Migration Intention of Rural Farmers to Urban Areas in Bangladesh and Its Determinants: A Partial Least Squares Structural Equation Modelling Approach

Abstract

Internal rural-urban migration is becoming a priority in social research. However, no one has systematically studied the intentions of farmers to migrate internally to Bangladesh to seek permanent urban residence via paid employment yet. This paper develops a TPB (theory of planned behaviour) model to explain farmers’ intentions to migrate or not migrate to cities for paid employment; then, this paper tests the model against the data collected. A total 372 individuals (migrated farmers) were randomly chosen from 11,200 families who had left a rural area in Bangladesh to collect quantitative data about the issues raised in the model; the data were collected by a telephone interview. The socioeconomic profile of the respondents fits that of internal migrants identified in the literature, and the Cronbach’s alpha as well as composite reliability statistics suggest that the data are representative of the population. According to the structural equation modelling (SEM) data, attitudes towards migration (ATTs) and subjective norms (SNs) have an impact on migration intention (MI), while perceived behavioural control (PBC) show no significant effect. This study used a primary dataset from interviews with rural migrants working in different sectors in different cities in Bangladesh. This suggests that the planned behaviour model may help explain farmers’ decisions to migrate internally in Bangladesh and that policies based on the model may successfully reduce such migration.

1. Introduction

Rural–urban (R–U) migration is the (temporary or indefinite) transfer of residence from the country to the city, usually within a nation’s borders [1]. The migration of individuals across regions has been examined as a complicated phenomenon starting from the earliest scientific study, which primarily focused on demographic and economic factors [2]. The single person or family making migration decisions is the focal point of the micro-viewpoint of migration research. From an empirical perspective, migration studies may be divided into those seeking to understand the causes of migration or those examining its effects. While there are many empirical studies on the factors that influence migration, e.g., refs. [3,4], several surveys on migration emphasise the need for more significant research on the impact of migration.

There are around 740 million domestic migrants, four times those who migrated internationally [5]. South Asia has a more excellent internal-to-external migration ratio at roughly 10:1 [6]. India and China have the highest rates of internal R–U migration, where the number of internal migrants surpasses the total number of migrants worldwide [7].

Bangladesh’s urbanisation rate increased by roughly 3% between 1975 and 2009, putting it among the nations with the highest urbanisation rates. The dynamics of migration within the country are reflected in this increase in the urbanisation rate [8]. According to World Bank statistics, the country’s rural population quadrupled from 1960 to 2018, while R–U migration caused the number of urban residents to increase by more than 22 times. The ratio of rural to urban residents was 18.5:1 in 1960 but reached 1.7:1 by 2020 (

Table 1. Rural and urban population in Bangladesh 1960–2022.

Year	Population (in Million)			Rural–Urban Ratio
	Rural	Urban	Total
1960	45.55	2.47	48.01	18.47:1
1970	59.36	4.88	64.23	12.17:1
1980	67.81	11.83	79.64	5.73:1
1990	82.73	20.44	103.17	4.05:1
2000	97.54	30.11	127.66	3.24:1
2010	102.62	44.95	147.58	2.28:1
2018	102.25	59.11	161.36	1.73:1
2022	101.41	67.69	169.1	1.49:1

Adapted from: World Bank Annual Report (2022) [11].

). Rapid R–U migration has been cited in earlier studies as a critical factor in Bangladesh’s industrial growth [9]. Given the interdependence of urbanisation and industrialisation [10], Bangladesh’s urban population has rapidly increased, transforming the country’s economy from one based primarily on agriculture to one with a sizable and expanding industrial sector.

