Calling the Shots: Determinants of Financial Decision-making and Behavior in Domestic Migrant Households in India

Abstract

This article explores financial decision-making and behavior in migrant households. Literature on migration and financial inclusion usually focuses on either migrant workers and their financial needs or remittance flows and their effects on development, leaving the subject of household decision-making significantly underresearched. Using primary data from two sample surveys, one with migrant workers and one with their household members, we employ descriptive analysis to study the financial decision-making processes and outcomes. Our sample is mostly composed of male Indian domestic migrants from Bihar, Jharkhand, and eastern Uttar Pradesh. Our analysis considers the following migrant typology dimensions: duration of migration cycle, skills, and destination. Key household characteristics explored in our study include household size, the number of financial contributors in the household, the presence of an older male and children below the age of 18, and overall household income. Our results show that household members compete for influence over financial decisions and power balances change significantly whether the migrant is at home or at destination. These dynamics play an important role in determining household financial preferences. This suggests that financial products and interventions targeting specific financial behavior (for instance, financial literacy programs) need to take these factors into account since different households and different migrant types make these choices differently.

JEL Codes: D14, O15, O16

Keywords

Migration household finance financial decision-making financial preferences household decision-making India

1. Introduction

How do individuals and households make financial decisions and what factors influence the financial behavior of diverse types of households? This article studies household financial decision-making through the lens of migration and uses a comparative approach to examine how factors such as gender, household composition, migrant typology,¹ risk preferences, financial sophistication, and information asymmetry influence financial decision-making and financial behavior in migrant households. The study uses data collected from Computer Assisted Telephonic Interviews (CATI) surveys administered to migrant and non-migrant members of households.

Researchers have been developing models describing agents’ financial choices for many years (Guiso & Sodini, 2013). The branch of consumer finance has been studying these choices at the individual level, but in the past 15 years, the study of how households use financial instruments and markets to achieve their objectives, that is, household finance, has emerged as a field of considerable interest (Campbell, 2006). Our study focuses on household finance with the aim of contributing to a nascent but growing body of literature on financial decisions and strategies employed by households to meet their financial needs in pursuit of upward mobility. More precisely, our study focuses on a specific kind of household, that is, the migrant household, by which we mean one where at least one household member is a migrant.

Migration itself constitutes one of the most widely used household financial strategies (Seshan, 2020). The Economic Survey of India (Ministry of Finance, 2017) estimates that the magnitude of interstate migration in India was close to 9 million annually between 2011 and 2016. Estimates place the size of the domestic remittance market in India at US $24 billion, the second highest in the world.

The importance of understanding the financial decision-making process in migrant households is clear. First, the existing literature reveals the shortcomings of a purely individual-based approach to understand financial decisions, a finding that this study confirms as well. Such an approach does not consider the evolving needs of low-income and marginalized households and the varying needs and bargaining power of the different members in a household. Second, individuals seldom make financial decisions in isolation. Instead, they normally consult other household members and often their extended family and social networks. Finally, we recognize that migrants follow different paths, have different skills, and are motivated by different goals. Therefore, our study distinguishes between several types of migrants, which allows us to understand how they differ in their financial relationship with their originating household.

Our study largely focuses on male internal migrants from the Bihar–Jharkhand–eastern Uttar Pradesh to Delhi/NCR migration corridor. This is a well-documented corridor for economic migration among low-income households varying in terms of skill, duration, and nature of migration which is conducive to the objectives of this study. Given the remote nature of data collection, we were able to conduct surveys with a significant number of migrants who were from the same source region but worked in destinations other than Delhi/NCR.

The findings of this article have several implications for further research and policy discourse. First, our empirical findings provide valuable insight about the financial choices and decision-making in migrant households. These insights have significant implications for the development of demand-driven financial products for a large customer segment. Second, an improved understanding of the financial preferences and risk appetites of migrant households can help inform targeted information campaigns and better financial literacy and awareness interventions for research and practice. Our findings show that the use of formal savings and credit continues to be low, despite high rates of reported awareness. Our article discusses several interesting findings on how women’s decision-making power in migrant households is vary by factors such as migration duration and household composition. The findings from our study, in this regard, can help inform not only the work of the aforementioned institutions but also future research on these topics. Existing evidence further supports this argument showing the positive effect of greater collective decision-making in household financial matters on women’s agency, their financial literacy, and household allocation on children’s education and health, leading to improved long-term financial outcomes for the household (Shwe et al., 2020).

