HIAS-E-109

Mobile money and shock-coping:
Urban migrants and rural families in Bangladesh under the
COVID-19 shock
Hiroyuki Egami (a) , Yukichi Mano (b) , Tomoya Matsumoto (c)(a) Nihon University(b) Hitotsubashi University(c) Otaru University of Commerce

August 2021

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo 186-8601, Japan
tel:+81 42 580 8668 http://hias.hit-u.ac.jp/
HIAS discussion papers can be downloaded without charge from:https://hdl.handle.net/10086/27202
https://ideas.repec.org/s/hit/hiasdp.html

All rights reserved.

Title page (with author details)

Mobile money and shock-coping:
Urban migrants and rural families in Bangladesh under the COVID-19 shock

Hiroyuki Egami†*, Yukichi Mano‡, and Tomoya Matsumoto§†

Nihon University, 1-3-2 Misakicho, Chiyoda-ku, Tokyo 101-8360, Japan. E-mail address:

phd15410@grips.ac.jp‡

Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo 186-8601, Japan§

Otaru University of Commerce, 3-5-21 Midori, Otaru, Hokkaido 047-8501, Japan* Corresponding author

1Funding
We gratefully acknowledge fundings from the following organizations: JSPS KAKENHI (Grant
Numbers: JP18J20988, JP18H00838), The Telecommunications Advancement Foundation, The
Mitsubishi Foundation, The Kikawada Foundation, The Zengin Foundation for Studies on Economics
and Finance, The Yamada Foundation, The Nihon Housei Gakkai, and Japan Center for Economic
Research.
Acknowledgements
We are thankful to Stephan Litschig, Yoko Kijima, and Masahiro Shoji for their insightful comments
and suggestions on earlier drafts of this paper. This project would not have been possible without the
support of Md. Mizanur Rahman, Md. Shafiur Rahman, Md. Mahfuzur Rahman, the entire team of
the Global Public Health Research Foundation, Maheen Matin, Rajibul Alam, the Momoda
foundation, and Abu S. Shonchoy. We thank Chihiro Egami and Masayuki Egami for providing
superb research assistance. All errors are our own.

2Conflict of Interest Statement
The authors declare no conflict of interest. The funders had no role in the design of the study; in thecollection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.

3

Manuscript (without Author Details)
Mobile money and shock-coping:
Urban migrants and rural families in Bangladesh under the COVID-19 shock

July 2021

Abstract
People in developing economies face substantial income risks and use diverse strategies to mitigate
the negative welfare impact. Rural households often send migrants to diversify income sources anddepend on remittances to cope with income risks. To examine the risk-coping mechanism of urban
migrants and their rural families against the aggregate shock due to the COVID-19 pandemic, we
analyze the seven-round Bangladeshi household panel covering the period before and after the first
implementation of COVID-19 lockdown policies. Our event study finds that urban migrants
experienced more substantial income loss than their rural families and reduced but not ceased
remittances to cope with the aggregate shock jointly. Notably, mobile money services allowed themto continue sending remittances even under the lockdown policies.
Keywords: migrants, remittances, risk coping, aggregate shock, mobile money, COVID-19
JEL code: O12, O15, F23

4

1. Introduction
People in developing economies face substantial risks, such as sickness of family members, crop pests
and diseases, and erratic rainfalls. However, rural households have inadequate assets to cope with theseshocks fully, intra-village informal insurance is often imperfect, and formal insurance is unavailable(Dercon, 2002; Rosenzweig, 1988; Townsend, 1994, 1995). Thus, they usually send migrants to urban
areas to diversify income sources and rely on migrants’ remittances to smooth consumption (De Janvry& Sadoulet, 2016; Lagakos, 2020; UNDP, 2009; World Bank, 2020a). Indeed, remittances enable rural
households to meet daily needs (De Weerdt & Dercon, 2006; Lucas & Stark, 1985; Rosenzweig &Stark, 1989) and cope with health and weather shocks (De Weerdt & Dercon, 2006; Gröger &Zylberberg, 2016). Recently, there is a renewed scholarly interest in remittances because of theincreased use of mobile money1 in developing countries, dramatically reducing the cost of sending
and receiving remittances (Aron, 2018; Jack & Suri, 2014; Munyegera & Matsumoto, 2016; Riley,2018; UNDP, 2012).
Nevertheless, the existing studies typically focus on rural households’ welfare and pay
insufficient attention to migrant workers’ welfare, e.g., whether intra-family remittances also help
migrant workers to cope with shocks. This study attempts to address this gap by simultaneously
examining the shock-coping behaviors of migrant workers in urban areas and their families in rural
areas in Bangladesh under the COVID-19 pandemic. The spread of COVID-19 and related
containment policies substantially reduced the employment and earnings opportunities, particularly inurban areas, and affected migrant workers’ welfare probably more severely than rural households’

Mobile money is an innovative technology that allows people to receive, store, and spend money using a mobile phone
and has been increasingly used in developing economies (See, for example, Aron, 2018).
1

5

(World Bank, 2020a). In Bangladesh, the first cases of COVID-19 were reported on March 8, 2020,2
and the government declared the nationwide lockdown and imposed movement restrictions on March
26, 2020, to contain the pandemic. Furthermore, with the earlier outbreak of COVID-19 in Europe andthe United States, apparel companies in these countries suddenly canceled or postponed export orders
from the garment factories in Bangladesh. 3 (Hossain, 2019) Consequently, urban migrant workers,
particularly in the export sector, faced the closure of their workplaces and lost their earning
opportunities for an extended period.
This study explores how domestic migrant workers and their rural families responded to andshared these shocks associated with the COVID-19 pandemic. We thus analyze a seven-round panelof 723 pairs of migrant workers of garment factories in Dhaka and their families in the rural area. The
panel survey, which collected information such as income, consumption, and remittances roughly
every three months, covers two years from October 2018 to August 2020, five rounds before and tworounds during and after the COVID-19 national lockdown through phone interviews. 4 We adopt an
event study approach to find how the migrant workers and their rural families responded to a significant
drop in income under the COVID-19 pandemic. We find that the migrant workers reduced but notceased their remittances to rural families. Consequently, both the migrant workers and their ruralfamilies shared the income shock. They prevented a considerable reduction of consumption of theurban migrant workers as well as their rural families. Notably, the migrant workers managed to send
remittances to rural families via mobile money services despite travel restrictions under the COVID19 pandemic. 5

The cumulative number of confirmed cases exceeded 0.74 million, which is the second-worst in South Asia after India,
according to World Meters (https://www.worldometers.info/coronavirus/#countries: last access April 25, 2021).
3
The garment industry accounts for over 80% of Bangladesh’s export (Hossain, 2019).
4
The phone interviews allow researchers to contact many remote and dispersed populations with a feasible logistics
operation (Dabalen et al., 2016).
5
Bangladesh has shown high growth in mobile penetration, which reached 87% in 2017 (GSMA, 2018).
2

6

The main contribution of this study is to shed light on the shock-coping strategies of migrant
workers and their rural families against the unprecedented aggregate shock. McKenzie (2003) showsthat internal remittances and other strategies to cope with idiosyncratic shocks failed to cope with anaggregate shock of the Mexican peso crisis. Gröger & Zylberberg (2016) document that rural
households in Vietnam coped with a catastrophic typhoon through long-distance migration andremittances. Furthermore, earthquakes in Pakistan and rainfall shocks and hurricanes in the Philippines
increased remittances from abroad (Suleri & Savage, 2006; Yang & Choi, 2007; Yang, 2008). Our
study contributes to the literature by showing that urban migrant workers and their rural families jointly
coped with the aggregate shock. They smoothed food consumption fully and more general
consumption partially—on both sides of migrants and rural families—by adjusting remittances from
migrants to rural families in the face of the COVID-19 pandemic.
Secondly, this study also contributes to the literature on the consumption smoothing of
migrant workers. To our knowledge, few empirical studies have investigated whether migrant workers
receive insurance from rural families and their findings are mixed. De Weerdt & Hirvonen (2016)
present that rural families did not bear migrants’ adverse shocks in Tanzania, suggesting a unilateralrisk-sharing, whereas Millán (2020) documents a bilateral risk-sharing network in which rural-tourban migrants are insured in Nicaragua. The current study adds to the literature by providing empiricalevidence of bilateral risk sharing.
The third strand of the related literature examines how mobile money facilitated urban-torural remittances and consumption smoothing against shocks. Jack & Suri (2014) find that remittances
through mobile money help smooth consumption against idiosyncratic shocks such as illness. Riley(2018) finds that remittances through mobile money successfully smooth consumption against weather
shocks, including droughts and floods. We find that the adoption of mobile money allowed urban
migrants to continue sending remittances through mobile money despite the travel restrictions of
COVID-19 lockdown policies, which in contrast, hindered hand-carry remittances.
7

The rest of this paper is organized as follows. Section 2 provides the background of the
COVID-19 pandemic and the emergence of mobile money services in Bangladesh. Section 3 describes
the panel survey and the data collected. Section 4 proposes the empirical strategies. Section 5 presents
the estimation results regarding the urban migrants’ and their rural families’ responses to the COVID19 shock. Section 6 concludes the paper.

2. Background
COVID-19 pandemic and containment policies 6
Shortly after the arrival of COVID-19 in March 2020, the Bangladesh government embarked on a
lockdown. The first case was confirmed on March 8, 2020, and the Bangladesh government closed all
the educational institutions on March 17. On March 23, the government announced the closure of all
public and private offices from March 26 (i.e., lockdown 7 ), and they implemented it accordingly.
Initially, the lockdown was planned to end on April 4 but was extended to April 11. Eventually, thelockdown was extended seven times and continued until May 30. The government gradually lifted the
lockdown by easing restrictions and reopening factories, markets, and offices except for educational
institutions.8 The garment factories—the leading industry of Bangladesh—reopened on April 27.

