Modelling structural breaks in social cash transfers effects on poverty and inequality reduction in Africa: A case of Nigeria

Modelling structural breaks in social cash transfers effects on poverty and inequality reduction in Africa: A case of Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modelling structural breaks in social cash transfers effects on poverty and inequality reduction in Africa: A case of Nigeria Rufus Adebayo Ajisafe, Solomon Oluwaseun Okunade, Musbau Olaniyan Fatai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3384456/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Mar, 2024 Read the published version in Scientific African → Version 1 posted You are reading this latest preprint version Abstract Poverty and inequality have become persistent challenges in African countries, hindering sustainable development and equitable economic growth. Against this backdrop, the Nigerian government introduced social cash transfer (SCT) programmes to provide targeted financial assistance to vulnerable populations and foster inclusive social welfare. However, the impact has been somewhat indefinable given the continued increase in poverty and inequality levels in Nigeria. Thus, this present study focuses on the effect of SCTs on poverty, inequality, and macroeconomic instability in Nigeria using secondary data for the periods of 1984 to 2021. The results of the Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS) with structural breaks showed that, individually, SCTs significantly increase the levels of poverty, inequality and inflation in Nigeria, but when we considered structural breaks and moderated effects of institutional peculiarities of less developed countries such as Nigeria using control of corruption and accountability, social cash transfers reduced poverty and inequality levels as well as macroeconomic instability in Nigeria. Findings from this study reveal that SCTs have played a pivotal role in improving the well-being of beneficiaries by providing crucial financial support, which, in turn, has contributed to reducing the incidence of poverty and mitigating income disparities among households. Our findings serve as a useful benchmark for the government, policymakers, and Non-Governmental Organizations (NGOs) for evaluating the effectiveness of SCT policies and other social welfare strategies in Nigeria toward evidence-based poverty alleviation and inequality reduction strategies, given the present socio-economic challenges. JEL Classification: E02, H55, I32, M14 Social cash transfers Poverty Inequality Structural breaks Nigeria Figures Figure 1 1. INTRODUCTION Poverty and inequality have become persistent challenges in the country, hindering sustainable development and equitable economic growth in developing countries, particularly in Sub-Saharan Africa, beyond the expiration of the Millennium Development Goals (MDGs) in 2015. According to the United Nations, all regions of the world attained a reasonable level in the target of poverty reduction as entrenched in MDG 1, except Sub-Saharan Africa. The target to eradicate extreme poverty and hunger as entrenched in the Sustainable Development Goals (SDGs) was almost elusive in the region, especially in Nigeria, the most populous African country (Bukari, et al., 2019; Fuseini et al., 2017 ; Anyanwu & Anyanwu, 2017). Also, the income gap between the rich and the poor in Nigeria continued to become wider, indicating that widespread income inequality coexists with extreme poverty in Nigeria. Social Cash Transfer (SCT) programmes have been used as an instrument to mitigate the menace of poverty and inequality in many developing economies, such as Nigeria. The SCT schemes surfaced and gained prominence as a global remedy to address poverty and income inequality in the 1980s and 1990s, respectively (Fuseini et al., 2017 ; Johannsen et al., 2010). The social cash transfer is a steady, regular, and non-contributory allocation of cash to the most vulnerable in society to address the prevalent poverty and inequality levels by governments and other non-state actors, otherwise known as non-governmental organizations (NGOs). The central goal of social cash transfers in Nigeria is to combat widespread absolute poverty and chronic income inequality by directly providing cash transfers to the country’s most vulnerable and poor households. In Nigeria, the National Cash Transfer Programme, otherwise known as the Household Uplifting Programme (HUP) which is one of the Federal Government of Nigeria's social safety net programmes, is funded by both the government through the National Cash Transfer Office (NCTO) under the Federal Ministry of Humanitarian Affairs, Disaster Management and Social Development and other local and international partnering organizations. Meanwhile, social benefits are grouped into contributory and non-contributory benefits (Kumar & Sakthivel, 2020 ). However, the non-contributory cash transfer component has attracted the attention of social researchers because of its perceived impact on the poorest households. According to Rabi ( 2011 ), examples of non-contributory cash transfers include but are not limited to conditional cash transfers, child allowances, parental benefits, housing allowances, unemployment benefits, students’ bursaries, and so on. This category does not include the contributory benefits or social insurance. For clarity, Conditional cash transfers (CCTs) are schemes in which recipients are only given money if they meet certain criteria, while Unconditional cash transfers (UCTs) refer to schemes in which recipients do not need to fulfil any requirements or criteria. Theoretically, social cash transfer reduces poverty and inequality among most socio-economically vulnerable or disadvantaged people by improving access to food or providing households with income that can be used to solve daily needs problems (Kumar & Sakthivel, 2020 ; Miller, Tsoka & Reichert, 2014 ; Miller, et al., 2010 ). The underlying assumption is that the poor have access to cash for the provision of basic needs such as food, healthcare services, and clothes and that by increasing income to the poorest households, families will opt to spend some of their monthly income on food (Pruce, 2022 ; Kumar & Sakthivel, 2020 ). This would ultimately reduce the poverty and income inequality levels in the country. Poverty and Inequality Theories: The study aligns with and enriches theories related to poverty and inequality reduction. Examining the impact of SCTs on vulnerable populations provides empirical evidence supporting the effectiveness of such interventions in addressing these complex socio-economic issues. The significance of the study on social cash transfers, poverty, and inequality reduction in Nigeria lies in its potential to inform and guide policies and interventions aimed at addressing socio-economic challenges in the country, and insights can have various practical implications and broader impacts. Consequently, in Nigeria, to increase the likelihood of the poor, the Federal Government has formed six different interventions under the National Social Investment Programme (NSIP) which include National Home-Grown School Feeding Programme (NHGSFP), N-Power, Alternate School Programme (ASP), Government Enterprise and Empowerment Programme (GEEP), Grant for Vulnerable Groups (GVG) and National Cash Transfer Office (NCTO), also known as Trader Money. Furthermore, the role of institutional quality has been entrenched in theory. Acemoglu, et al. ( 2005 ) opined that institutional quality and economic development reinforce each other in the long run, but less developed intuitions could foster the virtuous circle of poverty and widen the gap between the rich and the poor in an economy. The quality of institutions such as accountability and corruption control could provide an enabling environment that promotes growth, and reduces poverty and inequality when institutional quality is sufficiently developed, yet inhibit growth and poverty reduction mechanisms if otherwise (Acemoglu & Robinson, 2013 ; Acemoglu, et al. 2005 ). Therefore, the quality of institutions is theoretically important when development issues such as the nexus between social cash transfers, poverty, and income inequality are analyzed. It is arguably stated in the literature that social cash transfers disbursement disrupts macroeconomic stability in developing countries by leading to rising inflationary pressure in such economies (Mncube et al., 2023 ; Patel-Campillo & Garcia, 2022 ; Kitaura & Miyazawa, 2021 ; Yu & Li, 2021 ) without due recourse to strong institutional quality. Despite the implementation of social cash transfer programmes in Nigeria as a social protection strategy, poverty, and inequality remain persistent challenges in the country. While there is evidence of some positive impacts of social cash transfers on poverty reduction and human development outcomes in other countries (see Pruce, 2022 ; Kabamba, et al. 2021; Kumar & Sakthivel, 2020 ; Bukari, et al., 2019), it is essential to investigate whether these programmes have been effective in alleviating poverty and reducing inequality in the unique socio-economic context of Nigeria. However, the impact has been somewhat indefinable given the continued increase in poverty and inequality levels in Nigeria. Consequently, while there is empirical evidence of the beneficial impact of social cash transfers on the poorest households from developing European, Asian, Latin American, and a few African countries (see Pruce, 2022 ; Kabamba, et al. 2021; Kumar & Sakthivel, 2020 ; Bukari, et al., 2019; Fuseini et al., 2017 ; Asfaw, et al., 2016 ; Rabi, 2011 ; Miller, et al., 2010 ), there is a dearth of evidence on the impact of social cash transfers on poverty and inequality from poverty-ridden countries in Africa, such as Nigeria. Also, the roles of quality institutions like accountability and corruption control which may affect the allocation and distribution of social cash transfers have not been explained in the limited extant studies in Nigeria (see Okoli et al., 2014 ). No empirical study has also looked at the impact of social cash transfers on macroeconomic stability in Nigeria. These constitute some gaps that we strive to fill in this present study. The study contributes to the understanding of social cash transfer theory, particularly in the context of a developing country like Nigeria by exploring the mechanisms through which SCTs can alleviate poverty and reduce inequality, shedding light on the underlying theoretical foundations of these interventions. It also aligns with and enriches theories related to poverty and inequality reduction by examining the impact of SCTs on vulnerable populations and providing empirical evidence supporting the effectiveness of such interventions in addressing these complex socio-economic issues. This paper also contributes to the strands of literature in different ways. Firstly, we add to a broad literature on the effects of social cash transfer policy on poverty and inequality in Nigeria. Existing studies consistently highlight the limited redistributive power of social protection policy in developing countries due to limited coverage of the schemes (Warwick et al., 2022 ; Brum & De Rosa, 2021 ; Bukari, et al., 2019). The roles of institutional factors such as accountability and corruption control are a pervasive feature of redistributive policy in a developing country like Nigeria, yet while the basis for such policies has been discussed theoretically (See Acemoglu & Robinson, 2013 ; Acemoglu, et al. 2005 ) there is little existing empirical country-specific studies in Nigeria. A few studies of this nature have concentrated on the effect of cash transfers on food security, poverty, and child health in Zambia, Malawi, and Ethiopia (See Pruce, 2022 ; Kabamba, et al. 2021; Kumar & Sakthivel, 2020 ; Fuseini et al., 2017 ; Asfaw, et al., 2016 ; Rabi, 2011 ; Miller, et al., 2010 ). Second, by considering the role of instructional factors, we shed new light on the re-distributional effects of social cash transfer on poverty and inequality in Nigeria and, crucially, consider the potential for alternative policies to control the high rocketing inflation rate social protection policy to be more effective. Thus, this present study focuses on the effect of social cash transfers on poverty, inequality, and macroeconomic stability in Nigeria while taking into consideration the moderating roles of institutional factors of accountability and corruption control using secondary data for the periods of 1984 to 2021. The remainder of the paper is structured as follows: Section 2 deals with the survey of literature which includes both the theoretical and empirical review as well as the conceptual framework. The theoretical foundation and methodology used are discussed in Section 3 , and the data are analyzed and presented in Section 4 . The study ends with a discussion of findings and policy recommendations in Section 5 . 2. LITERATURE REVIEW 2.1 Theoretical and conceptual framework Social protection and mediation theories address all major aspects of poverty and income inequality reductions and empowerment of the vulnerable and disadvantaged by providing social cash transfers (Kabamba, et al. 2021). The social protection theory is rooted in the theory of Justice as Fairness, postulated by John Rawls in 1985 to illustrate how justice may be attained in society for fair socio-economic wealth. It is an essential component of social policy and human empowerment that entails real action to address levels of poverty, vulnerability, risk, and deprivation (Sichula, 2018 ). According to Schuring and Lawson-McDowall ( 2011 ), it also refers to any sort of inequity or deprivation that is seen as unpleasant and unacceptable in any economy. The fact that social protection theory is an effective solution to poverty, inequality, and vulnerability in developing countries, as well as an integral component of economic and social development plans that address macroeconomic instability, led to its selection for this study (Kabamba, et al. 2021; Sichula, 2018 ). To strengthen the inclusiveness of our framework, mediation theory is incorporated to explain the role of accountability and corruption as our institutional variables that are relevant to the allocation and distribution of social cash transfers and the poverty-inequality nexus (Kabamba, et al. 2021; Changala, et al., 2015 ). Conceptually, these theories explain the connections that exist among social cash transfers, poverty, inequality, macroeconomic stability, and institutional quality. Social cash transfers may have a direct effect on poverty and inequality or stability by directly providing cash incomes to the countries’ most vulnerable and poor households, and improving their access to food, shelter, and other basic needs through the provision of extra incomes (Kumar & Sakthivel, 2020 ). Indirectly, the quality of institutions may reshape the direction as well as the magnitude of the effect of social cash transfers on poverty and income inequality in developing countries. As a result, it is appropriate to utilize social protection and mediation theories as a lens for analyzing and interpreting the findings on how accountability and corruption control might be used to mediate the relationship between social cash transfers, poverty, and inequality in Nigeria. This relationship is depicted in the conceptual framework in Fig. 1. In the framework, the purple and red arrows indicate direct effects and indirect effects respectively. 