and WEMI pilot, Bangladesh
WS2 used quantitative approaches to assess the effectiveness of WiF-2 interventions along the migration pathway with a focus of Bangladesh as the country of origin. The workstream also aimed to identify additional interventions for actors in the migration space to consider. WS2 answered the following research questions: How and to what extent do WiF-2 interventions influence Bangladeshi women’s decision-making processes (to stay/leave exploitative work conditions) and agency? How were women migrants supported by their immediate family members? and What additional interventions could WiF-2 introduce in the focus countries?
To answer these questions, we collected primary intrahousehold data from potential migrants, returnee migrants, and non-migrant women in Bangladesh to (1) assess the impact of the training activities being implemented by the WiF-2 intervention in migration-prone districts, and (2) develop and pilot the Women’s Empowerment in Migration Index (WEMI) to explore the decision-making and agency of returnee migrants.
Through the WEMI, we aimed to develop a metric that can be used to assess the effectiveness of programs intended to enhance outcomes for migrant women and to identify potential entry points for increasing migrant women’s empowerment. The WEMI includes information on respect among household members and attitudes toward violence and gendered decision-making to reflect the role of immediate family members in making migration decisions, as well as the role played by employers and other stakeholders with whom women interact in the migration process, to better understand woman migrants’ vulnerabilities.
In addition to these research questions, CEDIL advised us to either adjust existing or add additional research to pro-actively consider the COVID-19 pandemic. In response to this suggestion, we implemented a phone survey with Bangladeshi returnee migrants to assess how COVID-19 had shaped vulnerabilities in migration. In this section, we present summary results from three papers.
1. Choudhury, Z., Sufian, S., Alvi, M., Hasan, A., Ratna, N. & C. Ringler. Inequitable or Partial Diffusion? An Assessment of Intervention to Reduce Bangladesh Women Migrants’ Trafficking and Forced Labor
2. Sufian, F., Alvi, M., Ratna, N. & C. Ringler. COVID-19 and vulnerability of low-skilled female migrants: Findings from phone survey with Bangladeshi returnee migrants from West Asia
3. Sufian, F., Alvi, M., Ratna, N., Choudhury, Z. & C. Ringler. Development and Validation of a Women’s Empowerment in Migration Index (WEMI): Evidence from Bangladeshi Returnee Migrants (working title)
WS2.1 Impact of WiF-2 activities in Bangladesh
Data and Methodology
The study evaluating the impact of WiF-2 activities in Bangladesh is based on primary data collection implemented from May to July 2022 in the country’s district with the most migration. The survey collected information about women villagers, in a program intervention area and in a control area, on various empowerment and migration-related issues.16 The control area resembles the intervention area in most respects, except for the intervention.
In the absence of a randomized experimental design, a standard recourse to establish causal claims is to utilize a matching technique, where each treatment case is matched with one or more control subjects. Such a procedure removes biases introduced by confounding factors common in observational data and approximates an experimental condition (Rosenbaum and Rubin, 1983; Agresti, 2007, p.105), which enhances the validity of the causal claim about the program’s impact (Bai and Clark, 2019, pp. 5-6).
In an experimental setup, the treatment and control groups would be balanced on average by random assignment of subjects. However, observational surveys cannot always guarantee such balancing. Variables like, as in our case, age, migration status, area of living (urban, semi-urban, rural), and district of the respondents may have confounded the program–outcome relationship. Standard matching designs, such as propensity score matching (PSM), allow us to account for these confounding variables by calculating the predicted probability of treatment-vs.-control group memberships by regressing these variables using a logistic link function on the binary response variable identifying the treatment group.17
However, due to its focus on balancing the distribution of the confounding variables, PSM discards non-matched pairs, sometimes losing a substantial proportion of the data. As a result of the reduced sample size, the matched data may no longer represent the population and so lose sufficient statistical power in estimating the treatment effect (Bai and Clark, 2018, p. 91). In our case, the nearest neighbour (Mahalanobis distance) matching reduced the sample size by about 20 percent, losing valuable information.