The information related to the lockdown was drawn from the following websites: (i) https://tbsnews.net/coronaviruschronicle/COVID-19-bangladesh/general-holiday-not-extend-further-state-minister-85870;(ii)
https://spc.jst.go.jp/experiences/asiaplan/asiaplan_2032.html; (iii) https://tbsnews.net/bangladesh/education/govt-ordersclosure-all-educational-institutions-march-17-56947;(iv)
https://www.business-humanrights.org/en/latestnews/bangladesh-500-garment-factories-reopen-amid-COVID-19-risks/;(v)
https://www.aa.com.tr/en/asiapacific/bangladesh-imposes-new-restrictions-on-public-movement/1828684
7
The government called the lockdown “general holidays.”8
Educational institutions have remained shut since March 16, 2020. The closure was extended several times, and mostrecently, until July 31, 2021.
68

During the lockdown, the government restricted public movement and ordered people to stayhome except for emergencies such as medical needs, treatment, and funerals. Travel on water, rail, andair routes was banned, and road transportation was suspended. People were not allowed to go out from
8 p.m. to 6 a.m. Nevertheless, experts doubted the practicality and effectiveness of social distancing
and staying at home for a densely populated country like Bangladesh. Indeed, most COVID-19 cases
spread from Dhaka to all over the country quickly during the lockdown (Shammi, Bodrud-Doza, Islam,& Rahman, 2020). 9 Given the failure, the government shifted from the nationwide lockdown to theflexible area-wise lockdown.

Pandemic influence on the garment sector in Bangladesh
Europe and the U.S. account for 80% of Bangladesh’s exports; the economic downturn in the westerncountries critically affects the Bangladeshi economy (The Financial Express, 2020b). 10 Because of
the early COVID-19 outbreak and the lockdown policies in Europe and the U.S. in February 2020, 11
Bangladesh’s exports substantially dropped by 20.1% year-on-year in March, 85.2% in April, and62.0% in May (Anner, 2020). When the western economy recovered from the first wave of the COVID19 outbreak around May (German press agency, 2020), 12 the exports quickly recovered after June. 13
The year-on-year decline in export was only 11.1% in June and 2.1 % in July. Further, in August, theexports showed a sharp rebound of 45.3% year-on-year growth.
Due to the declining global orders that began in late February 2020, the garment workers
experienced dismissal, furlough, back-pay, or partial payment of their salaries (NPR, 2020; The Daily

9
Despite the lockdown, people moved from the capital city to villages, and the COVID-19 quickly spread to the rural
areas.
10
The garment industry contributes over 80 percent of the exports (Hossain, 2019).
11
The United States declared public health emergency on February 3, 2020 (AJMC, 2020), and the first COVID-19
lockdown in Europe was implemented in Italy on February 21, 2020 (Metro, 2020).
12
Italy ended its national lockdown in May 2020 (German press agency, 2020), while Britain also began easing its
lockdown in May (BBC News, 2020).
13
A table of the export values is given in Appendix A.

9Star, 2020). On March 25, 2020, the Prime Minister of Bangladesh announced a 588 million USD
stimulus package for the garment industry to cover the salaries and wages of the workers (BenarNews,
2020). Still, factory owners did not pay the full salaries, nor did they pay on time. Some garment
workers could no longer survive in Dhaka and returned to their home villages (World Economic Forum,2020). Other thousands of garment workers blocked streets or highways demanding their unpaid wages(Reuters, 2020a). The government responded to this protest by warning factory owners to complete
wage payments in March (Reuters, 2020b). The government also announced that it would pay at least
60% of April’s salary to the workers of the temporarily closed factories (The Financial Express, 2020c).
On April 27, the government granted garment factories to resume operation, and the workers returned
to work (Aljazeera, 2020).

Mobile money in Bangladesh
Mobile money service companies in Bangladesh started to operate in 2011 (GSMA, 2016), and the
proportion of mobile money access among adults increased rapidly from 22% in 2013 to 45% in 2017
(Financial inclusion insights, 2018). The penetration was fast because most adults (87 % in 2017) in
Bangladesh had mobile phones (GSMA, 2018). 14 In contrast, only 20% of adults owned bank accounts(GSMA, 2016).
Nevertheless, only less than half of the mobile money users owned their mobile money
accounts (Financial inclusion insights, 2018). 15 Instead, the rest of the users ask local mobile money
agents to manage mobile money on their behalf and receive cash from the agents. In our sample(n=6,318), 73.1% of the garment workers, who regularly remitted money to rural families, used mobile
money in 2018, and 49.6% owned their mobile money account. These figures are higher than those

14
GSMA (2014) explains the situation of mobile phone penetration in Bangladesh as follows: “Bangladesh is a country
ahead of its time in terms of mobile access. Despite being ranked as a low income country, mobile penetration levels are
relatively high, even in rural areas”.
15
While mobile money users account for 45% of the population, mobile money account holders account for 18%. The
urban-rural gap in the account ownership is narrow, 21% in urban and 17% in rural population.

10

from the Global Findex report 2017 because migrant workers are more regularly remit than ordinary
people.

3. Survey and data
This study analyzes a seven-round panel on migrant workers in the garment industry in Dhaka andtheir rural families. The data covers two years from October 2018 to August 2020, five rounds before
and two rounds during and after the first implementation of the COVID-19 lockdown policies (seeTable 1). Unlike existing studies interviewing only rural households (De Weerdt & Dercon, 2006; Jack& Suri, 2014), we interviewed both the migrants (we call them the Dhaka households) and the village
households that the migrants most frequently remit to. Thus, the data allows us to scrutinize how
migrants and their rural families jointly coped with the aggregate shock due to the COVID-19
pandemic.

11

Table 1. Overview of EduMatch survey
Pre-baseline survey

Baseline survey
Round 1

Follow-up
Round 2

Follow-up
Round 3

Follow-up
Round 4

Follow-up
Round 5

Follow-up
Round 6

Interview
12/12/2017~8/10/2018
10/21/2018~11/30
4/19/2019~6/28
7/7/2019~8/20
9/13/2019~10/26 12/5/2019~1/1/2020 3/20/2020~5/16
Periods
Dhaka
Notes on HHs
One
HH
Ten
HHs
40 HHs interviewed
interviewed oninterviewed oninterviewed
onon 1/5/2019 ~ 4/17
different dates
9/19/2019
1/22/2020~1/30# of HHs
6318
723
545
686
661
622
595
#
of
HHs
returnedtovillages
Village NotesonFour
HHs Four
HHs
households
40 HHs interviewed
33 HHs interviewed
interviewed on interviewed
onon 1/3/2019~6/24
on 1/22/2020~1/30
interviewed on
9/22/2019~9/30 11/9/2019~11/20
different dates# of HHs
672
533
692
632
660
628
Note: The attrition rate for the Round 2 survey is relatively high because it was the first phone survey. We improved the survey implementation procedure in the following rounds.

12

Follow-up
Round 7
7/20/2020~8/30

546
111

643

We started the data collection initially to assess the impact of remittances on educational
investment and children’s academic performance (“EduMatch project”). At 13 randomly selected large
garment factories in Dhaka, we sampled migrant workers who (i) regularly remit to rural families and(ii) finance education of children left in villages.
Figure 1 describes the sampling procedures. We conducted face-to-face interviews with the
migrant workers in the pre-baseline and baseline surveys. In the remaining surveys, we conducted
phone interviews. As for the village households, we conducted interviews over the phone in thebaseline and follow-up surveys. Figure 2 presents the locations of the garment factories and the village
households. 16

16

The survey details are given in Appendix B.

13

Figure 1. Sampling and contacting respondents before and after COVID-19 pandemic
Factories which had more than 500 workers in the BGMEA Factory list.
Random sampling
13 factories
Systematic sampling of factory workers for pre-baseline survey
6318 garment workers
Selected the factory workers satisfying the study ctiteria*1
1154 garment workers

414 workers (failed to conduct baseline survey *2)

Successfully conducted the baseline survey
17 families (failed to conduct surveys
before the COVID-19 pandemic)

740 families (Dhaka & village households)

Both Dhaka and village households answered surveys before the COVID-19
pandemic at least once
723 families (Dhaka & village household)Group 1: 611 families(Workers who stayed
Dhaka between Mar.
& Aug. 2020 andanswered surveys
after the COVID-19
pandemic)Group 2: 59 families(Workers to whom
we failed to conduct
surveys after the
COVID-19 pandemic)

Group 3: 33 families(Workers who left
Dhaka before Feb.
2020 and responded
to surveys after the
COVID-19 pandemic)*1 The study criteria are shown in Appendix B.
*2 Or not eligible for the EduMatch program.

14Group 4: 20 families(Workers who left
Dhaka between Mar.
& Aug. 2020 and
responded tosurveys after the
COVID-19 pandemic)

Figure 2. Location of factories and village households

Notes: The location information is derived from the survey in round seven.

15

The entire sample consists of 723 pairs of the Dhaka households and their corresponding village
households, 17 both of which responded at least once before the COVID-19 pandemic (Table 1). The
lockdown started on March 26, 2020, during the Round 6 survey. We conducted the Round 7 survey
after the lockdown ended on May 30. Figure 3 presents the timing and frequency of interviews by theinterview dates and the bins used in our event study—explained in the following section.

We interviewed 740 families in the baseline survey. For 17 families, we could only contact either the migrant workers
or the village households until the Round 5 survey. We exclude these households from analysis because we want to examine
the shock-coping behaviors of both the migrants and village households simultaneously.

