Migration has long been recognized as a central process shaping socio-economic transformation across the world. It influences labor markets, income distribution, urbanization, and social structures, thereby becoming an integral component of development discourse (Kannappan, 1985). Both international and internal migration plays a pivotal role in enhancing household welfare and addressing income inequalities. Internal migration, in particular, often emerges as a survival strategy for households living in resource - constrained and disaster-prone environments. In Bangladesh, migration is not only a demographic phenomenon but also a livelihood strategy that contributes significantly to poverty reduction and income diversification (Biswas & Mallick, 2021). Rural households migrate primarily due to persistent unemployment, underemployment, low agricultural productivity, and frequent natural disasters. The process is accelerated by rural - urban wage differentials, availability of better services, and opportunities in urban centers (Tacoli, 2003). Several studies have shown that migration improves access to income, education, and healthcare, although it may simultaneously create challenges related to family separation and social adaptation (Ratha et al., 2011).

The Southwestern region of Bangladesh, particularly Khulna District, exemplifies this migration dynamic. Koyra Upazila, situated in this district, is highly vulnerable to cyclones, tidal surges, and salinity intrusion. Frequent disasters severely damage agricultural land and undermine traditional livelihood opportunities, forcing many households to migrate either temporarily or permanently in search of survival and better prospects. This makes the region a critical setting for studying how migration affects income and household welfare. 

While migration provides opportunities through remittances, asset accumulation, and improved savings (Rapoport & Docquier, 2006), it also creates socio - economic trade - offs. Migrant households often benefit from higher income and better access to services, whereas non-migrant households lag behind. At the same time, family disintegration and social costs cannot be overlooked. These contrasting outcomes underline the importance of assessing migration not only as an economic driver but also as a social process. Against this backdrop, the present study examines the impact of internal migration on household income in Koyra Upazila. The research also identifies the socio - economic and demographic factors influencing the migration decision of rural households. By analyzing household - level data through econometric models, the study seeks to provide insights that can guide policies for poverty reduction, sustainable rural livelihoods, and disaster resilience in vulnerable areas of Bangladesh.

Objectives of the Study

• To examine the impact of internal migration on household income in Koyra Upazila of Southwestern Bangladesh.
• To identify the socio - economic and demographic factors influencing rural households migration decisions.

The literature about migration is very large but the literation about internal migration is very small especially about south western region of Bangladesh. We are not aware of any studies that have looked at the factors impacts of internal migration on migrant households in rural areas. Following is a brief review of some major literatures.

Literature review on Impact Migration

Migration is a broad term which incorporates all kind of the movement from one place to another (Dingle & Drake, 2007). A good number of studies have been published about migration in which some studies are mainly concentrated about drivers and policy issues about migration, migration and development, migrants livelihood, pattern, trend and determinant of migration etc. But a few numbers of studies which tend to concentrate about the cause of migration and impact of migration on poverty. A recent study of Sarker et al., 2020, tried to explain the impact of internal migration on poverty alleviation in Bangladesh. But this study is based on the secondary data. This study finds few research works which are based on primary data as Factor behind internal migrations and migrants livelihood (Asfaw et al., 2010).

Migration has a strong nexus with unemployment and development. Searching employment, more income is the reason for migration. Natural calamities are also responsible for internal migration. Flood hazards in combination with loss of assets increase the likelihood of internal migration (Ton et al., 2025) Though some studies are available for internal migration, the effective result of these study are not well. Internal migration study based on sample survey only fulfil the gap of the readers they actually gather their wants after reading the study. There are large number of studies have been already published about internal migration but this study is unique because this study shows the impact of internal migration on poverty based on the sample data and draw a statistical result which can capable to identify the relation between poverty and migration (Sabates-Wheeler et al., 2008).

Hildebrandt et al. (2005) in their study, "The Impacts of Migration on Child Health in Mexico" in their World Bank Policy implication. Research Paper investigated the impact of internal migration on child health consequences in rural Mexico using a locally representative demographic survey. It shows that children in migrant households are found to have lower rates of infant mortality and higher birth weights. It also finds that preventative health care, such as breastfeeding and vaccination, is less likely for children in migrant households. Gkiouleka et al. (2018) conducted a study on consequence of migration by comparing between some socio-economic indicators of two group of non - migrants and migrants from two regions by using hypothesis testing. There are 95 migrants and 125 non-migrant households. They find a significance differences between these two groups. Migration has a strong nexus with unemployment and development. Searching employment, more income is the reason for migration. Natural calamities are also respon-sible for internal migration. Flood hazards in combination with loss of assets increase the likelihood of internal migration (Petrova, 2021). Though some studies are available for internal migration, the effective result of these study are not well. Internal migration study based on sample survey only fulfil the gap of the readers they actually gather their wants after reading the study (King & Skeldon, 2010). There are large number of studies have been already published about internal migration but this study is unique because this study shows the impact of internal migration on poverty based on the sample data and draw a statistical result which can capable to identify the relation between poverty and migration. 

Internal Migration in Asia 

In developing countries, internal migration plays an important role for income generation and economic development. It is seen that in rural area, population pressure is greater than urban areas and occurs deforestation. For this reason, most of the people migrate from their route to other better place for income (Lipton, 1980). In China, there are some people who are migrated from agricultural based region to industrial based region and about 50 percent people are migrated from least developing countries. About 70 percent of the migrated people are aged between 16 and 35 and they think that migration is a life stage between leaving school life and getting married and having children (Kley, 2011). In some parts of India, the three forth people includes migrant. Here labor migration affects the markets and it lower the cost of labor. It also affects the origin of the labor those people who are migrated affect income, expenditure patterns and investment at household levels. Migration is act as a safety valve in poor regions (Wickramasekara, 2016). It has some positive impact on incomes and investment. On the other hand, internal migration is difficult when urbanization is not occurred properly in developing countries (Lucas, 2016).

