Impact of armed conflicts on trust in government: Study of rural area of Nigeria

Impact of armed conflicts on trust in government: Study of rural area 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 Impact of armed conflicts on trust in government: Study of rural area of Nigeria Umeoka Chibuike This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8992161/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Households in rural areas are predominantly engaged in farming activities and are particularly vulnerable to external shocks, including violent conflicts perpetrated by various actors. These households often rely on the government to provide protection and to take responsibility for mitigating such threats. This study examines how exposure to conflict influences households’ trust in government, comparing rural and urban populations in Nigeria. Using cross-sectional data, we employ a linear probability model with fixed effects to estimate the relationship between conflict incidents and levels of institutional trust. The results indicate that an increase in conflict incidents in rural areas is significantly associated with a decline in trust in the government’s ability to perform effectively. This finding is not entirely unexpected, given that a large proportion of the population living in poverty resides in rural regions and thus experiences a higher degree of vulnerability to insecurity. The study therefore underscores the need to strengthen the security architecture in rural communities, as improving safety and stability in these areas could play a crucial role in restoring and sustaining public trust in government institutions Agricultural Economics & Policy Trust Conflict. Government Poverty Rural. Nigeria Figures Figure 1 Figure 2 Figure 3 1.0 Introduction Building public trust is not merely a social investment but a fundamental pillar for economic recovery, political stability, and social cohesion. Understanding the determinants of citizens’ trust in government is therefore both essential and urgent for sustaining economic growth and improving overall welfare over time. In the contemporary era, characterized by the rapid and diverse flow of information through social media, the Internet, and civil society networks, maintaining public trust has become an increasingly complex challenge particularly for governments in developing countries such as Nigeria. As Ban Ki-moon emphasized at the 7th Global Forum on Reinventing Government in 2017, trust in government remains the cornerstone of global peace and human well-being. Indeed, the realization of the Millennium Development Goals, the promotion of security, and the protection of human rights all depend fundamentally on citizens’ confidence in their governing institutions (Cheema & Popovski, 2010 , p. 1). However, in contexts marked by endemic mistrust of public officials, fragility and conflict often emerge as citizens become reluctant to participate constructively in government-led state-building processes (Cham et al., 2021 , p. 110). Declining levels of trust can further erode state capacity by reducing public compliance with laws and regulations. Moreover, diminished trust can increase risk aversion among citizens and businesses, discouraging investment, innovation, and employment factors that are critical for restoring competitiveness and catalyzing economic growth (OECD, 2013). However, scholars have offered divergent perspectives on how conflict influences individuals’ behavior and attitudes toward society and government. On the one hand, conflict can induce positive changes in political, socioeconomic, and personal behavior, thereby potentially strengthening social preferences and relationships and increasing trust in government. On the other hand, conflict can undermine social cohesion, reduce cooperation, and diminish participation in community-building initiatives (Fiedler, 2023 ). For instance, De Juan and Pierskalla ( 2016 ) employed geo-referenced survey data to examine the impact of exposure to violence on political trust in Nepal, finding that exposure to violence significantly reduced trust in the national government. Similarly, a nationally representative survey of 39,500 individuals across 35 countries revealed that individual experiences of conflict negatively affected political trust and perceived governmental effectiveness (Grosjean, 2014 ). Further evidence from Africa demonstrates the role of institutional capacity: Hutchison and Johnson ( 2011 ) analyzed Afrobarometer survey data for sixteen African countries between 2000 and 2005, showing that higher institutional capacity is associated with increased levels of individual trust in government. Conversely, Blair and Morse ( 2021 ), through a survey and field experiment in Liberia, found that rebel-perpetrated violence was strongly correlated with increased trust and reliance on positive police responses. Bakke et al. ( 2014 ), using original data from a 2010 survey in Abkhazia, reported that individuals who had experienced violence exhibited significantly higher trust in the president. Despite these findings, systematic investigations of conflict’s effect on political trust remain limited; according to Fiedler ( 2023 ), only a few studies have rigorously examined this relationship compared to the extensive literature on other dimensions of social cohesion. Our study contributes to this limited body of literature by examining the impact of recurring conflict on trust in government, using both qualitative and quantitative analyses. Unlike most studies that focus on conflicts associated with wars with defined end dates, we consider recurring conflicts and their cumulative effect, recognizing that any increase in trust following government intervention, such as enhanced policing, can be reversed by subsequent incidents of conflict or misconduct (Blair & Morse, 2021 ). Following the approach of Hutchison and Johnson ( 2011 ) and De Juan and Pierskalla ( 2016 ), we aggregate six years of conflict data (2010–2015) at the Local Government Area (LGA) level in Nigeria. We posit that recurring conflicts, whether or not mitigated effectively, influence behavioral change; for instance, effective government intervention may foster greater trust in institutions, while inadequate responses may erode it. Most studies indicate a negative relationship between conflict and trust in government, although some find the opposite. Key questions, however, remain unresolved, such as whether conflicts affect perceptions of government uniformly across populations (Fiedler, 2023 ). This study provides a novel perspective by focusing on the often-overlooked comparison between urban and rural populations. To our knowledge, no prior study has systematically examined how conflict affects trust in government differently in urban and rural contexts. This distinction is critical because approximately 80% of the world’s poor live in rural areas, with more than half engaged in agriculture and smallholder businesses (JICA, 2022 ). In Nigeria, the National Bureau of Statistics (NBS, 2022 ) reported that as of November 2022, over 133 million Nigerians, about 63% of the population, live in multidimensional poverty, with rural areas disproportionately affected (72% of rural residents live in poverty, compared to 42% in urban areas). Agriculture, the dominant occupation in rural regions, is particularly vulnerable to external shocks, including armed conflict, which manifests through killings, injuries, threats, fear, migration, and displacement (Odozi & Uwaifo Oyelere, 2021 ). Conflict further disrupts infrastructure and social services, such as transportation, suggesting that rural households may experience the effects of conflict differently than urban households, which often benefit from stronger infrastructure and security mechanisms. Specifically, this study investigates: The effect of conflict incidents in rural areas on trust in the government’s ability to perform effectively. Our results indicate that households in rural areas affected by conflict are likely to lose trust in the government. In contrast, urban households experiencing conflict may exhibit increased trust in the government. One possible explanation is that urban areas, such as Lagos and Port Harcourt, host more diverse economic activities, enabling residents to recover more quickly from conflict shocks. Another explanation is that urban populations have greater access to news media, awareness campaigns, and government security agencies, which may prompt faster intervention and foster trust. The paper proceeds as follows: Section 2 discusses the fragility of the Nigerian state. Section 3 presents the data, Section 4 outlines the empirical framework, Section 5 presents the results, and Section 6 concludes. 2.0 Fragility of the Nigerian State We employ state fragility theory to underpin the assumption that a lack of citizen trust often reflects the government’s ineffectiveness and fragility in managing and controlling conflict within society. Conversely, trust from citizens is essential for the government to function effectively. Conflict is widely recognized as a key source of state fragility due to its destructive effects on human development, while low development levels can, in turn, exacerbate conflict, creating a self-reinforcing cycle of underdevelopment (Naudé et al., 2011). Fragile states require enhanced capacity to cultivate mutually constructive relations with society and often need stronger institutional capabilities to perform even basic governance functions. The significance of fragile states is further underscored by their disproportionate share of the global poor population (OECD, 2013, p. 13). Despite global increases in income levels, poverty remains heavily concentrated in fragile states; by 2015, approximately half of the world’s population living on less than $ 1.25 per day resided in these states. This persistent poverty reflects the slower progress of development in fragile states compared with other regions, largely driven by high income inequality and weak institutional capacity (OECD, 2013, p. 13). Source: The graphs are created by the author based on the data obtained from ACLED Figure 1 illustrates a yearly increase in the number of conflict incidents in Nigeria. As a fragile state, Nigeria faces significant governance challenges, struggling to control its territory and meet the basic needs of its citizens. It is the most populous country in sub-Saharan Africa and potentially the largest economy in the region, bordered by Benin to the west, Cameroon to the east, Chad to the northeast, and Niger to the north. Nigeria is highly diverse, with more than 250 ethnic groups speaking over 500 languages and a wide range of religious affiliations. Ethnic and religious diversity has been identified as a primary driver of conflict in the country. Since independence, Nigeria has experienced terrorism, banditry, armed conflicts, ethno-religious clashes, and full-scale insurgencies, exacerbated in recent years by criminal and militant activities in the south and Boko Haram, armed herders, and bandits in the north (Abiodun, 2018 ). These conflicts have resulted in thousands of deaths, numerous injuries, and the destruction of property valued at billions of naira, pushing many citizens into extreme poverty. These violent conflicts are typically perpetrated by a variety of groups, including but not limited to the Movement for the Emancipation of the Niger Delta (MEND), the Arewa People’s Congress (APC), Bakassi Boys, the Movement for the Actualization of the Sovereign State of Biafra (MASSOB), Boko Haram, and herders (Onuoha, 2012 ). The Fourth Republic of Nigeria has experienced approximately 40% of all ethno-religious crises, with the frequency of terrorist attacks peaking between 2012 and 2014 despite government efforts to contain