17

16

Figure 3. Histogram of the number of interviews (with Dhaka respondents and village respondents)
by interview dates

Notes: 1) March 26th, 2020 was the date of implementation of the lockdown in Bangladesh. 2) The bins for the event studies are shown.

17

In the course of the panel survey, attritions occur due to missed phone connections for interviews
or relocations of the target households at different timings. There are complete 611 cases in which we
successfully collected information from both the Dhaka households and their corresponding village
households before and after the COVID-19 lockdown. However, there are 112 cases of attritions. We
attempt to address possible attrition biases with several methods described in the next section. 18
Tables 2 and 3 present baseline characteristics of the Dhaka households and their corresponding
village households collected from October to November 2018. 19 The characteristics are separately
displayed by the attrition status of the Dhaka households: 1) the non-attriting sample of Dhaka
households, which continuously stayed in Dhaka and were successfully tracked until the Round 7
survey (Group 1 in Figure 1) and 2) the attriting sample of Dhaka households, which moved outsideof Dhaka or were failed to be tracked (Groups 2, 3, and 4 in Figure 1). Among the non-attriting sample(Column 1 in Table 2), the Dhaka workers are 29 years old on average; 57% are male; 77% are married;
65% are household heads. These households have two household members, earn a total monthly
income of over 15,000 Taka (or 188 USD), 20 spend over 10,000 Taka on monthly consumption, andremit 3,000 Taka monthly. About 80% of them used mobile money in October 2018, while almost
100% used mobile money one year later, right before the COVID-19 pandemic. The Dhaka workers
in the attriting sample (Column 2 in Table 2) are significantly younger, more likely to be female, andless likely to be married than the Dhaka workers in the non-attriting sample. Furthermore, they havefewer household members and consume less than the non-attriting sample.

Appendix C explains the attritions in detail.
For households that did not respond to the round 1 survey but responded to the following surveys (round 2~5), values
answered at the following surveys are used as the baseline characteristics for complementation.
20
1 USD = 81.7 Taka in January 2019.
18
19

18

Table 2. Baseline characteristics of Dhaka households by Dhaka household attrition status

Variable
Dhaka households
Age of the Dhaka worker

N611

1 if the Dhaka worker is male611

1 if the Dhaka worker is married611

1 if the Dhaka worker is household head611

# of household members in Dhaka

611

1 if using mobile money

611

Household income of last 30 days (Taka)

611

Consumption of last 30 days (Taka)

611

Amount of remittances sent to village HH (last 30 days) (Taka)

611

(1)
The non-attriting sample
Mean/SD
28.856[6.689]0.565[0.496]0.768[0.423]0.655[0.476]2.013[0.956]0.823[0.382]
15484.710[5748.215]
10340.616[3721.261]
3064.746[2707.509]

N
112
112
112
112
112
112
112
112
112

(2)
The attriting sample
Mean/SD
26.652[6.336]0.429[0.497]0.634[0.484]0.580[0.496]1.830[0.815]0.821[0.385]15532.634[6519.007]
9617.301[3517.172]
3205.357[2591.178]

Diff in mean(1)-(2)2.204***
0.136***
0.134***
0.074
0.183*
0.002-47.924
723.315*-140.611

Notes: 1) The value displayed for t-tests are the differences in the means across the groups. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level. 2) Taka is the currency ofBangladesh. As of December 21st, 2020, 1 USD is 85 Taka.

19

Table 3. Baseline characteristics of village households by Dhaka household attrition status

Variable
Village households
Age of HH headN
611

1 if HH head is male611

1 if HH head is married611

HH head: wife/husband/son/daughter of the Dhaka worker (=1)

611

HH head: father/mother/brother/sister/grandfa/grandmo of the Dhaka worker (=1)

611

HH head: (In law) father/mother/brother/sister of the Dhaka worker (=1)

611

# of household members

611

1 if using mobile money

611

Total value of assets per HH member (Taka)

611

Total value of productive assets per HH member (Taka)

611

Consumption of last 30 days (Taka)

611

F-test of joint significance (F-stat)F-test, number of observations(1)
The non-attriting sample
Mean/SD
54.227[13.304]
0.728[0.445]0.828[0.378]0.074[0.261]0.710[0.454]0.187[0.390]4.597[1.611]0.866[0.341]3857.787[4971.054]
4921.773[8212.589]
12525.885[6322.756]

N
112
112
112
112
112
112
112
112
112
112
112

(2)
The attriting sample
Mean/SD
54.545[11.532]
0.723[0.449]0.813[0.392]0.045[0.207]0.723[0.449]0.214[0.412]4.563[1.626]0.821[0.385]3888.132[4570.210]
4489.689[6072.310]
12134.615[5006.142]

Diff in mean(1)-(2)-0.317
0.005
0.016
0.029
-0.013-0.0280.035
0.044-30.345432.084391.270
1.496**
723

Notes: 1) The value displayed for t-tests are the differences in the means between the groups. The value displayed for F-test is the F-statistic of the joint significance test for 49 baselinecharacteristics between the groups, including not only those shown in Table 2 and 3 but also the unreported observables. ***, **, and * indicate significance at the 1, 5, and 10 percent criticallevel. 2) Taka is the currency of Bangladesh. As of December 21st, 2020, 1 USD is 85 Taka.

20

In Table 3, the baseline characteristics of village households are similar between the nonattriting and the attriting samples. The heads of village households are 54 years old on average; 72%
are male; 82% are married. The village households have 4.6 members on average and consume 12,000
Taka worth of goods and services per month. About 86% of the village households used mobile moneyat the baseline, while almost 100% used it one year later. Note that about 70% of the village household
heads are the migrant workers’ parents or siblings, while 20% of them are the migrant workers’
parents-in-law or siblings-in-law. In other words, some migrant workers regularly remit to support
their spouses’ families in our sample. In contrast, most existing studies focus only on migrant-sending
households (De Weerdt & Hirvonen, 2016; Millán, 2020).
Some baseline characteristics of the non-attriting and attriting samples are statisticallydifferent in their mean values, as shown in Column 3 in Tables 2 and 3. We also observe the joint
significance of the differences in the full list of the baseline characteristics between the attriting andnon-attriting samples, reflected in the significant F-value. 21 In addition, we conduct an analogous joint
significance test of the baseline characteristics by the attrition status of the village households and findthat they are significantly different. 22 We address possible attrition biases in the analysis below.

4. Empirical strategies
We analyze how Dhaka households (or village households) responded to the COVID-19 pandemic andcontainment policies by estimating the following equation:
𝑦𝑦ℎ𝑏𝑏𝑏𝑏 = ∑𝑘𝑘=3
𝑘𝑘=−3 𝛽𝛽𝑘𝑘 𝐸𝐸𝑘𝑘 + 𝜇𝜇ℎ + 𝛾𝛾𝑏𝑏 + 𝜔𝜔𝑡𝑡 + 𝜖𝜖ℎ𝑏𝑏𝑏𝑏 ,(1)

where 𝑦𝑦 represents outcome variables of Dhaka household (or village household) ℎ in two-monthseason 𝑏𝑏 in year 𝑡𝑡, 𝐸𝐸𝑘𝑘 the event study dummy, indicating time windows before or after the starting
21
The joint significance test is conducted by using the complete set of the baseline characteristics of both the Dhaka
households and the village households. The full list of the baseline characteristics is presented in Table A1 in Appendix C.
22
A baseline characteristics table by the attrition status of the village households, including the joint-test result, are shownin Table A2 in Appendix C.

21

date of the lockdown (March 26, 2020). More precisely, 𝐸𝐸−3 to 𝐸𝐸3 corresponds to each of thefollowing period, respectively; [-542, -211], [-150, -91], [-90, -31], [-30, +30], [+31, +90], [+91, +150],[+151, +158], where the two numbers in a bracket indicate the first and the last day of the window
period 𝐸𝐸𝑘𝑘 counted from the day 0, or March 26, 2020. We set period [-210, -151] (i.e., October 27 to
December 26, 2019) as the reference period. 23 We define the event dummy 𝐸𝐸0 to represent [-30, +30]

instead of [0, +60] because the drop in the garment export demand due to the COVID-19 pandemic
started before the beginning of the lockdown. 24 It is worth noting that, in this specification, thecoefficient of the event dummy 𝐸𝐸0 will capture the difference in the outcome variables of Dhaka

households (or village households) in the period of 30 days before and after the implementation of the
lockdown in comparison with those in the reference period. The household fixed effect is denoted by𝜇𝜇ℎ ; 𝛾𝛾𝑏𝑏 denotes the bi-monthly season effect, 𝜔𝜔𝑡𝑡 the year effect, and 𝜖𝜖ℎ𝑏𝑏𝑏𝑏 the random error term. 25

The non-random attrition discussed in the previous section may bias the estimated coefficient.

To address possible attrition biases, we adopt the inverse probability weighting (IPW) method(Wooldridge, 2011). Specifically, we run probit regressions of non-attrition of the Dhaka households
and the village households, respectively. Then, we estimate regression Equation (1) using the inverseof the predicted non-attrition probability of the Dhaka households and the village households—
respectively for regressions on each side—as weight. 26
We do not claim that this event study is for causal inference because the pandemic affected
the entire sample and also because we do not make counterfactual comparisons. However, we can still
make before-after comparisons under control for household-specific unobserved time-invariant factors

Note that most of the outcome variables capture activities of the past 30 days from the interview dates. For example, afigure reported at an interview conducted on March 26th, 2020, is equal to the activity of [-30, 0] days relative to the
lockdown implementation.
24
To examine the robustness of this approach, we also analyzed the data by dividing the period [-30, +30] into two
subperiods: [-30, -1] and [0, +30] and present the results in Appendix D. We discuss the results using this alternative
definition in Section 5.
25
Because the baseline survey started in October 2018, we define the year dummy taking one from October 2019 to
September 2020 and zero from October 2018 to September 2019.
26
See Appendix E for the estimation results of the first-stage probit regressions.
23

22

by exploiting the household-level panel structure of the data. Moreover, we can test the validity of ourevent study by checking the pre-trend. For example, a significant coefficient 𝛽𝛽−1 implies that ourcontrol variables are inadequate to obtain causal evidence.

