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Article

How Do Left-Behind Families Adapt to the Salinity-Induced Male Out-Migration Context? A Case Study of Shyamnagar Sub-District in Coastal Bangladesh

by
Tahmina Chumky
1,*,
Mrittika Basu
1,
Kenichiro Onitsuka
1,
Md Lamiur Raihan
2 and
Satoshi Hoshino
1
1
Graduate School of Global Environmental Studies, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
2
The Organization for the Strategic Coordination of Research and Intellectual Properties, Meiji University, Kawasaki 214-8571, Japan
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(3), 2756; https://doi.org/10.3390/su15032756
Submission received: 11 December 2022 / Revised: 24 January 2023 / Accepted: 24 January 2023 / Published: 2 February 2023
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Abstract

:
The knowledge regarding male out-migration due to climate change and large-scale, rapid-onset disasters and their impacts on the left-behind families is well known. However, research on the adaptation strategies for the families left behind due to disaster-induced male-out seasonal migration is rarely carried out. Thus, this study attempts to explore the coping and adaptation strategies adopted by the left-behind families in the salinity-induced male out-migration context. Analyzing the factors affecting the adaptation behaviors is also a major objective of this study. The study was carried out in Shyamnagar sub-district of coastal Bangladesh, where male-out seasonal migration for both rapid and slow-onset disasters is evidenced. The data regarding the adaptation measures were explored through different participatory rural appraisal techniques. Primary data were collected from 213 women from the left-behind families through a semi-structured questionnaire. Descriptive statistics as well as multiple linear regression for analyzing the factors affecting adaptation behaviors were applied. The results revealed that the left-behind families, especially the women and children, adopted a total of 35 coping and adaptation strategies in five different aspects, such as economic adaptation, social adaptation measures, environmental measures, educational measures, and health-related measures. Migrant husbands’ age and their education, the household’s alternative income sources’ availability, receiving loans, disaster history, and migration history variables contributed most significantly to the adaptation behavior. This study provides a new perspective on seasonal male out-migration and the adaptation strategies of the left-behind families, which could be helpful for disaster-induced human migration management and enhancing the resilience of vulnerable communities.

1. Introduction

Climate change and associated hazards are matters of great concern in the current world. Climate change will affect many millions, if not billions, of people in the coming decades [1]. Climate change and large-scale natural disasters have drawn attention due to their negative effects on environmentally influenced human migration [2,3] Climate change-related factors, such as increased disasters, sea-level rise, and saline intrusion, are promoting internal and international human migration [4,5]. Migration is a silent, cyclical social process that has existed throughout human history and is as old as civilization itself [6]. In both developing and developed countries, migration is regarded as an indirect consequence of natural disasters [7]. In 2015, there were 19.2 million newly displaced people worldwide, across all regions, as a result of catastrophes like storms, earthquakes, volcanic eruptions, excessive temperatures, and avalanches [8]. In 2020, various natural catastrophes forced 30.7 million people from 149 nations and territories to flee their homes [9].
Climate change and associated natural catastrophes have had a direct impact on coastal people’s habitations and livelihoods worldwide [10]. As a result of climate change and sea level rise, saltwater intrusion has had a significant impact on coastal settlements in terms of social disturbance and cutbacks in local economies. As a result of their vulnerability to growing salinity intrusion, coastal people’s livelihoods are unsustainable [10]. Sea level rise-related enhanced salinization poses a water resource limitation for groundwater supplies [1]. A rise in salinity could have an impact on the almost 600 million people who currently reside in low-lying coastal areas globally [11]. Over the previous 20 years, the soil’s salt levels have sharply increased [12]. A record-breaking amount of seawater intrusion occurred in 2016, up to more than 90 km inland in Vietnam. This incident had a significant negative impact on agricultural production in 11 out of the Mekong Delta’s 13 provinces and contributed to water constraint [13]. There was also a lack of fresh water, which caused damage to 250,000 homes (or 1.3 million people), schools, clinics, hotels, production facilities, and 210,000 acres of farmland. Climate change, extreme weather events, and climate-induced disasters have a significant impact on the coastal areas of developing countries’ economies, such as Bangladesh [14].
Bangladesh is one of the world’s most climate-vulnerable nations, and its coastline is its most climate-vulnerable region [15]. The coastal region is susceptible to frequent cyclones, saline intrusion, and flash flooding produced by tidal surges [16], and these natural disasters have serious repercussions on the livelihood and way of life of vulnerable communities [17]. However, due to a series of rapid and slow-onset natural disasters, a lack of efficient policy planning, zoning and land use planning and control, and a lack of the implementation of law and order, coastal Bangladesh’s scarce land resources face a variety of risks and vulnerabilities [18]. Coastal areas of Bangladesh face slow socioeconomic development, the threat of various natural disasters, environmental degradation, and the global climate change process because of rapid, unmanaged, and unexpected changes in land use [18,19]. This vulnerable coastal zone encompasses 19 of Bangladesh’s 64 districts, where 30% of the country’s population and more than half of the impoverished reside [20]. In addition, the coastal population is projected to expand from 36.8 million in 2001 to 60.80 million in 2050.
In Bangladesh’s coastal regions, salt water intrusion has gradually increased over time. The resulting increases in salinity have had an effect on the functions and services of coastal ecosystems, and consequently, on the surrounding community. Salt water intrusion can increase salt levels in soils which can make agricultural and food production more variable. Due to a lack of freshwater input from upstream, salt intrusion in Bangladesh is anticipated to worsen due to climate change and sea level rise [21]. Salinity increases due to saltwater invasion along coastlines, which could have negative consequences for forestry, agriculture, and fisheries [22]. The National Adaptation Program of Action (NAPA) predicts that climate change-related water hazards will affect Bangladesh severely. In Bangladesh’s coastal zone, salinity in surface water, ground water, and soil has emerged as a major risk factor [23]. The coastline and offshore areas of Bangladesh make up 30% of the arable land and the degree of inundation is about 50% of the coastal areas, which prevents them from being used effectively [24].
Salinity intrusion has a significant impact on coastal cultivable land. Due to climate change and its associated catastrophic dangers, including sea level rise, cyclones and storm surge, salinity has also greatly increased on captured land [25]. In the salinity-intruded area, agricultural land utilization is about 53% of the national average [26]. Salinity intrusion also contributes to land degradation and a shortage of irrigation water [27]. Thus, coastal livelihoods and agriculture can be greatly affected by the salinity intrusion. In the past few years, drinking water scarcity and salinity intrusion have become new, serious hazards to public health. Human health crises may contribute to increased migration in the future because of the effects of climate change, sea level rise, and salinity, together with family economies disrupted by disasters [28].
In developing countries, migration primarily consists of male family members who leave their female counterparts behind. This will have both positive and negative effects on the well-being of the female family members [6,29]. Climate-induced migration in Bangladesh is mainly male; younger people are more likely to migrate [30]. In salinity-affected coastal areas, the main migration pattern is male outmigration, leaving the family at origin, as most of the migration is for a short period of time and economic factors are playing a key role [31]. It is evident that there are significant disparities between men and women in terms of vulnerabilities and coping strategies when it comes to natural catastrophes and climatic impacts like extreme weather and seasonality in a male-dominated culture where women have less control over productive resources [32].
Most of the research related to salinity induced migration focused on origin-destination issues, push-pull factors, economic contribution to families left behind, various adaptation strategies practiced by farmers (for example, temporary migration, changing cropping pattern practice). But very little literature is found regarding well-being, vulnerability, and adaptation strategies taken by left-behind families. Thus, this paper attempts to explore the adaptation strategies of left-behind families at the origin during the period of seasonal male out-migration. One of the goals of this study was to analyze the influencing factors that affect the adoption of different adaptation strategies by the left-behind families. The unique contribution of the findings of this research will help the policy planners, disaster risk reduction experts, and concerned stakeholders to design and formulate the effective adaptation strategies to reduce the vulnerabilities of left-behind families.
This paper is mainly structured with 6 sections. Section 1 presents the introduction. Section 2 presents the literature review and conceptual framework development. Section 3 describes the methodology, where the details of the study area, data collection, and analysis were given. Section 4 shows the results of the study. Section 5 discusses the results. Finally, Section 6 concludes the study, which provides a summary of the study with some policy recommendations accordingly.

