Next Article in Journal
Integrated Organic-Inorganic Nitrogen Fertilization Mitigates Nitrous Oxide Emissions by Regulating Ammonia-Oxidizing Bacteria in Purple Caitai Fields
Previous Article in Journal
Research Progress on the Theory and Practice of Grassland Eco-Compensation in China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 12
Issue 5
10.3390/agriculture12050722
agriculture-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Exploring the Factors of Farmers’ Rural–Urban Migration Decisions in Bangladesh

by
Abdullah Al-Maruf
1,
A. K. M. Kanak Pervez
2,
Pradip Kumar Sarker
3,*,
Md Saifur Rahman
4 and
Jorge Ruiz-Menjivar
5
1
Department of Geography & Environmental Studies, University of Rajshahi, Rajshahi 6205, Bangladesh
2
Department of Agronomy & Agricultural Extension, University of Rajshahi, Rajshahi 6205, Bangladesh
3
Forest and Nature Conservation Policy, Georg-August University, D-37077 Göttingen, Germany
4
Ministry of Public Administration, Bangladesh Secretariat, Dhaka 1000, Bangladesh
5
Department of Family, Youth and Community Sciences, University of Florida, Gainesville, FL 32611, USA
*
Author to whom correspondence should be addressed.
Agriculture 2022, 12(5), 722; https://doi.org/10.3390/agriculture12050722
Submission received: 2 May 2022 / Revised: 16 May 2022 / Accepted: 17 May 2022 / Published: 19 May 2022
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
Browse Figure
Review Reports Versions Notes

Abstract

:
In Bangladesh, rural–urban migration is widespread. Many earlier studies discussed the factors, patterns, causes, and consequences and the socio-economic and environmental impact of migration from the general perspective. However, rural–urban migration with a particular focus on particular communities or migrants’ employment profiles, for instance, farmers, is poorly described. In contrast, many farmers move from rural to urban areas every year in Bangladesh. However, the factors that affect farmers’ rural-to-urban migration are a primary concern to academia and key actors, as the country’s economy mainly depends on agriculture and farming. This paper, therefore, aimed to identify the underlying factors of the rural–urban (R–U) migration of farmers in Bangladesh. Data for this study came from phone interviews conducted with 254 migrant farmers living in city districts in Bangladesh. We adopted a three-step approach to select and identify factors that impacted farmers’ decision to move from rural to urban settings. First, we reviewed the extant literature and compiled more than 70 variables of interest relevant to farmers’ migration. Second, 30 variables were selected for data collection after consultations with key informants (KIIs) and informal discussions (IDs) with farmers and local community leaders. Besides, the Q-methodology was used to assess the level of importance of the selected variables. Lastly, principal component analysis (PCA) was performed to extract salient dimensions of farmers’ rural-to-urban migration, where 21 variables were detected that consistently exceeded a threshold value of 0.50 of communality for further analysis. Our findings show that six dimensions—i.e., individual, household, economic, attitudinal, spatial, and climate-induced extremes—significantly influence and contribute to rural urban migration decisions for farmers. Further, our results indicated that age, agricultural knowledge, household debt, seasonal famine/poverty (Monga), unemployment in rural areas, availability of anticipated job opportunities in urban areas, shortage of agricultural inputs, and river erosion significantly influenced farmers’ decision to leave their farms in Bangladesh. Findings from this study may be used as inputs in predictive models and benchmark guidelines for assessing trends and patterns of rural-to-urban migration and for the formulation of policy and programs targeting domestic migration in Bangladesh for proper urban planning and further rural development.

1. Introduction

Urbanization and industrialization have resulted in drastic economic development globally [1]. As a result, the income gap between the agricultural and non-agricultural sectors has gradually expanded. Many countries have experienced considerable changes in the income and structure of rural households [2]. Consequently, the most significant aspect driven by the economic benefits is that many rural households go out to work due to the lack of agricultural laborers and the ageing of agricultural production in rural areas [3]. R–U migration can be defined as temporary or permanent migration from the countryside to urban cities, typically within the country’s national boundaries [4]. Globally, about 740 million people are R–U migrants—four-times as many migrants who moved outside their countries [5,6]. The internal to international migration ratio in South Asia is comparatively higher than in other continents, about 10:1 [7]. Mainly, internal R–U migration is most prevalent in China and India, where the number of R–U migrants in these two countries exceeds the total number of migrants globally [8]. In Bangladesh, the increase in the urbanization rate reflects internal migration dynamics; from 1975 to 2009, this nation’s urbanization rate rose by about 3%—one of the highest worldwide [9]. According to the World Bank data (see Table 1), from 1960 to 2018, the country’s rural population doubled, while the urban population increased 23-fold due to R–U migration [10]. In 1960, the rural–urban population ratio was 18.47:1; however, by 2018, the rural–urban population ratio had notably changed to 1.73:1 [11]. Prior research has identified the rapid R–U migration as one of the vital determinants of the industrial development of Bangladesh [9,12,13]. Given that urbanization and industrialization are symbiotic processes [9], Bangladesh’s urban population’s accelerated growth has shifted from a mainly agricultural-based economy to one with a large and growing industrial sector. In 1961, the contributions of the agricultural and industrial sectors to Bangladesh’s total gross domestic product (GPD) were 57.98% and 6.79%, respectively [14]. By 2018, the contributions of the sectors mentioned above to the national GDP have changed significantly, 13.07% and 28.53%, respectively [11,15,16].
Rural households’ farmland abandonment has become a universal phenomenon of economic development in the world [17]. Previous research has examined labor migration and farmland abandonment in developing nations in Latin America, and Southeast Asia [18]. However, the literature related to labor migration in Bangladesh, particularly farmers from rural to urban areas, is limited [4].
Existing literature emphasized several factors that have contributed to the increase of R–U migration in Bangladesh; for instance, the textile industry in the country is the primary driver of within-country migration, where job opportunities in the ready-made clothing industry attract millions of rural people to urban areas, particularly women and the young, who seek financial independence [19,20]. Further, some studies focused on how coastal communities’ vulnerability to climate change effects contributes to climate change displacement from rural to larger and urban cities [11]. According to Salam et al. [21], the Shock Index indicated that around 8.9% of the country’s population lived in low-lying coastal, rural areas that were highly susceptible to frequent and intense weather events, such as typhoons, floods, and tsunamis. Estimates by the World Bank suggest that sea-level rise could result in up to 18% of coastal land being inundated and the displacement of 20–30 million coastal residents by 2100 [16,22]. Prior research has shown that R–U migration drivers include environmental, social, and economic challenges faced by farming communities in rural areas [23]. Other studies explored how crop seasonality, soil erosion problems, groundwater degradation, scarcity of water for drinking and irrigation, and climate-related financial risks to agricultural productivity are linked to rural poverty among farmers in Bangladesh [12,24]. R–U migration might be a livelihood strategy for smallholder farmers to secure stable job opportunities and higher income levels [25]. In Bangladesh, rural–urban migration is widespread. The factors, patterns, causes, and consequences and the socio-economic and environmental impact of migration from the general perspective are frequently discussed and cited [26,27]. However, research related to rural–urban migration with a particular focus on particular communities or migrants’ employment profiles, for instance, farmers, remains limited. This paper aimed to explore and identify the underlying factors that significantly affect the rural–urban (R–U) migration of farmers in Bangladesh.