In the regressions of the Dhaka households, standard errors are clustered at the household
level, and there are about 700 clusters. In the regressions of the village households, standard errors are
clustered at the Upazila level, the minimum administrative unit, and there are about 230 Upazila
clusters.

5. Empirical results
We use the regression analyses to describe the COVID-19 pandemic shock to various economic
activities of the migrant households and village households. In particular, we are interested in thechange in the trend of their remittances and consumption before and after the implementation of the
lockdown policy. The figures below present the estimated coefficients of the event dummies with 95%
confidence intervals. 27

COVID-19 shock to income
Figure 4 presents the event study estimates on the Dhaka households’ income, and we do not observea significant pre-trend. One may be surprised that there was no significant change in the income in [30, +30], which is the period covering 30 days before and 30 days after the lockdown starting date.
This is possible because the salary was paid on a monthly basis and reflected their work in the month
before the lockdown. 28 Consistently with the explanation, the Dhaka households experienced a

See Appendix I for the descriptive statistics of all the outcome variables examined.
According to the anecdotal evidence collected from garment workers, although garment factories were affected by the
downturn of the US and European economies, factory production in March 2020 did not dramatically decrease. This isbecause garment factories increased production before the lockdown start date (March 26, 2020) to cover the productions
that had been scheduled during the lockdown. Indeed, we confirmed from the Dhaka household data that March's salary ofgarment workers (which were supposed to be paid in April) did not decrease significantly.
27
28

23

substantial income drop in [+31, +90] by about 3,000 Taka or 20% of average income. After the fall,in the period of [+91, +150], income recovered to the pre-shock level. These findings are consistent
with the fact that the lockdown lasted for two months, and garment factories were closed forapproximately 30 days.
Figure 4. COVID-19 shock to Dhaka households’ income

Notes: 1) Standard errors are clustered at household-level. 2) Together with 95% confidence intervals, the figure reports the dynamic
coefficients obtained from the specification of equation (1) in the main text.

COVID-19 shock to remittances
Figure 5 presents the event study estimates on the remittance sent by the Dhaka households to thevillage households during the 30 days before the interview date. We display two aspects of theremittances: the amount (Panel a) and a binary indicator of whether the Dhaka households sent24

remittances to the village households (Panel b)—both based on the village household surveys. 29 The
remittance amount significantly decreased by 2,000 Taka in [+31, +90], when the migrants’ income
dropped by 3,000 Taka. The likelihood of village households receiving remittances also decreased but
not significantly in the same period. Both the amount and the likelihood of remittances recovered tothe pre-shock level in [+91, +150].

29

Few households received international remittances.

25

Figure 5. Cut of remittances sent from Dhaka workers to village households in response to COVID-19 lockdown(a) Amount of remittances (Taka)(b) Dummy of remittances

Notes: 1) Standard errors are clustered at Upazila-level. 2) Together with 95% confidence intervals, the figures report the dynamic coefficients obtained from the specification of equation (1) inthe main text. 3) The measures of remittances in the figures are those answered by the village households.

26

Figure 6 presents the event study estimates on the remittance amount from the village
households to their Dhaka families during the 30 days before the interview date (Panel a) and a binary
indicator of whether the village households sent the remittance to the Dhaka households (Panel b).
Because there is a marginally significant pre-trend in the remittance amount in [-150,-91], the estimates
may not reflect only the pandemic effect. We find no significant change in the amount and thelikelihood of remittances from the village households in response to the pandemic, unlike thesignificant reduction in migrant worker’s remittance to the rural families (Figure 5).

27

Figure 6. No additional remittances sent from village households to Dhaka workers in response to COVID-19 shock(a) Amount of remittances (Taka)(b) Dummy of remittances

Notes: 1) Standard errors are clustered at household-level. 2) Together with 95% confidence intervals, the figures report the dynamic coefficients obtained from the specification of equation (1)in the main text. 3) The measures of remittances in the figures are those answered by the Dhaka households.

28

To sum up, the migrant workers, facing a substantial income drop, reduced but not ceased
remittances to their village households, whereas the remittances from the village households did notchange. These findings suggest that the adverse shock was more substantial for the migrant workers,
which is consistent with the World Bank reporting that the COVID-19 shock was, at least in the shortterm, more significant in the urban area than in the rural area (World Bank, 2020b).

COVID-19 shock to asset sales
Following Fafchamps & Lund (2003), we examine whether the migrants or the rural families soldassets to cope with the COVID-19 shock (figures shown in Appendix F). 30 The value of asset holdingsof both the Dhaka households and the village households did not significantly change, suggesting noasset sales in response to the COVID-19 shock.

COVID-19 shock to consumption
Figure 7 presents the event study results regarding the consumption of the Dhaka and the village
households. 31 The Dhaka households’ consumption decreased by about 2,000 Taka in [-30, +30] andmore substantially by 4,000 Taka in [+31, +90] when they experienced a sharp income decline. The
village households’ consumption decreased by about 5,000 Taka in [-30, +30] and [+31, +90]. 32

30
Asset values are calculated based on self-reports. The questions are: i) Does your household have these assets now? ii)
Report current value (=if the asset is sold today, how much will you receive?) in Taka.
31
We use household-level consumption, which is not divided by the number of household members. This is useful forcomparing with other household-level variables such as remittances and other kinds of money inflows and outflows. As arobustness check, we examine per-capita consumption and find that the results are essentially the same (Appendix G).
32
At the pre-COVID-19 periods, one may notice that the point estimates are already negative, though those are notstatistically significant. This implies that the magnitude of the reduction in consumption after the COVID-19 shock is likely
to be overestimated.

29

Figure 7. COVID-19 shock to consumption of Dhaka households and village households(a) Dhaka household consumption of last 30 days (Taka)(b) Village household consumption of last 30 days (Taka)

Notes: 1) For panel (a), standard errors are clustered at household-level. For panel (b), standard errors are clustered at upazila-level. 2) Together with 95% confidence intervals, the figures report
the dynamic coefficients obtained from the specification of equation (1) in the main text.

30

To separately examine the effect of the early economic downturn right before the beginningof the lockdown and the effect of the lockdown implementation, we run a similar event study using analternative definition of the event dummies (Appendix D). We divide 𝐸𝐸𝑜𝑜 representing [-30, +30] into

two subperiods, [-30, -1] and [0, +30], allowing us to differentiate the effect of the early economic

downturn and the lockdown. The results indicate that consumption declined more substantially after
the lockdown for both the migrant households and the village households.
Although the Dhaka households’ income did not change in [-30, +30], their consumption
declined substantially in the same period. 33 The migrant workers likely anticipated the future income
drop because the foreign buyers canceled orders from the garment factories, and the COVID-19
pandemic changed their life substantially. By reducing consumption, the Dhaka households might havesaved cash or stocked up on food and daily necessities to prepare for the anticipated income shock andfuture lockdown. However, our data does not allow us to confirm these possibilities directly. But wefind suggestive evidence that the Dhaka households’ savings increased by 25% in [-30, 30].34
Overall, the event study results imply that both the Dhaka households and the village
households collectively tried to reduce the influence of the COVID-19 shock, although they did notentirely smooth consumption against it. Indeed, when we restrict the analysis on food consumption,
presented in Figure 8, we find that food consumption was fully smoothed against the COVID-19 shockin both the Dhaka households and the village households. This is consistent with the literature such as
McKenzie (2006) that shows reducing expenditure on other items in order to smooth food consumption
is a major tool to cope with aggregate shocks.

33
With respect to the decrease of consumption of the village households during the period [-30, 30], it is relatively easier
to conjecture the reason. As the figure in Appendix D indicates that there was a sharp decrease in consumption in the period[0,30], the decrease was likely to be a response to the lockdown implementation. Thereby, they might have simply
responded to their income decrease caused by the lockdown. Also, they might have curtailed consumption and stocked up
on food or everyday supplies in preparation (even if they did not experience a sudden income decline).
34
We run a simple regression of savings controlling for household fixed effects. The estimation results are given in
Appendix H. We collected the data of savings less frequently than the data of other variables, thus we cannot run the same
regression used in our main analyses.

31

Figure 8. Fully smoothed food consumption of Dhaka households and village households(a) Dhaka household food consumption of last 7 days (Taka)(b) Village household food consumption of last 7 days (Taka)

Notes: 1) For panel (a), standard errors are clustered at household-level. For panel (b), standard errors are clustered at upazila-level. 2) Together with 95% confidence intervals, the figures report
the dynamic coefficients obtained from the specification of equation (1) in the main text.

