Examining the Impact of Hawking on Personal Development: Predictors and Determinants

Examining the Impact of Hawking on Personal Development: Predictors and Determinants

Otunomeruke James Allen, Itiveh Francis, Eriyeva A. Godspower, Ejemah Emumena Thomas

Delta State University of Science and Technology, Ozoro, Delta State, Nigeria

DOI: https://doi.org/10.51584/IJRIAS.2024.910045

Received: 08 October 2024; Accepted: 17 October 2024; Published: 20 November 2024

ABSTRACT          

Hawking is a prevalent economic activity in many developing countries, contributing significantly to the informal economy and the livelihoods of individuals. This is a cross-sectional study using data collected from household-based survey on April 2024 in Isoko Communities with 500 women and men aged 15-65 that had at least one vendor in the last two years. The study assessed the impacts of hawking and personal development using statistical methods. Analysis was conducted in SPSS vs 26 that provided parameters such as descriptive statistic, correlation, regression models, and -2 Likelihood ratio test. Identified push factors contributing to the prevalence of hawking are poverty (88.6%), underemployment (84.2%), low income (82.6%), unemployment (80.2%), food insecurity (75.6%), and broken-homes (58.4%). The associated “Pull factors” include poverty alleviation (79.2%), and income earnings (9.4%). The study documented some effects which include low academic performance (26.2%), unwanted pregnancy (23.8%), and early marriage (6.4%). The results reveal that multiple socioeconomic and personal factors significantly increase the likelihood of engaging in hawking. The most influential predictors are “low income” and “underemployment”, both of which dramatically increase the odds of individuals resulting to hawking.

“Food insecurity” and “family instability” add further explanatory power, but their contribution is relatively smaller. Notwithstanding, all the models are statistically significant (with p-values < 0.05), meaning the relationships between these factors and hawking are valid and reliable.

Keywords: Unemployment, Hawking Actors, Economic, Predictors

INTRODUCTION

Every human being needs certain level of development and comfort in life. And the desires or expectations of every parent is to be able to provide basic needs of life to their households including food, shelter, health and protection. There is no parent that wants to depend on his/her fellow for the survival of the family. Healthy and able parents and adults avoid dependency, hence, devised survival strategies that will enhance their life sustenance, self-esteem and freedom depending on the level of education, exposure, available resources and economic empowerment. The expectations to achieve life-sustenance, shelter and protection, poverty, underemployment, low literacy, etc. serve as push factors among the adults especially the parents to drifting their adults and school-aged children in the household into street hawking (vending). This is because anytime any of these basic needs is absent or in critical short supply, there would arise a problem for such households. In other hand, the pull factors into hawking are creation of wealth, earning income to put food on the household table, volume of sales, no restriction to hawking actors, etc.

Hawking actors refer to individuals involved in street vending or informal sector activities.

According to rational expectations hypothesis (REH) by John F. Muth (1961) defines expectations to be rational if they are essentially the same as the predictions of the relevant economic theory. The key idea of rational expectation is that individuals make decisions based on all available information including their own expectations about future events. This implies that individuals are rational and use all available information to make decision. The hawking actors through their intuitiveness and prior information knowledge at their disposal perceived that hawking will enable them to generate income, so they put in every effort to achieve their expectations. With full knowledge of their daily sales and perceived demand through hawking, some mothers together with their school aged children engaged in hawking in anticipation of getting more income from the business. Information about the patronage, nature of products demanded, clientele volume and time bound also serve as pull factors into hawking with view of meeting their expectations. Directly or indirectly, hawking has become one of the economic coping strategies to providing relief from helplessness, misery, agony and pain which high level of unemployment and underemployment, low income and poverty have caused. Hawking as perceived by the mothers most especially, enable the family to provide temporary solution from lack of food, shelter, health and protection. Hawking has not just started yesteryears but many decades ago when people engaged in street hawking or trading by poverty-stricken families. The habit of hawking is age long tradition which is predominant in sub-Sahara Africa. The hawking actors (adult males, females, adolescents, and school-aged children) believed that through hawking they would be able to garner additional income to support their petty trade and sustainability of themselves and the family. Therefore, such adults especially parents in which mother play crucial roles do not see anything wrong in hawking their goods or agricultural produce. According to W. Arthur Lewis (2003) the pioneer of development Economics opined in his article titled: “Development with unlimited supplies of labour (1954)”, theorized that economic growth required a capitalist sector able to internalize capital accumulation by ploughing back profit to expand employment. He further stressed that advantage of economic growth is not that wealth increases happiness, but that it increases the range of human choices. This implies that wealth can enable man to gain greater control over nature and physical environment that can lead to production of food, clothing and shelter, than if he remains poor. The hawking actors or vendors believe that moving from one place to another with their wares increases sales volume and additional income which indirectly affects their freedom from dependency. Children from poor families or those that their parents had passed on are drifted into the trade anytime the parents or guardians consider appropriate for hawking. Little did such parents/guardians know that they are indirectly abusing or infringing on the right of the children. The study assessed the root causes – and effects of hawking and personal growth with view to recommend actionable outcomes for policy makers in the LGAs and the State.

Research Objective

The primary objective of this study is to gain insights about the predictors, determinants, and effects of hawking among the actors, with a focus on socioeconomic, regulatory, and demographic factors. By examining the relationships between these factors and hawking outcomes, the study provided insights into the impacts on hawking actors and LGAs policymakers, ultimately informing strategies for supporting hawking livelihoods and promoting inclusive LGAs development.