In light of the evolving situation around COVID-19 over the course of this study, we adopted a completely remote data collection strategy. Respondents were surveyed at two stages. We partnered with GramVaani, a social technology solutions provider with an established customer base in our study location, to recruit respondents. The first stage employed an interactive voice response (IVR) recruitment survey that asked listeners to identify whether they were a migrant or if a member of their household was a migrant, and if they wanted to take part in our study. The IVR recruitment resulted in two pools of respondents, one composed by migrants and one by non-migrating members of a household that has at least one migrant. We followed this up with CATI to conduct quantitative surveys with both sets of respondents and matched responses between the migrant and non-migrant members within the same household.

Our article is further divided into four sections. We present a review of relevant literature and research questions in Section 2 and outline the study methodology in Section 3. Section 4 discusses the findings from primary quantitative data, and Section 5 concludes.

2. Literature Review

Households, like individuals, rely on a variety of financial products and services: they use cash and credit cards to make payments and they make investments to finance future expenses, buy insurance to manage risks to themselves and their assets, and borrow money to finance large expenses or in times of emergency (Tufano, 2009).

A widely employed household financial strategy is migration (Singh et al., 2018),² which can be used both as a livelihood strategy to amplify household income and as a risk diversification strategy (Deshingkar, 2016). Defined in a different way, migration typically entails high up-front costs with the expectation of consistent future benefits, which makes it a form of investment (Banerjee & Duflo, 2011). Most of the existing literature on migration focuses on either the individual migrants or the beneficiaries of their remittances. However, household financial decision-making in the context of migrant households remains a relatively underexplored topic. A vast body of empirical evidence on non-migrant households supports a bargaining power theory of household financial decision-making, which suggests that the household member with the highest education, employment, and wages assumes the most weight in financial decisions (Bertocchi et al., 2014). Still, the relationship between migrants and their households in financial decision-making and the influence of variables like gender and household composition on household finance remain underresearched.

In the case of economic migration, people move in search of steady employment and net positive financial outcomes for their households, outcomes that they deemed unlikely in the did-not-migrate scenario. In this narrative, migrants are often reduced to providers of remittance. Moreover, this literature typically treats remittances akin to windfall gains for their beneficiaries, instead of as a household financial strategy or an investment (Clemens & Ogden, 2014). However, migration can also alter the dynamics of decision-making in migrant households (Singh, 2019). Research in India has shown that in households where the migrating members are male, the impact of migration on agency and labor force participation of both male and female household members depends on the structure of the household itself (Desai & Banerji, 2008). Further, while male migrants are away from their households, the share of resources spent on girls relative to boys, and the decision-making power of spouses shows significant differences (Antman, 2015). A recent paper that uses longitudinal data over an 8-year period to study determinants of financial decision-making found that decision-making power increases proportionally with wages and employment, but results vary with male and female reports of who is the decision maker (Johnston et al., 2016). It is reasonable to expect a similar dynamic in migrant households as well, with financial decision-making power being allocated between various household members based on both their contribution to household income and the norms around the same. These differences in household decision-making have implications on financial outcomes, such as on the allocations of expenditures (e.g., on health and education) or on the choice of formal or informal financial instruments (Shwe et al., 2020). This is substantiated by evidence drawing on NSSO data: Mahapatro et al. (2015) show that households that receive remittances spend less on food and more on education and health care (Mahapatro et al., 2015).

The effects of migration can also vary with migration type. A study on temporary Filipino migrant workers in Korea found that migration causes large changes in household financial behavior, as seen in their spending and savings decisions (Clemens & Tiongson, 2017). Moreover, these changes are not merely a result of remittance inflows but also because of migration-induced changes in household decision-making power. The study looked at the effects of migration on households within three to 5 years and found that it did not have any effect on the labor force participation of other household members. However, the study found evidence that the returns to migration include improved financial behavior. As a result of migration, the household expenditure on health and education increased substantially, while quality-of-life expenses such as celebrating festivals saw a nominal increase. Furthermore, no increase was found in consumption expenditure, but in terms of the effect on financial preferences, migration reduced borrowing and increased savings in these households.