2. Literature Review and Conceptual Framework Development

The famous framework given by Black et al. (2011) proposed five types of drivers which were responsible for the migration [33]. However, this is extremely useful to understand the contexts and interplay among different drivers in migration-decision making. However, it is expected and found from several previous studies that migration occurs mostly due to economic drivers and as an adaptation strategy to reduce the vulnerabilities caused by various disasters or distress. This landmark research also showed that there are two groups that remain in the migration process. The first is for those who have migrated (temporarily), and the second is for those who have remained in their place of origin. The latter group is called the “trapped” family or the “left behind” family. The decision to migrate, according to the neoclassical theory of migration, is the result of a calculation of the costs and benefits of moving. However, the end part of the framework given by Black et al. is a migration decision where involuntary non-migration occurs mostly among women and children [33]. The elderly people also face such situations.
The male-out seasonal or temporary migration is an effect of vulnerability which also acts as a cause of new vulnerabilities for the left-behind families at the origin. The consequences of male-out temporary migration also enhance the development of new vulnerabilities. The vulnerabilities of left-behind families involve several types. In disaster risk science, it is believed that where there are vulnerabilities, there are adaptations or coping mechanisms to reduce the vulnerabilities. This initiates the activities or adaptations. Research on gender and climate change mostly focuses on the negative impacts on women and children, where men are portrayed as somewhat irresponsible, migrating to the cities and leaving behind helpless women to face multiple adversities [32,34]. It is expected and proved in several studies that temporary male-out temporary migration induced by natural disasters helps to improve the economic conditions of the left-behind families at the source or origin. The economic drivers that drive male out-migration gradually diminish after the migration and flow of income in the destinations. However, the case is not always linear and there are many stories of increasing economic vulnerabilities of the left-behind families at the origin, even after the male out-migration. This situation induces the left-behind family’s adoption of economic adaptation or coping strategies, especially by the women [32,35].
In left-behind families, the feminization of agriculture occurs [6]. Because of the absence of the male members or household heads, several social vulnerabilities for women and children arise in male out-migration contexts. In such cases, the women adopt several social adaptations or coping measures. Temporary women headed households (in absence of men) face a series of vulnerabilities and therefore adapt different coping and or adaptation strategies accordingly [35]. Similarly, the left-behind families face a series of other vulnerabilities. The children of the left-behind families sometimes get a better opportunity due to their father’s greater income and remittances. Therefore, the left-behind families can spend more money on their children’s education. However, like the opposite side of the coin, the children also face various social and educational vulnerabilities. Therefore, women have to adopt different adaptation and coping mechanisms to reduce such vulnerabilities.
The occurrence of other natural disasters (mostly sudden-onset disasters) during the male out-migration period makes the left-behind families more vulnerable. As a result, environmental adaptation or coping at the household level in the source is also anticipated. Similarly, the members of the left-behind families face health-related vulnerabilities, especially the women. Reproductive and psychological health issues are very important for women, which is hindered during the male out-migration. The left-behind wives had lower self-rated health than the wives of non-migrants. Part of this negative health impact was driven by the low remittances sent by the migrant husbands. For both women in nuclear families and women in extended families, the negative health impact was partially attributable to women’s added responsibilities, such as animal care and managing a bank account. For women in nuclear families, the negative health effect of their husbands’ migration has been partially suppressed by women’s increased autonomy [36]. In such vulnerable contexts, different health-related adaptation and coping strategies are expected, especially for women, children, and elderly people.
Adaptation refers to the adjustment of human-environmental, ecological, social, or economic processes in response to observed or expected climatic stimulus changes and their consequences [37]. Adaptation is a crucial topic of discussion in contemporary climate change as well as migration-related research. It has also been viewed as an effective response option that warrants additional research and evaluation [38], not only to guide the selection of the most effective mitigation strategies but also to minimize the vulnerability of a community or group of people to climate change impacts, thereby reducing the risks associated with climate change [38]. Adaptation experiences are gendered and even though exposure to climate variations may be the same for men and women in any given location, there are gender-based differences in vulnerability and consequently in adaptation and adaptive capacity [39,40]. The adaptation or coping behavior of climate-vulnerable people is influenced by several social, economic, demographic, cognitive (perceptions and knowledge-based) and other factors [41]. The adaptation is highly context-specific. Therefore, it is expected that several socio-economic and demographic factors will affect the left-behind family’s adaptation behavior. Moreover, capacity development initiatives such as training should have a positive effect on adaptation behaviors [42]. Support systems (e.g., loan and incentives, safety-net programs, etc.) during a vulnerable situation, like other climate-related adaptation behaviors, should have a significant impact on the adaptation of left-behind families. As the vulnerability of the left-behind families is affected by the disaster history (exposure and sensitivity), so the adaptation behavior of the left-behind families is assumed to be affected by this variable. Similarly, migration history (times of male out-migration per year) is also assumed to be a predictor variable of left-behind families’ adaptation or coping strategies [43].
There are various studies we can find where it is shown that the male out-migration induces women’s empowerment because of their responsibilities, sense of identity, recognition, autonomy, and participation in decision-making processes [6,35,44]. Furthermore, all those attributes flourish in a given context and in different family or social environments, as well as individual characteristics. Those attributes help in the development of the resilience of vulnerable communities [45,46]. Figure 1 shows the conceptual framework of the current study where the straight arrows show the direct relations between the previous studies and the process of the adaptation behaviors of the left-behind families. The expected relationships, on the other hand, are shown by the dotted arrows, which will not be directly looked into by the empirical evidence in this study.