Migration is an alternative way to improve individual social welfare and to increase income of Indonesian people. Migration helps those people who cannot find any job in his origin due to lack of employment opportunities (Sriskandarajah, 2005). A study in the Philippines showed that offering wage level influences the migration (Carlos, 2002). Those areas where wage level is high, the number of migrants is also high. Khan (2016) finds that there is a significant variation of wage level of both migrants and non - migrants. In China, rural-urban migration changed the pattern of rural household income (Xiao & Zhao, 2018). In Africa, Migration decision is not depended on individual person rather collective decision. Migration also influences the capacity of local investment and also favors the transformation of traditional agriculture to modern agriculture (Ge et al., 2020). Thus, rural-urban migration has an important effect on the overall growth of a country. 

Rural Urban Migration in Bangladesh 

Bangladesh is now feeling superior movement of rural people to the urban region due to increasing of its population. Besides, a high wage of urban job influences the rural people to migrate. In this way, rural - urban migration increases urban unemploy-ment problem and other urban problem such as corruption, robbery etc. as well as it affects rural economy (Ezeudu & Tukur, 2024). Many of the migrants who live in urban slum areas which are very high room density (living more than two people in one room), high population density, and poor housing system with inadequate access to basic needs. But in Bangladesh, the opportunity of urbanization is very low. Only 15 percent of its people live in urban area which is very minor (Dye, 2008). Although in recent time Bangladesh is experiencing a highly rural-urban migration (Rahman et al., 2018). 

Normally, the migrants who come from poor families have a tendency to move a small urban area. For this reason, they get in low-paid urban jobs as day labors. Their earnings are too little and they send a little amount to their rural families. Khan (2012) also states that migrants are lesser illiterates than non-migrant people and most of the male migrants are single and they are migrated due to their large family size. More than 15 million people are migrated from different rural areas of Bangladesh and they live in over - populated area of urban region. They live under the poverty threshold line and do not get any formal job. They cannot achieve a sustainable secure livelihood for a long time. They want to make their family members into productive so that they can help their families and they also try to avoid many basic necessities etc. In contrast, migration plays a vital role to income generation and to ensure the economic and social development of a country. When the aim of any person is fulfilled due to migration, then the outcomes of migration become positive (Hendriks & Bartram, 2019). In Bangladesh, most of the rural women are comparatively less powerful than men. Migration helps women to be more powerful. Andersson et al. (2016) states that a huge number of migrants people get urban opportunities like modern sanitation, safe drinking water, electricity facility, developed housing, education and so on. Thus, the migration process has a great impact on economic development as well as other social and demographic development of migrant families.

Factors Affecting Migration in Bangladesh 

There are many reasons for migration, but people are not always conscious about their migration motive. Migration may be permanent or temporary, in contrast, it can be forced or voluntary. But it is explained by push or pulls factors. Those factors which has negative characteristics at the center of origin are called push factors. And pull factors has positive characteristics at the center of destination. Ishtiaque and Ullah, (2013) states that the main factors of rural urban migration are push factor. Besides, there are also some other pull factors which influence the people being migrate. On the other hand, Ishtiaque and Ullah, (2013) states that the expectation of higher income of an individual also determines the decision of migration. As most of the rural people have a little land or no land, poor financial condition and having poor facilities, so they are bound to migrate in urban areas. Due to natural storm and other natural factors, some people are also migrated. Hence, it is said that both push as well as pull factors affect the migration process. Good health is one of the crucial things for the migrants. When a person wishes to migrate into other place, he has to adjust himself with that environment. Thats why a good health status is very much needed. To become a productive member of a workplace in a destination place, a migrant needs a good health (Adam et al., 2023).

Migration and Urbanization in Bangladesh 

Urbanization means the development of city area. When people migrate from a rural area to a city area and get better facilities, work in an industrial zone, increase industrial activities and hence increase GDP growth and then urbanization happens. Besides, urbanization is considered as a prerequisite to the economic growth. Internal migration is very much associated with urbanization. Migration is considered as one of the main drivers of city area growth in Bangladesh. In 20th century, urbanization has become most important transformations. The urbanization growth rate is higher in least developed countries. At the time period of 1970 to 1990, there is 6.5 percent urban growth happens in Bangladesh. The growth rate of urbanization has increased rapidly after liberation war of Bangladesh with a high inflation. Now, rural - urban migration domin-ates the urban growth and this migration contributes a large in the economy. Some factor such as caste, education, sex, occupation, and age influence rural-urban migration.

Internal Migration and Environment

In China, there are 36 percent of total labor forces of around 770 million who are come from rural area as a migrant and they have a crucial role in the economy. But there are some problems. This internal migration creates environmental damage for the society. In present world, China is one of the largest environment polluted country. In late 2015, it consumes 17 percent more than previous years which release more acidic pollution for the environment (Bhatti et al., 1992). Besides, in Europe, the rural people are migrated for their environmental calamities. Floods, droughts etc. are the common natural calamities in some region of Europe. Some natural calamities such as drought may demote the quality of living standard and also demote to cope with the existence poverty. So some people wants to migrate to overcome this problem.

