate the application of Leigh's six categories of evidence across various policy cycle stages. The analysis particularly emphasizes on Bangladesh's policy creation phase to assess the degree to which different types of evidence have impacted decision-making. By highlighting trends, weaknesses, and possible areas for improvement in the incorporation of empirical evidence into policy design, the study provides crucial insights into the practice of evidence-based policymaking within the Bangladeshi policy context. 4. Study Methodology In this study purposive sampling method used as the goal of the research is to select respondents who are familiar with the NCLEP 2010 and have special knowledge of the policy process. 4.1. Sampling Procedure Bangladeshi individuals working in the public and private sectors, as well as academicians, and national and international organizations, are the primary focus of the study. The study undertakes 203 respondents who participated in a self-administered survey questionnaire. In the survey, 77.37% participants have Master’s degree and 47.83% respondents working experience are between one to ten years, 39.13% respondents have working experience are between eleven to twenty years. Descriptive analysis of respondents is exhibited below in Table 1 . Table 1 Percentage Distribution of the respondents by last academic degree and years in job Characteristics n % Last Academic Degree Honors 24 12.63 Masters 147 77.37 M.Phil. 0 0 PhD 3 1.58 Others 16 8.42 Years in current position 1–10 88 47.83 11–20 72 39.13 21–30 22 11.96 30+ 2 1.09 Source: Field Survey 2023 4.2. Techniques of data analysis A content analysis approach was employed to systematically identify themes and patterns related to evidence use in policymaking (Braun & Clarke, 2021 ; Phillips et al., 2020 ). For this study data were collected through KIIs and FGDs were transcribed for the analysis. In parallel, data obtained from the structured questionnaire were analyzed using descriptive statistics and Structural Equation Modeling (SEM) to assess the extent to which the policymaking process can be characterized as evidence-based. Variable description are as follows: We have used the following model to analyze the collected data using Structural Equation Modelling Approach (SEM): Study model: The study model of this study is- $$\:Evidence\:Based\:Policy=Public\:Policy\:Process+Policy\:Enacting\:Process+Institutional\:Framework$$ Description of the variables are provided in Table 2 . Table 2 Description and Details of the Variables Variables Description and Details EBP Evidence-Based Policy- 1. Systematic reviews (meta-analyses) of multiple randomized studies; 2. High-quality randomized studies; 3. Systematic evaluations 4. Natural experiments 5. Before-after (pre-post) studies; 6. Theoretical conjectures and opinions of experts (Leigh, 2011 ) PPP Public Policy Process- (Agenda Setting and Policy Formulation) PIP Policy Implementation Process- (Policy Selection and Policy Implementation) IF Institutional Framework- (Monitoring, Evaluation and Feedback (ME&F), Leadership, Role of Institutions, Culture) Note: Opinion of the policy makers, stakeholders in likert scale was taken 5. Findings 5.1. Quantitative Data Analysis 5.1.1. Model Evaluation This investigation used SEM, or structural equation modelling, which is regarded as a reliable analytical method because of its capacity to represent intricate relationships. Its adaptability to model misspecification and the ongoing development of estimating techniques that lessen sensitivity to statistical assumption violations serve as additional evidence of its robustness. The integrated model is reliable for simultaneous estimation and provide comprehensive framework to analyze complex relationship to yield an accurate result for the estimators (Kock et al., 2021 ). In the fields of business, social science, medicine, health research, and natural science, route analysis and confirmatory factor analysis are more common (Information & Chin, 2013). 5.1.2. Measurement Model Evaluation A confirmatory factor analysis (CFA) test was conducted on the measurement model. These variables' factor loadings are checked to verify the model's measurement. Factor loading values nearer 1 show that the factors have a significant impact on the variable (Astrachan et al., 2014 ). Every factor's score is stored for the primary estimation. On a 5-point Likert scale, with 1 denoting "strongly disagree" and 5 denoting "strongly agree," respondents are asked to answer 10–12 items related to a single issue. Finally, the Cronbach's Alpha value is determined to assess the reliability of the questionnaire for this variable. If a regression model is to be used for this investigation, it suggests that the questionnaire is internally consistent (Henseler et al., 2016 ). The measurement model demonstrates an acceptable level of goodness of fit (GFI) (Henseler et al., 2016 ), as indicated by multiple fit indices (Table 3 ). GFI is 0.96, exceeding the commonly recommended threshold of 0.90, which suggests an adequate fit between the hypothesized model and the observed data. Similarly, both the CFI at 0.92 and the Tucker-Lewis Index (TLI) at 0.91 meet the conventional cutoff value of ≥ 0.90, indicating a good comparative fit (Bentler, 1990 ). Table 3 Goodness of fit of the measurement model GFI CFI TLI REMSA RMR SRMR Measurement model 0.96 0.92 0.91 0.10 0.81 0.10 However, in the Table 3 the Root Mean Square Error of Approximation (RMSEA) is reported at 0.10, which slightly exceeds the acceptable upper limit of 0.08 for a reasonable error of approximation. While this suggests some room for improvement, it does not necessarily indicate poor model fit, especially when considered alongside the other fit indices (Rigdon, 1996 ). The Root Mean Square Residual (RMR) value of 0.81 appears to be unusually high and may indicate either a reporting or scaling issue, as typical acceptable values for RMR are ≤ 0.08. Conversely, the Standardized Root Mean Square Residual (SRMR) value of 0.10 is on the threshold of acceptability, as values less than or equal to 0.10 are considered acceptable for model fit (Bentler, 1990 ). Taken together, the majority of fit indices support the acceptability of the measurement model, with strong performance on absolute and incremental fit measures, albeit with minor limitations in residual-based metrics. Thus, the model can be considered to demonstrate an overall adequate fit to the data. 5.1.3. Common Method Bias To mitigate the potential influence of common method bias (CMB), several procedural and statistical remedies were employed, consistent with established best practices (Kock et al., 2021 ). First, ethical research protocols were rigorously followed to enhance the accuracy and reliability of responses. Participants were assured of both confidentiality and anonymity, thereby reducing social desirability and response bias. Second, Harman’s single-factor test was conducted to statistically assess the presence of CMB (Kock et al., 2021 ). The analysis indicated that the first factor accounted for 30% of the total variance, which is substantially below the conventional threshold of 50%, suggesting that common method variance does not pose a significant concern in this study (Table 4 ). Furthermore, multicollinearity was assessed through the calculation of Variance Inflation Factor (VIF) values for the independent variables, all of which were within acceptable thresholds. These results indicate the absence of serious multicollinearity issues, thereby affirming the robustness and suitability of the dataset for subsequent analytical procedures. Table 4 Common method Bias (Harman’s single -factor test) PPP PIP IF % Of variance explained by single factor 30% 43% 34% Note: Variance explained by a single factor for PPP, PIP and IF is less than 50%; therefore, there is no common method bias. Table 5 displays the correlation matrix among the key structural constructs—PPP, PEP, and IF. There is no indication of multicollinearity between the variables, as the maximum correlation coefficient found between PPP and PIP at 0.274, is much below the generally recognized cutoff of 0.90. Furthermore, the generally modest inter-construct correlations imply that the data's integrity is not seriously threatened by common method variance. These results validate the statistical validity and methodological soundness of the structural model used in this work when paired with other diagnostic tests. Table 5 The criterion for validity in a correlation matrix of the structural parameters PPP PPP PEP IF 1 PIP 0.274 *** 1 IF 0.229 *** 0.137 *** 1 *** Indicates the correlation is significant at 0.01 and * indicates the correlation is significant at 0.05 Source: Calculated by the Authors 5.1.4. Findings In this study it is empirically tested whether policy making process, the policy implementation process and institutional framework have a significant influence on evidence-based policy making or not. Here confirmatory factor analysis (CFA) is used to test the validity of the model (Information & Chin, 2013). Several criteria were employed to support the model's fitness. The structural model index was investigated and provided in the study, and it was determined that it was an acceptable fit. By utilizing internal consistency by utilizing Cronbach’s Alpha test for the variables if show that the variables are not problematic. We also perform CFA whose result suffices the fitness of the data [CFI 0.92 (closer to 1.00), TLI 0.91 (closer to 1.00). Variance explained by a single factor, Harman’s test for PPP, PEP, and IF is less than 50%; therefore, there is no common method bias. 5.1.5. Measurable Variables For the data set of this component (PPP to EBP) Bartlett’s test is highly significant, χ 2(55) = 507.515, p < .001, indicating that the variables exhibit acceptable intercorrelations to justify the application of factor analysis, Kaiser-Meyer-Oklin (KMO) test value is 0.56 which is greater than 0.5, exceeds the minimum acceptable threshold of 0.50 which indicates factor analysis is appropriate (Ponnam et al., 2014 ). The determinant of the selected variables is 0.0766. This value is greater than the necessary value of 0.00001, indicating that there is no multicollinearity among the variables (Kalnins, 2018 ). To check the reliability of the data we go for Cronbach’s test, where public policy process had high reliability and the Cronbach’s \(\:\alpha\:\) = 0.79. As such, our determinants of the selected variables do not seem problematic, indicating that multicollinearity or singularity is not a concern among the variables (Kalnins, 2018 ). Structural Equation Model Evaluation Table 6 Effect of structural equation model (NCLEP) Path Estimate Std. Error Z-Statistic P-value EBP \(\:\leftarrow\:\) PPP -0.413 0.197 -2.090 0.037 EBP \(\:\leftarrow\:\) PIP 0.781 0.302 2.592 0.010 EBP \(\:\leftarrow\:\) IF -0.526 0.251 -2.097 0.036 p -value: *** < 0.01, ** < 0.05, * < 0.1 Source: Calculated by the Authors From Table 6 , it is indicated that the relationship between policy-making process (PPP) and evidence-based policy (EBP) is negative and statistically significant, with a coefficient of -0.413 (p < 0.05). This finding reveals that, within the context of this study, the policy-making process exerts a significant negative direct effect on the adoption of EBP. Regarding the relationship between the policy implementation process (PIP) and EBP, the factor analysis was confirmed by a highly significant Bartlett’s test of sphericity, χ²(6) = 114.28, p < 0.001, and a Kaiser-Meyer-Olkin (KMO) value of 0.523, exceeding the minimum acceptable threshold of 0.50 (Cerny & Kaiser, 1977 ). Additionally, the determinant of the correlation matrix was 0.564, which is above the critical value of 0.00001, indicating the absence of multicollinearity concerns (Kalnins, 2018 ). Cronbach's alpha, a measure of the PIP construct's