Poverty and Migration in the Digital Age:

Experimental Evidence on Mobile Banking in Bangladesh

Jean Lee, Jonathan Morduch, Saravana Ravindran,

Abu Shonchoy, Hassan Zaman

April 26, 20171

Context

MigrationUrbanizationMobile MoneyPoverty and risk

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Two sites

Dhaka: Capital city, home to garment factories

Gaibandha district, RangpurOne of poorest regions of Bangladesh, with exposure to monga (seasonal famine, September through November).

Rangpur has significantly lower rates of food consumption per capita than other regions.

Dhaka

Gaibandha

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Focus: impact of remittances via mobile money

Factory workers

Remittances

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Urbanization

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From rural to Dhaka

(lifetime net migration rate

2001-11)

Absolute rural decline

Working age, 20-44

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Mobile Money in Bangladesh

“[E]xperts at Bangladesh Bank, the country’s central bank, describe mobile money as a key strategy to expand financial access in this nation of 160 million people, where fewer than 30% have a bank account.”

- Wall Street Journal, 2015• Wide range of bank-based, interoperable

mobile money providers: Dutch Bangla Bank, bKash, etc.

• Potential to mitigate economic shocks (Jack and Suri, AER 2014; Batista and Vicente 2016)

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bKash

• Leading mobile money service provided by BRAC Bank

• Mobile wallet and person-to-person transfers

• Individuals deposit and withdraw money through agent network

• Launched in 2011. Handles about 70 million transactions per day (Wall Street Journal, 2015)

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Rapid Expansion

Microfinance: After 4 decades, 21 million users. 90% women.

Mobile money: In 5 years, 21 million accounts. 18% women.

Leesa Shrader, CGAP 2015

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bKash

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Partner: Gana Unnayan Kendra (GUK), a local NGO in Gaibandha.

Works to train garment workers and place them in jobs in Dhaka.

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Research Questions

• Context: Seasonal variability in incomes, as well as the relatively higher incomes in factory jobs in Dhaka, motivates migration.

• Project: Introduce mobile banking accounts to a sample of migrants and rural families.

• Questions: Does the technology improve well-being of rural households (transfer recipients), particularly through the annual pre-harvest (monga season) famine. – Reduce food consumption variability? Improve education? Improve

financial conditions?– Improve recipient household members’ health through the difficult famine

season?

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Experimental Design

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Linked samples

• Rural families: Rural families of migrants • Urban migrants: Migrants to Dhaka from these same rural

households (70% male, 30% female).• Rural households trained through GUK

– Targeted for this intervention after identified as ultra-poor• 99% have mobile phones• 11 % have bank accounts• Avg land: about 0.1 acre• Many have incomes < $1 per day per person

• Encouragement design: Half of the sample is experimentally introduced to the technology

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Overview of Design• Rural household-urban migrant pairs are the unit of

randomization• 817 household-migrant pairs in final sample• Recruited through use of a garments training program

(SHIREE) roster and snowball sampling• Three cross-randomized experiments:

1. bKash training vs. no bKash training2. Messaging about individual benefits vs. messaging about

family/social benefits3. In a willingness-to-pay survey, priming to think about bKash or

cash• We focus here on Experiment 1

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Training Intervention

• 30- to 45-minute intervention. How to sign up and use bKash.

• Information about bKash mobile money (hard copy)• Technical assistance with enrollment:

– locating necessary identification, – completing application– locating vendor who could accept application

• 200 Taka (<3 USD) compensation for participation in the training

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Timeline

• Study recruitment (Sept 2014 to Feb 2015)• Baseline survey (Dec 2014 to March 2015)• Introduce bKash (April 2015 to May 2015)

– Treatment: 415 households (bKash training and incentive)– Control: 400 households– Marketing: Within treatment arm, cross-randomized order in

which households and migrants were approached – whether or not migrant is “first mover” – and pro-social marketing strategy

• Midline survey (August 2015 to September 2015)• Endline survey (January 2016 to March 2016)

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Administrative Data

• In addition to survey data, we collected administrative data from bKash on accounts held by households and migrants in our sample

• Data range from shortly following the end of the treatment phase to one year later (July 2015 to July 2016)

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Estimation

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First Stage• Compare “active” accounts by treatment status,

where active is defined as having at least one transaction in the period July 2015 to July 2016

• Transactions include transfers (sent or received), withdrawals, deposits and airtime top-ups

• Data are from bKash admin files

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Frequency of Transactions

• 73%% of bKash account holders make more than one transaction in their account per month

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First Stage: Rural

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Controls: gender, age, primary school completion of head of the household, and household size

First Stage: Urban

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Controls: gender, age, primary school completion of head of the household, and household size