The high level of internal R–U migration in Bangladesh has several causes. Within-country migration is fuelled mainly by the textile industry, where work prospects in the ready-to-wear factories have drawn millions of rural residents, notably women and young people looking for financial independence, towards metropolitan regions since the 1980s [11]. In addition, the vulnerability of coastal populations to the effects of climate change has led to the migration of largely rural coastal populations to large metropolitan areas for physical security from flash flooding and increasingly endemic droughts and deluges on the coast that destroy crops, thus making their food and clothing supply, as well as their homes, unreliable. According to the shock index, 8.9% of the nation’s population resides in rural, low-lying coastal regions that are especially susceptible to frequent, severe weather events like typhoons, floods, and tsunamis [12]. By 2100, the World Bank predicts that sea level rise will inundate 18% of coastal land and displace close to 30,000,000 coastal residents [13]. The difficulties that agricultural communities in rural regions experience due to the instability of their environment, society, and economy stimulate R–U migration and thus are relevant to this research.

The leading causes of rural poverty among farmers in Bangladesh are crop seasonality, soil erosion issues, groundwater degradation, lack of water for drinking and irrigation, and climate-related economic threats to agricultural production [14]. R–U migration presents a viable solution to small-scale farmers seeking a way out of poverty, with its prospects of steady employment at wages that are higher than what they could earn from working on their land [15].

As it affects population distribution rather than population change, internal migration is not as well researched in Bangladesh than in many other countries. However, there is a favourable correlation between internal migration intention and the economy, and moving internally has been identified by many rural Bangladeshis as a viable strategy to escape poverty [15,16]. This migration has led millions of underemployed and jobless rural people into full-time employment in cities.

The published literature demonstrates that internal migration is primarily influenced by socioeconomic, conflict, and climatic variables [17,18]. The first significant study on internal migration in Bangladesh was published in 1992. It attempted to demonstrate the patterns and factors influencing internal migration using census data from 1974 to 1981 [19]. Since then, a sizable number of works have been published, but no one has systematically studied the intentions of farmers to migrate to urban areas to work internally in Bangladesh. The focus has instead been on variables influencing the level of internal migration from a theoretical perspective, e.g., correlation.

Estimating internal migration intention is a more comprehensive and pragmatic approach that can allow future migration trends to be predicted and may assist in enlightening the theory already developed by the empirical confirmation of—or challenge to—those theories. Therefore, this research collected data about farmers’ intention to migrate to cities for internal work in Bangladesh, and conclusions were drawn about the variables influencing such intentions.

2. Theoretical Background and Hypothesis of the Research

Since the early 1980s, discussions on migration have been divided. Historical-structuralist frameworks (like neo-Marxist and global systems theories) often clash with neo-classical approaches [20]. However, neither has fully explained why some people migrate to specific places while others do not or why migration patterns are not random [21]. Neo-classical theory suggests people move to maximise income, assuming they know all about pay levels and job opportunities [22].

Recently, researchers have looked at non-economic factors (such as infant mortality rates, carbon dioxide emissions, crime rates, and political freedom) in migration decisions [23,24]. These studies highlight social, cultural, and demographic reasons for migration. Though behavioural and psychological theories have been used to study migration [25,26], the intention to migrate has not been widely examined.

Some researchers have used Fishbein’s theory of reasoned action (TRA) to explain migration intentions [27,28]. However, the TRA does not account for people’s ability to perform actions or control situations that might stop them from acting on their intentions [29]. The theory of planned behaviour (TPB) is a suitable theory to replace the limitations of the TRA [30], which considers behaviours as intentional efforts and careful decisions [31].

The TPB highlights the importance of migration decision making in predicting future behaviour. It suggests that a person’s intention to act, influenced by attitude, subjective norms, and perceived behavioural control, determines their behaviour [32,33]. Research shows differences between those who plan to move and those who do, indicating that traditional models might underestimate the constraints and facilitators’ role in migration decisions [34]. Shifting the migration decision process into pre-decisional and pre-actional stages can prevent biases [34].

Based on Ajzen’s work, the TPB model can predict and explain behaviours in specific settings [35]. It can be used to study migration, as shown in Figure 1, which illustrates farmers’ migration decisions.