reliability, came out at 0.54—a modest score that is appropriate for exploratory study in social scientific settings. With a coefficient of 0.78 (p < 0.01), the results of the structural equation model show a positive and statistically significant association between PIP and EBP. This indicates that the policy implementation process under the NCLEP (2010) is positively associated with evidence-based policy practices. Similarly, for the relationship between the institutional framework (IF) and EBP, Bartlett’s test of sphericity was highly significant, χ²(15) = 176.03, p < 0.001, with a KMO value of 0.523, indicating the appropriateness of the data for factor analysis. The determinant of the correlation matrix was again 0.564, confirming the absence of multicollinearity. The Cronbach’s alpha for the IF construct was 0.54, consistent with acceptable reliability levels for exploratory analysis. The structural equation modeling results reveal a negative and statistically significant relationship between IF and EBP, with a coefficient of -0.52 (p < 0.05). This finding suggests that the institutional framework associated with the NCLEP (2010) is negatively correlated with the application of evidence-based policy within this context. Collectively, these results provide a nuanced understanding of the determinants of evidence-based policy adoption within the framework of the NCLEP (2010), highlighting the differentiated impacts of policy formulation, implementation processes, and institutional structures on evidence-based policymaking in Bangladesh. Path Estimation of SEM The estimated result specifically shows that Public Policy Process and Institutional Framework are negatively affecting the Evidence-based Policymaking process of NCLEP '2010. The effect of the Policy Implementation Process is positively affecting the Evidence-based Policymaking Process. Therefore, we can say that the making of NCLEP '2010 Policy Implementation Process was evidence based but Policy Making Process and Institutional Framework were not evidence based. 5.2. Analysis of Qualitative Data 5.2.1. Findings obtained from KII From the qualitative segment, our data reveals a few factors determining challenges and a way forward to address evidence-based policy making in NCLEP’2010. The classified observations from our KII respondents are stated below: A. High-quality information and data To guarantee high-quality information, good-quality, reliable data, and a panel of expert’s expertise are required to initiate evidence-based policy. Departmental analysts should make an effort to understand the most concise explanation of the available evidence (Proma et al., 2023 ). One respondent opined that, “…. to avoid policy failure, evidence is a must. Child Labor survey report was the core evidence for initiating NCLEP’2010. High-quality evidence is required to guarantee high-quality information. For this, correct research methods should be used. Reliable literature is crucial since it will provide guidance on the way to formulate policies correctly based on its findings…. the secret to this is high caliber research.... the primary focus should be on analytical research, there isn't a lot of space for problem-solving research…. there's a sizable capacity deficit that needs to be solved. Policy failures will be reduced by high-quality data and strong analytical skill” . B. Leadership and place right person Having the right person in the right places is very important. Utilize and take into consideration the expert’s knowledge and beside that negotiation capability of the policy maker needs to be developed. One Participants from KII reported that, “ Stable service tenure is mandatory for effective evidence-based policy making, formulation and implementation. This should be taken into account not only for the proper implementation of NCLEP '2010 but also for the proper implementation of any policy. Frequent changes in the policy making level hampered the flow and speed of that particular project.” C. Social Safety Net Although the government of Bangladesh implemented a variety of incentive programs for children to cut down on child labor but the number is still substantial for hazardous jobs. Respondent from an international organization stated that: In our cultural and social purview people often consider starting families and relying on the next generation to support them. According to this principle, the cycle of sending kids to work never ends. This idea applies to both poverty and domestic choices. It is necessary to change the way the family members think . D. Institutional Capacity Action plans should be based on institutional capacity; without monitoring and coordination of actions, it is difficult to fulfill the objectives of NCLEP '2010. The following quotes of a respondent explain the situation: … in NCLEP '2010, there are nineteen ministries, along with the Ministry of Labor and Employment, who have responsibility for the successful implementation and monitoring of this policy. It’s necessary to operationalize ministry-wise functions to achieve the goals of this policy, where institutional capacity is core. Some recommendations came from the respondents. Engage more with the policymaker and share knowledge and research with them. Disseminate the work and try to develop the knowledge base on those. Furthermore, it needs to reach out to the general public and enhance their knowledge. Finally, to communicate uncertainty about evidence, it should be clearly spelled out before formulating the policy. 5.2.2. Findings from FGD Two Focus Group Discussions (FGDs) as a tool for research (Davies, 1999; Efimov et al., 2022) were conducted in June 2023 with 15 participants. They all were mid-level professionals with knowledge of the policy-making process. We captured data using memory, body language of the participants, taking field notes, handouts, and flip charts, along with audiotape. The answers to the key questions were identified, transcribed, and translated in line with the purpose of the study. For further exploration of certain circumstances, we use probes and follow-up questions to identify the situations. We have used an ending question like “what they consider to be the most important topic discussed? to identify the issue. At the concluding part of this round-robin question to the group was helped with the analysis part and provided a glimpse of where to put emphasis according to the importance placed by the participants. This insight was instrumental in depicting the result (Wilkinson, 2011). Moreover, we verified our findings from the FGD by offering a summary of the discussion session to the participants, asking them whether this was an appropriate overview of the important issues raised. Finally, a debriefing session among the research team members was conducted to compare notes, highlights, and to consider what others in the team have observed and heard to retain consistency among all FGDs. Focus groups and other qualitative techniques offer researchers an extra way to collect in-depth, first-hand input. From the FGD of this study, participants emphasized on coordination of actions. Alongside the Ministry of Labor and Employment, the involvement of other ministries is particularly important because the NCLEP 2010 has many aims. The communication issue ought to be effectively articulated. Institutions should be effective, credible, and impartial. Network management, administrative ethics, collective accountability, and institutional knowledge transfer is very crucial in this regard. Proper coordination direction will make this policy successful. The most important and difficult aspect of implementing this policy is changing people's minds. Improvement in the domain of institutional culture is vital. One respondent from FGD opined that, The NCLEP’2010 itself is not very structured and clear to achieve its goals….often donors gave a few criteria to set a standard rule for a policy… our officer need to follow strong evidence so that they can negotiate in favor of our interest…we should not copy others countries policies rather we can consider it as a best example, make comparison and take best decision based on our societal norms, values and other relevant socio, economic and cultural factors. Evidence should be gathered for the effective monitoring of this policy. It will result in sector-based data on child labor covering both urban and rural areas, including both the formal and informal sectors. One participant explained, Urban areas are getting more priority than rural areas in the evaluation and monitoring of NCLEP '2010… . this needs to be appropriately addressed. In the policy making level there are representative stakeholder’ participation but stakeholder selection is also very important. Even if meetings are held at the Zila and Upazila levels which were not always very effective. In this regard participants emphasized on, “ Participatory management system for evidence-based policy making. ’’ 6. Discussions This study critically examines the policymaking processes underpinning the NCLEP’2010 in Bangladesh, employing a mixed-methods approach that integrates both qualitative (FGDs, KIIs) and quantitative (survey) data, drawn from primary and secondary sources. The essence of effective EBP lies in combining logical reasoning, scientific evidence, and governance principles, while translating complex evidence into narratives that are accessible for policymakers and stakeholders (Oliver et al., 2014 ). The study positions evidence-based policymaking (EBP) as a crucial mechanism for improving policy effectiveness, asserting that policies grounded in robust evidence tend to yield more impactful outcomes. Within Bangladesh, interest in EBP has been growing, with stakeholders increasingly questioning the extent to which policy processes align with evidence-informed frameworks (FGD and KII findings, Field Survey, 2023). The application of an EBP approach to solve policy problems seems to be challenging when they are complex and multifaceted (Teirlinck et al., 2013 ). Notwithstanding the issues, the study's findings reveal that governments can benefit greatly from the insights found in EBP literature, which can provide them with useful policy recommendations, and the findings are compatible with the findings (Simons & Schniedermann, 2021 ). This study got some insights from the EBP literature on why and how evidence is essential and recommends that policymakers adopt an EBP approach, including appropriate stakeholders. However, there are a few contemporary analysts who now adopt such a rationalist position. The recent trajectory of EBP in the United Kingdom illustrates some of the current ambiguities (Pallett, 2020 ). Although there has been widespread support and acceptance for the increased focus on research and evaluation in the policy cycle (Sanderson, 2002 ). Evidence plays a crucial role in shaping the policy process not only during the agenda-setting and formulation phases but also during the decision-making, policy implementation, monitoring, and evaluation phases to gather input (Knill et al., 2024 ). This study reveals that evidence is not only vital during the formulation stage but also plays a critical role across the policy cycle, including decision-making, implementation, and monitoring and evaluation stages. Integrating evidence in the early stages of policymaking is particularly effective, a finding supported by Hythar, 2021 . The evolution of the evidence-based policy movement and high-quality data (Kassi et al., 2023 ) is useful for the operationalization of policy making process. This study finds that EBP is that policy settings can be improved on the basis of high-quality evidence and reliable knowledge. Policymakers need to develop strategies to deal with uncertainty and minimize risk, particularly when external factors like labor unions and international bodies are involved (Hope, 