Migration and Household Income in Bangladesh 

Migration is considered as a key source of income for more poor population across the LDC world. Compared to their situation before they migrated, the income of migrants often increases significantly. Most of the people are wanted to move out from low income area to high income area. That means people moves from rural area to urban area. After the migration, it also affects to the change of income that encourage them for internal migration to achieve the better living. Adjei et al. (2017) analyzes that migration changes the size of the household. That means when migrants income level changes then their family size also changes. Many research works are found that the households of the migrant get more than non- migrant. On the other hand, Haas & Osland, (2014) claims that major migrate people spend their income for housing and transport purpose. Sometimes, the result of migration can be negative. Poor people migrate from rural to urban area to get better facility. But unfortunately they fall into trap and become poorer rather than richer and they also create hazard in the urban areas. So, migration does not always create positive effect for the migrant people.

Research Gap of the Study

From the above discussion, it is clearly said that many researchers have worked on migration in different countries in different time period. Resear-chers do the work from different perspective such as economic aspect of migration, environmental aspect of migration, social aspect of migration etc. The impact of internal migration on income in south-western Bangladesh is an area that remains significantly under-researched, despite its potential to influence local economies and development outcomes. Most existing studies have focused on migration patterns to urban centers like Dhaka, largely overlooking the dynamics of internal migration within rural and peri-urban regions such as Khulna, Satkhira, and Jessore, which are charac-terized by their agricultural and coastal economies. The economic effects of internal migration in Koyra upazila including changes in household income, local development, have not been fully explored.

Additionally, the socio - economic status like (income generation, Health and education facilities, job opportunity etc.) of internal migration and its impact on income in southwestern Bangladesh like Koyra upazila is another research gap. Most of the migration literature tends to focus on internal migration patterns other areas, with less attention to the socio-economic consequences at koyra upazila in Khulna. In southwestern Bangladesh, Koyra is agriculture   dominant livelihood and disaster prone area, the internal migration often alters income generation strategies, yet their economic contributions are often underreported or not sufficiently studied. This studies warrants further investigation to understand how internal migration affect income and socio-economic condition within households. Addressing these gaps is crucial for informing policies that could better manage migration flows and their economic consequences in southwestern Bangladesh.

Study Area and Survey Design

Koyra Upazila, one of the largest upazilas of Khulna District, was chosen as the study area due to its high vulnerability to recurrent natural disasters such as cyclones, tidal surges, and salinity intrusion (Bang-lapedia, 2014). These climatic shocks frequently damage agricultural land and disrupt traditional livelihoods, leaving households with limited income opportunities. As a result, a significant portion of the population resorts to migration as a survival and income-generating strategy. According to the Bangladesh census (2011), Koyra has a population of approximately 1 lac 93 thousand with agriculture serving as the primary source of livelihood for about two-thirds of the residents. The literacy rate is about 50.4%, with a noticeable gender gap between males and females. To capture the dynamics of migration and its impact on household income, the study focused on two unions, Baghali and Moheswaripur, selected purposively from the seven unions of Koyra Upazila. Within these unions, four villages - Shathalia and Ghilabari (from Moheswaripur), and Fotekati and Naryanpur (from Baghali) - were randomly chosen. This combination of purposive and random selection ensured the representation of both migrant and non-migrant households.

The population of the study consisted of all households in these four villages, and the sample size was set at 200 households, with equal representation from migrant and non-migrant families (50 households from each village). Given the large size of the upazila and the difficulty of identifying all migrant households, a purposive non-random sampling design was employed to select households, with migrants considered as the experimental group and non - migrants as the control group. The research adopted a cross -sectional study design, involving both qualitative and quantitative methods. Data were collected exclusively from primary sources through field surveys using a structured interview schedule. The survey instrument included both open - and close-ended questions designed to capture household - level information on income, expenditure, savings, assets, education, occupation, and migration patterns. Household heads were targeted as respondents, as they typically play the key decision - making role in relation to migration and livelihood strategies.

To ensure accuracy and efficiency in data collection, the author personally conducted the interviews with the assistance of trained enumerators. Data were collected through direct person-to-person interaction in the selected villages, with careful attention to building trust and ensuring reliable responses. The field survey allowed for the collection of inform-ation from both migrant and non - migrant house-holds, thereby enabling a comparative analysis of their socio-economic conditions. This systematic design and multi - stage sampling framework provided a representative overview of Koyra upazila household dynamics, ensuring that the findings reflect the diverse realities of a disaster - prone and migration - affected region. In addition, the reliance on primary data strengthens the validity of the study and its ability to contribute meaningful insights into the relationship between migration and household income in rural Bangladesh.

Theoretical Framework

Migration is a multifaceted phenomenon that has been explained through different theoretical traditions. The neoclassical economic theory posits that migration is driven by wage differentials and employment opportunities across regions. Individuals are assumed to act rationally, migrating from low - income, labor - surplus areas to higher - income, labor - scarce areas with the objective of maximizing lifetime earnings. Within this frame-work, migration is understood as an investment in human capital that yields improved income streams at the household level. In contrast, the new economics of labor migration (NELM) emphasizes that migration decisions are rarely taken by individuals alone; rather, they are collective household strategies. Migration is used to overcome market imperfections in credit, insurance, and labor markets. By sending members to migrate, households diversify income sources, reduce vulnerability to risks, and enhance long - term livelihood security. Thus, migration is not solely an income -maximizing response but also a risk - sharing mechanism at the household level.

