Factors influencing economic wellbeing by Resettlement in urban market redevelopment: exploring the Kejetia project in Ghana

ABSTRACT

Urban market redevelopment has become the catalyst to re-invent space and alleviate poverty by local governments, yet, it poses economic wellbeing concerns, particularly for the resettled traders. The present study seeks to examine the factors influencing the economic wellbeing of the affected traders, using the Kejetia project in Ghana as a case study. The study responded to two questions: what are the factors, and how do the factors affect each other? A proposed model was developed and hypotheses were initiated. A quantitative methodology was used to elicit and analyse data using SmartPLS 4.0. A purposive sampling technique was used to select 240 respondents from the drivers and sellers’ groups. The measurement model tested reliable and valid. The findings listed deprivation, economic security, income, consumption, and wealth as the reliable factors. These factors were determined by certain economic indicators. The hypotheses were significant and supported. Its findings implied that the factors directly impacted others. The study concludes that resettlement in the Kejetia project triggered complex economic issues for affected traders, particularly drivers. The key implication of the findings is to inform policy that the economic wellbeing of drivers and sellers is better improved if the impeding factors and indicators are well-managed.

KEYWORDS:

• Economic wellbeing
• Kejetia project-Ghana
• PLS-SEM
• Resettlement
• urban market redevelopment

1. Introduction

Resettlement in Urban Market Redevelopment (RUMR) has become a catalyst for reinventing space and alleviating poverty by local governments, yet poses economic wellbeing challenges (Bonnin & Moore-Cherry, 2023; Endres, 2014; Mensah et al., 2022). Resettlement is the relocation of people to a place other than their former residence and having achieved safe, dignified, and sustainable integration in the location (Vanclay, 2017). However, the study extends this definition to include the return of displaced people after redevelopment. Redevelopment is the physical demolition and reconstruction of an existing building (Manganelli et al., 2020), serving the same initial purpose.

The worst-case examples of economic challenges by Resettlement in Urban Market Redevelopment(RUMR) include Mensah et al. (2022) who concluded that the Kejetia market redevelopment in Ghana caused complex forms and trends in the income inequality of affected traders across the entire project cycle. Also, Endres (2014) claimed that ‘Downgraded by Upgrading in urban market redevelopment in Hanoi, Vietnam’, caused small-scale traders to lose their means of economic survival in the marketplace through dispossessions and displacements. Again, Bonnin and Moore-Cherry (2023) highlighted that traditional markets are under threat globally as urban renewal supporting economic development rather causes reverse outcomes.

For the concept of RUMR to cause constant economic challenges instead of restoring or improving them, demonstrates a lack of policy planning. That is the planning design is inadequate to sustain the economic conditions of affected persons post-development. Economic wellbeing may vary in definition, but the present study adopts the definition as the ability to ‘command over resources’ (OECD, 2013; Osberg & Sharpe, 2010). That is people have the resource capability to meet their economic needs (OECD, 2013; Osberg & Sharpe, 2010).

In Ghana, like other developing countries, urban market redevelopment has been contentious due to the high reliance of the urban poor, women, and the informal economy for income (Peprah et al., 2019). Traders have often raised economic concerns, especially about job and financial securities, as they claim relocation sites are often far-flung and non-market areas (Mensah et al., 2022). Against this backdrop, the traders express their displeasure about the perceived economic risks through street protests and demonstrations (Asante & Helbrecht, 2019; Okoye, 2021).

The Kejetia redevelopment project is a recent case example of Resettlement in Urban Market Redevelopment(RUMR) in Ghana.

Previous studies in the region have significantly contributed to the challenges of RUMR (Adinyira et al., 2020; Asante & Helbrecht, 2020). One of the studies in the region that is relevant to this research is Mensah et al. (2022), which uses the level of income as the determinant factor for the economic wellbeing of traders affected by resettlement in the Kejetia project.

However, given the relevance of urban market redevelopment in the literature, empirical research examining the economic impeding factors is rare. Therefore, the present study seeks to examine the economic impeding factors of traders affected by resettlement in the Kejetia project in Ghana. This objective is executed by developing a framework for identifying all factors. The identification of the economic factors is significant because ‘what we identify affects our judgments and what policy responses are needed’.

The Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to test the measurement and structural functions of the proposed framework. The study preferred PLS-SEM because it enables the estimation of complex models with many factors, indicators, and structural paths with or without imposing distributional assumptions on the data (Joseph et al, 2014). Unlike this study, previous studies on economic wellbeing used separate statistical formulas to compute its performance (OECD, 2013; Osberg & Sharpe, 2010).