2009 ; Liverani et al., 2013 ). During the policy selection phase, participants showed favorable sentiments regarding the assessment of evidence-based policy alternatives (Listorti et al., 2020 ), but they also showed a more cautious degree of faith in the knowledge of policymakers. This suggests that policymakers need to develop their skills in order to better use evidence in their decision-making. This case study demonstrates that evidence plays a significant role in the initial stages of policymaking, emphasizing the need for ongoing evidence generation and stakeholder collaboration. Therefore, addressing the complexities of stakeholder interests and community values is essential to EBP's effectiveness (Mills et al., 2022 ). Acknowledging the practical constraints, this study focuses on the NCLEP 2010, offers important insights into how evidence can be methodically incorporated into policymaking in Bangladesh for sustainable development. Therefore, the channels through which rigorous evidence might influence policymaking are somewhat fragile, and are sometimes readily disrupted by political and organisational pressures. Hence, the communication channels need specific care and attention both to understand their characteristics and to improve their outcomes (Boaz & Nutley, 2023 ; Oliver et al., 2014 ). EBP will continue to inspire efforts for better performance in the policy sciences, but will always be constrained by the realities of community values, political systems in place, and interest of the policy players. However, challenges such as resource constraints, evolving social conditions, and a lack of monitoring and evaluation mechanisms pose risks to effective EBP (Listorti et al., 2020 ). Policymakers are encouraged to prioritize the monitoring and evaluation phase, addressing challenges in implementation, and changing societal mindsets. Furthermore, this focus aligns with the Sustainable Development Goals (SDGs), emphasizing the significance of eradicate child labour. Therefore, the channels through which rigorous evidence might influence policymaking are somewhat fragile, and are sometimes readily disrupted by political and organisational pressures. Hence, the communication channels need specific care and attention both to understand their characteristics and to improve their outcomes (Nutley et al., 2007; Ouimet, Landry, Ziam, & Bedard, 2009). EBP will continue to inspire efforts for better performance in the policy sciences, but will always be constrained by the realities of community values, political systems in place, and interest of the policy players. This study reasons behind this rapid and remarkably enduring transformation identifies the main dynamics of policy making, and explores some future challenges. Additionally, the study recommends incorporating evidence at all stages of policymaking, including agenda-setting, formulation, implementation, and monitoring and evaluation, to improve policy outcomes. 7. Limitations and Future Research Scope In this study the survey population was confined to officials of the Ministry of Labor and Employment (MLE), given their direct involvement in the policymaking process of the NCLEP 2010. This focus, combined with a low response rate, posed challenges in data collection process. The policy under examination was formulated in 2010, recall bias may have influenced respondents' accuracy in reporting the details of the policy formulation process. Although triangulation across survey responses, key informant interviews, and focus group discussions was employed to enhance the robustness of the findings, the study remains reliant on self-reported perceptions. Future research focusing on the policy implementation stage could provide deeper insights into the practice of evidence-based policymaking in this context. 9. Conclusion and Policy Implementation This study comprehensively examined the hypothesized relationships aligned with its research objectives. Using a Structural Equation Modeling (SEM) approach, the results indicate that all theorized direct effects are statistically significant. Notably, the findings reveal that both the public policy process and the institutional framework exert a significant negative influence on the adoption of evidence-based policymaking in relation to the National Child Labor Elimination Policy (NCLEP) 2010. In response to the complex and persistent challenges posed by child labor, countries worldwide are increasingly adopting legal and policy frameworks to articulate targeted interventions. However, the extent to which these policy measures are underpinned by robust evidence remains a critical concern. Assessing the state of evidence-based policymaking and understanding the factors that shape it are essential for strengthening each phase of the policy cycle from initial formulation and implementation to evaluation and refinement. This study addresses this gap by critically examining the status of evidence-based policymaking within Bangladesh’s public policy sphere, using the NCLEP 2010 as a case study. Drawing on both qualitative and quantitative evidence, the analysis demonstrates that while the policy enactment process shows a significant positive association with evidence-based policymaking, the broader policy process and institutional structures present notable constraints, negatively influencing the integration of evidence into policy decisions. These quantitative results are corroborated by insights from key informant interviews (KIIs) and focus group discussions (FGDs), adding contextual depth to the findings. The results underscore the importance of embedding credible evidence and suggest policy input for sound institutional mechanisms into the policy domain. Advancing evidence-based policymaking requires not only logical reasoning and the integration of scientific evidence with governance practices but also the capacity to translate complex evidence into clear, actionable narratives. Addressing the systemic barriers identified in this study calls for the introduction of a dedicated coordinating Act and the development of supporting institutional mechanisms to enable a more coherent, evidence-driven policy process. As seen from the qualitative analysis, challenges in implementation often stem from poorly synthesized action plans. To ensure successful implementation, action plans must be concise and effectively synthesized. Additionally, establishing robust institutional frameworks is essential to facilitate and support the implementation process. However, child labour policy requires an integrated, evidence-based, cross-sectoral approach to address SDS goals for sustainable development. Declarations Funding Acknowledgement : This research was supported by the Ministry of Public Administration (MoPA), Government of Bangladesh, under the Research Grant (Code No. 3257103) for the Financial Year 2023–2024. The authors gratefully acknowledge the financial support provided by MoPA for conducting this study. Ethical Approval The study protocol was approved by the Institutional Review Board of Bangladesh Institute of Governance and Management (BIGM) and the certificate reference number is: BIGM/Research & Publication/01/21/23–93. The study was conducted in accordance with the Bangladesh Institute of Governance and Management Research Ethics Board (BIGMREB) relevant guidelines and regulations. Consent to Participate Informed consent was obtained from all participants prior to data collection. For this study participation was voluntary, and respondents were assured of confidentiality and they were assured that their responses would remain confidential and used only for academic purposes. Consent to Publish Not Applicable Competing Interests The authors declare that they have no competing interests. Author Contribution Conceptualization, literature review, methodology, writing original draft, editing, referencing and final format: Fataraz Zahan. Conceptualization, methodology and writing Mowshumi Sharmin. Literature review: Shafin Haque Omlan. Quantitative data analysis: Md. Injamamul Haq Methun. Supervision, proofreading and review: Krishna Gayen (PhD), Overall guidance, feedback and insights: Mohammad Tareque (PhD). Data Availability The datasets used and analysed during the current study are available from the corresponding author on reasonable request. References Ahad MA, Parry YK, Willis E, Ullah S. Child Laborers’ Exposure to Neglect in Rural Bangladesh: Prevalence and Risk Factors. Child Indic Res. 2024;17(3):1115–35. https://doi.org/10.1007/s12187-024-10129-2 . Amin A, Dogan E. The role of economic policy uncertainty in the energy-environment nexus for China: Evidence from the novel dynamic simulations method. 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Supplementary Files 1.png Evidence-based Policymaking Source: Adopted and adapted from Leigh, (2011) 2.png Structural model and corresponding path estimates (NCLEP) Source: Calculated by the Authors Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 01 May, 2026 Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers invited by journal 29 Mar, 2026 Editor assigned by journal 29 Mar, 2026 Editor invited by journal 26 Mar, 2026 Submission checks completed at journal 21 Mar, 2026 First submitted to journal 21 Mar, 2026 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. 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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-8937382","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615874850,"identity":"f2bf576e-0079-4142-8d07-8b1dc2d02c53","order_by":0,"name":"Fataraz Zahan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBAC9gYogx9EJBQQoYXnADOEIQnSm2BAihaDA2CSGC3s5w9+rqipszc+vzrxwwMDBnl+sQMEtPAkM0ueOcbGbHbj7WYJoMMMZ85OwK/FniEZ6A02HjazG2c3gLQkGNwmoIWH/zHzz4Z/EjzGM85u/kGcFolkNsnGNgMJA/7ebUTaIvHYzLKxL8FA4gbvNgsgRdgvPPyJj282fKuz5+8/u/nmjwobeX5pAloQQAKsUoJY5SDAf4AU1aNgFIyCUTCSAABNND3sDefabAAAAABJRU5ErkJggg==","orcid":"","institution":"Bangladesh Institute of Governance and Management (BIGM)","correspondingAuthor":true,"prefix":"","firstName":"Fataraz","middleName":"","lastName":"Zahan","suffix":""},{"id":615874851,"identity":"9277b953-7535-4b39-b78b-20f1a57b7090","order_by":1,"name":"Mowshumi Sharmin","email":"","orcid":"","institution":"Bangladesh Institute of Governance and Management (BIGM)","correspondingAuthor":false,"prefix":"","firstName":"Mowshumi","middleName":"","lastName":"Sharmin","suffix":""},{"id":615874852,"identity":"b9e3e406-ab92-4f3d-a07d-03c9e7bbf9ee","order_by":2,"name":"Shafin Haque Omlan","email":"","orcid":"","institution":"Bangladesh Institute of Governance and Management (BIGM)","correspondingAuthor":false,"prefix":"","firstName":"Shafin","middleName":"Haque","lastName":"Omlan","suffix":""},{"id":615874853,"identity":"1a254825-144c-4da9-905e-d4959ea3a62a","order_by":3,"name":"Md. Injamamul Haq Methun","email":"","orcid":"","institution":"Bangladesh Institute of Governance and Management (BIGM)","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Injamamul Haq","lastName":"Methun","suffix":""},{"id":615874855,"identity":"e95b92f8-b588-4c3b-a3e4-0bed0941d236","order_by":4,"name":"Dr. Krishna Gayen","email":"","orcid":"","institution":"Bangladesh Institute of Governance and Management (BIGM)","correspondingAuthor":false,"prefix":"Dr.","firstName":"Krishna","middleName":"","lastName":"Gayen","suffix":""},{"id":615874857,"identity":"8d3b59de-9656-419b-9342-2b3a208d5ede","order_by":5,"name":"Mohammad Tareque (PhD)","email":"","orcid":"","institution":"Bangladesh Institute of Governance and Management (BIGM)","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Tareque","suffix":"PhD"}],"badges":[],"createdAt":"2026-02-22 06:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8937382/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8937382/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106005692,"identity":"afb782cd-c451-45b1-b875-b54b36c333d6","added_by":"auto","created_at":"2026-04-02 10:42:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":876476,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8937382/v1/191f100e-8317-4df1-90ea-f5172f006b5e.pdf"},{"id":106005515,"identity":"854c62a4-ccf9-4daf-a786-c7c9a299221b","added_by":"auto","created_at":"2026-04-02 10:42:00","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":82323,"visible":true,"origin":"","legend":"\u003cp\u003eEvidence-based Policymaking\u003c/p\u003e\n\u003cp\u003eSource: Adopted and adapted from Leigh, (2011)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8937382/v1/f00093d50b51908ff10729c2.png"},{"id":106005518,"identity":"9a03eee0-c4c7-4113-8075-78876684698c","added_by":"auto","created_at":"2026-04-02 10:42:01","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26103,"visible":true,"origin":"","legend":"\u003cp\u003eStructural model and corresponding path estimates (NCLEP)\u003c/p\u003e\n\u003cp\u003eSource: Calculated by the Authors\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8937382/v1/5c5033808056a9ff0647ad5f.