3. Methodology

3.1. Study Area

For the present study, Shyamnagar upazila of Satkhira district located in the south-western coastal area of Bangladesh was selected which encounter frequent storm surges, cyclones, and increased soil and water salinization [47,48]. Shyamnagar upazila has an area of 1968.24 square kilometers. It is situated between latitudes 21°36′ and 22°24′ north and longitudes 89°00′ and 89°19′ east [49]. The two unions named Nurnagar and Ramjannagar were purposively chosen for data collection because of the extent of natural disasters occurrence and rate of internal migration (Bangladesh is divided into seven divisions, which are administratively major regions. Each division is subdivided into multiple districts (zilas), for a total of 64 districts. A district contains multiple subdistricts (upazila), while a subdistrict contains several unions. In rural Bangladesh, the lowest administrative entity is the union.). After detailed literature review and discussion with the Upazila Agriculture department of the concerned areas, we selected these respective unions. The majority of people in Shyamnagar upazila are directly or indirectly managing multi-type, highly fertile agricultural land. The production of shrimp has also played a significant role in the land usage of this upazila. The Jamuna, Raymangal, Arpangachhia, Malancha, Hariabhanga, and Chunar Rivers are the primary rivers in this upazila, and the Bhet canal is another significant water body. Agriculture has dominated the vast majority of the upazila’s socioeconomic activity. This upazila was one of the important areas which was subsequently affected by the great cyclones Aila in 2007 and Sidr in 2009. The map of the study area is shown in Figure 2. A previous study found Shyamnagar upzila to be the most salinity-prone (>20 parts per thousand) compared to all other south-western coastal areas of Bangladesh [50]. Figure 3 shows the salinity level map of the south-western coastal areas of Bangladesh.

3.2. Data Collection

The data of this study were collected in two different ways. Firstly, we conducted 4 focus groups discussions (FGDs) with the women of the left-behind families where we came to know different adaptation and coping strategies during the male out-migration. Moreover, the literature review was done to understand the adaptation strategies being practiced by the left-behind families. FGDs were conducted from 19 to 24 March 2022. In the second step, the required data for the present study were collected through face-to-face surveys with the women of the left-behind families where male-out seasonal migration was occurred. The data collection timeline was 5 to 19 July 2022. The data was collected using a structured questionnaire. A random sampling technique was used for selecting the respondents of the study during the data collection. Before the simple random sampling, the list of the migrated families was garnered from the Upazila Agriculture Office. The total number of households in the two unions were 14,730 and according to the Upazila Agriculture Office, seasonal male out-migration occurs in 25% households. Therefore, the total population of migrated households in the study areas was 3683 people. So, the total number of samples was calculated by a survey system calculator (https://www.calculator.net/sample-size-calculator.html (accessed on 15 September 2022)) following the Cochran’s formula [51]. The confidence interval level was 95%, and the margin of error was set at 7%. The expected sample size was 187. However, a total of 217 respondents’ data were collected and 213 responses were valid and retained for the subsequent study. Therefore, the actual margin of error was 6.6%.
The semi-structured questionnaire was designed as per the objectives of the study. The questionnaire was divided by two parts. The first part was involved with the socio-economic and demographic information of the respondents. Respondents’ age, respondents’ level of education, husbands’ age, husbands’ education, alternative income sources, number of children, number of elderly people, husbands’ communication during their migration, training, receiving loans, disaster history, and migration history were collected. These are the independent variables which are selected based on the secondary literature as stated above as well as from the key informants’ interviews (KIIs) considering the situation of the study area. The list and sources of the independent variables used in this study are shown in Table 1. The data were both continuous as well as categorical (see Table 2). However, the second part of the questionnaire was only concerned with the adaptation strategies (binary responses: adopted, i.e., yes, or not adopted, i.e., no). The total number of adaptation strategies (in total, 35 adaptation strategies) as well as in groups such as economic adaptation (16 practices), social adaptation (5 practices), environmental adaptation (5 practices), educational adaptation (6 practices), and health-related adaptation (3 practices) were the sum of the responses.

3.3. Data Analysis

Simple descriptive analysis was carried out to show the percentage of the different characteristics of the respondents, the mean, and the standard deviations. Moreover, simple bar diagrams were made to show the adoption of different adaptations or coping strategies by the left-behind families. MS Excel was used to present the results. For the quantitative data from semi-structured surveys, the factors affecting the adaptation behavior were analyzed by the simple multiple linear regression analysis [63]. We run a total of six multiple linear regression models. Model 1 was concerned with the analysis of the influence of predictor variables on total adaptation behavior. For model 1, the dependent variable was the sum of the adaptation or coping strategies. For example, if a respondent practiced all 35 adaptation measures, then the total score would be 35. If none of the adaptation measures are practiced, then the total score for the respective respondent would be 0. However, Model 2 was an analysis of the influence of predictor variables on economic adaptation behaviors. A total of 16 economic adaptation measures (out of 35) were practiced by the respondents. The dependent variable for model 2 was the sum of the 16 adaptation measures. Therefore, the highest score for a respondent would be 16 and the lowest score would be 0, which were considered as the dependent variables. The dependent variables for models 3, 4, 5, and 6 were also calculated in the same way. The predictor or independent variables were the different characteristics of the respondents and their perceptions regarding the migration and disaster as explained in Section 3.2. The data were analyzed by SPSS (version 29) [64]. The study area map was created by ArcGIS.