2. Theoretical Background

Neoclassical economies highlight the theoretical orientation of R–U migration. According to Ravenstein’s (1885) [27,28] “laws of migration”, most migration is from agricultural to industrial areas (Law 2), which combines individual rational choice theory with the broader structures of rural–urban and developmental inequalities, which is found in the much-vaunted push–pull framework. The push–pull theory by Lee (1966) [29] reveals migration to be driven by several push factors functioning from the region or country of origin (e.g., poverty, unemployment, landlessness, population growth, low social status, poor marriage prospects) and pull factors operating from the place or country of destination (e.g., better income, better education, and welfare systems, land to settle and farm, good environmental and living conditions, political freedom). In the context of migration, transitions, and development, very different from the individual-level rational choice decision-making of “neoclassical” migrants, is the broadly sweeping historical generalizations of Wilbur Zelinsky’s “hypothesis of the mobility transition” (1971), as he pointed to early transitional society: mass rural–urban migration; emigration to attractive foreign destinations for settlement and colonization [30]. Todaro’s model (1969) [31]— a model of R–U migration that seeks to interpret growing urbanization in developing countries—argues that migration is seen as an individual investment, increasing the possibility of acquiring a better job with a higher wage. Nevertheless, the interaction mechanism of labor migration from rural to urban, focusing on any particular community/group (e.g., farmers, teachers, fishers) in developing countries is not precise [17,32].
Migration studies from the perspective of developing countries have generally highlighted the economic aspects of migration [33]. Most of these investigations have emphasized the differentials and determinants of migration, mainly focusing on the causes and consequences of migration [34,35]. Regarding social and economic impacts, an individual’s migration has demographic effects. The separation between members of households has contributed to low fertility rates in migrant families [36]. Some earlier studies focused on factors affecting the migration of household members from rural to urban and vice versa and the impact on land-use changes, urbanization, and food security from the general perspective [37,38]. However, limited studies have focused on adults, male and female [39], and tribal groups [40]. The earlier investigations widely analyzed rural laborers’ decisions to migrate (participate in off-farm activities) and their destination (e.g., local employment, within the county, within the province) [37]. Though some studies have examined the time-use of off-farm jobs, they have mainly focused on proactivity (participating in both on- and off-farm work) [17]. They have usually paid more attention to the off-farm income of the whole household rather than the off-farm time-use of the individual [3]. Several studies focused on the correlations between rural–urban migration and land transfer. Some literature has distinguished the differences in factors that affect farmers’ decisions in short-term and long-term off-farm employment, which are not clearly understood regarding the pattern of rural migration (e.g., rural–urban or urban–rural). The time-use of rural laborers’ off-farm jobs points to their transition from the farm to the off-farm sector and shows the urbanization process at the micro-level [41]. Still, there are fewer empirical studies exploring the factors affecting particular groups’ rural–urban migration, such as farmers [42]. Without a detailed categorization of the migration pattern of specific groups (e.g., farmers), critical information may be omitted when formulating labor and workforce policies [43].
Prior research has paid great attention to international migration patterns among Bangladeshis [8,44,45,46,47]. However, there is a lack of studies dealing with the dynamics of domestic migration, particularly with farming households. The current literature on internal migration has primarily focused on the effects of climate change and economic factors on coastal communities in Bangladesh [48,49,50]. The existing micro-level studies primarily investigated the characteristics of migrants at destination places, mainly Dhaka City, giving little attention to the causes of out-migration from rural areas [51]. Shams et al. (2014) studied the economic consequences of migration based on sample surveys conducted in Dhaka City [52]. Jahan (2012) found that out-migration is generally higher in the villages characterized by land scarcity, unequal distribution of land, and a high proportion of agricultural laborers [53]. Haque and Islam (2012) pointed out that migrants often benefited more than nonemigrants because of their innovative and risk-taking nature [54]. The benefits included higher income, a gain in wealth, and greater access to public services and education. However, with a single study, most of the investigation was conducted on diverse people, such as daily laborers, slum dwellers, rickshaw pullers, and government and non-government professionals [55]. However, it is essential to give attention to rural–urban migration, particularly farmers, using micro-level studies based on sample surveys that have the advantage of identifying regional heterogeneity [19,56,57,58,59]. The current study sought to fill this gap by examining underlying multidimensional factors affecting farmers’ decision to migrate from rural to urban cities in northwestern Bangladesh.

3. Materials and Methods

3.1. Sample and Data Collection

Data for this research were collected in subdistricts in Bangladesh: Gaibandha Sadar Upazilla (sub-district) in Gaibandha and Chilmari Upazilla in Kurigram (see Figure 1). The selection of these subdistricts was informed by discussions held with district office representatives from the Department of Agriculture Extension and prior studies indicating these areas have the highest percent of internal migrants [51,60,61]. To determine the sample size for data collection, we consulted household records at the Upazilla Agriculture Offices, which indicated that there were 2598 households from which at least one member has migrated from rural areas. Given time and financial costs, we randomly selected ten percent of these households for phone interviews—this approach met the necessary sample size of 243 individuals based on a 95% confidence level and a 6% margin of error [62]. The target sample size for interviews in each study site was 250 individuals, hence generating subsamples (representing between 8 and 10% of the population, respectively), allowing for relatively meaningful statistical analyses [63]. To (geographically) cover the different locations of the settled union area, field assistants—students and graduates of Rajshahi University—were asked to identify all mouzas across the union, allocate a similar target subsample to each, and approach households by moving from the center of the settlement (e.g., market square) towards the outer edges of the settlement along a major road/track and, depending on the size of the mouza, contact every third to fifth household (or the next household if no one was available at the targeted household). At least one migrant member in all 254 households agreed to participate in this study.