them. These incidents have cost lives, disrupted economic activities, and inflicted immense suffering on Nigeria’s impoverished population. Rebuilding a fragile state requires nurturing trust to manage and resolve conflicts and to promote positive development outcomes. Citizens must be able to rely on the government’s capacity to provide essential services and maintain territorial integrity. Trust is crucial for economic development (Cham et al., 2021 , p. 107), as declining trust can reduce compliance with rules and regulations, increase risk aversion among citizens and businesses, and delay investment, innovation, and employment decisions—factors essential for restoring competitiveness and jumpstarting growth (OECD, 2013) 3.0 Data The data used in this study were drawn from three sources: the Nigeria General Household Survey (GHS), World Bank indicators, and the Armed Conflict Location & Event Data Project (ACLED). All variables, except those related to conflict, were obtained from the GHS. The GHS is a robust instrument for analyzing household welfare, particularly in the context of agricultural livelihoods. It provides comprehensive information on household, farm, and location characteristics. This study uses post-harvest data from Wave 3, collected between January and April 2016, which originally included 5,000 households. However, most files in Wave 3 contained an average of 4,568 households. After data cleaning and modification, 510 households were excluded, resulting in a final sample of 4,068 households across 439 Local Government Areas (LGAs) in 37 states of Nigeria. Conflict-related data were obtained from ACLED, which provides real-time information on the location, date, actors, fatalities, and types of reported political violence and protest events worldwide. For this study, all recorded incidents from January 2010 to December 2015 were aggregated at the LGA level. Figure 2 presents the distribution of conflict incidents across the LGAs. Table 1 presents the descriptive statistics for all variables employed in this study, providing an overview of their means, standard deviations, and other relevant summary measures. Analysis of the data indicates that, among the 4,068 households included in this study, 2,638 households experienced at least one conflict incident between 2010 and 2015. Conflict was observed in all 37 states. Geographically, 18.73% of incidents occurred in the Southeast, 17.82% in the South-South, and 17.17% in the Southwest. The North Central accounted for 16.68%, the Northeast 16.53%, and the Northwest 13.08% of incidents, highlighting a relatively widespread distribution of conflict across the country. Table 1: Descriptive Statistics at the Household Level Obs Mean SD Min Max Binary Outcome The Government can Be Trusted to do a Good Job = 1 4,068 0.91 0.29 0 1 Explanatory Variable Rural * incidents (interaction) 4,068 3.68 15.57 0 290 LGA Variables The sum of Incidents from 2010-2015 at LGA 4,068 11.49 39.83 0 413 Household Demographics Agricultural household (YES=1, NO=0) 4,068 0.643 0.479 0 1 Natural log household consumption per capita 4,068 11.6 0.75 8.96 14.82 Natural log monetary value of education 4,068 5.64 3.95 0 12.8 Rural = 1 4,068 0.68 0.467 0 1 Sex of Household head (female =0, male=1) 4,068 0.852 0.354 0 1 Age of the household Head 4,068 53.45 14.31 20 103 Household head Status (Married=1, otherwise= 0) 4,068 0.809 0.393 0 1 Number of individuals in a household 4,068 5.907 3.285 1 31 Has any HH member received an assistant? =1 4,068 0.021 .143 0 1 Has HH been affected by any shock in the past 5 years? = 1 4,068 0.300 0.459 0 1 Household Geographic HH Distance in (KMs) to Nearest Major Road 4,068 5.892 7.917 0 67.9 HH Distance in (KMs) to Nearest Population Centre with +20,000 4,068 24.06 20.34 0.1 130.5 HH Distance in (KMs) to Nearest Market 4,068 68.03 43.64 0.4 214.3 HH Distance in (KMs) to Nearest Border Crossing 4,068 314.8 177.9 5.2 671.2 HH Distance in (KMs) to Capital of State of Residence 4,068 64.18 55.42 .2 291.6 Elevation (m) 4,068 277.2 214.09 0 1280 Terrain Roughness 4,068 2.906 2.132 1 12 LGA 439 State 37 3.1 Conflict and Rural Interaction as Variables of Interest. Our primary variable of interest is the interaction between households residing in rural areas and the total number of conflict incidents, comprising all reported political violence and protest events recorded in each Local Government Area (LGA) of Nigeria from January 2010 to December 2015. This interaction captures the intensity of conflict experienced specifically by rural households. Figure 3 illustrates the annual increase in conflict incidents affecting these rural households, highlighting trends in exposure over the study period. 3.2 Outcome variable Our primary dependent variable is whether at least one household member perceives the government as trustworthy and capable of performing its duties more effectively. Trust in government was quantified as a binary variable, with a value of 1 indicating agreement and 0 indicating disagreement. Table 2 presents the distribution of households according to their level of trust in government. The data indicate that 91.1% of households expressed trust in the government’s ability to perform its functions effectively. Table 2 : Showing the number of households that trust the government or not Household location Trust Distrust Total Rural 2529 237 2766 Urban 1178 124 1302 Total 3707 361 4068 Source: (GHS), Author. Trust as predictability refers to a situation in which one party, in this case, a citizen, expects that another party, the government, will act in a particular way and can therefore be confident that these expectations will be fulfilled (Cham et al., 2021, p. 108). A capable and effective government tends to enhance public trust, whereas weak or ineffective governance diminishes political trust (Hutchison & Johnson, 2011). Based on this framework, we hypothesize that an increase in conflict in rural areas will have a more pronounced negative effect on trust in government compared to similar increases in urban areas. While governments bear the primary responsibility for building inclusive and resilient societies, many governments in developing countries face significant challenges in fulfilling this role (Cham et al., 2021, p. 110). 3.3 Control variables. In addition to conflict, several other factors are expected to influence citizens’ perceptions of trust in government. We categorize these variables into household demographic and geographic characteristics. First, agricultural households may experience lower trust in government, as owning a farm often exposes households to conflicts with herders and climate-related shocks that reduce agricultural productivity. Second, consumption per capita and the monetary value of education are included as proxies for household well-being and poverty. Higher consumption per capita and greater educational expenditure are likely to enhance trust in government, consistent with Catterberg and Moreno (2006), who argue that citizens’ confidence in government is closely linked to the state’s ability to maintain or improve their well-being. Third, population-related proxies, including household size and distance (in kilometers) to the nearest population center of over 20,000 inhabitants, are expected to affect trust. Larger populations are associated with higher exposure to conflict, which may undermine trust (Fearon et al., 2003). Fourth, geographic proxies such as distance from the state capital, the nearest border crossing, and the closest major road are anticipated to influence trust. Households closer to state capitals are likely to experience greater security, whereas those situated farther from borders may be more exposed to conflict, potentially reducing trust in government. Finally, socio-demographic characteristics of households including urban versus rural residence, experience of economic shocks, receipt of government assistance, and the age, sex, and marital status of the household head are also expected to affect trust. For example, economic shocks may decrease trust in government, whereas receiving government assistance may enhance it. Collectively, these variables provide a comprehensive set of controls for understanding the determinants of political trust in the Nigerian context. 4.0 Empirical framework We employ the Linear Probability model with fixed effects as given below: Where: Y is the Trust in Government, is the household living in the rural area, is the number of incidents in LGA from 2010 to 2015, while is the interaction. is the vector of household demographic variables, is the vector of household Geographic variables, stands for Local Government Areas (LGA) while household. Then is the LGA fixed effect and is the Idiosyncratic error terms. 4.1 Threats to identification. A key challenge in estimating the causal effect of endogenous explanatory variables on outcomes is the potential for bias. To address this, our first approach involves assessing potential attrition bias by comparing the 510 households excluded during data cleaning with the 4,068 households included in the analysis. Table A3 presents the mean differences in covariates between the two groups. Overall, the two groups are largely similar; however, included households have more members and a higher proportion engaged in agricultural activities than attritor households. Additionally, household heads in the included sample are more likely to reside in their households, whereas attritor households appear relatively wealthier and report more deaths. Despite these differences, the figures suggest that selection or attrition bias is unlikely to substantially influence our results. Our second approach to mitigating bias involves including all observable variables that may affect the outcome of interest. Given the cross-sectional nature of our data, household-level fixed effects cannot be included in the model. Instead, we employ Local Government Area (LGA) fixed effects to control for unobserved heterogeneity across locations. This approach mitigates potential omitted variable bias and reverse causality that may arise when conflict is modelled as the explanatory variable and trust in government as the outcome variable. By accounting for all time-invariant characteristics at the LGA level, we effectively capture systematic differences across areas that could otherwise confound the estimated relationship between conflict exposure and institutional trust. Comparable empirical strategies have been applied in related studies. For instance, Umeoka and Sakurai (2025) examined the effects of conflicts on cassava consumption and production, using household fixed effects in their primary specification. In their robustness analysis, they replaced household fixed effects with LGA-level fixed effects and obtained consistent results. This suggests that the LGA level serves as a reasonable approximation of the household environment, given that most households within the same LGA tend to share similar socioeconomic and environmental characteristics. Hence, applying LGA fixed effects in the current analysis provides a credible and theoretically justified approach to addressing spatially correlated unobserved factors. Conflict data are measured at the LGA level, whereas the outcome variable is measured at the household level. Under this setup, we assume that reverse causality is limited (Umeoka & Sakurai, 2025). As Blattman et al. (2010) note, reverse causality typically arises at the macro or meso level, where national or regional variables may be correlated with conflict incidents, but it is generally minimal at the micro, household level. Nonetheless, we conduct additional robustness checks to ensure the validity and reliability of our results. 