2.2 Empirical Review In the extant studies, social protection policy has been used to battle absolute poverty and income inequality in developing Asian, Latin, and Southern American countries (see Warwick et al., 2022 ; Patel-Campillo & Garcia, 2022 ; Brum & De Rosa, 2021 ; Yu & Li, 2021 ). For instance, Yu and Li ( 2021 ) examined the effects of social security expenditure on income inequality and rural poverty reduction in China covering the period of 2003 to 2017 using the Johansen cointegration test and vector error correction model. The study found that social security expenditure has a significant negative effect on income inequality and rural poverty in China, indicating that social security expenditure reduced income inequality and rural absolute poverty in China. In a different study, Warwick et al. ( 2022 ) examined and compared the effectiveness of cash transfers and VAT exemptions in low- and middle-income countries (LMICs) by estimating their impact on tax revenues, inequality, and poverty. The study employed tax-benefit microsimulation models that incorporate input–output tables, to find that preferential VAT rates reduced poverty, but was not well targeted towards poor households. However, cash transfer schemes were better targeted towards poor households but have a diminutive effect on poverty due to limited coverage. Also, Patel-Campillo and Garcia ( 2022 ) analyzed the effects of the Peruvian 2005 Juntos Conditional Cash Transfer scheme on higher education attainment based on gender. The study employed the Young Lives Survey and matching techniques, to find that Juntos conditional cash transfer has a positive effect on higher education attainment especially for men, while a gender gap in women's higher education attainment among Juntos recipients was reported. In Uruguay, Brum and De Rosa ( 2021 ) analyzed the impact of key public policies such as cash transfers, and unemployment insurance on the poverty level, and forecasts of GDP contraction during the COVID-19 crisis. The finding showed that during the first full trimester of the crisis, the poverty rate grew by more than 38%, reaching 11.8% up from 8.5%. Also, the cash transfer programme of Uruguay’s government had a positive but very limited effect in reducing the poverty spike during the COVID-19 crisis. There have been limited studies critically examining the rationale for unconditional cash transfer size, its determination, and its impacts in Sub-Saharan Africa. Bukari, et al., (2019) adopted content analysis of mainstream literature on the incidence of poverty and social protection strategies involving social cash transfer for the protection of vulnerable groups and found that a relatively higher proportion of 35.5% of the people in Sub-Saharan Africa fell below the poverty line within the period despite the implementation of several unconditional cash transfer schemes when only 9.6% of global population remained below the global poverty line for the achievement of Millennium Development Goal 1 by 2015. In country-specific studies in Sub-Saharan Africa, specifically in Zambia, Chad, Kenya, Malawi, South Africa, and Zimbabwe, studies find that social protection policies including social cash transfers and other unconditional cash transfers programme have yielded positive effects in reducing the level of absolute poverty and income inequality as well improving food security and health outcomes (Mncube et al., 2023 ; Pruce, 2022 ; Kabamba, et al. 2021; Kumar & Sakthivel, 2020 ; Fuseini et al., 2017 ; Asfaw, et al., 2016 ; Rabi, 2011 ; Miller, et al., 2010 ). For instance, Kumar and Sakthivel ( 2020 ) focused on the relevance, appropriateness, sustainability, and impact of social cash transfer schemes on the livelihood of rural households in Shamwinda Village in Chibombo District, Zambia by interviewing the beneficiaries. The study reported that social cash transfer reduced the poverty level in the village. However, the study also found that a significant percentage of the beneficiaries expended the transferred cash on food rather than on investment, therefore leaving them in perpetual poverty when the scheme comes to an end. In a different work, Pruce ( 2022 ) investigated the targeting choices of deservingness and dependency among communities receiving cash transfers in Zambia by drawing on interviews with government and policy actors, as well as focus group discussions in the cash transfer-receiving communities. The study employed Van Oorschot’s deservingness heuristic to collect data and found that popular perceptions of deservingness and the broader social justice implications need to be taken seriously in the design and analysis of targeting. Also, using both qualitative and quantitative approaches, Kabamba, et al. (2021) investigated how social cash transfer can be used to promote the socioeconomic rights of the elderly in Zambia by administering a questionnaire to 102 elderly participants from the age of 65 years. The study found that the social cash transfer is a mediating factor in reducing the socioeconomic inequalities faced by the elderly. However, this mediation varied with the level of formal education attained by the participants. These findings also emphasized the role of other mediating factors in the nexus between SCT and inequalities. Asfaw, et al. ( 2016 ) examined the impact of the Ethiopia Social Cash Transfer Pilot Programme (SCTPP) on household behavior and decision-making using two-year impact evaluation data and compared programme beneficiaries with a group of controls interviewed in 2012 and 2014, using difference-in-difference estimators combined with propensity score matching methods. They found that the programme significantly increased the livelihood strategies of the poor (subjective well-being), social capital, and household food security in Ethiopia. The study concluded that some other important heterogeneity in programme implementation such as institutional factors should be considered. Similarly, Guardia, Lake, and Schnitzer ( 2022 ) investigated both the economic and social implications of targeting cash transfer programmes on poverty in Chad and found significant positive economic effects on non-beneficiaries, but considerable social and punitive costs for recipients as a result of their inclusion in the programme, indicating ineffectiveness of cash transfer programmes towards achieving poverty reduction. In Malawi, Miller, et al. ( 2010 ) examined the extent to which social cash transfers have reduced the intergenerational cycle of poverty and improved the health status of children using mixed methods of longitudinal household qualitative interviews and focus groups as well as the double difference impact estimates to find that social cash transfer is a vital tool to fight poverty with its positive impacts on child lifelong health and growth in Malawi. Miller, Tsoka, and Reichert (2011) and Mncube et al. ( 2023 ) investigated the impact of the social cash transfer scheme on food security in Malawi and South Africa respectively. Miller, Tsoka, and Reichert (2011) conducted a longitudinal, randomized community control study of the pilot SCTS in Mchinji, Malawi from March 2007 to April 2008. The study reported a robust positive impact of cash transfers on food security in rural Malawi. In their attempt to explain the challenge of improving energy access for the ultra-poor in Malawi, Aung, et al. ( 2021 ) investigated if unconditional social cash transfers close the energy access gap. The study reported that ultra-poor households are faced with high energy poverty, but the unconditional social cash transfer payments increased their energy access. To the best of our knowledge, very few studies on social protection policy have been conducted in Nigeria aside from Paul (2022), Ezenwaka et al. (2021), Okoli et al. ( 2014 ), and Holmes et al. (2011). Paul (2022) identified the pitfalls in conditional cash transfer and suggested the best practices to enhance the performance of the social policy instrument in Nigeria and found that conditional cash transfer in Nigeria is characterized by several anomalies, such diversion of funds by the beneficiaries, improper definition of exit and entry period and that beneficiaries are randomly selected in Nigeria, thus leading to obvious errors of exclusion and inclusion. However, the study, being a library research is devoid of any empirical investigation and solution to poverty and inequality targets of the social protection policy. Moreover, Ezenwaka et al. (2021) and Okoli et al. ( 2014 ) provided some clinical recommendations without empirical and economic implications. Also, Holmes et al. (2011) empirically examined how cash transfers influenced poverty, inequality, and instability in Nigeria and found that Care of the People (COPE) as a government-run conditional cash transfer (CCT) has been effective in helping households meet their daily consumption needs, and increased their access to health services and schooling for children in the selected four states (Adamawa, Bayelsa, Edo and Kano), yet, the conditional cash transfer could not reduce poverty and inequality levels in Nigeria. This study fails to account for the role of structural and institutional factors that could affect the distribution of social cash transfers in Nigeria. All the aforementioned weaknesses of the existing few studies on Nigeria constitute some gaps that we intend to fill in this present study. 3. METHODOLOGY 3.1 Empirical model Following the theoretical foundation of this study and in line with extant studies in the literature (Mncube et al., 2023 ; Yu & Li, 2021 and Kitaura & Miyazawa, 2021 ), we begin by investigating the direct effect of social cash transfers on poverty and income inequality in Nigeria by estimating the baseline model explicitly specified Eq. 1: \({Y}_{t}^{{\prime }}= {\alpha }_{i}+\delta {SCT}_{t}+{\mu }_{t}\) 1 where \({Y}_{t}^{{\prime }}\) is a 3x1 vector of the dependent variables, poverty ( \({POV}_{t}\) ), income inequality ( \({INQ}_{t}\) ), and macroeconomic stability proxied by inflation rate ( \({INF}_{t}\) ). \({SCT}_{t}\) is the growth rate of social cash transfers and \({\mu }_{i,t}\) is the white noise error term. To account for structural and institutional factors that could mediate the impact of social cash transfers on poverty and income inequality such as accountability ( \({ACC}_{t}\) ) and control of corruption ( \({COR}_{t}\) ) based on the theoretical and empirical expositions ( see Acemoglu & Robinson, 2013 ; Acemoglu, et al. 2005 ), we modified Eq. 1 to reflect the mediating role of institutional quality as well as other control variables such as financial development ( \({FIND}_{t}\) ) and interest rate ( \({INT}_{t}\) ). Financial development is important in the model because governments at various levels utilize financial institutions for the allocation and distribution of social cash transfers among the recipients. Also, interest rate enters into the model as a determinant of how cash benefit is consumed rather than invested. This is presented in Eq. 2: \({Y}_{t}^{{\prime }}= {\alpha }_{i}+\delta {SCT}_{t}+\sigma {ACC}_{t}+\beta {COR}_{t}+\gamma {FID}_{t}+{\beta }_{1}{SCT}_{t}*{ACC}_{t}+{\beta }_{2}{SCT}_{t}*{ COR}_{t}+{\mu }_{t}\) 2 Where \({ACC}_{t}\) is the institutional quality proxied by accountability, \({COR}_{t}\) is the institutional quality proxied by accountability, and \({FID}_{t}\) is the financial development during the periods. 3.2 Data and Sources The study examines the effect of social cash transfers on poverty, inequality, and macroeconomic instability in Nigeria using secondary data for the periods of 1984 to 2021. The choice of 1984 as the start date is informed by the era of massive policies to fight corruption as part of the Structural Adjustment Programme (SAP) introduced in 1986 in Nigeria as well as the data availability on the accountability and corruption index from the International Country Risk Guide (ICRG) database. Table 1 shows the description and measurement variables of interest as well as their sources. Table 1 Data Description, Measurements and Sources Variable Description and Measurements Source SCT Social cash transfers are proxied by the growth rate of Federal Government Recurrent Expenditure (₦' Billion) on Other Social and Community Services aside from health and education. It reflects cash transfers of government based on the prevailing social protection policy and programme. CBN, 2021 POV The poverty index was generated via Principal Component Analysis (PCA) using the natural logarithms of variables such as real Agriculture, forestry, and fishing, value added per worker (lagrva), real GDP per capita (lrgdpc), real households and NPISHs final consumption expenditure per capita (lhcepc), and total Life expectancy at birth (lleb) in line with Ajisafe and Okunade ( 2016 ), Olofin ( 2012 ). Data sourced from WDI, 2022; Index generated via PCA INQ The income inequality estimate indicates the disparities in income in an economy. SWIID, 2022 INF Macroeconomic instability measured by inflation rate reflects the rate at which prices for goods and services are generally increasing and, the purchasing power of money is declining. WDI, 2022 ACC The democratic accountability measures how responsive and responsible the government is to its people in any democratic system like Nigeria. ICRG, 2021 COR This depicts the corruption level in the country. The corruption index rescaled variable was reversed to portray the corruption level in line with Okunade ( 2022 ), Okunade and Ajisafe ( 2022 ), and D’Agostino et al. ( 2016 ) to reflect the control of financial corruption in the economy. ICRG, 2021 FID Financial development is proxied by Broad money (% of GDP) which measures the policies, variables, and institutions that result in effective financial markets and intermediation. WDI, 2022 Source: Authors’ Compilation, 2023 3.3 Method of Analysis Some of the major issues in time series data analysis are the issues of serial correlation, endogeneity problems, and stationarity of the variables of interest. These problems weaken the OLS coefficient estimates. One of the methods that address these issues is the Dynamic Ordinary Least Square (DOLS) or Fully Modified Ordinary Least Square (FMOLS) (Yorucu & Kirikkaleli, 2017 ; Kirikkaleli, 2016; Yorucu & Bahramian, 2015 ). The DOLS method is built on the standard error that adopts a parametric covariance matrix estimator that yields adjusted heteroskedasticity and autocorrelation that are robust to spatial and all forms of dependence, while FMOLS is a nonparametric test. The parametric DOLS is preferred over the nonparametric FMOLS because it imposes additional requirements that all variables be integrated in the same sequence, I(1) in contrast to the nonparametric FMOLS, which is the case in this study. Thus, DOLS estimates are reported as the baseline model for this parametric study. However, the FMOLS is also reported for comparison and robustness. Following several studies (See Yorucu & Kirikkaleli, 2017 ; Kirikkaleli, 2016; Yorucu & Bahramian, 2015 ), the DOLS method has proven to yield robust estimates in empirical studies. 