2. Literature review

2.1 The concept of resettlement in urban market redevelopment (RUMR)

A market is simply a place where people trade their goods and services for money (Omole et al., 2017). According to Okosun (2017), a market is a place that facilitates trade and enables parties to engage in an exchange of goods and services which promotes production, distribution, and consumption activities and improves the welfare and social life of the people. The existence of markets in any city is expedient because commercial activities are the backbone of several economies (Omole et al., 2017).

Though markets grow anywhere, that of the urban centres has been high due to rapid urbanization and the need to make ends meet. The pressures of urbanization on these urban marketplaces have justified the need for their redevelopment.

Moreover, resettlement has equally become an inevitable and necessary tool for the implementation of urban market redevelopment (Mensah et al., 2022). The policy is a land redevelopment process that often involves the demolition of old building structures and reconstruction of them (Mensah et al., 2022). This has given rise to the concept of Resettlement in Urban Market Redevelopment(RUMR), particularly in developing countries. Economic wellbeing challenges have been a constant outcome of the concept of RUMR (Bonnin & Moore-Cherry, 2023; Endres, 2014; Mensah et al., 2022). The section below discusses the economic challenges of RUMR in detail.

2.2 Economic impacts of market redevelopment

This section reviews the economic happenings of the affected population post-market redevelopment projects. Studies have significantly contributed to the economic impact of market redevelopment in various approaches (Endres, 2014; Kamakia et al., 2018; Mensah et al., 2022).

Kamakia et al. (2018) in examining the economic displacement of urban micro-enterprises in Nairobi, Kenya, indicated that even though the project had adequate income and livelihood restoration components, they were non-prioritized hence causing negative economic outcomes to the dislocated micro-enterprises. They highlighted the negative outcomes to include but not limited to reduced savings & investment, loss of capital assets and equipment, a decline in business partnerships, and unaffordability for food. Again, Endres (2014) examined some of the ruptures and contestations that have emerged in the context of urban restructuring and market redevelopment policies in Hanoi, Vietnam. And further claimed that the process caused small-scale traders to lose their means of economic survival in the marketplace through dispossessions and displacements. Such economic happenings of the traders include low market demand causing their low incomes and consumption capacity (Endres, 2014).

Also, Einiö and Overman (2016) investigated the ‘Displacement Effects of Spatially Targeted Enterprise Initiatives, using the UK LEGI exemplar’. The results indicated unequal job opportunities; i.e. job increases in treated areas close to the treatment area boundary at the cost of significant job losses in untreated localities just across the boundary.

Moreover, Bonnin and Moore-Cherry (2023) claimed that economic opportunities should be regarded as the everyday heritage for the urban redevelopment of marketplaces in Moore Street, Dublin rather than focusing on the physical development of the places. They claimed that traditional markets are under threat globally as urban renewal supporting particular forms of economic development comes center-stage (Bonnin & Moore-Cherry, 2023). The affected traders suffer income insecurity if livelihoods are not prioritized as everyday heritage in market redevelopment.

In Ghana, the concept of RUMR is not different from the other developing countries. The affected traders go through some level of insecurity before and after redevelopment (Asante & Helbrecht, 2020; Mensah et al., 2022). Asante and Helbrecht (2020) analysed the impact of the regeneration of the Kotokuraba market in Ghana, using Politically-induced Displacement (PID) as a theoretical construct. They concluded that PID was very pervasive in the regeneration process, prioritizing the party faithful in the purchase of the redeveloped stores over other traders. This poses high insecurities to the businesses of the other traders. Moreso, Mensah et al. (2022) highlighted the impacts of the Kejetia market redevelopment on the incomes of the affected traders in Ghana. They concluded that it caused complex forms and trends of income inequality for the affected traders further claiming low consumption for family needs, maintaining capital goods, and fulfilling their loan repayments.

According to Robert Lillibridge in 1948, to become a successful ‘social cementing agent’ in the community, and at the same time maintain their economic health, the shopping centres must be well-planned in both the physical and the economic senses. Again, he further alleged that to plan these community shopping centres so that they can become pleasant and profitable gathering places, it is necessary to estimate the degree of their needs, allow for their proper location, and determine their preferred design.

Against this backdrop, it is fair to conclude that the economic conditions for market redevelopment are multidimensional requiring a holistic investigation. Therefore, the present study aimed to examine the factors influencing the economic conditions of traders affected by Resettlement in Urban Market Redevelopment (RMUR). This is aided by developing a framework for the economic wellbeing of the traders. This contributes to the knowledge gap in the literature and the research problem of the study, i.e. economic wellbeing challenges.