5.0 Result The results reveal a consistent negative and statistically significant interaction between Rural and Conflict Incidents (2010–2015) across all model specifications. This indicates that conflict exposure has a differential effect on trust in government between rural and urban households. Specifically, the interaction coefficients (−0.00101, −0.000743, and −0.000554) imply that as the number of conflict incidents increases within a Local Government Area (LGA), trust in government declines more sharply among rural households compared to urban ones. The coefficient on “Sum of incidents”, which captures the effect of conflict exposure in urban areas (where Rural = 0), is statistically insignificant in all models. This suggests that conflict incidents do not have a meaningful impact on urban residents’ trust in the government’s ability to perform better. Thus, while urban trust levels remain relatively stable, rural trust deteriorates significantly with rising conflict exposure. This pattern highlights that rural populations are often more dependent on state protection and more directly affected by insecurity, and experience a stronger erosion of institutional trust in the presence of violent conflict. Model 3 further reveals that greater household distance (in kilometers) to the nearest border crossing is positively associated with trust in government. One explanation is that proximity to government services, including customs, immigration, and security personnel, may facilitate access to these services, generating a positive relationship with government trust. In Nigeria, border proximity may also provide economic benefits, such as easier cross-border trade, improved food security, and facilitated travel, which could enhance citizens’ perception of government effectiveness. Interestingly, household distance to the state capital is also positively associated with trust in government, contrary to expectations. This may reflect that rural households farther from state authorities rely more heavily on community governance structures, which can improve welfare outcomes and foster greater trust in government. As anticipated, households located in areas with rougher terrain exhibit lower trust in government, likely due to challenges in accessing government services and increased vulnerability to natural disasters. Other control variables, including consumption per capita and the monetary value of education, do not show statistically significant effects. Table 3: Baseline Result Trust in the government to do a better job. (1)LPM regression without fixed effects. (2)LPM regression with state fixed effects. (3) LPM regression with LGA fixed effect Rural * Incidents from 2010-2015 at LGA -0.00101*** (0.000264) -0.000743** (0.000313) -0.000554** (0.000224) The sum of Incidents from 2010-2015 at LGA -0.000425 (0.000403) 0.000202 (0.000256) Agricultural household (YES=1, NO=0) 0.0220 (0.0135) 0.00660 (0.0129) 0.000311 (0.0153) Natural log of the Household consumption per capita -0.00754 (0.00723) 0.000507 (0.00728) 0.00463 (0.00785) Natural log of the monetary value of education -0.00115 (0.00133) 0.000000223 (0.00136) -0.000535 (0.00140) Sex of Household head (female =0, male=1) 0.0168 (0.0201) -0.0346 (0.0214) 0.0320 * (0.0204) Household head Status (Married=1, otherwise= 0) -0.000225 (0.000347) 0.0253 (0.0196) -0.0000299 (0.000379) Age of the household Head 0.00291 (0.00153) -0.000224 (0.000341) 0.00246 (0.00172) Number of individuals in a household 0.00291* (0.00153) 0.00271* (0.00161) 0.00246 (0.00172) Any HH member received an assistant =1 -0.0256 (0.0279) -0.0431 (0.0300) -0.0183 (0.0327) HH has been affected by any shock in the past 5 years = 1 -0.00177 (0.0112) -0.0141 (0.0111) 0.00818 (0.0117) Household Distance in (KMs) to Nearest Major Road -0.00169 (0.000889) 0.0000937 (0.000775) -0.00176 (0.00149) Household Distance in (KMs) to Nearest Population Centre with +20,000 0.000263 (0.000350) -0.0000252 (0.000301) -0.000503 (0.00131) Household Distance in (KMs) to Nearest Market -0.000414** (0.000173) -0.0000988 (0.000172) 0.000835 (0.000866) Household Distance in (KMs) to Nearest Border Crossing 0.0000680* (0.0000364) -0.000147 (0.0000906) 0.00205*** (0.000567) Household Distance in (KMs) to Capital of State of Residence 0.000119 (0.000114) 0.000292*** (0.000106) 0.00215*** (0.000589) Elevation (m) 0.000146*** (0.0000287) 0.000116*** (0.0000409) -0.000000571 (0.000194) Terrain Roughness -0.00775*** (0.00266) -0.00337 (0.00253) -0.00905** (0.00381) STATE Fixed effect NO YES NO LGA Fixed effect NO NO YES Clusters in LGA. YES YES YES N 4068 4068 4068 R-sq 0.008 Standard errors in parentheses *p<0.10,**p<0.05,***p<0.01. Note: The cumulative number of incidents from 2010 to 2015 at the LGA level was omitted from the regression due to the inclusion of LGA fixed effects. 5.1 Mechanism Understanding the mechanisms through which households affected by conflict may lose trust in government is crucial for this study. According to Ayodele (2014), a Gallup poll conducted among Nigerians revealed that 94% of respondents distrust the government, largely due to perceptions of corruption. Conflict can exacerbate corruption and poor governance, often through the diversion of public funds, resulting in inadequate government performance and diminished citizen trust. Political and ethnic polarization represents another mechanism that can erode trust. Nigeria is highly diverse, with over 250 ethnic groups and more than 500 languages. If a particular ethnic group perceives that the government is favoring another group during conflict, citizens from the disadvantaged group may lose confidence in government institutions. As of November 2021, both armed and unarmed conflicts had displaced over 3 million Nigerians (UNHCR, 2021), causing significant loss of livelihoods and disruption of communities. When the government fails to assume full responsibility for addressing these crises, public trust declines further. Similarly, trust diminishes when the government is unable to provide security or maintain peace. In summary, public trust in government reflects citizens’ evaluation of governance performance and their responsiveness to the state (Ayodele, 2014). 5 1 . Robustness check Table 4: Interaction between rural and conflict each year. Trust in the government to do a better job 2010 2011 2012 2013 2014 2015 All-year 1 2 3 4 5 6 7 Rural * Incidents 2010 -0.00448* (0.00268) -0.0452*** (0.0142) Rural * Incidents 2011 -0.00435 (0.00391) 0.0473** (0.0235) Rural * Incidents 2012 -0.00547 (0.00343) 0.0180 (0.0173) Rural * Incidents 2013 -0.00360*** (0.00116) 0.00163 (0.0211) Rural * Incidents 2014 -0.00153*** (0.000509) 0.00803 (0.0124) Rural * Incidents 2015 -0.00225** (0.000983) -0.00949 (0.0141) Household controls YES YES YES YES YES YES YES LGA Fixed effect YES YES YES YES YES YES YES N 4068 4068 4068 4068 4068 4068 4068 R 0.008 0.007 0.008 0.008 0.007 0.008 0.010 Clusters in LGA. YES YES YES YES YES YES YES Standard errors are reported in parentheses. Significance levels are indicated as p < 0.10, p < 0.05, and p < 0.01. All models include household-level control variables, Local Government Area (LGA) fixed effects, and clustering of standard errors at the LGA level. In each model, yearly conflict incidents were interacted with rural area status. In Model 7, we include all yearly conflicts simultaneously, along with their interactions with rural households, to assess the cumulative effect. Table 5: Interaction between rural and conflict of years. Trust in the government to do a better job. 6years 5years 4years 3years 2years 1 year 1 2 3 4 5 6 Rural * Incidents ( 2010-2015 ) -0.000554** (0.000224) Rural * Incidents ( 2011-2015) -0.000617** (0.000240) Rural * Incidents (2012-2015) -0.000671** (0.000264) Rural * Incidents (2013-2015) -0.000741*** (0.000276) Rural * Incidents (2014-2015) -0.000929*** (0.000357) Rural * Incidents (2015 ) -0.00225** (0.000983) Household controls YES YES YES YES YES YES LGA Fixed effect YES YES YES YES YES YES N 4068 4068 4068 4068 4068 4068 R 0.008 0.008 0.008 0.008 0.008 0.008 Standard errors in parentheses *p<0.10,**p<0.05,***p<0.01. All the models contain household control, LGA fixed effect, and cluster at the LGA level. For the continuous identification of threats in this study, we conducted several robustness checks. Table 4 partially demonstrates that reverse causality is likely limited, as the outcome variable trust in government was measured in 2016, while conflicts in 2013 and 2014 already show a negative association with trust, even with a temporal gap in 2015. Although the 2015 gap does not completely rule out reverse causality, it suggests the likely direction of the effect. To examine whether the effect of the interaction between rural residence and conflict on trust in government differs by year, we summed conflict incidents in each LGA separately for each year. Table 4 indicates that conflicts in 2013, 2014, and 2015 are statistically significant, whereas conflicts in 2010, 2011, and 2012 are not. This suggests that more recent conflicts have a stronger negative effect on citizens’ trust in government than older incidents. Our findings align with De Juan and Pierskalla (2016), who note that the association between conflict and individual behavior can change over time, potentially influenced by peacebuilding activities or community healing processes. An additional interpretation is that trust in government tends to decline with increasing yearly conflicts, consistent with the upward trend in conflict illustrated in Figures 1 and 2. In Model 7, we include all yearly conflicts simultaneously. The results show that the 2010 conflict negatively affects trust in government, while the 2011 conflict appears positive and significant, reflecting a temporary reduction in conflict intensity, as seen in Figures 1 and 2. This pattern supports our hypothesis that the differential impact of conflict in rural versus urban areas may depend on the level of government intervention. Interventions, whether military or welfare-based, are often more rapid and effective in urban areas due to higher awareness, allowing households to recover more quickly from conflict shocks. Given the recurring nature of conflict, we also summed conflict incidents cumulatively from one to six years, as presented in Table 5. The results remain consistent and robust, confirming that our main findings hold even with shorter time windows. Following De Juan and Pierskalla (2016), we additionally estimated a random effects model with state fixed effects and obtained similar results (Table A1, Column 2). Finally, using state-level conflict data instead of LGA-level data yields a weaker association, as expected (Table A2). This is likely because households experience conflict more directly and intensely at the LGA level than across the broader state area. 6.0 Conclusion Our results indicate that conflict has a negative impact on political perceptions in rural areas of Nigeria. Conflicts in these areas often exacerbate food insecurity and poverty, as rural communities rely heavily on agriculture for their livelihoods (Umeoka & Sakurai, 2025). This finding contrasts with other studies that do not differentiate between urban and rural conflict contexts. Urban areas, which generally have better security and more diverse economic activities, appear to mitigate the negative effects of conflict on trust in government. A decline in trust can reduce compliance with rules and regulations, while citizens and businesses may become more risk-averse, delaying investment, innovation, and employment decisions necessary to restore competitiveness and stimulate growth (OECD, 2013). One potential implication of our findings is that many Nigerians may be relocating from rural to urban areas to avoid recurring conflicts and the limited presence of security personnel in rural communities. A limitation of this study is the aggregation of different types