The Impact of the Minimum Wage on Male and - Ricerca sull'intelligenza artificiale specializzata al MIT

The Impact of the Minimum Wage on Male and
Female Employment and Earnings in India
∗
NIDHIYA MENON AND YANA VAN DER MEULEN RODGERS

This study examines how employment and wages for men and women respond to
changes in the minimum wage in India, a country known for its extensive system
of minimum wage regulations across states and industries. Using repeated cross
sections of India’s National Sample Survey Organization employment survey
data for the period 1983–2008 merged with a newly created database of minimum
wage rates, we ﬁnd that, regardless of gender, minimum wages in urban areas
have little to no impact on labor market outcomes. Tuttavia, minimum wage rates
increase earnings in the rural sector, especially for men, without any employment
losses. Minimum wage rates also increase the residual gender wage gap, Quale
may be explained by weaker compliance among ﬁrms that hire female workers.

Keywords: employment, genere, India, minimum wage, wages
JEL codes: J52, J31, K31, O12, O14

IO. introduzione

The minimum wage is primarily used as a vehicle for lifting the incomes of
poor workers, but it can also entail distortionary costs. In a perfectly competitive
labor market, an increase in a binding minimum wage causes an unambiguous
decline in the demand for labor. Jobs become relatively scarce, some workers who
would ordinarily work at a lower market wage are displaced, and other workers see
an increase in their wages. Distortionary costs from minimum wages are potentially
more severe in developing economies given their large informal sectors. A minimum
wage primarily protects workers in the urban formal sector whose earnings already
exceed the earnings of workers in the rural and informal sectors by a wide margin.
Employment losses in the regulated formal sector translate into more workers
seeking jobs in the unregulated informal sector. This shift may result in lower,
not higher, wages for poor workers who are engaged predominantly in the informal

∗Nidhiya Menon: Department of Economics and International Business School, Brandeis University. E-mail:
nmenon@brandeis.edu. Yana van der Meulen Rodgers (corresponding author): Women’s and Gender Studies
Department, Rutgers University. E-mail: yrodgers@rci.rutgers.edu. The authors would like to thank Mihir Pandey
for helping them obtain the minimum wage reports from the Government of India’s Labour Bureau. Naﬁsa Tanjeem,
Rosemary Ndubuizu, and Sulagna Bhattacharya also provided excellent research assistance. The authors gratefully
acknowledge participants at the Beijing Normal University workshop on minimum wages; and seminar participants
from the Economics Departments of Brandeis University, Colorado State University, Cornell University, Rutgers
Università, and the University of Utah. They would also like to thank the managing editor and anonymous referees
for helpful comments. The usual disclaimer applies.

Asian Development Review, vol. 34, NO. 1, pag. 28–64

C(cid:3) 2017 Asian Development Bank
and Asian Development Bank Institute

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IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 29

sector. Even a small increase in the minimum wage can have sizable disemployment
effects in developing economies if the legal wage ﬂoor is high relative to prevailing
wage rates and a large proportion of workers earn the legislated minimum.

To the extent that female workers are relatively concentrated in the informal
sector and men in the formal sector, fewer women stand to gain from binding
minimum wages in the formal sector. Further, if minimum wages discourage formal
sector employment, a disproportionate number of women can experience decreased
access to formal sector jobs. For women who remain employed in the formal sector,
the minimum wage can help to raise their relative average earnings. Perché il
female earnings distribution falls to the left of the male earnings distribution in most
economies, a policy that raises the legal minimum wage irrespective of gender, if
properly enforced, should help to close the male–female earnings gap (Blau and
Kahn 1995). Although the gender wage gap in the formal sector shrinks, the wage
gain for women can come at the expense of job losses for low-wage female workers.
Hence, disemployment effects may be larger for women than men in the formal
sector.

Critics of the minimum wage state that employment losses from minimum-
wage-induced increases in production costs are substantial.1Advocates, Tuttavia,
argue that employment losses are small and any reallocation of resources that occurs
will result in a welfare-improving outcome through the reduction of poverty and an
improvement in productivity. Our study contributes to this debate by analyzing the
relationship between the minimum wage and employment and earnings outcomes
for men and women in India.

India constitutes an interesting case given its history of restrictive labor
market policies that have been blamed for lower output, productivity, investment,
and employment (Besley and Burgess 2004). As a federal constitutional republic,
India’s labor market exhibits substantial variation across its 28 geographical states
in terms of the regulatory environment. Labor regulations have historically fallen
under the purview of states, a framework that has allowed state governments to enact
their own legislation, which includes minimum wage rates that vary by age (child
workers, adolescents, and adults); skill level; and detailed job categories.2 Each
state sets minimum wage rates for particular occupational categories regardless of
whether the jobs are in the formal or informal sector, with the end result that there are
more than 1,000 different minimum wage rates across India in any given year. Questo
wide degree of variation and complexity may have hindered compliance relative to
a simpler system with a single wage set at the national or state level (Rani et al.
2013, Belser and Rani 2011).

1This debate is carefully reviewed in Card and Krueger (1995); Belman and Wolfson (2014); and Neumark,

Salas, and Wascher (2014).

2Importantly, there is no distinction in pay by gender. Tuttavia, given the complexity of enforcement arising

from the myriad wage levels, female workers and those in rural areas tend to be paid less than the legal wage.

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30 ASIAN DEVELOPMENT REVIEW

To examine how the minimum wage affects men and women’s employment
and wages in India, this study uses six waves of household survey data from the
National Sample Survey Ofﬁce (NSSO) spanning the 1983–2008 period, merged
with an extensive and unique database on minimum wage rates over time and across
states and industries. Also merged into the NSSO data are separate databases of
macroeconomic and regulatory variables at the state level that capture underlying
market trends. A priori, we expect that India’s minimum wage increases would bring
relatively fewer positive effects for women than men, particularly if women have
less bargaining power and face greater obstacles to being hired in the labor market.
Our empirical results conﬁrm these expectations in the case of women’s relative
wages, but we ﬁnd little evidence of disemployment effects either for them or for
men.

II. Literature Review

UN.

Employment and Wage Effects

The past quarter of a century has seen a surge in scholarly interest in the impact
of minimum wage legislation on labor market outcomes across economies, con
much of that research focusing on changes in employment. Results have varied across
studies, with some reporting statistically signiﬁcant and large negative employment
effects at one end of the spectrum and others ﬁnding small positive effects on
the other. In an effort to synthesize this large body of work, Belman and Wolfson
(2014) conducted a meta-analysis for a large number of studies of industrialized
economies and concluded that minimum wage increases may lead to a very small
disemployment effect: raising the minimum wage by 10% causes employment to
fall by between 0.03% E 0.6%.

For developing and transition economies, the estimated employment effects
also tend to be negative, but with more variation compared to industrialized
economies.3 Disemployment effects have been found for Bangladesh (Anderson,
Hossain, and Sahota 1991); Brasile (Neumark, Cunningham, and Siga 2006);
Colombia (Campana 1997, Maloney and Mendez 2004); Costa Rica (Gindling and Terrell
2007); Hungary (Kertesi and K¨ollo 2003); Indonesia (Rama 2001, Suryahadi et al.
2003); Nicaragua (Alaniz, Gindling, and Terrell 2011); Peru (Baanante 2004); E
Trinidad and Tobago (Strobl and Walsh 2003). But not all estimates are negative.
There has been no discernable impact on employment in Mexico (Campana 1997) E
Brasile (Lemos 2009). In the People’s Republic of China (PRC), the minimum wage

3For details, see two recently published meta-analyses for developing economies, Betcherman 2015 E
Nataraj et al. 2014. This section expands on the ﬁndings in these studies by focusing more on the gender-disaggregated
impacts of the minimum wage.

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IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 31

appears to have had a negative impact only in the eastern region of the country, while
it has had either no impact or a slightly positive impact elsewhere (Ni, Wang, E
Yao 2011; Fang and Lin 2013). Negligible or even small positive employment effects
have been found in other cases when national-level estimates are disaggregated, come
as in the case of workers in Indonesia’s large ﬁrms (Rama 2001; Alatas and Cameron
2008; Del Carpio, Nguyen, and Wang 2012).

Minimum wage impacts in developing economies vary considerably not
only because of labor market conditions and dynamics, but also because of
noncompliance, inappropriate benchmarks, and the presence of large informal
sectors.4 In fact, most of the negative minimum wage impacts across economies
are for formal sector employment where there is greater compliance among ﬁrms.
Noncompliance with minimum wage regulations is directly related to difﬁculties
in enforcement and can take the form of outright evasion, legal exemptions for
such categories as part-time and temporary workers, and cost shifting through
the avoidance of overtime premiums. Because minimum wages are relatively
more costly for small ﬁrms in the informal sector, noncompliance is pervasive
there.

Compliance costs are higher for smaller ﬁrms in the informal sector because
they tend to hire more unskilled workers, young workers, and female workers than
larger ﬁrms in the formal sector. Given that average wages for these demographic
groups are low, compliance is costly as the minimum wage is more binding. For
esempio, Rani et al. (2013) found an inverse relationship between compliance
and the ratio of the legislated minimum wage to median wages in a sample
Di 11 developing economies. Among individual economies, Gindling and Terrell
(2009) found that minimum wages in Honduras are enforced only in medium- E
large-scale ﬁrms where increases in the minimum wage lead to modest increases
in average wages but sizable declines in employment. There is no impact among
small-scale ﬁrms or among individuals who are self-employed. Similar evidence for
the positive relationship between ﬁrm size and compliance was found in Strobl and
Walsh (2003) in their study on Trinidad and Tobago.

Not surprisingly, most of these studies have found positive impacts of
the minimum wage on formal sector wages, with the strongest impact close to
the legislated minimum and declining effects further up the distribution. In a type
of “lighthouse effect,” wages in the informal sector may also rise if workers and
employers see the legislated minimum as a benchmark for their own wage-bargaining
and wage-setting practices, rispettivamente (per esempio., Maloney and Mendez 2004, Baanante
2004, and Lemos 2009). A number of studies have found that minimum wage
increases reduce wage compression since low-wage workers experience the strongest
wage boosts from the new legislated minimum (Betcherman 2015).

4For details, see Squire and Suthiwart-Narueput (1997), Nataraj et al. (2014), and Betcherman (2015).

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32 ASIAN DEVELOPMENT REVIEW

Gender Differences in Minimum Wage Impacts

While there is a large amount of empirical literature estimating minimum
wage impacts on employment and wages, relatively few studies have included
a gender dimension in their analysis. Among the exceptions for industrialized
economies is Addison and Ozturk (2012), who used a panel data set of 16
Organisation for Economic Co-operation and Development economies and found
substantial disemployment effects for women: UN 10% increase in the minimum wage
causes the employment-to-population ratio to fall by up to 7.3%. Among studies
for individual economies, Shannon (1996) found that adverse employment effects
from Canada’s minimum wage are more severe for women than men, although the
gender earnings gap shrank for women who kept their jobs. A similar result is found
for Japan in Kambayashi, Kawaguchi, and Yamada (2013), who identiﬁed sizable
disemployment effects for women and a compression in overall wage inequality.
Yet not all employment effects for women are negative. In the United Kingdom,
for instance, minimum wages are associated with a 4% increase in employment for
women while the estimated employment increase for men is less robust (Dickens,
Riley, and Wilkinson 2014). Further, not all gender-focused studies on industrialized
economies have found reductions in the gender earnings gap. For instance, Cerejeira
et al. (2012) found that an amendment to the minimum wage law in Portugal that
applied to young workers increased the gender earnings gap because of the associated
restructuring of fringe beneﬁts and overtime payments that favored men.

Among developing economies, evidence for Colombia indicates that
minimum wage increases during the 1980s and 1990s caused larger disemployment
effects for female heads of households relative to their male counterparts (Arango
and Pach´on 2004). Larger adverse employment effects for women than men were also
found in the PRC for less educated workers (Jia 2014) and in particular regions (Fang
and Lin 2013, Wang and Gunderson 2012). The sharp increase in the real minimum
wage in Indonesia since 2001 has contributed to relatively larger disemployment
effects for women in the formal sector (Suryahadi et al. 2003, Comola and de Mello
2011) and among nonproduction workers (Del Carpio, Nguyen, and Wang 2012).
In Mexico, among low-skilled workers, women’s employment was found to be quite
sensitive to minimum wage changes (with elasticities ranging from –0.6 to –1.3),
while men’s employment was more insensitive (Feliciano 1998).

Not all studies with a gender dimension have found disemployment effects for
women. For instance, Montenegro and Pag´es (2003) studied changes in the national
minimum wage over time in Chile and found that the demand for male workers fell
and the supply of female workers rose, resulting in small net employment gains for
women. The explanation for their ﬁnding is the existence of imperfect competition
in the female labor market that caused women’s wages to fall below their marginal
Prodotto. Further, Muravyev and Oshchepkov (2013) argued that the imposition
of minimum wages in the Russian Federation during 2001–2010 resulted in no

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IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 33

statistically signiﬁcant effects on unemployment rates for prime-age workers as a
whole or for prime-age working women.

Evidence of the impact of a minimum wage on women’s wages and the gender
wage gap is mixed essentially because it depends on the extent to which employers
comply with the legislation. Greater noncompliance for female workers has been
documented for a number of economies across developing regions. Minimum wage
legislation in Kenya was found to increase wages for women in nonagricultural
activities but not in agriculture, mostly because compliance rates were lower in
agricultural occupations (Andalon and Pag´es 2009). Also ﬁnding mixed results
for women’s earnings were Hallward-Driemeier, Rijkers, and Waxman (2015),
who showed that increases in Indonesia’s minimum wage contributed to a smaller
gender wage gap among more educated production workers but a larger gap among
production workers with the least amount of education. The authors suggest that
more educated women have relatively more bargaining power, which induces ﬁrms
to comply with minimum wage legislation. As another example, the Costa Rican
government implemented a comprehensive minimum wage compliance program
In 2010 based on greater public awareness of the minimum wage, new methods
for employees to report compliance violations, and increased inspections. As a
result, the average wage of workers who earned less than the minimum wage before
the program rose by about 10%, with the largest wage gains for women, workers
with less schooling, and younger workers. Inoltre, there was little evidence of a
disemployment effect for full-time male and female workers (Gindling, Mossaad,
and Trejos 2015).