3. Developing economic wellbeing model for RUMR

3.1 The proposed model

The challenge of economic well-being is one of the most complex but still not a well-studied area especially in development-induced resettlement projects (Mensah et al., 2022). The present study argues that identifying all the indicators influencing economic wellbeing improves the planning design for urban redevelopment projects. The study responds by developing a framework for such a purpose.

In the search for a more relevant and sustainable framework for economic wellbeing, two indices were adopted by the study among many others including the Index for Economic Wellbeing (IEWB) and Income, Consumption, and Wealth (ICW) framework. First, the Index for Economic Wellbeing (IEWB) was developed by Lars Osberg from Dalhousie University and Andrew Sharpe of the Centre for the Study of Living Standards in Canada (Osberg & Sharpe, 2005). The main factors of the IEWB included the level of consumption flows, stock of wealth, inequality in the distribution of individual incomes, and insecurity in the anticipation of future incomes (Osberg & Sharpe, 2010). For these authors, Consumption flows are the personal expenditure on goods and services that may also be affected by the changes in family size; Stock of wealth is a large amount of money in accumulation or valuable possessions; Inequality levels are the lack of equality; and Economic insecurity is the treat of achieving material possessions. The second adopted index is the Income, Consumption, and Wealth (ICW) framework developed by the Organization for Economic Cooperation and Development (OECD), primarily for household economic wellbeing.

Generally, an integrated analysis of factors influencing economic wellbeing is critical but it demands a single formula for significant data requirements (OECD, 2013). Currently, there is no internationally recognized framework to underpin such works even though the ICW framework by OECD is an essential effort contributing to the practicalities of collecting and presenting the required data.

Though the two adopted indices are relevant to the present study, their factors are not enough to reflect the economic wellbeing of Urban Market Redevelopment and Resettlement. That is, normally the factors and indicators determining the economic wellbeing of any place depend on the stakeholders involved (Negovan, 2010). For instance, wellbeing factors for hospitals will be different from that of schools.

In response, the study makes an extension to the adopted indices in terms of their factors. The level of deprivation will be the additional factor to those adopted to determine the economic wellbeing of people. Literature supports deprivation as an index for economic wellbeing measurement (Aassve et al., 2006). However, the indicators determining the factors are identified based on literature and responses from the pilot study conducted (see Table 1).The proposed framework uses the subjective approach to collect data on the factors instead of the usual statistical approach for most economic wellbeing indices. Studies like Stiglitz et al. (2009) reaffirm the need for a subjective approach to economic performance by arguing that if the statistical approach is flawed, decisions may be distorted. Table 1 below demonstrates the factors and the indicators for the proposed model (Figure 1).

Table 1. Study constructs and items (indicators) source: field survey 2022.

Code	Items
LDEP	Level of Deprivation (LDEP)
LDEP1	Access to Compensation & Assistance
LDEP2	Information Access & Accuracy
LDEP3	Access to Meaningful Participation
LDEP4	Access to Relocation Options
ECOS	Economic Security (ECOS)
ECOS1	Job Security
ECOS2	Financial Security
ECOS3	Representation Security
ECOS4	Allocation Assurance
INCO	Income (INCO)
INCO1	Earned Income
INCO2	Credit Facility
INCO3	Place Equity
INCO4	Market Demand
LWET	Level of Wealth (LWET)
LWET1	Accumulation of past incomes
LWET2	Secured Customer Base
LWET3	Liability/Debt
LWET4	Material Capital
COSU	Consumption (COSU)
COSU1	Affordability of Regrettable Expenditure
COSU2	Affordability of Consumer Goods
COSU3	Affordability of Capital Goods
COSU4	Family Expenditure

Figure 1. Proposed economic wellbeing model. Source: authors’ design.

3.2 Developing hypothesis

After careful observation of the proposed model (Figure 1) and in the quest to examine the relationship between factors influencing economic wellbeing, these initial hypotheses are developed.