3.2. Variable Selection and Analysis

Identifying relevant and robust variables to understand the factors affecting farmers’ rural–urban migration was a crucial step. To improve the suitability of the variables of rural–urban migration in the study area, the selected variables had to satisfy the following criteria: (i) the selected variables should be justified by previous studies on rural migration; (ii) the variables must be relevant to the scale of assessment (e.g., individual, households, and community); (iii) the variables must be measurable and easily interpretable; (iv) their measurement must be robust; (v) they should be used particularly in rural–urban migration farmers [62,63].
To ensure the criteria of variable selection, this study, therefore, employed a three-step approach to identify and select salient variables that affect farmers’ decision to migrate internally from rural to urban cities in Bangladesh. First, we reviewed the extant literature related to R–U migration in Bangladesh from 2000 to 2020 via Google Scholar. We compiled a list of more than 70 variables that have been reported as influential factors for R–U migration. Next, we refined the list of variables in consultation with key informants (KII) and stakeholders from different Government and non-government organizations, such as the Department of Agriculture Extension, Union Parishad (lowest administrative unit of Bangladesh), Palli Karma-Sahayak Foundation (PKSF) (PKSF’s overall goal is to create conditions for the people to move ahead not only in economic terms, but also in social terms and in terms of increased capacity to deal with environmental problems), Gana Unnayan Kendra (GUK) (a group of dedicated social activists founded Gana Unnayan Kendra (GUK) in 1985 to reduce poverty by bringing positive, sustainable changes in the lives and livelihoods of disadvantaged communities), and Migrant Resource Centre Bangladesh, who has a wide range of experience on migration in Bangladesh, particularly rural–urban migration [32,64]. Specifically, we conducted five focus group discussions with Agricultural Extension officers from the research sites (i.e., Gaibandha and Kurigram districts).
We held formal and informal discussion meetings with local leaders and migrant farmers. The direct output of these focus groups and discussions was a list of 30 variables deemed influential in farmers’ decision-making to migrate internally (Table 1). The variables and their measurement are shown in Table 1. Afterward, Q-methodology was used to assess the importance (dominance) level for variables influencing farmers’ R-U migration (Table 2). Lastly, principal component analysis (PCA) was performed to extract salient dimensions of farmers’ rural-to-urban migration. The key variables of R–U migration as directed by the results of a median analysis were used in the principal component analysis with varimax rotation. The factor analysis technique was performed to extract and quantify the underlying factors of farmers’ internal migration. Factor analysis identified patterns and revealed the underlying factors that accurately described the variation in the data [65]. Factor analysis was performed on each component to identify the variables with the highest variance in this study. The technique was used to investigate key traits from an array of overlapping relationships [66].
Several preconditions are needed to determine the suitability of PCA [67]. First, the sample size should include at least 200 participants. Second, the correlation matrix of the observed variables should display at least modest correlations to extract coherent factors [68]. In our research, the Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy (≥0.60) and Bartlett’s Test of Sphericity (p < 0.001) were used to test for the correlation among factors. We extracted only factors with an eigenvalue of 1.0 or more, in line with Kaiser’s assumption. We reported factor loadings for each observed variable, indicating the correlations between the observed variables and the latent factors. A varimax rotation was performed to minimize the number of variables with high loadings on certain factors and to adjust for multiple scales in our measurement items. To understand the influence of variables on each component of R–U migration, we checked the communality for 21 variables that consistently exceeded a threshold value of 0.50 (Table 3). A reliability assessment was conducted to assess internal consistency to represent the overall decision of farmers’ rural–urban migration. This helps check whether the components (e.g., individuals, household, economic, farmer’s attitude towards farming, spatial, climate-induced extremes) had adequate precision. Cronbach’s alpha coefficients were used to examine the reliability of each item. Cronbach’s alpha coefficients can vary from 0 to 1, where 1 indicates perfect reliability and 0 indicates a very unreliable measure. In the early stages of research, a Cronbach’s alpha coefficient approaching more than 0.60 is acceptable [69]. The study results found Cronbach’s alpha coefficients of each item to be higher than 0.60. Thus, generally, these items indicate a relatively high level of internal consistency. The analysis of the data was conducted using SPSS 22.0.
Table
Table 1. Description of selected variables and sources for assessing farmers’ rural–urban migration.
Table 1. Description of selected variables and sources for assessing farmers’ rural–urban migration.
Selected VariablesDescription of VariablesScalingSources
AgeYears15+[70]
EducationNumbers of years of schooling∑(Statements) of years[71,72]
Organizational participationAccess to government, non-government, and community-based organizations∑(Statements), each statement has a value of 0–3 (No = 0; Low = 1; Medium = 2; High = 3)[62]
CosmopolitanismFrequent internal traveler compared to someone who has never visited any place∑(Statements), each statement has a value of 0–3 (No = 0; Low = 1; Medium = 2; High = 3)[73,74]
Accessibility of mass mediaRadio, television, local newspaper∑(Statements), each statement has a value of 0–4[75]
Agricultural knowledgeAccess to knowledge of HYV, harvesting, seeding, pesticide, and land-use pattern∑(Statements), each statement has a value of 0–3 (No = 0; Low = 1; Medium = 2; High = 3)[76]
Training receivedAccess to the various training programs1 for each training day[71,77]
Family farm sizeTotal farm sizeIn hectares (ha)[78]
Family incomeTotal monthly incomeIn thousand Bangladesh taka (BDT)[79]
Family sizeNumber of family membersNumber of family members[80]
Family debtTotal monthly debtIn thousand Bangladesh taka (BDT)[81]
Unemployment in rural areasStatus of unemployment in rural areas of farmersFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[55]
Income fluctuationStability of monthly incomeFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[19,82]
Seasonal famine/poverty (Monga)Effects
of the pre-harvest period from September to November, plagued by seasonal
hunger/poverty in the northwestern part of Bangladesh		Five-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[80,83]
Low price of agricultural productsLevel of the price of agriculture productsFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[21,84]
Low agricultural wages in a rural areaAgricultural wage in rural areas of farmersFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[85]
Low agricultural investments (e.g., seeds, fertilizer, cash, credit) from the GovernmentAccess to agriculture investment by the governmentFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[86]
Low/no turnover in farmingSupports (e.g., money, seeds, land) taken by individuals, GOs, and NGOs in a particular periodFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[87]
Perceived production riskLow production due to seasonal variation and insectsFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[19,88]
Better communication infrastructure in urban areasBetter medium of transportationFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[61]
Availability of anticipated jobs in the urban areaJob availabilityFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[89]
Fascination with urban settingsBetter livelihood facilities (e.g., schooling, health, and safe food)Five-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[4]
Inadequate arable landStatus of cultivable landFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[46]
Single-cropped areaThe area only used for one cropFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[90]
Low agricultural mechanizationLevel of accessibility to agriculture machinesFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[25]
Low availability of agricultural inputsStatus of access to agriculture inputs (e.g., tractor, harvester, HYV seeds)Five-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[91]
Seasonal floodingFrequency of seasonal floodingFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[60]
DroughtIntensity of droughtFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[26,92]
Abnormal rainfall and heatwavesPeriod of rainfall and heatwaveFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[93,94]
River erosionFrequency of river erosion adjacent to the settlementFive-point Likert scale (0–4): very rare = 1; rare = 2: frequent = 3, and very frequent = 4[23,95]
Compiled by the authors, 2021.
Table
Table 2. Relatively important variables (based on the Q-methodology) influencing farmers’ internal migration (n = 254).
Table 2. Relatively important variables (based on the Q-methodology) influencing farmers’ internal migration (n = 254).
Degree of ImportanceVariables
Most importantAgriculture knowledge, training received, seasonal flooding, river erosion, income fluctuation, organizational participation
Highly importantInadequate arable land, seasonal famine/poverty (Monga), unemployment in rural areas, household income, low agricultural wage in rural areas, family farm size
Very importantAvailability of anticipated jobs in urban areas, low availability of agricultural inputs, abnormal rainfall and heatwaves, household debt, age
Fairly importantSingle-cropped area, low/no turnover in farming, perceived production risk, education
Less importantCosmopolitanism, accessibility to mass media, family size, better communication infrastructure in urban areas, fascination with urban settings, low agricultural mechanization, drought, low agricultural investments (e.g., seeds, fertilizer, cash, credit) from the Government, low price of agricultural products
G
Table
Table 3. Factor loadings and communality values of contributing variables under each factor (n = 254).
Table 3. Factor loadings and communality values of contributing variables under each factor (n = 254).
ComponentsContributing Variables of ComponentsFactor Loadings (h2)Communality
IndividualAge 0.6210.887
Organizational participation 0.7220.793
Agricultural knowledge0.8530.885
Training received0.8220.745
HouseholdsFamily farm size0.7730.743
Household income0.8800.684
Household debt0.6780.779
EconomicUnemployment in the rural area0.8890.780
Income fluctuation0.7730.709
Low agricultural wages in rural areas0.6740.774
Seasonal famine/poverty (Monga)0.7650.782
Farmers’ attitude toward farmingLow/no turnover in farming 0.7850.674
Perceived production risk business0.5900.689
Availability of anticipated jobs in an urban area0.6640.779
SpatialUnavailable arable land0.8700.773
Single-cropped area0.6850.756
Low availability of agricultural inputs0.6540.779
Climate-induced extremesSeasonal flooding0.6530.664
Abnormal rainfall and heatwaves0.6780.731
Drought0.5960.699
River erosion0.7750.880

4. Results and Discussion

4.1. Level of Importance (Dominance) for Variables Influencing Farmers’ R-U Migration

Selected variables of farmers’ R–U migration were assessed through median analysis; the median analysis of the sample of respondents assisted in identifying 30 key variables influencing farmers’ R–U migration. Based on the Q-methodology, the study detected 20 “relatively important” variables (e.g., most, highly, very, fairly, less) influencing farmers’ R-U migration (Table 2). The Q-methodology also elicited subjective viewpoints and identified shared patterns among the local farmers. The Q-methodology provides a solid basis for the systematic study of subjectivity [96]. Uniquely, the Q-methodology combines the strengths of both qualitative and quantitative methods. Typically, in a Q-methodological study, respondents are presented with a sample of statements about some topic (e.g., factors for rural–urban migration), called the Q-set. Respondents, called the P-set, are asked to rank-order the statements from their individual point of view, according to some preference, judgment, or feeling, mostly using a quasi-normal distribution. By Q-sorting, people give their subjective meaning to the statements and, by doing so, reveal their subjective viewpoint. These individual rankings or viewpoints are then employed in the factor and median analyses [97].
As shown in Table 2, agriculture knowledge, training received, seasonal flooding, river erosion, income fluctuation, and organizational participation were the most important factors. Next, scarce arable land, poverty, unemployment in rural areas, household income, low agricultural wage, and family farm size were deemed highly significant factors of R–U migration. Moreover, our results indicated that the availability of anticipated jobs in the urban area, low availability of agricultural inputs, abnormal rainfall and heatwaves, household debt, and age played a significant role in migrating from rural to urban settings. Likewise, single-cropped areas, low/no turnover in farming, perceived production risk, and anticipated jobs in urban areas were important aspects that impacted farmers’ decision to migrate internally. Importantly, our results suggested that better communication infrastructure in urban areas, fascination with urban settings, low agricultural mechanization, drought, education, cosmopolitanism, extensive media contact, low price of agricultural products, and low agricultural investment from the Government were less influential on farmers’ decision to migrate to the country’s cities.