The TPB states that behavioural intention directly leads to actual behaviour. A person’s “behavioural intention” (BI) depends on attitude (ATT), subjective norms (SNs), and perceived behavioural control (PBC). A person’s beliefs and judgments influence attitudes. Normative beliefs influence the individual’s motivation to comply with SNs. PBC reflects a person’s perceived ability to control outcomes.

According to the TPB, the desire to act is the main factor in determining behaviour. Intentions show individuals’ effort to perform the behaviour [31,36]. Thus, understanding a person’s intentions can help predict and explain their future actions, such as migrating within a country [37]. This research aims to apply these conclusions to study farmers’ migration from rural to urban areas in Bangladesh [38].

The TPB model has rarely been used to evaluate the intentions and social-psychological elements that influence a person’s choice to migrate, despite the findings of several authors [39,40], who noted that the desire to migrate may be a valuable predictor of actual migration. This research is intended to fill that research gap.

Despite its widespread use, the TPB has drawbacks. It assumes that individuals have the opportunities and means to perform the intended behaviour. It does not account for fear, danger, mood, or past experiences that might influence intentions and motivation [41]. Additionally, while normative factors are considered, environmental or economic factors that might impact behaviour are often overlooked [42]. The TPB also assumes a linear decision-making process and does not consider that behaviour might change over time. This research seeks to fill these gaps using TPB to evaluate the intentions and social-psychological factors influencing migration decisions [39,40].

2.1. The Model

This research begins by developing a TPB model to explain migration intention (MI) in terms of (i) ATT (ii) SNs, and (iii) PBC. From that model, we have a lens through which to view the data about farmers’ intentions to migrate internally to the city in Bangladesh. Migration intention (MI) is the dependent variable. This variable is proxied using [31] through details like better accommodation, a healthier life, quality education for kids, etc. (details in

Table 2. Summary of the construct and statement items for the SEM model using TPB.

Variables (Construct)	Questions (7-Point Likert Scale)	Mean	SD
Migration Intention (MI) (Dependent variable)	1. I think city areas have better living places than villages (MI1)	4.32	1.32
	2. Migrated areas have a healthier life than villages (MI2)	4.31	1.28
	3. Urban areas have quality education for kids (MI3)	4.15	1.36
	4. Urban areas have higher social security (MI4)	4.11	1.40
	5. Urban areas have good recreational facilities (MI5)	4.43	1.37
Attitude (ATT)	1. Migrated areas a satisfactory modern life (ATT1)	4.84	1.27
	2. Higher wages (payment/day) than village areas (ATT2)	4.94	1.21
	3. Better job availabilities than villages (ATT3)	4.92	1.20
	4. Urban lifestyle is more impressive than villages (ATT4)	4.79	1.20
	5. Urban areas have high-income sources other than salary (ATT5)	4.76	1.13
Subjective Norms (SNs)	1. Urban areas have an excellent social network (SN1)	4.54	1.32
	2. Fewer social barriers (like criticism, neglect, intolerance, misjudge from others) in urban areas (SN2)	4.43	1.37
	3. Honourable job opportunities in urban areas (SN3)	4.22	1.40
	4. Migrated areas have an opportunity to increase social position (SN4)	4.69	1.19
	5. Working in urban areas has a recognition of one’s job by near peers (SN5)	4.92	1.23
Perceived Behavioural Control (PBC)	1. Urban work is relatively more profitable than agricultural work (PBC1)	4.88	1.23
	2. In urban areas, there are good career development opportunities for the future (RPBC2)	5.00	1.09
	3. Higher freedom in urban life than in villages (PBC3)	5.06	1.05
	4. Relatively higher opportunity to obtain highly paid jobs than in villages (PBC4)	4.67	1.37
	5. Relatively higher accepted position than farming (PBC5)	4.51	1.38

). Attitude (ATT) is an independent variable from TPB theory [43]. This research proxied ATT in three statement items to gauge attitudes among rural residents, e.g., the influence of urban life, modern lifestyle, and the migration sector’s influence.