H1a

Level of Deprivation → Economic Security

H1b

level of Deprivation → Income Inequality

H1c

Level of Deprivation → Consumption

H2a

Economic Security → Level of Income

H2b

Economic Security → Level of Consumption

H2c

Economic Security → Level of Wealth

H3a

Level of Income → Level of Wealth

H3b

Level of Income → Level of Consumption

H4a

Level of Wealth → Level of Consumption

4. Methodology

4.1 Study area

The present study was conducted at the Kejetia Market, which is in the heart of Kumasi Metropolis (Figure 2), an administrative city for the Ashanti region in Ghana. The Kumasi metropolis is popularly known for its trading activities, having several satellite markets, and experiencing many cases of redevelopment-induced resettlement (Mensah et al., 2022). The Kejetia market redevelopment is a recent case example and the largest single market in Ghana with more than 8400 stores and stalls (Mensah et al., 2022). Due to urbanization pressures and the need to improve revenue generation, the kejetia market redevelopment was deemed necessary (Mensah et al., 2022). The market redevelopment is in three phases but the first phase is completed, and upon which the study is conducted. The first phase displaced both sellers and drivers temporarily in 2016, through 2019 at the relocation sites and returned them after the redevelopment in late 2019. The various relocation sites physically surrounded the Kejetia Redevelopment Market by a few distances and they include the Racecourse, Batama market, Adehy3 market, and Abinkyi (Afia Kobi) (Figure 2). Before the redevelopment, the kejetia market area was made up of 21 local government-approved shops, 13 Bot shops, 76 container shops, and several table stalls (Field Engineer, 2022).

Figure 2. A location map of the case study area in Kumasi, Ghana. In the second and small map, the purple color represents the Kumasi metropolis and blue indicates the Kejetia New market area. Source: Mensah et al., 2022.

4.2 Research approach

A quantitative case study method is appropriate for this study because researchers who collect accurate data have more reliable results (Creswell & Plano Clark, 2018). That is, the quantitative method provides fast, focused, scientific, and relatable results (Creswell & Plano Clark, 2018. The case study method is suitable for research seeking to answer ‘how’ and ‘what’ questions (Robert, 2003), which are critical for providing answers to the problem. For instance, to respond to the objective of the study, the below research questions were answered;

• What are the factors influencing economic wellbeing?

• How do the factors influence each other?

4.3 Target population

The study targeted the market traders who were victims of the resettlement in the Kejetia redevelopment project. The study targeted the sellers’ and drivers’ groups as the two main types of trading groups in the Kejetia market. The sellers consist of those who sell in shops, stalls, and small tables. Generally, the targeted population was the traders who were relocated temporarily and returned after the redevelopment of the Kejetia project.

4.4 Sampling method

A purposive sampling technique was used to identify the sample population because there was a need to get the respondents who were affected and have returned from the various relocation sites to the completed marketplace.

A sample size of 240 (160 drivers & 80 sellers) was purposely selected from a total population of 2300 (1700 drivers and 600 sellers) because it meets the 10 percent quota of the total population considered a good maximum (Andrade, 2019). Such sample size also meets the ’10 times rule’ method; a widely used minimum sample size estimation method in PLS-SEM which builds on the assumption that the sample size should be greater than 10 times the maximum number of inner or outer model links pointing at any latent factor in the model (Sarstedt et al., 2022).

4.5 Data collection method

A quantitative research approach was used to collect and analyse field data using structured questionnaire surveys. The data collection was done in two main ways; the pilot study (preliminary) and the main study survey.

The pilot study was conducted in July 2022 to identify the indicators that influence the already highlighted factors of the proposed economic wellbeing framework (see Table 1). About 15 respondents were selected including 5 monitoring & evaluation team officers from the Department of Development Planning in the Kumasi Metropolitan Assembly, one Field engineer, two trading association heads, and seven affected traders.

The main study survey was conducted from September to November 2022, to know traders’ level of command over resources post-resettlement project. Structured questionnaires were used because it is easy to administer, and simple to code, and the data are relatively easy to analyse and compare (Creswell & Plano Clark, 2018).

During the survey, multiple-item, five-point Likert scales were adopted for all indicators (items), with value 1 denoting ‘much worse’, value 2 indicating worse, value 3 demonstrating neutral, value 4 denoting better, and value 5 indicating ‘much better’, determining the command over resources. The questionnaire instrument was checked and questions that were unclear to the respondents were rewritten to ensure clarity and understanding of respondents.

4.6 Data analysis method- PLS-SEM

Partial Least Squares Structural Equation Modelling (PLS-SEM) is a part of multivariate statistical techniques employed to measure the overall fit of a model and to test the structural model together (Hoyle, 2012). That is, the measurement model (i.e. the outer model) specifies the relationships between a latent factor and its manifest indicators (items), whereas the structural model (i.e. the inner model) specifies the relationships between unobserved or latent factors (Hair et al., 2011). The measurement model can be established by examining its internal consistency, indicator reliability, convergent validity, and discriminant validity (Joseph et al., 2014). The structural model can only be analysed after the measurement model has been validated successfully. In PLS, a structural model can be evaluated using the coefficient of determination (R²) and path coefficients (Hair et al., 2011). The particular type of PLS-SEM used by the study to perform these functions is called SMARTPLS 4.0.