Therefore, we used inverse propensity weighting (IPW) in our multivariate analyses. IPW takes the inverses of the predicted probability to balance the global distribution of propensity scores (without truncating the data) so that subjects with low propensity receive higher weights and vice versa (Hernán and Robins, 2010; Heiss, 2021).18 Figure 2.1 shows that most IPW adjustment happens in our sample in the urban category, indicating that urban women were starkly dissimilar before the adjustment. This way we did not lose any information despite having a more balanced dataset.
16 The survey used purposive, non-probability samples targeting experienced, potential and non-migrants.
17 For an excellent interpretation of why ‘logit’ link is better over ‘probit’ link, consult Agresti (2007, 105).
18 We used the following formula: ipw = (treatment/propensity) + ((1 - treatment)/(1-propensity)), where treatment indicates if the subject is in the treatment group and propensity is the predicted probability calculated by regressing confounders on the treatment using logistic link function log[p/(1-p)] (Heiss, 2021, p. 26).
Figure 2.1 Inverse Probability Weighted Distribution of the Confounding Variables
Sampling
We began by making a list of villages reached by the WiF-2 implementing NGOs in the eight most-migration-prone districts in the country.19 As shown in Figure 2.2, these districts are in the center-eastern part of the country and geographically proximate to each other. These districts can be treated equally (e.g., due their migration proneness) except for Dhaka, which is the capital of Bangladesh and one of South Asia’s megacities, and Brahmanbaria, which borders with India.
“Non-WiF-2” or control villages came from within the Unions (the lowest administrative tier above the villages) under these districts where these NGOs operated but did not implement the WiF-2 program. Bangladesh’s population and housing census of 2011 was used to identify and create the list of control villages. Finally, 186 villages were randomly selected from each list, creating an overall list of 336 villages.
19 The districts were Faridpur, Dhaka, Narayanganj, Manikganj, Gazipur, Narshingdi, Brahmanbaria, and Kishoreganj.
Figure 1.2 Intervention and Study Area (at the district level)
Note: The left panel shows sample size distribution in the project areas demarcated at the district level. The darker the district, the higher the proportion it contributed to the sample. The right panel shows the distribution of the overall sample across the districts. Darker shades indicate a higher proportion of WiF-2 or intervention area.
In each of the listed villages, three types of women were targeted: non-migrant (women with no prior international migration experience), returnee migrant (women with their latest international migration experience in the last five years), and potential migrants (women with no prior international migration experience and planning to migrate in the next 12 months). Identifying households with women meeting the criteria turned out to be challenging, even in areas with highest migration.20 Therefore, we adopted two strategies to maximize our chances of reaching women meeting the above criteria. First, we took assistance from the community social workers employed by the implementing NGOs, and second, utilized snowball sampling. Moreover, given the social stigma around migration decisions, sometimes we took assistance from the villagers who had better knowledge about potential migrants.
20 The nature of the information campaign and empowerment training used by the WiF-2 implementing NGOs limited the scope of measuring whether a respondent was touched by the specific messages of the program for three reasons. First, the community diffusion model adapted by the implementers thrived on the open-for-all nature of the trainings, without targeting specific individuals. Second, the NGOs had similar training programs in place prior to collaborating with the WiF-2 program in the target areas. The WiF-2 training materials were not branded as a separate initiative. Third, the presence of other NGOs working in the target areas on similar topics, commissioned and funded by other international and government projects, also made it difficult to rely on self-reported beneficiaries of the WiF-2 program.
A list of WiF-2’s community social workers was created with the help of the NGOs. Since the NGOs made the pool of social workers from the intervention areas, they had native knowledge about the villages chosen for the survey. These social workers played a crucial role in identifying survey respondents. At the end of each interview, the respondents were asked if they knew someone like them and if they could provide the enumerator with the acquaintances’ addresses, including phone numbers. Enumerators used these addresses and phone numbers to reach the new respondents, although they kept the snowballing process within the lists of sample villages already prepared. In addition to the assistance of social workers, the simple random method for non-migrants and the snowballing process for other categories further helped to reach the overall sample target, which was set to be at least 2,500.