The push - pull perspective provides further explanatory power by highlighting the contextual forces shaping migration. Push factors such as poverty, unemployment, environmental stress, and agricultural decline encourage households to seek alternatives beyond their locality, while pull factors such as employment opportunities, higher wages, and urban facilities attract migrants to destination areas. This dual structure helps explain why households in disaster - prone regions like Koyra, frequently exposed to cyclones, tidal surges, and salinity intrusion, resort to migration as both a necessity and an opportunity. Taken together, these theories illuminate the rationale for migration as both an individual - level response to wage differentials and a household - level strategy to manage risk and stabilize livelihoods. In the context of Koyra Upazila, recurrent environmental shocks function as strong push factors, while income opportunities outside the locality act as pull factors. At the same time, households adopt migration as a deliberate strategy to sustain income flows, smooth consumption, and accumulate assets.

This theoretical framing directly aligns with the studys objectives and analytical focus. The dependent variable, household income, is expected to be influenced by socio-economic and demo -graphic factors such as household size, education, landholding, occupation, and assets. Migration status operates as a critical explanatory factor that mediates these relationships by providing remit-tances and alternative income sources. Hence, the framework establishes a logical basis for analyzing how migration contributes to household income differentials between migrant and non - migrant households in disaster-affected rural Bangladesh.

Analytical Framework and Econometric Specification

Migration is modeled as a binary decision outcome, where households either send members to migrate or not. To understand the determinants of internal migration in Koyra Upazila, a multidimensional analytical framework is employed that integrates socio - economic, demographic, cultural, and environmental factors.

Let Mi denote the migration status of the ith household, where Mi=1 if the household has at least one migrant and Mi=0 otherwise. The probability of migration is expressed as: 

Here, the explanatory variables (Xij) represent a range of household- and individual-level factors that may influence migration decisions. The expected signs of coefficients vary according to theoretical predictions: for instance, larger household size and dependency ratio are generally expected to increase the likelihood of migration, while greater land-holding or asset ownership may reduce it by providing local livelihood opportunities. The set of explanatory variables considered in the model is summarized in Table 1. These include demographic characteristics (age, gender, marital status, family size), socio - economic attributes (education, occupation, income, savings, assets, debt), and contextual factors (housing pattern, health facilities, remittances, migration pattern, duration, and number of migrants per household). The binary logit specification for the migration decision is thus given by:

Interpretation is conducted through marginal effects, which measure the change in the probability of migration associated with a unit change in each explanatory variable, holding others constant.

Summary Statistics

The main concern of this paper is to find out the impact of internal migration on income status of rural people. The average age of the sample is 38.5 years, with participants ranging from 22 to 60 years. This indicates that most individuals are in their late 30s to early 40s, but there is also a presence of younger and older individuals. In terms of educational qualification, the average number of years of education is about 11.7 years, suggesting that most individuals in this sample have completed secondary education. However, the range spans from no formal education (0 years) to higher education (18 years), indicating variability in educational attainment. The average family size is 4.66 members, with family sizes ranging from 2 to 10 members. This shows a mixture of smaller and larger households within the sample, though most families consist of a few members. On average, households have 1.44 earning members, with some families having up to three earners. This suggests that most families rely on a single primary earner, with a few households having more than one source of income. There is an average of 3.24 dependent members per household. Dependent members include children, elderly, or unemployed individuals, and the number of dependents ranges from 1 to 8. This indicates that households generally have a moderate number of dependents. The mean monthly income is BDT 34,523.75, but there is significant variation in income levels, ranging from BDT 3,500 to BDT 600,000. This large income disparity suggests that there are both low- and high-income households within the sample.

Table 1: Summary Statistics (Source: Authors Compilation, 2024).

Fig. 1: Migration pull factor index in percentage (Source: Authors Compilation, 2024).

The chart illustrates the "Migration pull factor index in percentage," focusing on various reasons influencing migration decisions. Respondents expressed their views across five categories: Strongly Agree, Agree, Neutral, Disagree, and Strongly Disagree for each factor. The data shows a significant variation in how these factors are perceived, with some showing near-unanimous agreement and others eliciting mixed responses. Factors such as Higher Educational Facilities and Higher Amenities stand out, with 100% of respondents strongly agreeing that these are critical reasons for migration. Similarly, Better Treatment Facilities received overwhelming support, with 87% strongly agreeing and 11% agreeing. These results highlight the universal appeal of improved education, healthcare, and amenities as primary motivators for migration. Economic factors also play a crucial role in migration decisions. High Labor Demand in city areas is strongly supported by 87% of respondents, with an additional 12.5% agreeing. Similarly, Higher Wages received strong approval, with 73% strongly agreeing and 22% agreeing. These findings underscore the importance of economic stability and job opportunities in urban areas as significant pull factors. Higher Communication Facilities (50.5% strongly agree, 33% agree) and Good Association with People (44% strongly agree, 41.5% agree) are also prominent migration factors. These results show that accessible communication networks and positive social environments contribute to migration, though to a lesser extent compared to education and wages. 

The factors of Religious Freedom (29.5% strongly agree, 45% agree) and Political Freedom (32.16% strongly agree, 42.21% agree) elicited more varied responses. While a majority agreed on their importance, a noticeable portion remained neutral (14% for religious freedom, 16.08% for political freedom), and some even disagreed. This suggests that while freedom is valued, they may not be as universally decisive as economic or educational factors. A Higher Standard of Living is another significant pull factor, with 81% strongly agreeing and 6% agreeing. However, a notable 13% of respondents remained neutral, indicating some diversity in how respondents perceive this factors role in migration decisions. The chart highlights that education, healthcare, and economic opportunities such as wages and labor demand are the most influential pull factors driving migration. While infrastructure, freedoms, and social connections also play a role, their influence appears more varied. This demonstrates that migration decisions are often shaped by a combination of practical, economic, and social considerations.