LITERATURE REVIEW

Poverty as one of the determinants of hawking by actors. Poverty is characterized as lack of purchasing power, rural predominance, exposure to environmental risk, insufficient access to social and economic services, lack of political right and few opportunities for formal-sector income generation (Kempe 2005: 22). Poverty is one of the predictors and determinants of hawking activities in the Local Government Areas. The two LGAs inhabitant’s occupation is agrarian but as result of land degradation due to oil exploration and oil spills, land is no more yielding its full capacity. As result of this, people drift into different types of jobs, and one of such jobs is hawking. Hawking is considered as a part of the informal sector in the economy where the less privileged can engage herself / himself in business that could provide for their temporal needs. Although, we could not put all the blame only on land degradation, there are some other reasons why most of the basic needs are not met. Part of the reasons for engaging in hawking include large family size, poor parenthood, poor home condition, low income, and unemployment that serve as catalysts into street hawking.

Okafor (2011) attributed street hawking to low and inadequate income to cater for the children, hence, he claimed that the practice of child labour in form of hawking cut across the north, east, south and west zones of the country.

Basu and Tzannatos (2003:148) in their study ex-rayed the political economy model and came up with the findings that income inequality gives rise to the incidence of children involving in the labour force. Davies (2010: 32) investigation shows that informal economies have grown in developing countries, and that many households supplement their incomes formerly earned in the formal sector with income earned informally.

Bonnet (1993: 375) for example asserted that the poor economic and social environments in which families live make them send their children to hawk. According to the researcher, the poorer a family is, the more vulnerable it is to events, be it natural, social, war, death of breadwinner or during drought. Bonnet points out that economic environments offer neither stability nor the flexibility needed to overcome difficulties. Cleaver (2005) in his assertion linked poverty to social capital and gave explanation on how social capital leads to a better understanding of the extent to which social relationships enable or constrain the livelihood of the poor and what kind of opportunity such people have within the existing social structure. He further reiterated that, a social capital approach also gives a better understanding of processes of institutional inclusion and exclusion, and limits to the emancipatory possibilities of development interventions that aim at promoting public participation (2005: 895). This means, any poverty-stricken family that lack coping strategies drifts or compels the adults including children to engage in menial jobs. In contrast to Cleaver views, Admassie et al (2002: 251) claimed that poverty is not the only push factors for parents sending their children to work in less developed countries like Nigeria. He claimed that there are other factors like, a poorly developed agricultural sector, high fertility rate that leads to population growth and low literacy rates that influence hawking.  He pointed out that a comprehensive and multifaceted intervention like poverty reduction strategies, provision of mass primary education and mobilization of community awareness can solve the problem of working children. Brass (2004) put up a defense of parents that drifted their children to hawking, and attributed this act to structural constraints challenges being faced by many countries in sub-Saharan such as poverty, debt, corruption, war, ethnic conflict, diseases, international competition and ineffective legislation which make the issue of child work and labour inevitable (Bass, 2004: 43). According to the investigator, the political and economic factors provide diminishing educational opportunities and expand work responsibilities for children.

RESEARCH METHODOLOGY

Brief description of study area

Delta State is one of the States in Southwest of Nigeria, located in the core of the Niger Delta region, created in 1991 and was carved out from Bendel State in the Federation. Asaba is the State capital, while Warri is the economic hub of the state. The State Population is 5,636,100, and covers projection Area of 16,986 km² with population density of 331.8/km². The State has 25 local government areas (LGAs). Delta is one of the states that sustain Nigeria’s annual budget by contributing largely to oil exports. One of the largest onshore blocks OML 30 is located in Delta State with nine oil fields that have been producing for decades. The two Isoko North and South LGAs have a population of 519,000 with sizeable oil wells in the two LGAs. The major occupation of the people is farming and trading. Finance and time constraints are the limiting factors for the restricted coverage area of the research.

Sample Design

A cross-sectional population household-based survey in the Isoko LGAs was conducted. The survey used a sampling frame, the list of enumeration areas (EAs) prepared for the 2006 population Census of the Federal Republic of Nigeria provided by the National Population Commission (NPC). The sample was designed to provide useful information on hawking actors in Isoko LGAs. The sample design allowed for specific indicators to be calculated and learnings. A multi-stage cluster sampling design was used to select clusters from the two pre-defined LGAs.  LGAs form the primary sampling units and communities (clusters) were the secondary sampling units. An 80% power, assuming design effect of 1.5, and 95% confidence interval was adopted. Enumeration areas (EAs) maps of the selected areas were obtained from National Population Commission for identification purposes. The primary sampling unit (PSU), referred to as a cluster in the 2024 study and is defined on the basis of EAs from the 2006 EA Census frame. The cross-sectional household-based sample was selected using a multi-stage cluster design consisting of 50 clusters, that is, 25 clusters in Isoko North LGA and 25clusters in Isoko South LGA. A representative sample of 500 households was selected for the survey, with a target of 250 completed interviews per LGA. Due to insecurity situation and raining season a complete listing of households and a mapping exercise were not carried out for each cluster. Instead, a random route walk (Serpentine Movement) was adopted after identification of the starting point. A systematic sampling method using sampling interval of 1 in 5 was adopted to selecting the households and the potential respondents for the survey after successful interview outcome. The survey targeted audience were in both urban and rural communities respectively. The member of households aged 15 – 65 years were interviewed.  The study was conducted in April and May 2024.

Household is being defined as a group of people living together under one roof, having everything in common, eating from the same pot and recognized one person as the head of the household. The head of the household could be male or female or both depending on the operational definition of household head.

Structured questionnaire was designed first in the English language and key words in the questionnaire translated to Pidgin and Isoko languages for easy understanding and uniformity purpose during the fieldwork. Pre-testing of the questionnaire was conducted in one of the wards in Isoko North, which, was not selected into the sample, and the results were used to refine the final questionnaire.