Subjective norms (SNs) are an independent variable from TPB theory [31]. In this model, SNs represent the societal pressures that people experience, which cause them to behave as they do in their social situations. This model identifies external factors in society that lead farmers to desire to migrate to cities. An example of SNs can be a social network, social status of work promotion from the poor rural condition, etc.

Control of behaviour as perceived (CBP) may be considered a person’s perception of how tough or straightforward it is to carry out a specific behaviour [31]. CBP is the product of a person’s alternative career opportunities and finances, including savings, marketable assets, and the relative income projected from agriculture and urban employment, etc. (Table 2).

The more options and resources a person has, the more their decisions reflect their preferences and values. Without these resources, decisions may be desperate, where personal preferences and values do not matter as much. For example, rural women and children might have limited career opportunities, finances, and talents because they must obey their husbands. Similarly, a farmer with good opportunities might feel unable to leave their village to earn more in the city because they need to care for elderly parents who cannot or would not move with them.

Therefore, perceived behavioural control (PBC) is influenced by external factors like social and practical obstacles or the availability of social support, which can either promote or hinder behaviour.

2.2. Hypotheses and Conceptual Models

The TPB and earlier theories served as the foundation for this research’s hypotheses. The TPB hypothesises that SNs, PBC, and ATTs all impact the actor’s intention, in this case, the farmer’s intention to migrate to the city. Thus, we derive the following hypotheses for this research:

H1: 

Attitude toward migration (ATT) is significantly and positively correlated to migration intention (MI) (ATT↔MI). The degree to which a person evaluates something as excellent or unpleasant is their attitude toward the conduct. This element is essential for an individual who wants to migrate. Ref. [44] concluded that attitude significantly influences people’s intentions to become migrants. There are various markers of attitude in their measures. Personality traits associated with attitudes that dispose a person towards migration include creativity, risk taking, tolerance, independence, proactiveness, innovativeness, self-confidence, time appreciation, hard work, integrity, toughness, autonomy, leadership, spiritualism, and future orientation.

H2: 

The subjective norm (SN) variables are significantly and positively correlated with migration intention (MI) (SN↔MI). This component is perceived as societal pressure to behave or not behave in a given way. When generating indicators for latent variables, the argument is that maintaining normative hope and believing in its normative referent is crucial. Adopting an organisation’s, parents’, family’s, teachers’, and friends’ expectations logically implies belief in a normative referent of hope that these expectations usually produce a better result than the individual’s norms, which are being overridden by others’ expectations in that adoption [45]. The individual must have, in some sense—even in a sub-conscious sense—concluded that “If I listen to them, I will succeed, but if I listen only to myself, I will fail”, or else the adoption of others’ norms in place of one’s deeply held values is illogical behaviour. Such a conclusion is a product of socialisation, so it is thus more likely to occur in collectivist and/or traditionalist societies, which put a higher value on conformity than individual liberty.

H3: 

Perceived behavioural control (PBC) correlates significantly and positively with migration intention (PBC↔MI). PBC is the ease with which an action is perceived or constructed as possible for the decision maker based on prior experiences and anticipated barriers. People do not generally decide to perform actions that they think are impossible for them unless they are obsessed or addicted to the behaviour to the extent that their behaviour has become illogical. An average person will not decide to jump off a roof and fly, but an addicted person, whose perceptions are altered by a drug, or a child, whose ability to predict the result is not yet developed, might do so. To put it more relevantly to this discussion, a person who perceives migrants as happier, more fulfilled people who have a better standard of living than they have is more likely to migrate than a person who has heard a lot of stories about migrants drowning in leaky boats at sea, being locked up in jail, becoming drug addicts, or are victims of crime in the city or are being enslaved in sweatshops in deeper poverty with no escape. This will largely depend on the information available to the person and any prior experiences with attempts at migration. The conceptual framework of this research is shown in Figure 1.