Gender Description

In study area, most of the respondents are male and a few are female respondents. According to gender, living standards are also affected. Among non-migrant household heads, 81% are male, while only 19% are female. The gender imbalance is even more pronounced among migrant household heads, with 94% being male and just 6% female. Overall, the data indicates a significant gender skew, as males make up 87.5% of the total respondents, compared to only 12.5% female respondents. This suggests that male - headed households dominate both migrant and non-migrant categories, with migration being particularly male-dominated. The findings highlight a gendered pattern in household leadership and migration, where men are overwhelmingly the heads of households, especially in migration contexts (Basu et al., 2020).

Housing Pattern of Respondents

In rural area, most of the housing pattern are either kacha or semi pacca. But most of people have a pacca house whose living standard is better. The housing pattern of the People of Koyra is shown in Table 4. This table shows the distribution of household heads by housing pattern (categorized as Kacha, Pacca, and Semi - pacca), divided into non-migrant and migrant households. The total number of households is 200, with 100 non - migrants and 100 migrants. Among the non-migrant households, the largest group lives in Pacca houses, with 52 non - migrant heads. This is followed by 36 non-migrants living in Semi - pacca houses and 12 non-migrants living in Kacha houses. The majority of non-migrants thus live in either Pacca or Semi-pacca houses, indicating that most non-migrant households have relatively stable housing, with a smaller portion residing in temporary or poorly constructed homes.

In contrast, migrant households show a somewhat different distribution. The largest group of migrant household heads live in Semi - pacca houses, with 51 migrant heads. This is followed by 44 migrant heads in Pacca houses and 5 migrant heads in Kacha houses. Migrants are somewhat more likely to live in Semi - pacca houses compared to non-migrants, suggesting that migrants might be residing in less permanent housing, which could be due to temporary living arrangements or migration-related factors. Looking at the total distribution, the majority of households (both migrant and non-migrant) live in Pacca houses, with 96 heads in this category, followed by 87 in Semi - pacca houses and 17 in Kacha houses. This indicates that Pacca housing is the most common across both groups, with a substantial number also living in Semi - pacca houses. The small number of households living in Kacha houses suggests that both migrant and non - migrant households have access to relatively durable housing, though migrants are slightly more likely to live in Semi - pacca houses.

Health Facilities of Respondents

Survey revealed health facilities, categorized into three types: Kabiraj (traditional healer), Special Doctor, and Local Doctor, and further divided into non-migrant and migrant households. The total number of household heads is 200, with 100 non-migrants and 100 migrants. Among the non-migrant households, the largest group accesses health care from a Special Doctor, with 64 non-migrant heads in this category. This is followed by 24 non-migrants who consult a Local Doctor and 12 non-migrants who rely on a Kabiraj (traditional healer). This indicates that non-migrant households tend to prefer more formal health care services, with the majority seeking help from Special Doctors, followed by Local Doctors for primary health care. 

For migrant households, the distribution is slightly different. While the largest group of migrant household heads (47) also consults a Local Doctor, there are 45 migrant heads who seek care from a Special Doctor, and 8 who rely on a Kabiraj. Migrants, therefore, have a higher reliance on Local Doctors compared to non-migrants, but Special Doctors are still the second most common source of health care. This could suggest that migrants may be more likely to use locally available health services or primary health care due to factors like cost, convenience, or accessibility. Looking at the total distribution, the majority of household heads, regardless of migration status, consult Special Doctors, with 109 total cases in this category. This is followed by 71 heads who visit Local Doctors and 20 heads who rely on a Kabiraj. The data indicates that Special Doctors are the most commonly accessed health care providers overall, with Local Doctors being the second most frequent choice, and Kabiraj services being relatively less common.

Number of earning members the household

The total number of households is 200, with 100 non-migrants and 100 migrants. Among the non - migrant households, the largest group (66 non - migrants) has 1 earning member, followed by 29 non - migrants with 2 earning members and only 5 non - migrants with 3 earning members. This suggests that most non-migrant households rely on a single primary earner, with a smaller proportion having two earners and very few households with three earners. For migrant households, the distribution is somewhat similar but with a slightly higher proportion of households having 2 earning members. There are 57 migrant households with 1 earning member, and 38 migrant households with 2 earning members, while 5 migrant households have 3 earning members. This indicates that while the majority of migrant households also have just one earning member, a larger proportion of migrants compared to non - migrants have two earners. This may suggest that migrant households might require more financial support or are engaged in more diversified income-generating activities compared to non - migrants. Looking at the total distribution, 123 households (the largest group) have 1 earning member, 67 households have 2 earning members, and 10 households have 3 earning members. This shows that most households, regardless of migration status, rely on a single earner. However, the presence of two earners in 67 households suggests that a significant number of households, both migrant and non-migrant, have more than one person contributing to the household income.

Migration Pattern of Respondents

This table presents the distribution of household heads based on the type of migration, categorized into Permanent and Temporary migration. The data shows the frequency, percent, and cumulative percent of each migration type among the sample. Out of the 100 households, 44 household heads have experienced Permanent Migration, which accounts for 44% of the total sample. The cumulative percent for permanent migration is also 44%, reflecting that 44% of the sample has permanently migrated.