4. RESULTS AND DISCUSSION 4.1 Preliminary Analyses It is imperative to examine the normality, distribution, and degree of multicollinearity among variables before the model estimations. The statistical features of our data are presented in Table 2 . The results presented in Table 2 showed that the means of all variables employed lie between the minimum and maximum values, indicating that our data series are consistent. In terms of variability, it is discovered that poverty proxied by real household final consumption expenditure per capita is the most volatile among the variables, followed by social cash transfers with standard deviations of 458.9 and 144.46 respectively. The probability of Jarque-Bera statistics showed that most of the variables employed in the study were not normally distributed, which is the case for most economic variables of less developed countries. Also, we present the results of the correlation matrix in Table 3 where the degree of multicollinearity among the independent variables was examined. The examination of the correlation matrix shows that none of the pairs of the regressors has a value higher than 50%. The result showed that the degree of correlation among the independent variables (SCT, ACC, COR, and FID) of the study is low. Hence, the problem of multicollinearity is not expected to manifest in the model. Table 2 Descriptive Characteristics of the variables POV HCE INQ INF SCT ACC COR FID Mean -5.79E-08 1504.676 42.85579 19.08389 104.4418 3.200658 1.592105 16.95533 Median 0.076633 1617.343 43.10000 12.71577 13.04873 3.166667 1.500000 14.45851 Maximum 2.976778 2196.420 43.70000 72.83550 448.9378 5.775000 2.000000 27.37879 Minimum -2.684111 871.4839 37.20000 5.388008 0.031208 0.500000 1.000000 9.063329 Std. Dev. 1.981496 458.9145 1.061097 17.20498 144.4587 1.181972 0.348091 5.956911 Skewness 0.103009 0.005176 -4.127838 1.804963 1.077681 -0.075573 -0.255517 0.382797 Kurtosis 1.371267 1.364280 22.51991 4.990024 2.745772 3.264104 2.085522 1.488972 Jarque-Bera 4.267423 4.236504 711.2063 26.90362 7.457844 0.146610 1.737592 4.543119 Probability 0.118397 0.120242 0.000000 0.000001 0.024019 0.929317 0.419456 0.103151 Observations 38 38 38 38 38 38 38 38 Source: Authors’ Compilation, 2023 Table 3 Correlation matrix of the variables of interest POV HCE INQ INF SCT ACC COR FID POV 1.0000 HCE 0.9457 1.0000 INQ -0.1257 -0.1050 1.0000 INF -0.4102 -0.4207 0.1929 1.0000 SCT 0.8543 0.7231 -0.2776 -0.2775 1.0000 ACC 0.7566 0.6687 -0.3919 -0.2044 0.1766 1.0000 COR -0.4913 -0.4836 0.0601 0.4767 -0.2204 -0.3623 1.0000 FID 0.8657 0.7821 -0.2169 -0.2923 0.3538 0.3213 -0.2732 1.0000 Source: Authors’ Compilation, 2023 It is important to test the stationarity of the variables to avoid spurious regression estimates. Thus, the Augmented Dickey-Fuller (ADF) and Phillips-Peron (PP) were employed to investigate the stationarity of the variables. In addition, According to Perron ( 1989 ) and Kunitomo ( 1996 ), potential structural breaks in time series can yield invalid estimates if ignored. Hence, it is on this basis that the study accounts for structural breaks using Zivot-Andrews structural break unit root test following Ertugrul et al., ( 2016 ) and Altinaya and Karagol ( 2004 ). The unit root test guides to ascertain whether DOLS is applicable. The DOLS requires all variables to be integrated in the same order, I(1). The results from Table 4 and Table 5 show that all variables are stationary at the first difference, thus validating the application of DOLS as the choice of method to estimate the baseline model in Eq. 2. Table 4 Augmented Dickey-Fuller (ADF) and Phillips-Peron (PP) Unit Root Tests Augmented Dickey-Fuller (ADF) UNIT ROOT TEST With Constant & Trend At Level Variable POV INQ INF SCT_GR ACC COR FID t-Stat. -1.4021 -2.3037 -1.9876 -3.3233* -3.3088* -0.4087 -2.0983 Prob. 0.8334 0.4149 0.7645 0.0874 0.0921 0.3456 0.0913 At First Difference t-Stat. -5.9201*** -5.778** -5.066** -5.7304*** -5.7535*** -3.7484** -8.559*** Prob. 0.0005 0.0294 0.0027 0.0007 0.0008 0.0401 0.0000 Status I(1) I(1) I(1) I(1) I(1) I(1) I(1) Phillips-Peron (PP) UNIT ROOT TEST With Constant & Trend At Level t-Stat. -1.5713 -0.4955 -1.0793 -1.1057** -2.4852 -2.8358* -0.4878** Prob. 0.4806 0.8752 0.6494 0.7074 0.1323 0.0689 0.9029 At First Difference t-Stat. -5.653*** -3.9997*** -3.0793** -3.2057** -9.2955*** -5.1272*** -3.3879** Prob. 0.0001 0.0060 0.0424 0.0327 0.0000 0.0005 0.0222 Status I(1) I(1) I(1) I(1) I(1) I(1) I(1) Notes : (***), (**) and (*) indicate significant at the 1%, 5% and 10% respectively. POV, INQ, INF, SCT_GR, ACC, COR, and FID represent poverty level index, inequality, macroeconomic stability proxied by inflation rate, social cash transfer growth rate, accountability, corruption index, and financial development index respectively. *MacKinnon (1996) one-sided p-values. Moreover, the Zivot-Andrews unit root test in Table 5 shows that the breakpoint occurred in 2001 for POV series, in 2003 for SCT_GR, in 2004 for INQ series, and in 2007 for the FID series. These breaks may be due to the rapid increase in the economy in the 2000s and banking consolidation which occurred after the financial crisis causing a sharp deterioration in the financial sector. Also, the breakpoint occurred in 1996 for INF series, in 1998 for COR, and in 1999 for ACC series. This may be due to political instability and regime shifts with conflicting economic policies that became the order for the period. The evidence of structural breaks therefore necessitate the use of econometric techniques that account for the breaks. Table 5 Zivot-Andrews Unit Root Test (break in both the intercept and trend) Variables t-stat. Decision Break Time POV -3.6699*** (0.0001) I(1) 2001 INQ -109.475*** (0.0014) I(1) 2004 INF -7.73814*** (0.0000) I(1) 1996 SCT_GR -8.049829*** (0.0439) I(1) 2003 ACC -5.12178** (0.0000) I(1) 1999 COR -8.209549** (0.0000) I(1) 1998 FID -5.931915*** (0.0053) I(1) 2007 Source: Authors , 2023. Notes: ***, ** and * indicate 1%, 5% and 10% significant levels respectively. Values in parenthesis () are the probability values. To employ the DOLS method, it is important to establish cointegration in the model. Having reported the stationarity of the series after the first difference process in the model and the structural breaks, it is imperative to adopt an econometric method that addresses this issue. Thus, Gregory and Hansen's (1996) cointegration test with structural breaks was employed to test the long-run relationship among the variables. The result in Table 6 shows that Zt (-5.83) is greater than the asymptotic critical values at 5% (-5.03), indicating the rejection of the null hypothesis of no cointegration among variables. We, therefore, conclude that there is long-run relationship among the variables. Also, since the test suggests the breakpoint date in 2001, other empirical analyses were carried out with dummy variables indicating the structural breaks. Table 6 Gregory-Hansen Test for Cointegration with Regime Shifts Test Statistic Breakpoint Breakpoint Date Asymptotic Critical Values 1% 5% 1% ADF -5.59 20 2003 -6.36 -5.39 -5.59 Z t -5.83* 18 2001 -6.36 -5.03* -5.59 Z a -65.44 18 2001 -76.95 -30.81 -60.12 Source: Authors, 2023 4.2 Effect of Social Cash Transfer on Poverty, Inequality and Inflation The paper examines the effects of social cash transfer on poverty, inequality, and macroeconomic instability (proxied by inflation rate). Having reported the stationarity of the series after the first difference process in the model and the structural breaks, it is imperative to adopt an econometric method that addresses this issue. To this end, the study adopts the DOLS method with structural breakpoint dummies following the model in Eq. 3. \(Z=\left\{{.}_{z=0 if otherwise}^{z=1 if year\ge 2001}\right.\) 3 The empirical results are presented in Table 7 . The first to third columns of Table 7 present empirical results using poverty reduction as the dependent variable, Columns 4 to Column 6 present the effect of social cash transfer on income inequality while macroeconomic instability (inflation) was treated as the dependent variable in Column 7 to Column 9 to depict the effect of social cash transfers on macroeconomic instability in Nigeria. The results of the three models show that structural breaks influenced the relational effects. The results showed that the level of social cash transfers in Nigeria reduces poverty and income inequality but increases the inflation rate in Nigeria especially when structural breaks in the time series were considered. This result is in line with the study of some extant studies (Mncube et al., 2023 ; Patel-Campillo & Garcia, 2022 ; Kitaura & Miyazawa, 2021 ; Yu & Li, 2021 ; Kumar & Sakthivel, 2020 ; Miller, Tsoka & Reichert, 2014 ; Miller, et al., 2010 ) who advocate the use of social cash transfers and other social protection policies to combat absolute poverty and inequality in Sub-Sahara African countries. But, the finding is contrary to some other studies (see Pruce, 2022 ; Kabamba, et al. 2021; Bukari, et al., 2019; Fuseini et al., 2017 ; Asfaw, et al., 2016 ) who concluded that the selection criteria as well as the allocation and distribution processes promote exclusion rather than inclusion of the most vulnerable households in developing countries, especially Malawi, Zambia and South Africa. Similar findings were reported on the individual effects of institutional factors that accountability and corruption control reduced poverty and income inequality when we accounted for structural breaks. The results also showed that the level of accountability in the public offices and corruption at various levels of government have dire implications on poverty and inequality levels, as well as the macroeconomic instability in Nigeria as a result of their consistent negative effects on various dependent variables. Table 7 Results of Dynamic Least Squares (DOLS) with Structural Breakpoint Dummies Dep. Var. POV POV POV INQ INQ INQ INF INF INF SCT_GR -0.1261 [0.2734] 0.1669 [0.1968] 4.3016*** [0.8637] -0.0349 [0.1496] 0.1086 [0.2034] -0.0620 [1.4336] 3.5553 [6.6255] -27.270** [8.4218] -31.349 [66.709] ACC -0.2936 [0.2841] 0.3139** [0.1235] 0.2599** [0.0721] -0.5377*** [0.0929] -0.4792*** [0.0656] -0.2800** [0.1197] -1.7272 [5.2353] -1.2576 [2.7144] 2.1583 [5.5678] COR 1.5459** [0.5926] 0.8469** [0.3066] 0.9419** [0.2881] -0.2866 [0.1833] 0.4353 [0.2713] -0.5415 [0.4782] 22.905** [10.289] 58.69*** [11.233] 26.328 [22.252] FID -0.2304*** [0.0499] -0.0402 [0.0561] -0.1160** [0.0356} 0.0069 [0.0156] 0.0548 [0.0377] 0.1204* [0.0590] -0.1598 [0.9121] 13.985*** [1.5603] 11.934*** [2.7473] Z 4.4214*** [1.2956] 3.7701*** [0.7693] 3.4094 [1.9055] 1.0179 [1.2769] 310.145** [78.886] 189.137** [59.417] Z_SCT_GR -0.4067* [0.2249] -0.8293 [0.4766] -0.4579 [0.2899] -0.7280 [0.7910] 23.064* [12.003] 2.1369 [36.809] Z_ACC -0.6654*** [0.1663] -0.7077*** [0.0891] 0.0210 [0.0884] -0.1383 [0.1479] 2.4687 [3.6611] 1.1394 [6.8801] Z_COR -2.7241** [0.7843] -2.8965*** [0.5084] -1.1597 [1.5848] 0.8864 [0.8439] -114.53 [65.610] -51.825 [39.268] Z_FID -0.0645 [0.0636] 0.0139 [0.0409] -0.0827 [0.0527] -0.1509** [0.0679] -12.434*** [2.1806] -10.995** [3.1584] SCT*ACC -0.8749*** [0.2486] -0.4422 [0.4126] 11.766 [19.201] SCT*COR -0.8665* [0.4212] -0.6675 [0.6991] -7.609 [32.533] C 7.3025*** [1.3202] 5.1464*** [1.0398] 6.0538*** [0.6302] 45.071*** [0.4389] 42.862*** [0.7223] 43.532*** [1.0462] -7.751 [23.166] -256.08*** [29.904] -173.38** [48.681] R 2 0.876 0.992 0.996 0.923 0.976 0.926 0.558 0.969 0.853 Adj. R 2 0.841 0.984 0.990 0.855 0.897 0.809 0.327 0.867 0.622 Source: Authors’ Compilation, 2023 However, when the effects of social cash transfers were moderated via institutional factors by interacting with social cash transfers through accountability and corruption control, the findings became more interesting. The results showed that the coefficient of the interaction terms has a stronger and more significant negative effect on poverty level, income inequality, and inflation. This finding implies that when structural breaks and institutional rigidities were factored in, social cash transfer schemes became a vital policy to reduce the level of poverty, inequality, and macroeconomic instability in Nigeria. This finding supports the social protection and mediation theories and the expositions of Acemoglu and Robinson, ( 2013 ) and Acemoglu, et al. ( 2005 ) that institutional quality and economic development reinforce each other in the long run, but less developed intuitions could foster the virtuous circle of poverty and widen the gap between the rich and the poor in an economy. It is worth noting that the results of the nonparametric test in Table 8 are also similar to the effect of social cash transfer on poverty, income inequality, and macroeconomic stability in Nigeria. Table 8 Results of Fully Modified Least Squares (FMOLS) with Structural Breakpoint Dummies - Robustness Analysis Dep. Var. POV POV POV INQ INQ INQ INF INF INF SCT_GR -0.0651 [0.1431] 0.0909 [0.0952] 1.8083*** [0.6432] 0.0221 [0.0738] -0.0672 [0.0723] -1.0406 [0.6246] 0.8310 [2.6845] -5.0811 [3.2664] -5.3935 [24.873] ACC -0.2910 [0.1973] 0.2775** [0.0806] 0.2878*** [0.0679] -0.5148*** [0.1018] -0.6084*** [0.0612] -0.6562*** [0.0659] -1.2624 [3.7014] 0.2137 [2.7670] 0.1039 [2.6246] COR 1.6895** [0.4859] 0.6848*** [0.2035] 0.4862** [0.1935] -0.2923 [0.2507] 0.4026** [0.1545] 0.4674** [0.1879] 23.7871** [9.1151] 29.204*** [6.9838] 36.893*** [7.4838] FID -0.2476*** [0.0385] -0.0492 [0.0398] -0.0546 [0.0324] 0.0117 [0.0199] 0.0724** [0.0303] 0.0761** [0.0315] -0.4710 [0.7225] 2.8336** [1.3681] 2.6322** [1.2544] Z 3.7508*** [0.8194] 3.4126*** [0.6901] 1.5270** [0.6221] 1.6637** [0.6701] 87.079** [28.126] 105.314*** [26.687] Z_SCT_GR -0.1698 [0.1063] -0.7717** [0.3202] 0.0668 [0.0807] 0.5917* [0.3109] 4.7477 [3.6498] 28.058** [12.379] Z_ACC -0.6454*** [0.1183] -0.6631*** [0.0961] 0.0750 [0.0898] 0.1412 [0.0932] 1.3649 [4.0596] -1.641 [3.7146] Z_COR -2.8755*** [0.4977] -2.6573*** [0.4171] 0.2361 [0.3779] 0.0738 [0.4051] -42.359** [17.083] -47.719** [16.1298] Z_FIND -0.0254 [0.0433] -0.0268 [0.0351] -0.0878** [0.0329] -0.0890** [0.0341] -2.8113* [1.4852] -2.5086* [1.3574] SCT*ACC -0.1256 [0.1445] -0.0349 [0.1403] -12.571** [5.5881] SCT*COR -0.7064** [0.2956] 0.5505* [0.2873] 16.678 [11.430] C 7.3891*** [1.0705] 5.6815 [0.6738] 6.1253*** [0.5770] 44.9106 [0.5523] 42.808*** [0.5115] 42.713*** [0.5603] -6.0827 [20.084] -62.638 [23.126] -74.621** [22.312] R 2 0.835 0.977 0.979 0.670967 0.728 0.691 0.632 0.685 0.586 Adj. R 2 0.814 0.970 0.970 0.629838 0.691 0.653 0.614 0.617 0.515 Source: Authors’ Compilation, 2023 5. CONCLUSION AND POLICY RECOMMENDATIONS Social Cash Transfer (SCT) programmes have been used as an instrument to tackle the menace of poverty and inequality in many developing economies such as Nigeria. Based on our survey of literature, the impact however has been somewhat indefinable given the continued increase in poverty, inequality levels as well and the triggered macroeconomic instability in Nigeria. This present study focused on the effect of social cash transfers on poverty, income inequality, and macroeconomic instability in Nigeria using secondary data for the periods of 1984 to 2021. The results of the Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS) with structural breaks showed that individual social cash transfers, accountability, and corruption control significantly increase the levels of poverty, inequality, and inflation in Nigeria, but when we considered structural breaks and moderated the effects with institutional peculiarities of less developed countries such as Nigeria using control of corruption and accountability, social cash transfers reduced poverty and inequality levels as well as the macroeconomic instability in Nigeria. Findings from this study reveal that social cash transfers have played a pivotal role in improving the well-being of beneficiaries by providing crucial financial support, which, in turn, has contributed to reducing the incidence of poverty. Furthermore, evidence indicates that social cash transfers have helped mitigate income disparities among households and have had positive implications for macroeconomic stability especially when the effects were moderated. This underscores the importance of social cash transfers in poverty and inequality reduction efforts in Nigeria by offering evidence-based insights to inform policymakers, researchers, and practitioners in their pursuit of more effective and inclusive social welfare strategies to foster sustainable development and equitable prosperity in the country. Our conclusion is centred on the important role that institutional factors such as accountability and corruption control play in stimulating the desirable effects of social cash transfers on inequality, poverty, and inflation by checkmating inherent loopholes and market distortions, and by addressing the triggered inflation in the prices of major goods and services in the economy as a result of excessive cash made available in the economy. Based on our findings, our policy implication is that more accountable, equitable, inclusive, and corrupt-free social cash transfer schemes as a social protection policy should be encouraged. It will become one of the major anti-poverty/inequality strategies in Nigeria to win the battle against absolute poverty of the most vulnerable households. Policymakers, government officials at various levels as well and Non-Governmental Organizations (NGOs) can draw practical guidance from this study to refine and optimize the design and implementation of social cash transfer programmes in Nigeria. Lessons learned from the study can help enhance the impact of existing initiatives, and to create more effective policies that directly benefit vulnerable populations in Nigeria. 