These findings substantiate a more general body of evidence, which suggests that migration leads to greater financial inclusion (Agarwalla et al., 2016) and that migrants exhibit a higher risk appetite, which reflects in their financial preferences (Balaz & Williams, 2011). A study with transnational migrants in which the migrants were exposed to motivational workshops on improving financial habits and joint decision-making with their spouses at home found that the spouses of migrants who received the treatment improved their financial habits and became more likely to enroll in financial education. These households were also found to be more likely to make joint financial decisions (Seshan & Yang, 2014).

While these themes have featured prominently in literature on international migration, understanding financial decision-making in domestic migrant households is a topic that has been hitherto understudied. In India, internal migration is far greater than international migration in absolute numbers. Census 2011 data peg the total number of migrants who moved within the past 1 year for economic reasons at 3.5 million. More recently, the ESI (2017) estimates that the magnitude of flow of domestic, interstate migration (for employment or education) in India was close to 9 million annually between 2011 and 2016. Internal migration has had significant impacts at both an individual and household level, such as higher incomes, consumption smoothening, lower poverty, and nonfarm diversification (Shrivastava & Sutradhar, 2016). However, the outcomes of migration are not uniform across different types of migrant worker groups. Srivastava (2019) shows that there exists significant informalization, segmentation, and social discrimination, which has a disproportionate effect on workers at the lower end of the labor market, resulting in welfare disparities for them and their households. Seasonal and circular migrants and poor and low-caste workers constitute a major proportion of such labor, with the former typically forming the lowest rung at organized sector worksites (Keshri & Bhagat, 2012). There is little other evidence that systematically examines and explains the differences in migration returns to different migrant typologies, which represents a gap in literature. The differences between different migrant groups, defined in terms of duration or skill, on a variety of demographic, household, financial, and decision-making variables constitute an important contribution of our article to the existing body of literature.

Given the size of internal migration in India, the importance of bringing migrants and migrant households under the ambit of the formal financial system is substantial. Estimates place the domestic remittance market in India as the second largest in the world (Tumbe, 2011). However, these estimates indicate that only 30% of the total domestic remittance flows in India is routed through formal channels. This is in contrast, for example, with China, where 75% of the remittances are formally routed. These estimates, however, are a decade old and may need to be updated. With improvements in financial inclusion and the advent of schemes like the PMJDY, our data show that a considerably higher proportion of domestic remittances in India is sent via bank transfers. In the context of household finance, studying household financial decision-making in general, and remittance allocation in particular through the lens of migration presents us with a unique opportunity to improve not only our understanding of migrant households’ financial strategies but also of the differences in financial preferences between different types of migrants.

It is, therefore, evident that the decision to migrate and the act of migration influences household financial decision-making and behavior in several ways. Against this backdrop, this study attempts to address three gaps in literature which also serve as the key research questions for our study:

Research questions: How does financial decision-making unfold in migrant households?

What does their financial portfolio of products and services look like, and what are their preferences in terms of formal and informal instruments?

Who is the primary decision maker in these households, and how does that vary when the migrant is at home versus at destination?

How does decision-making in migrant households vary by type of migration and different financial decisions?

3. Research Design and Methodology

3.1 Research Framework and Sampling Strategy

Our study uses primary quantitative data. We conduct surveys with migrant workers, as well as a non-migrant member of their households. Our study location is the Bihar–Jharkhand–eastern Uttar Pradesh to Delhi/NCR migration corridor, a well-documented corridor for economic migration in northern India.

We partnered with GramVaani, a social technology solutions provider with an established customer base in our study location, to recruit respondents. To this end, we used several IVR platforms hosted by GramVaani, especially Saajha Manch, their dedicated platform for migrant listeners, and their state-based IVR blasts that are active in our source states. We first sent out an IVR recruitment survey that asked listeners whether they were a migrant or if a member of their household was a migrant, and if they wanted to take part in our study. The IVR recruitment resulted in two pools of respondents, one composed by migrants and one by non-migrants. After completing the survey with a respondent, we would collect contact information for the correspondent migrant or non-migrating member within the same household.³ Consequently, we obtained three data sets for our analysis: a migrant data set, a non-migrant data set, and a matched data set that contain responses for those households where we were able to survey both a migrant and non-migrant member. Table 1 presents a summary of our three data sets.