The final sample size was 2,645 women villagers, where each woman represented a household. About 60 percent (1,584) of the sample resided in the intervention area. The sample included 1,019 returnees, 814 potential migrants, and 812 non-migrants.21 The proportion of snowballed samples became relatively high, about 58 percent. About 73 percent was from rural, 16 percent semi-urban, and 11 percent from urban areas.
Measurement
In the general models, migration-related attitudes and behaviours are a function of eight covariates, including intervention or treatment, age, education, unemployment, ownership of assets, satisfaction with monthly income, residential area (urban/semi- urban/rural), and a binary measure of the respondent’s migration network. The outcome variables are exposure, migration-related deliberations, and perceived migration risks.
Table 1 provides each measure’s definition and descriptive statistics, including the control, treatment, and outcome variables. More details on the questions used for calculating the treatment and outcome variables are in Table B.1-B.5 at the end of this section.
The treatment variable, intervention, identifies if the respondent lived in the villages where the WiF-2 program was implemented. A related variable, exposure, is a summary of four survey items measuring if the respondents or their husbands knew about, were contacted by, or were involved in any ILO-run NGOs or their activities. We treated exposure as an outcome variable in the causal models, although one could use the variable as a proxy for treatment. Thus, we used exposure as a predictor of other outcomes in the subsequent models.
21 The survey was conducted by trained local female enumerators in local language. They received training on ethical research practices
and requested informed consent. Respondents were compensated for their time with an in-kind gift of household items.
Table 2.1 Descriptive Statistics of Relevant Variable
Variables Descriptive Statistics (N =
2645) Description
Range Median Mean SD Control Variables
Education 0 - 13 5 4.32 3.5 The level of education is measured in terms of the number of years in school.
Own Large Asset 0 – 5 0 0.40 0.73 The number of large assets owned by the respondent, a proxy for wealth.
Income Satisfaction 1-3 1 1.52 .80
Level of satisfaction with the overall household income, and ordinal variable with “Satisfied” = 1, “Neutral”
= 2, “Dissatisfied” = 3.
Unemployed 0 - 1 1 0.69 0.45 Indicator of unemployment status (yes/no).
Migrant 0 - 1 1 0.69 0.46 An indicator of whether the
respondent was a migrant (potential or returnee).
Area 1-3 1 1.384 .67 Nature of the residential area: Rural = 1, Semi Urban =2, Urban = 3 Treatment variables
Intervention 1 - 2 2 1.59 0.49 An indicator of whether the
respondent lived in the WiF-2 villages or not.
Outcome variables Exposure
(Binary Index) 0 - 1 1 0.55 0.45
A composite measure of five binary questions asking respondents’
exposure to programs by NGOs under the ILO’s WiF-2 project.
Deliberation
(Binary Index) 0 - 1 1 .58 0.49
A binary version of the migration- related deliberation index where:
deliberation = 1 if index <= median, &
0 otherwise.
Perceived Migration Risk
(Ordered Index: Likert Scale: “Very Unlikely”
… “Very Likely”)
1 - 5 2.75 2.79 0.78
A composite Likert-like measure of 8 risky situations that the respondents perceived might happen to women as migrant workers in a foreign country.
Notes: We created two alternative binary deliberation indexes, one using the mean as the cut-off point and a second, raw deliberation index, which can be treated as a continuous variable and be fitted in an ordinary least square (OLS) regression. Results from the logit models were almost the same.
Models
We have three regression models, one for each outcome variable. Logistic regression models with IPW weights (Agresti, 2007, Heiss, 2021) were used for the binary outcomes,
‘exposure’ and ‘deliberation.’ The outcome variable ‘perceived migration risk’ is a Likert- like index where respondents’ perception of migration risk is rated in an ordered sequence of five elements: “Very Unlikely”, “Unlikely”, “Neutral”, “Likely”, and “Very Likely”.
Thus, for the migration risk outcome, we utilized an ordered logistic regression of the form logit(P(Y(<= j))) = Bj0 + B1X1 + … + BPXP, where Bj0 and B1X1 + … + BPXP are model parameters with P predictors for j=1, … j-1 categories. In this specification, the intercepts will differ for each category, but the slopes are constant across categories because of the parallel line assumptions.22 Thus, the single regression coefficient applies to all categories (Bilder and Loughlin 2014).