4.2. Underlying Factors Affecting Farmers’ Internal Migration

This section presents and summarizes the results of factor analysis. Factor loadings and communalities are reported in Table 3, and eigenvalues and the percent contribution of factors to the total variance are reported in Table 4.

4.2.1. Factor I: Farmers’ Individual Characteristics

Based on PCA analysis, the first component of R–U migration can be explained through four variables: age, organizational participation, agriculture knowledge, and training received. The communality for extracted variables was 0.687, 0.793, 885, and 0.745 with factor loadings of 0.621, 0.722, 0.853, and 0.822, respectively.
Older farmers tend to have a relatively lower probability of engaging in rural-to-urban migration. Al-Maruf (2017) found almost similar findings that farmers aged 20–50 are much more interested in rural-to-urban migration due to greater physical capability [60]. Farmers with higher levels of agriculture knowledge create job opportunities in urban areas, which plays a crucial role in R–U migration. Organizational participation may contribute to farmers’ migration—typically, growers receive information from such organizations about occupations and jobs requiring agricultural skills in urban areas, creating migration channels. As such, farmers with high organizational participation represent a relatively higher percentage of R–U migrants [91]. Our results also indicated that farmers who have received training from governmental and not-for-profit organizations are more likely to migrate from rural to urban areas [71]. Trained farmers are exposed to a wide range of ideas and knowledge, such as seeding, pesticides, irrigation, and crop selection [59,62]. Training on seeding, planting, fertilizer use, harvesting methods, and land-use decisions are the type of training that enhance farmers’ urban working competitiveness. Table 4 shows that individual characteristics explained a higher percentage of the total variance (15.44), indicating that this particular dimension is vital for explaining R–U migration in Bangladesh.

4.2.2. Factor II: Household’s Characteristics

The second contributing dimension explaining rural–urban migration is household characteristics. This dimension consisted of three variables: family farm size, household income, and household debt with communality values of 0.743, 0.684, and 0.779 and factor loadings of 0.773, 0.880, and 0.678, respectively. In rural areas of Bangladesh, more than 70% of producers are smallholder farmers [98]. They tend to migrate to bear their family’s basic needs and expenditures. Therefore, farmers with small farms show a more migratory attitude than large-sized farm holders. Higher household income levels may ensure their overall livelihood expenditures and essential better services such as school, college, hospitals, other public authorities, and household members, which led to R–U migration. Household debt is another push factor for farmers for R–U migration. Hence, they have to cover their households’ expenditures through debt. To reduce debt pressure, farmers migrate from rural to urban areas. Other household members contribute to family expenses through the return of money from the migrated members of the family [19]. The household characteristics dimension explained 15.02% of the variance in R–U migration.

4.2.3. Factor III: Economic Characteristics

The third component of farmers’ internal migration is economic characteristics. Economic characteristics include four variables, namely unemployment in rural areas, income fluctuation, low agriculture wages in rural areas, and poverty. Employment opportunities are very limited in rural areas in Bangladesh. Hence, most marginal and middle-sized farm holders are interested in migrating from rural to urban areas through short-term and seasonal migration. However, overall income fluctuates due to limited employment opportunities and natural disasters. Farmers migrate internally to deal with this economic challenge [89]. Low agriculture wage is another critical factor for internal migration. Based on the informal discussion, the study found that although some farmers find limited employment opportunities in rural areas, wages are meager. Thus, this crucially influences migration from rural to urban areas, as Mallick [99] found. The current study also revealed that many marginal farmers migrate from rural to urban due to poverty [100]. The economic characteristics contributed 12.58% to the total variability of the data.

4.2.4. Factor IV: Farmers’ Perception/Attitude towards Farming

The fourth contributing component of farmers’ internal migration was attitudes towards farming, which included the following variables: low turnover, perceived production risk, and anticipated jobs in urban areas with communality and loadings of 0.674, 0.689, and 0.779 and 0.785, and 0.590 and 0.664, respectively. Rural farmers’ overall income negatively affects local farming, as most farmers are considered to have a low turnover rate (Mallick et al., 2015). In addition, current challenges in agricultural production, including the increased price of farming inputs, poor soil quality, crop disease, low sale prices, poor revenue for farmers, and environmental shocks, contribute to the notion of agriculture as a highly risky activity [27,63,101]. As a result, farmers may migrate to urban areas and engage in other non-agricultural sectors (e.g., tertiary economic activities). Many farmers in rural Bangladesh are involved in subsistence farming, where most food crops produced are grown to provide for the basic food needs of the household with a small surplus for commercialization [90]. Thus, farmers, especially those not engaged in commercial agriculture, move to urban areas to seek job opportunities with relatively higher salaries than in rural areas. Farmers’ perception/attitude toward farming shows an overall variance of 11.11%, which moderately influences rural–urban migration among farmers.

4.2.5. Factor V: Spatial Characteristics

Lake and Fenner [86] pointed out that land quality, spatial crop pattern, and water availability influence R–U migration in rural Bangladesh. In our study, spatial characteristics comprise three variables: unavailability of arable land, single-cropped area, and low availability of agriculture inputs—these factors have communality values and factor loadings of 0.773, 0.756, 0.779 and 0.870, and 0.685 and 0.654, respectively. This dimension explained about 9.68% of the total variance. In Bangladesh, two-thirds of the population lives in villages, where approximately 41.54% of the economically active population is engaged in agriculture, contributing 12.64% to the country’s total GDP [102]. In the absence of stringent land-use policies in rural areas, farming land has drastically decreased and has been transferred to commercial, residential, industrial, and other unplanned uses [103]. Moreover, the land-use and availability issues are exacerbated by the low availability of agricultural inputs coupled with higher price tags and the lack of crop diversification [27].

4.2.6. Factor VI: Climate-Induced Extremes

Climate-induced extreme events, for example, floods, cyclones, and droughts are predicted to increase in Bangladesh [6]. The effects of climate shock negatively impact ecosystems, exacerbate the local water crisis and land degradation, and threaten the livelihoods of millions of rural residents and farming communities in Bangladesh [49]. Smallholder farmers are among the most vulnerable groups to climatic risks as they face chronic poverty and food insecurity [102]. The sixth influential dimension contribution to R–U migration is climate-induced disasters. This dimension consisted of four variables: seasonal flooding, abnormal rainfall and heatwaves, drought, and river erosion. The communality values and factor loadings for seasonal flooding, irregular rainfall and heatwaves, drought, and river erosion were 0.664, 0.731, 0.699, and 0.880 and 0.653, 0.678, 0.596, and 0.775, respectively. This dimension explained only 9.14% of the total variability in the data.