Looking more broadly at the gendered effects of the minimum wage on
measures of well-being, Sabia (2008) found that minimum wage increases in the
United States did not help to reduce poverty among single working mothers because
the minimum wage was not binding for some and led to disemployment and fewer
working hours for others. Among developing economies, Menon and Rodgers (2013)
found that restrictive labor market policies in India that favor workers (including the
minimum wage) contribute to improved job quality for women for most measures.
Tuttavia, such regulations bring fewer beneﬁts for men. Estimates indicate that for
men, higher wages come at the expense of fewer hours, substitution toward in-kind
compensation, and less job security.

Looking beyond labor market effects, Del Carpio, Messina, and Sanz
de Galdeano (2014) analyzed the impact of province-level minimum wages on
employment and household consumption in Thailand and found that exogenously
set regional wage ﬂoors are associated with small negative employment effects
for women, the elderly, and less educated workers, while they are associated with
large positive wage gains for working-age men. These wage gains contributed to
increases in average household consumption, although such improvements tended
to be concentrated around the median of the distribution. Closely related to these
ﬁndings, Lemos (2006) found that minimum wages in Brazil have had deleterious

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34 ASIAN DEVELOPMENT REVIEW

effects on the poor by raising the prices of the labor-intensive goods that they
purchase. These adverse price impacts are strongest in poorer regions of the country.

III. Methodology and Data

Our analysis uses an empirical speciﬁcation adapted from Neumark, Salas,
and Wascher (2014) and Allegretto, Dube, and Reich (2011) that relates employment
outcomes to productivity characteristics and minimum wage regulations across space
and time. A sample of individual-level, repeated, cross-sectional data from India’s
NSSO for the period 1983–2008 is used to identify the effects of the minimum wage
on employment and earnings outcomes, conditional on state and year variations.

The determinants of employment for an individual are expressed as follows:

Ei jst = a + β1MW jst + β2 Xi jst + β3 Pst + β4∅s + β5Tt + β6 (∅s ∗ Tt ) + ϑi jst

(1)

where i denotes an employee, j denotes an industry, s denotes a state, and t denotes
time. The dependent variable Eijst represents whether or not an individual of working
age is employed in a job that pays cash wages. The notation MWjst represents
minimum wage rates across industries, stati, and time. The notation Xijst is a set
of individual and household characteristics that inﬂuences people’s employment
decisions. These characteristics include gender, education level attained, years of
potential experience and its square, marital status, membership in a disadvantaged
group, religion, household headship, rural versus urban residence, and the number
of preschool children in the household. Most of these variables are fairly standard
control variables in wage regressions across economies. Speciﬁc to India, wages
tend to be lower for individuals belonging to castes that are perceived as being
deprived or disadvantaged; these castes are commonly referred to as the “scheduled”
castes or tribes. Wages are also typically lower for individuals whose religion is not
Hinduism. The matrix Pst represents a set of control variables for a variety of
economic indicators at the state level: net real domestic product, the unemployment
rate, indicators of minimum wage enforcement, and variables for the labor market
regulatory environment.

The Øs notation is a state-speciﬁc effect that is common to all individuals in
each state, and Tt is a year dummy that is common to all individuals in each year.
The state dummies, the year dummies, and the state-level economic indicators help
to control for observed and unobserved local labor market conditions that affect men
and women’s employment and earnings. In particular, the state and year dummies are
important to control for state-level shocks that may be correlated with the timing of
minimum wage legislation (Card 1992, Card and Krueger 1995). Equazione (1) also
allows state effects to vary by time to address the fact that, individually, these controls
may be insufﬁcient to capture all of the heterogeneity in the underlying economic
conditions (Allegretto, Dube, and Reich 2011). Finalmente, ϑi jst is an individual-speciﬁc

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IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 35

idiosyncratic error term.5 Equation (1) is estimated separately by gender and by rural
and urban status.

Our analysis also considers the impact of the minimum wage on the residual
wage gap between men and women. All regressions are weighted using sample
weights provided in the NSSO data for the relevant years and standard errors are
clustered at the state level. All regressions are separately estimated with real and
nominal minimum wage rates. Since the results are similar, the tables only report
estimations for the real minimum wage. The movement of workers into and out of
states with prolabor or proemployer legislative activity is unlikely to contaminate
results since migration rates are low in India (Munshi and Rosenzweig 2009, Klasen
and Pieters 2015).

We use six cross sections of household survey data collected by the NSSO.
As shown in Table A.1, the data include the years 1983 (38th round), 1987–1988
(43rd round), 1993–1994 (50th round), 1999–2000 (55th round), 2004–2005 (60th
round), and 2007–2008 (64th round). We utilize the Employment and Unemployment
Module—Household Schedule 10 for each round. These surveys have detailed
information on employment status, wages, and a host of individual and household
characteristics.

To construct the full sample for the employment regressions, we appended
each cross section across years and retained all individuals of prime working age
(15–65 years old) in agriculture, services, and manufacturing with measured values
for all indicators. The pooled full sample has 3,332,094 observations. To construct
the sample for the wage regressions, we restricted the full sample to all individuals
with positive daily cash wages. The pooled wage sample has 597,621 observations.
One of the steps in preparing the data entailed reconciling changes over time in
NSSO state codes that arose, in parte, from the creation of new states in India
(per esempio., the creation of Jharkhand from southern Bihar in 2000). Newly created states
were combined with the original states from which they were created in order to
maintain a consistent set of state codes across years. Inoltre, Union Territories
were combined with the states to which they are located closest in geographic
terms.

Sample statistics for the pooled full sample in Table 1 indicate that a fairly low
percentage of individuals were employed for cash wages during the period, with men
experiencing a sizable advantage relative to women in both 1983 E 2008. The table
further shows considerable gender differences in educational attainment. In 1983,
42% of men were illiterate compared with 74% of women, while 15% of men and 6%
of women had at least a secondary school education. These percentages changed

5We follow equation (1) to be consistent with Neumark, Salas, and Wascher (2014) and Allegretto, Dube,
and Reich (2011). This equation is an incomplete version of a difference-in-difference model since it includes one of
the three two-way interaction terms (between minimum wages, stati, and years) and does not include the three-way
interaction term (between minimum wages, stati, and years). We estimated the difference-in-difference counterpart
for male employment and the results are qualitatively the same.

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36 ASIAN DEVELOPMENT REVIEW

Tavolo 1. Full Sample Means by Gender

1983

2008

Employed for cash wages

Educational attainment

Illiterate

Less than primary school

Primary school

Middle school

Secondary school

Graduate school

Potential experience in years

Potential experience squared/100

Age in years

Currently married

Scheduled tribe or caste

Hindu

Household headed by a man

Rural

No. of preschool children in household

No. of observations

Men
0.189
(0.392)

0.417
(0.493)
0.134
(0.341)
0.158
(0.365)
0.139
(0.346)
0.113
(0.316)
0.040
(0.196)
23.875
(14.780)
7.885
(8.386)
34.040
(13.270)
0.722
(0.448)
0.256
(0.436)
0.843
(0.364)
0.967
(0.179)
0.733
(0.442)
0.762
(0.958)
391,157

Women
0.087
(0.282)

0.737
(0.440)
0.067
(0.250)
0.084
(0.278)
0.055
(0.228)
0.043
(0.202)
0.014
(0.119)
26.002
(14.533)
8.873
(8.652)
33.736
(13.355)
0.753
(0.431)
0.283
(0.450)
0.856
(0.351)
0.883
(0.321)
0.789
(0.408)
0.775
(0.957)
244,302

Men
0.328
(0.470)

0.237
(0.426)
0.102
(0.302)
0.158
(0.365)
0.207
(0.405)
0.135
(0.342)
0.160
(0.367)
22.154
(15.684)
7.368
(8.336)
34.814
(13.692)
0.684
(0.465)
0.291
(0.454)
0.831
(0.375)
0.946
(0.226)
0.735
(0.442)
0.484
(0.808)
221,443

Women
0.119
(0.324)

0.462
(0.499)
0.089
(0.285)
0.125
(0.331)
0.141
(0.348)
0.088
(0.284)
0.095
(0.294)
24.623
(15.921)
8.598
(8.910)
35.023
(13.474)
0.746
(0.435)
0.287
(0.452)
0.834
(0.372)
0.876
(0.330)
0.747
(0.435)
0.516
(0.830)
212,877

Note: Standard deviations are in parentheses and sample means are weighted. All means are expressed
in percentage terms unless otherwise noted.
Fonte: Authors’ calculations.

markedly over time, especially for women. By 2008, the percentage of illiterate
women had dropped to 46%, and the percentage of women with at least secondary
schooling had risen to 18%. The data also show a sizable gender differential in
geographical residence—73% of men lived in rural areas in 1983 compared with
79% of women. This difference shrank during the period but did not disappear.
The bulk of the sample was married, lived in households headed by men, E
claimed Hinduism as their religion. On average, between 25% E 30% of individuals
belonged to the scheduled castes or tribes.

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IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 37

We merged the NSSO data with a separate database on daily minimum
wage rates across states, industries, and years to create a database on state- E
industry-level daily minimum wage rates using the annual Report on the Working
of the Minimum Wages Act, 1948 published by the Government of India’s Labour
Bureau. Only very recent issues of this report are available electronically; earlier
years had to be obtained from local sources as hard copies and converted into
an electronic database. For each year, we obtained the minimum wage report for
the year preceding the NSSO data wave, whenever possible, in order to allow for
adjustment lags. We were able to obtain reports for the following years: 1983 (1983
NSSO wave), 1986 (1987–1988 NSSO wave), 1993 (1993–1994 NSSO wave), 1998
(1999–2000 NSSO wave), 2004 (2004–2005 NSSO wave), E 2006 (2007–2008
NSSO wave).

We then merged the minimum wage data into the pooled NSSO data using
state codes and industry codes aggregated into ﬁve broad categories (agriculture
and forestry, mining, construction, services, and manufacturing). At least two-thirds
of women were employed in agriculture during the period of analysis; for men, Questo
share was closer to one-half. Men were more concentrated in construction, services,
and manufacturing, while over time, women increased their relative representation
in services. For any individuals in the full sample who did not report an industry
to which they belonged, this merging process entailed using the median legislated
minimum wage rate for each individual’s state and sector (urban or rural) in un
particular year. Assigning all individuals a relevant minimum wage regardless
of their employment status allowed us to estimate minimum wage impacts on
the likelihood of cash-based employment relative to all other types of activities,
including those performed by individuals of working age who were not employed
(and therefore did not report an industry).

For each of the broad categories deﬁned above, we utilized the median
minimum wage rate across the detailed job categories as most states had minimum
wage rates speciﬁed for multiple occupations within the broad groups. Further, given
that smaller states are combined with larger ones in order to maintain consistency
in the NSSO data, utilizing the median rate across states, years, and job categories
avoids problems with especially large or small values. Inoltre, if values were
missing for the minimum wage for a broad industry category in a particular state,
we used the value of the minimum wage for that industry from the previous time
period for which data was available for that state. Underlying this step was the
assumption that the minimum wage data are recorded in a particular year only if
states actually legislated a change in that year. Allo stesso modo, the minimum wages for the
aggregate industry categories in a state that was missing all values were assumed to
be the same as the minimum wages in this state in the preceding time period.

IL 1983 and 1985–1986 minimum wage reports differed from subsequent
years in several ways. Primo, these two earlier reports published rates for detailed
job categories based on an entirely different set of labels. Hence, the aggregation

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38 ASIAN DEVELOPMENT REVIEW

procedure into the ﬁve broad categories involved reconciling the two different sets
of labels. Secondo, the earlier reports published monthly rates for some detailed
categorie; these rates were converted to daily rates using the assumption of 22
working days per month. Third, the two earlier reports published numerical values
for piece rate compensation, while the latter four reports simply speciﬁed the words
“piece rate” as the compensation instead of providing a numerical value. For the two
earlier reports, the piece rate compensation was converted into daily wage values
using additional information in the reports on total output per day and minimum
compensation rates. For the latter four reports, because very few detailed industries
paid on a piece rate basis and those that did speciﬁed no numerical values, we
assigned a missing value to the minimum wage rate. The two earlier reports also
speciﬁed minimum wage rates for children; these observations were removed from
the database of minimum wage rates because our NSSO sample consists only of
individuals 15–65 years of age.

Also merged into the NSSO data were separate databases of macroeconomic
and regulatory variables at the state level that capture underlying labor market
trends. The variables cover 15 states for each of the 6 years of the NSSO data
and include net real domestic product, unemployment rates, indicators of minimum
wage enforcement, and indicators of the regulatory environment in the labor market.
The domestic product data were taken from Reserve Bank of India (2014) E
the state-level unemployment data merged into the sample were obtained from
NSSO reports on employment and unemployment during each survey year (Indiastat
various years, NSSO various years). Also merged into the full sample are four
indicators of minimum wage enforcement by state and year. These indicators include
the number of inspections undertaken, number of irregularities detected, number of
cases in which ﬁnes were imposed, and total value of ﬁnes imposed in (real) rupees.
The data on minimum wage enforcement are available from the same annual reports
(Report on the Working of the Minimum Wages Act, 1948) that were used to construct
the minimum wage rate database.

Finalmente, we control for two labor market regulation variables. The ﬁrst variable
(adjustments) relates to legal reforms that affect the ability of ﬁrms to hire and
ﬁre workers in response to changing business conditions. Positive values for this
variable indicate regulatory changes that strengthen workers’ job security through
reductions in ﬁrms’ ability to retrench, increases in the cost of layoffs, and restrictions
on ﬁrm closures. Negative values indicate regulatory changes that weaken workers’
job security and strengthen the capacity of ﬁrms to adjust employment. The second
variable (disputes) relates to legal changes affecting industrial disputes. Positive
values indicate reforms that make it easier for workers to initiate and sustain
industrial disputes or that lengthen the resolution of industrial disputes. Negative
values indicate state amendments that limit the capacity of workers to initiate and
sustain an industrial dispute or that facilitate the resolution of industrial disputes.
The underlying data are from Ahsan and Pag´es (2009) and further discussion of

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IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 39

Tavolo 2. Average Daily Minimum Wage Rates by Industry and State

Panel A: Nominal

Agriculture

Mining

Construction

Services

Manufacturing

1983
14.1
11.5
9.3
15.2
19.8
10.0
7.5
10.7
11.8
9.5
10.3
22.0
10.0
9.0
23.0

2008
74.0
72.4
77.0
94.1
95.6
73.1
101.0
79.0
94.0
55.0
98.5
73.0
70.8
85.9
134.5

1983
12.3
13.8
14.1
14.9
21.0
11.2
6.6
10.7
9.9
15.3
12.6
22.0
16.6
9.5
28.0

2008
92.5
55.0
77.0
93.0
95.6
79.3
276.2
95.0
87.0
55.0
98.5
80.4
94.9
112.7
134.5

1983
14.6
12.0
18.8
16.3
21.1
11.8
17.1
14.3
22.5
15.3
17.1
22.0
19.0
9.5
24.8

2008
99.9
72.4
77.0
95.3
95.6
83.6
165.7
95.0
87.0
55.0
98.5
73.0
113.8
100.2
134.5

1983
17.0
11.0
20.9
15.1
28.1
13.2
13.5
15.9
12.5
15.1
14.7
22.0
9.5
11.4
31.5

2008
95.2
55.0
77.0
95.1
95.6
84.6
123.0
95.0
87.0
55.0
127.0
73.0
86.4
100.2
144.8

1983
11.2
11.5
14.0
14.9
23.6
10.5
7.9
17.0
13.7
17.0
14.5
22.0
5.5
14.5
23.6

2008
93.9
55.0
77.0
94.7
95.6
81.0
114.6
95.0
87.0
55.0
127.0
73.0
77.2
100.2
134.5

Agriculture

Mining

Construction

Services

Manufacturing

1983
14.1
11.5
9.3
15.2
19.8
10.0
7.5
10.7
11.8
9.5
10.3
22.0
10.0
9.0
23.0

2008
14.9
14.6
15.5
18.9
19.2
14.7
20.3
15.9
18.9
11.1
19.8
14.7
14.3
17.3
27.1

1983
12.3
13.8
14.1
14.9
21.0
11.2
6.6
10.7
9.9
15.3
12.6
22.0
16.6
9.5
28.0

2008
18.6
11.1
15.5
18.7
19.2
16.0
55.6
19.1
17.5
11.1
19.8
16.2
19.1
22.7
27.1

1983
14.6
12.0
18.8
16.3
21.1
11.8
17.1
14.3
22.5
15.3
17.1
22.0
19.0
9.5
24.8

2008
20.1
14.6
15.5
19.2
19.2
16.8
33.3
19.1
17.5
11.1
19.8
14.7
22.9
20.2
27.1

1983
17.0
11.0
20.9
15.1
28.1
13.2
13.5
15.9
12.5
15.1
14.7
22.0
9.5
11.4
31.5

2008
19.2
11.1
15.5
19.1
19.2
17.0
24.8
19.1
17.5
11.1
25.6
14.7
17.4
20.2
29.1

1983
11.2
11.5
14.0
14.9
23.6
10.5
7.9
17.0
13.7
17.0
14.5
22.0
5.5
14.5
23.6

2008
18.9
11.1
15.5
19.1
19.2
16.3
23.1
19.1
17.5
11.1
25.6
14.7
15.5
20.2
27.1

Andhra Pradesh
Assam
Bihar
Gujarat
Haryana
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Odisha
Punjab
Rajasthan
Tamil Nadu
Uttar Pradesh
West Bengal

Panel B: Real

Andhra Pradesh
Assam
Bihar
Gujarat
Haryana
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Odisha
Punjab
Rajasthan
Tamil Nadu
Uttar Pradesh
West Bengal

Notes: Nominal wages in rupees, real wages are pegged to price indices with a base year of 1983. As a
point of information, the average exchange rate was $1 = Rs44 in 2008.
Fonte: Government of India, Labour Bureau. Various years. Report on the Working of the Minimum Wages Act,
1948. Shimla.

the coding and interpretation of these variables is found in Menon and Rodgers
(2013).

Tavolo 2 presents sample statistics for average minimum wage rates by industry
across states. In 1983, some of the highest legislated minimum wage rates were found
in Haryana, Rajasthan, and West Bengal. By 2008, Tuttavia, Haryana and Rajasthan
had been replaced by Kerala, known for its relatively high social development

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40 ASIAN DEVELOPMENT REVIEW

Figura 1. Kernel Density Estimates of the Relative Real Wage across Formal and Informal
Sector Workers in India

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Fonte: Authors’ calculations.

indicators, and Punjab. Among industries, minimum wage rates tended to be the
highest on average in construction, mining, and services, the ﬁrst two of which are
male-dominated industries. Rates tended to be the lowest in agriculture, che è
where women are concentrated.

Figura 1 presents a set of wage distributions around the average statutory
minimum wage in 1983 E 2008. The ﬁgure shows the distributions for male and
female workers in India in the formal and informal sectors. Following convention,
we construct the kernel density estimates as the log of actual daily wages minus
the log of the relevant daily minimum wage for each worker, all in real terms (Rani
et al. 2013). In each plot, the vertical line at zero indicates that a worker’s wage is
on par with the statutory minimum wage in his or her industry and state in that year,
indicating that the minimum wage is binding and that ﬁrms are in compliance with
the legislation. Weighted kernel densities are estimated using standard bandwidths
that are selected nonparametrically.

Figura 1 shows that the wage distributions around the average statutory
minimum wage are closer to zero in 2008 than in 1983 for both male and female
workers. The shifts in the distributions suggest that compliance has increased over
time with proportionately more workers engaged in jobs in which they are paid the
legislated wage. For both men and women, the rightward shift in the wage distribution

IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 41

occurred in both the formal sector and the informal sector, which is consistent with
the ﬁndings for other economies of a lighthouse effect in which informal sector wages
increase when workers and employers use the minimum wage as a benchmark in
wage negotiations. Tuttavia, the improvement in compliance holds more for male
workers as most of the distributions for female workers in 2008 are still to the left
of the point that indicates full compliance. A higher degree of compliance for male
workers holds for both the formal and informal sectors.

These kernel density graphs are important in that they depict relative positions
of real wages in comparison to what is legally binding, with peaks at zero suggesting
compliance by ﬁrms. Such compliance could come from a variety of sources,
including better enforcement of laws (which is included in the regression models),
better agency on the part of workers (which would result from increased worker
representation and unionization), or a combination of these factors such as the
sorting of workers into occupations that are subject to stronger enforcement and
better representation. Per esempio, Kerala’s historical record of relatively high
rates of unionization and worker unrest (Menon and Sanyal 2005) may underlie
the state’s apparently high rate of compliance as depicted in Figure A.1, Quale
presents kernel density estimations for each state. The NSSO data do not allow
for consistent controls for worker agency since questions on union existence and
membership are not asked every year. Tuttavia, the enforcement variables and the
regulatory environment control variables should control for at least some of these
effects.

We note two more issues related to sorting. Primo, workers might move across
states seeking conditions that are more favorable for the occupations in which they
are trained. Because questions about migration were not asked consistently in the
1983–2008 NSSO data, we cannot control for this directly. Tuttavia, as noted above,
rates of migration in India are generally quite low and state characteristics that could
drive these types of movements are accounted for in the regression framework with
the inclusion of state and time ﬁxed effects and their interactions. Secondo, there
may be sorting by workers into industries both across and within states depending
on skill and training levels. Again, the NSSO modules do not consistently ask
whether there were recent job changes or for the details of such changes (per esempio.,
switches in industry afﬁliations). We control for possible sorting on observables
by including a full set of education, experience, and demographic characteristics
that conceivably inﬂuence choice of industries and possible movements between
them. This approach is supported by recent work indicating that controlling for
individual-level characteristics may absorb variations in both observable and
unobservable attributes under certain circumstances (Altonji and Mansﬁeld
2014).6

6Previous studies have used worker ﬁxed effects to control for sorting on unobservables (Vedere, Per esempio,

D’Costa and Overman 2014), but our data are repeated cross sections and not panel in nature.

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42 ASIAN DEVELOPMENT REVIEW

Tavolo 3. Determinants of Employment and Wages for Men in the Rural Sector

Employment Probability

Log Wages

Variable

Coefﬁcient Standard Error Coefﬁcient Standard Error

Minimum wage
Education (reference group = illiterate)

Less than primary school
Primary school
Middle school
Secondary school
Graduate school

Years of potential experience
Potential experience squared/100
Currently married
Scheduled tribe or caste
Hindu
Household headed by a man
Number of preschool children
Net state domestic product
State unemployment rate
State regulations: Adjustments
State regulations: Disputes
Enforcement: Inspections
Enforcement: Irregularities
Enforcement: Cases w/ ﬁnes
Enforcement: Value of ﬁnes
No. of observations

0.634∗∗∗

—0.061∗∗∗
−0.063∗∗∗
−0.059∗∗∗
−0.043∗∗
0.073∗∗
0.010∗∗∗
−0.017∗∗∗
0.053∗∗∗
0.064∗∗∗
0.000
−0.041∗∗
−0.005∗∗
0.002∗∗∗
0.009∗∗∗
−0.019∗∗∗
−0.024∗∗∗
0.030∗∗∗
−0.011∗∗∗
−0.085∗∗∗
0.008∗∗∗
1,216,259

(0.078)

(0.009)
(0.008)
(0.013)
(0.017)
(0.031)
(0.001)
(0.001)
(0.008)
(0.009)
(0.008)
(0.014)
(0.002)
(0.000)
(0.001)
(0.006)
(0.004)
(0.003)
(0.001)
(0.011)
(0.001)

1.078∗∗∗

0.110∗∗∗
0.179∗∗∗
0.334∗∗∗
0.736∗∗∗
1.237∗∗∗
0.036∗∗∗
−0.047∗∗∗
0.005
−0.040∗∗
−0.047
−0.007
−0.004

0.005∗∗∗
0.025∗∗∗
−0.147∗∗∗
−0.025∗∗∗
0.083∗∗∗
−0.013∗∗∗
0.333∗∗∗
0.017∗∗∗
218,506

(0.213)

(0.020)
(0.036)
(0.043)
(0.067)
(0.086)
(0.002)
(0.004)
(0.021)
(0.016)
(0.027)
(0.045)
(0.008)
(0.000)
(0.003)
(0.028)
(0.005)
(0.011)
(0.003)
(0.014)
(0.002)

Notes: Weighted to national level with National Sample Survey Organization sample weights. Standard errors, In
parentheses, are clustered by state. ∗∗∗ = p < 0.01, ∗∗ = p < 0.05, ∗ = p < 0.10. Both regressions include state dummies, time dummies, and state–time interaction terms. Source: Authors’ calculations. IV. Results Table 3 presents the regression results for the determinants of men’s employment and wages in the rural sector. The results show that the real minimum wage has a positive and statistically signiﬁcant impact on men’s likelihood of being employed for cash wages in the rural sector. For a 10% increase in the real minimum wage, the linear probability of employment increases by 6.34% on average for men in rural areas of India. Other variables in these models show that the likelihood of employment falls with all levels of education up through secondary school, but then rises with a graduate education. The probability of cash-based employment for rural men is higher with potential experience, marriage, scheduled tribe or caste status, net state domestic product, state unemployment, and two measures of enforcement (inspections and value of ﬁnes). But the probability of cash-based employment in rural areas is lower in households that are male headed and in households with preschool children. It also falls with both measures of the regulatory environment and two measures of enforcement. On balance, it appears that all else being equal, l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 43 Table 4. Determinants of Employment and Wages for Women in the Rural Sector Employment Probability Log Wages Variable Coefﬁcient Standard Error Coefﬁcient Standard Error Minimum wage Education (reference group = illiterate) Less than primary school Primary school Middle school Secondary school Graduate school Years of potential experience Potential experience squared/100 Currently married Scheduled tribe or caste Hindu Household headed by a man Number of preschool children Net state domestic product State unemployment rate State regulations: Adjustments State regulations: Disputes Enforcement: Inspections Enforcement: Irregularities Enforcement: Cases w/ ﬁnes Enforcement: Value of ﬁnes No. of observations 0.602∗∗∗ −0.058∗∗∗ −0.060∗∗∗ −0.075∗∗∗ −0.043∗∗ 0.084∗∗∗ 0.005∗∗∗ −0.008∗∗∗ 0.007∗ 0.053∗∗∗ 0.006 −0.073∗∗∗ −0.005∗∗∗ −0.001∗∗∗ −0.003∗∗∗ −0.076∗∗∗ −0.039∗∗∗ 0.027∗∗∗ −0.003∗∗∗ −0.149∗∗∗ 0.007∗∗∗ 963,269 (0.093) (0.014) (0.014) (0.016) (0.018) (0.022) (0.001) (0.001) (0.004) (0.008) (0.008) (0.010) (0.002) (0.000) (0.000) (0.016) (0.003) (0.004) (0.000) (0.016) (0.001) 0.687∗∗ 0.097∗∗∗ 0.161∗∗ 0.199∗∗∗ 0.804∗∗∗ 1.329∗∗∗ 0.022∗∗∗ −0.031∗∗∗ −0.012 0.028 −0.006 −0.049 −0.010 0.003∗∗∗ −0.001 −0.230∗∗∗ 0.060∗∗∗ 0.036∗∗∗ −0.004∗∗∗ 0.146∗∗∗ 0.002 85,753 (0.248) (0.030) (0.066) (0.044) (0.085) (0.132) (0.005) (0.007) (0.013) (0.021) (0.043) (0.033) (0.009) (0.000) (0.001) (0.044) (0.004) (0.011) (0.001) (0.032) (0.001) Notes: Weighted to national level with National Sample Survey Organization sample weights. Standard errors, in parentheses, are clustered by state. ∗∗∗ = p < 0.01, ∗∗ = p < 0.05, ∗ = p < 0.10. Both regressions include state dummies, time dummies, and state−time interaction terms. Source: Authors’ calculations. the employment probability for men in the rural sector is negatively affected by a regulatory and enforcement structure that appears to be restrictive for employers. Table 3 also reports results for real wages for men in the rural sector. The coefﬁcient for the real minimum wage shows that for a 10% increase in the minimum wage, real wages rise by 10.78%. Relative to being illiterate, all levels of education have positive and statistically signiﬁcant impacts on wages. As expected, wages rise with potential experience at a decreasing rate. Unlike with the case of employment, membership in one of the scheduled castes has a negative effect on real wages. Real wages also rise with net state domestic product and the unemployment rate. As one would expect, real wages for rural men rise with three of the four measures of minimum wage enforcement. Other labor regulations associated with adjustments and disputes have the opposite effect on real wages, suggesting that men experience a pay penalty in the face of a regulatory environment in which employers have more difﬁculty in adjusting the size of their workforce or ending disputes. Table 4 presents results for the determinants of cash-based employment and wages for women in the rural sector. Like the results for men in the rural sector, l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 44 ASIAN DEVELOPMENT REVIEW women experience a positive impact on employment from the minimum wage. For a 10% increase in the real minimum wage, the linear probability of employment increases by 6.02% on average for women in rural areas. Although this estimate is smaller than the estimate for men in the rural sector, tests reveal that these coefﬁcients are not statistically distinct. Lower levels of education are negatively associated with employment for women, but completing graduate school has a positive effect. The negative association may reﬂect the fact that women with lower levels of education are less likely to hold cash-based jobs in the rural sector. Married women and women who are members of the backward castes are more likely to be employed. In contrast, rural women are less likely to be employed if the household is headed by a man or if there are preschool-aged children in the household. In keeping with intuition, labor regulations that strengthen workers’ ability to initiate or sustain industrial disputes are associated with lower levels of employment. As in the case with rural men, the enforcement variables that most directly affect ﬁrms (inspections and the value of ﬁnes) are positively related to women’s likelihood of employment in the rural sector, while women’s employment falls with both measures of the regulatory environment and the other two measures of enforcement. Table 4 further indicates that for rural women receiving cash wages, the real minimum wage has a positive effect on wages. Controlling for state-level, time-varying heterogeneity, a 10% increase in the real minimum wage increases real wages by 6.87%. Although this increase is smaller than the 10.78% wage increase reported for rural men, the difference between the male and female coefﬁcients is not statistically signiﬁcant. Education has a positive impact on real wages, with higher levels of education associated with considerable wage premiums relative to having no education. Work experience matters positively, as does net state domestic product. Labor regulations associated with disputes have a beneﬁcial impact on wages too. Among the enforcement variables, as with men, rural women’s wages on balance are positively affected by minimum wage enforcement, with the number of cases with ﬁnes imposed having the largest positive impact. Table 5, which reports results for the determinants of men’s cash-based employment and wage levels in the urban sector, shows that the minimum wage rate has no statistically signiﬁcant effect on these outcomes. This result most likely suggests that in urban areas, perhaps as a consequence of better enforcement and/or increased awareness on the part of workers, men are paid at least the legislated minimum wage. The absence of an impact on urban sector employment is similar to ﬁndings in numerous other studies, suggesting that India’s urban sector labor market has characteristics consistent with those of other labor markets around the world. The effect of the education variables in Table 5 are similar to those for men in the rural sector except that the positive effects of schooling on employment become evident at much lower levels of education. The positive employment impacts of potential experience, marriage, and membership in scheduled tribes or scheduled castes are also similar to those for men in rural India. However, in contrast to l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 45 Table 5. Determinants of Employment and Wages for Men in the Urban Sector Employment Probability Log Wages Variable Coefﬁcient Standard Error Coefﬁcient Standard Error Minimum wage Education (reference group = illiterate) Less than primary school Primary school Middle school Secondary school Graduate school Years of potential experience Potential experience squared/100 Currently married Scheduled tribe or caste Hindu Household headed by a man Number of preschool children Net state domestic product State unemployment rate State regulations: Adjustments State regulations: Disputes Enforcement: Inspections Enforcement: Irregularities Enforcement: Cases w/ ﬁnes Enforcement: Value of ﬁnes No. of observations 0.132 −0.024∗∗ 0.045∗∗∗ 0.078∗∗∗ 0.110∗∗∗ 0.197∗∗∗ 0.018∗∗∗ −0.029∗∗∗ 0.123∗∗∗ 0.038∗∗∗ 0.032∗∗∗ −0.088∗∗∗ −0.016∗∗∗ 0.000 0.001 −0.015 −0.009 0.000 −0.002∗∗ −0.052∗∗ 0.002 690,342 (0.221) (0.010) (0.014) (0.019) (0.022) (0.019) (0.001) (0.002) (0.017) (0.008) (0.007) (0.012) (0.004) (0.000) (0.001) (0.036) (0.014) (0.004) (0.001) (0.022) (0.003) 0.247 0.170∗∗∗ 0.248∗∗∗ 0.375∗∗∗ 0.748∗∗∗ 1.309∗∗∗ 0.051∗∗∗ −0.068∗∗∗ 0.179∗∗∗ −0.041∗∗ −0.041∗∗ 0.014 −0.009 0.000∗ −0.005∗∗∗ −0.053 0.046∗∗∗ 0.007∗∗∗ 0.009∗∗∗ 0.134∗∗∗ 0.000 239,534 (0.191) (0.033) (0.045) (0.045) (0.053) (0.060) (0.004) (0.006) (0.027) (0.015) (0.019) (0.033) (0.011) (0.000) (0.000) (0.031) (0.010) (0.002) (0.000) (0.030) (0.002) Notes: Weighted to national level with National Sample Survey Organization sample weights. Standard errors, in parentheses, are clustered by state. ∗∗∗ = p < 0.01, ∗∗ = p < 0.05, ∗ = p < 0.10. Both regressions include state dummies, time dummies, and state–time interaction terms. Source: Authors’ calculations. their rural counterparts, Hindu men in the urban sector are more likely to be employed. Results for the other controls for men’s wages in the urban sector in Table 5 are similar to the results for rural men. In particular, potential experience and higher levels of education are associated with substantial wage premiums. In contrast to their rural counterparts, the wages of urban men are positively impacted from marriage. Working against higher wages for urban men is membership in a disadvantaged caste and being Hindu. Finally, regulations associated with disputes have positive impacts on the wages of urban men as do three of the four enforcement measures. Table 6 presents results for the determinants of cash-based employment and wages for women in the urban sector. Again, conditional on enforcement, real minimum wages have no statistically discernible impact on employment or wages. This result is similar to the ﬁnding for urban men and is in keeping with the intuition that India’s urban sector labor market, despite its inefﬁciencies, operates more like labor markets in other economies where minimum wage laws have been found to have negligible impacts on aggregate employment and wages. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 46 ASIAN DEVELOPMENT REVIEW Table 6. Determinants of Employment and Wages for Women in the Urban Sector Employment Probability Log Wages Variable Minimum wage Education (reference group = illiterate) Less than primary school Primary school Middle school Secondary school Graduate school Years of potential experience Potential experience squared/100 Currently married Scheduled tribe or caste Hindu Household headed by a man Number of preschool children Net state domestic product State unemployment rate State regulations: Adjustments State regulations: Disputes Enforcement: Inspections Enforcement: Irregularities Enforcement: Cases w/ ﬁnes Enforcement: Value of ﬁnes No. of observations Coefﬁcient Standard Error Coefﬁcient Standard Error −0.342 (0.321) (0.313) 0.432 −0.053∗∗∗ −0.055∗∗∗ −0.046∗∗∗ 0.017 0.184∗∗∗ 0.009∗∗∗ −0.015∗∗∗ −0.032∗∗∗ 0.039∗∗∗ 0.011 −0.114∗∗∗ −0.015∗∗∗ 0.001 0.001 0.065∗∗ 0.018 0.001∗∗∗ 0.002 0.066 −0.004 462,224 (0.014) (0.014) (0.014) (0.013) (0.019) (0.001) (0.002) (0.008) (0.006) (0.007) (0.014) (0.002) (0.001) (0.001) (0.029) (0.020) (0.000) (0.002) (0.077) (0.004) 0.244∗∗ 0.317∗∗∗ 0.492∗∗∗ 1.107∗∗∗ 1.663∗∗∗ 0.048∗∗∗ −0.065∗∗∗ 0.136∗∗ 0.078∗ 0.006 −0.247∗∗∗ 0.002 0.001∗∗∗ −0.001 −0.165∗∗∗ 0.029 0.008∗∗∗ 0.010∗∗∗ 0.052 0.003 53,828 (0.089) (0.095) (0.131) (0.108) (0.071) (0.005) (0.008) (0.051) (0.039) (0.083) (0.047) (0.029) (0.000) (0.001) (0.034) (0.019) (0.002) (0.001) (0.078) (0.003) Notes: Weighted to national level with National Sample Survey Organization sample weights. Standard errors, in parentheses, are clustered by state. ∗∗∗ = p < 0.01, ∗∗ = p < 0.05, ∗ = p < 0.10. Both regressions include state dummies, time dummies, and state–time interaction terms. Source: Authors’ calculations. For urban women, being married reduces the likelihood of employment but increases real wages, and women who live in households headed by men are less likely to be employed and to have lower real wages. Net state domestic product matters only for real wages. Labor regulations related to adjustments that are proworker in orientation have a positive impact on employment and a negative impact on wages for urban women. This result indicates that limitations imposed on ﬁrms’ abilities to adjust their workforce help to protect urban women’s jobs, but some of the cost may be passed along in the form of lower wages for women. Finally, the number of inspections to ensure enforcement has a positive effect on women’s employment, while both inspections and the number of irregularities detected matter for their wages.7 7We combined ﬁve measures of enforcement and created an index (dummy) based on each measure exceeding its median value to create a single aggregate indicator for overall enforcement that varied by state and year. We then included this index in the models for Tables 3–6 in place of the disaggregated measures and added an interaction term of the legal minimum wage and this index, allowing us to determine the impact in states that have more stringent controls. Our results remain the same in the rural sector. However, in the urban sector, minimum wages marginally l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 47 Table 7. Minimum Wage Coefﬁcients from Employment Estimations across Sectors, before and after 2005 Men’s Employment Women’s Employment Coefﬁcient Standard Error Coefﬁcient Standard Error Panel A. Formal sector Rural: Total Rural: Pre-2005 Rural: Post-2005 Urban: Total Urban: Pre-2005 Urban: Post-2005 Panel B. Informal sector Rural: Total Rural: Pre-2005 Rural: Post-2005 Urban: Total Urban: Pre-2005 Urban: Post-2005 Panel C. Self-employment Rural: Total Rural: Pre-2005 Rural: Post-2005 Urban: Total Urban: Pre-2005 Urban: Post-2005 0.654∗∗∗ 0.655∗∗∗ 0.414 −0.050 −0.050 −0.358 −0.650∗∗∗ −0.651∗∗∗ −0.402 0.038 0.038 0.353 −0.084∗∗ −0.084∗∗ −0.059 −0.010 −0.010 −0.008 (0.162) (0.162) (0.304) (0.324) (0.324) (0.233) (0.173) (0.173) (0.297) (0.328) (0.328) (0.232) (0.033) (0.033) (0.035) (0.006) (0.006) (0.010) 0.696∗∗∗ 0.696∗∗∗ 0.844∗∗∗ 0.376 0.375 0.773∗ −0.749∗∗∗ −0.748∗∗∗ −0.868∗∗∗ −0.374 −0.374 −0.787∗ −0.016 −0.016 −0.006 −0.021∗∗∗ −0.021∗∗∗ −0.001 (0.165) (0.165) (0.265) (0.297) (0.297) (0.435) (0.159) (0.159) (0.281) (0.302) (0.302) (0.435) (0.010) (0.010) (0.012) (0.006) (0.006) (0.004) Notes: Weighted to national level with National Sample Survey Organization sample weights. Standard errors, in parentheses, are clustered by state. ∗∗∗ = p < 0.01, ∗∗ = p < 0.05, ∗ = p < 0.10. Results are reported for the coefﬁcient on the real minimum wage from separate regressions for whether or not an individual is employed in a particular sector (formal, informal, or self-employment). All regressions include the full set of control variables shown in Tables 3–6 plus state dummies, time dummies, and state–time interaction terms. Pre-2005 years are based on 1983 through 1999–2000 NSSO data, and post-2005 years are based on 2004–2005 through 2007–2008 NSSO data. Source: Authors’ calculations. To shed more light on the employment results, minimum wage effects were estimated for different sectors of employment: formal sector, informal sector, and self-employment.8 These results are found in Table 7 where only the minimum wage coefﬁcients are reported.9 Note that the estimations are performed using the sample of all individuals of working age who are employed for cash wages. Hence, results in Panel A represent the likelihood of formal sector employment relative to other types of employment in which people earn cash wages, where the formal sector includes those who reported their current employment status as reduce employment and increase real wages for workers. Since this does not contradict the results in Tables 3–6, the results are not reported in this paper. 8We did not study wages in these disaggregated sectors as the concept of a wage is difﬁcult to interpret for informal and self-employed workers. 9Complete regression results are found in Tables A.2a–c. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 48 ASIAN DEVELOPMENT REVIEW regular salaried employees. Similarly, Panel B reports the likelihood of informal sector employment relative to engagement in other cash-based employment, where the informal sector includes those who reported their current employment status as own-account workers, employers, unpaid family workers, casual wage laborers in public works, and casual laborers in other types of work.10 In the same spirit, Panel C shows the likelihood of being self-employed relative to work in other employment with cash wages. Tabulations reveal that there is no overlap between formal sector employment and the other two categories of work. That is, formal sector status is mutually exclusive from informal sector status and self-employment. However, a small percentage of individuals are both self-employed and employed in the informal sector (about 2% of the sample). Table 7 reports these results for the formal sector, informal sector, and self- employment using the full sample for each sector as well as subsamples differentiated by year. We divided the sample into the pre-2005 years (1983 through 1999–2000) and the post-2005 years (2004–2005 through 2007–2008) in an effort to gauge the impact of India’s National Rural Employment Guarantee Act, 2005 (NREGA), a large job guarantee scheme that can be considered a mechanism for enforcing the minimum wage in rural areas. This act, which assures all rural households at least 100 days of paid work per year at the statutory minimum wage, has had a large positive effect on public sector employment in India’s rural areas according to Azam (2012) and Imbert and Papp (2015). These two studies, however, have conﬂicting results for NREGA’s effect with regard to gender. Azam (2012) ﬁnds that the act had a large positive impact on the labor force participation of women but not men, while Imbert and Papp (2015) found that the inclusion of proxy variables for other shocks unrelated to the program reversed this conclusion. The aggregate results in Table 7 indicate that for both men and women, most of the positive employment effects observed for all rural sector individuals in the aggregate employment results come from formal sector employment. A possible explanation is the migration of industries to rural areas in order to take advantage of competitive wages (Foster and Rosenzweig 2004). Such industrial migration could also drive the results for the rural informal sector where a sizable disemployment effect is evident for both men and women. The results for self-employment are lower in magnitude and differ by gender; while rural men see small reductions in self-employment with increases in the minimum wage, it is urban women who exhibit the disemployment effect when it comes to this category of work. The time-differentiated results in Table 7 reveal that in the formal sector, the positive and statistically signiﬁcant impact of the minimum wage on the employment of rural men occurred mostly before 2005, while the impact occurred both before and after NREGA was implemented for rural women. Urban women in the formal sector also experienced an employment boost during the post-2005 years, suggesting 10We thank Uma Rani for guidance on India’s deﬁnition of informal sector employment. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 49 that minimum wage increases combined with a strict enforcement scheme helped to pull women into the formal labor market across the board, possibly due to spillovers of the scheme in urban areas. Similarly, Panel B shows that the disemployment effect for informal sector work among rural men occurred only before NREGA was implemented, while rural women showed a lower likelihood of informal sector employment with minimum wage increases both before and after its implementation. This negative employment effect from the minimum wage for women employed in the informal sector during the post-2005 years also extends to urban areas, though this is not the case for men. In sum, minimum wages strengthened formal sector employment in rural areas for men and women. Potentially, there could be two reasons for this. First, employment elasticities could have increased for men and women. Second, this employment boost could be the direct impact of NREGA. The speciﬁcation test results in Table 7 indicate that very little to none of the positive impact of minimum wages in the rural sector for men could be explained by NREGA. For women, some of the positive impact in the rural sector occurred before NREGA was implemented—suggesting a possible role for an increase in employment elasticities from another cause, perhaps as outlined in Foster and Rosenzweig (2004)—and some occurred after its implementation. The estimation is based on variation in minimum wage rates across states and industries, while NREGA was applied at the national level and did not vary by industry. Any variation in how states applied NREGA should be captured by the time-varying state control variables included in the speciﬁcation, which implies that any impact that is measured net of these controls may be attributed separately to positive employment elasticities. This appears to be the case for rural men. However, some of the increase in women’s formal employment in the rural sector after 2005 could be attributed to the enforcement mechanism built into NREGA. Although we are not able to pinpoint how much, we can be reasonably sure that the state control variables are picking up much of the employment effects of NREGA even though we do not include a speciﬁc NREGA-related variable in the models for Table 7. This conclusion is consistent with the argument in Imbert and Papp (2015) that some of the positive labor market outcomes for women ascribed to NREGA are actually due to changes unrelated to the program. We further explored the positive employment results in rural areas by using the NSSO data to construct labor force participation rates by state, year, gender, and rural or urban areas; and we tested for the relationship between minimum wage rates and labor force participation rates with controls for state and year effects. These tests indicate that there is strong evidence of increased labor force participation rates in rural areas in states that have relatively high minimum wages.11 Interestingly, when we added a gender dimension by interacting the minimum wage and a dummy variable for male workers, we found that for women, the increase in labor force 11The results are found in Table A.3. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 50 ASIAN DEVELOPMENT REVIEW Table 8. Residual Wage Gap Covariates at the State Level Minimum wage Net state domestic product Rural male unemployment Urban male unemployment Rural female unemployment Urban female unemployment State regulations: Adjustments State regulations: Disputes Enforcement: Inspections Enforcement: Irregularities Enforcement: Cases w/ ﬁnes Enforcement: Value of ﬁnes Coefﬁcient Estimate 0.128∗ (0.060) 0.001∗∗∗ (0.000) 0.003∗∗∗ (0.001) −0.001 (0.001) −0.001∗∗ (0.000) 0.001 (0.001) −0.005 (0.016) 0.007 (0.009) 0.002∗∗ (0.001) −0.006∗∗ (0.003) −0.032 (0.047) −0.002∗ (0.001) level with National Sample Notes: Weighted to national Survey Organization sample weights. Standard errors, in parentheses, are clustered by state. ∗∗∗ = p < 0.01, ∗∗ = p < 0.05, ∗ = p < 0.10. All regressions have 90 observations at the state–year level and are estimated with an ordinary least squares regression. The residual wage gap is constructed with the pooled sample of male wage earners (458,040 observations) and includes controls for worker productivity characteristics, state dummies, year dummies, and state–year interaction terms. Source: Authors’ calculations. participation rates in rural areas is higher than that for men in the post-2005 period in states with relatively high minimum wages. This result helps to explain the minimum wage effects we document in rural areas for women. The ﬁnal part of the analysis considers the impact of the minimum wage on the residual wage gap between men and women. The residual wage gap is estimated using the Oaxaca–Blinder decomposition procedure, a technique that decomposes the wage gap in a particular year into a portion explained by average group differences in productivity characteristics and a residual portion that is often attributed to discrimination (Blinder 1973, Oaxaca 1973). We used the coefﬁcients from a regression of men’s wages on the full set of worker productivity characteristics, state dummies, year dummies, and state–year interaction terms, l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 51 estimated with the pooled sample of male wage earners (458,040 observations). The residual wage gaps are averaged to the state and year level and are regressed on controls that vary at this level: minimum wage, net state domestic product, gender- and sector-speciﬁc unemployment rates, regulatory environment in each state’s labor market, and four measures of minimum wage enforcement. The results in Table 8 indicate that the minimum wage is positively associated with the residual gender wage gap. A 10% increase in the minimum wage results in a 1.28% increase in the unexplained portion of the gender wage gap. This ﬁnding is consistent with the argument that noncompliance could be greater in the case of female workers, which is also evident in the kernel density ﬁgures for women.12 Average wages are lower for women than for men, so the minimum wage is more binding and compliance is relatively costlier for them. This explains why ﬁrms might not fully comply with the legislated minimum wage for female workers, which is all the more likely in cases where enforcement is weak and the legal machinery for enforcing contracts is either inefﬁcient or absent. V. Conclusion This study examined the extent to which minimum wage rates affect labor market outcomes for men and women in India. The empirical results indicate that regardless of gender, the legislated minimum wage has positive and statistically signiﬁcant impacts on rural sector employment and real earnings. These positive impacts in rural areas occur primarily in the formal sector, with sizable disemployment effects observed for informal sector workers (especially women) and self-employed individuals (especially men). Hence, we ﬁnd that a higher minimum wage appears to attract more employment for both genders in the formal sector in rural areas. This ﬁnding is not inconsistent with the studies reviewed above, especially those that have examined minimum wage impacts across wage distributions, sectors, and geographic areas and found employment growth in sectors and areas with high proportions of low-wage workers and relatively more underemployment (e.g., Stewart 2002). This ﬁnding is also consistent with evidence in Foster and Rosenzweig (2004) that a great deal of industrial capital moved to India’s rural areas during this period to set up new enterprises that could employ relatively cheaper labor. Further, we cannot rule out that the positive employment results in the rural sector for women partly reﬂect the minimum wage enforcement mechanism built into NREGA. In contrast, minimum wages in India’s urban areas have little to no impact on overall employment or wages. These urban sector results are consistent with previous 12In kernel density graphs by industry, women in agriculture and services (the female-dominated industries in our sample) move closer to the line indicating full compliance between 1983 and 2008, but still earn below the level of full compliance at the end of the review period. This pattern is not observed for men, who by 2008 earn wages that are on par with those legislated by law. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 52 ASIAN DEVELOPMENT REVIEW work in both industrialized and developing economies. However, a closer look at different sectors within India’s urban areas yields some evidence of disemployment effects for women who are self-employed or work in informal sector jobs, but not for men. These results suggest that NREGA may have drawn some urban women from informal sector jobs and self-employment. Our study indicates that the main cost associated with India’s minimum wage is an increase in the residual gender wage gap over the period 1983–2008. This widening in the gender wage gap is consistent with previous work that highlighted women’s relatively weak position in the labor market after reforms, as well as studies that note the persistent clustering of women into low-wage jobs and pay inequities within the same jobs in India (Menon and Rodgers 2009). The relatively adverse impact of the minimum wage on women is also consistent with ﬁndings in advanced economies and in middle-income economies such as the PRC, Indonesia, and Mexico. The growing residual gender wage gap is most likely explained by weak compliance among ﬁrms that predominantly hire female workers. Noncompliance with minimum wage regulations that is widespread in developing economies is directly related to difﬁculties in enforcement. Our ﬁndings suggest that women may bear the burden of this lack of compliance. For the minimum wage to be considered a gender-sensitive policy intervention in a shared prosperity approach to economic growth, governments must pay more attention to improving enforcement and compliance, especially in industries that employ large concentrations of female workers. Greater emphasis on compliance can help to prevent increases in the gender wage gap and ensure that the minimum wage is a more integral component in the toolkit to promote well-being. Policies that work in tandem to improve women’s education and their experience in the workplace would help to complement these objectives and further strengthen the effectiveness of a statutory minimum wage. A possible extension of this research would be to examine how India’s minimum wage legislation has affected household well-being as measured by poverty incidence, household consumption, and human capital investments in children. 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Variable Descriptions and Data Sources Description Individual and household characteristics Source and Years of Data NSSO: 1983, 1987–1988, 1993–1994, 1999–2000, 2004–2005, 2007–2008 State-level net real domestic product State-level unemployment rates Reserve Bank of India: 1983, 1987, 1993, 1999, 2004, 2007 Indiastat; NSSO: 1983, 1987–1988, 1993–1994, 1999–2000, State-level indicators of minimum Labour Bureau: 1983, 1986, 1993, 1998, 2004, 2006 2004–2005, 2007–2008 wage enforcement State-level labor market regulations on Ahsan and Pag´es (2009): 1983, 1986, 1993, 1998, 2004, adjustment and disputes 2006 State- and industry-level minimum Labour Bureau: 1983, 1986, 1993, 1998, 2004, 2006 wages Source: Authors’ compilation. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 57 n a b r U l a r u R n e m o W n e M n e m o W n e M 5 0 0 2 r e t f a d n a e r o f e b , r o t c e S l a m r o F e h t n i s n o i t a m i t s E t n e m y o l p m E r o f s t l u s e R n o i s s e r g e R e t e l p m o C . a 2 . A e l b a T 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P s t l u s e R r o t c e S l a m r o F ∗ 3 7 7 . 0 ) 5 3 4 . 0 ( 5 7 3 . 0 ) 7 9 2 . 0 ( ) 3 3 2 . 0 ( 8 5 3 . 0 − ) 4 2 3 . 0 ( 0 5 0 . 0 − ∗ ∗ ∗ 4 4 8 . 0 ) 5 6 2 . 0 ( ∗ ∗ ∗ 6 9 6 . 0 ) 5 6 1 . 0 ( 4 1 4 . 0 ) 4 0 3 . 0 ( ∗ ∗ ∗ 5 5 6 . 0 ) 2 6 1 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ 2 1 1 . 0 ) 9 6 0 . 0 ( 5 4 1 . 0 ) 4 4 0 . 0 ( 0 3 2 . 0 ) 7 5 0 . 0 ( 5 6 4 . 0 ) 4 5 0 . 0 ( 5 4 5 . 0 ) 3 5 0 . 0 ( ∗ 5 0 0 . 0 ) 2 0 0 . 0 ( ∗ ∗ ∗ 5 0 0 . 0 − ) 5 0 0 . 0 ( 0 8 0 . 0 − ) 0 2 0 . 0 ( ) 8 1 0 . 0 ( 6 0 0 . 0 − 7 1 0 . 0 − ) 5 2 0 . 0 ( 4 1 0 . 0 − ) 5 1 0 . 0 ( 7 1 0 . 0 − ∗ ∗ ∗ ∗ ∗ ∗ 7 5 0 . 0 − ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ 6 3 1 . 0 ) 7 2 0 . 0 ( 2 5 2 . 0 ) 4 3 0 . 0 ( 4 6 4 . 0 ) 9 3 0 . 0 ( 2 0 6 . 0 ) 3 4 0 . 0 ( 6 2 6 . 0 ) 9 4 0 . 0 ( 0 0 0 . 0 ) 2 0 0 . 0 ( ∗ 6 0 0 . 0 ) 3 0 0 . 0 ( 4 5 0 . − ) 7 1 0 . 0 ( ∗ ∗ ∗ ) 2 1 0 . 0 ( 0 2 0 . 0 ) 3 2 0 . 0 ( 5 3 0 . 0 ) 0 4 0 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ 4 4 1 . 0 ) 6 1 0 . 0 ( 4 3 2 . 0 ) 8 1 0 . 0 ( 5 3 3 . 0 ) 5 1 0 . 0 ( 3 8 4 . 0 ) 3 2 0 . 0 ( 1 9 5 . 0 ) 6 3 0 . 0 ( 6 0 0 . 0 ) 1 0 0 . 0 ( 6 0 0 . 0 − ) 2 0 0 . 0 ( ) 2 1 0 . 0 ( 3 1 0 . 0 − 4 7 0 . 0 − ) 3 1 0 . 0 ( ∗ 8 2 0 . 0 ) 5 1 0 . 0 ( ∗ 4 3 0 . 0 ) 7 1 0 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ 7 8 1 . 0 ) 3 2 0 . 0 ( 4 5 2 . 0 ) 2 2 0 . 0 ( 7 5 3 . 0 ) 0 2 0 . 0 ( 4 3 5 . 0 ) 8 2 0 . 0 ( 8 0 6 . 0 ) 1 3 0 . 0 ( 7 0 0 . 0 ) 2 0 0 . 0 ( 4 0 0 . 0 − ) 3 0 0 . 0 ( ) 0 1 0 . 0 ( 6 0 0 . 0 − ∗ ∗ ∗ ) 6 1 0 . 0 ( 4 3 0 . 0 ) 0 2 0 . 0 ( 7 7 0 . 0 ) 4 2 0 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ 3 6 0 . 0 ) 0 1 0 . 0 ( 4 0 1 . 0 ) 3 1 0 . 0 ( 0 3 2 . 0 ) 2 3 0 . 0 ( 3 9 5 . 0 ) 8 4 0 . 0 ( 8 6 8 . 0 ) 8 3 0 . 0 ( 1 1 0 . 0 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ) 1 0 0 . 0 ( 4 1 0 . 0 − ) 2 0 0 . 0 ( 7 3 0 . 0 − ) 7 0 0 . 0 ( 2 2 0 . 0 − ) 5 0 0 . 0 ( ∗ 4 1 0 . 0 − ) 7 0 0 . 0 ( 8 1 0 . 0 − ) 1 1 0 . 0 ( ∗ ∗ 8 3 0 . 0 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ) 5 1 0 . 0 ( 1 3 1 . 0 ) 9 3 0 . 0 ( 7 8 1 . 0 ) 0 3 0 . 0 ( 7 0 6 . 0 ) 1 3 0 . 0 ( 7 1 8 . 0 ) 6 6 0 . 0 ( 7 0 0 . 0 ) 2 0 0 . 0 ( 9 0 0 . 0 − ) 3 0 0 . 0 ( ∗ 6 1 0 . 0 − ) 9 0 0 . 0 ( 6 0 0 . 0 − ) 9 0 0 . 0 ( 3 1 0 . 0 ) 1 1 0 . 0 ( ) 0 1 0 . 0 ( 5 1 0 . 0 − ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ 7 4 0 . 0 ) 5 0 0 . 0 ( 0 1 1 . 0 ) 9 0 0 . 0 ( 2 3 2 . 0 ) 1 1 0 . 0 ( 6 7 4 . 0 ) 2 2 0 . 0 ( 6 7 7 . 0 ) 4 2 0 . 0 ( 3 1 0 . 0 ) 1 0 0 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ 6 6 0 . 0 ) 8 0 0 . 0 ( 8 1 1 . 0 ) 5 1 0 . 0 ( 6 5 2 . 0 ) 3 2 0 . 0 ( 4 2 5 . 0 ) 7 2 0 . 0 ( 7 7 7 . 0 ) 9 3 0 . 0 ( 5 1 0 . 0 ) 1 0 0 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ 7 1 0 . 0 − ) 2 0 0 . 0 ( 8 3 0 . 0 − ) 8 0 0 . 0 ( 8 7 0 . 0 − ∗ ∗ ∗ ) 2 0 0 . 0 ( ∗ ∗ 0 2 0 . 0 − 0 2 0 . 0 − ∗ ∗ ∗ ) 8 0 0 . 0 ( 2 5 0 . 0 − ) 6 1 0 . 0 ( 4 1 0 . 0 ) 6 1 0 . 0 ( 3 1 0 . 0 ) 3 1 0 . 0 ( ) 1 1 0 . 0 ( 4 1 0 . 0 ) 3 1 0 . 0 ( 4 3 0 . 0 ) 0 3 0 . 0 ( ) e t a r e t i l l i = p u o r g e c n e r e f e r ( n o i t a c u d E l o o h c s y r a m i r p n a h t s s e L e g a w m u m i n i M 0 0 1 / d e r a u q s e c n e i r e p x e l a i t n e t o P e c n e i r e p x e l a i t n e t o p f o s r a e Y e t s a c r o e b i r t d e l u d e h c S d e i r r a m y l t n e r r u C u d n i H n a m a y b d e d a e h d l o h e s u o H l o o h c s y r a m i r P l o o h c s e l d d i M l o o h c s y r a d n o c e S l o o h c s e t a u d a r G . d e u n i t n o C l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 58 ASIAN DEVELOPMENT REVIEW n e m o W n e M n e m o W n e M n a b r U . d e u n i t n o C . a 2 . A e l b a T l a r u R 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P s t l u s e R r o t c e S l a m r o F ) 4 0 0 . 0 ( 1 0 0 . 0 − ∗ ∗ ∗ ) 1 0 0 . 0 ( 4 0 0 . 0 − 6 2 4 , 2 8 1 2 2 9 , 7 2 1 3 8 , 7 5 7 0 0 . 0 − ) 1 1 0 . 0 ( 1 0 0 . 0 − ) 1 0 0 . 0 ( ∗ 1 0 0 . 0 − ) 1 0 0 . 0 ( 7 0 1 . 0 − ) 0 8 0 . 0 ( 8 5 0 . 0 − ) 1 4 0 . 0 ( ∗ 3 1 0 . 0 − ) 7 0 0 . 0 ( 1 0 0 . 0 ) 0 1 0 . 0 ( . . . . ∗ ∗ ∗ 5 0 0 . 0 − ) 1 0 0 . 0 ( 4 0 0 . 0 − ) 8 0 0 . 0 ( 0 0 0 . 0 − ) 0 0 0 . 0 ( 1 0 0 . 0 − ) 1 0 0 . 0 ( 4 2 0 . 0 ) 0 3 0 . 0 ( 7 0 0 . 0 − ) 8 1 0 . 0 ( 8 1 0 . 0 ) 2 1 0 . 0 ( 5 0 0 . 0 ) 1 0 0 . 0 ( 8 8 0 . 0 ) 4 1 0 . 0 ( 2 0 0 . 0 ) 3 0 0 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ 5 2 6 4 1 , 3 0 2 9 3 , ∗ ∗ 1 0 0 . 0 ) 8 0 0 . 0 ( ) 0 0 0 . 0 ( 1 0 0 . 0 − ∗ ∗ ∗ ∗ ∗ ∗ ) 2 0 0 . 0 ( 3 5 0 . 0 ) 0 5 0 . 0 ( 1 7 0 . 0 ) 6 0 0 . 0 ( 6 0 0 . 0 ) 1 0 0 . 0 ( ∗ ∗ 1 2 0 . 0 − ) 9 0 0 . 0 ( 2 0 0 . 0 ) 1 0 0 . 0 ( 8 0 1 , 7 5 . . . . ) 8 0 0 . 0 ( 0 0 0 . 0 ) 2 0 0 . 0 ( 2 0 0 . 0 ) 6 0 0 . 0 ( ) 1 2 0 . 0 ( 0 2 0 . 0 − 4 0 0 . 0 − ) 1 3 0 . 0 ( ) 0 0 0 . 0 ( 2 0 0 . 0 ) 4 0 0 . 0 ( 0 5 0 . 0 ) 4 8 0 . 0 ( ∗ ∗ 7 0 0 . 0 ) 3 0 0 . 0 ( ) 0 0 0 . 0 ( ∗ ∗ 1 0 0 . 0 − ) 0 0 0 . 0 ( ∗ ∗ 5 8 0 . 0 − ) 0 3 0 . 0 ( ∗ ∗ 8 7 0 . 0 − 1 0 0 . 0 − ) 1 3 0 . 0 ( ) 3 0 0 . 0 ( 1 1 0 . 0 ) 0 1 0 . 0 ( . . . . ∗ ∗ ∗ 4 0 0 . 0 − ∗ ∗ ∗ 0 1 0 . 0 − ∗ ∗ 1 2 0 . 0 − ∗ ∗ 7 1 0 . 0 − ∗ ∗ ∗ ∗ ∗ ∗ ) 6 0 0 . 0 ( 1 0 0 . 0 ) 0 0 0 . 0 ( 5 0 0 . 0 − ∗ ∗ ∗ 3 0 0 . 0 − ) 1 0 0 . 0 ( ∗ ∗ ∗ 2 5 1 . 0 − ∗ ∗ ∗ ) 8 4 0 . 0 ( 8 6 0 . 0 ∗ ∗ 2 1 0 . 0 ) 3 1 0 . 0 ( ) 4 0 0 . 0 ( ∗ ∗ ∗ 8 0 0 . 0 − ∗ ∗ ∗ ) 2 0 0 . 0 ( 3 0 1 . 0 − ∗ ∗ ∗ ) 7 1 0 . 0 ( 2 0 0 . 0 ) 1 0 0 . 0 ( ∗ ∗ ∗ 4 0 0 . 0 − ) 0 0 2 . 0 ( 1 0 0 . 0 − ) 1 0 0 . 0 ( 9 0 0 . 0 − ) 5 0 0 . 0 ( ∗ 8 4 1 . 0 − ) 3 8 0 . 0 ( 0 1 0 . 0 ) 3 0 0 . 0 ( 2 0 0 . 0 ) 2 0 0 . 0 ( ∗ 8 4 0 . 0 − ) 3 2 0 . 0 ( . . 8 0 0 . 0 ) 5 0 0 . 0 ( 2 5 1 , 8 7 . . ∗ ∗ ∗ ∗ ∗ ∗ ) 4 0 0 . 0 ( ∗ 8 0 0 . 0 − 0 0 0 . 0 − ) 0 0 0 . 0 ( 7 0 0 . 0 ) 2 0 0 . 0 ( ∗ ∗ ∗ 0 1 1 . 0 − ) 8 2 0 . 0 ( ∗ ∗ ∗ 9 3 0 . 0 − ∗ ∗ ∗ ) 6 0 0 . 0 ( 6 2 0 . 0 ) 7 0 0 . 0 ( ∗ ∗ ∗ ) 2 0 0 . 0 ( ∗ ∗ 7 5 0 . 0 − 9 0 0 . 0 − ∗ ∗ ∗ ) 0 2 0 . 0 ( 7 0 0 . 0 ) 2 0 0 . 0 ( d l o h e s u o h n i n e r d l i h c l o o h c s e r p f o . o N s t n e m t s u j d A : s n o i t a l u g e r e t a t S t c u d o r p c i t s e m o d e t a t s t e N e t a r t n e m y o l p m e n u e t a t S s e t u p s i D : s n o i t a l u g e r e t a t S s n o i t c e p s n I : t n e m e c r o f n E s e i t i r a l u g e r r I : t n e m e c r o f n E s e n ﬁ / w s e s a C : t n e m e c r o f n E s e n ﬁ f o e u l a V : t n e m e c r o f n E 4 5 3 , 0 4 1 s n o i t a v r e s b o f o . o N < p = ∗ ∗ ∗ . e t a t s y b d e r e t s u l c e r a , s e s e h t n e r a p n i , s r o r r e d r a d n a t S . s t h g i e w e l p m a s n o i t a z i n a g r O y e v r u S e l p m a S l a n o i t a N h t i w . s m r e t n o i t c a r e t n i e m i t – e t a t s d n a , s e i m m u d e m i t , s e i m m u d e t a t s e d u l c n i s n o i s s e r g e r l l A . 0 1 . 0 < p = ∗ , 5 0 . 0 < p = ∗ ∗ , 1 0 . 0 l e v e l l a n o i t a n o t d e t h g i e W : s e t o N l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 59 n a b r U l a r u R n e m o W n e M n e m o W n e M 5 0 0 2 r e t f a d n a e r o f e b , r o t c e S l a m r o f n I e h t n i s n o i t a m i t s E t n e m y o l p m E r o f s t l u s e R n o i s s e r g e R e t e l p m o C . b 2 . A e l b a T 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P s t l u s e R r o t c e S l a m r o f n I ) 5 3 4 . 0 ( ∗ 7 8 7 . 0 − ) 2 0 3 . 0 ( 4 7 3 . 0 − 3 5 3 . 0 ) 2 3 2 . 0 ( 8 3 0 . 0 ) 8 2 3 . 0 ( ∗ ∗ ∗ 8 6 8 . 0 − ) 1 8 2 . 0 ( ∗ ∗ ∗ 8 4 7 . 0 − ) 9 5 1 . 0 ( ) 7 9 2 . 0 ( 2 0 4 . 0 − ∗ ∗ ∗ 1 5 6 . 0 − ) 3 7 1 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ 8 0 1 . 0 − ) 7 6 0 . 0 ( 3 5 1 . 0 − ) 7 4 0 . 0 ( 6 3 2 . 0 − ) 3 5 0 . 0 ( 8 6 4 . 0 − ) 1 5 0 . 0 ( 2 5 5 . 0 − ) 1 5 0 . 0 ( ∗ 5 0 0 . 0 − ) 2 0 0 . 0 ( ∗ ∗ ∗ 5 0 0 . 0 ) 6 0 0 . 0 ( 5 7 0 . 0 ) 9 1 0 . 0 ( 0 0 0 . 0 ) 7 1 0 . 0 ( 6 1 0 . 0 ) 4 2 0 . 0 ( 3 1 0 . 0 ) 5 1 0 . 0 ( ∗ ∗ ∗ 3 3 1 . 0 − ∗ ∗ ∗ 1 4 1 . 0 − ∗ ∗ ∗ 9 8 1 . 0 − ) 9 2 0 . 0 ( ) 7 1 0 . 0 ( ) 3 2 0 . 0 ( ∗ ∗ ∗ 2 5 2 . 0 − ∗ ∗ ∗ 1 3 2 . 0 − ∗ ∗ ∗ 8 5 2 . 0 − ∗ ∗ ∗ 1 6 0 . 0 − ) 9 0 0 . 0 ( ∗ ∗ ∗ 5 0 1 . 0 − ) 9 1 0 . 0 ( 0 3 0 . 0 − ∗ ∗ ∗ 6 4 0 . 0 − ∗ ∗ ∗ 6 6 0 . 0 − ) 5 0 0 . 0 ( ) 8 0 0 . 0 ( ∗ ∗ ∗ 6 3 1 . 0 − ∗ ∗ ∗ 0 1 1 . 0 − ∗ ∗ ∗ 8 1 1 . 0 − ) 6 3 0 . 0 ( ) 9 1 0 . 0 ( ∗ ∗ ∗ 4 6 4 . 0 − ∗ ∗ ∗ 2 3 3 . 0 − ∗ ∗ ∗ ) 0 4 0 . 0 ( ) 5 1 0 . 0 ( ∗ ∗ ∗ 6 0 6 . 0 − ∗ ∗ ∗ 0 8 4 . 0 − ∗ ∗ ∗ ) 2 4 0 . 0 ( ) 3 2 0 . 0 ( ∗ ∗ ∗ 4 3 6 . 0 − ∗ ∗ ∗ 0 9 5 . 0 − ∗ ∗ ∗ ) 5 3 0 . 0 ( ∗ ∗ ∗ 6 0 0 . 0 − ∗ ∗ ∗ ) 1 5 0 . 0 ( 0 0 0 . 0 ) 2 0 0 . 0 ( ∗ 6 0 0 . 0 − ) 3 0 0 . 0 ( ∗ ∗ 1 4 0 . 0 ) 4 1 0 . 0 ( 2 2 0 . 0 ) 3 1 0 . 0 ( ) 6 2 0 . 0 ( 7 2 0 . 0 − 6 2 0 . 0 − ) 7 3 0 . 0 ( ∗ ∗ ∗ ) 1 0 0 . 0 ( 6 0 0 . 0 ) 2 0 0 . 0 ( 1 1 0 . 0 ) 1 1 0 . 0 ( 2 7 0 . 0 ) 1 1 0 . 0 ( 5 2 0 . 0 − ) 5 1 0 . 0 ( ∗ 3 3 0 . 0 − ) 7 1 0 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ) 2 2 0 . 0 ( 6 5 3 . 0 − ) 0 2 0 . 0 ( 8 3 5 . 0 − ) 8 2 0 . 0 ( 0 1 6 . 0 − ) 2 3 0 . 0 ( 7 0 0 . 0 − ) 2 0 0 . 0 ( 4 0 0 . 0 ) 3 0 0 . 0 ( 6 0 0 . 0 ) 9 0 0 . 0 ( 1 6 0 . 0 ∗ ∗ ) 6 1 0 . 0 ( ∗ 7 3 0 . 0 − ) 0 2 0 . 0 ( 8 7 0 . 0 − ) 7 2 0 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ) 3 1 0 . 0 ( 6 2 2 . 0 − ) 0 3 0 . 0 ( 5 9 5 . 0 − ) 0 5 0 . 0 ( 6 6 8 . 0 − ) 0 4 0 . 0 ( 1 1 0 . 0 − ) 1 0 0 . 0 ( 5 1 0 . 0 ) 2 0 0 . 0 ( 6 3 0 . 0 ) 9 0 0 . 0 ( 1 2 0 . 0 ) 7 0 0 . 0 ( 3 1 0 . 0 ) 8 0 0 . 0 ( 6 1 0 . 0 ) 3 1 0 . 0 ( ) 2 4 0 . 0 ( ∗ ∗ ∗ 5 8 1 . 0 − ) 2 3 0 . 0 ( ∗ ∗ ∗ 0 0 6 . 0 − ) 3 3 0 . 0 ( ∗ ∗ ∗ 5 3 8 . 0 − ) 8 5 0 . 0 ( ∗ ∗ ∗ 7 0 0 . 0 − ∗ ∗ ∗ ) 2 0 0 . 0 ( 9 0 0 . 0 ) 3 0 0 . 0 ( ∗ 9 1 0 . 0 ) 9 0 0 . 0 ( 0 0 0 . 0 ) 9 0 0 . 0 ( ) 0 1 0 . 0 ( 2 1 0 . 0 ) 1 1 0 . 0 ( 7 1 0 . 0 − ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ) 9 0 0 . 0 ( 1 3 2 . 0 − ) 1 1 0 . 0 ( 3 7 4 . 0 − ) 3 2 0 . 0 ( 6 7 7 . 0 − ) 5 2 0 . 0 ( 3 1 0 . 0 − ) 1 0 0 . 0 ( 7 1 0 . 0 ) 2 0 0 . 0 ( 7 3 0 . 0 ) 8 0 0 . 0 ( 8 7 0 . 0 ) 6 1 0 . 0 ( 3 1 0 . 0 − ) 7 1 0 . 0 ( 2 1 0 . 0 − ) 2 1 0 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ) 5 1 0 . 0 ( 9 5 2 . 0 − ) 3 2 0 . 0 ( 1 3 5 . 0 − ) 7 2 0 . 0 ( 8 8 7 . 0 − ) 3 4 0 . 0 ( 5 1 0 . 0 − ∗ ∗ ∗ ) 1 0 0 . 0 ( 0 2 0 . 0 ∗ ∗ 2 2 0 . 0 ) 2 0 0 . 0 ( ) 9 0 0 . 0 ( 1 5 0 . 0 ) 2 1 0 . 0 ( 4 1 0 . 0 − ) 2 1 0 . 0 ( 7 2 0 . 0 − ) 7 2 0 . 