3. Materials and Methods

Information for this study was gathered in the following places: Sundorganj Upazilla and Saghata Upazilla (sub-districts) of the Gaibandha district (See Figure 2) in Bangladesh. These are rural areas where most people are farmers. The decision to focus on these places was inspired by discussions with staff members from the Department of Agricultural Extension (DAE) district offices and past research that revealed that these were the areas with the highest number of internal migrants. We searched household records at the Upazilla agriculture offices using a fixed sample size for data gathering. We found 11,300 farming households with one or more members who had left the area. Considering the time and budgetary constraints, we randomly chose (using the table of random numbers) 372 residences for phone interviews (the migrated persons) following the equation provided by [46]. We selected only migrated people because only they can understand the actual situation in urban areas and compare rural and urban life with those of other people living in villages [7].

The original questionnaire was in Bengali. The researchers took sufficient time to describe the questionnaire and establishment of rapport using the Bengali language and local dialect. The data collection started on 22 March 2023 and continued until 20 September 2023.

n = N × X/(X Hi N − 1)

where X = Zα/22 × p × (1 − p).

The confidence level was 95 percent, and the critical value was 1.96 percent. MOE is the margin of error, p is the sample percentage, and N is the population size. In the sample size formula, a finite population correction was used.

Each village in Upazilla was given a similar target sub-sample, and field assistants from the University of Rajshahi were asked to find them all. The collected data were evaluated using structural equation modelling (SEM) and a descriptive analysis (See Table 2).

The data were developed using the PLS-SEM approach, suitable for small sample sizes and new statistical factors [47]. The SmartPLS 4.0 program was used for analysis, evaluating reliability using Cronbach’s alpha and composite reliability. The confirmation of convergent and discriminant validities was performed using the Fornell–Larcker criteria [48]. A bootstrapping approach was used to create fresh samples of similar sizes to determine the importance of research hypotheses for a route model [49]. A non-parametric bootstrapping approach assessed structural links at a 0.05 significance level [50].

4. Results and Discussion

Most respondents (67.21%) to the questionnaire were in the middle-aged category (36–55 years) (

Table 3. Socio-demographic characteristics of the respondents (n = 372).

The Variable and Scoring Technique	Categories	n = 372	%	SD	Mean	Mode
Age (1 for each year)	Young (≤35) Middle-aged (36–55) Old (≥56)	70 250 56	18.82 67.21 13.97	10.36	44.76	45
Sex (1 = male, 0 = female)	Male Female	311 61	83.61 16.39	0.37	0.83	1
Education (1 = 1 schooling Yr)	Primary (≤5) Secondary (6–12) Higher secondary (≥12)	135 189 48	36.29 50.81 12.90	3.70	8.39	5
Annual Family Income (in 000 BDT)	Low (≤100) Medium (101–200) High (≥201)	263 88 21	70.69 23.66 5.65	33.99	89.75	80
Organizational memberships (No = 0, yes = 1)	No Yes	214 158	57.5 42.5	0.49	0.43	0
Credit facilities (No = 0, yes = 1)	No Yes	176 196	47.3 52.7	0.49	0.52	1

), representing a significant and active portion of the labour force. In recent years, these people have left agriculture for urban, non-agricultural industries in Bangladesh [51]. Migration has been shown in the published literature to be an age-selective process, and young adults are often the most mobile age group [52]. The migration rate was also more significant for those with low educational levels (87.1% below ten years of education) and for families earning less than BDT 50 thousand annually [53]. Other essential characteristics of migrants included inadequate agricultural extension facilities, low organisational involvement, and low credit facilities from any organisation [54]. So, Table 3 shows that the sample was probably representative of the population we were studying.

The findings of the validity and reliability studies are shown in

Table 4. Results of the reliability and validity analyses.