5. Conclusions and Recommendations

The main objective of this paper was to identify the underlying factors in the rural–urban (R–U) migration of farmers in Bangladesh. For instance, the factors, economic, demographic, social, and environmental, that affect this R–U migration are multiple and complex in nature [104]. However, our findings indicated that factors drive farmer’s R–U migration in six main dimensions: individual, households, economic, farmer’s attitude towards farming, spatial, and climate-induced disaster. Further, our results indicated that agriculture knowledge, training received, seasonal flooding, river erosion, income fluctuation, and organizational participation are the most influential factors that affect R–U migration, as cited by Kumar et al. [105]. In addition, age, agricultural knowledge, household debt, seasonal famine/poverty (Monga), unemployment in rural areas, availability of job opportunities in urban areas, shortage of agricultural inputs, and river erosion significantly influenced farmers’ decision to leave their farms in Bangladesh.
Since rural–urban migration mainly originates from a lack of rural economic development, creating jobs and other opportunities for earning income in rural regions can address the current problem [106]. There should thus be three different kinds of investment in the rural areas, as also noted by Walter [104]. First of all, to increase agricultural production, the Government should continue to promote the modernization of the agricultural sector. More efforts to adapt to environmental risks and diversify agricultural output will also increase agricultural productivity. The State needs to research and develop more flood-tolerant, drought-resistant, heat-resistant, and salinity-resistant crops and train farmers to grow them, providing reliable seeds at a moderate cost. Secondly, Bangladesh should develop rural industries and infrastructure and upgrade health and learning in rural areas, so that farm families believe do not need to migrate to cities to avail themselves of these services. Subsidies in rural areas should be encouraged. Guaranteeing balanced national development requires economic growth’s regionalization (i.e., decentralization). Political parties have successfully campaigned on this issue for 30 years and on the related issue of moving the capital out of Dhaka to stop concentrating development there, but no action has been taken. Establishing industrial estates, education facilities, and private investment in semi-urban centers may accomplish this. Lastly, an integrated R–U migration strategy is necessary, and these policies should be designed to work on the underlying causes of rural–urban migration. Such a strategy must be proactive in addressing current problems and also forestalling future ones.
The Bangladesh Government published a draft of the country’s national urban policy in 2011 for the coming years, entitled “National Urban Sector Policy” draft [104]. This document recognizes the current spatial imbalance in the pattern of urbanization in the country and also the drastic pace of urban population growth in Dhaka and other big cities. It also shows both positive and negative results of R–U migration. R–U migration needs to be guided appropriately to build balanced urbanization to avoid mass population aggregation in one or few cities [107].
The Department of Agricultural Extension (DAE) should take necessary actions to provide practical training to the rural farmers to increase their agricultural knowledge and increase the overall yield of crops. Furthermore, the Government should create an insurance scheme against agricultural risks, such as floods, disasters, and crop losses due to pest infestations. Concerning the prevention and management of natural disasters, short-term strategies include constructing additional drainage systems that disperse water surpluses and minimize floods. In addition, improving flood protection is an essential issue in large metropolitan centers. To halt the present degree of disorganization in flood prevention and reduction, the Government should establish a hierarchical chain of command for a Department of Disaster Management to govern.

Author Contributions

Conceptualization, A.A.-M. and A.K.M.K.P.; methodology, A.A.-M. and A.K.M.K.P.; formal analysis, A.A.-M.; field investigation and interviews, A.K.M.K.P.; resources, A.A.-M. and A.K.M.K.P.; writing—original draft preparation, A.A.-M. and P.K.S.; writing—review and editing, A.A.-M., A.K.M.K.P., P.K.S., M.S.R. and J.R.-M.; visualization, A.A.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Supporting data of the findings of this study are available from the second author upon reasonable request.