Table 1.
Sample Description

Survey Data Statistics Surveys Completed Number of Variables

Surveys with migrants as respondents 649 1,062

Surveys with Non-migrant household members as respondents 494 800

Households with both migrant and Non-migrant member surveys completed 384 1,861

Total surveys completed 1,143

Survey Data Statistics	Surveys Completed	Number of Variables
Surveys with migrants as respondents	649	1,062
Surveys with Non-migrant household members as respondents	494	800
Households with both migrant and Non-migrant member surveys completed	384	1,861
Total surveys completed	1,143

Source: The authors.

Given the challenges with in-person data collection in 2020, we used CATI to conduct quantitative surveys with both sets of respondents. The two sets of surveys were structured similarly but administered differently. The migrant survey asked the respondent direct questions about their migration history, financial preferences, and the intra-household dynamics of decision-making, while the non-migrant survey asked the respondent the same questions about their migrant relative. We first analyze both data sets separately and then match respondents from the same household to construct a matched data set for our key variable transformations and regression analysis. We used an approach of collecting matched responses from migrant and non-migrant members of the same household, similar to Seshan and Yang (2014). Findings from these analyses are presented in Section 4.

3.2 Comparability of Measurements

Another notable aspect of our quantitative survey is our focus on making our data as comparable as possible with that of other researchers. As a result, we have drawn upon well-recognized literature to standardize the measurement of indicators that can be used across studies. Table A2 presents the different sections of our survey, the variables measured in each section, literature where this is drawn from, and how we ensure external validity. Mapping the financial decision-making process in migrant households is a key component of our data collection exercise. We adapt a decision-making matrix developed by Peterman et al. (2021) and identify the decision maker across seven decision types when the migrant is at home or at destination.

4. Findings

In the following sections, we describe both our migrant and non-migrant survey samples, followed by a descriptive analysis of migrant and household financial portfolios and primary decision makers across a variety of household financial decisions.

4.1 Demographic Data

As shown in Table 2, most migrants have some level of high school education, are, on average, in their early 30s and tend to belong to the “Other Backward Caste” (OBC) category.⁴

Table 2.
Migrants Demographic Information

Age of Migrant 31.4 years Type of Migration (%)

Educational Status (%) Temporary/seasonal 57.0

No formal education 4.5 Semi-permanent 35.0

Primary education (Classes 1–5) 12.3 Permanent 8.0

Middle school (Classes 6–8) 18.5 Skill Level (ISCO-08)

High school (Classes 9–12) 47.8 Level 1 80.0

Diploma certificate 1.4 Level 2 20.0

Graduate and above 15.6 Destination of Migration (%)

Social Category (%) Within source states 19.0

OBC 59.6 NCR 19.9

SC 19.4 North 14.2

ST 6.8 South 16.3

East 11.7

West 19.0

Age of Migrant	31.4 years	Type of Migration (%)
Educational Status (%)	Temporary/seasonal	57.0
No formal education	4.5	Semi-permanent	35.0
Primary education (Classes 1–5)	12.3	Permanent	8.0
Middle school (Classes 6–8)	18.5	Skill Level (ISCO-08)
High school (Classes 9–12)	47.8	Level 1	80.0
Diploma certificate	1.4	Level 2	20.0
Graduate and above	15.6	Destination of Migration (%)
Social Category (%)	Within source states	19.0
OBC	59.6	NCR	19.9
SC	19.4	North	14.2
ST	6.8	South	16.3
		East	11.7
		West	19.0

Source: The authors.

Migration is explored along three dimensions, which are commonly employed in the literature: duration, skill, and destination.

In terms of duration, 57% of the migrants in our sample are classified as temporary or seasonal, while over a third as semi-permanent. Following definitions provided in the latest Indian Census (2011), migration cycles up to 6 months are considered to be temporary/seasonal, and over 6 months as semi-permanent. Finally, we classify migrants that are at their destination for over a year as permanent migrants.

The majority of our sample (80%) is low skilled, falling under Level 1 of International Labour Organization (ILO) International Standard Classification of Occupation (ISCO-08)⁵ and reporting to be construction site workers and factory labor. This is consistent with existing literature on prevalent occupations in the study locations (Srivastava & Sutradhar, 2016).