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Empirical Analysis to Integrate Public Value and Child Labour Policy through an Evidence-based Approach for Sustainable Development.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEvidence-based policymaking (EBP) has gained momentum recently, and the presence of well-built evidence is crucial for policymaking. EBP is a crucial approach to creating the necessary policies to solve a country's issues with securing the effective and efficient use of public funds (Eden \u0026amp; Wagstaff, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; MacKillop \u0026amp; Downe, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). An EBP consequently focuses on topics like how evidence is to be located, categorized, utilized, co-created, and reinterpreted within particular policy-making contexts (Baron, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A more accurate understanding of the problems that restrict and those that demand the use of information and empirical evidence in the process of decision-making is one of the most important circumstances for coordinated action and transformative solutions to any social change (Jabali et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, evidence-based policymaking has garnered increasing attention across a range of social policy domains, including health, education, and child protection (Ahad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), underscoring its potential to improve policy outcomes and facilitate the efficient allocation of resources (Khan, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Quattri \u0026amp; Watkins, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The effectiveness of such policy initiatives, however, is contingent not only upon their formulation but also on the extent to which they are grounded in credible and systematic evidence (Fran\u0026ccedil;oise et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a scenario based on the theory of policy, Cairney, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e describes how choice and necessity result in a decentralized state. The centralization of policymaking is commonly rejected by their governments, but even if it were implemented, it would be a failure. An impact on policies can be made by using well-constructed evidence. A strong governance framework and improved policy foundation support the ability of policymakers to assist with and carry out standardized, uniform policies (Chriqui et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Edler et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, child labor continues to pose a significant challenge in many developing countries, where it undermines fundamental human rights, restricts access to education, and sustains cycles of poverty (Heather, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; M. M. Rahman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In the context of Bangladesh, although we have made notable progress in poverty reduction and human development, eliminating child labor remains a complex and persistent issue. (Dodd et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nath \u0026amp; Hadi, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; S. Rahman et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The NCLEP is closely linked with Sustainable Development Goals (SDGs), i.e., reduced inequalities, decent work and economic growth, peace justice which are pertinent for achieving vision 2041 of Bangladesh (UNICEF, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcknowledging the need for focused and structured interventions, the Government of Bangladesh adopted the National Child Labor Elimination Policy (NCLEP) in 2010, establishing a comprehensive framework to address child labor through legal, social, and institutional mechanisms (Ahad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Costa et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite this, Bangladesh has a thorough legal system and ratified important international accords. Since the NCLEP mainly covers the official sector, most child workers involved in informal jobs like domestic work and agriculture are not protected by the law (Hoque, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, a crucial element in policy execution is the discrepancy between policy and actual monitoring (Howard et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pallett, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; S. S. Rahman, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, the widespread lack of birth registration complicates age verification, while existing laws fail to sufficiently address the issue of hazardous child labor. NCLEP is not fully effective or scientific in the sense of being evidence-based and outcome-driven; its success depends on context, enforcement, and integration with other interventions (From FGDs, Field Survey 2023).\u003c/p\u003e \u003cp\u003eDespite these impediments, aligning robust research evidence remains essential for addressing the complex and deeply rooted challenges (Awaworyi Churchill et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) associated with child labor in Bangladesh. To give a thorough evaluation of the situation of EBP inside the NCLEP 2010, this study employs a combination of techniques that blends quantitative analysis using structural equation modelling (SEM) with qualitative observations from focus group discussions (FGDs) and key informant interviews (KIIs).\u003c/p\u003e \u003cp\u003eHowever, there is much concern among policymakers and stakeholders about the evidence-based policy-making level across Bangladesh. There are several studies conducted to address child labor causes, consequences in general but yet no studies conducted to know the EBP process of NCLEP using a case study method employing SEM model. The study contributes to the existing literature both theoretically and methodologically.\u003c/p\u003e \u003cp\u003eThis study advances Leigh\u0026rsquo;s (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) framework by aligning the stages of the policy cycle with Leigh\u0026rsquo;s evidence typology to identify at which phases evidence is most utilized within the policymaking process. On the other hand, many previous studies in this field that have predominantly employed qualitative methods, this research adopts both qualitative and quantitative analyses to assess the state of EBP within Bangladesh\u0026rsquo;s public sector. Bangladesh has implemented numerous policies critical to its broader development trajectory. Within this context, the present case study focuses on the NCLEP 2010. Accordingly, a central aim of this study is to examine the current status of evidence-based policymaking in Bangladesh\u0026rsquo;s public policy domain, using the NCLEP2010 as a focal case to generate new insights that can contribute to strengthening the policymaking process in Bangladesh which is very pertinent to the current spectrum of socio-economic spectrum related to SDG.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Study Objectives\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo find the state of evidence-based policy making in the National Child Labor Elimination Policy\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo find out the challenges in employing evidence-based policymaking in Bangladesh\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThrough the objectives we found out whether the policy-making process was evidence-based in the case of NCLEP 2010 and could compare how much it was evidence-based in Bangladesh. We could also determine the various types of evidence used to formulate this policy.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eThere is considerable potential to strengthen EBP, underpinned by expanded access to data and strategic investments in high-quality research (Frith, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Integrating robust evidence into policy decisions enhances the efficiency of resource allocation (Braun \u0026amp; Clarke, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Head, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) by reducing expenditure on ineffective initiatives and directing investments toward interventions that demonstrate clear economic and social benefits (Behague et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wolffe et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In democratic systems, it is seen as a means of accumulating knowledge that guides iterative policy learning and has its roots in the rationalist tradition (Dowding et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSystematically using the best available data to guide policy choices and improve the effectiveness, efficiency, and accountability of policy initiatives (Bandelow et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wenzelburger \u0026amp; Jensen, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The most important evidence includes data that is credible, valid, and directly connected to the policy\u0026rsquo;s goals and outcomes. This evidence can come from scientific research, program evaluations, expert analysis, and practical experience (Castanho et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Listorti et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nonetheless, prioritize the indigenous knowledge and inclusive stakeholder participation alongside the intricacies of institutional dynamics (Bijlsma et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEven though many countries have enacted laws and policies to prevent child labor, the methodical integration of trustworthy data into the planning and implementation of these programs is often essential to their effectiveness. According to research, methods based on comprehensive and well-organized data are usually more successful in reducing child labor (Heather, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; M. M. Rahman et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The effective translation of evidence into policy action is frequently hindered by inadequate institutional capacities, limited use of data in decision-making, and insufficient coordination among key stakeholders (Delap, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Head, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHoque, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e evaluate the facts on child labor trends and emphasizes on countries child labor from both the \"demand\" and \"supply\" sides in order to effectively combat it. On the basis of the lessons learned from the available data, they also examine policies to combat child labor. There is various study that reveals a number of supply and demand barriers exist that prevent better use of data and assessment in policy decisions on both the supply and demand sides. Despite their knowledge and potential contribution to the policy process, some academics find it challenging to engage in it effectively. Policies were frequently created in a way that prevented thorough review due to a lack of reliable usable data and the possibility that input would be biased (Braun \u0026amp; Clarke, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Head, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe prevalence of bonded child labor, its root causes, and its adverse effects on the physical, emotional, and educational well-being in South Asian perspective was studied by various research papers. The study of Ahad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e emphasizes the urgent need for effective policy frameworks and interventions and contributes significantly which should be evidence base, providing valuable knowledge that can guide policy engagement efforts to combat bonded child labor in South Asia effectively.