The major impacts of climate change play a substantial role in triggering human migration, especially in the coastal areas. The individual or combined effects of climate change are likely to trigger mass human movement both within and across international borders. People rarely move for a single reason; the motivation to migrate is complex of many factors. The main goal of this article is to identify the factors related to the decision to migrate taken by refugees in the coastal area. To assess this objective we employ exploratory factor analysis and structural equation modeling (SEM) and find that different factors influence refugees’ migration decision differently. From the findings, it is seen that loss of shelter, extreme events, decreasing soil fertility and food shortage, variability in temperature patterns and exhaustion of natural resources are the most important environmental factors that affect the decision to migrate of climate refugees. Low income, increasing price, decreasing purchasing power are the most important economic factors that influence migration decision. No social factors have significant effect on migration decision while safety as a political factor has a moderate influence on refugees’ decision to migrate. Finally, this article provides some recommendations for recognition of and protection for migrants forced to move to safer places due to certain direct impacts of climate change, notwithstanding the existence of multi-causality.
For the last few years, there has been an expanding concern in connections between climate change and migration in all spheres. Climate change is intrinsically influencing the existence of millions of coastal people who are being imposed to leave their habitat to seek refuge in other areas. It is indicated by the Intergovernmental Panel on Climate Change (IPCC) that migration flows associated with climate change are predicted to rise, specifically in the world’s poorest countries due to extreme weather events, such as stronger and more frequent storms, floods, and droughts, as well as long term problems, such as desertification, rising sea levels and riverbank erosion [
Climate variability has appeared as a key discussion for environmentally vulnerable countries especially Bangladesh because this country is widely conceded as one of the most climate vulnerable countries in all over the world. Bangladesh is a flood plain basin contemplated as the coast of rivers and canals [
To define people displaced due to climate change Norman Myers (2005) in 2005 defined climate refugees as:
“People who can no longer gain a secure livelihood in their homelands because of drought, soil erosion, desertification and other environmental problems, together with associated problems of population pressures, and profound poverty [
Even the International Organization for Migration (IOM) has proposed the following definition to be able to categorize these people:
“Environmental migrants or climate migrants are persons or groups of persons, who, for compelling reasons of sudden or progressive changes in the environment that adversely affect their lives or living conditions, are obliged to leave their habitual homes, or chose to do so, either temporarily or permanently, and who move either within their country or abroad [
Both environmental refugees and climate refugees are invoked to describe populations that have been displaced or are at risk of displacement associated with environmental changes. The term climate refugee especially has been mobilizing to describe as:
“Large numbers of people predicted to be permanently or temporarily displaced by climate change effects such as drought, desertification, deforestation, soil erosion, water shortages and rising sea level [
“People who have to leave their habitats, immediately or in the near future, because of sudden or gradual alternations in their natural environment related to at least one of the three impacts of climate change: sea-level rise, extreme weather events, drought and water scarcity [
According to Castles (2002) environmental conditions are part of a complex pattern of causality. They argue that environmental, economic, social, and political factors are interrelated and need to be examined jointly in order to understand the role environmental factors play in population movements (
Temperature variations and disastrous events incite short-distance internal relocation. Among various examinations exploring the connection among drought and migration, decreased precipitation is connected to expanded movement to urban territories in sub-Saharan Africa [
Mali and Burkina Faso [
H1: Environmental factors have high influence on refugees’ migration decision