4.7 Demographics of the respondents

After cautious data screening, examining sample errors, and missing data, there was a pre-analysis of the demographics of the respondents before the main analysis of the proposed model. Based on Table 2, the affected traders look more youthful and are dominated by the male population constituting 71.4% out of 100%. As about 34.9% of the affected traders were married, a majority of 42.3% were single. Most of the traders did not have any schooling and they constituted exactly 40% of the total traders. Again, there were more drivers than sellers of the traders’ population in the market and the majority of those groups were in the annual income group of $5500 and below.

Table 2. Descriptive profile of respondents. Source: authors’ design.

Variable	Measure	Frequency	Percent
		N = 350	%
Age	Youth (15–24)	150	42.9
	Adults (25–64)	130	37.1
	Older Adults (65 and above)	70	20
Gender	Male	250	71.4
	female	100	28.6
Marital status	Married	122	34.9
	Divorce	39	11.1
	Single	148	42.3
	Widow	41	11.7
Education background	No education	140	40
	Primary school	80	22.9
	Junior high school	50	14.3
	Senior-high school	48	13.7
	Tertiary level	32	9.1
Composition of traders	Sellers	124	35.5
	Drivers	226	64.5
Annual Income Group Currency= $ USA	5500 and below	151	43.1
	5600–9500	74	21.1
	9501–15000	65	18.6
	15001 and above	60	17.1

Generally, Table 2 demonstrates that the resettlement in the Kejetia redevelopment project rendered an alarming economic profile of the traders.

5. Results

5.1 Measurement model assessment

The measurement model especially the reflective measurement is usually assessed through the validity and reliability of factors (Joseph et al., 2014). The validity and reliability of the measurement model are evaluated by comparing the thumb rule to the factors of; (1) internal consistency reliability is the extent to which indicators measuring the same factor are associated with each other (i.e. delivers reliable scores). There is satisfactory internal consistency reliability if the values of composite reliability (CR) and Cronbach Alpha are greater than 0.7; (2) indicator reliability examines the reliability of the items’ loadings and they become satisfactory when their loadings are greater than 0.7; (3) convergent validity shows that a set of indicators represents the same underlying construct. An Average Variance Extracted (AVE) value of at least 0.5 indicates sufficient convergent validity; (4) discriminant validity is the dissimilarity in a measurement of a construct with other different factors. If the Heterotrait-Monotrait Ratio (HTMT) is less than 0.9, then discriminant validity can be regarded as established (Henseler et al., 2015; Joseph et al., 2014). Though other approaches such as the Fornell-Larcker criterion and Cross Loadings can be used to test the discriminant validity between two reflective factors, HTMT is recommended to be the improved approach over the previous two (Ringle et al., 2022).

Table 3 and Table 4 below evidence the outcomes of the various tests for the reliability and validity of the proposed factors and indicators.

Table 3. Construct and indicators reliability and validity. Source: authors design.

Construct	Indicators (items)	Type of measurement.	Indicator Reliability. Item loadings	Internal Consistency Reliability.	Cronbach CR Alpha		Convergent Validity. AVE
			>0.7	>0.7	>0.7		>0.5
LDEP		Reflective		0.911		0.921	0.787
	LDEP1		0.889
	LDEP2		0.912
	LDEP3		0.843
	LDEP4		0.904
ECOS		Reflective		0.898		0.913`	0.770
	ECOS1		0.948
	ECOS2		0.856
	ECOS3		0.940
	ECOS4		0.753
INCO		Reflective		0.855		0.860	0.696
	INCO1		0.852
	INCO2		0.846
	INCO3		0.863
	INCO4		0.771
LWET		Reflective		0.980		0.981	0.943
	LWET1		0.988
	LWET2		0.970
	LWET3		0.988
	LWET4		0.937
COSU		Reflective		0.936		0.940	0.955
	COSU1		0.790
	COSU2		0.960
	COSU3		0.946
	COSU4		0.964

Table 4. Heterotrait-monotrait ratio (HTMT). Source: authors’ design (SMART PLS 4.0).

Correlation between Constructs	Heterotrait-Monotrait Ratio (HTMT) <0.90
ECOS <-> COSU	0.761
INCO <-> COSU	0.724
INCO <-> ECOS	0.813
LDEP <-> COSU	0.768
LDEP <-> ECOS	0.870
LDEP <-> INCO	0.832
LWET <-> COSU	0.650
LWET <-> ECOS	0.771
LWET <-> INCO	0.718
LWET <-> LDEP	0.694

First, Table 3 demonstrates that all indicators have loadings exceeding the threshold of 0.7, ranging from a lower bound of 0.753 to an upper bound of 0.988. Thus, all values demonstrate satisfactory indicator reliability to their constructs based on the thumb rule for PLS-SEM. These findings mean that the indicators were major determinants of the factors influencing the economic wellbeing of both trading groups (i.e. sellers and drivers).