4. Results

4.1. Characteristics of Left-Behind Families

The average age of the respondents was 34 years, which indicated that the maximum number of women from the left-behind families were middle-aged. However, the average age of the spouses of the respondents was almost 42 years old, which also indicates the middle-aged category. The average education score of the women was 3.69, which is within the primary school background. Moreover, the average education score of the respondents’ husband was 6.87, which means that most of the male partners have a secondary school background. The respondent’s opinion regarding the receiving of the required family maintenance costs from their husbands during the seasonal migration was a bit strange because almost 60% of the women reported that they didn’t receive family maintenance costs, which were actually required to support the livelihoods as well as other necessary costs. However, about 40% of women received the required family maintenance. About 61% of respondents felt that they had no alternative income sources to support their livelihood during their spouse’s seasonal migration. The average number of children (less than 28 years old) per family was almost 3, and the average number of elderly people (more than 60 years old) was slightly above 1, which indicated the tendency of nuclear families nowadays to have the presence of 1 elderly person in their families, which means the separation of the elderly people (mostly father and mother) among the male members (sons) of the family. Almost 66% of respondents stated that their husbands maintained continuous communication and cooperation during their seasonal migration. Most of the respondents (about 60%) didn’t get any training, neither from any government organizations nor from any non-government agencies, to uplift their livelihoods, adapt during the disasters and deal with issues associated with seasonal migration. 63% of women received loans from different NGOs. Moreover, almost 62% of respondents indicated that they didn’t face any significant disaster during their husband’s seasonal migration due to salinity. The average migration history score per family was 1.31 times per year, which indicates that the male-out seasonal migration occurred in each family more than once a year.

4.2. Coping or Adaptation Strategies Adopted by the Left-Behind Families

We found 35 coping or adaptation strategies in total practiced by the left-behind families. However, among the economic coping and adaptation strategies, there were 11 measures that were related to the farm, and 5 practices were related to non-farm activities. Regarding the social adaptations or coping practices, the women of the left-behind families have adopted five adaptations or coping strategies. Moreover, they adopted five environmental strategies, six educational strategies, and three health-related strategies, respectively.

4.2.1. Economic Adaptation or Coping Strategies

Figure 4 depicts two types of economic adaptation or coping strategies used by left-behind families to adapt in vulnerable contexts.
The farm-related strategies are mostly involved with homestead or household activities for increasing agricultural production (crops, livestock, and dairy). Homestead vegetable cultivation ranked number 1 (76% of the left-behind families) in the farm-related adaptation or coping strategies, followed by poultry rearing (66% of the respondents), and dairy animal rearing (58%). The poultry animals are mostly managed by the children of the left-behind families. In the absence of their husbands, 36% of the women of the left-behind families of Shyamangar upzila are also engaged in crop cultivation directly. Planned utilization of the land for household vegetable production or seed production during the lean periods was also an important strategy.
The non-farm economic strategies were mostly related to coping strategies to adjust to the vulnerable situations raised by the male out-migration. Therefore, most of the strategies are not planned adaptations but rather context-specific coping mechanisms. There were 91% of the left-behind families that received credit from different NGOs to support the livelihoods of the families, which is a bit ridiculous considering the male out-migration, which is mostly induced by economic drivers. It means that the male partners of the families migrated seasonally to reduce the economic vulnerabilities of their families caused by slow-onset disasters, e.g., salinity, which still remains after their migration and left-behind families had to borrow money from NGOs to support livelihoods. Saving money was found as an important adaptation measure which was practiced by 77% of the respondents, followed by loans from NGOs to ensure the children’s education (73% of the respondents). Non-farm economic adaptation and coping mechanisms used by left-behind families included selling cow manure stick fuel (44%), sewing clothes (36%), and sending children to labor work (26% of respondents). Figure 5a shows that the women of the left-behind families of Shyamnagar upzila were working in the crop fields in the absence of their male partners. Figure 5b represented the cow manure stick fuel made by the women of the left-behind families of Shyamanagar upazila. Figure 6a–c shows that women of the left-behind families work in poultry and dairy farming as an adaptation strategy during male out-migration.

4.2.2. Social Adaptation or Coping Strategies

Figure 7 shows different social, environmental, educational and health-related adaptation and coping mechanisms adopted by the left-behind families. They have adopted various social adaptation methods to cope with the social vulnerabilities raised due to the temporary male-out temporary migration.
Among the five social adaptation or coping strategies, joining the social safety net programs (76% of the respondents) was the most important one. Women from left-behind families join the social safety net programs of different government and non-government organizations to reduce their social vulnerabilities. Joining different cooperatives or NGOs also helps them to reduce their social vulnerability (56% of the respondents). To handle the social vulnerability situation caused by the male out-migration, women of the left-behind families also took part in decision-making on family affairs, which is also a major portion (54% of the respondents). In the absence of the family head, women and children become more vulnerable socially, and in some cases, the issue of security is also a major concern. Therefore, 39% of families restricted their children’s movement to protect them from any unwanted situations or moral degradation. Moreover, 31% of women brought their paternal family members into their houses during their husbands’ absence.

4.2.3. Environmental Adaptation or Coping Strategies

The occurrence of various sudden-onset disasters such as floods, storms, cyclones, etc. throughout the year is not so uncommon in the coastal areas of Bangladesh. The left-behind families of Shyamnagar upazila also have to face such incidences during their husbands’ outmigration temporarily. Moreover, to handle the continuous salinity problem and water distress issues in the household, women also adopt several adaptations and or coping measures. A total of 97% of the respondents reported that they harvest safe drinking water from the designated water sources (mostly reserved water bodies such as ponds) during their husbands’ absence. A total of 94% of women also harvested the rainwater for the drinking demands of household members. Taking community shelter during the sudden-onset disasters is a common coping mechanism for the left-behind families, which is not so usual during their family heads’ presence. There were 97% of respondents answered that they took designated community shelter (locally known as “cyclone shelter”) during the sudden-onset disaster in their husbands’ temporal migration contexts. Building protected houses and toilets were also important adaptation measures to reduce the environmental vulnerabilities of the left-behind families.