5.1. Limitation to the study The major caveat of this study is found in the use of a macroeconomic approach to analyze the effect of social cash transfers on poverty and income inequality, by employing aggregated macro-variables. The macro data may significantly affect the empirical findings and implications thereof. Future research should focus on household-level analysis by using micro-data which could render more appropriate results. Also, the findings have established the effect of social cash transfers on poverty and inequality levels, establishing direct causality among these variables may be necessary given the potential for reverse causality in the relationship. Subsequent research in this area can focus on this direction. Lastly, defining and measuring poverty and inequality can be complex. Different indicators and methodologies may yield different results. The indicators and measurement approach used in this study could influence the conclusions drawn. However, despite these limitations, the study contributes valuable insights into the effectiveness of social cash transfer programmes in reducing poverty, inequality, and macroeconomic instability in Nigeria. By acknowledging these limitations, researchers and policymakers can interpret the findings critically and consider them within the broader context of socioeconomic development efforts, as the identified limitations do not undermine the relevance of the study or lessen the robustness of the findings herein. They are only acknowledged to direct and inform future researchers in this area. Declarations Availability of Data and Materials Ajisafe, Rufus Adebayo; Okunade, Solomon; Fatai, Musbau (2023). Modelling structural breaks in social cash transfers effects on poverty and inequality reduction in Africa: A case of Nigeria. figshare. Dataset. https://doi.org/10.6084/m9.figshare.24188529 Conflict of Interest The authors declare that there is no competing interests be it financial or non-financial. Funding We declare that there was no funding or financial support of any form for this research work. 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Journal of Integrative Agriculture 2021, 20(4): 1060–1067. i>https://doi.org/10.1016/S2095-3119(20)63404-9 Cite Share Download PDF Status: Published Journal Publication published 01 Mar, 2024 Read the published version in Scientific African → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3384456","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317907998,"identity":"739c4b8f-5e64-4bac-b70f-389f49d9bd71","order_by":0,"name":"Rufus Adebayo Ajisafe","email":"","orcid":"","institution":"Obafemi Awolowo University","correspondingAuthor":false,"prefix":"","firstName":"Rufus","middleName":"Adebayo","lastName":"Ajisafe","suffix":""},{"id":317907999,"identity":"f01288a3-aecc-4446-af5c-5285ddb27b7d","order_by":1,"name":"Solomon Oluwaseun Okunade","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYHACxgMgkp+Z+fiHD0AGGzsResBaJNvb0hhngLQwE6vF4MwZM2YeEIuQFn7+wwcOfMyxyWO4kZb22ObXNnk+ZgbGDx9zcGuRnJGWcHDmtrRixhnJx41z+24btjEzMEvO3IZbi8ENHoPDvNsOJzZLpCVI5/bcZgRqYWPmxafl/BmIljaJHANpy57b9oS1HMiBaOnhOWMmzfDjdiJBLTC/JM5gb0s27G24ndzGzNiM1y/AEDv44OM2m8T9h5kPPvjx57bt/Pbmgx8+4tGCChjbwGQDsepB4A8pikfBKBgFo2CkAADkvVbtxpj3mQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1593-5672","institution":"Chrisland University","correspondingAuthor":true,"prefix":"","firstName":"Solomon","middleName":"Oluwaseun","lastName":"Okunade","suffix":""},{"id":317908000,"identity":"bf9a85c3-65ce-44a3-824f-4d64c935230b","order_by":2,"name":"Musbau Olaniyan Fatai","email":"","orcid":"","institution":"Obafemi Awolowo University","correspondingAuthor":false,"prefix":"","firstName":"Musbau","middleName":"Olaniyan","lastName":"Fatai","suffix":""}],"badges":[],"createdAt":"2023-09-25 10:53:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3384456/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3384456/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.sciaf.2024.e02106","type":"published","date":"2024-03-01T20:32:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60617782,"identity":"4ae86b09-9746-44de-9037-cd433045b0ca","added_by":"auto","created_at":"2024-07-18 20:32:12","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151885,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3384456/v1/055b40950ac2839ca10936a0.jpeg"},{"id":60618824,"identity":"d893ed04-9c7d-4281-b47d-14eaaa5a6c70","added_by":"auto","created_at":"2024-07-18 20:40:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1232322,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3384456/v1/1384dd4f-1f5c-4f60-a2ea-76e727037c59.pdf"}],"financialInterests":"","formattedTitle":"Modelling structural breaks in social cash transfers effects on poverty and inequality reduction in Africa: A case of Nigeria","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003ePoverty and inequality have become persistent challenges in the country, hindering sustainable development and equitable economic growth in developing countries, particularly in Sub-Saharan Africa, beyond the expiration of the Millennium Development Goals (MDGs) in 2015. According to the United Nations, all regions of the world attained a reasonable level in the target of poverty reduction as entrenched in MDG 1, except Sub-Saharan Africa. The target to eradicate extreme poverty and hunger as entrenched in the Sustainable Development Goals (SDGs) was almost elusive in the region, especially in Nigeria, the most populous African country (Bukari, et al., 2019; Fuseini et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Anyanwu \u0026amp; Anyanwu, 2017). Also, the income gap between the rich and the poor in Nigeria continued to become wider, indicating that widespread income inequality coexists with extreme poverty in Nigeria. Social Cash Transfer (SCT) programmes have been used as an instrument to mitigate the menace of poverty and inequality in many developing economies, such as Nigeria. The SCT schemes surfaced and gained prominence as a global remedy to address poverty and income inequality in the 1980s and 1990s, respectively (Fuseini et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Johannsen et al., 2010). The social cash transfer is a steady, regular, and non-contributory allocation of cash to the most vulnerable in society to address the prevalent poverty and inequality levels by governments and other non-state actors, otherwise known as non-governmental organizations (NGOs).\u003c/p\u003e \u003cp\u003eThe central goal of social cash transfers in Nigeria is to combat widespread absolute poverty and chronic income inequality by directly providing cash transfers to the country\u0026rsquo;s most vulnerable and poor households. In Nigeria, the National Cash Transfer Programme, otherwise known as the Household Uplifting Programme (HUP) which is one of the Federal Government of Nigeria's social safety net programmes, is funded by both the government through the National Cash Transfer Office (NCTO) under the Federal Ministry of Humanitarian Affairs, Disaster Management and Social Development and other local and international partnering organizations. Meanwhile, social benefits are grouped into contributory and non-contributory benefits (Kumar \u0026amp; Sakthivel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the non-contributory cash transfer component has attracted the attention of social researchers because of its perceived impact on the poorest households. According to Rabi (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), examples of non-contributory cash transfers include but are not limited to conditional cash transfers, child allowances, parental benefits, housing allowances, unemployment benefits, students\u0026rsquo; bursaries, and so on. This category does not include the contributory benefits or social insurance. For clarity, Conditional cash transfers (CCTs) are schemes in which recipients are only given money if they meet certain criteria, while Unconditional cash transfers (UCTs) refer to schemes in which recipients do not need to fulfil any requirements or criteria.\u003c/p\u003e \u003cp\u003eTheoretically, social cash transfer reduces poverty and inequality among most socio-economically vulnerable or disadvantaged people by improving access to food or providing households with income that can be used to solve daily needs problems (Kumar \u0026amp; Sakthivel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Miller, Tsoka \u0026amp; Reichert, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Miller, et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The underlying assumption is that the poor have access to cash for the provision of basic needs such as food, healthcare services, and clothes and that by increasing income to the poorest households, families will opt to spend some of their monthly income on food (Pruce, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kumar \u0026amp; Sakthivel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This would ultimately reduce the poverty and income inequality levels in the country. Poverty and Inequality Theories: The study aligns with and enriches theories related to poverty and inequality reduction. Examining the impact of SCTs on vulnerable populations provides empirical evidence supporting the effectiveness of such interventions in addressing these complex socio-economic issues. The significance of the study on social cash transfers, poverty, and inequality reduction in Nigeria lies in its potential to inform and guide policies and interventions aimed at addressing socio-economic challenges in the country, and insights can have various practical implications and broader impacts. Consequently, in Nigeria, to increase the likelihood of the poor, the Federal Government has formed six different interventions under the National Social Investment Programme (NSIP) which include National Home-Grown School Feeding Programme (NHGSFP), N-Power, Alternate School Programme (ASP), Government Enterprise and Empowerment Programme (GEEP), Grant for Vulnerable Groups (GVG) and National Cash Transfer Office (NCTO), also known as Trader Money.\u003c/p\u003e \u003cp\u003eFurthermore, the role of institutional quality has been entrenched in theory. Acemoglu, et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) opined that institutional quality and economic development reinforce each other in the long run, but less developed intuitions could foster the virtuous circle of poverty and widen the gap between the rich and the poor in an economy. The quality of institutions such as accountability and corruption control could provide an enabling environment that promotes growth, and reduces poverty and inequality when institutional quality is sufficiently developed, yet inhibit growth and poverty reduction mechanisms if otherwise (Acemoglu \u0026amp; Robinson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Acemoglu, et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Therefore, the quality of institutions is theoretically important when development issues such as the nexus between social cash transfers, poverty, and income inequality are analyzed. It is arguably stated in the literature that social cash transfers disbursement disrupts macroeconomic stability in developing countries by leading to rising inflationary pressure in such economies (Mncube et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Patel-Campillo \u0026amp; Garcia, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kitaura \u0026amp; Miyazawa, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yu \u0026amp; Li, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) without due recourse to strong institutional quality.\u003c/p\u003e \u003cp\u003eDespite the implementation of social cash transfer programmes in Nigeria as a social protection strategy, poverty, and inequality remain persistent challenges in the country. While there is evidence of some positive impacts of social cash transfers on poverty reduction and human development outcomes in other countries (see Pruce, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kabamba, et al. 2021; Kumar \u0026amp; Sakthivel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bukari, et al., 2019), it is essential to investigate whether these programmes have been effective in alleviating poverty and reducing inequality in the unique socio-economic context of Nigeria. However, the impact has been somewhat indefinable given the continued increase in poverty and inequality levels in Nigeria. Consequently, while there is empirical evidence of the beneficial impact of social cash transfers on the poorest households from developing European, Asian, Latin American, and a few African countries (see Pruce, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kabamba, et al. 2021; Kumar \u0026amp; Sakthivel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bukari, et al., 2019; Fuseini et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Asfaw, et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rabi, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Miller, et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), there is a dearth of evidence on the impact of social cash transfers on poverty and inequality from poverty-ridden countries in Africa, such as Nigeria. Also, the roles of quality institutions like accountability and corruption control which may affect the allocation and distribution of social cash transfers have not been explained in the limited extant studies in Nigeria (see Okoli et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). No empirical study has also looked at the impact of social cash transfers on macroeconomic stability in Nigeria. These constitute some gaps that we strive to fill in this present study.\u003c/p\u003e \u003cp\u003eThe study contributes to the understanding of social cash transfer theory, particularly in the context of a developing country like Nigeria by exploring the mechanisms through which SCTs can alleviate poverty and reduce inequality, shedding light on the underlying theoretical foundations of these interventions. It also aligns with and enriches theories related to poverty and inequality reduction by examining the impact of SCTs on vulnerable populations and providing empirical evidence supporting the effectiveness of such interventions in addressing these complex socio-economic issues. This paper also contributes to the strands of literature in different ways. Firstly, we add to a broad literature on the effects of social cash transfer policy on poverty and inequality in Nigeria. Existing studies consistently highlight the limited redistributive power of social protection policy in developing countries due to limited coverage of the schemes (Warwick et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Brum \u0026amp; De Rosa, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bukari, et al., 2019). The roles of institutional factors such as accountability and corruption control are a pervasive feature of redistributive policy in a developing country like Nigeria, yet while the basis for such policies has been discussed theoretically (See Acemoglu \u0026amp; Robinson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Acemoglu, et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) there is little existing empirical country-specific studies in Nigeria. A few studies of this nature have concentrated on the effect of cash transfers on food security, poverty, and child health in Zambia, Malawi, and Ethiopia (See Pruce, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kabamba, et al. 2021; Kumar \u0026amp; Sakthivel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fuseini et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Asfaw, et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rabi, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Miller, et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Second, by considering the role of instructional factors, we shed new light on the re-distributional effects of social cash transfer on poverty and inequality in Nigeria and, crucially, consider the potential for alternative policies to control the high rocketing inflation rate social protection policy to be more effective. Thus, this present study focuses on the effect of social cash transfers on poverty, inequality, and macroeconomic stability in Nigeria while taking into consideration the moderating roles of institutional factors of accountability and corruption control using secondary data for the periods of 1984 to 2021.