32

The results of the event study of the total consumption presented in Figure 7 has anotherimportant implication. The average consumption in the reference period was 10,228 Taka for the
Dhaka households and 12,465 Taka for their village households. Due to the pandemic, the Dhaka
households’ consumption declined by 39% (about 4,000 Taka), and the village households’
consumption dropped by 40% (about 5,000 Taka). Recall that the urban-to-rural remittances dropped
by 2,000 Taka. If there were no drop in remittances, the Dhaka households’ consumption could have
dropped by 59% (about 6,000 Taka), whereas the village households’ consumption by only 24% (3,000Taka). These results imply that the risk-sharing mechanism through the family network was effective
in sharing the shock between the migrants and the village households. 35
There is a question remaining. Why did the Dhaka households decrease their consumption by
4,000 Taka in [+31, 90]? Although their income declined by 3,000 Taka, they reduced remittances by
2,000 Taka; this suggests that their disposal income declined only by 1,000 Taka. Therefore, besides
the possibility of precautionary saving discussed above, we also check other kinds of money flows.

COVID-19 shock to borrowing and loaning behavior
To explore how the COVID-19 pandemic affected money flows of the Dhaka households other thanremittances, we examine the change in borrowing and loaning behavior of the migrant workers. Figure
9 presents the event study regarding borrowing money from friends and neighbors 36 and borrowing
money from institutions such as shops and microfinance institutions, which we call “loaning.” 37 The
COVID-19 shock made it difficult for the Dhaka households to borrow money from individuals. The
amount and the likelihood of borrowing money declined from the period of the lockdown

The hypothetical case of “no remittance cuts” should not be taken as a counterfactual. Rather, it is an extreme case. For
example, if there were no remittance cuts, the migrant workers might have used loans or other methods to avoid further
consumption reductions. The hypothetical case of “no remittance cuts” assumes that the migrant workers did not exploit
such alternative shock coping strategies.
36
This can include money borrowed with interest. Also, repaid money is included in “borrowing.”
37
Pawning, using rent arrears, and other informal lending schemes such as ROSCA are also included.
35

33

implementation. In contrast, the loaning behavior did not change. This constraint of borrowing fromfriends and neighbors during the pandemic seems to be a key to explain the consumption decline of
the Dhaka households during the pandemic.

34

Figure 9. COVID-19 shock to borrowing and loaning behavior(a) Amount of money borrowed by Dhaka HHs (Taka)(b) Dummy of money borrowed by Dhaka HHs(c) Amount of loan by Dhaka HHs (Taka)(d) Dummy of loan by Dhaka HHs

Notes: 1) Standard errors are clustered at household-level. 2) Together with 95% confidence intervals, the figures report the dynamic coefficients obtained from the specification of equation (1)in the main text.

35Mode of remittances amid COVID-19 lockdown
Before the pandemic, the Dhaka workers used to bring money by themselves or ask someone to do sofor 21% of the remittances, whereas they remit through mobile money for 77%. However, thecontainment regulations prevented the Dhaka workers from physically bringing money. We examine
the role of mobile money in remittance during the pandemic. Figure 10 presents the event study
regarding the amount and the likelihood of remittances from the migrant workers to the village
households by mode. 38 The hand-carry remittances significantly declined during the pandemic (panelsc and d of Figure 10). In contrast, the amount and the likelihood of remittances through mobile money
did not significantly change (panels a and b), despite the income drop. The mobile money services
enabled the migrant workers to remit to rural families in spite of the travel restrictions.

38
The migrants also used bank transfer, post office, and other modes for less than 1% of the remittances. We categorized
them into hand-carry remittances.

36

Figure 10. Stable mobile money remittances and no hand-carry remittances amid COVID-19 lockdown(a) Amount of mobile money remittances (Taka)(b) Dummy of mobile money remittances(c) Amount of hand-carry remittances (Taka)(d) Dummy of hand-carry remittances

Notes: 1) Standard errors are clustered at Upazila-level. 2) Together with 95% confidence intervals, the figures report the dynamic coefficients obtained from the specification of equation (1) inthe main text. 3) The measures of remittances are those answered by the village households.

37

Limitations
We admit that this study has some limitations. First, the event study is not a causal inference but adescription of facts. In order to serve as causal inference, the event study needs to control all theconfounding factors. It is difficult to make valid counterfactual comparisons because the COVID-19
shock affected everyone. We still examined the existence of a pre-trend to check the validity of theanalysis.
Second, some migrants returned home during the pandemic (World Economic Forum, 2020),
but we excluded them from our sample—as mentioned in the explanation of the attrition issue. We
admit that returning home is another shock-coping strategy, but we did not observe many returning
migrants in our sample (only 20 out of 723 families). We thus decided to focus on shock-coping
through remittances within the family network.
Third, we do not have data on village households’ income and detailed information on money
inflows and outflows other than remittances. It is challenging to conduct an extended interview throughphone surveys. We rather concentrated on collecting accurate data regarding village households’
consumption and shock-coping strategies within the family network.

6. Conclusion
The COVID-19 pandemic has affected more severely in the urban area than in the rural area (World
Bank, 2020b). This study examines how migrant workers and their rural families coped with the shockwith an event study approach. Firstly, we show that the migrant workers responded to a substantial
income drop after the lockdown mainly by reducing, but not ceasing, remittances to their rural families.
McKenzie (2003) argued that internal remittances—which is known as a major self-insurance tool
against idiosyncratic shocks—declined their role in coping with aggregate shocks. However,
McKenzie did not directly examine the role of internal remittances during the crisis due to data
limitations. Importantly, we find that the consumption of both the migrants and their rural families
38declined in a similar proportion due to the flexible adjustment of remittances. Thus, this study
contributes to understanding the roles of family networks in coping with aggregate shocks. Moreover,
our study serves as evidence of bilateral risk-sharing between migrant workers and their rural families.
Secondly, we reconfirm the importance of mobile money. This innovative technology allowed
people to mitigate the negative influence of the income shock through the remittance between family
members even during the lockdown period. In contrast, hand-carry remittances decreased during the
lockdown. This contrast sheds light on the importance of mobile money as a tool of financial inclusionthat helps poor households’ self-insurance against the COVID-19 pandemic.
Finally, we find the resilience of migrants and their families from the first wave of the
COVID-19 pandemic; the income of the migrant workers and the consumption of both the migrant andthe village households recovered, responding to the quick recovery of garment exports. Given the
country's current situation of the COVID-19 pandemic coupled with various extended restrictions on
movement, business activities, and education among others, however, we are concerned about thepersistence of family networks in mitigating the adverse effects. In addition, our study reveals that the
migrant workers remained unable to borrow money from the local network after the lockdown incontrast to the quick recovery of the remittance. It is plausible that people might have believed that therecovery in income was only temporary, and they might be reluctant to lend money to others. The
disruption in the local informal risk-sharing network would deteriorate the livelihood of the poor,
particularly those isolated from family networks, suggesting room for public interventions.

39

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World Economic Forum. (2020). In Bangladesh, COVID-19 threatens to cause a humanitarian crisis.
Retrieved December 14, 2020, from https://www.weforum.org/agenda/2020/04/in-bangladeshcovid-19-could-cause-a-humanitarian-crisis/42

Appendix A.
Bangladesh RMG (Ready-Made-Garments) Export Value per monthCalendar Month,Year

RMG export
nominal value(Million US$)

Jan-2020
Feb-2020
Mar-2020
Apr-2020
May-2020
Jun-2020
Jul-2020
Aug-2020

3,039
2,784
2,256
375
1,231
2,240
3,240
3,240

Source: The Financial Express (2020a), which brought the data from BGMEA.

43

Year to year
growth-3.0%-4.3%-20.1%-85.2%-62.0%
-6.6%
-2.1%
45.3%

Appendix B.
Survey details
Pre-baseline survey
To create a list of garment workers, we conducted the pre-baseline survey at 13 randomly selectedfactories 39 from garment factories in Dhaka, 40 the capital city of Bangladesh. Creating the list ofgarment workers was necessary because we aimed to conduct panel surveys to the garment workers
who had migrated from outside of the capital city. To do so, we needed a list of garment workers with
the information of where they were from. In the pre-baseline survey, we collected a list of basic
information (i.e., household structure, age of children) on about 6,318 workers and their originalvillage families. We used systematic sampling at each factory and interviewed the randomly selectedgarment workers. 41

Baseline survey
Using the list of 6,318 garment workers obtained in the pre-baseline survey, we conducted the baseline
survey to 740 families of migrant workers who were eligible for the EduMatch project since October

We used the factory list of the BGMEA (Bangladesh Garment Manufacturers and Exporters Association) website(https://www.bgmea.com.bd/) and selected the factories randomly from those which had more than 500 workers (we did
not include small factories in the pool because we wanted to secure a certain number of samples from each factory).
However, one should not interpret that those randomly drawn factories represent the garment factories in Dhaka. Those
factory managements that rejected our request of conducting surveys to their workers are not included in the 13 factories.
Obviously, the factory managements that continue to support the project team do not represent the garment factory.
Although, at least, we conducted the sampling of factories in a transparent manner.
40
Most of the factories are located in Gazipur, which is a town next to Dhaka. For simplicity, when the word “Dhaka” isused, it may include Gazipur in this study.
41
The number of samples drawn from each factory is calculated based on the number of workers working in each factory.
Namely, at a large factory we interviewed a relatively large number of workers while at a small factory we interviewed arelatively small number of workers. As a result, the minimum number of samples from each factory is 137 and the
maximum is 1140.
39

44

2018. From 6,318 workers, we contacted 1,154 migrant workers that were eligible to the project. The
eligibility conditions are given as the following two points: 1) a student of a particular grade who wasscheduled to take the exam was in the Dhaka worker's village home (students who will be in the grade
4, 5, 7, 8, 10 in 2019), and 2) the worker remitted money to his or her village home on a regular basis.
As a result, 740 families responded and accepted to be interviewed in the coming surveys. 42
From both the Dhaka workers and the village households, we collected a number of basic
socio-economic information. When we collect the information of flow of funds such as income,consumption, and remittances, for most of the variables, we asked about the information of the last 30
days from the interview dates.