4.2.4. Educational Adaptation or Coping Strategies

The left-behind families adopt several coping and adaptation strategies related to the education of their children during their household head’s absence. The school-going children become more vulnerable due to their father’s temporary migration. A total of 46% of respondents indicated that they send their children to religious institutions such as Moktab, Madrasa, etc. so that the children’s moral degradation doesn’t occur. Moreover, the social and religious values and norms are taught in those institutions by which the other family members feel relieved about their children. Spending more money on children’s education was also an important coping mechanism of the left-behind families by which the quality of their children’s education was ensured. Some left-behind families (19%) also sent their children to orphanages where they don’t have to worry about their food and education as well as their basic needs. In some cases (19% of the respondents), the women managed private tuition of their children and managed home visits of their school teachers for better education, which indicates a positive change in children’s education.

4.2.5. Health-Related Adaptation or Coping Strategies

The women as well as the elderly people of the left-behind families go through health-related vulnerabilities and adopt different adaptations or coping strategies. A total of 81% of respondents reported that they went through loneliness and/or mental illness during their husbands’ out migration. Therefore, they increased peer relations with neighbors and relatives to minimize health-related vulnerabilities. More than half (59%) of respondents said they contact fostering women, midwives, and local family planning officers to take care of their reproductive health. Furthermore, frequent doctor visits during family members’ illnesses are an important coping mechanism for reducing health-related vulnerabilities.

4.3. Factors Affecting Left-Behind Family’s Adaptation Strategies

The ability of thirteen explanatory variables (respondents’ age, respondents’ level of education, husbands’ age, husbands’ education, alternative income sources, number of children, number of elderly people, husbands’ communication, training, receiving loans, disaster history, and migration history) to predict the total adaptation or coping strategies was evaluated using multiple regression analysis. There were six regression models analyzed to see how various factors influence various adaption or coping strategies. Nevertheless, model 1 was associated with the elements influencing the whole adaptation or coping technique. Models 2, 3, 4, 5, and 6 were related to the economic, social, environmental, educational, and health-related aspects impacting coping and adaptation techniques, respectively. In Table 3 are displayed two models (model 1 and model 2). Table 4 offers models 3 and 4, while Table 5 presents models 5 and 6, respectively.
Preliminary investigations were performed to ensure that the assumptions of normality, linearity, multicollinearity, and homoscedasticity were not violated. First, boxplots revealed that all variables in the regression were normally distributed and lacked univariate outliers. Secondly, an examination of the normal probability plot of standardized residuals and the scatterplot of standardized residuals versus standardized expected values revealed that the residuals satisfied the assumptions of normality, linearity, and homoscedasticity. Thirdly, Mahalanobis distance and Cooks distance demonstrated that the critical χ2 value for df = 12 (at = 0.001) did not exceed the conventional limit, showing that multivariate outliers were not a reason to be concerned. The relatively high tolerances for all predictors in the regression model demonstrate that multicollinearity will not hinder our ability to comprehend the results of the regression models.

4.3.1. Factors Affecting Total Adaptation or Coping Strategies Adopted by the Left-Behind Families

From Table 3, we can see that for the case of model 1, in combination, all the predictor variables accounted for 19.0% of the variability in adoption of total adaptation or coping measures, R2 = 0.19, adjusted R2 = 0.13, F (13, 199) = 4.03, p < 0.01, with the disaster history recording the highest value (beta = 0.24, p < 0.01), followed by receiving a loan (beta = 0.19, p = 0.03), husbands’ age (beta = 0.14, p = 0.04), and alterative income sources (beta = 0.13, p = 0.05). If the disaster history score increases by one standard deviation (SD), the adoption of total adaptation or coping measures score would be likely to increase by 0.24 standard deviation units. Similar to the variable disaster history, if the receiving loan score increases by one SD, the adoption of total adaptation or coping measures would be likely to increase by 0.19 SD units. On the other hand, if the training score increases by one standard deviation (SD), the adoption of total adaptation or coping measures score would be likely to drop by 0.14 standard deviation units. A similar explanation will apply to the other important variables, such as the husband’s age and alternative income sources.

4.3.2. Factors Affecting Economic Adaptation or Coping Strategies Adopted by the Left-Behind Families

From Table 3, we can see that for the case of model 2, i.e., the model for economic adaptation and coping strategies, in combination, all the predictor variables accounted for 19.0% of the variability in adoption of economic (both on- and off-farm) adaptation or coping strategies, R2 = 0.19, adjusted R2 = 0.14, F (13, 199) = 3.66, p < 0.01, with the disaster history recording the highest value (beta = 0.30, p < 0.01) followed by receiving loan (beta = 0.23, p < 0.01). Husbands’ education and age contributed significantly to the model and their beta values were the same (beta = 0.14), though husbands’ education negatively correlated with the economic adaptation and coping strategies. The same similar explanations as for model 1 will be applicable for all the significant variables of model 2.

4.3.3. Factors Affecting Social Adaptation or Coping Strategies Adopted by the Left-Behind Families

From Table 4, we can see that for the case of model 3, i.e., the model for social adaptation and coping strategies, in combination, all the predictor variables accounted for 14.0% of the variability in adoption of social adaptation or coping strategies, R2 = 0.14, adjusted R2 = 0.08, F (13, 199) = 2.50, p < 0.01, with the receiving loan recording the highest value (beta = 0.23, p < 0.01) followed by training (beta = 0.22, p < 0.01). Similar explanations as for model 1 will be applicable for both the significant variables of model 3.

4.3.4. Factors Affecting Environmental Adaptation or Coping Strategies Adopted by the Left-Behind Families

From Table 4, we can see that for the case of model 4, i.e., the model for environmental adaptation and coping strategies, in combination, all the predictor variables accounted for 20.0% of the variability in adoption of environmental adaptation or coping measures, R2 = 0.20, adjusted R2 = 0.15, F (13, 199) = 3.85, p < 0.01, with the receiving loan recording the highest value (beta = 0.38, p < 0.01). Disaster history and receiving required family maintenance costs contributed to the model exactly in the amount (beta = 0.13, p < 0.01). The same explanations that apply to model 1 will apply to all three of model 4’s significant variables.