• Average month-end balances are low (< $3)

• $1 = 78 taka• PPP$1 = 31 taka

• Rising over time

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Average Month-End Balances

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Low Balances

Number of Remittances

• Treatment households send about double the number of remittances over the period as control households

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Value of Mobile Remittances

• Similar pattern for value of remittances sent

• Peaks and valleys somewhat correspond to festivals and harvest seasons

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Aus

Local boro HYV

boro

Migrant Income and Remittances

Average monthly income: 7830 Taka per migrant.– $100, PPP$300, (PPP$10/day)

Remittances in past 7 months: 17,279 Taka– $222, PPP$557 – 2468 Taka per month ($32, PPP$80)– Large fraction of monthly income (2468/7830 =

31.5%).

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Key findings: impact of treatment

Rural households:– Reduced borrowing and increased savings– Improved education, health and agricultural outcomes– Improved resilience to negative economic shocks

Urban migrants:– Increased savings and reduced poverty– Increased employment in formal sector– Worsened self-reported health indicators

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Results: Rural Households

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Estimating Impacts• We then estimate Intent-to-Treat and

Instrumental Variables estimates of the treatment effect using the following specification:

• Our outcomes include individual outcomes and indices of outcomes within an outcome locus, such as education or health, as constructed following Kling, Liebman and Katz (2007)

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• At baseline, the total size of loans taken by rural households over the last 12 months was 6798 Taka.

• Monthly remittances are large in comparison to the size of total loans (2486/6798 = 36.6%)

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Borrowing

Borrowing

• Significant reduction in reported need to borrow over past 1 year

• Reduction in total value of loans (p-value = 0.107)

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Borrowing Index

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Savings

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44 percentage points more likely to save on a control mean of 43%

Education• Significant

increases in daily hours spent studying and aspirations for children

• Insignificant increases in rates of passing last exam, enrollment, and attendance

• No effect on education expenditures

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Education Index

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Health• Significant

decrease in number of sick household members

• Insignificant decreases in weeks ill over past year and average medical expenses

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Health Index

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Agriculture• Fewer negative

agricultural productivity shocks

• Insignificant positive increase in agricultural productivity

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Agriculture Index

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Consumption

• No significant impacts on consumption levels

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Consumption Index

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Resilience to Shocks

• To compare the outcomes of rural households in the treatment group hit by shocks with outcomes of rural households in the control group hit by shocks, we estimate:

• is the coefficient of interest (on treatment group * shock)

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Shocks – Remittance Flows• Large and

significant increases in remittances sent by migrants via bKash

• Pattern very similar to estimates for consumption

• Illustrates mechanism for consumption smoothing when hit by shocks

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Shocks - Consumption• Significant

increase in consumption when hit by agricultural shocks(32%)

• Impact larger than that estimated by Jack and Suri, 2014 (7%)

• Insignificant increase in consumption when hit by health shocks

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Shocks – Food Consumption• Similar pattern

when we look at food consumption

• Magnitudes are slightly larger, since food consumption is likely hardest hit when households are faced with shocks

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Positive Shocks

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Are Migrants Key for Resilience to Shocks?

• To compare outcomes of rural households in the treatment group hit by shocks whose paired migrants are also hit with shocks, with rural households in the control group hit by shocks whose paired migrants are hit with shocks:

• is the coefficient of interest (rural and urban pairs experience shocks at same time)

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Household Shock + Migrant Shock • No impact on

consumption for rural households hit by shocks, when migrant is hit by a health shock

• Migrants are key for provision of insurance in bad times

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Household Shock + Migrant Shock

• Migrants unable to send more remittances when they are hit by health shocks

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Results: Urban Migrants

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Remittances

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Remittances

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Savings

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Formal Employment

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Work in Garments Industry

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Poverty

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Migrant Health• Decreases in self-

reported health indicators in terms of physical health problems, ease of daily work, bodily pain, social activities, emotional problems and severe emotional problems

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Health Index

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Higher Income at Expense of Health

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Results: Robustness Checks

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Spillover Analysis - Rural

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Impact on control group of adoption – based on having more villagers in the treatment group

Spillover Analysis - Urban

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Impact on control group of adoption – based on having more people locally in the treatment group

Network Spillovers - Urban

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Discussed bKash with social network, but that did not affect adoption.

Conclusion

2 big trends gaining speed:• Movement of people• Movement of money

• Extreme poverty will become more difficult to reduce

• Migration with remittances as a poverty reduction strategy

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Strategy 1: Local development

MicrofinanceGraduation/ultrapoor programsTrainingSME

(Capital, Human capital, X)Labor

Strategy 2: Migrate and connect

(Capital, Human capital, X)Labor

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Summary Statistics

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