Acknowledgments

We are very grateful to the GO’s and NGOs’ executive officials and the local farmers who provided insightful information regarding rural–urban migration. The authors are, of course, responsible for errors of facts and interpretation. We acknowledge the Open Access Publication Funds by the Georg-August University, Göttingen, Germany.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Deng, X.; Xu, D.; Qi, Y.; Zeng, M. Labor off-farm employment and cropland abandonment in rural China: Spatial distribution and empirical analysis. Int. J. Environ. Res. Public Health 2018, 15, 1808. [Google Scholar] [CrossRef] [Green Version]
  2. Shirai, Y.; Rambo, A.T. Household structure and sources of income in a rice-growing village in northeast Thailand. Southeast Asian Stud. 2017, 6, 275–292. [Google Scholar]
  3. Sen, B.; Dorosh, P.; Ahmed, M. Moving out of agriculture in Bangladesh: The role of farm, non-farm and mixed households. World Dev. 2021, 144, 105479. [Google Scholar] [CrossRef]
  4. Farhana, D.K.M.; Rahman, S.A.; Rahman, M. Factors of migration in urban Bangladesh: An empirical study of poor migrants in rahshahi city. Bangladesh e-J. Sociol. 2012, 9, 107–117. [Google Scholar] [CrossRef] [Green Version]
  5. Human Development Report 2009: Overcoming Barriers: Human Mobility and Development; UNDP: New York, NY, USA, 2009.
  6. Tawsif, S.; Alam, M.S.; Al-Maruf, A. How households adapt to heat wave for livable habitat? A case of medium-sized city in Bangladesh. Curr. Res. Environ. Sustain. 2022, 4, 100159. [Google Scholar] [CrossRef]
  7. Wickramasekara, P. Labour migration in South Asia: A review of issues, policies and practices. Int. Migr. Work. Pap. 2011, 108, 1–49. [Google Scholar] [CrossRef] [Green Version]
  8. Donato, K.M.; Carrico, A.R.; Sisk, B.; Piya, B. Different but the same: How legal status affects international migration from Bangladesh. ANNALS Am. Acad. Political Soc. Sci. 2016, 666, 203–218. [Google Scholar] [CrossRef]
  9. Nabi, A.N. Dynamics of internal migration in Bangladesh. Can. Stud. Popul. 1992, 19, 81–98. [Google Scholar] [CrossRef] [Green Version]
  10. World Bank. United Nations Population Division. World Population Prospects: 2019 Revision, The World Bank. 2021. Available online: https://data.worldbank.org/indicator/SP.POP.TOTL?locations=BD (accessed on 22 August 2021).
  11. World Bank. Bangladesh: Growing the Economy through Advances in Agriculture. 2016. Available online: https://www.worldbank.org/en/results/2016/10/07/bangladesh-growing-economy-through-advances-in-agriculture (accessed on 28 July 2021).
  12. Kabir, M.J.; Cramb, R.; Alauddin, M.; Roth, C.; Crimp, S. Farmers’ perceptions of and responses to environmental change in southwest coastal Bangladesh. Asia Pac. Viewp. 2017, 58, 362–378. [Google Scholar] [CrossRef]
  13. Ajaero, C.K.; Onokala, P.C. The effects of rural-urban migration on rural communities of southeastern Nigeria. Int. J. Popul. Res. 2013, 2013, 610193. [Google Scholar] [CrossRef] [Green Version]
  14. World Bank. Turn Down the Heat: Climate Extremes, Regional Impacts, and the Case for Resilience, 1464800561; The World Bank (WB): Washington, DC, USA, 2013. [Google Scholar]
  15. BBS. Bangladesh Bureau of Statistics; Ministry of Planning, Government of the People’s Republic of Bangladesh Household Income and Expenditure Survey (HIES): Dhaka, Bangladesh, 2016. [Google Scholar]
  16. World Bank Report. 2020. Available online: https://www.macrotrends.net/countries/BGD/bangladesh/urban-population’>BangladeshUrbanPopulation1960-2022</a> (accessed on 22 August 2021).
  17. Renwick, A.; Jansson, T.; Verburg, P.H.; Revoredo-Giha, C.; Britz, W.; Gocht, A.; McCracken, D. Policy reform and agricultural land abandonment in the EU. Land Use Policy 2013, 30, 446–457. [Google Scholar] [CrossRef]
  18. Xu, D.; Deng, X.; Guo, S.; Liu, S. Labor migration and farmland abandonment in rural China: Empirical results and policy implications. J. Environ. Manag. 2018, 232, 738–750. [Google Scholar] [CrossRef]
  19. Kartiki, K. Climate change and migration: A case study from rural Bangladesh. Gend. Dev. 2011, 19, 23–38. [Google Scholar] [CrossRef]
  20. Li, M.; Zhang, W. Does ‘Landless’ Stimulate Bangladesh Rural Migration under Weather Risk? 2020. Available online: https://ideas.repec.org/p/ags/aaea20/304538.html (accessed on 21 July 2021).
  21. Salam, S.; Azad, M.; Kalam, A.; Salam, M.; Islam, M.M. Rural-to-Urban Migration and Realization of Expected Better Life in Bangladesh: An Empirical Study in Rajshahi City Corporation. Int. J. Asian Soc. Sci. 2020, 10, 612–622. [Google Scholar] [CrossRef]
  22. Haque, A.K.M.; Al-Maruf, A.; Rida, T.N. Governance for Sustainable Development to Combat the Impact of Climate Change in a Medium-Sized City in Bangladesh. In Bangladesh II: Climate Change Impacts, Mitigation and Adaptation in Developing Countries; Springer: Cham, Switzerland, 2021; pp. 357–373. [Google Scholar]
  23. Pyka, L.M.; Al-Maruf, A.; Jenkins, J.C.; Shamsuzzoha, M.; Braun, B. Floating gardening in coastal Bangladesh: Evidence of sustainable farming for food security under climate change. J. Agric. Food Environ. 2021, 1, 161–168. [Google Scholar] [CrossRef]
  24. Al-Maruf, A.; Mira, S.A.; Rida, T.N.; Rahman, M.S.; Sarker, P.K.; Jenkins, J.C. Piloting a Weather-Index-Based Crop Insurance System in Bangladesh: Understanding the Challenges of Financial Instruments for Tackling Climate Risks. Sustainability 2021, 13, 8616. [Google Scholar] [CrossRef]
  25. Bhuyan, R.A.; Khan, H.A.R.; Ahmed, S.U. Rural Urban Migration and Poverty: The Case for Reverse Migration in Bangladesh; MAP Focus Study Series, no. 10; Centre on Integrated Rural Development for Asia and the Pacific: Dhaka, Bangladesh, 2001. [Google Scholar]
  26. Adkoli, B.V. Migration of Health Workers: Perspectives from Bangladesh, India, Nepal, Pakistan and Sri Lanka. In Regional Health Forum; World Health Organization, Regional Office for South-East Asia: Geneva, Switzerland, 2006; Volume 10, pp. 49–58. [Google Scholar]
  27. Ravenstein, E.G. The laws of migration. J. Stat. Soc. Lond. 1885, 48, 167–235. [Google Scholar] [CrossRef]
  28. Lee, E.S. A theory of migration. Demography 1966, 3, 47–57. [Google Scholar] [CrossRef]
  29. Zelinsky, W. The Hypothesis of the Mobility Transition. Geogr. Rev. 1971, 61, 219–249. [Google Scholar]
  30. Todaro, M.P. A model of labor migration and urban unemployment in less developed countries. Am. Econ. Rev. 1969, 59, 138–148. [Google Scholar]
  31. Kaushik, H. Theories and Typologies of Migration: An Overview. IUP J. Int. Relat. 2021, 15, 13–22. [Google Scholar]
  32. Miklian, J.; Hoelscher, K. Entrepreneurial Strategies to Address Rural-Urban Climate-Induced Vulnerabilities: Assessing Adaptation and Innovation Measures in Dhaka, Bangladesh. Sustainability 2020, 12, 9115. [Google Scholar] [CrossRef]
  33. Uddin, M.; Firoj, M. Causes and Consequences of Rural-Urban Migration in Bangladesh: An Empirical Study in Chittagong City. 2013. Available online: http://dspace.iiuc.ac.bd:8080/xmlui/handle/88203/247 (accessed on 17 June 2021).
  34. Xie, F.; Liu, S.; Xu, D. Gender difference in time-use of off-farm employment in rural Sichuan, China. J. Rural. Stud. 2019; in press. [Google Scholar]
  35. Kuhn, R.S. The Determinants of Family and Individual Migration: A Case-Study of Rural Bangladesh. IBS Res. Program Popul. Processes Work. Paper. 2005. Available online: https://ibs.colorado.edu/pubs/pop/pop2005-0005.pdf (accessed on 30 March 2022).
  36. Huang, K.; Deng, X.; Liu, Y.; Yong, Z.; Xu, D. Does off-farm migration of female laborers inhibit land transfer? Evidence from Sichuan Province, China. Land 2020, 9, 14. [Google Scholar] [CrossRef] [Green Version]
  37. Wang, P.; Lyu, L.; Xu, J. Factors Influencing Rural Households’ Decision-Making Behavior on Residential Relocation: Willingness and Destination. Land 2021, 10, 1285. [Google Scholar] [CrossRef]