of conflict, as various conflict forms may influence trust in government differently. For example, civil protests in rural areas aimed at demanding improved welfare could, in the short term, enhance citizen trust if they lead to tangible benefits. Future research could therefore focus on conflicts involving specific actors or compare the effects of different conflict types. Additionally, the use of geo-referenced data could improve accuracy by measuring household proximity to conflict sites, ideally within a 6 km radius, to better capture exposure effects. References Abiodun AK (2018) A review of the Boko Haram insurgency and armed conflicts in Nigeria under international humanitarian law . https://core.ac.uk/download/234651908.pdf ACLED (2025) Armed Conflict Location & Event Data Project (ACLED) Codebook, 2023 Ayodele J (2014) Trust in government and the politics of fuel subsidy removal in Lagos, Nigeria . https://www.researchgate.net/publication/309231072_Trust_in_government_and_the_politics_of_ful_subsidy_removal_in_Lagos_Nigeria Bakke KM, O'Loughlin J, Toal G, Ward MD (2014) Convincing state-builders? Disaggregating internal legitimacy in Abkhazia. Int Stud Quart 58(3):591–607 Blair RA, Morse BS (2021) Policing and the legacies of wartime state predation: Evidence from a survey and field experiment in Liberia. J Conflict Resolut 65(10):1709–1737 Blattman C (2009) From violence to voting: War and political participation in Uganda. Am Polit Sci Rev 103(2):231–247 Blattman C, Miguel E (2010) Civil war. J Econ Lit 48(1):3–57. http://www.jstor.org/stable/40651577 Cappelli F, Conigliani C, Consoli D (2023) Climate change and armed conflicts in Africa: Temporal persistence, non-linear climate impact and geographical spillovers. Econ Polit 40:517–560. https://doi-org.utokyo.idm.oclc .org/10.1007/s40888-022-00271-x Catterberg G, Moreno A (2006) The individual bases of political trust: Trends in new and established democracies. Int J Public Opin Res 18(1):31–48 Cham R, Espinoza R, Montiel P (2021) Macroeconomic policy in fragile states. Oxford University Press. https://doi.org/10.1093/oso/9780198853091.001.0001 Cheema GS, Popovski V (2010) Building trust in government: Innovations in governance reform in Asia. UN University. https://digitallibrary.un.org/record/688912?ln=ar Coleman JS (1990) Foundations of social theory. Harvard University Press Dedewanou FA, Tossou RCBK (2022) Remittances and agricultural productivity in Burkina Faso. Appl Economic Perspect Policy 44(3):1573–1590. https://doi.org/10.1002/aepp.13188 De Juan A, Pierskalla JH (2016) Civil war violence and political trust: Microlevel evidence from Nepal. Confl Manag Peace Sci 33(1):67–88 Fearon JD, Laitin DD (2003) Ethnicity, insurgency, and civil war. Am Polit Sci Rev 97(1):75–90 Fiedler C (2023) What do we know about how armed conflict affects social cohesion? A review of the empirical literature. Int Stud Rev, 25 Gates S, Justesen MK (2020) Political trust, shocks, and accountability: Quasi-experimental evidence from a rebel attack. J Conflict Resolut 64(9):1693–1723 Grosjean P (2014) Conflict and social and political preferences: Evidence from World War II and civil conflict in 35 European countries. Comp Econ Stud 56(3):424–451 Hutchison ML, Johnson K (2011) Capacity to trust? Institutional capacity, conflict, and political trust in Africa, 2000–2005. J Peace Res 48(6):737–752 JICA (2022) Works toward the achievement of the Sustainable Development Goals (SDGs). https://www.jica.go.jp/english/our_work/thematic_issues/index.html Kim SE (2005) The role of trust in the modern administrative state: An integrative model. Adm Soc 37(5):611–635. https://doi.org/10.1177/0095399705278596 Lee Y, Schachter HL (2019) Exploring the relationship between trust in government and citizen participation. Int J Public Adm 42(5):405–416. https://doi.org/10.1080/01900692.2018.1465956 Naude W, Paulino S, McGillivray M (2011) Fragile states: Cause, costs and responses. Oxford University Press NBS. (2020) 2019 poverty and inequality in Nigeria. National Bureau of Statistics. https://nigerianstat.gov.ng/elibrary/read/1092 NBS (2022) Multi-dimensional poverty in Nigeria. National Bureau of Statistics. https://nigerianstat.gov.ng/elibrary/read/1241254 Odozi JC, Uwaifo Oyelere R (2021) Does violent conflict affect the labor supply of farm households? The Nigerian experience. Agric Resour Econ Rev 50(3):401–435. https://doi.org/10.1017/age.2021.14 OECD (2013a) Fragile states: Resource flows and trends, conflict and fragility. OECD Publishing. http://dx.doi.org/10.1787/9789264190399-en OECD (2013b) Trust in government, policy effectiveness, and the governance agenda . https://doi.org/10.1787/gov_glance-2013-6-en Onuoha FC (2012) The audacity of the Boko Haram: Background, analysis and emerging trend failure. J Sustainable Soc 2(1):20–30. https://link.springer.com/article/ 10.1057/sj.2011.15 Rousseau DM, Sitkin SB, Burt RS, Camerer C (1998) Not so different after all: A cross-discipline view of trust. Acad Manage Rev 23:393–404 UNDP (2023) Journey to extremism in Africa: Pathways to recruitment and disengagement . https://www.journey-to-extremism.undp.org Umeoka C, Sakurai T (2025) Investigating direct economic and market shocks of conflict on agriculture production in Nigeria. Agric Food Secur 14:55. https://doi.org/10.1186/s40066-025-00565-w Umeoka C, Sakurai T (2025) The effects of conflicts on the consumption and production of cassava: a comparative study of the northeast and southeast of Nigeria. Agric Food Secur. https://doi.org/10.1186/s40066-025-00545-0 https://www.researchgate.net/publication/396657514_The_effects_of_conflicts_on_the_consumption_and_production_of_cassava_a_comparative_study_of_the_northeast_and_southeast_of_Nigeria Worldometer (2023) Nigeria population (live). https://www.worldometers.info/world-population/nigeria-population Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8992161","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598405102,"identity":"6e07ee19-2072-412e-bca1-544db777fb77","order_by":0,"name":"Umeoka Chibuike","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYPACCQYG9gYGZhK18BwgTQtIVwKRWvjFDh/88OOPRR6/5BvDzwUVNgz87d0JeLVIzk5LluxtkyiWnJ1jLD3jTBqDxJmzG/BqMbidY8bA2yCRuOF2joE0b9thBgOJXMJaGP/8AWq5ecb4N9FamHnYgFpu8JgRZwvIL9KybRKJM3vSyqx5zqTxEPQLv3TywY9v/tQl9rMf3nybp8JGjr+9F78WJMBhACJ5iFUOAuwPSFE9CkbBKBgFIwgAAGYLQi4P7lEzAAAAAElFTkSuQmCC","orcid":"","institution":"University of Tokyo","correspondingAuthor":true,"prefix":"","firstName":"Umeoka","middleName":"","lastName":"Chibuike","suffix":""}],"badges":[],"createdAt":"2026-02-28 03:46:49","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8992161/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8992161/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103803275,"identity":"cbf62317-b1b9-472b-ad30-8355d77e87d3","added_by":"auto","created_at":"2026-03-03 06:33:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37162,"visible":true,"origin":"","legend":"\u003cp\u003eSource: The graphs are created by the author based on the data obtained from ACLED\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8992161/v1/1175691b45837b175e03b371.jpg"},{"id":103803276,"identity":"7b1e622c-886c-4ce6-996e-6d7eb8a9cf78","added_by":"auto","created_at":"2026-03-03 06:33:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":223061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncidents by LGAS from 2010 to 2015\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: The graphs are created by the author based on the data obtained from ACLED\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8992161/v1/5b6e2f18de12ece4feaee8e1.jpg"},{"id":103803274,"identity":"ff79dd84-35f6-451c-996c-85587e4bd6f7","added_by":"auto","created_at":"2026-03-03 06:33:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33300,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction between Rural and Conflict from 2010 to 2015\u003c/p\u003e\n\u003cp\u003eSource: Computed by Author\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8992161/v1/0e111507a4c6a69b835bae3b.png"},{"id":104407959,"identity":"c3fa88c8-6159-445f-a296-c4807cae545e","added_by":"auto","created_at":"2026-03-11 12:41:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1004868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8992161/v1/5d079432-09a0-4801-9715-4eb9f63ac106.pdf"},{"id":104400411,"identity":"6ba62171-bf02-4b93-83cb-9ba25f60dfe7","added_by":"auto","created_at":"2026-03-11 12:09:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":211136,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8992161/v1/9e518355f35f84f6bf96f9d0.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eImpact of armed conflicts on trust in government: Study of rural area of Nigeria\u003c/p\u003e","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eBuilding public trust is not merely a social investment but a fundamental pillar for economic recovery, political stability, and social cohesion. Understanding the determinants of citizens\u0026rsquo; trust in government is therefore both essential and urgent for sustaining economic growth and improving overall welfare over time. In the contemporary era, characterized by the rapid and diverse flow of information through social media, the Internet, and civil society networks, maintaining public trust has become an increasingly complex challenge particularly for governments in developing countries such as Nigeria. As Ban Ki-moon emphasized at the 7th Global Forum on Reinventing Government in 2017, trust in government remains the cornerstone of global peace and human well-being. Indeed, the realization of the Millennium Development Goals, the promotion of security, and the protection of human rights all depend fundamentally on citizens\u0026rsquo; confidence in their governing institutions (Cheema \u0026amp; Popovski, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, p. 1). However, in contexts marked by endemic mistrust of public officials, fragility and conflict often emerge as citizens become reluctant to participate constructively in government-led state-building processes (Cham et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p. 110). Declining levels of trust can further erode state capacity by reducing public compliance with laws and regulations. Moreover, diminished trust can increase risk aversion among citizens and businesses, discouraging investment, innovation, and employment factors that are critical for restoring competitiveness and catalyzing economic growth (OECD, 2013).\u003c/p\u003e \u003cp\u003eHowever, scholars have offered divergent perspectives on how conflict influences individuals\u0026rsquo; behavior and attitudes toward society and government. On the one hand, conflict can induce positive changes in political, socioeconomic, and personal behavior, thereby potentially strengthening social preferences and relationships and increasing trust in government. On the other hand, conflict can undermine social cohesion, reduce cooperation, and diminish participation in community-building initiatives (Fiedler, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, De Juan and Pierskalla (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) employed geo-referenced survey data to examine the impact of exposure to violence on political trust in Nepal, finding that exposure to violence significantly reduced trust in the national government. Similarly, a nationally representative survey of 39,500 individuals across 35 countries revealed that individual experiences of conflict negatively affected political trust and perceived governmental effectiveness (Grosjean, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther evidence from Africa demonstrates the role of institutional capacity: Hutchison and Johnson (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) analyzed Afrobarometer survey data for sixteen African countries between 2000 and 2005, showing that higher institutional capacity is associated with increased levels of individual trust in government. Conversely, Blair and Morse (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), through a survey and field experiment in Liberia, found that rebel-perpetrated violence was strongly correlated with increased trust and reliance on positive police responses. Bakke et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), using original data from a 2010 survey in Abkhazia, reported that individuals who had experienced violence exhibited significantly higher trust in the president. Despite these findings, systematic investigations of conflict\u0026rsquo;s effect on political trust remain limited; according to Fiedler (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), only a few studies have rigorously examined this relationship compared to the extensive literature on other dimensions of social cohesion.