 

Table 2: Migration Type (Source: Authors Compilation, 2024).

The remaining 56 household heads have experienced Temporary Migration, which constitutes 56% of the sample. Since temporary migration includes all households other than those with permanent migration, the cumulative percent for temporary migration is 100%, indicating that all 100 households are accounted for. In summary, the data shows that 56% of the household heads have migrated temporarily, while 44% have migrated permanently. The cumulative percent confirms that all households have been classified into one of these two migration types, with the majority of the sample being involved in temporary migration.

Keep Savings of Migrants

The Bank category accounts for the largest proportion, representing 39% of the total. This indicates that banks are the most common and trusted institution for savings, highlighting their prominent role in financial security. The second-largest category is NGO, which comprises 28% of the total. This shows that NGOs play a significant role in providing savings opportunities, particularly for communities or groups that might not have easy access to formal banking systems. The others category makes up 24% of the distribution. This reflects the diversity in saving practices beyond conventional sources like banks or NGOs, possibly including informal savings, cooperatives, or other local initiatives. Finally, No Savings accounts for 9% of the total. This indicates that a smaller, yet notable, portion of respondents do not engage in any form of saving. This group may face financial vulnerability or lack the means to save regularly. Overall, the data reveals that while banks are the dominant savings source, NGOs and other informal mechanisms also play significant roles. The presence of a "No Savings" group highlights the need for further financial inclusion efforts.

Destination of the Migrants

Among the destinations, Khulna stands out as the most preferred location, attracting 37 respondents, likely due to its economic opportunities or urban facilities. Dhaka, the capital city, follows closely with 27 respondents, reflecting its status as a hub for jobs, education, and healthcare. Chattogram ranks third with 11 respondents, indicating its appeal as a commercial and industrial center. Bagerhat and Barishal receive moderate attention, with 10 and 7 respondents, respectively, suggesting fewer pull factors compared to larger cities. In contrast, Rajshahi has the lowest number of migrants, with only 2 respondents, possibly due to limited job prospects or development. The overall trend reveals a clear preference for more urbanized and economically active regions, highlighting the influence of urbanization and economic opportunities on migration patterns.

Amount of Remittance of Migrants 

Remittance distribution of the migrants across various ranges, segmented into permanent and temporary statuses shows lowest range (1000-5000), 25 individuals (25% of all cases) received remittances, with 10 (22.73%) being permanent and 15 (26.79%) temporary. The largest group falls within the 5100-10000 range, comprising 28 individuals (28% of all cases), with a balanced distribution of 12 (27.27%) permanent and 16 (28.57%) temporary cases. The 10100-15000 range accounts for 23 individuals (23% of all cases), followed by the 15100-20000 range with 12 individuals (12% of all cases). Notably, the higher ranges (20100-25000 and above) see fewer cases, with a declining trend in both permanent and temporary remittances. Permanent remittances are absent in the highest ranges (25100-50000 and 50100-90000), where only temporary remittances appear, albeit minimally. This indicates that smaller remittance amounts are more common across both statuses, while larger amounts tend to be temporary in nature.

Remittance Use of the Respondents

The largest share of remittance, 38%, is used for Consumption. This indicates that a significant portion of remittance is spent on meeting day-to-day household needs such as food, clothing, and utilities, reflecting its essential role in maintaining household consumption. The second - largest share, 31%, is allocated to House Repair, showing that remittance also plays a critical role in improving housing conditions, likely enhancing living standards and addressing structural needs. About 13% of the remittance is spent on purchasing Land, indicating an investment - oriented use where families seek to acquire assets for long - term benefits. This highlights the importance of remittance in securing property ownership and increasing family wealth. A smaller portion, 12%, is spent on Medicine, reflecting the role of remittance in ensuring access to healthcare services and meeting medical expenses for family members. Lastly, 6% of the remittance is used for other purposes, which could include miscellaneous expenses such as education, ceremonies, or savings. Although a minor share, it indicates some level of flexibility in remittance utilization. 

Number of Migrants of the Household

The table shows the distribution of migrants in households based on type of migration (Permanent or Temporary) and the total number of migrants. For households with 1 migrant, there are 5 permanent migrants and 14 temporary migrants, totaling 19 migrants. In households with 2 migrants, the numbers increase to 10 permanent migrants and 14 temporary migrants, giving a total of 24 migrants. Households with 3 migrants have 11 permanent migrants and 6 temporary migrants, making a total of 17 migrants.

 

Table 3: Number of Migrants of the Household (Source: Authors Compilation, 2024).

In households with 4 migrants, there are 10 permanent migrants and 17 temporary migrants, which sums up to 27 migrants, the highest total among the groups. Households with 5 migrants consist of 7 permanent migrants and 3 temporary migrants, adding up to 10 migrants. For households with 6 migrants, there are 0 permanent migrants and 2 temporary migrants, resulting in a total of 2 migrants. Lastly, households with 7 migrants include 1 permanent migrant and no temporary migrants, making a total of 1 migrant. Total number of permanent migrants across all households is 44, while the total number of temporary migrants is 56. Combined, there are 100 migrants in total. This indicates that temporary migration (56%) is more common compared to permanent migration (44%).

Table 4: Logistic Analysis (Source: Authors Compilation, 2024).