4.2 Household Composition and Income

Table 3 presents information on household composition in our sample, from both the migrant and non-migrant surveys. In our sample, average household size is 6.5 people, and most households have children and live with the parents of either the migrant or his spouse. A third of the surveyed migrants self-identified as the household’s head, which is higher than what was observed in the non-migrant survey (25%). Aside from the migrant, 50% of all respondents reported that the migrant’s father was the head of the household. From a gender perspective, over three-fourths of the sample in both surveys reported a male head of household.

Table 3.
Household Demographic Characteristics and Income

Migrant Survey (N = 649) Non-migrant Member Survey (N = 494)

Household Composition Mean Mean

HH size 6.4 6.5

% HH with child below 18 years 80.4 80.6

% HH with spouse of migrant 67.3 65.6

% HH with older male 67.2 64.0

% HH with older female 73.3 71.1

Head of household (%)

Migrant 32.0 25.7

Father/father-in-law 49.6 50.0

Other members 18.4 24.3

No. of Members Contributing Financially

None other than migrant (%) 27.6 26.3

Average HH income 11,110.8 10,308.3

Primary source of HH income (%)

Remittance from migrant 73.7 70.9

Agricultural activities 17.8 18.2

Others (%) 8.5 10.9

	Migrant Survey (N = 649)	Non-migrant Member Survey (N = 494)
Household Composition	Mean	Mean
HH size	6.4	6.5
% HH with child below 18 years	80.4	80.6
% HH with spouse of migrant	67.3	65.6
% HH with older male	67.2	64.0
% HH with older female	73.3	71.1
Head of household (%)
Migrant	32.0	25.7
Father/father-in-law	49.6	50.0
Other members	18.4	24.3
No. of Members Contributing Financially
None other than migrant (%)	27.6	26.3
Average HH income	11,110.8	10,308.3
Primary source of HH income (%)
Remittance from migrant	73.7	70.9
Agricultural activities	17.8	18.2
Others (%)	8.5	10.9

Source: The authors.

Over 70% of the respondents reported that remittances are the primary source of income of their households, which is, on average, ₹11,110.8 (USD 150)⁶ per month in the migrant survey and ₹10,308.3 (USD 139) in the non-migrant survey.

4.3 Migrant Income and Remittances

Focusing on financial decisions and behaviors, we notice that migrants, on average, earn a monthly income of ₹11,743 (USD 158) and send around 56% of that back home as remittances. Furthermore, our data suggest that, on average, semi-permanent migrants earn and remit marginally higher amounts than temporary migrants. Predictably, we find that migrants in higher skilled jobs earn and remit more than migrants in lower skilled jobs. Finally, we find that migrants who travel further away, which in our sample means to southern and western states, both earn and remit more. A potential interpretation of this result could be that migrants are motivated to travel far from home by higher paying jobs. Interestingly, migrants to the NCR reported higher incomes than their peers in other northern destinations but lower remittance sizes. This can be explained by the higher incomes and higher costs of living, respectively, in the NCR. These results are summarized in Table 4.

Table 4.
Migrant Income and Remittances

Migrant Average Monthly Income
Average Size of Remittance

Mean Median N Mean Median N

Overall sample 11,742.3 12,000 649 6,635.90 6,000 622

Duration of migration

Temporary/seasonal 11,426.2 10,500 370 6,596.64 6,000 357

Semi-permanent 12,161.5 12,000 279 6,688.68 6,000 265

Migrant skill level

Level 1 11,521.5 11,000 519 6,519.96 6,000 501

Level 2 12,623.9 12,000 130 7,115.70 6,000 121

Migration destination

Source states 11,313.0 10,000 123 6,415.25 6,000 118

North 11,111.6 10,250 92 6,810.35 6,000 87

South 12,646.2 12,000 106 7,219.05 7,000 105

West 12,203.3 12,000 123 7,056.03 6,000 116

East 10,750.0 10,000 76 6,200.00 5,000 70

NCR 12,003.9 12,000 129 6,091.27 5,000 126

	Migrant Average Monthly Income	Average Size of Remittance
Overall sample	11,742.3	12,000	649	6,635.90	6,000	622
Duration of migration
Temporary/seasonal	11,426.2	10,500	370	6,596.64	6,000	357
Semi-permanent	12,161.5	12,000	279	6,688.68	6,000	265
Migrant skill level
Level 1	11,521.5	11,000	519	6,519.96	6,000	501
Level 2	12,623.9	12,000	130	7,115.70	6,000	121
Migration destination
Source states	11,313.0	10,000	123	6,415.25	6,000	118
North	11,111.6	10,250	92	6,810.35	6,000	87
South	12,646.2	12,000	106	7,219.05	7,000	105
West	12,203.3	12,000	123	7,056.03	6,000	116
East	10,750.0	10,000	76	6,200.00	5,000	70
NCR	12,003.9	12,000	129	6,091.27	5,000	126

Source: The authors.