\u003c/p\u003e \u003cp\u003eOur study contributes to this limited body of literature by examining the impact of recurring conflict on trust in government, using both qualitative and quantitative analyses. Unlike most studies that focus on conflicts associated with wars with defined end dates, we consider recurring conflicts and their cumulative effect, recognizing that any increase in trust following government intervention, such as enhanced policing, can be reversed by subsequent incidents of conflict or misconduct (Blair \u0026amp; Morse, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Following the approach of Hutchison and Johnson (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and De Juan and Pierskalla (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), we aggregate six years of conflict data (2010\u0026ndash;2015) at the Local Government Area (LGA) level in Nigeria. We posit that recurring conflicts, whether or not mitigated effectively, influence behavioral change; for instance, effective government intervention may foster greater trust in institutions, while inadequate responses may erode it. Most studies indicate a negative relationship between conflict and trust in government, although some find the opposite. Key questions, however, remain unresolved, such as whether conflicts affect perceptions of government uniformly across populations (Fiedler, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study provides a novel perspective by focusing on the often-overlooked comparison between urban and rural populations. To our knowledge, no prior study has systematically examined how conflict affects trust in government differently in urban and rural contexts. This distinction is critical because approximately 80% of the world\u0026rsquo;s poor live in rural areas, with more than half engaged in agriculture and smallholder businesses (JICA, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Nigeria, the National Bureau of Statistics (NBS, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported that as of November 2022, over 133\u0026nbsp;million Nigerians, about 63% of the population, live in multidimensional poverty, with rural areas disproportionately affected (72% of rural residents live in poverty, compared to 42% in urban areas). Agriculture, the dominant occupation in rural regions, is particularly vulnerable to external shocks, including armed conflict, which manifests through killings, injuries, threats, fear, migration, and displacement (Odozi \u0026amp; Uwaifo Oyelere, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conflict further disrupts infrastructure and social services, such as transportation, suggesting that rural households may experience the effects of conflict differently than urban households, which often benefit from stronger infrastructure and security mechanisms.\u003c/p\u003e \u003cp\u003eSpecifically, this study investigates:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe effect of conflict incidents in rural areas on trust in the government\u0026rsquo;s ability to perform effectively.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eOur results indicate that households in rural areas affected by conflict are likely to lose trust in the government. In contrast, urban households experiencing conflict may exhibit increased trust in the government. One possible explanation is that urban areas, such as Lagos and Port Harcourt, host more diverse economic activities, enabling residents to recover more quickly from conflict shocks. Another explanation is that urban populations have greater access to news media, awareness campaigns, and government security agencies, which may prompt faster intervention and foster trust.\u003c/p\u003e \u003cp\u003eThe paper proceeds as follows: Section 2 discusses the fragility of the Nigerian state. Section 3 presents the data, Section 4 outlines the empirical framework, Section 5 presents the results, and Section 6 concludes.\u003c/p\u003e"},{"header":"2.0 Fragility of the Nigerian State","content":"\u003cp\u003eWe employ state fragility theory to underpin the assumption that a lack of citizen trust often reflects the government\u0026rsquo;s ineffectiveness and fragility in managing and controlling conflict within society. Conversely, trust from citizens is essential for the government to function effectively. Conflict is widely recognized as a key source of state fragility due to its destructive effects on human development, while low development levels can, in turn, exacerbate conflict, creating a self-reinforcing cycle of underdevelopment (Naud\u0026eacute; et al., 2011). Fragile states require enhanced capacity to cultivate mutually constructive relations with society and often need stronger institutional capabilities to perform even basic governance functions. The significance of fragile states is further underscored by their disproportionate share of the global poor population (OECD, 2013, p. 13). Despite global increases in income levels, poverty remains heavily concentrated in fragile states; by 2015, approximately half of the world\u0026rsquo;s population living on less than \u003cspan\u003e$\u003c/span\u003e1.25 per day resided in these states. This persistent poverty reflects the slower progress of development in fragile states compared with other regions, largely driven by high income inequality and weak institutional capacity (OECD, 2013, p. 13).\u003c/p\u003e \u003cp\u003e \u003cdiv description=\"A graph with a line going upDescription automatically generated\" class=\"Drawing\" id=\"8\" name=\"Content Placeholder 7\"\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: The graphs are created by the author based on the data obtained from ACLED\u003c/p\u003e \u003cp\u003eFigure 1 illustrates a yearly increase in the number of conflict incidents in Nigeria. As a fragile state, Nigeria faces significant governance challenges, struggling to control its territory and meet the basic needs of its citizens. It is the most populous country in sub-Saharan Africa and potentially the largest economy in the region, bordered by Benin to the west, Cameroon to the east, Chad to the northeast, and Niger to the north. Nigeria is highly diverse, with more than 250 ethnic groups speaking over 500 languages and a wide range of religious affiliations. Ethnic and religious diversity has been identified as a primary driver of conflict in the country. Since independence, Nigeria has experienced terrorism, banditry, armed conflicts, ethno-religious clashes, and full-scale insurgencies, exacerbated in recent years by criminal and militant activities in the south and Boko Haram, armed herders, and bandits in the north (Abiodun, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These conflicts have resulted in thousands of deaths, numerous injuries, and the destruction of property valued at billions of naira, pushing many citizens into extreme poverty.\u003c/p\u003e \u003cp\u003eThese violent conflicts are typically perpetrated by a variety of groups, including but not limited to the Movement for the Emancipation of the Niger Delta (MEND), the Arewa People\u0026rsquo;s Congress (APC), Bakassi Boys, the Movement for the Actualization of the Sovereign State of Biafra (MASSOB), Boko Haram, and herders (Onuoha, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The Fourth Republic of Nigeria has experienced approximately 40% of all ethno-religious crises, with the frequency of terrorist attacks peaking between 2012 and 2014 despite government efforts to contain them. These incidents have cost lives, disrupted economic activities, and inflicted immense suffering on Nigeria\u0026rsquo;s impoverished population.\u003c/p\u003e \u003cp\u003eRebuilding a fragile state requires nurturing trust to manage and resolve conflicts and to promote positive development outcomes. Citizens must be able to rely on the government\u0026rsquo;s capacity to provide essential services and maintain territorial integrity. Trust is crucial for economic development (Cham et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p. 107), as declining trust can reduce compliance with rules and regulations, increase risk aversion among citizens and businesses, and delay investment, innovation, and employment decisions\u0026mdash;factors essential for restoring competitiveness and jumpstarting growth (OECD, 2013)\u003c/p\u003e"},{"header":"3.0 Data","content":"\u003cp\u003eThe data used in this study were drawn from three sources: the Nigeria General Household Survey (GHS), World Bank indicators, and the Armed Conflict Location \u0026amp; Event Data Project (ACLED). All variables, except those related to conflict, were obtained from the GHS.\u003c/p\u003e\n\u003cp\u003eThe GHS is a robust instrument for analyzing household welfare, particularly in the context of agricultural livelihoods. It provides comprehensive information on household, farm, and location characteristics. This study uses post-harvest data from Wave 3, collected between January and April 2016, which originally included 5,000 households. However, most files in Wave 3 contained an average of 4,568 households. After data cleaning and modification, 510 households were excluded, resulting in a final sample of 4,068 households across 439 Local Government Areas (LGAs) in 37 states of Nigeria.\u003c/p\u003e\n\u003cp\u003eConflict-related data were obtained from ACLED, which provides real-time information on the location, date, actors, fatalities, and types of reported political violence and protest events worldwide. For this study, all recorded incidents from January 2010 to December 2015 were aggregated at the LGA level. Figure 2\u0026nbsp;presents the distribution of conflict incidents across the LGAs.\u003c/p\u003e\n\u003cp\u003eTable 1\u0026nbsp;presents the descriptive statistics for all variables employed in this study, providing an overview of their means, standard deviations, and other relevant summary measures. Analysis of the data indicates that, among the 4,068 households included in this study, 2,638 households experienced at least one conflict incident between 2010 and 2015. Conflict was observed in all 37 states. Geographically, 18.73% of incidents occurred in the Southeast, 17.82% in the South-South, and 17.17% in the Southwest. The North Central accounted for 16.68%, the Northeast 16.53%, and the Northwest 13.08% of incidents, highlighting a relatively widespread distribution of conflict across the country.