Follow-up surveys
The migrant workers and their original households answered the follow-up surveys up to six times.
The timings of the follow-up surveys are displayed in Table 1.
The migrant workers of our sample occasionally go back to their home villages and stay there
for a while. Some of them decided to leave the capital city and moved to their home villages during
the survey period. All of those may create missing observations and affect our regression results.
Thereby, we explain how we treat the issue in the following paragraphs.
As for Round 2-7, if a migrant worker answered that he/she is not in the capital city, thesurveys for the migrant workers (hereafter, ‘Dhaka survey’) were not conducted to that person. Thus,
when the Dhaka workers went back to their home village, attritions of the data from the Dhaka surveys
occur.

42
The reasons why around 400 families were dropped is as follows: (i) the subject did not answer the survey phone, (ii)the phone number was no longer used, (iii) the subject refused to cooperate with the survey, (iv) the worker had alreadyfinished migrating and returned to his or her village home. (v) the subject had called his or her village family to the capitalcity, and lived together when the survey was conducted. (vi) there were errors in the answers from the pre-baseline survey,
and subjects were not actually eligible for the EduMatch surveys (for example, the one was not from outside of the capitalcity).

45

In contrast, whether the Dhaka workers left Dhaka or not does not matter for conducting thesurveys to the village households (hereafter, ‘village survey’). Thus, village surveys’ attritions only
include missing values due to no-response to our mobile phone surveys. In the regressions, we attempt
to treat those attrition problems by the inverse probability weighting model.
To know how the COVID-19 shock influenced the migrant workers’ decision on where towork, at Round 7 (July-August of 2020), we additionally collected information on where the migrant
workers live. At that time, we conducted the survey to the people including those who returned to their
original village. When we contacted the migrant workers and found that they were not in Dhaka
anymore, we asked when they had left Dhaka. Thus, for the migrant workers who answered the surveyof Round 7, we have the information on whether the migrant workers left Dhaka or not and when theyleft Dhaka.
There is another type of information that was supplementarily collected. In addition to the last
30 days, we collected the information of the last 31-60 days at Round 3 and Round 7. This is to makeup for the information when the survey interval was too long. The information collected as such ismoney inflow, money outflow (i.e., remittances), and income.

46

Appendix C.
Attritions of Dhaka households
Initially, we categorized samples into 4 groups based on the status and timing of attritions of the Dhaka
households: Group 1 is the Dhaka households that continued to stay in Dhaka (as Round 7 survey
conducted in August 2020); Group 2 is the Dhaka households that we lost contact with since March
2020; Group 3 is the Dhaka households that left Dhaka by February 2020; Group 4 is the Dhaka
households that left Dhaka after March 2020. Comparing their baseline characteristics between groups,
we found that the baseline characteristics are statistically different between Group 1 and other groups
but not different between Groups 2 to 4 based on the F-test statistics for the joint significance tests.
Thus, we regroup the samples into two groups: the non-attriting sample (corresponding to Group 1)
and the attriting sample (corresponding to households in Group 2-4). Table A1 presents the baselinecharacteristics by the four groups and their mean differences. The attrition rate is 15.4 percent.

Attritions of village households during and after COVID-19 crisis
Next, we compare the baseline characteristics by the attrition status of the village households. If avillage household answered surveys at least once between March 2020 and August 2020, the household
is counted as a non-attiring sample and an attriting sample otherwise. The attrition rate of the village
household surveys is 7.7 percent. Between the non-attriting and attriting samples, a significant
difference is found by the joint F-test. Table A2 presents the baseline characteristics by the attrition
status by the village households and their mean differences.

47

Table A1. Full list of baseline characteristics of Dhaka households and village households by attrition status of Dhaka households
(1)

(2)

(3)

(4)

Stay inDhaka
until Aug
2020

Missing in
Mar-Aug
2020

Left
Dhaka
before Feb
2020

Left
Dhaka
Mar-Aug
2020

Diff inmean

Diff inmean

Diff inmean

Variable
Dhaka households
Age of the Dhaka worker

N

Mean/SDN

Mean/SDN

Mean/SDN

Mean/SD(1)-(2)(1)-(3)(1)-(4)611

59

2.886**

3.706**

0.141**

0.140

0.115

611

0.191***

0.162**-0.082

1 if Dhaka respondent is widow/widower/divorced/separated

611-0.103***

0.019

0.049

1 if the Dhaka worker is household head

611

0.028

0.109

0.155

1 if the Dhaka worker is spouse of the household head

611

0.009

-0.080-0.186*# of household members in Dhaka

611

0.216*

0.195

0.063

Dhaka worker did not get PSC or did not attend school (=1)

611

-0.105**0.042-0.117

Dhaka worker completed PSC (=1)

611

-0.015

0.006

0.038

Dhaka respondent completed grade 6 ~ grade 9 (=1)

611

-0.006-0.0230.051

Dhaka respondent graduated from SSC or above SSC (=1)

611

0.119**-0.0310.022

1 if Dhaka respondent's job: RMG worker

611

0.009

-0.0080.042*

1 if Dhaka respondent's job: Housewifery

611

0.002

0.002

0.002

1 if Dhaka respondent's job: Business, Other industries' employee, else

611

0.007

0.007

0.007

1 if using mobile money

611

25.150[5.133]0.450[0.510]0.850[0.366]0.000[0.000]0.500[0.513]0.500[0.513]1.950[0.826]0.250[0.444]0.150[0.366]0.350[0.489]0.250[0.444]0.950[0.224]0.000[0.000]0.000[0.000]0.750[0.444]

1.314

1 if the Dhaka worker is married

25.970[6.018]0.424[0.502]0.606[0.496]0.030[0.174]0.545[0.506]0.394[0.496]1.818[0.917]0.091[0.292]0.182[0.392]0.424[0.502]0.303[0.467]1.000[0.000]0.000[0.000]0.000[0.000]0.818[0.392]20611

27.542[6.809]0.424[0.498]0.576[0.498]0.153[0.363]0.627[0.488]0.305[0.464]1.797[0.761]
0.237[0.429]0.203[0.406]0.407[0.495]0.153[0.363]0.983[0.130]0.000[0.000]0.000[0.000]0.847[0.363]33

1 if the Dhaka worker is male

28.856[6.689]0.565[0.496]0.768[0.423]0.049[0.216]0.655[0.476]0.314[0.465]2.013[0.956]0.133[0.339]0.188[0.391]0.401[0.490]0.272[0.445]0.992[0.090]0.002[0.040]0.007[0.081]0.823[0.382]-0.024

0.005

0.073

59
59
59
59
59
59
59
59
59
59
59
59
59
59

48

33
33
33
33
33
33
33
33
33
33
33
33
33
33

20
20
20
20
20
20
20
20
20
20
20
20
20
20

1 if using mobile money through agent account611

1 if using mobile money through own account611

1 if using mobile money through family members' account

611

Household income of last 30 days (Taka)

611

Total value of assets (Taka) per HH member

611

Consumption of last 30 days (Taka)

611

Amount of sent remittances to village HH (last 30 days) (Taka)

611

Cognitive skill measure of the Dhaka worker (low~high:0~6)611

Village households
Age of HH head611

1 if HH head is male611

1 if HH head is married611

1 if HH head is widow/widower/divorced/separated

611

HH head: wife/husband/son/daughter of the Dhaka worker (=1)

611

HH head: father/mother/brother/sister/grandfa/grandmo of the Dhaka worker (=1)

611

HH head: (In law) father/mother/brother/sister of the Dhaka worker (=1)

611

# of household members

611

HH head did not get PSC or did not attend school (=1)

611

HH head completed PSC (=1)

611

0.311
[0.463]0.453[0.498]0.057[0.233]15484.710[5748.215]

59

5796.609

59[7341.398]
10340.616[3721.261]
3064.746[2707.509]
4.108[1.653]54.227[13.304]
0.728[0.445]0.828[0.378]0.160[0.367]0.074[0.261]0.710[0.454]0.187[0.390]4.597[1.611]0.661[0.474]0.124[0.330]

49

59
59
59

59
59
59

59
59
59
59
59
59
59
59
59
59

0.356
[0.483]0.424[0.498]0.068[0.254]
15822.153[6416.066]

33

8960.11333[18651.414]
9359.579[3006.655]
3296.610[2598.273]
4.085[1.601]54.339[12.619]0.695[0.464]0.780[0.418]0.220[0.418]0.034[0.183]0.746[0.439]0.203[0.406]4.627[1.639]0.695[0.464]0.186[0.393]

33
33
33

33
33
33

33
33
33
33
33
33
33
33
33
33

0.364
[0.489]0.364[0.489]0.091[0.292]15325.091[7602.970]

206910.354

20[7983.285]
9321.753[3507.740]
3424.242[2989.882]
3.879[1.746]53.909[9.674]0.727[0.452]0.818[0.392]0.182[0.392]0.061[0.242]0.667[0.479]0.242[0.435]4.364[1.765]0.818[0.392]0.091[0.292]

20
20
20

20
20
20

20
20
20
20
20
20
20
20
20
20

0.300
[0.470]0.400[0.503]0.050[0.224]15021.000[4964.228]
5387.500[4297.070]
10865.233[4685.495]
2575.000[1741.710]
4.300[1.895]56.200[11.381]0.800[0.410]0.900[0.308]0.100[0.308]0.050[0.224]0.750[0.444]0.200[0.410]4.700[1.380]0.650[0.489]0.150[0.366]-0.045-0.053