Second, in terms of internal consistency reliability for grouped indicators, the values for Cronbach Alpha and Composite Reliability were greater than 0.7: meeting the threshold for satisfactory internal consistency reliability. This means that the indicators determining the factors had similar high effects. Last but not least, Table 3 shows that all of the constructs had AVE values greater than the threshold value of 0.5, denoting sufficient convergent validity. This means that all the constructs possess the same high influence on economic wellbeing. Table 4 below shows the results for discriminant validity.

Table 4 demonstrates that discriminant validity is established as HTMT values ranged from 0.650 to 0.870, meeting the threshold of < 0.90. That is, the correlation of indicators within the same factor is stronger than the correlation with other factors. This means the indicators are major determinants for their factors than other factors influencing the economic wellbeing of sellers and drivers.

5.2 Structural model assessment

The structural model assessment may be examined through the tests on the coefficient of determination (R²) and the size of the path coefficients with the significance level (Hair et al., 2014). First, the R² value indicates the amount of variance in a dependent construct that is explained by the independent construct (Oliver et al., 2010). In other words, it is the proportion of variability in the data that the model can explain. According to the rule of thumb for PLS-SEM, R² is substantial, moderate, and weak, when its values are above 0.75, 0.50–0.75, and below 0.50 (Hair et al., 2014). A larger R² value increases the predictive ability of the structural model (Oliver et al., 2010).

The results in Figure 3 show that all the R² values (0.608, 0.625, 0.617, 0.575) are moderate in predictive ability by the statistical model. This means that 60.8%, 62.5%, 61.7%, and 57.5% of the outcomes in consumption (COSU), economic security (ECOS), Income (INCO), and Level of Wealth (LWET), are explained by the structural model.

Figure 3. The structural model with path coefficient, item loadings, and R square. Source: authors design.

The path coefficients in Figure 3 indicate the direct effects of the independent factors on the dependent factors (Hair et al., 2022). In PLS-SEM, the paths between factors represent the hypothesis (relationship). Therefore, examining the significance of path coefficients using t-statistics and p-values confirms or denies the hypothesis. The p-values and t-statistics are statistically significant when p < 0.05 and t > 1.960, respectively (Hair et al., 2022).

Table 5 shows that the path coefficients and the hypotheses are statistically significant and are supported by the study as the p-values and t-statistics meet the thumb rule.

Table 5. Significance of path coefficient and hypotheses.

Hypotheses	Path (Relationship)	Path coefficients	T statistics	P values	Supported
H1a	LDEP -> ECOS	0.790	32.749	0.000	YES
H1b	LDEP -> INCO	0.483	7.404	0.000	YES
H1c	LDEP -> COSU	0.333	5.642	0.000	YES
H2a	ECOS -> INCO	0.346	5.068	0.000	YES
H2b	ECOS -> COSU	0.232	3.513	0.000	YES
H2c	ECOS -> LWET	0.514	9.054	0.000	YES
H3a	INCO -> LWET	0.294	5.628	0.000	YES
H3b	INCO -> COSU	0.164	2.953	0.003	YES
H4a	LWET -> COSU	0.138	2.492	0.013	YES

The findings on the hypothesis in Table 5 mean that there were significant relationship effects between the factors influencing the economic wellbeing of drivers and sellers. These relationship effects (i.e. hypotheses) are explained as follows: Hypothesis H1_a states that Level of Deprivation (LDEP) had a significant impact on Economic Security (ECOS), H1_b states that Level of Deprivation (LDEP) has a significant impact on Income (INCO), H1_c states that Level of Deprivation (LDEP) has a significant impact on Consumption (COSU), H2_a states that Economic Security (ECOS) has a significant impact on Income (INCO), H2_b states that Economic Security (ECOS) has a significant impact on Consumption (COSU), H2_c states that Economic Security (ECOS) has a significant impact on Level of Wealth (LWET), H3_a states that Income (INCO) has a significant impact on Level of Wealth (LWET), H3_b states that Income (INCO) has a significant impact on Consumption (COSU), and H4_a states that Level of Wealth (LWET) has a significant impact on Consumption (COSU).

Generally, the findings revealed that the Level of Deprivation (LDEP) was a major independent predictor for most factors influencing economic wellbeing.

6. Discussion

6.1 Discussing the findings

This section discussed the findings of the study in response to the research questions of the paper (i.e. What are the factors influencing the economic wellbeing of traders? And, how do the factors relate to each other in economic wellbeing?)