\u003c/p\u003e\n\u003cp\u003eTable 1: Descriptive Statistics at the Household Level\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBinary Outcome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe Government can Be Trusted to do a Good Job = 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExplanatory Variable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * incidents (interaction)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLGA Variables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe sum of Incidents from 2010-2015 at LGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Demographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAgricultural household (YES=1, NO=0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNatural log household consumption per capita\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNatural log monetary value of education\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural = 1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex of Household head (female =0, male=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge of the household Head\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHousehold head Status (Married=1, otherwise= 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of individuals in a household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHas any HH member received an assistant? =1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHas HH been affected by any shock in the past 5 years? = 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Geographic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHH Distance in (KMs) to Nearest Major Road\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHH Distance in (KMs) to Nearest Population Centre\u003c/p\u003e\n \u003cp\u003ewith +20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHH Distance in (KMs) to Nearest Market\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e214.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHH Distance in (KMs) to Nearest Border Crossing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e314.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e177.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e671.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHH Distance in (KMs) to Capital of State of Residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e291.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElevation (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e277.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e214.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTerrain Roughness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;LGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.1 \u003cstrong\u003eConflict and Rural Interaction as Variables of Interest.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur primary variable of interest is the interaction between households residing in rural areas and the total number of conflict incidents, comprising all reported political violence and protest events recorded in each Local Government Area (LGA) of Nigeria from January 2010 to December 2015. This interaction captures the intensity of conflict experienced specifically by rural households. Figure 3 illustrates the annual increase in conflict incidents affecting these rural households, highlighting trends in exposure over the study period.\u003c/p\u003e\n\u003cp\u003e3.2\u003cstrong\u003e\u0026nbsp;Outcome variable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur primary dependent variable is whether at least one household member perceives the government as trustworthy and capable of performing its duties more effectively. Trust in government was quantified as a binary variable, with a value of 1 indicating agreement and 0 indicating disagreement. Table 2 presents the distribution of households according to their level of trust in government. The data indicate that 91.1% of households expressed trust in the government\u0026rsquo;s ability to perform its functions effectively.\u003c/p\u003e\n\u003cp\u003eTable 2\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eShowing the number of households that trust the government or not\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Household location\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Trust\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDistrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: (GHS), Author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTrust as predictability refers to a situation in which one party, in this case, a citizen, expects that another party, the government, will act in a particular way and can therefore be confident that these expectations will be fulfilled (Cham et al., 2021, p. 108). A capable and effective government tends to enhance public trust, whereas weak or ineffective governance diminishes political trust (Hutchison \u0026amp; Johnson, 2011). Based on this framework, we hypothesize that an increase in conflict in rural areas will have a more pronounced negative effect on trust in government compared to similar increases in urban areas. While governments bear the primary responsibility for building inclusive and resilient societies, many governments in developing countries face significant challenges in fulfilling this role (Cham et al., 2021, p. 110).\u003c/p\u003e\n\u003cp\u003e3.3 \u003cstrong\u003eControl variables.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to conflict, several other factors are expected to influence citizens\u0026rsquo; perceptions of trust in government. We categorize these variables into household demographic and geographic characteristics. First, agricultural households may experience lower trust in government, as owning a farm often exposes households to conflicts with herders and climate-related shocks that reduce agricultural productivity. Second, consumption per capita and the monetary value of education are included as proxies for household well-being and poverty. Higher consumption per capita and greater educational expenditure are likely to enhance trust in government, consistent with Catterberg and Moreno (2006), who argue that citizens\u0026rsquo; confidence in government is closely linked to the state\u0026rsquo;s ability to maintain or improve their well-being.\u003c/p\u003e\n\u003cp\u003eThird, population-related proxies, including household size and distance (in kilometers) to the nearest population center of over 20,000 inhabitants, are expected to affect trust. Larger populations are associated with higher exposure to conflict, which may undermine trust (Fearon et al., 2003). Fourth, geographic proxies such as distance from the state capital, the nearest border crossing, and the closest major road are anticipated to influence trust. Households closer to state capitals are likely to experience greater security, whereas those situated farther from borders may be more exposed to conflict, potentially reducing trust in government.\u003c/p\u003e\n\u003cp\u003eFinally, socio-demographic characteristics of households including urban versus rural residence, experience of economic shocks, receipt of government assistance, and the age, sex, and marital status of the household head are also expected to affect trust. For example, economic shocks may decrease trust in government, whereas receiving government assistance may enhance it. Collectively, these variables provide a comprehensive set of controls for understanding the determinants of political trust in the Nigerian context.\u003c/p\u003e"},{"header":"4.0 Empirical framework ","content":"\u003cp\u003eWe employ the Linear Probability model with fixed effects as given below:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere: Y is the Trust in Government, \u0026nbsp; is the household living in the rural area, \u0026nbsp; \u0026nbsp;is the number of incidents in LGA from 2010 to 2015, while \u0026nbsp;is the interaction. \u0026nbsp; is the vector of household demographic variables, \u0026nbsp;is the vector of household Geographic variables, \u0026nbsp; \u0026nbsp;stands for Local Government Areas (LGA) while \u0026nbsp;household. Then is the LGA fixed effect and \u0026nbsp;is the Idiosyncratic error terms. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.1 \u003cstrong\u003eThreats to identification.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA key challenge in estimating the causal effect of endogenous explanatory variables on outcomes is the potential for bias. To address this, our first approach involves assessing potential attrition bias by comparing the 510 households excluded during data cleaning with the 4,068 households included in the analysis.\u0026nbsp;Table A3\u0026nbsp;presents the mean differences in covariates between the two groups. Overall, the two groups are largely similar; however, included households have more members and a higher proportion engaged in agricultural activities than attritor households. Additionally, household heads in the included sample are more likely to reside in their households, whereas attritor households appear relatively wealthier and report more deaths. Despite these differences, the figures suggest that selection or attrition bias is unlikely to substantially influence our results.\u003c/p\u003e\n\u003cp\u003eOur second approach to mitigating bias involves including all observable variables that may affect the outcome of interest. Given the cross-sectional nature of our data, household-level fixed effects cannot be included in the model. Instead, we employ Local Government Area (LGA) fixed effects to control for unobserved heterogeneity across locations. This approach mitigates potential omitted variable bias and reverse causality that may arise when conflict is modelled as the explanatory variable and trust in government as the outcome variable. By accounting for all time-invariant characteristics at the LGA level, we effectively capture systematic differences across areas that could otherwise confound the estimated relationship between conflict exposure and institutional trust. Comparable empirical strategies have been applied in related studies. For instance,\u0026nbsp;Umeoka and Sakurai (2025)\u0026nbsp;examined the effects of conflicts on cassava consumption and production, using household fixed effects in their primary specification. In their robustness analysis, they replaced household fixed effects with LGA-level fixed effects and obtained consistent results. This suggests that the LGA level serves as a reasonable approximation of the household environment, given that most households within the same LGA tend to share similar socioeconomic and environmental characteristics. Hence, applying LGA fixed effects in the current analysis provides a credible and theoretically justified approach to addressing spatially correlated unobserved factors.\u003c/p\u003e\n\u003cp\u003eConflict data are measured at the LGA level, whereas the outcome variable is measured at the household level. Under this setup, we assume that reverse causality is limited (Umeoka \u0026amp; Sakurai, 2025). As Blattman et al. (2010) note, reverse causality typically arises at the macro or meso level, where national or regional variables may be correlated with conflict incidents, but it is generally minimal at the micro, household level. Nonetheless, we conduct additional robustness checks to ensure the validity and reliability of our results.\u003c/p\u003e"},{"header":"5.0 Result ","content":"\u003cp\u003eThe results reveal a consistent negative and statistically significant interaction between Rural and Conflict Incidents (2010–2015) across all model specifications. This indicates that conflict exposure has a differential effect on trust in government between rural and urban households. Specifically, the interaction coefficients (−0.00101, −0.000743, and −0.000554) imply that as the number of conflict incidents increases within a Local Government Area (LGA), trust in government declines more sharply among rural households compared to urban ones. The coefficient on “Sum of incidents”, which captures the effect of conflict exposure in urban areas (where Rural = 0), is statistically insignificant in all models. This suggests that conflict incidents do not have a meaningful impact on urban residents’ trust in the government’s ability to perform better.