\u003c/p\u003e \u003cp\u003eThe remainder of the paper is structured as follows: Section 2 deals with the survey of literature which includes both the theoretical and empirical review as well as the conceptual framework. The theoretical foundation and methodology used are discussed in Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and the data are analyzed and presented in Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The study ends with a discussion of findings and policy recommendations in Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e"},{"header":"2. LITERATURE REVIEW","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical and conceptual framework\u003c/h2\u003e \u003cp\u003eSocial protection and mediation theories address all major aspects of poverty and income inequality reductions and empowerment of the vulnerable and disadvantaged by providing social cash transfers (Kabamba, et al. 2021). The social protection theory is rooted in the theory of Justice as Fairness, postulated by John Rawls in 1985 to illustrate how justice may be attained in society for fair socio-economic wealth. It is an essential component of social policy and human empowerment that entails real action to address levels of poverty, vulnerability, risk, and deprivation (Sichula, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to Schuring and Lawson-McDowall (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), it also refers to any sort of inequity or deprivation that is seen as unpleasant and unacceptable in any economy. The fact that social protection theory is an effective solution to poverty, inequality, and vulnerability in developing countries, as well as an integral component of economic and social development plans that address macroeconomic instability, led to its selection for this study (Kabamba, et al. 2021; Sichula, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To strengthen the inclusiveness of our framework, mediation theory is incorporated to explain the role of accountability and corruption as our institutional variables that are relevant to the allocation and distribution of social cash transfers and the poverty-inequality nexus (Kabamba, et al. 2021; Changala, et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Conceptually, these theories explain the connections that exist among social cash transfers, poverty, inequality, macroeconomic stability, and institutional quality. Social cash transfers may have a direct effect on poverty and inequality or stability by directly providing cash incomes to the countries\u0026rsquo; most vulnerable and poor households, and improving their access to food, shelter, and other basic needs through the provision of extra incomes (Kumar \u0026amp; Sakthivel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Indirectly, the quality of institutions may reshape the direction as well as the magnitude of the effect of social cash transfers on poverty and income inequality in developing countries. As a result, it is appropriate to utilize social protection and mediation theories as a lens for analyzing and interpreting the findings on how accountability and corruption control might be used to mediate the relationship between social cash transfers, poverty, and inequality in Nigeria. This relationship is depicted in the conceptual framework in Fig.\u0026nbsp;1. In the framework, the purple and red arrows indicate direct effects and indirect effects respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Empirical Review\u003c/h2\u003e \u003cp\u003eIn the extant studies, social protection policy has been used to battle absolute poverty and income inequality in developing Asian, Latin, and Southern American countries (see Warwick et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Patel-Campillo \u0026amp; Garcia, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Brum \u0026amp; De Rosa, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yu \u0026amp; Li, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, Yu and Li (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined the effects of social security expenditure on income inequality and rural poverty reduction in China covering the period of 2003 to 2017 using the Johansen cointegration test and vector error correction model. The study found that social security expenditure has a significant negative effect on income inequality and rural poverty in China, indicating that social security expenditure reduced income inequality and rural absolute poverty in China. In a different study, Warwick et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) examined and compared the effectiveness of cash transfers and VAT exemptions in low- and middle-income countries (LMICs) by estimating their impact on tax revenues, inequality, and poverty. The study employed tax-benefit microsimulation models that incorporate input\u0026ndash;output tables, to find that preferential VAT rates reduced poverty, but was not well targeted towards poor households. However, cash transfer schemes were better targeted towards poor households but have a diminutive effect on poverty due to limited coverage. Also, Patel-Campillo and Garcia (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) analyzed the effects of the Peruvian 2005 Juntos Conditional Cash Transfer scheme on higher education attainment based on gender. The study employed the Young Lives Survey and matching techniques, to find that Juntos conditional cash transfer has a positive effect on higher education attainment especially for men, while a gender gap in women's higher education attainment among Juntos recipients was reported. In Uruguay, Brum and De Rosa (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) analyzed the impact of key public policies such as cash transfers, and unemployment insurance on the poverty level, and forecasts of GDP contraction during the COVID-19 crisis. The finding showed that during the first full trimester of the crisis, the poverty rate grew by more than 38%, reaching 11.8% up from 8.5%. Also, the cash transfer programme of Uruguay\u0026rsquo;s government had a positive but very limited effect in reducing the poverty spike during the COVID-19 crisis.\u003c/p\u003e \u003cp\u003eThere have been limited studies critically examining the rationale for unconditional cash transfer size, its determination, and its impacts in Sub-Saharan Africa. Bukari, et al., (2019) adopted content analysis of mainstream literature on the incidence of poverty and social protection strategies involving social cash transfer for the protection of vulnerable groups and found that a relatively higher proportion of 35.5% of the people in Sub-Saharan Africa fell below the poverty line within the period despite the implementation of several unconditional cash transfer schemes when only 9.6% of global population remained below the global poverty line for the achievement of Millennium Development Goal 1 by 2015. In country-specific studies in Sub-Saharan Africa, specifically in Zambia, Chad, Kenya, Malawi, South Africa, and Zimbabwe, studies find that social protection policies including social cash transfers and other unconditional cash transfers programme have yielded positive effects in reducing the level of absolute poverty and income inequality as well improving food security and health outcomes (Mncube et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pruce, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kabamba, et al. 2021; Kumar \u0026amp; Sakthivel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fuseini et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Asfaw, et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rabi, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Miller, et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For instance, Kumar and Sakthivel (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) focused on the relevance, appropriateness, sustainability, and impact of social cash transfer schemes on the livelihood of rural households in Shamwinda Village in Chibombo District, Zambia by interviewing the beneficiaries. The study reported that social cash transfer reduced the poverty level in the village. However, the study also found that a significant percentage of the beneficiaries expended the transferred cash on food rather than on investment, therefore leaving them in perpetual poverty when the scheme comes to an end.\u003c/p\u003e \u003cp\u003eIn a different work, Pruce (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated the targeting choices of deservingness and dependency among communities receiving cash transfers in Zambia by drawing on interviews with government and policy actors, as well as focus group discussions in the cash transfer-receiving communities. The study employed Van Oorschot\u0026rsquo;s deservingness heuristic to collect data and found that popular perceptions of deservingness and the broader social justice implications need to be taken seriously in the design and analysis of targeting. Also, using both qualitative and quantitative approaches, Kabamba, et al. (2021) investigated how social cash transfer can be used to promote the socioeconomic rights of the elderly in Zambia by administering a questionnaire to 102 elderly participants from the age of 65 years. The study found that the social cash transfer is a mediating factor in reducing the socioeconomic inequalities faced by the elderly. However, this mediation varied with the level of formal education attained by the participants. These findings also emphasized the role of other mediating factors in the nexus between SCT and inequalities.\u003c/p\u003e \u003cp\u003eAsfaw, et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) examined the impact of the Ethiopia Social Cash Transfer Pilot Programme (SCTPP) on household behavior and decision-making using two-year impact evaluation data and compared programme beneficiaries with a group of controls interviewed in 2012 and 2014, using difference-in-difference estimators combined with propensity score matching methods. They found that the programme significantly increased the livelihood strategies of the poor (subjective well-being), social capital, and household food security in Ethiopia. The study concluded that some other important heterogeneity in programme implementation such as institutional factors should be considered. Similarly, Guardia, Lake, and Schnitzer (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated both the economic and social implications of targeting cash transfer programmes on poverty in Chad and found significant positive economic effects on non-beneficiaries, but considerable social and punitive costs for recipients as a result of their inclusion in the programme, indicating ineffectiveness of cash transfer programmes towards achieving poverty reduction.\u003c/p\u003e \u003cp\u003eIn Malawi, Miller, et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) examined the extent to which social cash transfers have reduced the intergenerational cycle of poverty and improved the health status of children using mixed methods of longitudinal household qualitative interviews and focus groups as well as the double difference impact estimates to find that social cash transfer is a vital tool to fight poverty with its positive impacts on child lifelong health and growth in Malawi. Miller, Tsoka, and Reichert (2011) and Mncube et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) investigated the impact of the social cash transfer scheme on food security in Malawi and South Africa respectively. Miller, Tsoka, and Reichert (2011) conducted a longitudinal, randomized community control study of the pilot SCTS in Mchinji, Malawi from March 2007 to April 2008. The study reported a robust positive impact of cash transfers on food security in rural Malawi. In their attempt to explain the challenge of improving energy access for the ultra-poor in Malawi, Aung, et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) investigated if unconditional social cash transfers close the energy access gap. The study reported that ultra-poor households are faced with high energy poverty, but the unconditional social cash transfer payments increased their energy access.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, very few studies on social protection policy have been conducted in Nigeria aside from Paul (2022), Ezenwaka et al. (2021), Okoli et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and Holmes et al. (2011). Paul (2022) identified the pitfalls in conditional cash transfer and suggested the best practices to enhance the performance of the social policy instrument in Nigeria and found that conditional cash transfer in Nigeria is characterized by several anomalies, such diversion of funds by the beneficiaries, improper definition of exit and entry period and that beneficiaries are randomly selected in Nigeria, thus leading to obvious errors of exclusion and inclusion. However, the study, being a library research is devoid of any empirical investigation and solution to poverty and inequality targets of the social protection policy. Moreover, Ezenwaka et al. (2021) and Okoli et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) provided some clinical recommendations without empirical and economic implications. Also, Holmes et al. (2011) empirically examined how cash transfers influenced poverty, inequality, and instability in Nigeria and found that Care of the People (COPE) as a government-run conditional cash transfer (CCT) has been effective in helping households meet their daily consumption needs, and increased their access to health services and schooling for children in the selected four states (Adamawa, Bayelsa, Edo and Kano), yet, the conditional cash transfer could not reduce poverty and inequality levels in Nigeria. This study fails to account for the role of structural and institutional factors that could affect the distribution of social cash transfers in Nigeria. All the aforementioned weaknesses of the existing few studies on Nigeria constitute some gaps that we intend to fill in this present study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. METHODOLOGY","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Empirical model\u003c/h2\u003e \u003cp\u003eFollowing the theoretical foundation of this study and in line with extant studies in the literature (Mncube et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yu \u0026amp; Li, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e and Kitaura \u0026amp; Miyazawa, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we begin by investigating the direct effect of social cash transfers on poverty and income inequality in Nigeria by estimating the baseline model explicitly specified Eq.\u0026nbsp;1:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Y}_{t}^{{\\prime }}= {\\alpha }_{i}+\\delta {SCT}_{t}+{\\mu }_{t}\\)\u003c/span\u003e \u003c/span\u003e 1\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{t}^{{\\prime }}\\)\u003c/span\u003e\u003c/span\u003e is a 3x1 vector of the dependent variables, poverty (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({POV}_{t}\\)\u003c/span\u003e\u003c/span\u003e), income inequality (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({INQ}_{t}\\)\u003c/span\u003e\u003c/span\u003e), and macroeconomic stability proxied by inflation rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({INF}_{t}\\)\u003c/span\u003e\u003c/span\u003e). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({SCT}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the growth rate of social cash transfers and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }_{i,t}\\)\u003c/span\u003e\u003c/span\u003e is the white noise error term. To account for structural and institutional factors that could mediate the impact of social cash transfers on poverty and income inequality such as accountability (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ACC}_{t}\\)\u003c/span\u003e\u003c/span\u003e) and control of corruption (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({COR}_{t}\\)\u003c/span\u003e\u003c/span\u003e) based on the theoretical and empirical expositions (\u003cem\u003esee\u003c/em\u003e Acemoglu \u0026amp; Robinson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Acemoglu, et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), we modified Eq.\u0026nbsp;1 to reflect the mediating role of institutional quality as well as other control variables such as financial development (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({FIND}_{t}\\)\u003c/span\u003e\u003c/span\u003e) and interest rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({INT}_{t}\\)\u003c/span\u003e\u003c/span\u003e). Financial development is important in the model because governments at various levels utilize financial institutions for the allocation and distribution of social cash transfers among the recipients. Also, interest rate enters into the model as a determinant of how cash benefit is consumed rather than invested. This is presented in Eq.\u0026nbsp;2:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Y}_{t}^{{\\prime }}= {\\alpha }_{i}+\\delta {SCT}_{t}+\\sigma {ACC}_{t}+\\beta {COR}_{t}+\\gamma {FID}_{t}+{\\beta }_{1}{SCT}_{t}*{ACC}_{t}+{\\beta }_{2}{SCT}_{t}*{ COR}_{t}+{\\mu }_{t}\\)\u003c/span\u003e \u003c/span\u003e 2\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ACC}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the institutional quality proxied by accountability, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({COR}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the institutional quality proxied by accountability, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({FID}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the financial development during the periods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data and Sources\u003c/h2\u003e \u003cp\u003eThe study examines the effect of social cash transfers on poverty, inequality, and macroeconomic instability in Nigeria using secondary data for the periods of 1984 to 2021. The choice of 1984 as the start date is informed by the era of massive policies to fight corruption as part of the Structural Adjustment Programme (SAP) introduced in 1986 in Nigeria as well as the data availability on the accountability and corruption index from the International Country Risk Guide (ICRG) database. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the description and measurement variables of interest as well as their sources.\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\u003eData Description, Measurements and Sources\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription and Measurements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial cash transfers are proxied by the growth rate of Federal Government Recurrent Expenditure (₦' Billion) on Other Social and Community Services aside from health and education. It reflects cash transfers of government based on the prevailing social protection policy and programme.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCBN, 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe poverty index was generated via Principal Component Analysis (PCA) using the natural logarithms of variables such as real Agriculture, forestry, and fishing, value added per worker (lagrva), real GDP per capita (lrgdpc), real households and NPISHs final consumption expenditure per capita (lhcepc), and total Life expectancy at birth (lleb) in line with Ajisafe and Okunade (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Olofin (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData sourced from WDI, 2022; Index generated via PCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe income inequality estimate indicates the disparities in income in an economy.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSWIID, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacroeconomic instability measured by inflation rate reflects the rate at which prices for goods and services are generally increasing and, the purchasing power of money is declining.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWDI, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe democratic accountability measures how responsive and responsible the government is to its people in any democratic system like Nigeria.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICRG, 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThis depicts the corruption level in the country. The corruption index rescaled variable was reversed to portray the corruption level in line with Okunade (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Okunade and Ajisafe (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and D\u0026rsquo;Agostino et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to reflect the control of financial corruption in the economy.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICRG, 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinancial development is proxied by Broad money (% of GDP) which measures the policies, variables, and institutions that result in effective financial markets and intermediation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWDI, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource: Authors\u0026rsquo; Compilation, 2023\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Method of Analysis\u003c/h2\u003e \u003cp\u003eSome of the major issues in time series data analysis are the issues of serial correlation, endogeneity problems, and stationarity of the variables of interest. These problems weaken the OLS coefficient estimates. One of the methods that address these issues is the Dynamic Ordinary Least Square (DOLS) or Fully Modified Ordinary Least Square (FMOLS) (Yorucu \u0026amp; Kirikkaleli, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kirikkaleli, 2016; Yorucu \u0026amp; Bahramian, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The DOLS method is built on the standard error that adopts a parametric covariance matrix estimator that yields adjusted heteroskedasticity and autocorrelation that are robust to spatial and all forms of dependence, while FMOLS is a nonparametric test. The parametric DOLS is preferred over the nonparametric FMOLS because it imposes additional requirements that all variables be integrated in the same sequence, I(1) in contrast to the nonparametric FMOLS, which is the case in this study. Thus, DOLS estimates are reported as the baseline model for this parametric study. However, the FMOLS is also reported for comparison and robustness. Following several studies (See Yorucu \u0026amp; Kirikkaleli, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kirikkaleli, 2016; Yorucu \u0026amp; Bahramian, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the DOLS method has proven to yield robust estimates in empirical studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Preliminary Analyses\u003c/h2\u003e \u003cp\u003eIt is imperative to examine the normality, distribution, and degree of multicollinearity among variables before the model estimations. The statistical features of our data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed that the means of all variables employed lie between the minimum and maximum values, indicating that our data series are consistent. In terms of variability, it is discovered that poverty proxied by real household final consumption expenditure per capita is the most volatile among the variables, followed by social cash transfers with standard deviations of 458.9 and 144.46 respectively. The probability of Jarque-Bera statistics showed that most of the variables employed in the study were not normally distributed, which is the case for most economic variables of less developed countries. Also, we present the results of the correlation matrix in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e where the degree of multicollinearity among the independent variables was examined. The examination of the correlation matrix shows that none of the pairs of the regressors has a value higher than 50%. The result showed that the degree of correlation among the independent variables (SCT, ACC, COR, and FID) of the study is low. Hence, the problem of multicollinearity is not expected to manifest in the model.\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\u003eDescriptive Characteristics of the variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSCT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.79E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1504.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.85579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.08389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e104.4418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.200658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.592105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.95533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.076633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1617.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.10000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.71577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.04873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.166667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.45851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.976778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2196.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.70000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.83550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e448.9378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.775000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.37879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.684111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e871.4839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.20000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.388008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.500000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.063329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.981496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458.9145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.061097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.20498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144.4587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.181972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.348091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.956911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.103009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.127838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.804963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.077681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.075573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.255517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.382797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.371267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.364280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.51991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.990024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.745772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.264104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.085522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.488972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJarque-Bera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.267423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.236504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e711.2063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.90362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.457844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.146610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.737592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.543119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.118397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.120242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.929317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.419456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.103151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38\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\u003e \u003cb\u003eSource: Authors\u0026rsquo; Compilation, 2023\u003c/b\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\u003eCorrelation matrix of the variables of interest\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSCT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.1050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.4102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.2776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.2775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.3919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.2044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.4913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.2204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.3623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.2169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.2923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.2732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eSource: Authors\u0026rsquo; Compilation, 2023\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIt is important to test the stationarity of the variables to avoid spurious regression estimates. Thus, the Augmented Dickey-Fuller (ADF) and Phillips-Peron (PP) were employed to investigate the stationarity of the variables. In addition, According to Perron (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) and Kunitomo (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), potential structural breaks in time series can yield invalid estimates if ignored. Hence, it is on this basis that the study accounts for structural breaks using Zivot-Andrews structural break unit root test following Ertugrul et al., (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Altinaya and Karagol (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The unit root test guides to ascertain whether DOLS is applicable. The DOLS requires all variables to be integrated in the same order, I(1). The results from Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show that all variables are stationary at the first difference, thus validating the application of DOLS as the choice of method to estimate the baseline model in Eq.\u0026nbsp;2.\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\u003eAugmented Dickey-Fuller (ADF) and Phillips-Peron (PP) Unit Root Tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eAugmented Dickey-Fuller (ADF) UNIT ROOT TEST With Constant \u0026amp; Trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eAt Level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eINQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSCT_GR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et-Stat.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.4021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.3037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.9876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.3233*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.3088*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.4087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.0983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProb.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.8334\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.4149\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.7645\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.0874\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.0921\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.3456\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e0.0913\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAt First Difference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et-Stat.