0.011

0.030

0.090

0.053

-0.011-0.0340.007-337.442

159.619463.7103163.504***-1113.744

409.109981.036**1018.863-524.618-231.864-359.496

489.746

0.023

0.229

-0.192-0.1110.318-1.973

0.033

0.001

-0.072

0.048

0.010

-0.072-0.060-0.021

0.060

0.040

0.013

0.024

-0.0350.044-0.040-0.017-0.056-0.013-0.0300.234-0.103-0.034-0.157*0.011-0.0620.033-0.026

HH head completed grade 6 ~ grade 9 (=1)

611

HH head graduated from SSC or above SSC (=1)

611

HH head's occupation: farming (=1)

611

HH head's occupation: self-employment, trader, wage-labor (=1)

611

HH head's occupation: salaried workers (i.e.goverment, teacher) (=1)

611

HH head's occupation: non-earning occupations (i.e.housewife) (=1)

611

1 if using mobile money

611

1 if using mobile money through agent account611

1 if using mobile money through own account611

1 if using mobile money through family members' account611

minutes to the closest mobile money agent by foot611

Total value of assets per HH member (Taka)

611

Total value of productive assets per HH member (Taka)

611

Total value of lands per HH member (Taka)

611

Consumption of last 30 days (Taka)

611

Educational consumption (~SSC) of last 30 days (Taka)

611

Number of students (~SSC)

611

0.154
[0.361]
0.061[0.239]0.411[0.492]0.124[0.330]0.016[0.127]0.448[0.498]0.866[0.341]0.399[0.490]0.403[0.491]
0.064[0.245]17.136[14.668]
3857.787[4971.054]
4921.773[8212.589]
1.08e+05[1.83e+05]
12525.885[6322.756]
1772.473[1248.472]
1.714[0.873]F-test of joint significance (F-stat)F-test, number of observations

59
59
59
59
59
59
59
59
59
59
59
59
59
59
59
59
59

0.051
[0.222]0.068[0.254]0.356[0.483]0.153[0.363]0.017[0.130]0.475[0.504]0.797[0.406]0.356[0.483]0.373[0.488]0.068[0.254]17.220[12.312]3284.187[3418.427]
3564.338[5608.176]
81377.253[1.04e+05]
11792.966[4494.306]
1678.158[876.405]
1.898[0.687]

33
33
33
33
33
33
33
33
33
33
33
33
33
33
33
33
33

0.030
[0.174]0.061[0.242]0.515[0.508]0.091[0.292]0.061[0.242]0.333[0.479]0.818[0.392]0.364[0.489]0.424[0.502]0.030[0.174]17.121[11.876]4258.086[5483.015]
5544.535[6680.123]
1.05e+05[2.85e+05]11897.919[5379.821]
1707.823[1144.332]
1.848[0.795]

20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20

0.150
[0.366]0.050[0.224]0.450[0.510]0.150[0.366]0.000[0.000]0.400[0.503]0.900[0.308]0.300[0.470]0.400[0.503]0.200[0.410]14.400[8.894]5059.345[5736.162]
5478.976[6215.090]
1.33e+05[1.32e+05]
13533.025[5786.947]
1907.958[962.648]
1.800[0.768]0.103**

0.124*

0.004-0.007-0.000

0.011

0.055

-0.104-0.039-0.0280.033-0.026-0.001-0.044*0.016-0.026

0.115

0.048

0.069

0.048

-0.034

0.043

0.036

0.099

0.030

-0.0220.003-0.0040.034-0.136**-0.084

0.015

2.736

573.600-400.299-1201.559

1357.435-622.762-557.203

26219.025

2490.219-2.55e+04732.919627.966-1007.140

94.31564.650-135.485-0.185-0.135-0.0861.480**
670

0.724
644

1.386**
631

Notes: 1) The value displayed for t-tests are the differences in the means across the groups. The value displayed for F-tests are the F-statistics. ***, **, and * indicate significance at the 1, 5, and 10 percent
critical level. 2) Taka is the currency of Bangladesh. As of December 21st,2020, 1 USD is 85 Taka.

50

Table A2. Baseline characteristics of Dhaka households and village households by village household attrition
statusVariable
Dhaka households
Age of the Dhaka worker

N(1)
The non-attriting sample
Mean/SD

667

N

28.573[6.646]0.549[0.498]0.760[0.427]0.642[0.480]0.327[0.469]1.994[0.946]

56

0.136

56

1 if the Dhaka worker is male667

1 if the Dhaka worker is married667

1 if the Dhaka worker is household head667

1 if the Dhaka worker is spouse of the household head667

# of household members in Dhaka

667

Dhaka worker did not get PSC or did not attend school
(=1)

667

Dhaka worker completed PSC (=1)

667

Dhaka respondent completed grade 6 ~ grade 9 (=1)

667

Dhaka respondent graduated from SSC or above SSC
(=1)

667

1 if Dhaka respondent's job: RMG worker

667

1 if Dhaka respondent's job: Housewifery

667

1 if Dhaka respondent's job: Business, Other industries'
employee, else

667

1 if using mobile money

667

1 if using mobile money through agent account667

1 if using mobile money through own account667

1 if using mobile money through family members'
account

667

0.058

Household income of last 30 days (Taka)

667

Total value of assets (Taka) per HH member[0.344]0.186[0.389]0.405[0.491]0.267[0.443]0.990[0.102]0.001[0.039]
0.006

56
56
56
56

56
56
56
56
56
56

27.821[7.084]0.482[0.504]0.589[0.496]0.661[0.478]0.268[0.447]1.875[0.833]0.214[0.414]0.214[0.414]0.357[0.483]0.214[0.414]1.000[0.000]0.000[0.000]0.000

Diff in mean(1)-(2)0.751
0.067
0.171***-0.0190.059
0.119-0.078-0.0280.048
0.053-0.0100.001
0.006[0.000]0.821[0.386]0.393[0.493]0.357[0.483]-0.08356

0.071-0.013[0.235]15469.334[5817.407]

56[0.260]15763.696[6508.240]-294.362

667

5835.663

56

9174.628

Consumption of last 30 days (Taka)

667

56

Amount of sent remittances to village HH (last 30 days)(Taka)[7287.031]
10290.252[3747.120][19104.134]
9493.851[2971.946]

667

3078.051

56

3187.500

Cognitive skill measure of the Dhaka worker(low~high:0~6)

667

Village households
Age of HH head
1 if HH head is male[0.077]0.823[0.382]0.310[0.463]0.453[0.498]56(2)
The attriting sample
Mean/SD

56
56
56[2711.591]
4.081

54.436[13.066]
0.729

667

51

0.096

3338.965***
796.401-109.449[2416.915]
56[1.674]
667

0.002

4.339

-0.258[1.443]56
56

52.375[12.669]
0.714

2.061
0.014

1 if HH head is married667

1 if HH head is widow/widower/divorced/separated

667

HH head: wife/husband/son/daughter of the Dhaka
worker (=1)

667

HH head: father/mother/brother/sister/grandfa/grandmoof the Dhaka worker (=1)

667

HH head: (In law) father/mother/brother/sister of the
Dhaka worker (=1)

667

# of household members

667

HH head did not get PSC or did not attend school (=1)

667

HH head completed PSC (=1)

667

HH head completed grade 6 ~ grade 9 (=1)

667

HH head graduated from SSC or above SSC (=1)

667

HH head's occupation: farming (=1)

667

HH head's occupation: self-employment, trader, wagelabor (=1)

667

HH head's occupation: salaried workers (i.e.goverment,teacher) (=1)

667

HH head's occupation: non-earning occupations(i.e.housewife) (=1)

667

1 if using mobile money

667

1 if using mobile money through agent account667

1 if using mobile money through own account667

1 if using mobile money through family members'
account

667

minutes to the closest mobile money agent by foot667

Total value of assets per HH member (Taka)

667

Total value of productive assets per HH member (Taka)

667

Total value of lands per HH member (Taka)

667

Consumption of last 30 days (Taka)

667

Educational consumption (~SSC) of last 30 days (Taka)

667

Number of students (~SSC)

667[0.445]0.823[0.382]0.166[0.373]
0.070

56
56
56

[0.256]0.708[0.398]4.586[1.637]0.675[0.469]0.118[0.323]0.147[0.354]0.060[0.238]0.420[0.494]
0.115

56
56
56
56
56
56
56
56
56

[0.498]0.865[0.342]0.388[0.488]0.409[0.492]0.067[0.251]16.997[14.354]3884.350[4904.284]
4903.956[8061.033]
1.06e+05[1.77e+05]
12498.091[6210.658]
1777.547[1232.242]
1.730[0.868]

F-test of joint significance (F-stat)F-test, number of observations

0.017

0.768

-0.0600.125[0.334]4.661[1.283]0.625[0.489]0.250[0.437]0.054[0.227]0.071[0.260]0.321[0.471]
0.250

0.071
-0.0750.050-0.132***
0.093*-0.011
0.098-0.135***[0.437]56[0.127]
0.448

0.024

[0.426][0.320]
0.016

0.054

-0.034[0.227][0.455]0.196[0.456]0.857[0.353]0.143[0.353]0.036-0.019[0.187]
56
56
56
56
56
56
56
56
56
56
56
56

0.393

0.055

[0.493]0.786[0.414]0.429[0.499]0.304[0.464]-0.040

0.054

0.014

[0.227]17.893[12.746]3602.090[4990.388]
4269.820[5962.580]
1.12e+05[2.40e+05]
12074.393[5194.444]
1622.955[887.795]
1.821[0.664]

0.079

0.106

-0.896282.260
634.135-6069.923
423.698
154.592-0.0911.564**
723

52

Notes: 1) The value displayed for t-tests are the differences in the means across the groups. The value displayed for F-tests are the F-statistics. ***,**, and * indicate significance at the 1, 5, and 10 percent critical level. 2) Taka is the currency of Bangladesh. As of December 21st,2020, 1 USD
is 85 Taka.