\u003c/p\u003e\n\u003cp\u003eThus, while urban trust levels remain relatively stable, rural trust deteriorates significantly with rising conflict exposure. This pattern highlights that rural populations are often more dependent on state protection and more directly affected by insecurity, and experience a stronger erosion of institutional trust in the presence of violent conflict.\u003c/p\u003e\n\u003cp\u003eModel 3\u0026nbsp;further reveals that greater household distance (in kilometers) to the nearest border crossing is positively associated with trust in government. One explanation is that proximity to government services, including customs, immigration, and security personnel, may facilitate access to these services, generating a positive relationship with government trust. In Nigeria, border proximity may also provide economic benefits, such as easier cross-border trade, improved food security, and facilitated travel, which could enhance citizens’ perception of government effectiveness.\u003c/p\u003e\n\u003cp\u003eInterestingly, household distance to the state capital is also positively associated with trust in government, contrary to expectations. This may reflect that rural households farther from state authorities rely more heavily on community governance structures, which can improve welfare outcomes and foster greater trust in government. As anticipated, households located in areas with rougher terrain exhibit lower trust in government, likely due to challenges in accessing government services and increased vulnerability to natural disasters. Other control variables, including consumption per capita and the monetary value of education, do not show statistically significant effects.\u003c/p\u003e\n\u003cp\u003eTable 3: \u0026nbsp; Baseline Result\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"584\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eTrust in the government to do a better job.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(1)LPM regression without fixed effects.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(2)LPM regression with state fixed effects.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(3)\u0026nbsp;LPM regression with LGA fixed effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents from 2010-2015 at LGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00101***\u003c/p\u003e\n \u003cp\u003e(0.000264)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000743**\u003c/p\u003e\n \u003cp\u003e(0.000313)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000554**\u003c/p\u003e\n \u003cp\u003e(0.000224)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe sum of Incidents from 2010-2015 at LGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000425\u003c/p\u003e\n \u003cp\u003e(0.000403)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000202\u003c/p\u003e\n \u003cp\u003e(0.000256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAgricultural household (YES=1, NO=0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0220\u003c/p\u003e\n \u003cp\u003e(0.0135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00660\u003c/p\u003e\n \u003cp\u003e(0.0129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000311\u003c/p\u003e\n \u003cp\u003e(0.0153)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNatural log of the Household consumption per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00754\u003c/p\u003e\n \u003cp\u003e(0.00723)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000507\u003c/p\u003e\n \u003cp\u003e(0.00728)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00463\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.00785)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNatural log of the monetary value of education\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00115\u003c/p\u003e\n \u003cp\u003e(0.00133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000000223\u003c/p\u003e\n \u003cp\u003e(0.00136)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000535\u003c/p\u003e\n \u003cp\u003e(0.00140)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex of Household head (female =0, male=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0168\u003c/p\u003e\n \u003cp\u003e(0.0201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0346\u003c/p\u003e\n \u003cp\u003e(0.0214)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0320 *\u003c/p\u003e\n \u003cp\u003e(0.0204)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHousehold head Status (Married=1, otherwise= 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000225\u003c/p\u003e\n \u003cp\u003e(0.000347)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0253\u003c/p\u003e\n \u003cp\u003e(0.0196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0000299\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.000379)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge of the household Head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00291\u003c/p\u003e\n \u003cp\u003e(0.00153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000224\u003c/p\u003e\n \u003cp\u003e(0.000341)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00246\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.00172)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of individuals in a household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00291*\u003c/p\u003e\n \u003cp\u003e(0.00153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00271*\u003c/p\u003e\n \u003cp\u003e(0.00161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00246\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.00172) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAny HH member received an assistant =1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0256\u003c/p\u003e\n \u003cp\u003e(0.0279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0431\u003c/p\u003e\n \u003cp\u003e(0.0300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0183\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.0327)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;HH has been affected by any shock in the past 5 years = 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00177\u003c/p\u003e\n \u003cp\u003e(0.0112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0141\u003c/p\u003e\n \u003cp\u003e(0.0111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00818\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.0117)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHousehold Distance in (KMs) to Nearest Major Road\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00169\u003c/p\u003e\n \u003cp\u003e(0.000889)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000937\u003c/p\u003e\n \u003cp\u003e(0.000775)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00176\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.00149)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHousehold Distance in (KMs) to Nearest Population Centre with +20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000263\u003c/p\u003e\n \u003cp\u003e(0.000350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0000252 (0.000301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000503\u003c/p\u003e\n \u003cp\u003e(0.00131)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHousehold Distance in (KMs) to Nearest Market\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000414**\u003c/p\u003e\n \u003cp\u003e(0.000173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0000988\u003c/p\u003e\n \u003cp\u003e(0.000172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000835\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.000866)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHousehold Distance in (KMs) to Nearest Border Crossing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000680*\u003c/p\u003e\n \u003cp\u003e(0.0000364)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000147\u003c/p\u003e\n \u003cp\u003e(0.0000906)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00205***\u003c/p\u003e\n \u003cp\u003e(0.000567)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHousehold Distance in (KMs) to Capital of State of Residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000119\u003c/p\u003e\n \u003cp\u003e(0.000114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000292***\u003c/p\u003e\n \u003cp\u003e(0.000106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00215***\u003c/p\u003e\n \u003cp\u003e(0.000589)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElevation (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000146***\u003c/p\u003e\n \u003cp\u003e(0.0000287)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000116***\u003c/p\u003e\n \u003cp\u003e(0.0000409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000000571\u003c/p\u003e\n \u003cp\u003e(0.000194)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTerrain Roughness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00775***\u003c/p\u003e\n \u003cp\u003e(0.00266)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00337\u003c/p\u003e\n \u003cp\u003e(0.00253)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00905**\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.00381)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSTATE Fixed effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLGA Fixed effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClusters in LGA.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR-sq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eStandard errors in parentheses *p\u0026lt;0.10,**p\u0026lt;0.05,***p\u0026lt;0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The cumulative number of incidents from 2010 to 2015 at the LGA level was omitted from the regression due to the inclusion of LGA fixed effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.1 Mechanism\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnderstanding the mechanisms through which households affected by conflict may lose trust in government is crucial for this study. According to Ayodele (2014),\u0026nbsp;a Gallup poll conducted among Nigerians revealed that 94% of respondents distrust the government, largely due to perceptions of corruption. Conflict can exacerbate corruption and poor governance, often through the diversion of public funds, resulting in inadequate government performance and diminished citizen trust.\u003c/p\u003e\n\u003cp\u003ePolitical and ethnic polarization represents another mechanism that can erode trust. Nigeria is highly diverse, with over 250 ethnic groups and more than 500 languages. If a particular ethnic group perceives that the government is favoring another group during conflict, citizens from the disadvantaged group may lose confidence in government institutions.\u003c/p\u003e\n\u003cp\u003eAs of November 2021, both armed and unarmed conflicts had displaced over 3 million Nigerians\u0026nbsp;(UNHCR, 2021), causing significant loss of livelihoods and disruption of communities. When the government fails to assume full responsibility for addressing these crises, public trust declines further. Similarly, trust diminishes when the government is unable to provide security or maintain peace. In summary, public trust in government reflects citizens’ evaluation of governance performance and their responsiveness to the state\u0026nbsp;(Ayodele, 2014).\u003c/p\u003e\n\u003cp\u003e5 1\u003cstrong\u003e. Robustness check\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4: Interaction between rural and conflict each year.