The neo-classical microeconomic perspectives tend to focus more on the human capital and economic dimensions of migration decision-making than environmental context during a hazardous situation. Here, migration is viewed as shaped by cost-benefit calculation with a personal investment in migration behavior only being justified by sufficient returns to the behavioral investment. Environmental considerations are, in a sense, implicit here since environmental hazard or other risks may represent negative locational characteristics, while positive environmental attributes likely increase destination attractiveness. Econometric migration models have disclosed associations with locational amenities and some suggest that an indication of the societal value placed upon such amenities, or dis-amenities, is reflected in wage differentials across locations [
H2: Economic factors have high influence on refugees’ migration decision
Recent studies have also identified social, political factors as a possible driver of human migration along with economic and environmental determinants [
H3: Social factors have high influence on refugees’ migration decision
H4: Political factors have high influence on refugees’ migration decision
In this research, quantitative research design has been employed for assessing the factors influencing migration decision. The study areas for this study have been selected from disaster-prone coastal areas like Haridhali Union of Paikgachha Upazila of Khulna District, Haimchar Union of Uttar Algi Durgapur Upazila of Chandpur District and Sreepur Union of Mehendigang Upazila Barisal District. There are no baseline data about the household numbers of climate refugees in the selected study areas. As such I have made a baseline survey and found approximately 1682 Households of climate refugees. For these 1682 households, 150 households from three different areas have been selected randomly by using the sampling size formula (n = Nz2pq/Nd2 + z2pq) as the sample of this study [
To ensure the validity of all measures regarding factors affecting migration decision, the measurement items for latent constructs were developed from prior studies. Then, its’ items have been measured on a Likert scale. The detailed items of each construct and their sources are listed in
Variable | Coding | Items | Source |
---|---|---|---|
Environmental Factors | ENV1 | Variability in Precipitation and temperature patterns | [ |
ENV2 | Extreme events such as cyclone, floods, droughts, erosion | ||
ENV3 | Decreasing soil fertility and food shortage | ||
ENV4 | Exhaustion of natural resources | ||
ENV5 | Loss of Shelter | ||
Economic Factors | ECO1 | Low income | [ |
ECO2 | Unemployment | ||
ECO3 | Underemployment | ||
ECO4 | Increasing price | ||
ECO5 | Decreasing purchasing power | ||
Social Factors | SOC1 | Family conflict | [ |
SOC2 | Welcome by relatives | ||
SOC3 | Welcome by NGO’S | ||
SOC4 | Urban oriented education | ||
Political Factors | POL1 | Welcome by Government | [ |
POL2 | Safety |
This study modified some items to better fit the current research context. Measurement items for Environmental Factors, Economic Factors, Social Factors, and Political Factors were adopted from the climate change and migration literature [Afifi et al. (2014), Barrios et al. (2006), Findley (1994), Henry et al. (2004), McLeman & Ploeger (2012), Lu et al. (2012), Salauddin & Ashikuzzaman (2012), Feng et al., (2010), Marchiori et al. (2012), Black et al. (2011), Castles (2002)].
The first output from factor analysis is a table of descriptive statistics that involves all the variables responsible for the migration decision is under investigation. Looking at the mean we can conclude that Loss of shelter (4.78), Extreme events (4.65), Decreasing soil fertility and food shortage (4.12), variability in temperature patterns (3.62) and exhaustion of natural resources (3.59) are the most important environmental factors that affect the decision to migrate. Low income (3.83), increasing price (3.07), Decreasing Purchasing Power (3.29) are the most important economic factors that influence migration decision. No social factors have significant effect on migration decision while safety (2.07) as a political factor has a moderate influence on migration decision (
Descriptive Statistics | ||||
---|---|---|---|---|
Factors Affecting the Migration Decision | Mean | Standard Deviation | Analysis N | |
Environmental Factors | Variability in precipitation and temperature patterns (ENV1) | 3.62 | 1.060 | 150 |
Extreme events such as cyclone, floods, droughts, erosion (ENV2) | 4.65 | 0.602 | 150 | |
Decreasing soil fertility and food shortage (ENV3) | 4.12 | 1.086 | 150 | |
Exhaustion of natural resources (ENV4) | 3.59 | 1.171 | 150 | |
Loss of shelter (ENV5) | 4.78 | 0.542 | 150 | |
Economic Factors | Low income (ECO1) | 3.83 | 1.163 | 150 |
Unemployment (ECO2) | 3.02 | 1.497 | 150 | |
Underemployment (ECO3) | 2.79 | 1.349 | 150 | |
Increasing Price (ECO4) | 3.07 | 1.332 | 150 | |
Decreasing Purchasing Power (ECO5) | 3.29 | 1.292 | 150 | |
Social Factors | Family conflict (SOC1) | 1.57 | 1.077 | 150 |
Welcome by relatives (SOC2) | 1.96 | 1.474 | 150 | |
Welcome by NGO’S (SOC3) | 1.37 | 0.799 | 150 | |
Urban oriented education (SOC4) | 1.31 | 0.741 | 150 | |
Political Factors | Welcome by Government (POL1) | 1.50 | 0.968 | 150 |
Safety (POL2) | 2.07 | 1.455 | 150 |
Source: Authors’ calculation.