\u003c/p\u003e \u003cp\u003eChild labor, which involves a number of parties, makes it challenging for the parties to work together effectively in order to harmonize laws, regulations, and law enforcement, increase access to social protection, expand and improve social protection (Howard et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The implementation of EBP in Bangladesh remains limited due to institutional, political, and financial constraints. Although efforts have been made to strengthen evidence systems through improved data collection and enhanced research utilization, and the integration of evidence into policy decisions.\u003c/p\u003e \u003cp\u003eTo produce effective policies, policymakers must prioritize evidence-based and data-driven decision-making, identify the most frequent causes of policy failure, and adopt an evidence-based approach to policymaking (Kamruzzaman \u0026amp; Hakim, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The 'National Child Labor Elimination Policy 2010' was formulated incorporating essential elements of national, regional, and global levels in socio-economic, cultural, and geopolitical arenas (UNICEF, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this regard, prioritizing stakeholders\u0026rsquo; involvement, evidence-based decisions for policy making are key to achieving the objective of the policy and SDG targets. Therefore, a comprehensive study is required to develop a workable plan for ending child labor that is appropriate for Bangladesh's unique social and economic situation.\u003c/p\u003e \u003cp\u003ePrevious studies have underscored the need for aligning policy interventions with research evidence to effectively tackle the multifaceted nature of child labor ((Mckinney et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). More specifically, evidence is rarely used when making decisions about policy in sectors such as the economy (Amin \u0026amp; Dogan, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, evidence is increasingly being used in our country's policymaking. However, little is known about the evidence's current status and usage patterns in the process of formulating labor policy. To try to address this gap, this work employs a mixed method approach.\u003c/p\u003e"},{"header":"3. Theoretical Framework","content":"\u003cp\u003eLeigh, (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) identifies six distinct categories of evidence that are particularly valuable in the formulation of social policies across sectors such as education, health, and social welfare. Leigh further suggests that the relevance and applicability of these evidence types may vary in other policy domains such as defense or fiscal policy (Leigh, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Our study evaluates the applicability of Leigh\u0026rsquo;s evidence typology within the context of three selected policy areas in Bangladesh, offering insights into the nature and quality of evidence underpinning national policy decisions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to systematically assess the degree and mode of evidence use in policymaking processes, this study aims to investigate the application of Leigh's six categories of evidence across various policy cycle stages. The analysis particularly emphasizes on Bangladesh's policy creation phase to assess the degree to which different types of evidence have impacted decision-making. By highlighting trends, weaknesses, and possible areas for improvement in the incorporation of empirical evidence into policy design, the study provides crucial insights into the practice of evidence-based policymaking within the Bangladeshi policy context.\u003c/p\u003e"},{"header":"4. Study Methodology","content":"\u003cp\u003eIn this study purposive sampling method used as the goal of the research is to select respondents who are familiar with the NCLEP 2010 and have special knowledge of the policy process.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e4.1. Sampling Procedure\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eBangladeshi individuals working in the public and private sectors, as well as academicians, and national and international organizations, are the primary focus of the study. The study undertakes 203 respondents who participated in a self-administered survey questionnaire. In the survey, 77.37% participants have Master\u0026rsquo;s degree and 47.83% respondents working experience are between one to ten years, 39.13% respondents have working experience are between eleven to twenty years. Descriptive analysis of respondents is exhibited below in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage Distribution of the respondents by last academic degree and years in job\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\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLast Academic Degree\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHonors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM.Phil.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears in current position\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: Field Survey 2023\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e4.2. Techniques of data analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eA content analysis approach was employed to systematically identify themes and patterns related to evidence use in policymaking (Braun \u0026amp; Clarke, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For this study data were collected through KIIs and FGDs were transcribed for the analysis. In parallel, data obtained from the structured questionnaire were analyzed using descriptive statistics and Structural Equation Modeling (SEM) to assess the extent to which the policymaking process can be characterized as evidence-based.\u003c/p\u003e \u003cp\u003eVariable description are as follows:\u003c/p\u003e \u003cp\u003eWe have used the following model to analyze the collected data using Structural Equation Modelling Approach (SEM):\u003c/p\u003e \u003cp\u003eStudy model:\u003c/p\u003e \u003cp\u003eThe study model of this study is-\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Evidence\\:Based\\:Policy=Public\\:Policy\\:Process+Policy\\:Enacting\\:Process+Institutional\\:Framework$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eDescription of the variables are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription and Details of the Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription and Details\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvidence-Based Policy-\u003c/p\u003e \u003cp\u003e1. Systematic reviews (meta-analyses) of multiple randomized studies;\u003c/p\u003e \u003cp\u003e2. High-quality randomized studies; 3. Systematic evaluations 4. Natural experiments 5. Before-after (pre-post) studies; 6. Theoretical conjectures and opinions of experts (Leigh, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic Policy Process- (Agenda Setting and Policy Formulation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolicy Implementation Process- (Policy Selection and Policy Implementation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitutional Framework- (Monitoring, Evaluation and Feedback (ME\u0026amp;F), Leadership, Role of Institutions, Culture)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNote: Opinion of the policy makers, stakeholders in likert scale was taken\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Findings","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e5.1. Quantitative Data Analysis\u003c/em\u003e\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e5.1.1. Model Evaluation\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThis investigation used SEM, or structural equation modelling, which is regarded as a reliable analytical method because of its capacity to represent intricate relationships. Its adaptability to model misspecification and the ongoing development of estimating techniques that lessen sensitivity to statistical assumption violations serve as additional evidence of its robustness. The integrated model is reliable for simultaneous estimation and provide comprehensive framework to analyze complex relationship to yield an accurate result for the estimators (Kock et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the fields of business, social science, medicine, health research, and natural science, route analysis and confirmatory factor analysis are more common (Information \u0026amp; Chin, 2013).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e5.1.2. Measurement Model Evaluation\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eA confirmatory factor analysis (CFA) test was conducted on the measurement model. These variables' factor loadings are checked to verify the model's measurement. Factor loading values nearer 1 show that the factors have a significant impact on the variable (Astrachan et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Every factor's score is stored for the primary estimation. On a 5-point Likert scale, with 1 denoting \"strongly disagree\" and 5 denoting \"strongly agree,\" respondents are asked to answer 10\u0026ndash;12 items related to a single issue. Finally, the Cronbach's Alpha value is determined to assess the reliability of the questionnaire for this variable. If a regression model is to be used for this investigation, it suggests that the questionnaire is internally consistent (Henseler et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe measurement model demonstrates an acceptable level of goodness of fit (GFI) (Henseler et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), as indicated by multiple fit indices (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). GFI is 0.96, exceeding the commonly recommended threshold of 0.90, which suggests an adequate fit between the hypothesized model and the observed data. Similarly, both the CFI at 0.92 and the Tucker-Lewis Index (TLI) at 0.91 meet the conventional cutoff value of \u0026ge;\u0026thinsp;0.90, indicating a good comparative fit (Bentler, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGoodness of fit of the measurement model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eREMSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurement model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\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\u003eHowever, in the Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e the Root Mean Square Error of Approximation (RMSEA) is reported at 0.10, which slightly exceeds the acceptable upper limit of 0.08 for a reasonable error of approximation. While this suggests some room for improvement, it does not necessarily indicate poor model fit, especially when considered alongside the other fit indices (Rigdon, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The