First, the study findings in Table 3 revealed the factors and the determinant indicators influencing economic wellbeing. The findings imply that in the resettlement process, the drivers and sellers’ accessibility to certain indicators influenced the factors impacting their economic wellbeing. That is, first, the Level of Deprivation (LDEP) influencing the economic wellbeing of drivers and sellers in the Kejetia project was determined by LDEP1- access to compensation & assistance, LDEP2- information access & accuracy, LDEP3- access to meaningful participation, and LDEP4- access to relocation options. Deprivation measures have potential influences on economic wellbeing (Aassve et al., 2006).

Also, Economic Security (ECOS) and its indicators that influenced the economic wellbeing of the drivers and sellers affected by the kejetia project included: ECOS1- job security, ECOS2- financial security, ECOS-3 representation security, and ECOS4- allocation assurance. Economic security has become a major measuring component of the economic wellbeing of individual groups (Osberg & Sharpe, 2010).

The other remaining factors influencing the economic wellbeing of the drivers and sellers were the three most recognized components of economic wellbeing; Income, Wealth, and Consumption (OECD, 2013; Osberg & Sharpe, 2010). The Level of Income (INCO) and its determinant indicators included: INCO1- earned income, INCO2- credit facility, INCO3- place equity, and INCO4- market demand. Access to equitable income and credit reduces economic hardships and improves other components like consumption and wealth (OECD, 2013; Osberg & Sharpe, 2010).

The Level of Wealth (LWET) and its indicators influencing economic wellbeing included: LWET1- accumulation of past incomes, LWET2- secured customer based, LWET3- liability/debt, and LWET4- material capital. Wealth has more positive evaluations on economic wellbeing and lack of it predicts greater discomfort (Office for National Statistics, 2015; C.; Robert, 2012).

Last but not least, the Level of Consumption (COSU) and its indicators influencing economic wellbeing included: COSU1- the affordability of regrettable expenditure, COSU2- the affordability of consumer goods, COSU3- the affordability of capital goods, and COSU4- family expenditure. All other things being equal, a person with a higher level of consumption is regarded as having a higher level of economic well-being than someone with a lower level of consumption (Osberg & Sharpe, 2010).

Generally, the findings imply that the resettlement in the Kejetia project triggered many economic concerns for the affected traders, particularly the drivers’ group due to their dominant population in the marketplace. This re-affirms the claim that often resettlement costs are transferred to the affected people instead of project developers absorbing them (Owen et al., 2020).

Second and last, the results on the relationship effects between factors influencing economic wellbeing (see Table 5) demonstrate their direct influence over others. These are the hypotheses that were proven significant and supported by the study and they are explained below. Hypothesis H1a (LDEP -> ECOS- 0.790) implies that traders who had their level of deprivation impacted, also had their economic security substantially impacted. Studies like Archibong and Ph (2020) support this claim with the conclusion that economic deprivation impacts the security of the people.

Hypothesis H1b (LDEP -> INCO- 0.483) implies that traders who had their level of deprivation impacted, also had their incomes impacted. Imedio-Olmedo et al. (2012) also believe that deprivation of resources is the major determinant of income levels for the targeted population.

Hypothesis H1c (LDEP -> COSU- 0.333) implies that traders who had their level of deprivation impacted, also had their consumption capacity impacted. Thomas et al. (2019) reiterated that deprivation influences the consumption of people. Hypothesis H2a (ECOS -> INCO- 0.346) implies that traders who had their economic security impacted, also had their incomes impacted. According to the International Labour Office (2004), changes in economic security have a potential impact on individual incomes. Hypothesis H2b (ECOS -> COSU- 0.232) implies that traders who had their economic security impacted, also had their consumption capacity impacted. Economic security is integrated with the component of consumption (Osberg & Sharpe, 2010). Hypothesis H2c (ECOS -> LWET- 0.514) implies that traders who had their economic security impacted, also had their level of wealth moderately impacted. Williams (2014) reiterated this position by saying economic security such as income & savings are the primary avenue for building wealth.

Hypothesis H3a (INCO -> LWET- 0.294) implies that traders who were impacted by their incomes, had their level of wealth impacted. This assertion is supported by OECD (2013) which claims incomes are the primary resources for wealth building. Hypothesis H3b (INCO -> COSU- 0.164) implies that traders who had their incomes impacted, also had their consumption capacity impacted. There is a consensus that income has a potential effect on consumption (OECD, 2013). Last but not least, hypothesis H4a (LWET -> COSU- 0.138) implies that traders who had their level of wealth impacted, also had their consumption capacity impacted. People with greater wealth can be regarded as having a higher level of economic well-being and the opportunity to increase consumption if desired (OECD, 2013).