  38. Razzaque, A.; Clair, K.; Chin, B.; Islam, M.Z.; Mia, M.N.; Chowdhury, R.; Mustafa, A.H.M.G.; Kuhn, R. Association of time since migration from rural to urban slums and maternal and child outcomes: Dhaka (north and south) and Gazipur City corporations. J. Urban Health 2020, 97, 158–170. [Google Scholar] [CrossRef]
  39. Chakma, B.M.; Akhy, A.A. Hill to plain: Causes and impacts of internal migration of indigenous peoples in Bangladesh. Sociol. Mind 2015, 5, 268. [Google Scholar] [CrossRef] [Green Version]
  40. Aryal, G.R.; Mann, J.; Loveridge, S.; Joshi, S. Drivers of differences in inventiveness across urban and rural regions. J. Urban Aff. 2021, 43, 640–657. [Google Scholar] [CrossRef]
  41. Akhter, S.; Bauer, S. Household level determinants of rural-urban migration in Bangladesh. Int. J. Humanit. Soc. Sci. 2014, 8, 24–27. [Google Scholar]
  42. Deng, X.; Xu, D.; Zeng, M.; Qi, Y. Does early-life famine experience impact rural land transfer? Evidence from China. Land Use Policy 2019, 81, 58–67. [Google Scholar] [CrossRef]
  43. Collins, W.J.; Wanamaker, M.H. Selection and economic gains in the great migration of African Americans: New evidence from linked census data. Am. Econ. J. Appl. Econ. 2014, 6, 220–252. [Google Scholar] [CrossRef] [Green Version]
  44. Cleland, J.; Phillips, J.F.; Amin, S.; Kamal, G.M. The Determinants of Reproductive Change in Bangladesh; The World Bank: Washington, DC, USA, 1994; Volume 72, pp. 1–55. [Google Scholar]
  45. Martin, M.; Billah, M.; Siddiqui, T.; Abrar, C.; Black, R.; Kniveton, D. Climate-related migration in rural Bangladesh: A behavioural model. Popul. Environ. 2014, 36, 85–110. [Google Scholar] [CrossRef]
  46. Chen, J.; Mueller, V. Coastal climate change, soil salinity and human migration in Bangladesh. Nat. Clim. Chang. 2018, 8, 981–985. [Google Scholar] [CrossRef]
  47. Khan, A. International Migration Literature Search in Bangladesh during the Period of 1971–2020. 2020. Available online: https://ssrn.com/abstract=3866504 (accessed on 22 May 2021).
  48. Martin, M.; Kang, Y.H.; Billah, M.; Siddiqui, T.; Black, R.; Kniveton, D. Climate-influenced migration in Bangladesh: The need for a policy realignment. Dev. Policy Rev. 2017, 35, O357–O379. [Google Scholar] [CrossRef]
  49. Bernzen, A.; Jenkins, J.C.; Braun, B. Climate change-induced migration in coastal Bangladesh? A critical assessment of migration drivers in rural households under economic and environmental stress. Geosciences 2019, 9, 51. [Google Scholar] [CrossRef] [Green Version]
  50. Castellano, R.; Dolšak, N.; Prakash, A. Willingness to help climate migrants: A survey experiment in the Korail slum of Dhaka, Bangladesh. PLoS ONE 2021, 16, e0249315. [Google Scholar] [CrossRef]
  51. Ishtiaque, A.; Ullah, M.S. The influence of factors of migration on the migration status of rural-urban migrants in Dhaka, Bangladesh. Hum. Geogr. 2013, 7, 45. [Google Scholar] [CrossRef] [Green Version]
  52. Shams, S.; Mahruf, M.; Shohel, C.; Ahsan, A. Housing problems for middle and low income people in Bangladesh: Challenges of Dhaka Megacity. Environ. Urban. Asia 2014, 5, 175–184. [Google Scholar] [CrossRef]
  53. Jahan, I. Development in Rural Bangladesh: A Critical Ethnography. Ph.D. Thesis, Durham University, Durham, UK, 2015. [Google Scholar]
  54. Haque, M.E.; Islam, M.M. Rural to urban migration and household living conditions in Bangladesh. Dhaka Univ. J. Sci. 2012, 60, 253–257. [Google Scholar] [CrossRef] [Green Version]
  55. Khan, M.A. Livelihood, WASH Related Hardships and Needs Assessment of Climate Migrants: Evidence from Urban Slums in Bangladesh. Heliyon 2022, 8, e09355. [Google Scholar] [CrossRef]
  56. Tankou, M. The Interactions of Human Mobility and Farming Systems on Biodiversity and Soil Quality in the Western Highlands of Cameroon; African Books Collective: Yaoundé, Cameroon, 2014. [Google Scholar]
  57. Gellrich, M.; Zimmermann, N.E. Investigating the regional-scale pattern of agricultural land abandonment in the Swiss mountains: A spatial statistical modelling approach. Landsc. Urban Plan. 2007, 79, 65–76. [Google Scholar] [CrossRef]
  58. Shackleton, C.M.; Mograbi, P.J.; Drimie, S.; Fay, D.; Hebinck, P.; Hoffman, M.T.; Maciejewski, K.; Twine, W. Deactivation of field cultivation in communal areas of South Africa: Patterns, drivers and socio-economic and ecological consequences. Land Use Policy 2019, 82, 686–699. [Google Scholar] [CrossRef] [Green Version]
  59. Marshall, R.; Rahman, S. Internal Migration in Bangladesh: Character, Drivers and Policy Issues; United Nations Development Programme (UNDP): New York, NY, USA, 2013. [Google Scholar]
  60. Al-Maruf, A. Enhancing Disaster Resilience through Human Capital: Prospects for Adaptation to Cyclones in Coastal Bangladesh. Ph.D. Thesis, Universität zu Köln, Köln, Germany, 2017. [Google Scholar]
  61. Imam, M.F.; Islam, M.A.; Alam, M.A.; Hossain, M.J.; Das, S. Small Area Estimation of Poverty in Rural Bangladesh. Bangladesh J. Agric. Econ. 2019, 40, 1–16. [Google Scholar]
  62. Kanak Pervez, A.K.M.; Gao, Q.; Uddin, M.E. Rural women’s awareness on indigenous technical knowledge: Case of northern Bangladesh. Anthropologist 2015, 21, 415–426. [Google Scholar] [CrossRef]
  63. Al-Maruf, A.; Jenkins, J.C.; Bernzen, A.; Braun, B. Measuring Household Resilience to Cyclone Disasters in Coastal Bangladesh. Climate 2021, 9, 97. [Google Scholar] [CrossRef]
  64. Al-Maruf, A.; Jenkins, C.; Islam, A.; Sarmin, S. Coastal Zone of Bangladesh: A Tale of Pessimism and Optimism. In Climate Adaptation for a Sustainable Economy: Lessons from Bangladesh, an Emerging Tiger of Asia; Asian Political, Economic and Social Issues; Nova Science Publishers: Hauppage, NY, USA, 2020. [Google Scholar]
  65. Hayton, J.C.; Allen, D.G.; Scarpello, V. Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organ. Res. Methods 2004, 7, 191–205. [Google Scholar] [CrossRef]
  66. Kumar, S.; Chand, P. Prevailing practices and dimensions of contract wheat seed farming in Haryana state. Agric. Econ. Res. Rev. 2004, 17, 149–161. [Google Scholar]
  67. Asadzadeh, A.; Kötter, T.; Zebardast, E. An augmented approach for measurement of disaster resilience using connective factor analysis and analytic network process (F’ANP) model. Int. J. Disaster Risk Reduct. 2015, 14, 504–518. [Google Scholar] [CrossRef]
  68. Beccari, B. A comparative analysis of disaster risk, vulnerability and resilience composite indicators. PLoS Curr. 2016, 8, ecurrents.dis.453df025e34b682e9737f95070f9b970. [Google Scholar] [CrossRef]
  69. Mayunga, J.S. Measuring the Measure: A Multidimensional Scale Model to Measure Community Disaster Resilience in the U.S. Gulf Coast. Ph.D. Thesis, Graduate Studies of Texas A & M University, College Station, TX, USA. Available online: http://oaktrust.library.tamu.edu/bitstream/handle/1969.1/ETD-TAMU-2009-05-769/MAYUNGA-DISSERTATION.pdf?sequence=1 (accessed on 11 April 2021).
  70. Rahman, M.M.; Alam, K. Clean energy, population density, urbanization and environmental pollution nexus: Evidence from Bangladesh. Renew. Energy 2021, 172, 1063–1072. [Google Scholar] [CrossRef]
  71. Rithu, M.N.A.; Rahman, M.M.; Al-Maruf, A. The Status of Geography and Environmental Studies at Secondary Level Education in Bangladesh. Arch. Curr. Res. Int. 2016, 5, 1–9. [Google Scholar] [CrossRef] [Green Version]
  72. Al-Maruf, A.; Braun, B. Contribution of the Vocational Education and Training (VET) to Enhance Disaster Resilience: A Case Study through Human Capital Lens in Coastal Area of Bangladesh. In Proceedings of the International Conference on Sustainable Development, Dhaka, Bangladesh, 4–5 February 2016; Available online: http://csd.ulab.edu.bd/wp-content/uploads/sites/19/2016/12/ICSD-Proceedings_2016.pdf (accessed on 22 November 2020).
  73. Mitra, A.; Murayama, M. Rural to Urban Migration: A District-Level Analysis for India. Int. J. Migr. Health Soc. Care 2009, 5, 17–47. [Google Scholar] [CrossRef] [Green Version]
  74. Gidwani, V.; Sivaramakrishnan, K. Circular migration and rural cosmopolitanism in India. Contrib. Indian Sociol. 2003, 37, 339–367. [Google Scholar] [CrossRef]