Root Mean Square Residual (RMR) value of 0.81 appears to be unusually high and may indicate either a reporting or scaling issue, as typical acceptable values for RMR are \u0026le;\u0026thinsp;0.08. Conversely, the Standardized Root Mean Square Residual (SRMR) value of 0.10 is on the threshold of acceptability, as values less than or equal to 0.10 are considered acceptable for model fit (Bentler, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTaken together, the majority of fit indices support the acceptability of the measurement model, with strong performance on absolute and incremental fit measures, albeit with minor limitations in residual-based metrics. Thus, the model can be considered to demonstrate an overall adequate fit to the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e5.1.3. Common Method Bias\u003c/h2\u003e \u003cp\u003eTo mitigate the potential influence of common method bias (CMB), several procedural and statistical remedies were employed, consistent with established best practices (Kock et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). First, ethical research protocols were rigorously followed to enhance the accuracy and reliability of responses. Participants were assured of both confidentiality and anonymity, thereby reducing social desirability and response bias. Second, Harman\u0026rsquo;s single-factor test was conducted to statistically assess the presence of CMB (Kock et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The analysis indicated that the first factor accounted for 30% of the total variance, which is substantially below the conventional threshold of 50%, suggesting that common method variance does not pose a significant concern in this study (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Furthermore, multicollinearity was assessed through the calculation of Variance Inflation Factor (VIF) values for the independent variables, all of which were within acceptable thresholds. These results indicate the absence of serious multicollinearity issues, thereby affirming the robustness and suitability of the dataset for subsequent analytical procedures.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCommon method Bias (Harman\u0026rsquo;s single -factor test)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Of variance explained by single factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Variance explained by a single factor for PPP, PIP and IF is less than 50%; therefore, there is no common method bias.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the correlation matrix among the key structural constructs\u0026mdash;PPP, PEP, and IF. There is no indication of multicollinearity between the variables, as the maximum correlation coefficient found between PPP and PIP at 0.274, is much below the generally recognized cutoff of 0.90. Furthermore, the generally modest inter-construct correlations imply that the data's integrity is not seriously threatened by common method variance. These results validate the statistical validity and methodological soundness of the structural model used in this work when paired with other diagnostic tests.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe criterion for validity in a correlation matrix of the structural parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePPP\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePEP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.274\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.229\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.137\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*** Indicates the correlation is significant at 0.01 and * indicates the correlation is significant at 0.05\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Calculated by the Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e5.1.4. Findings\u003c/h2\u003e \u003cp\u003eIn this study it is empirically tested whether policy making process, the policy implementation process and institutional framework have a significant influence on evidence-based policy making or not. Here confirmatory factor analysis (CFA) is used to test the validity of the model (Information \u0026amp; Chin, 2013). Several criteria were employed to support the model's fitness. The structural model index was investigated and provided in the study, and it was determined that it was an acceptable fit. By utilizing internal consistency by utilizing Cronbach\u0026rsquo;s Alpha test for the variables if show that the variables are not problematic. We also perform CFA whose result suffices the fitness of the data [CFI 0.92 (closer to 1.00), TLI 0.91 (closer to 1.00). Variance explained by a single factor, Harman\u0026rsquo;s test for PPP, PEP, and IF is less than 50%; therefore, there is no common method bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e5.1.5. Measurable Variables\u003c/h2\u003e \u003cp\u003eFor the data set of this component (PPP to EBP) Bartlett\u0026rsquo;s test is highly significant, \u003cem\u003eχ\u003c/em\u003e2(55)\u0026thinsp;=\u0026thinsp;507.515, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, indicating that the variables exhibit acceptable intercorrelations to justify the application of factor analysis, Kaiser-Meyer-Oklin (KMO) test value is 0.56 which is greater than 0.5, exceeds the minimum acceptable threshold of 0.50 which indicates factor analysis is appropriate (Ponnam et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The determinant of the selected variables is 0.0766. This value is greater than the necessary value of 0.00001, indicating that there is no multicollinearity among the variables (Kalnins, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To check the reliability of the data we go for Cronbach\u0026rsquo;s test, where public policy process had high reliability and the Cronbach\u0026rsquo;s \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e = 0.79. As such, our determinants of the selected variables do not seem problematic, indicating that multicollinearity or singularity is not a concern among the variables (Kalnins, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStructural Equation Model Evaluation\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of structural equation model (NCLEP)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEBP \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\leftarrow\\:\\)\u003c/span\u003e\u003c/span\u003e PPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEBP \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\leftarrow\\:\\)\u003c/span\u003e\u003c/span\u003e PIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEBP \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\leftarrow\\:\\)\u003c/span\u003e\u003c/span\u003e IF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003ep\u003c/em\u003e-value: *** \u0026lt; 0.01, ** \u0026lt; 0.05, * \u0026lt; 0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Calculated by the Authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, it is indicated that the relationship between policy-making process (PPP) and evidence-based policy (EBP) is negative and statistically significant, with a coefficient of -0.413 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This finding reveals that, within the context of this study, the policy-making process exerts a significant negative direct effect on the adoption of EBP.\u003c/p\u003e \u003cp\u003eRegarding the relationship between the policy implementation process (PIP) and EBP, the factor analysis was confirmed by a highly significant Bartlett\u0026rsquo;s test of sphericity, χ\u0026sup2;(6)\u0026thinsp;=\u0026thinsp;114.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and a Kaiser-Meyer-Olkin (KMO) value of 0.523, exceeding the minimum acceptable threshold of 0.50 (Cerny \u0026amp; Kaiser, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Additionally, the determinant of the correlation matrix was 0.564, which is above the critical value of 0.00001, indicating the absence of multicollinearity concerns (Kalnins, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Cronbach's alpha, a measure of the PIP construct's reliability, came out at 0.54\u0026mdash;a modest score that is appropriate for exploratory study in social scientific settings. With a coefficient of 0.78 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), the results of the structural equation model show a positive and statistically significant association between PIP and EBP. This indicates that the policy implementation process under the NCLEP (2010) is positively associated with evidence-based policy practices.\u003c/p\u003e \u003cp\u003eSimilarly, for the relationship between the institutional framework (IF) and EBP, Bartlett\u0026rsquo;s test of sphericity was highly significant, χ\u0026sup2;(15)\u0026thinsp;=\u0026thinsp;176.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, with a KMO value of 0.523, indicating the appropriateness of the data for factor analysis. The determinant of the correlation matrix was again 0.564, confirming the absence of multicollinearity. The Cronbach\u0026rsquo;s alpha for the IF construct was 0.54, consistent with acceptable reliability levels for exploratory analysis. The structural equation modeling results reveal a negative and statistically significant relationship between IF and EBP, with a coefficient of -0.52 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This finding suggests that the institutional framework associated with the NCLEP (2010) is negatively correlated with the application of evidence-based policy within this context.\u003c/p\u003e \u003cp\u003eCollectively, these results provide a nuanced understanding of the determinants of evidence-based policy adoption within the framework of the NCLEP (2010), highlighting the differentiated impacts of policy formulation, implementation processes, and institutional structures on evidence-based policymaking in Bangladesh.\u003c/p\u003e \u003cp\u003ePath Estimation of SEM\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe estimated result specifically shows that Public Policy Process and Institutional Framework are negatively affecting the Evidence-based Policymaking process of NCLEP '2010. The effect of the Policy Implementation Process is positively affecting the Evidence-based Policymaking Process. Therefore, we can say that the making of NCLEP '2010 Policy Implementation Process was evidence based but Policy Making Process and Institutional Framework were not evidence based.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Analysis of Qualitative Data\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1. Findings obtained from KII\u003c/h2\u003e \u003cp\u003eFrom the qualitative segment, our data reveals a few factors determining challenges and a way forward to address evidence-based policy making in NCLEP\u0026rsquo;2010. The classified observations from our KII respondents are stated below:\u003c/p\u003e \u003cp\u003eA. High-quality information and data\u003c/p\u003e \u003cp\u003eTo guarantee high-quality information, good-quality, reliable data, and a panel of expert\u0026rsquo;s expertise are required to initiate evidence-based policy. Departmental analysts should make an effort to understand the most concise explanation of the available evidence (Proma et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). One respondent opined that,\u003c/p\u003e \u003cp\u003e\u0026ldquo;\u0026hellip;.