It is concerned with the consistency and stability of the measurement. In the current study, there are four independent scales and one dependent scale used in survey questionnaire to measures the constructs of the research model. In this study, there were sixteen scales used in the survey questionnaire to measure the constructs in the proposed model; Environmental Factors (EF), Economic Factors (ECF), Social Factors (SF) and Political Factors (PF) of migrated people in the coastal areas. A reliability coefficient was run on SPSS for each set of constructs and the results are presented in
Kaiser Meyer Olkin (KMO) and Bartlett’s Test measures the strength of relationship among variables. The KMO measures the sampling adequacy which should be close than 0.5 for a satisfactory factor analysis to proceed. Kaiser (1974) recommend 0.5 (value for KMO) as minimum (barely accepted), values between 0.7 - 0.8 acceptable, and values above 0.9 are superb. Looking at the table below, the KMO measure is 0.622, which is close of 0.5 and therefore can be barely accepted (
Confirmatory Factor Analysis (CFA) entails associating the latent variables with their measured variables by restricting the former to load with their respective measured variables such that they are allowed to correlate. In the Confirmatory factor Analysis (CFA), convergent validity relies on the average variance extracted (AVE) and Composite Reliability (CR) as a base.
Variables | Items Number | Cronbach’s alpha | Comments |
---|---|---|---|
Environmental Factors | 5 | 0.885 | High |
Economic Factors | 5 | 0.836 | High |
Social Factors | 4 | 0.784 | Good |
Political Factors | 2 | 0.631 | Moderate |
Migration Decision | 2 | 0.912 | Excellent |
Source: Authors’ calculation.
KMO and Bartlett’s Test | ||
---|---|---|
Kaiser-Meyer-Olkin Measure of Sampling Adequacy | 0.622 | |
Bartlett’s Test of Sphericity | Approx. Chi-Square | 596.968 |
Degree of Freedom (df) | 120 | |
Significance (Sig.) | 0.000 |
Source: Authors’ calculation.
Variable | Item | Standardized Loading | AVE | CR |
---|---|---|---|---|
Environmental Factors | ENV1 | 0.74 | 0.639 | 0.898 |
ENV2 | 0.85 | |||
ENV3 | 0.87 | |||
ENV4 | 0.82 | |||
ENV5 | 0.71 | |||
Economic Factors | ECO1 | 0.64 | 0.587 | 0.875 |
ECO2 | 0.71 | |||
ECO3 | 0.70 | |||
ECO4 | 0.85 | |||
ECO5 | 0.89 | |||
Social Factors | SOC1 | 0.72 | 0.431 | 0.747 |
SOC2 | 0.67 | |||
SOC3 | 0.73 | |||
SOC4 | 0.48 | |||
Political Factors | POL1 | 0.59 | 0.484 | 0.648 |
POL2 | 0.78 | |||
Migration Decision | MG1 | 0.86 | 0.747 | 0.922 |
MG2 | 0.92 |
Source: Authors calculations.
0.48 to 0.74 and CR ranged from 0.64 to 0.92 are greater than the recommended levels [
In this study, discriminant validity was assessed by comparing the absolute value of the correlations between the constructs and the square root of the average variance extracted by a construct. When the correlations are lower than the square root of the average variance extracted by a construct, constructs are said to have discriminant validity. As shown in table, all squares roots of the AVEs are higher than the correlations between constructs and that definitely confirms adequately discriminant validity. The results shown in
ENV | ECO | SOC | POL | MIG | |
---|---|---|---|---|---|
ENV | 0.799 | ||||
ECO | 0.54 | 0.766 | |||
SOC | 0.41 | 0.67 | 0.690 | ||
POL | 0.32 | 0.70 | 0.49 | 0.696 | |
MIG | 0.49 | 0.55 | 0.63 | 0.60 | 0.865 |
Source: Authors calculations.
The structural model analyzes the relationships among the variables and the significance of these relationships (
Richard Black, after reviewing a wide range of studies on environmental degradation induced migration, claims that there is no convincing evidence that it leads to large-scale displacement. He also points out that the links postulated in the literature between environment and migration are not explicitly demonstrated. Black recognizes that environmental degradations and catastrophes, such as rising sea levels, flood, cyclones, and declining water supplies are very real and important factors in the decision to migrate. But he finds little evidence of actual permanent large scale displacement directly caused by these factors. But from my research findings, the statistical tables and the data show that environmental factors are the main factors that triggered massive displacement in the coast. It is because most of the climate migrants live alongside the coast which is geographically so much vulnerable to natural disasters. So when sea level rises all their locations go under water or when riverbank erosion happens all their land washed away so they do not have any other option without taking migration decision. So in my research areas, environmental factors are the main factors that trigged migration.