  75. Yue, Z.; Li, S.; Jin, X.; Feldman, M.W. The role of social networks in the integration of Chinese rural–urban migrants: A migrant–resident tie perspective. Urban Stud. 2013, 50, 1704–1723. [Google Scholar] [CrossRef] [Green Version]
  76. Zhao, Q.; Kulane, A.; Gao, Y.; Xu, B. Knowledge and attitude on maternal health care among rural-to-urban migrant women in Shanghai, China. BMC Women’s Health 2009, 9, 5. [Google Scholar] [CrossRef] [Green Version]
  77. Caldwell, J.C. Determinants of rural-urban migration in Ghana. Popul. Stud. 1968, 22, 361–377. [Google Scholar] [CrossRef]
  78. Brumer, A. Gender relations in family-farm agriculture and rural-urban migration in Brazil. Lat. Am. Perspect. 2008, 35, 11–28. [Google Scholar] [CrossRef]
  79. Mallick, B.; Vogt, J. Societal Dealings with Cyclone in Bangladesh–A proposal of Vulnerability Atlas for Sustainable Disaster Risk reduction. J. Coast. Zone Manag. 2015, 18, 67–87. [Google Scholar] [CrossRef]
  80. Mobarak, M.; Bryan, G.; Chowdhury, S. Encouraging seasonal migration to mitigate the consequences of a seasonal famine in rural Bangladesh. Policy Brief 2011, 7, 3014. [Google Scholar]
  81. Sarker, S.; Farukh, M.A.; Sharmin, N.; Ali, A. Assessing the Disaster Induced Migration and Displacement in the South-west of Bangladesh. J. Environ. Sci. Nat. Resour. 2019, 12, 135–141. [Google Scholar] [CrossRef]
  82. Lagakos, D.; Mobarak, A.M.; Waugh, M.E. The welfare effects of encouraging rural-urban migration (No. w24193). Natl. Bur. Econ. Res. 2018, 24–93. [Google Scholar]
  83. Mishra, A.K.; Khanal, A.R. Assessing Food Security in Rural Bangladesh: The Role of a Nonfarm Economy. Front. Econ. Glob. 2017, 17, 241–257. [Google Scholar]
  84. Roy, K.C.; Tisdell, C.; Alauddin, M. Rural-urban migration and poverty in South Asia. J. Contemp. Asia 1992, 22, 57–72. [Google Scholar] [CrossRef]
  85. Mallick, B.; Siddiqui, T. Disaster-induced migration and adaptation conversation in Bangladesh. Sustain. Dev. South Asian Conundrum 2015, 11, 155. [Google Scholar]
  86. Lake, P.; Fenner, R. The influence of underlying stresses from environmental hazards on resilience in Bangladesh: A system view. Int. J. Disaster Risk Sci. 2019, 10, 511–528. [Google Scholar] [CrossRef] [Green Version]
  87. Islam, M.S.; Rashid, H.A.; Howlader, A.; Ahmed, M.F. Rural–urban migration: A quest for economic development and poverty reduction. J. Econ. Policy Res. 2011, 6, 119–136. [Google Scholar]
  88. Anaglo, J.N.; Sakyi-Dawson, O.; Boateng, S.D.; Mahama, W.B. Perceived impacts of rural-urban migration on agricultural productivity in Nanumba South District of Northern Region of Ghana. Res. Humanit. Soc. Sci. 2014, 4, 126–133. [Google Scholar]
  89. Feng, S. What’s the Problem of ‘Climate Migration’ Represented to Be? A Discourse Analysis of Policies in Bangladesh; Lund University: Lund, Sweden, 2017. [Google Scholar]
  90. Dev, T.; Sultana, N.; Hossain, M.E. Analysis of the impact of income diversification strategies on food security status of rural households in Bangladesh: A case study of Rajshahi district. Am. J. Theor. Appl. Bus. 2016, 2, 46–56. [Google Scholar]
  91. Bhagat, R.B. Internal migration in India: Are the underprivileged migrating more. Asia-Pac. Popul. J. 2010, 25, 27–45. [Google Scholar] [CrossRef] [Green Version]
  92. Nawrotzki, R.J.; De Waard, J.; Bakhtsiyarava, M.; Ha, J.T. Climate shocks and rural-urban migration in Mexico: Exploring nonlinearities and thresholds. Clim. Chang. 2017, 140, 243–258. [Google Scholar] [CrossRef] [Green Version]
  93. Brueckner, J.K.; Lall, S.V. Cities in developing countries: Fueled by rural–urban migration, lacking in tenure security, and short of affordable housing. Handb. Reg. Urban Econ. 2015, 5, 1399–1455. [Google Scholar]
  94. Shamsuzzoha, M.; Al-Maruf, A. Post SIDR life strategy: Adaptation scenario of settlements of the south. J. IBS 2011, 19, 207–222. [Google Scholar]
  95. Rana, M.S.; Nessa, A.M. Impact of riverbank erosion on population migration and resettlement of Bangladesh. Sci. J. Appl. Math. Stat. 2017, 5, 60–69. [Google Scholar] [CrossRef] [Green Version]
  96. Shabila, N.P.; Al-Tawil, N.G.; Al-Hadithi, T.S.; Sondorp, E. Using Q-methodology to explore people’s health seeking behavior and perception of the quality of primary care services. BMC Public Health 2014, 14, 2. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Gao, J.; Soranzo, A. Applying Q-methodology to investigate people’preferences for multivariate stimuli. Front. Psychol. 2020, 23, 34–45. [Google Scholar]
  98. Osmani, A.G.; Hossain, E. Market participation decision of smallholder farmers and its determinants in Bangladesh. Екoнoмика Пoљoпривреде 2015, 62, 163–179. [Google Scholar]
  99. Mallick, B. Cyclone-induced migration in southwest coastal Bangladesh. ASIEN 2014, 130, 60–81. [Google Scholar]
  100. Biswas, R.K.; Kabir, E.; Khan, H.T. Causes of urban migration in Bangladesh: Evidence from the urban health survey. Popul. Res. Policy Rev. 2019, 38, 593–614. [Google Scholar] [CrossRef]
  101. DiCarlo, J.; Epstein, K.; Marsh, R.; Måren, I. Post-disaster agricultural transitions in Nepal. Ambio 2018, 47, 794–805. [Google Scholar] [CrossRef]
  102. Etzold, B.; Mallick, B. Moving beyond the focus on environmental migration towards recognizing the normality of trans-local lives: Insights from Bangladesh. In Migration, Risk Management, and Climate Change: Evidence and Policy Responses; Springer: Cham, Switzerland, 2016; pp. 105–128. [Google Scholar]
  103. Bernzen, A.; Al-Maruf, A.; Lin, A.; Ahmed, R. Landnutzungswandel im Küstenraum von Bangladesh: Management von Land auf Gemeinde-und Haushaltsebene. Geogr. Rundsch. 2016, 68, 16–22. [Google Scholar]
  104. Walter, P. Floods and rural-urban migration in Bangladesh. State Environ. Migr. 2015, 51, 64. [Google Scholar]
  105. Kumar, R.; Paul, S.; Singh, P.; Chahal, V.P. Factors determining farmers progressiveness: A principal component analysis. Indian J. Agric. Sci. 2015, 85, 19–37. [Google Scholar]
  106. Herrmann, M.; Svarin, D. Environmental Pressures and Rural-Urban Migration: The Case of Bangladesh; MPRA Paper No. 12879; MPRA: Munich, Germany, 2009. [Google Scholar]
  107. International Fund for Agricultural Development (IFAD). Investing in Rural People in Bangladesh. 2014. Available online: https://www.ifad.org/en/web/knowledge/-/publication/investing-in-rural-people-in-banglade-1 (accessed on 29 March 2022).
Agriculture 12 00722 g001 550
Figure 1. Research sites of the study. Source: own design.
Figure 1. Research sites of the study. Source: own design.
Agriculture 12 00722 g001
Table
Table 4. Eigenvalue and percent contribution of factors to the total variance (n = 254).
Table 4. Eigenvalue and percent contribution of factors to the total variance (n = 254).
ComponentsEigenvalue (λ)% of Variance
Individual2.60315.44
Household2.53115.02
Economic2.12012.58
Farmers’ attitude toward farming1.87311.11
Spatial1.6329.68
Climate-induced extremes1.5419.14
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Al-Maruf, A.; Pervez, A.K.M.K.; Sarker, P.K.; Rahman, M.S.; Ruiz-Menjivar, J. Exploring the Factors of Farmers’ Rural–Urban Migration Decisions in Bangladesh. Agriculture 2022, 12, 722. https://doi.org/10.3390/agriculture12050722

AMA Style

Al-Maruf A, Pervez AKMK, Sarker PK, Rahman MS, Ruiz-Menjivar J. Exploring the Factors of Farmers’ Rural–Urban Migration Decisions in Bangladesh. Agriculture. 2022; 12(5):722. https://doi.org/10.3390/agriculture12050722

Chicago/Turabian Style

Al-Maruf, Abdullah, A. K. M. Kanak Pervez, Pradip Kumar Sarker, Md Saifur Rahman, and Jorge Ruiz-Menjivar. 2022. "Exploring the Factors of Farmers’ Rural–Urban Migration Decisions in Bangladesh" Agriculture 12, no. 5: 722. https://doi.org/10.3390/agriculture12050722

APA Style

Al-Maruf, A., Pervez, A. K. M. K., Sarker, P. K., Rahman, M. S., & Ruiz-Menjivar, J. (2022). Exploring the Factors of Farmers’ Rural–Urban Migration Decisions in Bangladesh. Agriculture, 12(5), 722. https://doi.org/10.3390/agriculture12050722

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop