Migration, Information Gap, and Visible Consumption: Evidence from India* - arXiv
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Migration, Information Gap, and Visible Consumption:
Evidence from India*
Shihas Abdul Razak† Upasak Das‡
January 18, 2021
Abstract
Using representative migration survey data from the Indian state of Kerala, this paper as-
sesses the impact of transnational migration on social signaling through the consumption of
visible goods. Using the plausibly exogenous variation in migration networks in the neighbor-
hood and religious communities to account for the potential endogeneity, we find significant and
positive effects on conspicuous consumption. In terms of the mechanisms, we put forward three
possible channels. While we are unable to rule out the associated changes in preferences driving
up the spending on status goods, we observe only modest effects of peer group spending due
to higher status competition. A key channel that we propose through a theoretical framework
is a potential information gap among permanent residents about the income levels of a migrant.
This we argue can be leveraged by migrants to increase the visible consumption to gain higher
status in the society.
Keywords: Conspicuous Consumption, Information Gap, Migration, Kerala
JEL Classification: D12, D14, D82, O15
1 Introduction
Social status within a community remains one of the key elements of human behavior and works
by scholars including Veblen (1899) and Duesenberry (1952) among others have been profound
in establishing this. This is evident from Veblen (1899), who argues ”the members of each stratum
accept as their ideal of decency the scheme of life in vogue in the next higher stratum, and bend their
energies to live up to that ideal”. One common way of signaling social status is the consumption of
visible goods or conspicuous consumption which is often perceived as a way in which individuals
define themselves in society. Consequently, members of each social group seek to emulate or move
up in comparison to the other members of that particular group or other groups. As a method
of household reproduction in society, households strive for maintaining or improving their social
position through consumption, especially when unequal social groups co-exist (Carrier and McC,
1997).
Existing studies have empirically documented the existence and heterogeneity of conspicuous
consumption in different contexts. For example, extant literature has shown how a household’s
* We thank Soham Sahoo and Linh Tô, and the participants of Econometric Society DSE Winter School 2020, for their
useful comments.
† Unaffiliated, shihaspvkd@gmail.com
‡ Global Development Institute, University of Manchester and Centre for Social Norms and Behavioral Dynamics,
University of Pennsylvania, upasak.das@manchester.ac.uk
1socio-economic position within the own reference group and self-esteem of the individuals get as-
sociated with different levels of visible consumption, which is found to vary across different groups
as well (Bertrand and Morse, 2016; Bursztyn et al., 2017; Charles et al., 2009; Kaus, 2013; Khamis
et al., 2012). The quest for signaling through consumption is also found to have a bearing on
portfolio choices in the financial markets and household indebtedness (Bricker et al., 2020; Gali,
1994; Roussanov, 2010). In addition, studies have documented how inequality in the reference and
socio-economic groups affect conspicuous consumption in different contexts (Frank et al., 2014; Roy-
chowdhury, 2016). This paper adds to the literature by looking at the implications of transnational
migration on conspicuous consumption and social signaling by using recent household migration
data collected in 2018 from the Indian state of Kerala. The case of Kerala is important because
of the historically high prevalence of out-migration to the Middle-Eastern countries for low and
semi-skilled employment.
Using a basic regression framework, we first examine the effects of migration on household
conspicuous consumption. To account for the potential endogeneity because of the self-selection of
households into migration, we use variation in the proportion of migrant households in the locality
and that from the same religion within the corresponding block as the instrument variables. In
addition, we also assume these variables to be plausibly exogenous and use methods developed by
Conley et al. (2012) and van Kippersluis and Rietveld (2018) to test whether our estimates remain
significant even with substantial relaxation of the assumption of strict exogeneity of the instrument
variables. Notably, we also use a strategy developed by Altonji et al. (2005) and Oster (2019) that
assumes the presence of unobservables in the model to confirm that the results are not driven by a
potential uncontrolled factor. This enables us to look at the results from the causal framework and
ensure that the change in the outcome variables is a result of migration and no other confounding
factor.
Our findings indicate that the migrant households are more likely to spend on conspicuous
consumption and possess a luxury house in comparison to the non-migrant households, controlling
for the observable as well as the unobservable confounders. While no conclusive evidence is found
for savings, the migrant households tend to have lesser investment and higher debt than the non-
migrants indicating that the expenditure on visible consumption items is financed through debt,
though that requires further investigation. When we look into the heterogeneity of this impact,
we observe migrants from lower-income classes and lower castes have a higher likelihood of social
signaling indicating that they tend to keep up with the Joneses. Importantly, such heterogeneity is
not found between religions.
What is the potential relationship between transnational migration and social signaling? In other
words, why would a migrant from Kerala have different levels of the propensity of conspicuous con-
sumption? We hypothesize three possible channels that can potentially be responsible for systematic
higher spending among migrants. Firstly, the difference in the consumption basket of migrants and
non-migrants could be driven by changes in preferences of the migrants post-migration. However,
we did not find any conclusive evidence to show that transnational migration influences food and
educational expenditure, which are relatively less visible expenses. This indicates that changes in
preference may not actually drive our results on visible consumption though we are unable to rule
out the possibility of a preferential change.Secondly, we argue visible consumption among migrants
might be driven by peer effects where in changes in reference group may drive the results. This
emanates from the literature that documents reference or peer group effects on conspicuous con-
2sumption; income level and inequality of the reference group systematically associated with higher
status good possession (Charles et al., 2009; Roychowdhury, 2016). To test for this possibility, we
add indicators that capture reference group income distribution including mean and median among
othersas control variables. Nevertheless, no substantial changes in the migration effect size is ob-
served. This indicates while the reference group effect might be a plausible channel, it does not
explain the migration effect in this context.
The third argument we propose is increased incentives for status signaling through information
gaps. Literature indicates prevalence of significant gap in the information among the home com-
munity about a migrant’s job profile and actual wealth status while migrating outside (Seshan and
Zubrickas, 2017). It is possible that this information gap may incentivize the migrants to seek status
by increasing visible consumption, which can be leveraged to signal income levels. Accordingly,
in the paper, we develop a theoretical framework to put forward this idea. Relevant to this, we
observe migrant households are more likely to spend on goods that are less possessed by others
in the community. Further, migration effects on visible consumption is found to be higher among
migrants from the socially and economically deprived communities (belonging to lower castes).
Despite such indicative evidence in favor of this proposed information gap channel, this warrants
more formal and rigorous empirical testing to understand the relative importance of each channel,
which is posed as a future research question for exploration.
The paper contributes to two strands of literature. Firstly it adds to the literature on the eco-
nomics of conspicuous consumption. In this context, we contribute to a potential relationship be-
tween the information gap because of transnational migration and social status signaling, hence
visible consumption. Secondly, it contributes to the literature on transnational migration and remit-
tances and their effects on different dimensions of consumption. The study has wider implications
that go beyond the migrants. With an expansion of the economy and changing economic roles,
mobility, and employment opportunities, the returns from conspicuous consumption might also
increase disproportionately. This would have larger welfare consequences as more resources will
be diverted to the consumption of visible items (Frank, 2005). The mechanism we introduce, in-
creased incentives for conspicuous consumption due to information gap, could explain the increas-
ing consumption and consumer debt in the economy (Leicht, 2012; Weller, 2007), which in turn have
macroeconomic consequences because of potentially higher indebtedness and lesser private savings
and investments (Jordà et al., 2016; Mian et al., 2017). In terms of policy relevance, the paper opens
up the possibility of discussion around the implications of cash and in-kind transfer interventions as
welfare-enhancing instruments especially in areas with a high prevalence of migrants. In addition,
we formally propose the channel of information gap which can be used by migrants to increase
their visible consumption. This opens a potential debate and lays platform for further research to
empirically examine the relevance of information gap for raising conspicuous consumption.
The rest of the paper is organized as follows. The next section provides the background on
migration from Kerala and its distinctiveness. Section 3 explains the data and empirical strategy.
The results of the empirical analyses are presented in Section 4. The last section provides some
concluding remarks.
2 Kerala and Migration
”When we think we’re toiling for the sake of our loved ones we don’t feel the fatigue. We feel
3happy when the money sent would have fulfilled a need. Most of us are here only physically. Even
my wife is unaware of what my job is or my salary, not because I’m ashamed, but not to let them
know about the hardships. When we send Rs. 10000, what do people think? Out of 20000 we
earn, we sent 10000. But in reality, we earn 7000 and take a loan for the rest.”1
Kerala, a state in the Southern part of India, is renowned for its high levels of human develop-
ment and the developmental path it has gone through (CDS-UN, 1975; Isaac and Tharakan, 1995).
Though the history of migration in the state dates back to time immemorial, there has been a sys-
temic increase in migration from Kerala to foreign countries in search of jobs from the 1970s. Most
of this outflow has been for unskilled and semi-skilled work to Middle-Eastern countries, primarily
Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. This was in response
to the increased labor demand in the region that started from the 70s owing to the oil boom. This
migration was temporary and the migrant workers always had the idea of returning to the home-
land. The majority of these migrants lived alone without family in labor camps and temporary
residences (Seshan, 2012; Zachariah et al., 2004). The fact that most of the migrants left their fami-
lies back in Kerala along with the temporary nature of migration led to the inflow of a major chunk
of migrants’ earnings to the state’s households in the form of remittances (Zachariah et al., 2002;
Raman, 2015). The systemic migration and the subsequent foreign remittance flows, which accounts
for almost 20% of the State Domestic Product (Rajan and Zachariah, 2019), have changed the socio-
economic contour of the state. Remittances have been considered as the major factor contributing
to the increased economic growth of the state from the 80s (Kannan, 2005; Kannan and Hari, 2020).
Kerala has also been considered as the vanguard of consumption in India, which is majorly driven
through foreign remittances. Scholars have also noted the consequences of the temporary nature
of the migration on the financial decisions of the households in the state (Osella and Osella, 1999;
Gulati, 1993).
Peculiar features of the Kerala migration, and in general cross-country migration from develop-
ing regions, make it an appropriate setting to explore the link between migration and conspicuous
consumption through the information gap which we discuss in detail later. Firstly, unlike other
distress-induced unskilled and semi-skilled migration, international migration to the Middle-East
from South Asia did not accompany reputational damage. These migrants were regarded as fortune
bringers and considered with equal, if not higher, status in society (Dominic, 2017; Nidheesh, 2020).
Migrants are often seen as the driving force behind the consumption trends in the state, and the
consumption among migrants appealed to many young people and made them migrate (Pattath,
2020). The second feature is the temporary nature of the migration. The Middle-Eastern countries
never provided the migrants an option of settling down there permanently. Studies indicate for the
majority of these migrants, their family members always remained back in Kerala (Seshan, 2012;
Zachariah et al., 2004). Thirdly, Kerala is very distinct from the rest of India in the sense that it
has relatively lower role of structural factors such as caste because of the long history of social
movements with strong anti-caste sentiments (Deshpande, 2000; Devika, 2010). Therefore the role
of signalling mechanism might be different to that in comparison to the rest of the country. There
also exists significant information gap on what these migrants do and how much they earn (Seshan
and Zubrickas, 2017), which we argue might be among the primary reason for higher consumption
of visible goods among them.
1 This dialogue is from a Malayalam movie, Pathemari, which narrates the lives of gulf migrants from Kerala.
43 Data and Empirical Strategy
3.1 Data
Data used for this study comes from Kerala Migration Survey (KMS).2 KMS is a periodic house-
hold survey conducted by the Centre for Development Studies, Thiruvananthapuram, to monitor
migration from Kerala. This study uses the 2018 round of KMS. This survey data is a kind unique
resource for migration studies, especially given the context of minimal availability of reliable data on
migration globally. The first round of KMS was conducted in 1998, and since then there have been
seven more rounds with the latest data available one in 2018. From its 2008 round, the survey covers
15000 households across the state. The sample households are selected using a stratified multistage
random sampling, where district rural and urban areas are the strata. The number of sample house-
holds in each stratum is selected in proportion to the census data. One ward is selected randomly
from a selected number of localities (village/municipality). From each ward selected, 50 households
are selected randomly for the survey. The number of localities in each stratum is decided by the
number of households needed in the stratum. There are 300 localities divided proportionally in the
entire sample.3
3.2 Variables
To capture conspicuous consumption, we create various indices of conspicuous consumption from
the data on possession of different goods that possess high visibility and use those as the main
dependent variables. We follow the categorization of goods done by Khamis et al. (2012),4 , and take
ten goods that are highly visible compared to others. For each of ten goods, we first assign a value
of 1 for all households that possess the asset and 0 otherwise. Using this set of dummy variables,
we run a Principal Component Analysis (PCA) and then standardize this PCA value to obtain our
primary visible consumption index. Further, as a different index of conspicuous consumption, we
first obtain a standardized value for each of these ten goods. Then we obtain a sum of these ten stan-
dardized scores for each household, which is again standardized to obtain a standardized visible
consumption index. Notably, such methods of arriving at a standardized score have been widely
used including a recent study by Heath and Tan (2020) to obtain a female household autonomy
score using a wide range of empowerment indicators. We also define a household as a conspicuous
consumption household if the household possesses at least one of the five least possessed goods
in their locality. In addition to these three, we also use possession of a luxurious house defined
as houses that have at least two bedrooms with attached bathrooms with tile flooring as the third
variant of our dependent variables. Note that the first two variables are continuous in nature while
the third and fourth ones are binary.
We capture migration through three variables, which constitute our main variables of interest:
whether the household has at least one international migrant, whether it had one in the past, and the
logarithmic value of the current amount of remittance received from the migrants by the sampled
household every year. To avoid the possibility of getting an undefined value for households without
migrants while taking logarithmic value, we add one to the amount of remittance received. This
2 Kerala Migration Survey. Thiruvananthapuram: Centre for Development Studies, 2004. URL:
http://cds.edu/research/ru/migrationresearch/migration-survey-data
3 For more details on the sampling method, see Zachariah and Rajan (2015).
4 Khamis et al. (2012) conducts a survey, following Charles et al. (2009) and Heffetz (2011), in India to find out the
goods that possess high visibility compared to other goods.
5Table 1: Definition of Visible Consumption Indices
Does the household possess the following?
Coding of responses: 0 - No, 1 - Yes
(1) Motorcar
(2) Motorcycle
(3) Mobilephone
(4) Television
(5) Refrigerator
(6) Washing Machine
(7) Oven
(8) Computer
(9) Air Conditioner
(10) Inverter
Visible Consumption Index (PCA) – Z-score of the Principal Component Analysis value of the
above coded responses for 10 goods
Visible Consumption Index (Standarized) – After taking standardized value for each of these
ten goods, a sum total of these ten standardized scores is taken for each household, which is
again standardized
Conspicuous Consumption Household (Least Possessed) – Household possesses at least one of
the five least possessed goods in the region (Taluk)
Type of house which the household is now occupying
Coding of responses:
1 - Luxurious (3 or more bedrooms with attached bathrooms, concrete/tile roof, tiled floor)
2 - Very Good (2 bed rooms with attached bathrooms, concrete/tile roof, Mosaic floor)
3 - Good (1 bed room, brick and cement walls, concrete or tile roof)
4 - Poor (Brick walls, cement floor, tin or asbestos roof)
5 - Kutcha (Mud walls, Mud floor & Thatched roof)
Luxury House – Response 1 or 2
ensures that the value of this variable would be zero for non-migrant households.5 Of note is the
fact that the first two variables are binary in nature whereas the third variable is continuous.
We control for a range of household demographics and characteristics as control variables since
these are potential confounders of conspicuous consumption. We use log household income to con-
trol income effects on conspicuous consumption. Because it is possible that the marginal propensity
of visible consumption increases with income, it becomes necessary to incorporate the economic
condition of the household.Different demographic groups have different propensities to consume
visible goods, and demographics also affect the incentive for status-seeking of each household
(Charles et al., 2009; Khamis et al., 2012). Considering these demographic effects, we control for
religion, area of living, highest education in the household, and ration-card type. To control for the
differences in household sizes, we use the number of members in the household and the number of
earning members. We also control for the regional effects (Mejia and Restrepo, 2016) by including
district dummies. We consider the IV model as our standard model and in the later sections, only
5 This is a standard method that has been employed by a number of papers.
6IV models will be reported for brevity.
Table 1 lists all the variables we construct to capture conspicuous consumption and shows how
they have been calculated for our analysis. Table 2 presents the summary statistics of all these
variables separately for the non-migrant and the migrant households.6 We also add a third column
here to show the difference between these variables between migrant and non-migrant households
and test whether these differences are statistically indistinguishable from zero. The findings reveal
that there is a significant difference between the migrant and the non-migrant households in terms
of consumption of visible goods and household financials as well. As expected, migrant house-
holds have a lower proportion of male household heads. The number of earning members is also
significantly higher for migrant households.
3.3 Empirical Strategy
Guided by several studies in the Indian context that explain consumption pattern, our baseline
econometric model is given by
Yi = α + βDi + γXi + ei (1)
where Yi gives the measure/indices of visible consumption as discussed for household i, Xi is
the vector of control variables and Di captures the measures of migration of the same household as
already discussed in the previous section. ei is the error term and β gives the treatment effect.
The above regression estimates can yield unbiased estimates, adjusting for the other underlying
factors if the migration decision of the household and remittances are exogenous to conspicuous
consumption or it can be conditioned solely through the observable characteristics. Nevertheless, it
is likely that the migration decision of a household is not random and there may be unobservables
that may affect both, migration as well as visible consumption. This can potentially arise since the
household can self-select themselves into migration and the associated selection from unobservables
can bias the estimates of spending on conspicuous goods. As an instance, a dynamic household that
derives more utility from social signaling might also be the ones who are more likely to migrate.
Even after incorporating an elaborate set of confounding variables, we may not be able to control
for these unobserved characteristics, which can then possibly yield biased estimates of the impact.
More formally following equation 1, if the migration is exogenous, Di , should be uncorrelated with
the unobservable error term, ei , i.e. Cov( Di , ei ) = ρ = 0. However for reasons already discussed,
migration might be endogenous where the first stage model would look like
Di = δ + φCi + ηZi + νi (2)
where Ci is a vector of exogenous variables that are correlated to migration decision and Zi is
the vector of Instrument Variables (IV).
Therefore, to identify the endogenous migration decision, we use exogenous variation in the
prevalence of migrating households within the village as well as among the religious group within
the taluk of the corresponding household as the potential IVs. Notably, to gauge the effects of
migration, literature has used the prevalence of migration within the community as IVs as this
would be dependent on the pre-existing migration network and hence highly correlated with the
household migration decision (Amuedo-Dorantes and Pozo, 2010; Bouoiyour and Miftah, 2016;
6 Full sample summary statistics is provided in Table A1.
7Table 2: Difference between Migrant and Non-migrant Households (Key Variables)
(1) (2) (3)
Variable Non-migrant Migrant Difference
Household Household
Visible Consumption Index (PCA) -0.145 0.676 0.821***
(1.802) (1.983) (0.000)
Visible Consumption Index (Stand.) -0.070 0.325 0.395***
(0.976) (1.045) (0.000)
Conspicuous Consumption Household 0.320 0.511 0.191***
(0.467) (0.500) (0.000)
Luxury House 0.392 0.609 0.218***
(0.488) (0.488) (0.000)
Household Savings 39,027.941 58,534.859 19,506.918***
(105751.719) (133992.438) (0.000)
Household Investment 79,498.469 124703.430 45,204.965***
(348990.750) (427905.094) (0.000)
Household Debt 138566.641 209846.469 71,279.828***
(396067.313) (505044.781) (0.000)
Food Expenditure 5,648.249 6,646.379 998.130***
(3,874.706) (4,037.378) (0.000)
Education Expenditure 13,805.410 19,767.793 5,962.383***
(36,786.055) (46,977.391) (0.000)
Medical Expenditure 16,396.797 22,905.500 6,508.704***
(37,576.730) (47,533.227) (0.000)
Total Income 21,692.584 28,455.658 6,763.074***
(26,529.615) (31,456.730) (0.000)
Household Size 4.041 4.060 0.020
(1.684) (2.007) (0.637)
Number of Earning Members 1.401 1.908 0.507***
(0.848) (0.966) (0.000)
Male Household Head 0.761 0.509 -0.252***
(0.427) (0.500) (0.000)
Rural Household 0.519 0.515 -0.004
(0.500) (0.500) (0.735)
Observations 12,347 2,653 15,000
Note: Definition of the dependent variables are given in Table 1. Summary statistics for the entire sample
is given in A1. Standard Deviations and Standard Errors are reported in the parentheses. * p < 0.10, **
p < 0.05, *** p < 0.01
Datt et al., 2020). In our case, it must be noted locality specific migration networks often are the
key reasons that explain variation in migration across households and regions. In 2010, 78% of
the migrant households and 43% of non-migrant households knew at least one relative, friend, or
neighbor who has emigrated outside India to work.7 Hence these variables can potentially be highly
correlated with our variables of interest.
Apart from being highly correlated with the endogenous variable, the IVs need to be exogenous
to the error term, ei and hence unrelated to the outcome variable. Given the fact we are controlling
for a comprehensive set of controls, we argue our IVs would be exogenous to visible consumption.
7 The figures are calculated from Kerala Migration Survey (KMS) 2010 round data.
8Notably, we have controlled for the district level fixed effects as well to control for district-specific
characteristics that affect migration networks as well as visible consumption. Hence variations
across districts that may drive migration and also change visible consumption would be accounted
for by the district level dummies incorporated in the regression. Nevertheless, it is possible that if
our hypothesis of migration driving visible consumption is true, localities with a high migration
network would also have high conspicuous consumption. Since peer effects and social interaction
often increase the demand for social signaling, it may stand as an unobserved factor that may affect
the outcome variable. Hence the exclusion restriction might be debatable and therefore the IVs can
be weaker.
Further, in our model, we have income as a covariate which is highly likely to be endogenous
due to the same reasons we have stated for migration. Income is also correlated with our treatment
variable, migration. Therefore, we have a situation of having an endogenous control variable being
correlated with the treatment variable in the model. In such cases, both keeping and dropping the
endogenous control variable can potentially bias the estimates (Frölich, 2008).
Given this, we use the coefficient stability test by Altonji et al. (2005) and Oster (2019), which
provides the conditions on the unobservables under which the results from models with observed
controls hold. We also use a relatively recent methodology by van Kippersluis and Rietveld (2018)
based on Conley et al. (2012) that preserve the causal estimation but with weaker instruments. With
weaker assumptions, Conley et al. (2012) show that the bounds rather than the point estimations
can be obtained from the endogenous parameter of interest. van Kippersluis and Rietveld (2018)
build on this, by exploiting zero-first-stage sub-populations present in the sample, to estimate point
estimates. In other words, by using van Kippersluis and Rietveld (2018) and Conley et al. (2012),
we can get the econometric framework to understand the implication of migration on visible con-
sumption when the prevalence of migrants in the locality are imperfect instruments, and maybe
only plausibly exogenous. Therefore, we relax the strong exogeneity condition for the IVs and then
estimate the β to test if the effect becomes statistically indistinguishable from zero.
4 Results and Discussion
4.1 Baseline Results
We first discuss the results from the regression to estimate the effect of having migrants on the
indices of conspicuous consumption as discussed. Here we present the results to show the impact
of having a past migrant, a transnational migrant, and the logarithmic value of the remittances
received by the corresponding household separately. Because of the potential threat of non-random
selection of migrant households through unobservables, as IVs, we use the exogenous variation
of the migration network at both the village level as well as the taluk level though for the latter,
we consider the network at the religion level within a taluk. As an outcome variable, we take
the standardized value of the visible consumption index obtained through a Principal Component
Analysis (PCA). Accordingly, we present the results from simple OLS regression along with the IV
regressions in Table 3. Notably, the F-statistic of our IVs remains consistently well above the thumb
rule of 10.8
8 Conventional thumb-rule for considering a t-ratio inferences valid was an F-statistic value more than 10, Lee et al.
(2020) has shown that a true 5 percent test requires an F value more than 104.7. Most of our models are consistent with
this stricter threshold as well.
9Table 3: Regression Results for the Effect of Migration on Visible Consumption Index (PCA)
OLS IV-2SLS OLS IV-2SLS OLS IV-2SLS
International Migrant 0.228*** 0.408**
Household (0.023) (0.160)
Household Ever had 0.222*** 0.240***
International Migrant (0.019) (0.093)
Log Remittances 0.019*** 0.036**
(0.002) (0.014)
F-statistic 266.637 1159.263 218.153
Note: Definition of the dependent variables are given in Table 1. All models control for covariates in
Table A2. IV models are instrumented with migration network at Village level and Taluk-Religion level.
Clustered standard errors at the village level are reported in the parentheses. The F-statistic refers to the
Wald version of the Kleibergen and Paap rk-statistic. The number of observations is 14,325 in all models.
* p < 0.10, ** p < 0.05, *** p < 0.01
The findings reveal a robust and positive effect of having migrants in the households on con-
spicuous consumption, which is found to be statistically significant at the 5% level. In terms of
the effect size, we find that having an international migrant in the household increases the visible
consumption index by close to 0.23 standard deviations on average when observable confounders
are controlled for and by 0.41 standard deviations when both observable and unobservable factors
are accounted for through usage of IVs. We further observe one unit increase in the logarithmic
value of the remittances received increases the PCA value of the possession of visible consumption
assets by close to 0.04 standard deviations on average and this is statistically significant at the 5%
level. Note that we have controlled for income and a set of other economic factors including the
highest education in the household and ration card type among others. Hence, we argue that the
estimates give us the additional effect of having at least one migrant member in the household and
also the remittances received. These results, put together, give a definite indication of a significantly
positive effect of migration on the eagerness to spend more on conspicuous consumption that aids
in giving a clear signal of social status in her locality.
The full control covariates are reported in Table A2. The findings from the control covariates
indicate what one can observe. Household income has a secular positive effect on visible con-
sumption. Christians and Muslims are more likely to possess visible goods than Hindus. Higher
Education is also associated with higher possession of such goods. Rural households are less likely
to possess such goods compared to their urban counterparts. Household size and the number of
earning members seem to have no effect on visible consumption.
4.2 Validity of the Estimates
4.2.1 Altering the dependent variable
10Table 4: Robustness – Alternative Dependent Variables
Dependent Variable:
Standardized Visible Consumption Index
International Migrant 0.315*
Household (0.164)
Household Ever had 0.189**
International Migrant (0.095)
Log Remittances 0.028*
(0.015)
Dependent Variable:
Conspicuous Consumption Household
International Migrant 0.666**
Household (0.266)
Household Ever had 0.376**
International Migrant (0.160)
Log Remittances 0.060**
(0.024)
Dependent Variable: Luxury House
International Migrant 1.068***
Household (0.318)
Household Ever had 0.603***
International Migrant (0.204)
Log Remittances 0.096***
(0.028)
F-statistic 266.637 1159.263 218.153
Note: Definition of the dependent variables are given in Table 1. All
models control for covariates in Table A2. The results reported are of
IV models instrumented with migration network at Village level and
Taluk-Religion level. Clustered standard errors at the village level are
reported in the parentheses. The F-statistic refers to the Wald version
of the Kleibergen and Paap rk-statistic. The number of observations is
14,325 in all models. * p < 0.10, ** p < 0.05, *** p < 0.01
To capture visible consumption at the household level, we take the PCA index in terms of possession
of the relevant durable goods that give a definite signal of social status. As a robustness check, we
alter the dependent variable. We use three different dependent variables capturing conspicuous
consumption as discussed earlier. We ran the same IV regressions as done in the earlier case, and
11IV probit for luxury house possession and conspicuous consumption household. The estimates
yield qualitatively similar results again (see Table 4) indicating a definite positive effect of migration
on conspicuous spending.
4.2.2 Coefficient Stability Test
The credibility of our estimates depends on the assumption of exogeneity of the IV. Though dif-
ferent variants of migrant networks have been widely used across the literature as an instrument
for migration, one can raise questions about their validity. The existence of endogenous control
variable, income in our case, which is correlated with the treatment variable also might bias our
estimates. This endogeneity of income also rises from the plausible unobservables’ effect on in-
come and consumption simultaneously. Accordingly, the first question we ask is whether estimates
would change substantially and turn out statistically insignificant once we assume a higher extent
of unobservables? To examine this, we employ the coefficient stability test first introduced by Al-
tonji et al. (2005) and extended by Oster (2019). The method provides a strategy to generate bounds
of the treatment coefficient accounting for the unobservables. As discussed earlier, it assumes that
the relationship between the treatment and the observables are informative about the relationship
between the treatment and unobservables. The results of this exercise are given in Table 5. The
first two columns report the treatment effect with and without controls. The third column shows
the bounds of the treatment effect. The bounds are the β̂ from the controlled OLS model and the β
calculated accounting for the unobservables, by assuming the value for the relative degree of selec-
tion on observed and unobserved variables, δ = 1, and an arbitrary value of Rmax ∈ [ R̃, 1], where
R̃ is the R-squared in the regression with controls. We follow the value of Rmax suggested by Oster
(2019), Rmax = 1.3 ∗ R̃. If the identified does not include zero, it shows the stability of the treatment
coefficient and the robustness of the results. The migration treatment effect is robust across all our
model.
The last column reports the value of δ for which β = 0, for the given Rmax . δ value two implies
that the unobservables have to be two times as important as the observables to get a treatment effect
of zero. Results with δ value greater than one is in practice considered as robust. In our case, all
variables have values greater than one.
4.2.3 Plausible Exogeneity of the IV
The instrumental variable approach assumes the exogeneity of the instrument variable, which
means the direct effect of the instrument on the dependent variable, r, would be zero. Conley
et al. (2012) relaxes this assumption of zero direct effect and allows to offer a prior value or a range
of values for r. van Kippersluis and Rietveld (2018) recommend the use of a zero-first-stage test
on particular subsamples, for which the indirect effect is zero, to estimate the exact value of r, in-
stead of choosing arbitrary prior values. In our case, the sub-sample of non-migrants qualifies for
estimating the direct effect of the instrumental variable on the dependent variable since the indirect
effect through migration would be zero for them.
The results of this exercise are provided in Table 6. The third and fourth row presents the
plausibly exogenous results. The third row assumes no uncertainty over the r̂. Whereas the fourth
row allows for some uncertainty around r̂ by allowing non-zero elements in the variance-covariance
q
matrix, Ωr . Following van Kippersluis and Rietveld (2018), we set Ωγ = (0.125 s2i + s2r )2 , where
12Table 5: Coefficient Stability Conditions
Dependent Variable Baseline Effect Controlled Effect Identified Set δ for β = 0
(Std. Error) [ R2 ] (Std. Error) [ R2 ]
Visible Consumption 0.441*** 0.228*** [0.130, 0.414]† 2.039
Index (PCA) (0.029)[0.028] (0.023)[0.404]
Visible Consumption 0.395*** 0.192*** [0.099, 0.192]† 1.870
Index (Stand.) (0.029)[0.023] (0.022)[0.406]
Conspicuous
0.191*** 0.111*** [ 0.072, 0.111]† 2.384
Consumption
Household (0.129)[0.023] (0.012)[0.287]
Luxury House 0.218*** 0.145*** [0.111, 0.145]† 3.061
(0.014)[0.028] (0.013)[0.244]
Note: The treatment variable is ‘International Migrant Household’. Baseline Effect does not include any controls and
Controlled Effect has all the control for covariates in Table A2. The identified set is bounded by β̂ of OLS model with
controls and β calculated given Rmax = 1.3 ∗ R̃ and δ = 1. The last column shows the value of δ which would produce β
= 0 given the values of Rmax . † identified set does not include zero. * p < 0.10, ** p < 0.05, *** p < 0.01
Table 6: Plausibly Exogenous IV Analysis
Visible Visible Conspicuous
Consumption Consumption Consumption Luxury House
Index (PCA) Index (Stan.) Household
OLS 0.228*** 0.192*** 0.358*** 0.433***
(0.023) (0.022) (0.039) (0.040)
IV - 2SLS 0.408** 0.315* 0.666** 1.068***
(0.160) (0.164) (0.266) (0.318)
Plausibly Exogeneous 0.756*** 0.698*** 0.309*** 0.529***
(0.160) (0.165) (0.077) (0.111)
Plausibly Exogeneous 0.756*** 0.698*** 0.309*** 0.529***
(with uncertainity) (0.167) (0.172) (0.081) (0.116)
Note: The treatment variable is ‘International Migrant Household’. All models control for covariates in Table A2. Clus-
are reported in the parentheses. The row ‘Plausibly exogenous’ assumes Ωr = 0
tered standard errors at the village levelq
and ‘with uncertainty’ uses Ωr = (0.125 s2i + s2r )2 . * p < 0.10, ** p < 0.05, *** p < 0.01
si is the standard error of r̂ of the non-migrant group and sr is the standard error of γ̂ in the rest
of the sample. The treatment effect is significant in all the models. Also, in all the models, the
treatment effects have become larger when accounted for the direct effect of the migration network
on conspicuous consumption.
134.3 Sub-sample, over-time analysis, and effects on household financials
4.3.1 Across income class
Our findings indicate a significant positive impact of transnational migration on household visible
consumption. However, are these effects homogenous across different income cohorts? As what has
been observed by Bricker et al. (2020), do migrants households that are richer tend to signal more
on their social status through higher conspicuous consumption? Or do the poor migrant house-
holds have a higher tendency to disproportionately spend on these visible assets? We explore these
questions in this section, where we look at the heterogeneous effects of migration across households
of different income classes. For this purpose, we divide the sampled households into five equally
divided quantiles based on household income and run the IV regressions separately for households
belonging to these income quantiles. The marginal effects of the migration variable (whether the
household has an international migrant or not) are observed for each of the five regressions.
Figure 1 presents these effects on the standardized PCA index. Our findings indicate the house-
holds in lower-income classes have significant migration effects, though those from the richest
households have close to zero effect. Further, households in the middle-income category also
showed positive effects. While it is clear that among lower-income classes, the migrants tend to
spend more on visible consumption compared to non-migrants, nothing conclusive can be inferred
on whether the migrants from the richer households engage in status-seeking through such con-
sumption. Because the richer households are in any way likely to possess durable goods that signal
status and luxury household, irrespective of their migration status, the variation in the data is low.9
Very likely, the status-seeking for richer households would take manifestation through consumption
of expensive brands of these assets or construction of more luxurious houses compared to others.
Since the data do not capture this information, we are unable to comment on the migration effects
on the status-seeking behavior of the richer households. This again can classify as a further research
issue.
4.3.2 Across religion and caste
Given the importance of caste and religion in terms of spending on conspicuous consumption
goods in the context of India as documented by Khamis et al. (2012), we ran sub-sample similar
IV regressions to estimate the effects of migration on visible consumption. The objective is to
examine these effects across the households belonging to different caste and religion groups. Figure
1 presents the results from these regressions. As one may observe, we did not find any significant
effects of migration difference among households belonging to the three religious groups: Hindu,
Muslims, and Christians at a 5% level of significance. However, among the Hindus, we get a positive
effect for households belonging to lower caste social group that include Other Backward Classes and
the socially deprived Scheduled Castes. Whereas, there is no significant migration effect among the
upper castes.
4.3.3 Across time effects
Notably, a similar survey across the state of Kerala was conducted in 2016 and 2013. The survey
was conducted by the same organization covering around 15000 households. Similar information on
9 Among households in the richer quantile, only 29% does not have a luxury house. Among these richer households,
30% of households with a migrant and 29% of those with non-migrant do not possess a luxury house.
14Figure 1: Heterogeneity of Migration Effect - Sub-sample Regressions
(a) Income Class (b) Caste
(c) Religion
Note: The results reported are of IV models instrumented with migration network at Village level and Taluk-Religion
level. All models control for covariates in Table A2. The dependent variable in all regressions is ‘Visible Consumption
Index (PCA)’ and the treatment variable is ‘International Migrant Household’. 95% confidence interval is plotted along
with the coefficient.
household socioeconomic characteristics along with data on migration was gathered, which makes
it possible to compare the findings from both these surveys. Because the same households have
not been tracked by these rounds of primary data collection, we are unable to examine the migra-
tion impacts on conspicuous consumption using longitudinal household data. Yet pooling the two
data gives an opportunity to assess the effects in previous rounds and gauge if the implications of
migration on the consumption of status goods have magnified from 2013 to 2018. Table 7 presents
the relevant results. As one can observe, we find a significant and positive effect of transnational
migration on visible good consumption by the households with this pooled data as well. Compar-
ing with 2013, 2016 has registered an increase in visible consumption. This enables us to present
another robustness check ensuring that our inferences are strong and valid. The across-time effects
captured by the interaction term between time and migrant household indicate that the effects are
insignificant stressing the point that there has not been a decrease or even a discernible increase in
the influence of migration over the years.
15Table 7: Pooled Cross-sectional Data Analysis
(1) (2)
Migrant Household 0.441*** 0.732**
(0.138) (0.324)
2018 Round -0.080*** 0.007
(0.024) (0.066)
2016 Round 0.078*** 0.155**
(0.026) (0.061)
Migrant HH * 2018 -0.436
(0.295)
Migrant HH * 2016 -0.417
(0.300)
F-statistic 573.372 65.220
Note: The results reported are of IV models instrumented with migration
network at Village level and Taluk-Religion level. The dependent variable
in all regressions is ‘Visible Consumption Index (PCA)’ and the treatment
variable is ‘International Migrant Household’. All models control for
covariates in Table A2. Clustered standard errors at the village level are
reported in the parentheses. The F-statistic refers to the Wald version
of the Kleibergen and Paap rk-statistic. The number of observations is
41,782 in all models. * p < 0.10, ** p < 0.05, *** p < 0.01
4.3.4 Effects on savings, investment, and debt
Next, we look at the effects of migration on household savings, investment, and debt. The idea is
controlling for household income levels and other covariates that proxy for its economic condition,
if there is an increase in conspicuous expenditure that needs to be compensated with potentially
lower savings and investment, and high debts. Because we observe a significant rise in conspicuous
spending for migrant households, we hypothesize migration would then be associated with lower
saving and/or lower investment and higher debts. Table 8 presents the IV regression results with
the logarithmic value of savings, investment, and debt as dependent variables. While we observe
no significant effects of migration or remittance received on household savings, there seems to be
a substantial drop in their investments.10 Importantly, we find debt to have increased for these
households. In addition, a higher amount of remittance received from migrants is found to increase
the volume of debt.
Hence clearly we observe that there has been a negative effect of household transnational mi-
gration on household financials since we observe a significant increase in the volume of debt. This
brings us to the next question on whether debt finances the disproportionately higher conspicuous
spending among migrant households? However, we are unable to say this as this increase in debt
may have been driven by other factors including meeting the migration cost among others. Notably,
the economic cost of transnational migration for a household is considerable that may include the
cost towards the visa, transport, and the initial settling down expenses among others. It is possible
that these costs are met through debts by the migrating households. Yet, it is also possible that
the disproportionately large conspicuous consumption may also be debt-financed. Examining these
issues is beyond the scope of the paper, nevertheless stands as an important future research exer-
10 This is in contradiction with the findings of Lim and Basnet (2017), which finds a positive relationship between
remittances and investment for South Asian countries including India using aggregate-country level data.
16cise especially in terms of indebted status competition through visible good consumption and its
macroeconomic implications.
Table 8: IV Results for the Effect of Migration on Household Financials
Dependent Variable: Log Savings
International Migrant 1.520
Household (1.505)
Household Ever had 0.603
International Migrant (0.876)
Log Remittances 0.142
(0.135)
Dependent Variable: Log Investment
International Migrant -4.541***
Household (1.662)
Household Ever had -2.657***
International Migrant (0.984)
Log Remittances -0.406***
(0.149)
Dependent Variable: Log Debt
International Migrant 2.777*
Household (1.571)
Household Ever had 1.862**
International Migrant (0.908)
Log Remittances 0.242*
(0.140)
F-statistic 266.637 1159.263 218.153
Note: All models control for covariates in Table A2. The results reported
are of IV models instrumented with migration network at Village level
and Taluk-Religion level. Clustered standard errors at the village level
are reported in the parentheses. The F-statistic refers to the Wald version
of the Kleibergen and Paap rk-statistic. The number of observations is
14,325 in all models. * p < 0.10, ** p < 0.05, *** p < 0.01
4.4 Plausible Mechanisms
Our findings to this extent reveal a significant and positive effect of transnational migration on
household consumption of visible assets, even after accounting for the potential increase in income
17levels and the other associated observable and unobservable characteristics. A further question,
we attempt to address is what drives this disproportionate spending among migrant households.
What are the potential channels or reasons because of which we observe an increase in conspicuous
spending among migrants? We put forward three possible channels, preferential change due to
migration, consumption network or reference group change effect, and an information gap channel.
While the preferential change channel reflects on the direct utility from the consumption of the
visible goods and has nothing to do with signaling, the information gap channel deals with the
social utility through signaling. Reference group effect could mirror both a preferential change
through social learning and social utility (Bursztyn et al., 2014). We consider each of these channels
separately.
Table 9: Alternative Channels - Preferential Changes
Dependent Variable: Log Food Expenditure
International Migrant 0.076
Household (0.140)
Household Ever had 0.048
International Migrant (0.080)
Log Remittances 0.007
(0.013)
Dependent Variable: Log Education Expenditure
International Migrant -0.492
Household (0.789)
Household Ever had -0.245
International Migrant (0.458)
Log Remittances -0.045
(0.071)
F-statistic 266.7361 1157.058 218.0451
Note: All models control for covariates in Table A2. The results reported
are of IV models instrumented with migration network at Village level
and Taluk-Religion level. Clustered standard errors at the village level
are reported in the parentheses. The F-statistic refers to the Wald version
of the Kleibergen and Paap rk-statistic. The number of observations is
14,325 in all models. * p < 0.10, ** p < 0.05, *** p < 0.01
Firstly, migration of household members may be associated with an overhaul change in pref-
erences that may also incorporate consumption of visible durable goods among others. Related
literature has shown how changes in attitudes and utility function can be linked with migration
which in turn can lead to favorable outcomes in terms of health and education. For example, Hilde-
brandt et al. (2005) point out how acquiring health knowledge influenced health outcomes back at
home among the migrants from Mexico to the US. Because such knowledge acquisition can happen
in our case as well since the migrants are exposed to a different lifestyle and consumption pattern
18in comparison to that of their hometown, it can lead to incremental spending on visible goods.
To partly test this, we look at the transnational migration effects on food and educational, which
are generally not considered as conspicuous in nature. We expect an effect of migration on these
expenses as well if there is a comprehensive preferential change on the part of migrants. We use
similar IV regressions to gauge the impact. Table presents the marginal effects of the regressions,
where we find no significant effect on food and education expenditure. This shows no net changes
in preference in terms of food and education despite migration among the household members,
which potentially indicates preferential changes may not be possibly the reason for higher visisble
consumption among migrants.Nevertheless, this necessitates further research and hence we are un-
able to rule out the possibility of a preferential change in the consumption of visible goods with
certainty.
Our second proposition is a reference group effect that might drive the observed higher visible
spending among migrants. This is directly derived from the literature on the reference group income
distribution effects on visible consumption (Chai et al., 2019; Charles et al., 2009; Roychowdhury,
2016). Importantly, localities which have higher prevalence of migrants might be different from
those with lower prevalence. Accordingly, the income levels of peer-group or reference group of
a household, which is likely to be its neighborhood/locality can systematically be different for a
migrant in comparison to a non-migrant. This difference in the income levels may actually then
drive higher visible concumption among migrant households. Notably, Roychowdhury (2016) doc-
uments higher spending on conspicuous goods with a fall in visible inequality because of status
competition.
We test this possibility by utilizing different indicators that captures the distributional parame-
ters of the reference group income. In particular, we calculate the locality wise mean, median, and
90th percentile income levels along with another indicator of inequality, which is measured as the
90th percentile income level divided by the 25th percentile for migrant households as well as the
non-migrants. Table 10 presents the results from the regression where we regress our conspicuous
consumption indices with the income distribution parameter values of the reference group at the
locality level. The first row reports the estimate from our baseline model. The results show that
even after controlling for the reference group income level and inequality, the difference between
migrants and non-migrants is still remains statistically significant with no considerable changes in
the effect size. This suggests that unlike Charles et al. (2009), where the reference group income
level and variation explain the entire difference between races, here it does not explain the migration
effect.
Thirdly, we introduce an explanation for the differences in visible spending between migrants
and non-migrants which could not be entirely explained by behavioral change or a reference group
income effect. We propose that the differences in incentives to signal status due to the information
gap11 difference between migrants and non-migrants could be a probable channel for the migration
effect. The importance of social signaling wherein an individual derives utility from the status
that depends on the beliefs and information about others income. Notably, information within the
community on the income levels of the migrant is often disproportionately more incomplete. This
becomes clear from a set of literature that documents the existence of high information gap between
the migrant and family or society back home (Ambler, 2015; Chen, 2013; de Laat, 2014; McKenzie
11 For
the purposes of this paper, we use the term ”information gap” to refer to the extent of imperfection in society’s
information about the actual wealth statuses of individuals/households.
19Table 10: Alternative Explanantions - Reference Group Change
Reference Group Income
Migrant Household 0.408** 0.443** 0.443** 0.404** 0.471*** 0.513**
(0.160) (0.188) (0.225) (0.164) (0.169) (0.210)
Reference Group Income
Mean -0.031 -0.006
(0.038) (0.049)
Median -0.068 -0.188
(0.158) (0.213)
90th Percentile 0.042 0.109
(0.071) (0.079)
Inequality 0.798** 0.348
(0.310) (0.362)
F-statistic 266.6371 113.4875 52.37526 145.8725 169.1362 46.26582
Note: Reference group is migrant/non-migrant households in the locality (Taluk). Inequality is calculated as
90th Percentile/25th Percentile. All models control for covariates in Table A2. The results reported are of IV models
instrumented with migration network at Village level and Taluk-Religion level. Clustered standard errors at the village
level are reported in the parentheses. The F-statistic refers to the Wald version of the Kleibergen and Paap rk-statistic.
The number of observations is 14,325 in all models. * p < 0.10, ** p < 0.05, *** p < 0.01
et al., 2013). Seshan and Zubrickas (2017) has shown the existence of the same phenomenon for
the case of Kerala migration within the family. If the income levels of migrants are unknown to
the family, it is very likely that others within the community do not have full information on their
actual income as well. This can be leveraged by the migrant through high investment in status
goods to showcase the income level and derive utility through higher social status. The problem of
this information gap within the community for non-migrants can also be prevalent but the extent
would possibly be higher for migrants, whose work profile and living standards while migration
often remains unobservable. Accordingly, the migrants can then tend to move up the social status
ladder or keep up with the Joneses by spending potentially more on visible goods which they find
conducive to do in comparison to the the non-migrants because of information gap about their
income and job profile within the society. We use a short theoretical framework to explain the
mechanism of increased incentives through the information gap in the following section.
5 Information Asymmetry and Conspicuous Consumption
We formalize our proposed mechanism using a standard conspicuous consumption model in the
spirit of the canonical signaling model of Spence (1973). Consumption of visible good, c, could affect
an individual’s utility in a couple of ways.One, the utility derived through direct consumption of
the good. Second, through the people’s expectation about their status through signaling. People’s
expectations could have two different effects on utility. If there is information asymmetry about
income, people form expectations about the income from the consumption of visible goods. Thus
the individual derives indirect utility through signaling. Whereas, if there is perfect information on
the income, people will have an expectation on the level of consumption of visible goods given the
societal standards. Deviating from these standards will have dis-utility. Since anything more or less
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