Results
Table 2.2 Major Finding: Logit Estimates (log-odds) with Inverse Probability Weights
Exposure Deliberation Perceived Mig.
Risk
Intervention (WIF =1) 1.347*** -0.056 -0.291***
(0.083) (0.064) (0.055)
Age 0.006 -0.018*** 0.016***
(0.005) (0.004) (0.003)
Education 0.070*** -0.056*** 0.038***
(0.012) (0.009) (0.008)
Employment (Unemployed =1) -
0.371*** -0.300*** -0.221***
(0.082) (0.067) (0.058)
Own Large Asset 0.192*** -0.010 -0.157***
(0.053) (0.043) (0.037)
Level of Income Satisfaction (Neutral |
Unsatisfied) -0.179 -0.297** -0.040
(0.135) (0.105) (0.091)
Level of Income Satisfaction (Satisfied |
Unsatisfied) -0.289** -0.494*** -0.368***
(0.093) (0.076) (0.066)
Area (Semi-urban | Rural) 0.381*** 0.146+ 0.098
(0.099) (0.084) (0.071)
Area (Urban | Rural) -0.193 -0.650*** 0.510***
(0.128) (0.097) (0.079)
Migration Network (Yes =1) -
1.971*** -0.701*** -0.004
(0.079) (0.068) (0.061)
Exposure 0.888*** 0.024
(0.078) (0.068)
Intercept -
1.148*** 1.473*** #
(0.227) (0.183) #
AIC 4279.1 6597.4 13598.5
22 See Agresti (2007) and Bilder and Loughlin (2014) for the general frameworks of binary logistic and ordered logistic regression models. For the implementation of IPW weights within the logistic regression framework, see Heiss (2021)
RMSE 0.38 0.48 2.67 Significance: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. Note: Standard Errors are in
parentheses. # The intercepts or cut points of the ordered logit model were statistically significant.
To assess the impacts of WiF-2 activities in Bangladesh, we first assessed exposure to the program. Table 2.2, with “exposure” as the dependent variable, shows a significant positive coefficient for intervention. Women in the intervention area were 1.35 times more likely to be exposed than those in the non-intervention areas, all things being equal.
In the odds ratio interpretation, the estimated odds that a woman in the intervention area was exposed were exp(1.35) or 3.86 times the odds (about 286 percent more) for a woman living in the control area.
Women living in semi-urban areas were about 46 percent (exp(.38) = 1.46) more likely to be exposed. Moreover, the program intervention increased the probability of exposure significantly (Figure 2.3). Furthermore, the likelihood of exposure is systematically higher in the semi-urban areas across intervention and non-intervention areas.
The exposure model shows that an increase in one academic year of schooling multiplies exposure by exp(0.07) or 1.07 (a 7 percent increase). The probability of exposure significantly increases as the level of education increases (right panel of Figure 2.3). The relationship is more pronounced for the treated (intervention) women in semi-urban areas than others in the sample.
Figure 2.2 Marginal Effects of Intervention on Exposure and its Relationships with Area and Education
Deliberation
We defined deliberation as a binary index measuring women’s initiatives to consult and discuss migration topics with others like family, neighbours, friends, and local intermediaries. This increases the information available to them, including understanding of migration risks, and should help them to take more informed migration decisions. As a result of the WiF-2 intervention, we expected that treated women deliberated more than their counterparts in the non-intervention area. Similarly, women exposed to ILO’s and their partners’ activities (exposure) should be more knowledgeable about migration topics than those not exposed.
Figure 2.3 Marginal Effects of Exposure on Migration-Related Deliberation and its Relationships with Age and Education
The impact of the intervention was not straightforward. The coefficient for “intervention”
in the deliberation model (Table 2.2) is not statistically significant; its size and sign are also contrary to our expectations. However, as discussed before, the “exposure” variable can be considered a proxy of intervention, and its coefficient is statistically significant (p<0.001) and indicates a positive relationship with deliberation. Exposed women were 143 percent more likely to deliberate about migration-related topics than non-exposed women, ceteris paribus. Digging further into the exposure–deliberation relationship in Figure 2.4, we found that the probability of deliberation for semi-urban women increases with exposure more than for those in the urban and rural areas. Furthermore, older, and more educated women were significantly less likely to deliberate.