We also find that less than one-third (29.9%) of our sample receives payments in their bank account, but, interestingly most migrants (close to 80%) submit remittances via formal channels transfer, with bank-to-bank transfers being the most popular choice (70%). This is in contrast to a vast array of literature that alludes to the persistence of informal channels among migrants and their households. A plausible explanation for this discrepancy is that much of this literature is more than a decade old. Given the advancements in financial inclusion in India, particularly with the advent of the so-called JAM (Jan Dhan-Aadhaar-Mobile) trinity, migrant preferences have shifted toward formal modes such as bank transfers with increased access. However, these results need to be confirmed by surveys with larger samples. In both surveys, we find that remittances are received by the migrant’s father or spouse in over 70% of our sample, with the father being on average the most likely receiver as summarized in Table 5.

Table 5.

Remittances

Remittance Behavior	Migrant Survey (N = 649)		Non-migrant Member Survey (N = 494)
Remittance Behavior	Mean	Median	Mean	Median
Who receives remittance in the HH (%)
Father	41.0		42.5
Spouse	32.9		30.1
Mother	14.2		12.9
Others	11.9		14.5
Mode of sending remittance (%)
Bank transfer	70.9
Other formal channels	9.4
Informal channels	19.6
Average size of remittance	6,635.6	6,000	6,535.2	6,000
Allocation of remittances (reported)	% of HHs		% of HHs
General HH consumption	90.7		94.2
Healthcare	71.5		73.4
Education	58.9		61.0
Utility/bill payments	33.5		44.0

Source: The authors.

4.4 Financial Behavior

Next, we examine financial behaviors and preferences of both migrants and their households on savings, expenditure, borrowing, and bank account ownership, in line with the Reserve Bank of India’s basic criteria for financial inclusion (RBI, 2019).

Examining migrant’s financial behaviors at destination, we notice that most migrants spend⁷ whatever they do not remit, with only 30% of our sample reporting any savings, which is consistent with both the existing literature and our qualitative interviews. Those who do save, on the other hand, tend to save, on average, 18% of their income.

As seen in Table 6, a third (28%) of migrant respondents reported to have borrowed in the last 18 months, with the average number of loans subscribed being 3. However, we find that only 3% of all respondents reported having borrowed from financial institutions. While we observe a shift toward formal channels for remittances, migrants seem to still rely on informal sources for credit. As the data were collected 9 months after the COVID-19 pandemic breakout, it is possible that this result might be caused by formal credit being harder to obtain in this period compounded by other factors such as a lack of adequate collateral and less flexible terms associated with formal credit, also highlighted by the (RBI Household Finance Committee, 2017). Finally, we also find that bank account ownership is nearly universal in our migrant sample (92%) and that over three-fourths of the sample reported having their bank accounts at their destination.

Table 6.
Migrant Financial Behavior

Financial Behavior Migrant Survey (N = 649)

Mean Median

Migrant savings at destination

% of migrants having savings 28.0

Avg. monthly savings (N = 182) 2148.6 2,000

Migrant Expenditure at Destination^a

Housing expenses (%) 74.1

Food expenses (%) 84.8

Utilities/bill payments (%) 67.0

Fuel expenses (%) 41.3

Avg. monthly expenses (N = 649) 3793.6 3,399

Prop. of expenses on productive consumption (%) 1.3

Migrant borrowing at destination

% of migrants borrowed in last 18 months 28.0

Avg. no. of borrowings in last 18 months 2.8 2

Sources of borrowing (N = 182)

Borrow from friends (%) 63.2%

Borrow from moneylender (%) 12.1%

Borrow from local shop/business (%) 9.3%

Borrow from employer/contractor (%) 8.2%

Borrow from other migrants (%) 8.2%

Borrow from financial institution (%) 2.8%

Avg. borrowing amount (N = 182) 20,654 6,000

Has bank account ownership (%) 92.3

Location of bank account (N = 599)