\u003cem\u003eto avoid policy failure, evidence is a must. Child Labor survey report was the core evidence for initiating NCLEP\u0026rsquo;2010. High-quality evidence is required to guarantee high-quality information. For this, correct research methods should be used. Reliable literature is crucial since it will provide guidance on the way to formulate policies correctly based on its findings\u0026hellip;. the secret to this is high caliber research.... the primary focus should be on analytical research, there isn't a lot of space for problem-solving research\u0026hellip;. there's a sizable capacity deficit that needs to be solved. Policy failures will be reduced by high-quality data and strong analytical skill\u0026rdquo;\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eB. Leadership and place right person\u003c/p\u003e \u003cp\u003eHaving the right person in the right places is very important. Utilize and take into consideration the expert\u0026rsquo;s knowledge and beside that negotiation capability of the policy maker needs to be developed. One Participants from KII reported that,\u003c/p\u003e \u003cp\u003e\u0026ldquo;\u003cem\u003eStable service tenure is mandatory for effective evidence-based policy making, formulation and implementation. This should be taken into account not only for the proper implementation of NCLEP '2010 but also for the proper implementation of any policy. Frequent changes in the policy making level hampered the flow and speed of that particular project.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003cp\u003eC. Social Safety Net\u003c/p\u003e \u003cp\u003eAlthough the government of Bangladesh implemented a variety of incentive programs for children to cut down on child labor but the number is still substantial for hazardous jobs.\u003c/p\u003e \u003cp\u003eRespondent from an international organization stated that:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn our \u003cem\u003ecultural and social purview people often consider starting families and relying on the next generation to support them. According to this principle, the cycle of sending kids to work never ends. This idea applies to both poverty and domestic choices. It is necessary to change the way the family members think\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eD. Institutional Capacity\u003c/p\u003e \u003cp\u003eAction plans should be based on institutional capacity; without monitoring and coordination of actions, it is difficult to fulfill the objectives of NCLEP '2010. The following quotes of a respondent explain the situation:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u0026hellip;\u003cem\u003ein NCLEP '2010, there are nineteen ministries, along with the Ministry of Labor and Employment, who have responsibility for the successful implementation and monitoring of this policy. It\u0026rsquo;s necessary to operationalize ministry-wise functions to achieve the goals of this policy, where institutional capacity is core.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSome recommendations came from the respondents. Engage more with the policymaker and share knowledge and research with them. Disseminate the work and try to develop the knowledge base on those. Furthermore, it needs to reach out to the general public and enhance their knowledge. Finally, to communicate uncertainty about evidence, it should be clearly spelled out before formulating the policy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2. Findings from FGD\u003c/h2\u003e \u003cp\u003eTwo Focus Group Discussions (FGDs) as a tool for research (Davies, 1999; Efimov et al., 2022) were conducted in June 2023 with 15 participants. They all were mid-level professionals with knowledge of the policy-making process.\u003c/p\u003e \u003cp\u003e We captured data using memory, body language of the participants, taking field notes, handouts, and flip charts, along with audiotape. The answers to the key questions were identified, transcribed, and translated in line with the purpose of the study. For further exploration of certain circumstances, we use probes and follow-up questions to identify the situations. We have used an ending question like \u0026ldquo;what they consider to be the most important topic discussed? to identify the issue. At the concluding part of this round-robin question to the group was helped with the analysis part and provided a glimpse of where to put emphasis according to the importance placed by the participants.\u003c/p\u003e \u003cp\u003eThis insight was instrumental in depicting the result (Wilkinson, 2011). Moreover, we verified our findings from the FGD by offering a summary of the discussion session to the participants, asking them whether this was an appropriate overview of the important issues raised. Finally, a debriefing session among the research team members was conducted to compare notes, highlights, and to consider what others in the team have observed and heard to retain consistency among all FGDs. Focus groups and other qualitative techniques offer researchers an extra way to collect in-depth, first-hand input.\u003c/p\u003e \u003cp\u003eFrom the FGD of this study, participants emphasized on coordination of actions. Alongside the Ministry of Labor and Employment, the involvement of other ministries is particularly important because the NCLEP 2010 has many aims. The communication issue ought to be effectively articulated. Institutions should be effective, credible, and impartial. Network management, administrative ethics, collective accountability, and institutional knowledge transfer is very crucial in this regard. Proper coordination direction will make this policy successful.\u003c/p\u003e \u003cp\u003eThe most important and difficult aspect of implementing this policy is changing people's minds. Improvement in the domain of institutional culture is vital. One respondent from FGD opined that,\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eThe NCLEP\u0026rsquo;2010 itself is not very structured and clear to achieve its goals\u0026hellip;.often donors gave a few criteria to set a standard rule for a policy\u0026hellip; our officer need to follow strong evidence so that they can negotiate in favor of our interest\u0026hellip;we should not copy others countries policies rather we can consider it as a best example, make comparison and take best decision based on our societal norms, values and other relevant socio, economic and cultural factors.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEvidence should be gathered for the effective monitoring of this policy. It will result in sector-based data on child labor covering both urban and rural areas, including both the formal and informal sectors. One participant explained,\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eUrban areas are getting more priority than rural areas in the evaluation and monitoring of NCLEP '2010\u0026hellip;\u003c/em\u003e. \u003cem\u003ethis needs to be appropriately addressed.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the policy making level there are representative stakeholder\u0026rsquo; participation but stakeholder selection is also very important. Even if meetings are held at the Zila and Upazila levels which were not always very effective. In this regard participants emphasized on, \u0026ldquo;\u003cem\u003eParticipatory management system for evidence-based policy making.\u003c/em\u003e\u0026rsquo;\u0026rsquo;\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. Discussions","content":"\u003cp\u003eThis study critically examines the policymaking processes underpinning the NCLEP\u0026rsquo;2010 in Bangladesh, employing a mixed-methods approach that integrates both qualitative (FGDs, KIIs) and quantitative (survey) data, drawn from primary and secondary sources. The essence of effective EBP lies in combining logical reasoning, scientific evidence, and governance principles, while translating complex evidence into narratives that are accessible for policymakers and stakeholders (Oliver et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The study positions evidence-based policymaking (EBP) as a crucial mechanism for improving policy effectiveness, asserting that policies grounded in robust evidence tend to yield more impactful outcomes. Within Bangladesh, interest in EBP has been growing, with stakeholders increasingly questioning the extent to which policy processes align with evidence-informed frameworks (FGD and KII findings, Field Survey, 2023).\u003c/p\u003e \u003cp\u003eThe application of an EBP approach to solve policy problems seems to be challenging when they are complex and multifaceted (Teirlinck et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Notwithstanding the issues, the study's findings reveal that governments can benefit greatly from the insights found in EBP literature, which can provide them with useful policy recommendations, and the findings are compatible with the findings (Simons \u0026amp; Schniedermann, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study got some insights from the EBP literature on why and how evidence is essential and recommends that policymakers adopt an EBP approach, including appropriate stakeholders.\u003c/p\u003e \u003cp\u003eHowever, there are a few contemporary analysts who now adopt such a rationalist position. The recent trajectory of EBP in the United Kingdom illustrates some of the current ambiguities (Pallett, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although there has been widespread support and acceptance for the increased focus on research and evaluation in the policy cycle (Sanderson, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Evidence plays a crucial role in shaping the policy process not only during the agenda-setting and formulation phases but also during the decision-making, policy implementation, monitoring, and evaluation phases to gather input (Knill et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study reveals that evidence is not only vital during the formulation stage but also plays a critical role across the policy cycle, including decision-making, implementation, and monitoring and evaluation stages. Integrating evidence in the early stages of policymaking is particularly effective, a finding supported by Hythar, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e. The evolution of the evidence-based policy movement and high-quality data (Kassi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) is useful for the operationalization of policy making process. This study finds that EBP is that policy settings can be improved on the basis of high-quality evidence and reliable knowledge.\u003c/p\u003e \u003cp\u003ePolicymakers need to develop strategies to deal with uncertainty and minimize risk, particularly when external factors like labor unions and international bodies are involved (Hope, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Liverani et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). During the policy selection phase, participants showed favorable sentiments regarding the assessment of evidence-based policy alternatives (Listorti et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), but they also showed a more cautious degree of faith in the knowledge of policymakers. This suggests that policymakers need to develop their skills in order to better use evidence in their decision-making.\u003c/p\u003e \u003cp\u003eThis case study demonstrates that evidence plays a significant role in the initial stages of policymaking, emphasizing the need for ongoing evidence generation and stakeholder collaboration. Therefore, addressing the complexities of stakeholder interests and community values is essential to EBP's effectiveness (Mills et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Acknowledging the practical constraints, this study focuses on the NCLEP 2010, offers important insights into how evidence can be methodically incorporated into policymaking in Bangladesh for sustainable development.