4.3.5. Factors Affecting Health-Related Adaptation or Coping Strategies Adopted by the Left-Behind Families

From Table 5, we can see that for the case of model 5, i.e., the model for health-related adaptation and coping strategies, in combination, all the predictor variables accounted for 11.0% of the variability in adoption of educational adaptation or coping strategies, R2 = 0.11, adjusted R2 = 0.06, F (13, 199) = 1.97, p < 0.01, with the respondents’ age recording the highest value (beta = −0.37, p < 0.01), followed by migration history (beta = 0.20, p < 0.01), and the number of elderly people (beta=−0.16, p = 0.05). The same explanations that apply to model 1 will apply to all three of model 5’s significant variables.

4.3.6. Factors Affecting Educational Adaptation or Coping Strategies Adopted by the Left-Behind Families

From Table 5, we can see that for the case of model 6, i.e., the model for educational adaptation and coping strategies, in combination, all the predictor variables accounted for 17.0% of the variability in adoption of health-related adaptation or coping strategies, R2 = 0.17, adjusted R2 = 0.12, F (13, 199) = 3.17, p < 0.01, with the respondents’ training recording the highest value (beta = 0.299, p = 0.01), followed by husbands’ age (beta = 0.20, p = 0.01), and number of children (beta = 0.13, p = 0.04). The same explanations that apply to model 1 will apply to all three of model 6’s significant variables.

5. Discussion

The current study attempted to fulfill two research objectives. The first objective was to explore the different adaptation strategies or practices of the left-behind families, especially the women and children, in male-out seasonal migration contexts. The second objective was to analyze the factors affecting the left-behind family’s adaptation or coping strategies. We found 35 adaptation or coping strategies in total in the given contexts. The coping or adaptation strategies were classified into 5 types, such as economic, social, environmental, educational, and health-related. However, to the best of our knowledge, there are no studies we found that explored the left-behind family’s adaptation and coping mechanisms and where different influencing factors were analyzed. Therefore, it is critical to show the similarities or dissimilarities of the factors that influence the adaptation behavior of the left-behind families. The greatest number of coping measures were found as economic strategies. However, during regular times, given the contexts of vulnerability due to salinity and other natural disasters, some of those strategies are adopted by the women and children of the study area sometimes, but during the seasonal migration of the male partners, these practices are very common to adapt to the vulnerable situations because of the husband’s absence and family livelihood maintenance. Sometimes it seems difficult to differentiate between the adaptation and coping measures adopted by the women of the most vulnerable households and the women of the left-behind families.
Due to male out-migration, a significant number of women engaged in agricultural activities, which indicated an increase in their work loads and burdens. Agricultural activities were also hampered in some cases because of labor shortages. Other researchers also found that the women of the left-behind families were much more engaged with the agricultural activities [32]. This study demonstrated how male migration, combined with women’s farming and direct market interaction, has altered gender relations in India. Another previous study discovered that women from left-behind families engage in a variety of farm and off-farm activities, both in the home and outside, as a source of empowerment [44].
In the current study, we repeatedly found that the women of the left-behind families became more vulnerable after their husband’s temporary migration because the loan re-payment became the main focal point of their husband’s off-farm work. This cyclic relationship of debts in source or origin and the destination of the migration is a continuous process. We found that women in left-behind families take loans from different sources such as banks, non-governmental organizations (NGOs), friends and relatives, traditional money lenders (Mohajon), mortgaging of assets, etc. Another previous study also found that the loans of the household heads for seasonal migration are a burden for the women of the left-behind families, thereby the possibility of another loan is a very common coping mechanism during the financial transition [65]. There is a cycle of NGO loans where they act as a way of vulnerability reduction at the first stage of disaster occurrence and agricultural losses [31]. However, these types of loans later triggered seasonal male out-migration. Women of the left-behind migrant families adopted a series of on- and off-farm activities [4,31]. The list of similar adaptation or coping strategies we found are taking loans and joining adaptation strategies and, most importantly, involvement with agricultural activities, mostly crop cultivation. However, the major difference between the previous studies [4,31] and this study is the role of women as household heads.
With the adoption of different adaptation and coping strategies, especially economic and social strategies, the women of the left-behind families were found to be more confident and empowered. The current study found that most of the families were nuclear families with both husband and wife, which is not typical for rural Bangladesh. Therefore, the women of nuclear families faced higher responsibilities as well as higher autonomy, which helped in women’s autonomy and control over life, as well as women’s empowerment, which is also seen as significant. Similar findings were found in previous studies [4,31]. Adopting different non-farm economic activities helps the left-behind families be capable of solving different issues related to their family as well as environmental adaptation and reduction of future vulnerability, thereby enhancing social and economic resilience. Moreover, due to the inclusion of the left-behind families in different social safety programs, cooperatives, NGOs, and other financial and social organizations, the sense of identity and recognition of these families increases. Taking part in the family decision-making processes makes the women more empowered, which leads to successful adaptation in vulnerable contexts. Microcredit helped in climate change adaptation in the south-western coastal areas of Bangladesh [20]. Therefore, the involvement of women in the microcredit system to support the family’s livelihood and reduce their childrens’ educational vulnerability is considered an important strategy.
Training plays an important role in the adoption of different adaptation and coping strategies. Training was one of the important predictor variables which we found significant for 3 models, such as model 1 (total adaptation or coping strategy model), model 3 (social adaptation or coping model), and model 6 (educational adaptation or coping model), which indicates the significance of training on successful adaptation in vulnerable contexts. Similar findings were found in many other studies related to climate change adaptation [66,67,68]. The women who had more training were found to be more cautious about their childrens’ education and, thereby, it contributed to the education strategy model (model 6).
As shown in the Section 4, two important predictor variables which we found significant for most of the models are disaster history and migration history. The greater the history and sensitivity to sudden-onset disasters, the greater the adoption of environmental and economic adaptation and coping strategies by left-behind families. Similarly, the migration history explains the adaptation behavior. The higher the migration frequency and duration, the women of the left-behind families adopt more adaptation or coping measures. Therefore, we can say that male out-migration is sometimes positive in building the resilience of the women of left-behind families. A number of studies were found where the authors showed the social, educational, and psychological vulnerabilities of left-behind children were explored. These studies also demonstrated adaptation in such situations [34,69,70,71,72]. However, in this current study, we didn’t find any psychological adaptation of the left-behind children. Moreover, the social adaptation or coping strategies we found are mostly related to women, not children.
Several studies have found that male out-migration has a negative impact on the mental and reproductive health of women who are left behind. Therefore, contacting fostering women or peer groups for advice on maintaining good reproductive and mental health is an important coping strategy [36,73]. The health-related impacts due to male out-migration and the adaptation or coping strategies adopted by the wives of the left-behind families were important and shown in a few previous studies [74,75,76]. Remittances, increased household responsibilities, and increased autonomy all influence the effects of spousal migration on left-behind women and their coping mechanisms [36]. However, our study found that women can adapt a little in the face of such vulnerable situations, and low remittances as well as a significant increase in household responsibilities could be important factors behind this.