The logit estimation reveals several statistically significant predictors of migration. The variable Gender is significant at the 10% level (p = 0.085), with a positive coefficient (1.326). The marginal effect (dy/dx = 0.180) indicates that being male increases the probability of migration by 18%, holding other factors constant. This confirms that males are more likely to migrate than females. Accommodation emerges as a highly significant determinant (p = 0.000). The coefficient (-1.977) and marginal effect (-0.268) suggest that owning accommodation reduces the probability of migration by 26.8%. This finding highlights the stabilizing effect of home ownership, implying that households with secure housing are less likely to engage in migration. Economic factors are also important. Monthly income is highly significant (p = 0.000) with a positive coefficient (0.0008). While the marginal effect is small, the results suggest that higher household income increases the likelihood of migration, possibly because greater financial resources ease migration costs. Similarly, monthly savings shows a significant positive association (p = 0.008). The marginal effect indicates that households with higher savings are more likely to migrate, reflecting the role of financial security in enabling migration decisions.

The loan variable is significant at the 5% level (p = 0.012), with a coefficient of 1.249 and marginal effect of 0.170. This indicates that households that have taken loans are 17% more likely to migrate, suggesting that migration may serve as a strategy for financing or repaying household debt. Finally, the total pull score, which aggregates perceived opportunities at the destination, is significant at the 10% level (p = 0.095). The positive coefficient (0.135) and marginal effect (0.018) imply that stronger pull factors - such as better wages, labor demand, and improved facilities - increase the probability of migration by 1.8%. In contrast, age, education, family size, and household occupation are positively associated with migration but remain statistically insignificant, indicating weaker predictive power.

Summary statistics of the expenditure

The summary statistics of expenditure provide insights into the distribution of household costs across six key categories: food consumption, education costs, housing cost, fuel cost, medical cost, and clothing cost. For food consumption, the mean expenditure is 7,890.4 BDT with a standard deviation of 5,306.024, indicating considerable variation among households. The minimum expenditure is 0, while the maximum is 30,000 BDT, suggesting that some households may not spend on food while others spend significantly higher amounts. The education costs have a mean of 3,092.54 BDT and a standard deviation of 2,516.104, reflecting moderate variation. The minimum expenditure is 0, and the maximum is 15,000 BDT, indicating disparities in educational investments among households. For housing costs, the mean expenditure is 2,193.65 BDT, but the standard deviation is high at 4,426.365. The minimum is 0, and the maximum is 40,000 BDT, showing that while some households spend nothing on housing, others allocate a significant amount, leading to a wide range of expenditures. The fuel cost has a relatively low mean of 712.75 BDT and a standard deviation of 1,122.307. The minimum is 0, and the maximum is 10,000 BDT, suggesting that fuel expenditures are generally low for most households but can be higher for a few. For medical costs, the mean expenditure is 1,030.5 BDT with a standard deviation of 1,059.752. The minimum is 0, and the maximum is 5,000 BDT. This indicates that while healthcare costs remain modest on average, there is notable variation across households. Lastly, clothing costs have a mean of 1,703.7 BDT with a standard deviation of 2,459.813. The minimum is 0, and the maximum is 20,000 BDT, showing that spending on clothing varies significantly, with some households allocating nothing and others spending a substantial amount. 

Occupation pattern of the Respondents

The data reveals that the Business sector has the largest number of respondents, with 81 individuals, indicating that business is the most common occupation among the surveyed group. This highlights the prominence of entrepreneurial activities as a key source of livelihood. The Service sector follows as the second most common occupation, with 42 respondents engaged in jobs such as government or private employment. This suggests that a significant portion of the population finds stable income opportunities through service-based occupations. The Farmer category accounts for 28 respondents, reflecting the continued importance of agriculture in sustaining livelihoods, particularly in rural or semi-rural areas. Meanwhile, Others, which may include informal or unspecified jobs, represent 26 respondents, showing diversity in occupational choices beyond the listed categories. A smaller number of respondents, 14, are engaged as Day Laborers, indicating that casual or temporary labor plays a role in the livelihood strategies of some households. Additionally, the Driver category has the least representation, with only 9 respondents, suggesting limited engagement in driving-related occupations. Overall, the chart highlights that business and service occupations dominate the employment structure, while farming and other informal sectors also provide livelihood opportunities for a considerable portion of the respondents. The lower representation of day laborers and drivers reflects their relatively minor role within the surveyed group.

Income Comparison of the Respondents

For non-migrant households, the mean income is 696,904.32 BDT, with a standard deviation of 526,121.85 BDT, indicating substantial variation. The minimum income for non-migrant households is 65,678 BDT, while the maximum reaches 3,543,000 BDT. On the other hand, for migrant households, the mean income is higher at 789,806.78 BDT, with a slightly larger standard deviation of 584,086.84 BDT. The minimum income for migrant households is 83,000 BDT, and the maximum income is 3,650,000 BDT, which is also higher compared to non-migrant households. Overall, migrant households demonstrate a higher average income and a wider range of income distribution compared to non-migrant households. This suggests that migration may be associated with improved financial outcomes, reflected in the higher minimum, maximum, and average incomes for migrant household heads. However, the higher standard deviation also indicates greater variability in income levels among migrant households.

Econometric Analysis

To identify the determinants of migration, a binary logit model was estimated where the dependent variable is migration status (Mi =1) if the household has at least one migrant and Mi =0 otherwise). The model examines how demographic, socio-economic, and household-level factors shape the probability of migration. Table 4 presents the estimated coefficients, marginal effects (dy/dx), and standard errors.