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eTrust in the government to do a better job\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAll-year\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents\u003c/p\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00448* \u0026nbsp;(0.00268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0452***\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.0142)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents\u003c/p\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00435 (0.00391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0473**\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.0235)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents\u003c/p\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00547 (0.00343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0180\u003c/p\u003e\n \u003cp\u003e(0.0173)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents\u003c/p\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00360***\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.00116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00163\u003c/p\u003e\n \u003cp\u003e(0.0211)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents\u003c/p\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00153***\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.000509)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00803\u003c/p\u003e\n \u003cp\u003e(0.0124)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents\u003c/p\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00225**\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.000983)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00949\u003c/p\u003e\n \u003cp\u003e(0.0141)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHousehold controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLGA Fixed effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClusters in LGA.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eStandard errors are reported in parentheses. Significance levels are indicated as \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10, \u003cstrong\u003ep\u003c/strong\u003e \u0026lt; 0.05, and \u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e \u0026lt; 0.01. All models include household-level control variables, Local Government Area (LGA) fixed effects, and clustering of standard errors at the LGA level. In each model, yearly conflict incidents were interacted with rural area status. In Model 7, we include all yearly conflicts simultaneously, along with their interactions with rural households, to assess the cumulative effect.\u003c/p\u003e\n\u003cp\u003eTable 5: Interaction between rural and conflict of years.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"584\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eTrust in the government to do a better job.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e( 2010-2015 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000554**\u003c/p\u003e\n \u003cp\u003e(0.000224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e( 2011-2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000617**\u003c/p\u003e\n \u003cp\u003e(0.000240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2012-2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000671**\u003c/p\u003e\n \u003cp\u003e(0.000264)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2013-2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000741***\u003c/p\u003e\n \u003cp\u003e(0.000276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2014-2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.000929***\u003c/p\u003e\n \u003cp\u003e(0.000357)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRural * Incidents\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(2015 )\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.00225**\u003c/p\u003e\n \u003cp\u003e(0.000983)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHousehold controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLGA Fixed effect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eStandard errors in parentheses *p\u0026lt;0.10,**p\u0026lt;0.05,***p\u0026lt;0.01. All the models contain household control, LGA fixed effect, and cluster at the LGA level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the continuous identification of threats in this study, we conducted several robustness checks. Table 4 partially demonstrates that reverse causality is likely limited, as the outcome variable trust in government was measured in 2016, while conflicts in 2013 and 2014 already show a negative association with trust, even with a temporal gap in 2015. Although the 2015 gap does not completely rule out reverse causality, it suggests the likely direction of the effect.\u003c/p\u003e\n\u003cp\u003eTo examine whether the effect of the interaction between rural residence and conflict on trust in government differs by year, we summed conflict incidents in each LGA separately for each year. Table 4 indicates that conflicts in 2013, 2014, and 2015 are statistically significant, whereas conflicts in 2010, 2011, and 2012 are not. This suggests that more recent conflicts have a stronger negative effect on citizens’ trust in government than older incidents. Our findings align with De Juan and Pierskalla (2016), who note that the association between conflict and individual behavior can change over time, potentially influenced by peacebuilding activities or community healing processes. An additional interpretation is that trust in government tends to decline with increasing yearly conflicts, consistent with the upward trend in conflict illustrated in Figures 1 and 2.\u003c/p\u003e\n\u003cp\u003eIn Model 7, we include all yearly conflicts simultaneously. The results show that the 2010 conflict negatively affects trust in government, while the 2011 conflict appears positive and significant, reflecting a temporary reduction in conflict intensity, as seen in Figures 1 and 2. This pattern supports our hypothesis that the differential impact of conflict in rural versus urban areas may depend on the level of government intervention. Interventions, whether military or welfare-based, are often more rapid and effective in urban areas due to higher awareness, allowing households to recover more quickly from conflict shocks.\u003c/p\u003e\n\u003cp\u003eGiven the recurring nature of conflict, we also summed conflict incidents cumulatively from one to six years, as presented in Table 5. The results remain consistent and robust, confirming that our main findings hold even with shorter time windows. Following De Juan and Pierskalla (2016), we additionally estimated a random effects model with state fixed effects and obtained similar results (Table A1, Column 2). Finally, using state-level conflict data instead of LGA-level data yields a weaker association, as expected (Table A2). This is likely because households experience conflict more directly and intensely at the LGA level than across the broader state area.\u003c/p\u003e"},{"header":"6.0 Conclusion ","content":"\u003cp\u003eOur results indicate that conflict has a negative impact on political perceptions in rural areas of Nigeria. Conflicts in these areas often exacerbate food insecurity and poverty, as rural communities rely heavily on agriculture for their livelihoods (Umeoka \u0026amp; Sakurai, 2025). This finding contrasts with other studies that do not differentiate between urban and rural conflict contexts. Urban areas, which generally have better security and more diverse economic activities, appear to mitigate the negative effects of conflict on trust in government. A decline in trust can reduce compliance with rules and regulations, while citizens and businesses may become more risk-averse, delaying investment, innovation, and employment decisions necessary to restore competitiveness and stimulate growth (OECD, 2013). One potential implication of our findings is that many Nigerians may be relocating from rural to urban areas to avoid recurring conflicts and the limited presence of security personnel in rural communities.\u003c/p\u003e\n\u003cp\u003eA limitation of this study is the aggregation of different types of conflict, as various conflict forms may influence trust in government differently. For example, civil protests in rural areas aimed at demanding improved welfare could, in the short term, enhance citizen trust if they lead to tangible benefits. Future research could therefore focus on conflicts involving specific actors or compare the effects of different conflict types. Additionally, the use of geo-referenced data could improve accuracy by measuring household proximity to conflict sites, ideally within a 6 km radius, to better capture exposure effects.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbiodun AK (2018) \u003cem\u003eA review of the Boko Haram insurgency and armed conflicts in Nigeria under international humanitarian law\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://core.ac.uk/download/234651908.pdf\u003c/span\u003e\u003cspan address=\"https://core.ac.uk/download/234651908.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eACLED (2025) \u003cem\u003eArmed Conflict Location \u0026amp; Event Data Project (ACLED) Codebook, 2023\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyodele J (2014) \u003cem\u003eTrust in government and the politics of fuel subsidy removal in Lagos, Nigeria\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/309231072_Trust_in_government_and_the_politics_of_ful_subsidy_removal_in_Lagos_Nigeria\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/309231072_Trust_in_government_and_the_politics_of_ful_subsidy_removal_in_Lagos_Nigeria\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakke KM, O'Loughlin J, Toal G, Ward MD (2014) Convincing state-builders? 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Government, Poverty, Rural. Nigeria, ","lastPublishedDoi":"10.21203/rs.3.rs-8992161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8992161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHouseholds in rural areas are predominantly engaged in farming activities and are particularly vulnerable to external shocks, including violent conflicts perpetrated by various actors. These households often rely on the government to provide protection and to take responsibility for mitigating such threats. This study examines how exposure to conflict influences households’ trust in government, comparing rural and urban populations in Nigeria. Using cross-sectional data, we employ a linear probability model with fixed effects to estimate the relationship between conflict incidents and levels of institutional trust. The results indicate that an increase in conflict incidents in rural areas is significantly associated with a decline in trust in the government’s ability to perform effectively. This finding is not entirely unexpected, given that a large proportion of the population living in poverty resides in rural regions and thus experiences a higher degree of vulnerability to insecurity. The study therefore underscores the need to strengthen the security architecture in rural communities, as improving safety and stability in these areas could play a crucial role in restoring and sustaining public trust in government institutions\u003c/p\u003e","manuscriptTitle":"Impact of armed conflicts on trust in government: Study of rural area of Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 06:33:19","doi":"10.21203/rs.3.rs-8992161/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":"89c405b2-cb4e-4bf1-b119-90e275c29918","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63683808,"name":"Agricultural Economics \u0026 Policy"}],"tags":[],"updatedAt":"2026-03-03T06:33:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 06:33:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8992161","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8992161","identity":"rs-8992161","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}