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.9201***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.778**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.066**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.7304***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.7535***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.7484**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-8.559***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProb.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.0005\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.0294\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.0027\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.0007\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.0008\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.0401\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e0.0000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eStatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhillips-Peron (PP) UNIT ROOT TEST With Constant \u0026amp; Trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAt Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et-Stat.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.5713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.4955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.0793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.1057**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.4852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.8358*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.4878**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProb.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.4806\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.8752\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.6494\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.7074\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.1323\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.0689\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e0.9029\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAt First Difference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et-Stat.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.653***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.9997***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.0793**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.2057**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.2955***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.1272***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.3879**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProb.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.0001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.0060\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.0424\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.0327\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.0000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.0005\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e0.0222\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eStatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eNotes\u003c/b\u003e: (***), (**) and (*) indicate significant at the 1%, 5% and 10% respectively. POV, INQ, INF, SCT_GR, ACC, COR, and FID represent poverty level index, inequality, macroeconomic stability proxied by inflation rate, social cash transfer growth rate, accountability, corruption index, and financial development index respectively. *MacKinnon (1996) one-sided p-values.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMoreover, the Zivot-Andrews unit root test in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the breakpoint occurred in 2001 for POV series, in 2003 for SCT_GR, in 2004 for INQ series, and in 2007 for the FID series. These breaks may be due to the rapid increase in the economy in the 2000s and banking consolidation which occurred after the financial crisis causing a sharp deterioration in the financial sector. Also, the breakpoint occurred in 1996 for INF series, in 1998 for COR, and in 1999 for ACC series. This may be due to political instability and regime shifts with conflicting economic policies that became the order for the period. The evidence of structural breaks therefore necessitate the use of econometric techniques that account for the breaks.\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\u003eZivot-Andrews Unit Root Test (break in both the intercept and trend)\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\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003et-stat.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBreak Time\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.6699*** (0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-109.475*** (0.0014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.73814*** (0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCT_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.049829*** (0.0439)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.12178** (0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.209549** (0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.931915*** (0.0053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2007\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\u003e \u003cb\u003eSource: Authors\u003c/b\u003e, 2023. Notes: ***, ** and * indicate 1%, 5% and 10% significant levels respectively. Values in parenthesis () are the probability values.\u003c/p\u003e \u003cp\u003eTo employ the DOLS method, it is important to establish cointegration in the model. Having reported the stationarity of the series after the first difference process in the model and the structural breaks, it is imperative to adopt an econometric method that addresses this issue. Thus, Gregory and Hansen's (1996) cointegration test with structural breaks was employed to test the long-run relationship among the variables. The result in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that Zt (-5.83) is greater than the asymptotic critical values at 5% (-5.03), indicating the rejection of the null hypothesis of no cointegration among variables. We, therefore, conclude that there is long-run relationship among the variables. Also, since the test suggests the breakpoint date in 2001, other empirical analyses were carried out with dummy variables indicating the structural breaks.\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\u003eGregory-Hansen Test for Cointegration with Regime Shifts\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTest Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBreakpoint\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBreakpoint Date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAsymptotic Critical Values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.83*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-65.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-76.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-30.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-60.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eSource: Authors, 2023\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Effect of Social Cash Transfer on Poverty, Inequality and Inflation\u003c/h2\u003e \u003cp\u003eThe paper examines the effects of social cash transfer on poverty, inequality, and macroeconomic instability (proxied by inflation rate). Having reported the stationarity of the series after the first difference process in the model and the structural breaks, it is imperative to adopt an econometric method that addresses this issue. To this end, the study adopts the DOLS method with structural breakpoint dummies following the model in Eq.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Z=\\left\\{{.}_{z=0 if otherwise}^{z=1 if year\\ge 2001}\\right.\\)\u003c/span\u003e \u003c/span\u003e 3\u003c/p\u003e \u003cp\u003eThe empirical results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The first to third columns of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e present empirical results using poverty reduction as the dependent variable, Columns 4 to Column 6 present the effect of social cash transfer on income inequality while macroeconomic instability (inflation) was treated as the dependent variable in Column 7 to Column 9 to depict the effect of social cash transfers on macroeconomic instability in Nigeria. The results of the three models show that structural breaks influenced the relational effects. The results showed that the level of social cash transfers in Nigeria reduces poverty and income inequality but increases the inflation rate in Nigeria especially when structural breaks in the time series were considered. This result is in line with the study of some extant studies (Mncube et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Patel-Campillo \u0026amp; Garcia, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kitaura \u0026amp; Miyazawa, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yu \u0026amp; Li, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kumar \u0026amp; Sakthivel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Miller, Tsoka \u0026amp; Reichert, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Miller, et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) who advocate the use of social cash transfers and other social protection policies to combat absolute poverty and inequality in Sub-Sahara African countries. But, the finding is contrary to some other studies (see Pruce, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kabamba, et al. 2021; Bukari, et al., 2019; Fuseini et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Asfaw, et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) who concluded that the selection criteria as well as the allocation and distribution processes promote exclusion rather than inclusion of the most vulnerable households in developing countries, especially Malawi, Zambia and South Africa. Similar findings were reported on the individual effects of institutional factors that accountability and corruption control reduced poverty and income inequality when we accounted for structural breaks. The results also showed that the level of accountability in the public offices and corruption at various levels of government have dire implications on poverty and inequality levels, as well as the macroeconomic instability in Nigeria as a result of their consistent negative effects on various dependent variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Dynamic Least Squares (DOLS) with Structural Breakpoint Dummies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDep. Var.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eINQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eINQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eINQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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colname=\"c9\"\u003e \u003cp\u003e-27.270**\u003c/p\u003e \u003cp\u003e[8.4218]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-31.349\u003c/p\u003e \u003cp\u003e[66.709]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2936\u003c/p\u003e \u003cp\u003e[0.2841]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3139**\u003c/p\u003e \u003cp\u003e[0.1235]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2599**\u003c/p\u003e \u003cp\u003e[0.0721]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.5377***\u003c/p\u003e \u003cp\u003e[0.0929]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.4792***\u003c/p\u003e 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colname=\"c4\"\u003e \u003cp\u003e0.9419**\u003c/p\u003e \u003cp\u003e[0.2881]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.2866\u003c/p\u003e \u003cp\u003e[0.1833]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4353\u003c/p\u003e \u003cp\u003e[0.2713]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.5415\u003c/p\u003e \u003cp\u003e[0.4782]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.905**\u003c/p\u003e \u003cp\u003e[10.289]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.69***\u003c/p\u003e \u003cp\u003e[11.233]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.328\u003c/p\u003e \u003cp\u003e[22.252]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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colname=\"c9\"\u003e \u003cp\u003e13.985***\u003c/p\u003e \u003cp\u003e[1.5603]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.934***\u003c/p\u003e \u003cp\u003e[2.7473]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4214***\u003c/p\u003e \u003cp\u003e[1.2956]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7701***\u003c/p\u003e \u003cp\u003e[0.7693]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4094\u003c/p\u003e \u003cp\u003e[1.9055]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0179\u003c/p\u003e \u003cp\u003e[1.2769]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e310.145**\u003c/p\u003e \u003cp\u003e[78.886]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e189.137**\u003c/p\u003e \u003cp\u003e[59.417]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ_SCT_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.4067*\u003c/p\u003e \u003cp\u003e[0.2249]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.8293\u003c/p\u003e \u003cp\u003e[0.4766]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.4579\u003c/p\u003e \u003cp\u003e[0.2899]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.7280\u003c/p\u003e \u003cp\u003e[0.7910]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.064*\u003c/p\u003e \u003cp\u003e[12.003]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.1369\u003c/p\u003e \u003cp\u003e[36.809]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ_ACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.6654***\u003c/p\u003e \u003cp\u003e[0.1663]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.7077***\u003c/p\u003e \u003cp\u003e[0.0891]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0210\u003c/p\u003e \u003cp\u003e[0.0884]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1383\u003c/p\u003e \u003cp\u003e[0.1479]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.4687\u003c/p\u003e \u003cp\u003e[3.6611]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.1394\u003c/p\u003e \u003cp\u003e[6.8801]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ_COR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.7241**\u003c/p\u003e \u003cp\u003e[0.7843]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.8965***\u003c/p\u003e \u003cp\u003e[0.5084]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.1597\u003c/p\u003e \u003cp\u003e[1.5848]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8864\u003c/p\u003e \u003cp\u003e[0.8439]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-114.53\u003c/p\u003e \u003cp\u003e[65.610]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-51.825\u003c/p\u003e \u003cp\u003e[39.268]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ_FID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0645\u003c/p\u003e \u003cp\u003e[0.0636]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0139\u003c/p\u003e \u003cp\u003e[0.0409]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0827\u003c/p\u003e \u003cp\u003e[0.0527]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1509**\u003c/p\u003e \u003cp\u003e[0.0679]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-12.434***\u003c/p\u003e \u003cp\u003e[2.1806]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-10.995**\u003c/p\u003e \u003cp\u003e[3.1584]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCT*ACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.8749***\u003c/p\u003e \u003cp\u003e[0.2486]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.4422\u003c/p\u003e \u003cp\u003e[0.4126]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.766\u003c/p\u003e \u003cp\u003e[19.201]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCT*COR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.8665*\u003c/p\u003e \u003cp\u003e[0.4212]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.6675\u003c/p\u003e \u003cp\u003e[0.6991]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-7.609\u003c/p\u003e \u003cp\u003e[32.533]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3025***\u003c/p\u003e \u003cp\u003e[1.3202]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1464***\u003c/p\u003e \u003cp\u003e[1.0398]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0538***\u003c/p\u003e \u003cp\u003e[0.6302]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.071***\u003c/p\u003e \u003cp\u003e[0.4389]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.862***\u003c/p\u003e \u003cp\u003e[0.7223]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.532***\u003c/p\u003e \u003cp\u003e[1.0462]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-7.751\u003c/p\u003e \u003cp\u003e[23.166]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-256.08***\u003c/p\u003e \u003cp\u003e[29.904]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-173.38**\u003c/p\u003e \u003cp\u003e[48.681]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eSource: Authors\u0026rsquo; Compilation, 2023\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHowever, when the effects of social cash transfers were moderated via institutional factors by interacting with social cash transfers through accountability and corruption control, the findings became more interesting. The results showed that the coefficient of the interaction terms has a stronger and more significant negative effect on poverty level, income inequality, and inflation. This finding implies that when structural breaks and institutional rigidities were factored in, social cash transfer schemes became a vital policy to reduce the level of poverty, inequality, and macroeconomic instability in Nigeria. This finding supports the social protection and mediation theories and the expositions of Acemoglu and Robinson, (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Acemoglu, et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) that institutional quality and economic development reinforce each other in the long run, but less developed intuitions could foster the virtuous circle of poverty and widen the gap between the rich and the poor in an economy. It is worth noting that the results of the nonparametric test in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e are also similar to the effect of social cash transfer on poverty, income inequality, and macroeconomic stability in Nigeria.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Fully Modified Least Squares (FMOLS) with Structural Breakpoint Dummies - Robustness Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDep. Var.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eINQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eINQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eINQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCT_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0651\u003c/p\u003e \u003cp\u003e[0.1431]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003cp\u003e[0.0952]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8083***\u003c/p\u003e \u003cp\u003e[0.6432]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0221\u003c/p\u003e \u003cp\u003e[0.0738]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0672\u003c/p\u003e \u003cp\u003e[0.0723]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.0406\u003c/p\u003e \u003cp\u003e[0.6246]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8310\u003c/p\u003e \u003cp\u003e[2.6845]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-5.0811\u003c/p\u003e \u003cp\u003e[3.2664]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.3935\u003c/p\u003e \u003cp\u003e[24.873]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2910\u003c/p\u003e \u003cp\u003e[0.1973]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2775**\u003c/p\u003e \u003cp\u003e[0.0806]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2878***\u003c/p\u003e \u003cp\u003e[0.0679]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.5148***\u003c/p\u003e \u003cp\u003e[0.1018]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.6084***\u003c/p\u003e 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colname=\"c4\"\u003e \u003cp\u003e0.4862**\u003c/p\u003e \u003cp\u003e[0.1935]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.2923\u003c/p\u003e \u003cp\u003e[0.2507]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4026**\u003c/p\u003e \u003cp\u003e[0.1545]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4674**\u003c/p\u003e \u003cp\u003e[0.1879]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.7871**\u003c/p\u003e \u003cp\u003e[9.1151]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.204***\u003c/p\u003e \u003cp\u003e[6.9838]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36.893***\u003c/p\u003e \u003cp\u003e[7.4838]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0254\u003c/p\u003e \u003cp\u003e[0.0433]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0268\u003c/p\u003e \u003cp\u003e[0.0351]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0878**\u003c/p\u003e \u003cp\u003e[0.0329]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0890**\u003c/p\u003e \u003cp\u003e[0.0341]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.8113*\u003c/p\u003e \u003cp\u003e[1.4852]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.5086*\u003c/p\u003e \u003cp\u003e[1.3574]\u003c/p\u003e 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colname=\"c9\"\u003e \u003cp\u003e-62.638\u003c/p\u003e \u003cp\u003e[23.126]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-74.621**\u003c/p\u003e \u003cp\u003e[22.312]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.670967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.629838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eSource: Authors\u0026rsquo; Compilation, 2023\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. CONCLUSION AND POLICY RECOMMENDATIONS","content":"\u003cp\u003eSocial Cash Transfer (SCT) programmes have been used as an instrument to tackle the menace of poverty and inequality in many developing economies such as Nigeria. Based on our survey of literature, the impact however has been somewhat indefinable given the continued increase in poverty, inequality levels as well and the triggered macroeconomic instability in Nigeria. This present study focused on the effect of social cash transfers on poverty, income inequality, and macroeconomic instability in Nigeria using secondary data for the periods of 1984 to 2021. The results of the Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS) with structural breaks showed that individual social cash transfers, accountability, and corruption control significantly increase the levels of poverty, inequality, and inflation in Nigeria, but when we considered structural breaks and moderated the effects with institutional peculiarities of less developed countries such as Nigeria using control of corruption and accountability, social cash transfers reduced poverty and inequality levels as well as the macroeconomic instability in Nigeria. Findings from this study reveal that social cash transfers have played a pivotal role in improving the well-being of beneficiaries by providing crucial financial support, which, in turn, has contributed to reducing the incidence of poverty. Furthermore, evidence indicates that social cash transfers have helped mitigate income disparities among households and have had positive implications for macroeconomic stability especially when the effects were moderated. This underscores the importance of social cash transfers in poverty and inequality reduction efforts in Nigeria by offering evidence-based insights to inform policymakers, researchers, and practitioners in their pursuit of more effective and inclusive social welfare strategies to foster sustainable development and equitable prosperity in the country. Our conclusion is centred on the important role that institutional factors such as accountability and corruption control play in stimulating the desirable effects of social cash transfers on inequality, poverty, and inflation by checkmating inherent loopholes and market distortions, and by addressing the triggered inflation in the prices of major goods and services in the economy as a result of excessive cash made available in the economy. Based on our findings, our policy implication is that more accountable, equitable, inclusive, and corrupt-free social cash transfer schemes as a social protection policy should be encouraged. It will become one of the major anti-poverty/inequality strategies in Nigeria to win the battle against absolute poverty of the most vulnerable households. Policymakers, government officials at various levels as well and Non-Governmental Organizations (NGOs) can draw practical guidance from this study to refine and optimize the design and implementation of social cash transfer programmes in Nigeria. Lessons learned from the study can help enhance the impact of existing initiatives, and to create more effective policies that directly benefit vulnerable populations in Nigeria.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Limitation to the study\u003c/h2\u003e \u003cp\u003eThe major caveat of this study is found in the use of a macroeconomic approach to analyze the effect of social cash transfers on poverty and income inequality, by employing aggregated macro-variables. The macro data may significantly affect the empirical findings and implications thereof. Future research should focus on household-level analysis by using micro-data which could render more appropriate results. Also, the findings have established the effect of social cash transfers on poverty and inequality levels, establishing direct causality among these variables may be necessary given the potential for reverse causality in the relationship. Subsequent research in this area can focus on this direction. Lastly, defining and measuring poverty and inequality can be complex. Different indicators and methodologies may yield different results. The indicators and measurement approach used in this study could influence the conclusions drawn. However, despite these limitations, the study contributes valuable insights into the effectiveness of social cash transfer programmes in reducing poverty, inequality, and macroeconomic instability in Nigeria. By acknowledging these limitations, researchers and policymakers can interpret the findings critically and consider them within the broader context of socioeconomic development efforts, as the identified limitations do not undermine the relevance of the study or lessen the robustness of the findings herein. They are only acknowledged to direct and inform future researchers in this area.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAjisafe, Rufus Adebayo; Okunade, Solomon; Fatai, Musbau (2023). Modelling structural breaks in social cash transfers effects on poverty and inequality reduction in Africa: A case of Nigeria. figshare. Dataset. https://doi.org/10.6084/m9.figshare.24188529 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no competing interests be it financial or non-financial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that there was no funding or financial support of any form for this research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAjisafe R.A.:\u0026nbsp;\u003c/strong\u003eConceptualization, Resources, Writing-Original draft, Visualization, Funding Acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOkunade S.O.:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Software, Formal Analysis, Resources, Writing-Original draft, Visualization, Funding Acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFatai, M.O.:\u003c/strong\u003e Supervision, Writing-Reviewing and Editing, Funding Acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge two anonymous reviewers who have contributed significantly to improve this research through their constructive criticisms.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcemoglu D, Robinson JA (2013) Why nations fail: the origins of power, prosperity and poverty. 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Revista de Cercetare si Interventie Sociala, 56\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu L, Li X (2021) The effects of social security expenditure on reducing income inequality and rural poverty in China. \u003cem\u003eJournal of Integrative Agriculture 2021, 20(4): 1060\u0026ndash;1067.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ei\u0026gt;https://doi.org/10.1016/S2095-3119(20)63404-9\u003c/span\u003e\u003cspan address=\"http://i%3Ehttps://doi.org/10.1016/S2095-3119(20)63404-9\" targettype=\"URL\" 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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Social cash transfers, Poverty, Inequality, Structural breaks, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-3384456/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3384456/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePoverty and inequality have become persistent challenges in African countries, hindering sustainable development and equitable economic growth. Against this backdrop, the Nigerian government introduced social cash transfer (SCT) programmes to provide targeted financial assistance to vulnerable populations and foster inclusive social welfare. However, the impact has been somewhat indefinable given the continued increase in poverty and inequality levels in Nigeria. Thus, this present study focuses on the effect of SCTs on poverty, inequality, and macroeconomic instability in Nigeria using secondary data for the periods of 1984 to 2021. The results of the Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS) with structural breaks showed that, individually, SCTs significantly increase the levels of poverty, inequality and inflation in Nigeria, but when we considered structural breaks and moderated effects of institutional peculiarities of less developed countries such as Nigeria using control of corruption and accountability, social cash transfers reduced poverty and inequality levels as well as macroeconomic instability in Nigeria. Findings from this study reveal that SCTs have played a pivotal role in improving the well-being of beneficiaries by providing crucial financial support, which, in turn, has contributed to reducing the incidence of poverty and mitigating income disparities among households. Our findings serve as a useful benchmark for the government, policymakers, and Non-Governmental Organizations (NGOs) for evaluating the effectiveness of SCT policies and other social welfare strategies in Nigeria toward evidence-based poverty alleviation and inequality reduction strategies, given the present socio-economic challenges.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification:\u003c/strong\u003e E02, H55, I32, M14\u003c/p\u003e","manuscriptTitle":"Modelling structural breaks in social cash transfers effects on poverty and inequality reduction in Africa: A case of Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 20:32:07","doi":"10.21203/rs.3.rs-3384456/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf16cd16-c768-47dc-bb9a-867f4e4f6334","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-18T20:32:53+00:00","versionOfRecord":{"articleIdentity":"rs-3384456","link":"https://doi.org/10.1016/j.sciaf.2024.e02106","journal":{"identity":"scientific-african","isVorOnly":true,"title":"Scientific African"},"publishedOn":"2024-03-01 20:32:53","publishedOnDateReadable":"March 1st, 2024"},"versionCreatedAt":"2024-07-18 20:32:07","video":"","vorDoi":"10.1016/j.sciaf.2024.e02106","vorDoiUrl":"https://doi.org/10.1016/j.sciaf.2024.e02106","workflowStages":[]},"version":"v1","identity":"rs-3384456","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3384456","identity":"rs-3384456","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}