The findings of this study provide new evidence on the socio-economic, demographic, and contextual factors shaping internal migration in disaster-prone rural Bangladesh. Several results are consistent with the broader migration literature, while others offer nuanced insights specific to Koyra Upazila. First, the role of gender as a significant determinant of migration supports previous evidence that migration is a male-dominated phenomenon in South Asia (Khan, 2016; Ishtiaque & Ullah, 2013). The results indicate that male household heads are significantly more likely to migrate, reflecting cultural norms, labor market segmentation, and gendered household responsibilities that limit female mobility. This finding underscores the persistence of patriarchal structures in shaping household migration strategies. 

Second, housing and accommodation ownership demonstrate a strong negative effect on migration. Households with permanent housing are substantially less likely to send migrants, confirming earlier studies which highlighted that asset ownership, especially land and property, anchors households to their localities (Biswas & Mallick, 2021; Xiao & Zhao, 2018). This stability effect suggests that asset - rich households may rely more on local opportunities and are less compelled to migrate, while asset - poor households are more vulnerable and thus more inclined to pursue migration as a livelihood strategy.

Third, income, savings, and loan-taking behavior emerge as central economic drivers of migration. Higher income and greater savings increase the probability of migration, aligning with the New Economics of Labor Migration, which views migration as a household investment strategy (Rapoport & Docquier, 2006; Ratha et al., 2011). Similar results have been reported in rural Africa and Asia, where improved financial capacity facilitates the financing of migration costs (Asfaw et al., 2010; Adjei et al., 2017). The positive and significant role of loan-taking further reinforces the argument that house-holds often use migration as a means to service debt obligations, thereby linking credit constraints with mobility decisions (Sarker et al., 2020).

Fourth, pull factors - such as improved job opportunities, higher wages, and better social services at destinations - are found to significantly increase the likelihood of migration. This result is consistent with classical push - pull theories of migration (Dingle & Drake, 2007; Lucas, 2016), as well as more recent empirical findings from Bangladesh (Rahman et al., 2018; Petrova, 2021). In the context of Koyra, where recurrent cyclones, tidal surges, and salinity intrusion undermine local livelihoods, destination-side opportunities appear to play an even stronger role in shaping household migration strategies (Biswas & Mallick, 2021; Ton et al., 2025).

Interestingly, variables such as education, family size, and occupation were positively associated with migration but not statistically significant. This contrasts with some earlier studies that identified education as a key driver of mobility (King & Skeldon, 2010; Kley, 2011). The weak effect here may reflect the localized context of Koyra, where migration is driven less by human capital accumulation and more by immediate economic survival needs. Taken together, these results highlight that migration in disaster-prone rural Bangladesh is shaped by a complex interaction of economic capacity, asset ownership, and destination - side opportunities, rather than by demographic characteristics alone. In line with the theoretical underpinnings of the New Economics of Labor Migration (Rapoport & Docquier, 2006; Sarker et al., 2020), the findings suggest that households adopt migration as a strategy to diversify income sources, repay debts, and manage risks associated with environmental shocks.

This study explored the socio - economic and demographic determinants of migration in Koyra Upazila, with particular emphasis on the role of income, savings, loan-taking, housing conditions, and pull factors. The results demonstrate that migration is primarily economically motivated, with migrant households reporting significantly higher income and savings compared to non - migrant households. The econometric analysis further highlights that male-headed households are more likely to migrate, while accommodation ownership reduces mobility, suggesting that stable housing anchors households in place. 

Conversely, higher income, greater savings, and indebtedness significantly increase the likelihood of migration, reflecting the importance of financial capacity and household risk management strategies. Moreover, destination - side pull factors - including labor demand, better wages, and improved access to education and healthcare - serve as strong drivers of mobility. Taken together, these findings confirm that migration in disaster - prone rural Bangladesh functions as a household - level strategy for income diversification, debt repayment, and livelihood security, consistent with the New Economics of Labor Migration (NELM). While migration improves household income and savings, its benefits are maximized only when remittances are effectively mobilized for productive purposes and when vulnerabilities such as housing insecurity are addressed. Based on the findings, the following policy measures are recommended:

• Develop Affordable Housing Support Schemes

Given the strong negative association between accommodation ownership and migration, policies should promote low-cost, disaster-resilient housing and secure land tenure for rural households to reduce distress-driven migration.

• Expand Financial Services for Migrant Households

Since income, savings, and loans significantly influence migration, introducing microfinance tailored to migration costs and remittance-linked savings products can help households finance migration more sustainably and invest remittances in productive activities.

• Promote Productive Use of Remittances

With remittances currently concentrated in consumption and housing repairs, financial literacy and investment support programs should encourage households to channel remittances into education, skill-building, and small enterprises, enhancing the long-term developmental impact of migration.

The authors, M.M.H.; and M.N.U.: conducted a comprehensive study to investigate the socio-economic and demographic factors influencing internal migration in Koyra Upazila, Bangladesh. M.M.H.: as first author, accomplished the idea generation, methodological, data analysis, result and discussion part of the study. Whereas, M.N.U.: contributed on the introductory, literature review, data collection and management, and conclusion and findings part of the research.

At the outset, the authors express deep gratitude to the Almighty Allah for bestowing upon them the health, knowledge, ability, and scope to accomplish this research enterprise. The authors also extend their heartfelt thanks to all the respondents of Koyra upazila of Khulna district of Bangladesh who generously dedicated their time and provided invaluable insights, enriching the depth and quality of this study. In addition to this, authors acknowledge the assistance from faculties and students of Economics Discipline, Khulna University, Khulna, Bangladesh. 

The authors declare that there are no conflicts of interest.