Training and Fieldwork Management

The research assistants (interviewers) were recruited from the two LGAs of the survey for the ease in data collection. A three-day training which includes: act of conducting interview, establish contact with respondents, adhere to ethical principles, including obtaining informed consent from respondents, protecting their confidentiality and privacy, and minimizing any potential harm or risks associated with participation were the focus of the training. Respect for cultural sensitivities, local customs, and community norms when conducting research in urban and rural areas were not left out during the training. The researcher assistants were taught on how to engage with stakeholders in a collaborative and respectful manner. A four-group team technique was adopted with each team having a supervisor and 5 interviewers each. Each of the research assistants was given well-structured questionnaires, sketch map of the enumeration areas for easy identification, pen, and notepad and identity card. The sketch maps were used to identify the demarcation of the enumeration area, while a sampling gap of 1 in 5 Households after a successful interview was observed to identify the next household for the interview.

Advocacy visit to the community head was done a day or two days before the scheduled interview date.

The data gathering procedure start from identification of the boundary of the selected cluster(s) through random route walk in the chosen community. As soon as identification of an E.A boundary is completed, a decision on the start-point for the study is quickly taken; and this could be the compound beside the Palace of the Community Head or any major landmark for the start of listing. The interviewer enters the compound and introduce himself/herself before the listing of the households in Form A provided, identify the number of households with a hawking actor, use Kish Grid to determine who to interview among the potential respondents in the household. After which the interviewer restarts the introduction, explain the purpose of the survey, and asked for the verbal consent of the respondent in the household and also tell the respondent that the result of the study will not be shared with anyone except his/her supervisor who may want to conduct a back-check later. Once verbal consent is granted, he/she starts asking questions and elicits answer from the respondent; probing to clarify any grey area(s) was observed. After successful interview, the interviewer then observes a sampling gap of 1 in 5 households for the next interview. By this technique, supervision is made easy and tracing interviewers on the field is made possible. Any household without hawking actor was not listed in the sample. The qualified member of the households aged 15 – 65 years were interviewed.

Editing, Coding and Data Entry of Variables

The supervisors and researchers were involved in data checking for errors, missing variables during editing and coding the completed questionnaires from the field. The edited and coded data were inputted into the SPSS version 26, and the data cleaning was run before generation of frequency tables to check further for missing variables and determination of any outliers in the data.

ANALYSIS AND RESULTS

Respondents successfully interviewed were 500 out of 510 potential respondents sampled with response rate of 98.5 percent. We performed descriptive and multiple regression analysis on the effects of hawking actors, personal development and economic growth by identifying relevant variables related to hawking. These variables include demographic profile variables, predictors, determinants, push and pull factors, regulations affecting hawking, economic growth, etc.). We ensured that the collected data is reliable and consistent across the time period. Data were cleaned by checking for missing values, outliers, and inconsistencies. Also, we ensured that all variables are measured in the same units and are on a compatible scale. When necessary, we transformed variables to meet the assumptions of regression analysis, such as normalizing variables with skewed distributions. Model specification was not left out. We Specified the regression models by determining which independent variables to be included and guided by different theories. We used statistical software like SPSS to estimate the coefficients of the regression model; involves running the regression analysis and interpreting the results. We also assessed the statistical significance of the coefficients, their signs, and magnitudes. Checked for both autocorrelation and multicollinearity (correlation between independent variables), heteroscedasticity (unequal variance of residuals), and other assumptions of regression analysis. In terms of model fit, we employed the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) to arrive at valid and reliable model. Finally, the researchers interpreted the coefficients of the independent variables in the context of the research questions.

Demographic Variables

The descriptive analysis using percentages and mean scores analysis was carried out. The respondents surveyed from Isoko LGAs were 500 cuts across the gender cohorts. Gender analysis revealed that 28.8% of males and 71.2% of females were successfully interviewed.

Table 1: Demographical Variables of the Surveyed Respondents in Isoko LGAs

Source – Field Survey, April – May 2024.

Age and Gender of Respondents

About 34.2% of women interviewed were within the age of 45-54 years, followed by 35-44 years (17.6%) women and 65+ years had the lowest of 1.4%; while men in age cohort 45-54 years recorded 7.4%, aged 35-44 had 6.8% and no respondent for age 65+ years. Gender distribution show 24.4% of male and 75.6% females were interviewed.

Education

Almost one-third (32%) of the women had secondary school education, male secondary school leavers (12%), women with primary school (31.2%) and males (6.0%), tertiary education had 8.8% for women and 3.6% for males. Women without formal education recorded 4.0% and 2.8% for male counterparts respectively.

Marital Status

Married women interviewed were 45%, single females were 30.6%, while 17.6% and 6.8% were for married men and single males.

Types of Work

Majority (65.4%) are self-employed, farming (12.4%), employed (11.8%) and crafts recorded the least of 4.0%. In terms of gender, women folks recorded above half (53.2%), farming (9.4%) and 8.2% are employed; while about 5% of men are in business, self-employed (3.6%) and farming (3.0%) of the targeted population that are self-employed. It is noted that most of the employed and self-employed also engage in farming in subsistence level. Those that are into farming deal with crops such as cassava, yam, corn, groundnuts, fish farming and plantain plantation.

Gender Income Level Per Month

From the income distribution among the targeted audience, we observed that majority (33%) women claimed to earn N10,000 – N20000 from hawking per month, almost one-fifth (19.4%) earn income that lies between (20,000 -N40,000) and men (10.4%) claimed their income per month is N10,000 -N20,000, while 6.6% earns income (N20,000 – N40,000) from hawking. A very few women (2.6%) and men (1.2%) claimed they earns income between N40,000 -N60,000) per month.

Access to Credit Facility: Over one-quarter (28.4%) of the respondents claimed to have access to credit facilities, while 71.6% of the people interviewed had no access to credits facilities. Among gender respondents that had access to credits facilities, 22.2% are females and males (6.2%) respectively.

Regulatory Policy: Almost one-third (32.8%) of the respondents affirm the existence of regulatory policy; while majority (67.2%) of the respondents believe there are no regulatory policy guiding hawking in the targeted areas of study. In terms of implementation, only 15% believe that the policy is enforced; while 85% affirms none implementation of regulatory policy.

Perceived Reasons for Hawking: When the respondents were asked about what make people to go into hawking or vending, the responses reveal the perceived factors contributing to the prevalence of hawking activities in the two LGAs are: poverty (88.6%), underemployment (84.2%), low income (82.6%), unemployment (80.2%), expectations (77.8%), food insecurity (75.6%), mother trade (67.4%), household size (63.0%), and broken home (58.4%).

In attempt to determine key factors that actually influenced hawking, a multiple linear regression analysis was conducted and four key models emerged. Findings show that the low income, underemployment, food insecurity and broken homes (family instability) were identified as key variables that have influenced the act of vending, and they are all statistically significant.

Multiple Linear Regression Analysis Model

Hawking actors are predominant in both rural and urban areas in Isoko LGAs. To be able to accurately predict the future occurrence of such explanatory variables’ experiences, the regression equation below was applied.

Push Factors that Influence Hawking

Hawking is a prevalent economic activity in many developing countries, contributing significantly to the informal economy and the livelihoods of individuals. It does not happen overnight without genuine reasons for going into it as coping or survival strategies. The study evaluates these associated factors and found that underemployment, low income, food insecurity and broken homes (family instability) are some of the key associated factors that lead people to hawking as stated in table 4 below. Four key models emanate from the data analysis as some of the reasons why people hawk.

Table 4: Push Factors influence Hawking

Source: Field survey April-May, 2024

Model 1: Underemployment

In this model, underemployment is the sole predictor. The R value (0.510) represents the correlation coefficient between underemployment and hawking. It shows that underemployment explains about 51 percent of the variation in hawking behavior. The p-value of 0.000 indicates that this relationship is highly statistically significant; which means “Underemployment”, by itself, is a strong predictor of hawking.

Model 2: Underemployment + Low Income

In this model, low income is added alongside underemployment. The correlation (R) value increases to 0.561, meaning the combination of underemployment and low income explains about 56.1 percent of the variation in hawking. The p-value of 0.000 remains significant, showing that both underemployment and low income are important predictors. The improvement in R from 0.510 to 0.561 indicates that low income adds substantial explanatory power to the model.

Model 3: Underemployment + Low Income + Food Insecurity

In model 3, food insecurity is added to the predictors in model 2. The R value increases slightly to 0.572, meaning that the combined effect of underemployment, low income, and food insecurity explains about 57.2% of the variation in hawking behavior. The p-value of 0.003 shows that the model is still statistically significant. However, the smaller increase in R (from 0.561 to 0.572) suggests that food insecurity, while significant, does not add as much to the explanatory power of the model as low income did in model 2.

Model 4: Underemployment + Low Income + Food Insecurity + Broken Homes

In the final model, broken home (family instability) is added. The R value rises to 0.581, meaning the combined predictors explain about 58.1 percent of the variation in hawking behavior. The p-value of 0.006 confirms that the model remains statistically significant. While family instability does contribute to explaining hawking, the increase in R (from 0.572 to 0.581) is again modest. This indicates that broken homes, like food insecurity, add only a small additional predictive value when compared to the other stronger predictors (underemployment and low income).

Multinomial Logistic Regression Model

The multinomial logistics regression model analysis reveal the following parameter estimates which are key factors that are significantly associated with hawking: intercept (β=-1.661, p=0.000) underemployment (β=1.029, p<0.05); low-income (β=1.915, p=0.000), food insecurity (β=0.818, p=0.011); and broken homes (β=0.644, p=0.015). The p-values of all the parameter estimates are less than 0.05, this means all the estimates are statistically significant and reliable predictors. Hence, you can use the equation below to predict what will happen to outcome (Y) when there is a change in each of the independent variables.

Table 5: Parameter Estimates

Intercept

The intercept represents the log-odds of engaging in hawking when all other factors (underemployment, low income, food insecurity, and broken homes) are absent or set to their reference category. The study findings reveal a negative intercept (-1.661) suggests that, without these factors, the likelihood of engaging in hawking is low. However, a p-value of 0.000 shows that the intercept is statistically significant, meaning this baseline is crucial in the model.

Underemployed

The positive beta coefficient (1.029) means that being underemployed increases the likelihood of engaging in hawking. The odds ratio (e^{1.029}) is approximately 2.8, indicating that individuals who are underemployed are 2.8 times more likely to engage in hawking compared to those who are fully employed or not underemployed. The p-value of 0.001 suggests this is a statistically significant predictor. Underemployment strongly influences the decision to engage in hawking.

Low Income

The regression analysis reveals a beta coefficient ( of 1.915 which means that individuals with low income are much more likely to engage in hawking. The odds of engaging in hawking increase by approximately (e^{1.915}), which is about 6.8 times higher for those with low income with p-value of 0.000. The p-value of 0.000 indicates this is a highly significant predictor. Low income has a very strong impact on the likelihood of hawking, suggesting that financial hardship is one of the major drivers behind the decision to hawk.

Food Insecurity

With a beta coefficient ( of 0.818, individuals experiencing food insecurity are more likely to engage in hawking. The odds of hawking increase by a factor of (e^{0.818}), or about 2.3 times, for those who are food insecure compared to those who are not. The p-value of 0.011 shows that food insecurity is a statistically significant predictor, although its effect is somewhat smaller than low income or underemployment.

Broken Homes [Family Instability]

The family instability beta coefficient ( of 0.644 suggests that individuals from broken homes are more likely to engage in hawking than the family with stable homes. The odds of engaging in hawking are (e^{0.644}), or about 1.9 times higher for individuals from broken homes compared to those from stable homes. The p-value of 0.015 obtained in family instability shows that this factor is also statistically significant. While not as strong as low income or underemployment, it contributes to the decision to hawk.

The Durbin Watson (2.136) statistic is close to 2, suggesting that there is no significant autocorrelation in the residuals, which is a good sign for the validity of the regression model.

Table 6: Model fitting

The model fitting is adequate with the Akaike information criteria and Bayesian information criteria with values smaller than the intercept only. The -2 Log Likelihood shows value of 55.551 and Likelihood Ratio Test Chi-Square value of 157.363 with significant level of 0.000. The analysis of variance (ANOVA) results also shows that all the associated key factors to hawking are statistically significant, that is, p<0.05.

Unemployment as contributing factors to Hawking

The plot below shows that unemployment is a key factor that always influence the prevalence of hawking in a society. The graph shows upward movement in the study areas, this means, the higher unemployment rate, the higher the possibility of engaging in hawking. Therefore, there is a need for government to intervene to address the unemployment status of the active cohort in order to prevent the tide of increasing crime rate in the areas. Establishment of modular refinery, cottage industries or manufacturing company will definitely provide jobs opportunities to the teem productive youths especially in the Isoko communities.

Figure 1: Unemployment leads to hawking

Table 7: Mean Scores of Perceived Perceptions about Hawking Activities

The mean scores of Likert Scale Measurement (5) with decision rule of 2.5 as average score cut-off point reveals low business growth (2.24) and perception of hawking effects (2.41) had scores less that average cut-off point. The perceived perception of why people engage in hawking reveals “High growth of business” attracted (3.89) as one of the major reasons for hawking, closely follows by moderate growth of business (3.80), making good connection or meeting Godsent person to help recorded (3.08), sources of additional income (2.91), meet some of my needs (2.87), provide food for my family (2.75), and less dependent on people recorded 2.69 which is above threshold. Virtually, all the variables measured scored above average, indicating high positive perceptions about the hawking activities which invariably lead to economic empowerment and personal development.

Perceived Perceptions of Hawking

When the perception about hawking were subjected to multiple linear regression analysis the following results were obtained. From the (table 8) five models were identified as pull factors.

Table 8: Model Summary of Regression Analysis of Pull Factors influencing Hawking

1. Dependent Variable; Perception of hawking effects

The above table offers insights into the impact of different pull factors (reasons) for hawking with the dependent variable as “perception of hawking effect”.

The first model suggests a strong positive correlation (R= 0.715), that is, relationships between being “Less dependent on people” and the perception of hawking effects. The p-value = 0.000 confirms this variable significantly contributes to explaining the variance in the dependent variable.

The second model comprises of “less dependent on people, and provide food for my family” reveals that adding “Provide food for my family” to the model increases the R (correlation) value to 0.791, indicating a stronger relationship. We also observe an increase to 0.625, meaning that 62.5% of the variance in the perception of hawking effect is explained by these two predictors combined. However, “Provide food for my family” explains an additional 11.4 percent of the variance beyond what was explained by the first factor alone with the p-value = 0.000, showing that this additional predictor significantly improves the model.

The third model has “less dependent on people, provides food for my family and meets my needs” means adding “meet my needs” increases the correlation (R) value to 0.823, reflecting a very strong relationship with p-value less than 0.05 is a significant addition to the model.

Fourth Model comprises of “less dependent on people, provide food for my family, meet my needs and source of additional income”. Adding “source of additional income” to the 4^th model increases the relationship value to 0.712; further indicating a strong model with the p-value statistically significant (p=0.000); indicating that the source of additional income predictor is meaningful and improves the model.

The fifth model comprises of “less dependent on people, provides food for my family, meet my needs, source of additional income, and connection and/or God sent helpers can emanate from hawking”. The people perception that through hawking, God can connect someone to his/her destiny helper actually shows that including this additional factor has associated influence on the relationship. The impact of this variable increases the correlation value to 0.853, indicating an even stronger relationship. The p-value = 0.000 confirms that these additional factors significantly contribute to the model.

The Durbin Watson value (1.722) that measure autocorrelation in the model shows a value close to 2 which means there is no autocorrelation in the analyzed data.

Likelihood Ratio Tests Analysis

The Likelihood Ratio Test (LRT) along with the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values help in comparing models to determine which predictor variables contribute significantly to the model and should be retained. In the light of this, the researchers have noted the following performance of each of the predictor indicators.

Intercept: The intercept-only model (which includes no predictors) has the lowest AIC value, but its BIC is relatively high compare to the “Food Insecurity” and “Broken Home” models. This could indicate that, while adding predictors increases the complexity of the model (reflected in higher BIC values), certain predictors like “Food Insecurity” and “Broken Home” still provide a better balance between model fit and complexity than the intercept-only model.

Unemployment Indicator: The AIC and BIC values for the model including the “unemployment” predictor suggest that this variable contributes to the model, but whether it is better or worse than other models depend on comparing these values with those of the full model or other models.

Low-Income Indicator: The relatively high AIC and BIC values for the “Low-Income” predictor indicate that including this variable might not improve the model as much as the other predictors. Higher AIC and BIC values generally suggest that the model is less optimal.

Table 9: Likelihood Ratio Tests

Food Insecurity Indicator: The AIC and BIC values for “Food Insecurity” are lower compared to those of “Low-Income”, indicating that this variable might be a better predictor for the outcome (hawking) in the context of the model. Generally, lower AIC and BIC values suggest a more parsimonious model with better fit.

Broken Home Indicator: Similar to “Food Insecurity”, the “Broken home” predictor has relatively low AIC and BIC values. This suggests that it is a good fit for the model and is likely, a significant factor leading to hawking. The low AIC and BIC values of this indicator indicate a better model.

Comparative Analysis: The intercept-only model has the lowest AIC, but since it only includes a constant, it lacks explanatory power. Adding predictors like “Food Insecurity” and “Broken Home” increases the AIC and BIC slightly, but these variables still offer a good balance between fit and complexity, as indicated by their relatively low AIC and BIC values. Predictors such as “Food Insecurity” and “Broken Home” seem to be the strongest predictors in the model, given their lower AIC and BIC values. “Low-income” has the highest AIC and BIC, suggesting it may not be as valuable in the model or may add unnecessary complexity to the model and analysis.

Model Selection: We believe it is important to balance between model fit (lower AIC) and model complexity (lower BIC), so that a model with lower AIC and BIC values that includes meaningful predictors (like “Food Insecurity” and “Broken Home”) would generally be preferred and recommended.

Effects of Hawking

The perceived effects (positive and negative) documented in this study include poverty alleviation (79.2%), ability to contribute to household income (58.4%), livelihood diversification (20.2%), early exposure to money (11.6%) and generate more income (9.4%). While the negative effects of hawking range from child low academic performance (26.2%), fatigue (22.8%), drop-out of school (18.6%), lack of control (18%), unwanted pregnancy (23.8%), alcoholism (8.0%), and early marriage (6.4%) respectively.

DISCUSSIONS

Majority of the vendors (75.6%) are women and girl-child and almost one-quarter are male vendors, boy-child inclusive. In terms of educational level, we observed majority of the women (63.2%) had primary and secondary school education, and only 8.8% of women had tertiary education. Male category records 15.6% primary and secondary school leavers, while without formal education for males was 2.8% and females 4% respectively. The vendors with tertiary education claimed they had no job, hence, opted for hawking to make money to provide basic needs of life for themselves. This confirms the opinion of W. Arthur Lewis (2003) that wealth can enable man to gain greater control over nature and physical environment that can lead to provision of food, clothing and shelter, than if he remains poor. The hawking actors or vendors believe that through hawking they would be able to make money to cater for the basic needs and be less dependent.

In terms of marital status, about 45 % of women vendors are married, 30.6% are single; while married men vendors are 17.6% and 6.8% are single. If hawking time period is not well managed, it can lead to quarrel at home if the husband is not understanding time. This is because it may affect the married women duties at home especially time to prepare evening meal for the family. Majority (65.4%) are self-employed, farming (12.4%), employed (11.8%) and crafts recorded the least of 4.0%. It is noted that most of the employed and self-employed also engage in farming in subsistence level. Those into farming cultivate crops such as cassava, yam, corn, groundnut, fish farming, vegetables, potatoes, cucumber, melon, and plantation. In terms of income generation, females exhibit aggressive drive in hawking and this drive has paved way for higher income as reveal in the above table. About 33% of women earns income between N10,000 and N20,000 per month from the sales, 19.4% of women earns between N20,000-N40,000 and while 10.4% of men earns income between N10,000 -N20,000 and 1.2% of men folk claimed earnings between N40,000 and N60,000. It means, women are more aggressive in hawking and probably more serious in the business than male folks, hence, earn more money from the informal business as result of energy, time and right goods they vend. This strategy could be the reason behind high sales volume enjoy.

When we examined the possibility of access to credit facilities, over one-quarter (28.4%) of the respondents claimed to have access to credit facilities, while 71.6% of the people interviewed had no access to credits facilities.

Among gender respondents that had access to credits facilities, 22.2% are females and males (6.2%) respectively. The inability of majority especially women not having access to credit facilities is attributable to lack of collateral as requisite for collecting credits from the banks. In terms of regulatory policy, one-third (32.8%) of the respondents affirm the existence of regulatory policy; while majority (67.2%) of the respondents believe in non-existence of regulatory policy guiding hawking in the targeted areas of study. Notwithstanding, low awareness level of hawking bye-laws does not mean that government do not have regulatory apparatus to checkmate hawking practices in the LGAs. According to some government officials interviewed said the challenges for not enforcing hawking bye-laws is attributable to lack of industries or companies in the two LGAs that can create job opportunities for the inhabitants. Hence, the inhabitants are encouraged to be pro-active and engage in the business that can enhance their financial independency and personal growth. In terms of implementation, only 15% believe that the policy is enforced; while 85% affirms non implementation of regulatory policy.

The researchers also observed interplay of the push factors across the four models, underemployment and low income emerge as the most important push factors for hawking, with the largest increase in the correlation (R) value occurring when low income is added in Model 2. Food insecurity and broken homes add further explanatory power, but their contribution is relatively smaller. All models are statistically significant (with p-values < 0.05), meaning the relationships between the push factors and hawking are reliable.

The Significance of Push Factors: All the models indicate that each push factor (low income, underemployment, food insecurity, and broken home) significantly influenced the likelihood of engaging in hawking activities, with each additional factor slightly increasing the model’s explanatory power. Cross examination shows that “Underemployment alone” explains a substantial portion of the variation in hawking (51%); Low income significantly improves the model’s explanatory power; “Food insecurity” and “broken homes” add minor but meaningful contributions to the overall explanation of hawking behavior. The reliability of the push and pull associated factors that lead to hawking was further attest to by the Durbin Watson (2.136) statistic that is close to 2, suggesting that there is no significant autocorrelation in the residuals, which is a good sign for the validity of the regression model.

Model Fit Considerations: While “Low-income” is statistically significant, it drastically increases the -2 Log Likelihood value, which suggests that it might make the model more complex without sufficiently improving the fit. In contrast, “Food Insecurity” and “Broken Homes” have lower -2 Log Likelihood values, indicating better fit and efficiency. The practical implications reveal “Food Insecurity” and “Broken Homes” to be the most efficient predictors to include in the model due to their significant impact on the model’s fit with relatively small increases in -2 Log Likelihood. “Unemployment” is also a significant predictor, though it adds more complexity to the model. “Low-income” contributes significantly but may require careful consideration due to its large impacts on model complexity.

From the analysis of the five models that assess the pull factors to hawking, the analysis reveals that each added factor increases the model’s explanatory power. The significance of the F Change for each step shows that each additional variable significantly improves the model, though the incremental improvement decreases as more factors are included. The final model, with all included factors, explains a substantial 72.7% of the variance in the perception about hawking effect, indicating a robust model. This analysis helps the investigators and data users to understand the relative importance of different pull factors in shaping the perception of vendors and conclude that through hawking, someone can improve his/her personal status and obtain reliefs from some of the socio-economic challenges in the households or the family. The analysis of variance (ANOVA) results also supports the findings of all the five models’ regression analysis. Hence, a conclusion can be drawn that all the five models’ variables contribute to the improvement of the models and that all the variables are statistically significant as shown above. This means that the perceived perception of pull factors has strong abilities in convincing and attracting persons with low socio-economic status into engaging in hawking as means of livelihood or strategies for survival.

This analysis helps to understand the relative importance of different pull factors in shaping the perception of hawking activities. The -2 Log Likelihood values, Chi-Square statistics, degree of freedom (df), and significance levels provide insights into how well each predictor variable improves the model compared to a baseline (usually the intercept-only model). From this study findings, government should come to the aid of the community members by engaging in mechanized farming and other interventions that could make agricultural produce available at all time to reduce the effects of food insecurity in the areas. Religious leaders should also take it upon themselves to base their sermon on peaceful coexistence and importance of husband and wife living together without separation. This is because if home is peaceful and in harmony, separation or divorce issue will not arise and some women that are forced into hawking to make end meet may not happen.

Personal and Economic Development: Generally, hawking activities can be referred to as an integral part of the society informal economy, contributing significantly to employment, income generation, economic growth and personal development. Low-income earners resort to hawking as means to generating additional income for the upkeep of the entire household. This finding of this survey confirms the results of Okafor 2010, Kempe (2005), Bonnet (1993) and Admassie et al (2002: 251). Findings show that through hawking respondents are able to put food on their table without relying on either relatives or friends for the supply of food (less dependent on people). Full knowledge of the roles of hawking in society economies either in urban or rural can help policymakers recognize its economic contributions and develop strategies to harness its potential for sustainable urban development.

Livelihood Opportunities: For many rural and urban dwellers, hawking represents a crucial source of livelihood, especially for those with limited educational background or lack of formal job opportunities. We observed that many adults engage in vending in high traffic areas or roads especially during hold-ups in urban centers. And this singular act has enabled the actors to face life challenges squarely and be able to provide for their basic needs and also save for the raining day. This is in conformity with the pull factors mentioned in this study and Okafor (2010 assertion. The study also reveals that majority of the vendors are unemployed, while those employed claimed insufficient income led them into hawking or farming to supplement the family sustenance. If the targeted audience are low literate, lack adequate skills, and inability to engage in lifelong learning, can make it difficult for such person to get a job except menial jobs. In this situation, personal development will be very slow until the individual makes frantic efforts to break the yoke of low self-esteem. Some of the respondents believe that to improve on their self-esteem, psychological resilience, and stress management skill, they need to engage in informal business such as hawking. And through hawking and interaction, most of the productive labour force are able to join thrifts and cooperative organizations. And being a member of cooperative society, relationships are developed, networking opportunities and social support for the members are made available which put together enhance personal development.

Cultural Norms and Expectations: The culture of the Isoko Communities does not forbid hawking. The expectation is that parents should be able to provide for the family. Hence, everyone strives to meet up their obligations at all cost even if it means going into hawking to address the challenges of unemployment. The social acceptance of hawking in the community paves way for whosoever that want to go into hawking as petty business, hence, both adults and children that have dropped out-of-school engage in hawking. We noted that adolescent girls mostly go into selling food stuff ingredients in wheel barrows, while some become POS agents overnight due to the profit from it.

Government Policies and Interventions: The government policies encourage informal business activities including hawking in the two LGAs of the study areas. The only aspect frown at is blocking the road with their wheel barrow wares causing difficulties in vehicular movement. Once obstruction of movement is observed by government functionaries in particular in high traffic areas or market, the enforcement of bye-law will take place.

Peer Influence: Peer groups influence was observed also as one of the reasons for going into hawking especially among adolescent girls who has considered schooling as scam and also need freedom from parents or guardians. The peer group adolescents are majorly into the sales of food stuff both perishable and unperishable products. Encouragement from the parents or relatives enhances many women taking up hawking as viable business.

Gender issues: From the data, majority of the hawking actors are women and female children which means that if jobs are available such women are ever ready to pick up the jobs. In both day and evening markets, women both old and young roll wheel barrow containing their goods with the intention of making ends meet. The researchers believe that if leaders could come up with interventions such as knitting, poultry keeping, fish farming, fashion design, food processing and other viable business ventures, the women will easily embrace them. A soft loan or grant without stringent collateral to assist these vulnerable women and children to set up manageable ventures should be encouraged. These types of interventions will reduce or save the women especially the girl-child from sexual harassment, early marriage, rape, kidnaping and unwanted pregnancy. On the part of the boys, when they are actively engaged, the saying that say, “an idle hands are devil’s workshop” will be minimized and the society will not breed hooligans, rapists, armed robbers, or bandits that would cause sleepless night for the society in future. Skills acquisitions empowerment programs for adults, adolescents and out-of-school children should be introduced and implemented with minimal cost so that the participants will be able to access the program.

Monitoring and evaluation of established programs should be regularly conducted to know the interventions that are working and the ones not working and why. The monitoring activities should be able to provide answers to why some interventions are working and keep on making them to work; while the managers implementing the interventions may want to know the associated factors inhibiting the interventions that are not working, learn from them and strategize to make them work. If the programs are well-implemented, monitored and government learns from the findings/results, the money spent on such interventions will yield dividends and people will gain from them and sustainability would be guaranteed, hence, promoting accountability among the implementing managers.

Community Resilience and Social Capital: Hawking communities often exhibit resilience and solidarity in the face of economic, social, and environmental challenges. Through social capital and coping strategy that enhance well-being in the areas, part of the income generated from the hawking is used for thrift that serves as form of saving which in later future plough back into the business to build the business capital. This ventures and networking enhance personal development and economic growth in the society.

Research Contribution: This study adds to the body of knowledge on the informal economy; and its role in personal and economic development, providing a basis for in-depth future research in this area. By addressing these push and pull factors, the study offers a comprehensive analysis of key predictors and determinants of hawking and its role in personal and economic development contributing valuable insights for stakeholders at various levels. The application of statistical methods approaches to analysis of the predictors and determinants of vending gives in-depth insights to the key variables that influence hawking, which stakeholders can work on and come up with implementable interventions within the informal sectors. By so doing, poverty alleviation programs would be achieved with ease.

SUMMARY AND CONCLUSION

In all indications, the results reveal that multiple socioeconomic and personal factors significantly increase the likelihood of engaging in hawking. The most influential predictors are “low income” and “underemployment”, both of which dramatically increase the odds of individuals resulting to hawking. Notwithstanding, “Food Insecurity” and “Broken Homes” also significantly contribute, though to a slightly lesser extent. All the factors presented have p-values below 0.05, indicating that they are statistically significant and reliable predictors. This suggests that tackling issues like underemployment, low income, food insecurity, and family instability may be essential to reducing the prevalence of hawking and its associated effects on personal development. Government should focus their attention more on low income and underemployment variables which are key to hawking and intervene in these areas. If the issue of low income and underemployment are resolved food insecurity will invariably be solved and rancour, and fighting between husband and wife that could lead to separation and finally divorce will be minimized and peace and harmony will reign at home.

The study provides in-depth insights to predictors and determinants of hawking, both the push and pull factors were outlined. The positive effects on hawking can be improved upon by the hawking actors, family, and communities; while the adverse effects of hawking should be looked into by the LGAs government authorities. The issues relating to girl-child and boy-child should not be swept under the carpet but appropriate action is required to keep children out of hawking during week days when they are expected to be in the school learning. Free education at least up to Junior Secondary School is one option government could consider among other available interventions.

The findings from this study can serve as reference material for any region or country who needs information about hawking activities. The information could be used for proper planning and management of informal sectors and develop strategies to improve on the findings in their country. A study of the nuanced impacts of hawking on students’ lives and academic trajectories can be another suggested area for evaluation in near future. Conducting studies on hawking activities in urban areas is also significant for understanding the socio-economic dynamics of cities, informing urban policies and planning initiatives, promoting social inclusion and equity, and enhancing the livability and resilience of urban communities.

Recommendation: Government can provide enabling environment in which the citizenry can engage in active ventures that will generate income and add to the personal development and growth of the economy (Gross domestic product-GDP). Findings from hawking activities are signal to the leaders at all level to come up with resounding targeted interventions that are implementable to support hawkers’ livelihoods that would improve economic opportunities for the residence of both urban and/or rural dwellers respectively. Policy makers should be hungry for real-time data such as socio-economic factors influencing hawking participation for informed decision-making. Constituencies projects can emanate from such researches or even the Politicians who know their worths can commission studies to help them find out the actual needs of the people and strive to meet the needs without stress. Separation between husband and wife could be as a result of moral decay which needs to be looked into and community leaders and religious leaders should do something about it to bring sanity to the communities. Impact of low literacy has also influenced street hawking among the participants. Majority of the respondents are women and most of the them only had secondary education. Education can be described as the best legacy a parent can give to his/her child or children since it forms the foundation of what the child would be in future. A student that takes time to study and prepare well for examination is likely to end up with good grade. Most school-aged street hawkers seem not to have enough time to study and prepare for examination, attend classes’ regularly and punctually, or do assignment and read. Fatigue is one of the banes of hawking which negatively influence poor academic performance of the school-aged children involve in hawking. Students’ study habits can also be influenced negatively by lack of parental support, poor economic background, too much household chores, unseriousness, procrastination, distractions (social networking) and poor time management. Government at all level should look at all the issues observed and analyzed in the study and come up with actionable plan to proffer solutions to the push and pull factors that could enhance the livelihoods of the people especially the women folks, and children in the LGAs and/or society at large. This singular action can make serious minded children to pick up their study again and face it without any distractions, believing at the end, good academic performance would be the dividends of pro-active action taken.

ACKNOWLEDGMENT

The authors would like to thank the anonymous reviewers for their valuable comments and the journal for acceptance of this article. Thanks also go to the research assistants who participated in fieldwork and editing. This work was personally funded.

REFERENCES

1. Admassie A. et al (2002:251): Explaining the high incidence of child labor in Sub-Saharan Africa, Africa development review, 14(2): 251-275.
2. Arthur Lewis W. (1954): Developing with unlimited supplies of Labour
3. Basu and Tzainatos (2003:148). The role of street Vendors in the informal sector in Developing countries
4. Bass (2004:43): Hawking and impact on children’s education: A study in Urban Informal sector.
5. Bonnet, M. (1993). Child Labour in Africa’, International Labour Review, 132(2): 371-389.
6. Cleaver, F. (2004). The Inequality of Social Capital and the Reproduction of Chronic Poverty. World Development, 33(6): 893-906.
7. Davies, J. (2010). Selling Wares on the Street of Accra: A Case Study of Street Hawkers in Ghana’s Capital, Focus on Geography, 51(3): 32-36.
8. John F. Muth (1961). The Rational Expectations Hypothesis – An assessment on its real world
9. Kempe Ronald (2005). Child survival, poverty, and labor in Africa; March 2005. 11(1): 19-42
10. Okafor E. E (2011). Youth Unemployment and Implications for Stability of Democracy in Nigeria. Journal of Sustainability Development in Africa 13(1):1520-5509