Construct (Variable)	Items	Factor Loadings	Cronbach’s Alpha (α)	Composite Reliability (CR)	Average Variance Extracted (AVE)
Attitude (ATT)	ATT1	0.833	0.850	0.854	0.632
	ATT2	0.836
	ATT3	0.858
	ATT4	0.815
	ATT5	0.605
Social norms (SNs)	SN1	0.871	0.922	0.881	0.627
	SN2	0.890
	SN3	0.793
	SN4	0.687
	SN5	0.695
Perceived behavioural control (PBC)	PBC1	0.875	0.851	0.924	0.763
	PBC2	0.909
	PBC3	0.903
	PBC4	0.846
	PBC5	0.830
Migration intention (MI)	MI1	0.825	0.850	0.916	0.603
	MI2	0.837
	MI3	0.722
	MI4	0.778
	MI5	0.714

. The results showed acceptable factor loadings, as no indication showed values below 0.605. Each construct had composite reliability and a Cronbach’s alpha of over 0.7, indicating the model’s reliability [55]. Every average variance extracted (AVE) value was more than 0.5; thus, convergent validity was shown. Further evidence that the ranged AVE values of all constructs were significantly higher than the minimal threshold value was provided by the fact that each latent construct had a squared root of AVE greater than its latent factor correlation [56]. This demonstrated the discriminant validity of the constructs. As stated, off-diagonal correlation values for the latent variable were lower than diagonal values (

Table 5. Calculation of the Fornell–Larcker discriminant validity criterion.

	MI	ATT	SN	PBC
MI	0.795
ATT	0.616	0.810
SN	0.628	0.542	0.873
PBC	0.733	0.719	0.783	0.792

), indicating that both analyses were significant and confirmed the research hypotheses [57].

The standardised path (values) and p values are shown in

Table 6. Path analysis to test the hypotheses.

Hypothesis	Path	Coefficient	T Statistics	p Values	Hypothesis Status
H1	ATT -> MI	0.203	3.187	0.001	Supported
H2	SNs -> MI	0.635	8.974	0.000	Supported
H3	PBC -> MI	-0.083	1.496	0.135	Not supported

. The structural model assessment showed significant relationships between variables (i.e., the factors impacting migration choices): the ATT and SN coefficients were favourable and statistically significant at the 1% level. In other words, the correlations’ direction between the variables showed that the associations were strongly and favourably related, and all presumptive assumptions were confirmed. In particular, SNs were shown to have the most significant impact.

While ATTs and SNs were significant, perceived behavioural control (PBC) was not. This means that people’s attitudes and the influence of social norms strongly impact their decision to migrate, but their sense of control over the situation does not.

There may be a few reasons for this. First, in the context of migration, many factors are beyond an individual’s control, such as political conditions, economic opportunities, or social networks. This makes perceived control less relevant. Second, socio-demographic factors, such as education level, income, and access to information, might also play a role. For example, individuals with lower education or income might feel less able to control their migration decisions, relying more on social norms and attitudes. Thus, PBC is not a significant factor in this research context. Further research with more area coverage in Bangladesh is recommended.

According to Figure 3, the factor loadings of ‘High wages (ATT2)’ and ‘Good job opportunities (ATT3)’ had highly significant factor loadings. This suggests that the farmers are mainly migrating for economic reasons to improve their incomes. ‘A good social network in urban areas (SN1)’ and ‘Less social barriers in urban areas (SN2)’ yielded the most significant values for social norms (SNs). This suggests that the farmers are also looking for better social connections in the city; farm life can tend to be isolated. In contrast, cities are heavily populated areas where “everything is happening”. The factor loadings of ‘Relatively good career opportunities (RPBC2)’ and ‘Higher freedom in urban life (PBC3)’ confirm the economic motivation for moving to cities, as there are no career ladders for advancement on the farm, and it suggests that farmers are looking for more freedom and a less traditional lifestyle than that which exists in rural areas of Bangladesh. The adjusted R2 score, which shows the model’s solid predictive ability, indicated that the variables explained above 50% of the variation in MI, suggesting that these conclusions from the model are reliable. Overall, the tested structural model produced positive outcomes, having a significant and essential influence on the examined structured interactions.