6. Conclusions

Migration is considered a popular adaptation strategy in the face of climate change and disasters. Male-out seasonal migrations to reduce vulnerabilities exacerbate new vulnerabilities, requiring left-behind families to employ a variety of adaptation and coping strategies. This research explored the adaptation and coping strategies adopted by the left-behind families, especially women and children, which has wider implications in climate-vulnerable developing countries where migration is seen as a common adaptation strategy itself. The results of this study indicated that the left-behind families adopted a wide range of coping and adaptation measures to combat their vulnerabilities. Women’s involvement in agricultural activities as well as non-farm activities for the betterment of their livelihoods creates new opportunities. Women participated in the decision-making processes of family affairs, which is not so common in the typical rural setup of Bangladesh. The empowerment of women, recognition, and a sense of identity help build resilience in the left-behind families. More efforts should be directed by the concerned authorities to reduce the children’s vulnerability in terms of education and health during the male out-migration. The lack of communication and/or coordination between the origin and source (migrated husbands and left-behind women) during the migration affects the social and economic security of the left-behind families and thereby aggravates the cycle of interest-based loans. Sufficient training and extension activities should be offered to the left-behind families for effective coping and adaptation in their vulnerable contexts. This study provides a new perspective on seasonal male out-migration and its associated factors. One of the study’s limitations is the ambiguity of the adaptation and coping mechanisms induced by male out-migration vulnerabilities, as well as their close similarities to typical climate change adaptation in vulnerable coastal contexts. This study could be replicated in other areas globally where similar socio-economic situations remain, keeping in mind that adaptation is a context-specific issue and, therefore, adjusting the survey questions accordingly. This study would be generalized to those coastal areas with similar socio-economic conditions not only in Bangladesh but also in the climate-vulnerable global south where the salinity level is higher and, therefore, seasonal male-out migration occurs as an adaptation, thereby leaving behind families.

Author Contributions

Conceptualization, T.C.; Methodology, T.C.; Software, T.C.; Formal analysis, T.C.; Investigation, T.C. and M.L.R.; Data curation, T.C. and M.L.R.; Writing—original draft, T.C.; Writing—review & editing, T.C., M.B., K.O. and M.L.R.; Visualization, T.C.; Supervision, M.B., K.O. and S.H.; Funding acquisition, K.O. and M.L.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. The APC was funded by Graduate School of Global Environmental Studies, Kyoto University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all respondents involved in the study.

Data Availability Statement

Relevant data will be available upon reasonable request to the corresponding author.