53

Appendix D.
Consumption event study based on alternative event dummies(a) Dhaka household consumption of last 30 days (Taka)(b) Village household consumption of last 30 days (Taka)

Notes: 1) For Panel (a), standard errors are clustered at household-level. For Panel (b), standard errors are clustered at Upazila-level. 2) Together with 95% confidence intervals, the figures report
the dynamic coefficients obtained from the specification of equation (1), with a small modification as follows: the event dummy for the dates [-30, +30] is divided into two parts: [-30, -1] and [0,
+30].

54

Appendix E.
Estimation results for probit model for non-attrition of Dhaka households and village
households
Table A3. Estimation results for probit model
VARIABLES(1)
Non-attrition indicator based on
Dhaka responses(2)
Non-attrition indicator based onvillage responses.0349***(.0134).136(.226).252(.211).0113(.0167).129(.277).345(.27)-.0171-.14(.348).0832(.367)-.0254(.398).0849(.114)(.41).0522(.423).163(.483)-.0895(.145)-.644***-.728**

(.24)
-.201
(.205)-.242(.172)
.624
(.53)
4.02
(391)
-4.13
(391)
-3.97
(391)

(.307)-.394(.262)-.22(.225)

-4.16

-3.59

(391)
-.0000127(.0000145)-.000016**(6.84e-06).0000317(.0000276)(627)-.0000197(.0000176)-.0000231***(7.88e-06).0000594(.0000386)

7.81e-06

8.60e-06(.0000237)(.00003)-.0171-.13**(.0393)-.002(.00648)-.0847(.263)-4(138)(.0549)-.000408(.00829).299(.317)
-3.92
(206)

-4.15

-3.52

Age of the Dhaka worker
1 if the Dhaka worker is male
1 if the Dhaka worker is married
1 if Dhaka respondent iswidow/widower/divorced/separated
1 if the Dhaka worker is household head
1 if the Dhaka worker is spouse of the household head
# of household members in Dhaka
Dhaka worker did not get PSC or did not attend school(=1)
Dhaka worker completed PSC (=1)
Dhaka respondent completed grade 6 ~ grade 9 (=1)
1 if Dhaka respondent's job: RMG worker
1 if using mobile money
1 if using mobile money through agent account1 if using mobile money through own account1 if using mobile money through family members'
account
Household income of last 30 days (Taka)
Total value of assets (Taka) per HH member
Consumption of last 30 days (Taka)
Amount of sent remittances to village HH (last 30 days)(Taka)
Cognitive skill measure of the Dhaka worker(low~high:0~6)(village) age of HH head(village) 1 if HH head is male(village) 1 if HH head is married(village) 1 if HH head iswidow/widower/divorced/separated

55

3.17
(627)
-3.36
(627)
-3.15
(627)

(village)HH head: wife/husband/son/daughter of Dhaka
respondent(village)HH head:
father/mother/brother/sister/grandfa/grandmo.276(.289).177(.306).821**(.339)-.024(.186)(.378)-.265(.385).687(.444).0512(.246)-.13-.578*(.246)(.297)

-.265

-.67

(.453).135(.276).171(.249).0355(.247)(.544).386(.369)-.189(.339)-.0206(.342)-.000689-.00601(.00434)-7.26e-06(.0000152)(.00514).0000225(.0000229)

2.94e-06

5.32e-06(8.70e-06)
2.70e-07(4.24e-07)-.0000114(.0000154)(.0000125)-4.12e-07(5.17e-07)-8.60e-06(.0000188).000106.000131(.0000766)-.25**(.113)
3.11(138)(.000103)-.168(.145)
4.87
(206)

723

723

(village) HH head's occupation: farming(village) HH head's occupation: self-employment, trader,
wage-labor(village) HH head's occupation: salaried workers(i.e.government, teacher)(village) 1 if using mobile money(village) 1 if using mobile money through agent account(village) 1 if using mobile money through own account(village) Educational consumption (~SSC) of last 30
days (Taka)(village) Number of students (~SSC)
Constant
Observations

Notes: Standard errors are in parentheses. ***p < 0.01; **p<0.05; *p<0.1

56(.564).368(village) HH head completed grade 6 ~ grade 9(village) Consumption of last 30 days (Taka)(.499).19(village) HH head completed PSC(village) Total value of lands per HH member (Taka).25(.438).419(.484).0211(.0717)(village) HH head did not get PSC or did not attend
school(village) Total value of productive assets per HHmember (Taka)

-.236
-.111

(village) # of household members(village) Total value of assets per HH member (Taka)(206)(.414)-.124(.437).0755(.0576)(village)HH head: (In law) father/mother/brother/sister(village) minutes to the closest mobile money agent by
foot

(138)

Appendix F.
Asset sales in response to COVID-19 shock(a) Asset values of Dhaka HHs(b) Asset values of village HHs(c) Productive asset values of village HHs

Notes: 1) For panel (a), standard errors are clustered at household-level. For panel (b) and (c), standard errors are clustered at Upazila-level. 2) Together with 95% confidence intervals, the figures
report the dynamic coefficients obtained from the specification of equation (1) in the main text.

57

Appendix G.
Alternative measure of consumption: COVID-19 shock to consumption per capita(a) Consumption per capita of Dhaka HHs(b) Consumption per capita of village HHs

Notes: For panel (a), standard errors are clustered at household-level. For panel (b), standard errors are clustered at Upazila-level. Together with 95% confidence intervals, the figures report the
dynamic coefficients obtained from the specification of equation (1), in the main text.

58

Appendix H.
Savings of Dhaka households
Table A4. Savings of Dhaka households increased during COVID-19 crisis

VARIABLES

(1)
Dhaka
Total value of savings (Taka)

Dummy: 1 = [-30,30] and 0 = [-120,-31]

2,938**(1,462)

Observations
R-squared
Household FE
Mean

1,126
.759
Yes
11701.882

Notes: 1) The results of a regression based on the following equation is shown: 𝑦𝑦𝑖𝑖ℎ = 𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽 + 𝜇𝜇ℎ + 𝜖𝜖𝑖𝑖ℎ , where 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 is a dummy
variable that takes one for dates [-30,30] surrounding the lockdown implementation day (March 26th, 2020) and zero for dates [-120,-31].
Observations of the dates outside of [-120,30] are not used. The regression is weighted by the same weight used in the main regressions.
2) Standard errors are clustered at household-level. 3) Significance level: * p<0.1, ** p<0.05, *** p<0.01.

59Appendix I.
Descriptive statistics of outcome variables at the baseline
Table A5. Dhaka households’ outcome variables at the baseline by Dhaka attrition status(1)
The non-attriting
sampleMean/SDN(2)
The attriting
sampleMean/SDt-testDifference

Variable
Dhaka

NHousehold income of last 30 days (Taka)

611

112

15532.634[6519.007]-47.924

Amount of received remittances from village HH of last 30
days (Taka)

15484.710[5748.215]

611

18.003112

0.000

18.003

Dummy of received remittances from village HH of last 30
days

611

Consumption of last 30 days (Taka)

611

Food consumption of last 7 days (Taka)

611

Amount of money borrowed of last 30 days (Taka)

611

Dummy of money borrowed of last 30 days

611

Amount of loan borrowed of last 30 days (Taka)

611

Dummy of loan borrowed of last 30 days

611

[406.508]
0.003[0.057]10340.616[3721.261]
1338.849[511.328]
1030.278[11195.698]
0.070[0.256]2821.039[8782.235]
0.588[0.493](1)-(2)[0.000]
112
112
112
112
112
112
112

0.000
[0.000]9617.301[3517.172]
1239.529[492.341]
89.286[665.137]
0.018[0.133]1369.643[1814.883]
0.491[0.502]0.003
723.315*
99.320*940.993
0.053**
1451.396*
0.096*

Notes: 1) The value displayed for t-tests are the differences in the means across the groups. ***, **, and * indicate significance at the 1,
5, and 10 percent critical level. 2) Taka is the currency of Bangladesh. As of December 21st, 2020, 1 USD is 85 Taka.

60

Table A6. Village households’ outcome variables at the baseline by village attrition statusN(1)
The non-attriting
sampleMean/SD

Amount of remittances from Dhaka worker of last 30
days(Taka)

667

3123.568

Dummy of remittances from Dhaka worker of last 30 days

667

Consumption of last 30 days (Taka)

667

Food consumption of last 7 days (Taka)

667

Amount of remittances from Dhaka worker (mobile money)(Taka)

667

2668.846

Dummy of remittances from Dhaka worker (mobile money)

667

Amount of remittances from Dhaka worker (by hand/others)(Taka)
Dummy of remittances from Dhaka worker (by hand/others)

Variable
Village[2435.642]
0.867[0.340]12498.091[6210.658]
1874.321[904.524]

DifferenceN(2)
The attriting
sampleMean/SDt-test563251.786-128.217(1)-(2)[2184.490]
0.911[0.288]12074.393[5194.444]
1772.500[615.352]

423.69856

2671.429-2.583[2377.445]
0.777[0.417]56[2214.474]
0.786[0.414]-0.009667

442.72956

580.357-137.629

667[1618.517]
0.090[0.286]56[1739.323]
0.125[0.334]-0.03556
56
56-0.044101.821

Notes: 1) The value displayed for t-tests are the differences in the means across the groups. ***, **, and * indicate significance at the 1,
5, and 10 percent critical level. 2) Taka is the currency of Bangladesh. As of December 21st, 2020, 1 USD is 85 Taka.

61