Account at source (%) 17.4

Account at destination (%) 78.5

Accounts at both locations (%) 4.2

Financial Behavior	Migrant Survey (N = 649)
Migrant savings at destination
% of migrants having savings	28.0
Avg. monthly savings (N = 182)	2148.6	2,000
Migrant Expenditure at Destination^a
Housing expenses (%)	74.1
Food expenses (%)	84.8
Utilities/bill payments (%)	67.0
Fuel expenses (%)	41.3
Avg. monthly expenses (N = 649)	3793.6	3,399
Prop. of expenses on productive consumption (%)	1.3
Migrant borrowing at destination
% of migrants borrowed in last 18 months	28.0
Avg. no. of borrowings in last 18 months	2.8	2
Sources of borrowing (N = 182)
Borrow from friends (%)	63.2%
Borrow from moneylender (%)	12.1%
Borrow from local shop/business (%)	9.3%
Borrow from employer/contractor (%)	8.2%
Borrow from other migrants (%)	8.2%
Borrow from financial institution (%)	2.8%
Avg. borrowing amount (N = 182)	20,654	6,000
Has bank account ownership (%)	92.3
Location of bank account (N = 599)
Account at source (%)	17.4
Account at destination (%)	78.5
Accounts at both locations (%)	4.2

Source: The authors.

Note: ^aPlease note that these are multi-choice responses and do not aggregate to 100%.

At source, we find that, on average, migrant households save ₹2,629 (USD 35) per month, with a relatively high degree of variability (median savings are ₹2,000 with a standard deviation of ₹2,133) among the households that reported savings. However, this accounts for less than one-third of our sample that reports any savings at all, which, unlike the migrants’ saving behavior, is harder to explain. One plausible explanation is that savings are underreported in our data. In order to validate this assumption, we look at their expenditures.

The average monthly expenses at source in the non-migrant data amounts to ₹6,107 (USD 83), which accounts for nearly 60% of the average monthly household income. The median difference between average monthly household income and average monthly household expenditure comes to ₹3,000 (USD 41). However, this money is unaccounted for in their monthly allocation, despite accounting for all expenses and documenting allocations to informal savings and other financial instruments. Thus, it may be concluded that there is an opportunity to promote formal savings behaviors, perhaps through financial products tailored to the needs and characteristics of migrant households.

With regard to borrowing, we see that similar to our migrant data, 28% of the non-migrant sample reported having borrowed in the last 18 months, with the average and median number of loans in this period being 2. However, we see that a relatively higher percentage of respondents have borrowed formally (17%). Furthermore, we observe nearly universal bank account ownership in the non-migrant data as well. Last but not least, over 70% of the sample reported having received some kind of government payments or social entitlements in the last 12 months.

4.5 Measuring Household Financial Decision-making

We argue that the financial decision-making process in migrant households changes depending on whether the migrant is at source or at destination. Adapting the decision-making matrix developed by Peterman et al. (2021), we identify the decision maker across seven decision types when the migrant is at home or at destination. Results from this analysis are presented in Table 7.

Table 7.
Primary Decision Maker

Type of Decision Migrant (%) Spouse (%) Older Male (%) Older Female (%) Other HH Member (%)

When the migrant is at home (Non-migrant member survey N = 494)

Small daily purchases 32.6 15.2 31.4 12.6 8.3

Large financial purchases 49.4 5.3 29.8 5.5 10.1

Expenditure on education 44.7 5.9 33.4 5.9 10.1

Choosing where to keep HH Savings 40.3 7.3 33.8 9.1 9.5

How to spend remittances 37.9 6.9 37.5 8.1 9.7

Emergency borrowing (e.g., medical expenses) 41.1 4.5 37.7 5.5 11.3

Nonemergency borrowing (e.g., marriage) 39.3 4.3 40.3 5.9 10.3

When migrant is away (Non-migrant member survey N = 494)