Acknowledgments

The first author would like to express her sincere gratitude to the people of Shyamnagar and the local agriculture offices as well as the civil admiration of Shyamnagar, Satkhira. The first author also would like to thank to the MEXT scholarship authority of Japan for providing the funding during this research.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Conceptual framework of Bangladesh male out-migration study.
Figure 1. Conceptual framework of Bangladesh male out-migration study.
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Figure 2. Map of Bangladesh male out-migration study area.
Figure 2. Map of Bangladesh male out-migration study area.
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Figure 3. Salinity map of south-western coastal areas of Bangladesh [50].
Figure 3. Salinity map of south-western coastal areas of Bangladesh [50].
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Figure 4. Economic coping and adaptation strategies for Bangladesh families experiencing male out-migration (n = 213).
Figure 4. Economic coping and adaptation strategies for Bangladesh families experiencing male out-migration (n = 213).
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Figure 5. (a) Bangladesh women working in the field and the (b) cow manure sticks that they make.
Figure 5. (a) Bangladesh women working in the field and the (b) cow manure sticks that they make.
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Figure 6. Bangladesh women rearing (a) poultry and (b,c) dairy cattle.
Figure 6. Bangladesh women rearing (a) poultry and (b,c) dairy cattle.
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Figure 7. Social, environmental, educational, and health-related coping and adaptation strategies for Bangladesh families experiencing male out-migration (n = 213).
Figure 7. Social, environmental, educational, and health-related coping and adaptation strategies for Bangladesh families experiencing male out-migration (n = 213).
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Table
Table 1. List of variables used for Bangladesh male out-migration study.
Table 1. List of variables used for Bangladesh male out-migration study.
Major DimensionsVariablesSource
Socio-economic factorsAge of the respondent[46,47]
Age of the household head/ husband’s age[35,48]
Respondent’s education[52]
Education of the household head[52]
Receiving required family maintenance cost[50,51]
Alternative household income sources[51,53]
Number of children[49,54]
Number of elderly people[52,55]
Husband’s communication during migration[47,56]
Capacity development factorsTraining[47,57]
Support systemReceiving loan[55,58]
Disaster historyExposure to disaster during husband’s absence (male out-migration)[35]
Migration historyYearly frequency of migration[59,60,61,62]
Table
Table 2. Socio-economic, demographic, and other characteristics of Bangladesh left-behind families.
Table 2. Socio-economic, demographic, and other characteristics of Bangladesh left-behind families.
Selected CharacteristicsUnitCategoriesRespondents (%)MeanSD
Respondent’s ageYearContinuous10033.6811.83
Respondent’s educationYears of schoolingContinuous1003.692.37
Husband’s age YearContinuous10041.8513.95
Husband’s educationYears of schoolingContinuous1006.872.95
Receiving required family maintenance cost ScaleYes (1)39.90.400.50
No (0)60.1
Alternative income sourceScaleYes (1)39.00.390.49
No (0)61.0
Number of childrenActual numberContinuous1002.570.91
Number of elderly peopleActual numberContinuous1001.200.50
Husband’s communicationScaleYes (1)34.30.340.48
No (0)65.7
TrainingScaleYes (1)39.90.400.50
No (0)60.1
Receiving loanScaleYes (1)62.90.630.48
No (0)37.1
Disaster historyScaleYes (1)37.60.380.48
No (0)62.4
Migration historyActual numberContinuous1001.310.47
Note: SD means Standard Deviation; (n = 213).
Table
Table 3. Factors affecting Bangladesh’s left-behind families’ total and economic adaptation measures.
Table 3. Factors affecting Bangladesh’s left-behind families’ total and economic adaptation measures.
ModelModel 1 (Total Adaptation Measures)Model 2 (Economic Adaptation Measures)
VariablesStandardized
Coefficients		‘t’ ValueSig.Standardized
Coefficients		‘t’ ValueSig.
Respondent’s age−0.05−0.260.790.020.130.89
Respondent’s education0.020.100.92−0.10−0.510.61
Husband’s age0.142.030.04 *0.141.960.05 *
Husband’s education−0.09−1.410.16−0.14−2.140.03 *
Receiving required family maintenance cost0.091.360.17−0.04−0.610.54
Alternative income sources0.131.950.05 *0.233.42<0.01 ***
Number of children0.060.940.340.060.910.36
Number of elderly people0.020.280.77−0.04−0.470.64
Husband’s communication0.030.560.570.091.430.15
Training0.142.160.03 *−0.01−0.040.96
Receiving loan0.192.77<0.01 ***−0.06−0.870.38
Disaster history0.243.65<0.01 ***0.303.51<0.01 ***
Migration history0.111.350.180.040.370.71
Constant-11.29<0.01-8.97<0.01
R square, adjusted R20.19, 0.130.19, 0.14
F value3.513.66
n213
The symbols * and *** correspond to alphas = 0.05 and <0.01 respectively.
Table
Table 4. Factors affecting Bangladesh’s left-behind families’ social and environmental adaptation measures.
Table 4. Factors affecting Bangladesh’s left-behind families’ social and environmental adaptation measures.
ModelModel 3 (Social Adaptation Measures)Model 4 (Environmental Adaptation Measures)
VariablesStandardized Coefficients‘t’ ValueSig.Standardized Coefficients‘t’ ValueSig.
Respondent’s age0.301.530.12−0.18−0.990.32
Respondent’s education−0.29−1.460.150.160.870.38
Husband’s age−0.11−1.480.140.010.180.85
Husband’s education−0.07−0.970.330.010.100.92
Receiving required family maintenance cost0.020.260.790.131.940.05 *
Alternative income sources0.030.360.72−0.12−1.740.08
Number of children−0.05−0.660.510.030.440.66
Number of elderly people0.040.330.740.141.750.08
Husband’s cooperation−0.01−0.090.93−0.04−0.570.57
Training0.223.20<0.01 ***−0.09−1.440.15
Receiving loan0.233.25<0.01 ***0.385.66<0.01 ***
Disaster history0.040.560.570.131.100.04 *
Migration history0.101.290.20−0.02−0.220.82
Constant-10.13<0.01-3.82<0.01
R square, adjusted R20.14, 0.080.20, 0.15
F value2.503.85
n213
The symbols * and *** correspond to alphas = 0.05 and <0.01 respectively.
Table
Table 5. Factors affecting Bangladesh’s left-behind families’ health-related and educational adaptation measures.
Table 5. Factors affecting Bangladesh’s left-behind families’ health-related and educational adaptation measures.
ModelModel 5 (Health-Related Adaptation Measures)Model 6 (Educational Adaptation Measures)
VariablesStandardized Coefficients‘t’ valueSig.Standardized Coefficients‘t’ ValueSig.
Respondent’s age−0.37−1.870.05 *0.110.560.57
Respondent’s education0.251.280.200.040.190.85
Husband’s age−0.01−0.170.860.202.780.01 **
Husband’s education0.010.110.910.030.450.65
Receiving required family maintenance cost 0.111.640.100.071.010.31
Alternative income sources−0.09−1.270.200.121.770.07
Number of children−0.11−1.540.120.132.010.04 *
Number of elderly people−0.16−1.900.05 *0.070.900.37
Husband’s cooperation0.101.400.16−0.09−1.360.17
Training0.050.670.500.294.320.01 **
Receiving loan0.070.940.35−0.02−0.270.78
Disaster history0.060.900.360.020.280.77
Migration history0.202.400.01 **0.030.320.74
Constant-2.060.04-0.900.37
R square, adjusted R20.11, 0.060.17, 0.12
F value1.973.17
n213
The symbols * and ** correspond to alphas = 0.05 and 0.01 respectively.
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Chumky, T.; Basu, M.; Onitsuka, K.; Raihan, M.L.; Hoshino, S. How Do Left-Behind Families Adapt to the Salinity-Induced Male Out-Migration Context? A Case Study of Shyamnagar Sub-District in Coastal Bangladesh. Sustainability 2023, 15, 2756. https://doi.org/10.3390/su15032756

AMA Style

Chumky T, Basu M, Onitsuka K, Raihan ML, Hoshino S. How Do Left-Behind Families Adapt to the Salinity-Induced Male Out-Migration Context? A Case Study of Shyamnagar Sub-District in Coastal Bangladesh. Sustainability. 2023; 15(3):2756. https://doi.org/10.3390/su15032756

Chicago/Turabian Style

Chumky, Tahmina, Mrittika Basu, Kenichiro Onitsuka, Md Lamiur Raihan, and Satoshi Hoshino. 2023. "How Do Left-Behind Families Adapt to the Salinity-Induced Male Out-Migration Context? A Case Study of Shyamnagar Sub-District in Coastal Bangladesh" Sustainability 15, no. 3: 2756. https://doi.org/10.3390/su15032756

APA Style

Chumky, T., Basu, M., Onitsuka, K., Raihan, M. L., & Hoshino, S. (2023). How Do Left-Behind Families Adapt to the Salinity-Induced Male Out-Migration Context? A Case Study of Shyamnagar Sub-District in Coastal Bangladesh. Sustainability, 15(3), 2756. https://doi.org/10.3390/su15032756

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