Lonergan (1998) suggests that environmental factors cannot be easily separated from other socioeconomic and political factors and processes triggering migration. Castles takes a more nuanced view, noting that migration involves complex patterns of multiple causalities, in which natural and environmental factors are closely linked to economic, social, and political ones (Lonergan & Swain, 1998). But from my research findings, the statistical tables and the data show something different. Environmental factors trigged the economic factor in
Estimate (β) | P Value | Decision | ||
---|---|---|---|---|
H1 | Environmental Factors → Influence Migration Decision | 0.193 | 0.010 | Accept |
H2 | Economic Factors → Influence Migration Decision | 0.218 | 0.000 | Accept |
H3 | Social Factors → Influencing Migration Decision | 0.052 | 0.779 | Reject |
H4 | Political Factors → Influencing Migration Decision | −0.070 | 0.430 | Reject |
the study areas but not the social and political factors. Environmental factors such as extreme events, decreasing soil fertility and food shortage, variability in temperature patterns, loss of shelter and exhaustion of natural resources trigged economic factors like income, price, and refugees’ purchasing power. Social factors like family conflict, urban-oriented household, welcome by NGO’s or welcome by relatives didn’t play much significant role in taking migration decision. That is because of the economic hardship of their relatives is not so good to take the responsibilities of others. Another reason is that most of the refugee relatives stay in one room areas where they don’t want to take the burden of others. Another reason is that the relatives who are wealthy enough to take the responsibilities of climate refugees are unwilling to do so because of their status. In Bangladesh, the government built primary schools in remote places so like developed countries urban-oriented household does not play a crucial role for migration decision. In developed countries like the United States, Environmental NGOs (ENGOs) play a role in the establishment and enforcement of environmental priorities. Not exclusively are ENGOs conquering insufficiencies in great global law, they currently assume distinctive jobs in need setting and the implementation of worldwide standards. They can articulate powerful universal, single-purpose standards because they do not have to trade off for other objectives. They have little incentive to subordinate science to other political or economic considerations. Finally, they can regularly coordinate with neighborhood ecological gatherings. ENGOs are additionally specialists of social learning. They add to societal change by surrounding the issues, building networks, and setting precedents. But in Bangladesh, most of the ENGOs are not functioning properly. They just provide some relief when climate migration took place. Otherwise, their functions are limited. That’s why this factor plays less impact on the migration decision of refugees. Besides that refugee migration is also influenced by political factors like political safety and welcome by the government. In Bangladesh the government didn’t specify any priorities or policies for the people who could be displaced due to hazardous events. So most often government took necessary steps after migration has been initiated by the refugees, not before the migration. Thus, it does not play a crucial role in taking migration decision.
Using confirmatory factor analysis (CFA) and structural equation modeling (SEM) we explore the influential factors that affect the climate refugee’s decision to migrate. We have found that environmental degradation, resource depletion, and natural hazards play a contributing role as an important push factor in affecting population movement in coastline areas. Environmental degradation as a result of climate change may be one of the many triggering factors for migration but it is not the only cause. Environmental hardships are often aggravated by issues such as economic hardship also (unemployment, income, increase in price etc.). So when environmental deteriorations cause displacements, they are often the byproduct of economic factors also. This research considers the conceptualization of the environment as a primary cause of forced displacement. But as a whole migration involves complex patterns of multiple causalities, in which natural and environmental factors are closely linked mainly with economic factors as well. Considering the situations of climate refugees the following recommendations have been suggested:
1) A particular national plan for climate refugees should be illuminated by the Government to resolve climate displacement related issues. This national plan could be subsumed with Government’s climate change adaptation strategy also.
2) It is important that the Government clearly identifies the bodies with primary responsibility for climate displacement. In particular, climate displaced persons should have a clear understanding of which institutions are able to provide social, financial and resettlement assistance.
3) Government should provide emergency relief services and establish first aid centers in heavily remote and coastal areas for climate displaced persons.
4) All areas that cannot be protected through increased coastal defenses for practical or economic reasons need to be included early in long-term resettlement and reintegration programs that make the process acceptable for the affected people.
5) International communities, and especially donor countries, must also support efforts to eliminate corruption and vastly improve transparency. It is not enough to simply provide funds for climate displacement programmes and policies, it is essential that funds are monitored and effective implementation of programmes is ensured.
I express my most sincere gratitude and thanks to my supervisor, Professor XU Xiaojun, for his support, comments, guidance, and inspiration throughout the period of this paper writings. Besides that, I appreciate the support of my students from the Department of Sociology, University of Barishal, Bangladesh for helping me during my data collection. Finally, I would like to express special gratitude to all my family members for their emotional support and encouragement.
The authors declare no conflicts of interest regarding the publication of this paper.
Abir, T.M. and Xu, X.J. (2019) Assessing the Factors Influencing Migration Decision of Climate Refugees in Coastal Areas of Bangladesh. American Journal of Climate Change, 8, 190-204. https://doi.org/10.4236/ajcc.2019.82011