Small daily purchases 1.6 30.0 41.5 17.0 9.9

Large financial purchases 18.4 18.4 42.5 8.5 12.2

Expenditure on education 10.9 23.3 43.5 9.5 12.8

Choosing where to keep HH savings 10.9 23.7 42.1 12.2 11.1

How to spend remittances 11.5 22.9 42.9 11.5 11.1

Emergency borrowing (e.g., medical expenses) 14.8 19.8 45.8 7.5 12.2

Nonemergency borrowing (e.g., marriage) 17.8 16.0 48.0 7.3 10.9

Type of Decision	Migrant (%)	Spouse (%)	Older Male (%)	Older Female (%)	Other HH Member (%)
When the migrant is at home (Non-migrant member survey N = 494)
Small daily purchases	32.6	15.2	31.4	12.6	8.3
Large financial purchases	49.4	5.3	29.8	5.5	10.1
Expenditure on education	44.7	5.9	33.4	5.9	10.1
Choosing where to keep HH Savings	40.3	7.3	33.8	9.1	9.5
How to spend remittances	37.9	6.9	37.5	8.1	9.7
Emergency borrowing (e.g., medical expenses)	41.1	4.5	37.7	5.5	11.3
Nonemergency borrowing (e.g., marriage)	39.3	4.3	40.3	5.9	10.3
When migrant is away (Non-migrant member survey N = 494)
Small daily purchases	1.6	30.0	41.5	17.0	9.9
Large financial purchases	18.4	18.4	42.5	8.5	12.2
Expenditure on education	10.9	23.3	43.5	9.5	12.8
Choosing where to keep HH savings	10.9	23.7	42.1	12.2	11.1
How to spend remittances	11.5	22.9	42.9	11.5	11.1
Emergency borrowing (e.g., medical expenses)	14.8	19.8	45.8	7.5	12.2
Nonemergency borrowing (e.g., marriage)	17.8	16.0	48.0	7.3	10.9

Source: The authors.

We find that when the migrant is at home, the migrant himself tends to be the primary decision maker for most decisions. Aside from the migrant, the older male is found to be the other member of the household with decision-making power, indicating that decision-making power is concentrated in the hands of the male members of the household. Overall, there is relatively little variation in how decision-making power is shared when the migrant is at home.

When the migrant is away, we notice that the older male emerges as the primary decision maker for all decisions. This validates our finding that decision-making power is concentrated among male members of the household.

However, we see more variation in how decision-making is shared. We find that the spouse is identified as the primary decision maker in 30% of the sample for decisions on small daily purchases and in a quarter of the sample for decisions on education expenditure, household savings, and remittance allocations.

While migrants lose decision-making power when they are away, they are identified as the primary decision makers in nearly 20% of the sample for decisions on large financial purchases and nonemergency borrowing, which suggests that migrants maintain influence on important financial decisions even when they are away.

Additionally, with regard to other household members being consulted in decision-making, we find that decisions involving higher financial outlays and those that are made less frequently, such as large financial purchases (91.5%) and nonemergency borrowings (91.5%), are taken in consultation with other household members. In contrast, decisions involving low financial outlays and decisions made more frequently such as daily purchases of smaller ticket sizes (75%) and household savings (81%) are the least consultative. However, it is over decisions where there is greater consultation that we find that male members of the household (i.e., the migrant or the older male) retains final control, in the event of a disagreement.

5. Conclusions

This article explored financial portfolios and decision-making in migrant households. First, our data show that the incomes of migrant households in the sample are heavily reliant on remittances. Interestingly, we observe that remittances are mostly submitted and received through formal channels, showing a notable departure from previously observed data and literature. This formalization process, however, has not translated to other financial products yet. It can be observed, in fact, that both savings and borrowing still occur mostly through informal channels. This suggests that there is an opportunity to tailor financial products for migrants and their households that could capture this market gap.

In terms of financial decision-making, we observe that migrants have the greatest control over most financial decisions, especially when living at home. This could be due to the importance that remittances play in the financial portfolios of migrant households: as migrants bring the most significant contribution to household income, it is intuitive that they have the greatest influence on financial decisions. The allocation of financial decisions is more complex when the migrant is away. First of all, it is interesting to notice that migrants retain control on large, important financial decisions. Second, we observe that the “older men” in the household assume control over most financial decisions when the migrant is away, uncovering a clear gendered power dynamic. In fact, we observe that spouses, who in our sample are all women, become the principal financial decision makers only on a handful of choices, mostly related with the day-to-day management of household expenses and the education of children. Finally, it is worth noting that in terms of shared financial decision-making, large financial decisions are the most consultative. However, in case of a disagreement, it is a male member of the household (either the migrant or the older male) that has the final say, confirming the gendered power-dynamics outlined above.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The research was funded by a grant from the Dvara Research Foundation.

Appendix A

Notes

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