5. Conclusions and Recommendations

Utilising a dependable partial least squares structural equation modelling (PLS-SEM) methodology, this research has empirically examined the migration intention (MI) of farmers in Bangladesh and the factors affecting MI. The results of this research emphasise a complex interaction among human, economic, social, and environmental variables and provide insight into the dynamics of rural-to-urban migration in the setting of a developing nation.

The TPB model has been found to be helpful in explaining rural migrants’ decision to migrate to urban regions from rural areas in Bangladesh. The findings indicate that attitudes and subjective norms significantly affected rural residents’ intentions to leave agriculture, as the model predicts. Still, the research does not find an effective relationship between PBC and MI, as this study was conducted in specific areas of Bangladesh and with a particular number of farmers. We recommend conducting further research in broad areas in the country to retest the hypothesis again.

This study used PLS-SEM to model and assess the complex interactions between variables in rural-to-urban migration. It emphasised the need for a comprehensive understanding of migratory intentions, considering socioeconomic situations, policies, and environmental variables. Future research should continue to investigate these changes over time.

The most significant factors related to MI are economic factors. In contrast, some non-economic aspects are also described: for example, a healthy life, a good education for the child, career development opportunities, and urban lifestyle fascination are significant for rural–urban migration intention. Migrants seem to perceive the city as a place where they can obtain everything they need and rural areas as places where they can obtain nothing. Regardless of whether or not these perceptions confirm reality, policies to reduce migration need to address them. Part of the answer may lie in reality therapy about what life in the city is like, and part of the answer may lie in improving the lifestyle, educational opportunities, healthcare, and developing career ladders for farmers, perhaps through the agricultural extension service. Farming jobs in Bangladesh are mostly regarded as jobs for lower-class people, which makes them lower-class jobs. Thus, many farmers are shifting to urban areas to improve their social status. Education and marketing policies may be needed to raise the social status of farmers and farming as an important component of the nation’s economy, development, health, and well-being. The wealthiest superstar would be nothing without food. Mass and social media, as well as television, are all consumed in the country and could be helpful here. Television shows that value and respect farmer characters rather than patronise them as hopeless victims of poverty or lampoon them as useful idiots could have a big impact on farmers’ self-perception, as the nation does need them.

In addition, to better manage and harness the potential advantages of rural-to-urban migration while addressing its associated challenges, policymakers, development organisations, and stakeholders should consider the complex web of determinants identified in this study. This study contributes to our knowledge of the intricacies and subtleties surrounding this crucial socioeconomic problem by laying the groundwork for future research and policy conversations on migration in Bangladesh’s rural areas.

The research demonstrates that the psychological incentive to move is significantly influenced by the allure of the perceived possibilities found in contemporary metropolitan surroundings. However, “perception” is not necessarily a true and accurate reflection of the options and circumstances present in the destination. Although migration is often an unavoidable escape from circumstances where economic survival is not assured, erroneous beliefs and evaluations that underlie the choice to move have various unintended consequences. On the one hand, excessive and uncontrolled migration patterns have clogged up metropolitan areas, severely impacting both the viability of cities and the living and working circumstances of migrants. However, the drastic reduction in people and labour resources in rural areas has had significant consequences on the economy (such as in the area of variables impacting productivity) and sociocultural aspects (i.e., weakening the connective tissue and generational reproduction of rural communities). The difficulties policymakers face in dealing with these inequalities are evident from the experiences of other Asian nations (such as China and Vietnam). Rethinking the notion that “urban-centric” development is the only kind that can be pursued will help to improve the management of internal migration, and it should also be helpful to recover the perspective of a territorial rebalancing intended to close the income and job gaps between urban and rural contexts which, as we have seen, fuels the motivation to migrate.

Future studies should concentrate on the effects of migratory patterns on the rural environments in which migrants originate. The current work emphasises the pertinent aspects of rural-to-urban migration intention in Bangladesh. Bangladesh’s spatial and migratory rebalancing policy might benefit from the extra information this future research approach could provide.