Though the study agrees that there may be unknown intervening factors between each relationship (hypothesis), the already identified factors and indicators are significant and critical for future resettlement planning in urban market redevelopment.

6.2 Social and policy implications

The study findings responded to the research questions: what are the factors impacting economic wellbeing, and how do the factors affect each other? These findings have implications for policy and society.

The findings on the economic impeding factors inform policy that to better improve the economic wellbeing of the drivers and sellers affected by urban market redevelopment, policy practitioners should prioritize the various factors and their determinant indicators in future resettlement planning. On the other hand, the social implications are that, if the economic impeding factors are not well managed in the resettlement process, it may not only cause low economic wellbeing for the affected traders who are youthful males, unmarried, less educated, and low-income earners but may also lead to their indirect displacement and mistrust for future similar policies. Economic hardships in communities often lead to security challenges ranging from cultism, kidnapping, and robbery among other violent crimes (Archibong & Ph, 2020).

6.3 Limitations of the study

As with all research, the present study is toppled with two main limitations, each of which provides recommendations for future research. First, the limitation of the study context is concerned with the higher-level concept of marking out ‘what has been studied and what has not yet’. In terms of what has not been studied in the concept of Resettlement in Urban Market Redevelopment, the present study is the first initiative to focus on the factors influencing economic wellbeing, so it lacks the backing of literature. The study, therefore, recommends future research in this context to adopt, test, and verify the factors and indicators of the proposed model.

Second and last, the limitation of the present research is concerned with the specific limitation to identifying the affected traders who were resettled due to the Kejetia market redevelopment. It took the help of the traders’ association heads to identify the victims. The present study recommends that future research rely on the assigned local resettlement department for easy identification if association heads are not accessible.

7. Conclusion

The overarching aim of this research was to examine the economic impeding factors of traders affected by the Kejetia market redevelopment. A proposed model was developed and hypotheses were initiated to test. Generally, we believe that we have achieved the study’s aim by answering the research questions of the study: What are the factors, and How do the factors influence each other?

The measurement model proved reliable and valid based on these various tests; internal consistency reliability, indicator reliability, convergent validity, and discriminant validity. The study findings revealed that the factors that impacted the economic wellbeing of traders affected by the resettlement in the Kejetia project were determined by certain indicators. The factors and their indicators included a) Level of Deprivation: access to compensation & assistance, information access & accuracy, access to meaningful participation, and access to relocation options, b) Economic Security: job security, financial security, representation security, and allocation assurance, c) Level of Income: earned income, credit facility, place equity, and market demand, d) Level of Wealth: accumulation of past incomes, secured customer based, liability/debt, and material capital, e) Level of Consumption: affordability of regrettable expenditure, affordability of consumer goods, affordability of capital goods, and family expenditure.

In the structural model, the hypotheses between the factors were supported by the study and significant at the level where p values were < 0.05 and t values > 1.960 (see Table 5). The findings here revealed that the factors that impacted the economic wellbeing of traders influenced each other. The various hypothesis statements include: H1_a states that Level of Deprivation (LDEP) had a significant impact on Economic Security (ECOS), H1_b states that Level of Deprivation (LDEP) has a significant impact on Income (INCO), H1_c states that Level of Deprivation (LDEP) has a significant impact on Consumption (COSU), H2_a states that Economic Security (ECOS) has a significant impact on Income (INCO), H2_b states that Economic Security (ECOS) has a significant impact on Consumption (COSU), H2_c states that Economic Security (ECOS) has a significant impact on Level of Wealth (LWET), H3_a states that Income (INCO) has a significant impact on Level of Wealth (LWET), H3_b states that Income (INCO) has a significant impact on Consumption (COSU), and H4_a states that Level of Wealth (LWET) has a significant impact on Consumption (COSU).

The study concludes that resettlement in the Kejetia project failed to prioritize the factors that impacted the economic wellbeing of the affected traders.

The key implication of the research is to inform policy that the economic wellbeing of the traders affected by resettlement in urban market redevelopment is dependent on the various impeding factors and their indicators introduced.

The study concedes that there may be other factors influencing the relationship effects between factors. Therefore, it recommends that future research should focus on the other factors mediating the effects of factors impacting economic wellbeing. Also, similar research in different locations are recommended to test the potency of the proposed model. By and large, the study contributes to the literature on sustainability and development, especially in urban redevelopment.

Acknowledgments

We thank the editor and the reviewers of this manuscript for their insightful comments that have improved the paper. The authors also thank our field team and the respondents interviewed in the survey for their assistance and contribution to data collection, respectively.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data supporting the findings of this study is available in the supplementary link.