Perceived Risk of Migration
Respondents were asked to rate on a five-point Likert scale (from “Very unlikely” to “Very likely”) a battery of eight risky situations that can occur to women migrants, like losing money, getting beaten, trapped in slavery, being deprived of food and water, being
deported, or imprisoned, becoming extremely ill, or death. Forty-three percent of surveyed women remained neutral, while about 34 percent thought such situations were
“Unlikely”. We find that “risk perceptions” were linked to the WiF-2 intervention (Table 2.3).
The coefficient for intervention in Table 2.2 shows that the relationship is negative. In causal terms, for the treated women, the odds of their rating the risky situations as less likely to occur (i.e., “Very unlikely”, “Unlikely”, and “Neutral”) is 1.34 times that of the untreated women, ceteris paribus. In other words, treated women had about 34 percent higher odds of being less likely to think that risky events would occur to a migrant.23
Table 2.3 Distribution of Risk-Perception Categories by Intervention
Very Unlikely Unlikely Neutral Likely Very Likely Non-Intervention 0.024 0.126 0.172 0.066 0.013 Intervention 0.035 0.216 0.260 0.086 0.002
Total 0.059 0.342 0.432 0.152 0.015
X-squared = 39.577, df = 4, p-value = 5.295e-08
We further probed how the above relationship held for various levels of the respondent’s age, education, and the number of large assets she owned—some of the statistically significant variables in the risk model.
Figure 2.4 Marginal Effects of Intervention on Perceived Migration Risk and Their Relationships with Age, Education, and the Number of Assets Owned
23 We tested for the proportional odd assumptions for this model, and the results were reasonable.
As shown in the left panel of Figure 2.5, older respondents in the intervention area were less likely to select the “Unlikely” and more likely to select the “Likely” category than their younger counterparts. In the latter case, such thoughts were more pronounced in the non-intervention area than in the intervention area. The pattern repeats for the level of education. The second panel shows that the higher the level of education, the lower the probability of choosing “Unlikely”. In other words, less-educated respondents were more likely to select “Unlikely” than more-educated respondents. The right-most panel shows that the more affluent (in terms of owning assets) the respondents in the intervention area, the higher the probability that they chose the “Unlikely” category. In other words, relatively poorer respondents in the intervention area were less likely to choose the
“Unlikely” category and more likely to select the “Likely” category than their wealthier counterparts.
Robustness Check
These results remain robust across stepwise model specifications. All dependent variables were regressed on the treatment variables in the bivariate specification before other variables were introduced as controls in the equations. Models with IPW weights did better than weights such as the finite population corrections (FPC). The ordered logit regression for the perceived migration risk analysis had reasonable tolerance to the proportional odds assumption that the relationship between each pair of outcome groups is the same (Agresti 2007, 180-182). Since we chose IPW weighting, we decided not to control for intra-class correlation (ICC)—which was not more than 19 percent in this dataset—and, for that matter, random intercepts for study districts.
A limitation of the study stems from the assumption used for the sampling strategy that suggests living in the intervention area means a minimum exposure to the program. Since direct exposure cannot be measured due to the nature of the intervention, true identification of the causal effect of the WiF-2 programs on women was not feasible.
Conclusion
The study assessed the impact of WiF-2 in Bangladesh on key migration-related outcomes, including women’s exposure to the program-related organizations and activities, their initiatives to deliberate on migration topics, and their perception of risk in migration. Although we did not directly measure the issues of trafficking and forced labour, the analyses presented in this paper shed light on these issues by implication, that is exposure to information reduces the likelihood of risky migration, including trafficking and forced labour.
The survey was observational and retrospective. However, its scope included women and their households in the intervention and control villages in the country’s districts with highest levels of migration, where ILO implemented the WiF-2 program. Households in the control villages were like those in the intervention villages in most respects, except for the intervention.
Overall, we found positive impacts of the WiF-2 program on all three outcomes. Women in the intervention area had more exposure to information and knowledge about migration risks than those in the non-intervention areas. This also means WiF-2 was able to engage women (or male members of their households) in the target area. If considered