\u003c/p\u003e \u003cp\u003eTherefore, the channels through which rigorous evidence might influence policymaking are somewhat fragile, and are sometimes readily disrupted by political and organisational pressures. Hence, the communication channels need specific care and attention both to understand their characteristics and to improve their outcomes (Boaz \u0026amp; Nutley, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Oliver et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). EBP will continue to inspire efforts for better performance in the policy sciences, but will always be constrained by the realities of community values, political systems in place, and interest of the policy players.\u003c/p\u003e \u003cp\u003eHowever, challenges such as resource constraints, evolving social conditions, and a lack of monitoring and evaluation mechanisms pose risks to effective EBP (Listorti et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Policymakers are encouraged to prioritize the monitoring and evaluation phase, addressing challenges in implementation, and changing societal mindsets. Furthermore, this focus aligns with the Sustainable Development Goals (SDGs), emphasizing the significance of eradicate child labour.\u003c/p\u003e \u003cp\u003eTherefore, the channels through which rigorous evidence might influence policymaking are somewhat fragile, and are sometimes readily disrupted by political and organisational pressures. Hence, the communication channels need specific care and attention both to understand their characteristics and to improve their outcomes (Nutley et al., 2007; Ouimet, Landry, Ziam, \u0026amp; Bedard, 2009). EBP will continue to inspire efforts for better performance in the policy sciences, but will always be constrained by the realities of community values, political systems in place, and interest of the policy players.\u003c/p\u003e \u003cp\u003eThis study reasons behind this rapid and remarkably enduring transformation identifies the main dynamics of policy making, and explores some future challenges. Additionally, the study recommends incorporating evidence at all stages of policymaking, including agenda-setting, formulation, implementation, and monitoring and evaluation, to improve policy outcomes.\u003c/p\u003e"},{"header":"7. Limitations and Future Research Scope","content":"\u003cp\u003eIn this study the survey population was confined to officials of the Ministry of Labor and Employment (MLE), given their direct involvement in the policymaking process of the NCLEP 2010. This focus, combined with a low response rate, posed challenges in data collection process. The policy under examination was formulated in 2010, recall bias may have influenced respondents' accuracy in reporting the details of the policy formulation process. Although triangulation across survey responses, key informant interviews, and focus group discussions was employed to enhance the robustness of the findings, the study remains reliant on self-reported perceptions. Future research focusing on the policy implementation stage could provide deeper insights into the practice of evidence-based policymaking in this context.\u003c/p\u003e"},{"header":"9. Conclusion and Policy Implementation","content":"\u003cp\u003eThis study comprehensively examined the hypothesized relationships aligned with its research objectives. Using a Structural Equation Modeling (SEM) approach, the results indicate that all theorized direct effects are statistically significant. Notably, the findings reveal that both the public policy process and the institutional framework exert a significant negative influence on the adoption of evidence-based policymaking in relation to the National Child Labor Elimination Policy (NCLEP) 2010.\u003c/p\u003e \u003cp\u003eIn response to the complex and persistent challenges posed by child labor, countries worldwide are increasingly adopting legal and policy frameworks to articulate targeted interventions. However, the extent to which these policy measures are underpinned by robust evidence remains a critical concern. Assessing the state of evidence-based policymaking and understanding the factors that shape it are essential for strengthening each phase of the policy cycle from initial formulation and implementation to evaluation and refinement.\u003c/p\u003e \u003cp\u003eThis study addresses this gap by critically examining the status of evidence-based policymaking within Bangladesh\u0026rsquo;s public policy sphere, using the NCLEP 2010 as a case study. Drawing on both qualitative and quantitative evidence, the analysis demonstrates that while the policy enactment process shows a significant positive association with evidence-based policymaking, the broader policy process and institutional structures present notable constraints, negatively influencing the integration of evidence into policy decisions. These quantitative results are corroborated by insights from key informant interviews (KIIs) and focus group discussions (FGDs), adding contextual depth to the findings.\u003c/p\u003e \u003cp\u003eThe results underscore the importance of embedding credible evidence and suggest policy input for sound institutional mechanisms into the policy domain. Advancing evidence-based policymaking requires not only logical reasoning and the integration of scientific evidence with governance practices but also the capacity to translate complex evidence into clear, actionable narratives. Addressing the systemic barriers identified in this study calls for the introduction of a dedicated coordinating Act and the development of supporting institutional mechanisms to enable a more coherent, evidence-driven policy process. As seen from the qualitative analysis, challenges in implementation often stem from poorly synthesized action plans. To ensure successful implementation, action plans must be concise and effectively synthesized. Additionally, establishing robust institutional frameworks is essential to facilitate and support the implementation process. However, child labour policy requires an integrated, evidence-based, cross-sectoral approach to address SDS goals for sustainable development.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cb\u003eFunding Acknowledgement\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThis research was supported by the Ministry of Public Administration (MoPA), Government of Bangladesh, under the Research Grant (Code No. 3257103) for the Financial Year 2023\u0026ndash;2024. The authors gratefully acknowledge the financial support provided by MoPA for conducting this study.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical Approval\u003c/h2\u003e \u003cp\u003eThe study protocol was approved by the Institutional Review Board of Bangladesh Institute of Governance and Management (BIGM) and the certificate reference number is: BIGM/Research \u0026amp; Publication/01/21/23\u0026ndash;93. The study was conducted in accordance with the Bangladesh Institute of Governance and Management Research Ethics Board (BIGMREB) relevant guidelines and regulations.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all participants prior to data collection. For this study participation was voluntary, and respondents were assured of confidentiality and they were assured that their responses would remain confidential and used only for academic purposes.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent to Publish\u003c/h2\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, literature review, methodology, writing original draft, editing, referencing and final format: Fataraz Zahan. Conceptualization, methodology and writing Mowshumi Sharmin. Literature review: Shafin Haque Omlan. Quantitative data analysis: Md. Injamamul Haq Methun. Supervision, proofreading and review: Krishna Gayen (PhD), Overall guidance, feedback and insights: Mohammad Tareque (PhD).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhad MA, Parry YK, Willis E, Ullah S. Child Laborers\u0026rsquo; Exposure to Neglect in Rural Bangladesh: Prevalence and Risk Factors. Child Indic Res. 2024;17(3):1115\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12187-024-10129-2\u003c/span\u003e\u003cspan address=\"10.1007/s12187-024-10129-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmin A, Dogan E. 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Environ Int. 2019;130(May):104871. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envint.2019.05.065\u003c/span\u003e\u003cspan address=\"10.1016/j.envint.2019.05.065\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Public Value, Evidence-based policy, Policy analysis, Child labor elimination policy, Structural Equation Model","lastPublishedDoi":"10.21203/rs.3.rs-8937382/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8937382/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGovernments are currently prioritized to establish robust legal and policy adaption mechanisms to specify policy actions in order to address the public value and wide-ranging effects of child labor. However, one crucial question is the extent to which this policy is backed by evidence. The effective way to create a responsive and flexible policymaking process is to critically assess the state of evidence-based policymaking (EBP) and determine the circumstances that allow it to be used effectively throughout the policy cycle. This study examines the state of EBP within Bangladesh's public policy sphere, using the National Child Labor Elimination Policy 2010 (NCLEP) as a case study. Applying Structural Equation Modelling (SEM) and combining qualitative and quantitative methodologies, findings reveal that the Policy Implement Process has a positive impact on EBP in the setting of NCLEP, whereas the Public Policy Process and Institutional Framework have a negative impact. The findings are further supported by insights from focus group discussions (FGDs) and key informant interviews (KIIs). Effective policymaking is greatly influenced by strong governance structures and rigorously developed evidence. In this regard, prioritizing stakeholders\u0026rsquo; involvement, evidence-based decisions for policy making are key to achieve the objective of the policy and SDG targets. Evidence-based policymaking (EBP) demands not only sound reasoning but also the integration of governance principles with empirical data, along with the capacity to translate complex findings into accessible narratives.\u003c/p\u003e","manuscriptTitle":"An Empirical Analysis to Integrate Public Value and Child Labour Policy through an Evidence-based Approach for Sustainable Development.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 10:39:13","doi":"10.21203/rs.3.rs-8937382/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T06:44:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T17:10:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T17:34:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T11:14:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150013263674111224021227018542773886243","date":"2026-04-03T15:49:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274884008783579081941917342743127372697","date":"2026-04-03T15:36:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124746322821867774373198492390862752383","date":"2026-03-31T18:01:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T14:08:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-29T14:00:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-26T06:55:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-21T08:17:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-03-21T08:13:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff86de22-2432-41e4-9ef7-ec60a7dadf0c","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T06:44:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T17:10:34+00:00","index":89,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T17:34:22+00:00","index":88,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T06:54:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 10:39:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8937382","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8937382","identity":"rs-8937382","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}