0 ( ) e t a r e t i l l i = e c n e r e f e r ( n o i t a c u d E l o o h c s y r a m i r p n a h t s s e L e g a w m u m i n i M 0 0 1 / d e r a u q s e c n e i r e p x e l a i t n e t o P e c n e i r e p x e l a i t n e t o p f o s r a e Y e t s a c r o e b i r t d e l u d e h c S d e i r r a m y l t n e r r u C u d n i H n a m a y b d e d a e h d l o h e s u o H l o o h c s y r a m i r P l o o h c s e l d d i M l o o h c s y r a d n o c e S l o o h c s e t a u d a r G . d e u n i t n o C l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 60 ASIAN DEVELOPMENT REVIEW n e m o W n e M n e m o W n e M n a b r U . d e u n i t n o C . b 2 . A e l b a T l a r u R 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P s t l u s e R r o t c e S l a m r o f n I 8 0 0 . 0 ) 1 1 0 . 0 ( 1 0 0 . 0 ) 1 0 0 . 0 ( ∗ 1 0 0 . 0 ) 1 0 0 . 0 ( 0 1 1 . 0 ) 0 8 0 . 0 ( 7 6 0 . 0 ∗ ∗ 5 1 0 . 0 ) 1 4 0 . 0 ( ) 7 0 0 . 0 ( ) 0 1 0 . 0 ( 4 0 0 . 0 − . . . . ∗ ∗ ∗ 5 0 0 . 0 ) 1 0 0 . 0 ( 4 0 0 . 0 ) 8 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( 1 0 0 . 0 ) 1 0 0 . 0 ( ) 1 3 0 . 0 ( 8 0 0 . 0 ) 8 1 0 . 0 ( 6 2 0 . 0 − ) 2 1 0 . 0 ( 9 1 0 . 0 − ∗ ∗ ∗ 6 0 0 . 0 − ∗ ∗ ∗ ) 1 0 0 . 0 ( 2 9 0 . 0 − ) 4 1 0 . 0 ( 2 0 0 . 0 − ) 3 0 0 . 0 ( 5 2 6 4 1 , 3 0 2 9 3 , ∗ ∗ 1 2 0 . 0 ) 8 0 0 . 0 ( ∗ ∗ 1 0 0 . 0 − ) 0 0 0 . 0 ( 1 0 0 . 0 ) 2 0 0 . 0 ( ) 0 5 0 . 0 ( 4 5 0 . 0 − ∗ ∗ ∗ 7 6 0 . 0 − ) 6 0 0 . 0 ( ∗ ∗ ∗ 6 0 0 . 0 − ∗ ∗ 9 1 0 . 0 ) 1 0 0 . 0 ( ) 9 0 0 . 0 ( . . . . ) 1 0 0 . 0 ( 2 0 0 . 0 − 8 0 1 , 7 5 ∗ ∗ 7 1 0 . 0 ) 7 0 0 . 0 ( ) 2 0 0 . 0 ( 0 0 0 . 0 − 2 0 0 . 0 − ) 6 0 0 . 0 ( 7 1 0 . 0 ) 1 2 0 . 0 ( 4 0 0 . 0 ) 2 3 0 . 0 ( 4 0 0 . 0 0 0 0 . 0 ) 4 0 0 . 0 ( 2 0 0 . 0 − 2 4 0 . 0 − ) 5 8 0 . 0 ( 1 0 0 . 0 ) 5 0 0 . 0 ( ∗ ∗ ∗ ) 4 0 0 . 0 ( ∗ 8 0 0 . 0 − ∗ ∗ ∗ 1 0 0 . 0 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ 1 0 0 . 0 ) 0 0 0 . 0 ( ) 0 0 0 . 0 ( ∗ ∗ 0 7 0 . 0 ) 2 3 0 . 0 ( 9 9 0 . 0 ) 2 3 0 . 0 ( 4 1 0 . 0 ) 3 0 0 . 0 ( ∗ ∗ 6 2 0 . 0 − ) 0 1 0 . 0 ( . . . . ∗ ∗ ∗ 5 0 0 . 0 ) 1 0 0 . 0 ( 5 0 0 . 0 ) 4 0 0 . 0 ( ∗ ∗ ∗ 2 0 0 . 0 − ∗ ∗ ∗ ∗ ∗ ∗ ) 0 0 0 . 0 ( 3 0 0 . 0 ) 1 0 0 . 0 ( 7 6 1 . 0 ) 6 4 0 . 0 ( ∗ ∗ ∗ 2 7 0 . 0 − ) 2 1 0 . 0 ( ∗ ∗ ∗ 3 1 0 . 0 − ∗ ∗ ∗ ∗ ∗ ∗ ) 4 0 0 . 0 ( 9 0 0 . 0 ) 2 0 0 . 0 ( 2 1 1 . 0 ) 6 1 0 . 0 ( ∗ ∗ ∗ 3 0 0 . 0 − ) 1 0 0 . 0 ( 4 0 0 . 0 ) 0 0 3 . 0 ( ∗ 1 0 0 . 0 ) 1 0 0 . 0 ( ∗ 0 1 0 . 0 ) 5 0 0 . 0 ( ∗ 3 5 1 . 0 ) 1 8 0 . 0 ( ∗ ∗ 8 0 0 . 0 − ) 3 0 0 . 0 ( 1 0 0 . 0 − ∗ ∗ 1 5 0 . 0 ) 2 0 0 . 0 ( ) 3 2 0 . 0 ( . . . . ∗ ∗ ∗ ∗ 7 0 0 . 0 ) 4 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( ∗ ∗ ∗ 7 0 0 . 0 − ∗ ∗ ∗ ∗ ∗ ∗ ) 2 0 0 . 0 ( 2 1 1 . 0 ) 0 3 0 . 0 ( 8 3 0 . 0 ) 7 0 0 . 0 ( ∗ ∗ ∗ 5 2 0 . 0 − ∗ ∗ ∗ ) 8 0 0 . 0 ( 8 0 0 . 0 ∗ ∗ 2 6 0 . 0 ) 2 0 0 . 0 ( ) 1 2 0 . 0 ( ) 5 0 0 . 0 ( ∗ 0 1 0 . 0 − ∗ ∗ ∗ ) 2 0 0 . 0 ( 7 0 0 . 0 − d l o h e s u o h n i n e r d l i h c l o o h c s e r p f o . o N s t n e m t s u j d A : s n o i t a l u g e r e t a t S t c u d o r p c i t s e m o d e t a t s t e N e t a r t n e m y o l p m e n u e t a t S s e t u p s i D : s n o i t a l u g e r e t a t S s n o i t c e p s n I : t n e m e c r o f n E s e i t i r a l u g e r r I : t n e m e c r o f n E s e n ﬁ / w s e s a C : t n e m e c r o f n E s e n ﬁ f o e u l a V : t n e m e c r o f n E 6 2 4 , 2 8 1 2 2 9 , 7 2 1 3 8 , 7 5 2 5 1 , 8 7 4 5 3 , 0 4 1 s n o i t a v r e s b o f o . o N < p = ∗ ∗ ∗ . e t a t s y b d e r e t s u l c e r a , s e s e h t n e r a p n i , s r o r r e d r a d n a t S . s t h g i e w e l p m a s n o i t a z i n a g r O y e v r u S e l p m a S l a n o i t a N h t i w . s m r e t n o i t c a r e t n i e m i t – e t a t s d n a , s e i m m u d e m i t , s e i m m u d e t a t s e d u l c n i s n o i s s e r g e r l l A . 0 1 . 0 < p = ∗ , 5 0 . 0 < p = ∗ ∗ , 1 0 . 0 l e v e l l a n o i t a n o t d e t h g i e W : s e t o N l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 61 n a b r U l a r u R n e m o W n e M n e m o W n e M 5 0 0 2 r e t f a d n a e r o f e b , d e y o l p m E - f l e S e h t r o f s n o i t a m i t s E t n e m y o l p m E r o f s t l u s e R n o i s s e r g e R e t e l p m o C . c 2 . A e l b a T 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P s t l u s e R d e y o l p m E - f l e S ) 4 0 0 . 0 ( 1 0 0 . 0 − ∗ ∗ ∗ 1 2 0 . 0 − ) 6 0 0 . 0 ( ) 0 1 0 . 0 ( 8 0 0 . 0 − ) 6 0 0 . 0 ( 0 1 0 . 0 − ) 2 1 0 . 0 ( 6 0 0 . 0 − ) 0 1 0 . 0 ( 6 1 0 . 0 − ) 5 3 0 . 0 ( 9 5 0 . 0 − ∗ ∗ 4 8 0 . 0 − ) 3 3 0 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ∗ 6 0 0 . 0 ) 3 0 0 . 0 ( ) 1 0 0 . 0 ( ∗ 2 0 0 . 0 − 2 0 0 . 0 − ) 1 0 0 . 0 ( 3 0 0 . 0 − ) 1 0 0 . 0 ( 3 0 0 . 0 − ) 1 0 0 . 0 ( 0 0 0 . 0 − ) 0 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( 1 0 0 . 0 ) 1 0 0 . 0 ( 1 0 0 . 0 ) 1 0 0 . 0 ( ) 2 0 0 . 0 ( 1 0 0 . 0 − 2 0 0 . 0 − ) 1 0 0 . 0 ( 2 0 0 . 0 ) 4 0 0 . 0 ( 2 0 0 . 0 − ) 4 0 0 . 0 ( 2 0 0 . 0 − ) 4 0 0 . 0 ( ∗ 5 0 0 . 0 − ) 3 0 0 . 0 ( 3 0 0 . 0 − ) 2 0 0 . 0 ( 0 0 0 . 0 − ) 0 0 0 . 0 ( ∗ ∗ 4 0 0 . 0 ) 0 0 0 . 0 ( ) 2 0 0 . 0 ( 0 0 0 . 0 ) 2 0 0 . 0 ( 1 0 0 . 0 − 4 0 0 . 0 − ) 3 0 0 . 0 ( 5 0 0 . 0 − ) 4 0 0 . 0 ( ∗ ∗ 0 0 0 . 0 − ) 2 0 0 . 0 ( 2 0 0 . 0 − ) 2 0 0 . 0 ( 1 0 0 . 0 − ) 1 0 0 . 0 ( ∗ 3 0 0 . 0 − ) 1 0 0 . 0 ( 3 0 0 . 0 − ) 1 0 0 . 0 ( 0 0 0 . 0 − ) 0 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( 1 0 0 . 0 ) 1 0 0 . 0 ( 0 0 0 . 0 − ) 1 0 0 . 0 ( 0 0 0 . 0 ) 1 0 0 . 0 ( ) 1 0 0 . 0 ( 1 0 0 . 0 − 0 0 0 . 0 − ) 3 0 0 . 0 ( 0 0 0 . 0 − ) 2 0 0 . 0 ( ∗ 3 0 0 . 0 − ) 2 0 0 . 0 ( ∗ 3 0 0 . 0 − ) 2 0 0 . 0 ( ∗ 4 0 0 . 0 − ) 2 0 0 . 0 ( 0 0 0 . 0 − ) 0 0 0 . 0 ( ∗ ∗ 2 0 0 . 0 ) 0 0 0 . 0 ( ) 1 0 0 . 0 ( 0 0 0 . 0 ) 1 0 0 . 0 ( 0 0 0 . 0 − 0 0 0 . 0 − ) 1 0 0 . 0 ( 1 0 0 . 0 − ) 3 0 0 . 0 ( 5 0 0 . 0 ) 5 0 0 . 0 ( 0 0 0 . 0 ) 2 0 0 . 0 ( 4 0 0 . 0 ) 3 0 0 . 0 ( 0 0 0 . 0 ) 3 0 0 . 0 ( 1 0 0 . 0 ) 4 0 0 . 0 ( ∗ 1 0 0 . 0 ) 0 0 0 . 0 ( ) 0 0 0 . 0 ( ∗ 1 0 0 . 0 − 1 0 0 . 0 − ) 2 0 0 . 0 ( 1 0 0 . 0 − ) 1 0 0 . 0 ( ∗ ∗ 4 0 0 . 0 ) 2 0 0 . 0 ( ∗ ∗ ∗ 5 0 0 . 0 − ) 2 0 0 . 0 ( 1 0 0 . 0 − ) 4 0 0 . 0 ( 0 0 0 . 0 ) 4 0 0 . 0 ( 4 0 0 . 0 ) 4 0 0 . 0 ( 3 0 0 . 0 ) 5 0 0 . 0 ( 2 0 0 . 0 ) 4 0 0 . 0 ( ∗ 1 0 0 . 0 ) 0 0 0 . 0 ( ) 1 0 0 . 0 ( 1 0 0 . 0 − 2 0 0 . 0 − ) 4 0 0 . 0 ( 6 0 0 . 0 − ) 5 0 0 . 0 ( 4 0 0 . 0 ) 3 0 0 . 0 ( ) 7 0 0 . 0 ( 6 0 0 . 0 − 3 0 0 . 0 ) 3 0 0 . 0 ( 4 0 0 . 0 ) 5 0 0 . 0 ( 2 0 0 . 0 ) 5 0 0 . 0 ( ) 5 0 0 . 0 ( ∗ ∗ 9 0 0 . 0 − 5 0 0 . 0 − ∗ ∗ ∗ ∗ ∗ ∗ ) 4 0 0 . 0 ( 1 0 0 . 0 ) 0 0 0 . 0 ( 1 0 0 . 0 − ) 1 0 0 . 0 ( 1 1 0 . 0 ) 2 0 0 . 0 ( ∗ 5 0 0 . 0 − ) 3 0 0 . 0 ( 4 0 0 . 0 ) 3 0 0 . 0 ( 1 0 0 . 0 ) 2 0 0 . 0 ( ∗ ∗ ∗ ∗ 5 0 0 . 0 ) 3 0 0 . 0 ( 2 0 0 . 0 ) 4 0 0 . 0 ( 1 0 0 . 0 − ) 4 0 0 . 0 ( ∗ 8 0 0 . 0 − ) 4 0 0 . 0 ( 4 1 0 . 0 − ) 4 0 0 . 0 ( ∗ ∗ 1 0 0 . 0 ) 0 0 0 . 0 ( ∗ ∗ ∗ ) 1 0 0 . 0 ( 2 1 0 . 0 ) 3 0 0 . 0 ( ∗ ∗ 6 0 0 . 0 − 1 0 0 . 0 − ∗ ∗ 5 0 0 . 0 ) 2 0 0 . 0 ( ) 2 0 0 . 0 ( 4 0 0 . 0 ) 6 0 0 . 0 ( ) e t a r e t i l l i = p u o r g e c n e r e f e r ( n o i t a c u d E l o o h c s y r a m i r p n a h t s s e L e g a w m u m i n i M 0 0 1 / d e r a u q s e c n e i r e p x e l a i t n e t o P e c n e i r e p x e l a i t n e t o p f o s r a e Y e t s a c r o e b i r t d e l u d e h c S d e i r r a m y l t n e r r u C u d n i H n a m a y b d e d a e h d l o h e s u o H l o o h c s y r a m i r P l o o h c s e l d d i M l o o h c s y r a d n o c e S l o o h c s e t a u d a r G . d e u n i t n o C l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 62 ASIAN DEVELOPMENT REVIEW n e m o W n e M n e m o W n e M n a b r U . d e u n i t n o C . c 2 . A e l b a T l a r u R 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P 5 0 0 2 - t s o P 5 0 0 2 - e r P s t l u s e R d e y o l p m E - f l e S ) 1 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( 0 0 0 . 0 ) 1 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( 1 0 0 . 0 − ) 0 0 0 . 0 ( 0 0 0 . 0 − ∗ ∗ ∗ 1 0 0 . 0 − ) 0 0 0 . 0 ( . . . . ∗ ∗ ∗ 0 0 0 . 0 − ) 0 0 0 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ 0 0 0 . 0 ) 2 0 0 . 0 ( 1 0 0 . 0 ) 0 0 0 . 0 ( 1 0 0 . 0 ) 0 0 0 . 0 ( 3 0 0 . 0 ) 1 0 0 . 0 ( 6 0 0 . 0 ) 0 0 0 . 0 ( ∗ 0 0 0 . 0 ) 0 0 0 . 0 ( ∗ ∗ ∗ 2 0 0 . 0 − ∗ ∗ ∗ ) 0 0 0 . 0 ( 6 2 0 . 0 ) 0 0 0 . 0 ( ∗ ∗ ∗ 1 0 0 . 0 − ) 0 0 0 . 0 ( 1 0 0 . 0 ) 0 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( 3 0 0 . 0 ) 2 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( ∗ ∗ 1 0 0 . 0 ) 0 0 0 . 0 ( ) 0 0 0 . 0 ( 0 0 0 . 0 − . . . . ) 0 0 0 . 0 ( 0 0 0 . 0 − 8 0 1 , 7 5 0 0 0 . 0 ) 0 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ) 0 0 0 . 0 ( 7 0 0 . 0 ) 0 0 0 . 0 ( 5 0 0 . 0 ) 1 0 0 . 0 ( 1 0 0 . 0 ) 0 0 0 . 0 ( ∗ ∗ ∗ 1 0 0 . 0 − ) 0 0 0 . 0 ( 2 0 0 . 0 ) 2 0 0 . 0 ( 0 0 0 . 0 − ∗ ∗ ∗ 0 0 0 . 0 ) 1 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( 0 0 0 . 0 ∗ ∗ 3 0 0 . 0 ) 0 0 0 . 0 ( ) 1 0 0 . 0 ( 2 0 0 . 0 ) 1 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( ∗ ∗ 1 0 0 . 0 − ) 0 0 0 . 0 ( . . . . ∗ ∗ ∗ 1 0 0 . 0 − ) 0 0 0 . 0 ( ∗ ∗ ∗ ) 0 0 0 . 0 ( 0 0 0 . 0 − ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ 1 0 0 . 0 ) 2 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( 4 1 0 . 0 ) 3 0 0 . 0 ( 1 0 0 . 0 ) 1 0 0 . 0 ( ) 0 0 0 . 0 ( 0 0 0 . 0 − ∗ ∗ ∗ 0 0 0 . 0 − ∗ ∗ ∗ ) 0 0 0 . 0 ( 5 0 0 . 0 ) 1 0 0 . 0 ( ) 0 0 0 . 0 ( 0 0 0 . 0 − ∗ ∗ ∗ ) 1 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( 1 0 0 . 0 1 0 0 . 0 − ∗ ∗ ∗ ∗ ∗ 1 2 0 . 0 ) 1 0 0 . 0 ( ) 0 1 0 . 0 ( 3 0 0 . 0 ) 1 0 0 . 0 ( ) 0 0 0 . 0 ( 4 0 0 . 0 ) 3 0 0 . 0 ( 0 0 0 . 0 − . . . . ∗ ∗ 2 0 0 . 0 − ) 1 0 0 . 0 ( ∗ ∗ ∗ 1 0 0 . 0 ) 1 0 0 . 0 ( 0 0 0 . 0 ) 0 0 0 . 0 ( ∗ ∗ 1 0 0 . 0 − ∗ ∗ ∗ ∗ ∗ ∗ ) 0 0 0 . 0 ( 8 1 0 . 0 ) 5 0 0 . 0 ( 0 1 0 . 0 ) 1 0 0 . 0 ( ∗ ∗ 3 0 0 . 0 − ∗ ∗ ∗ ) 1 0 0 . 0 ( ) 0 0 0 . 0 ( 0 0 0 . 0 − 6 0 0 . 0 − ) 4 0 0 . 0 ( 1 0 0 . 0 − ) 0 0 0 . 0 ( d l o h e s u o h n i n e r d l i h c l o o h c s e r p f o . o N s t n e m t s u j d A : s n o i t a l u g e r e t a t S t c u d o r p c i t s e m o d e t a t s t e N e t a r t n e m y o l p m e n u e t a t S s e t u p s i D : s n o i t a l u g e r e t a t S s n o i t c e p s n I : t n e m e c r o f n E s e i t i r a l u g e r r I : t n e m e c r o f n E s e n ﬁ / w s e s a C : t n e m e c r o f n E s e n ﬁ f o e u l a V : t n e m e c r o f n E < p = ∗ ∗ ∗ . e t a t s y b d e r e t s u l c e r a , s e s e h t n e r a p n i , s r o r r e d r a d n a t S . s t h g i e w e l p m a s n o i t a z i n a g r O y e v r u S e l p m a S l a n o i t a N h t i w . s m r e t n o i t c a r e t n i e m i t – e t a t s d n a , s e i m m u d e m i t , s e i m m u d e t a t s e d u l c n i s n o i s s e r g e r l l A . 0 1 . 0 < p = ∗ , 5 0 . 0 < p = ∗ ∗ , 1 0 . 0 l e v e l l a n o i t a n o t d e t h g i e W : s e t o N l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 . s n o i t a l u c l a c ’ s r o h t u A : e c r u o S 5 2 6 4 1 , 3 0 2 9 3 , 6 2 4 , 2 8 1 2 2 9 , 7 2 1 3 8 , 7 5 2 5 1 , 8 7 4 5 3 , 0 4 1 s n o i t a v r e s b o f o . o N IMPACT OF THE MINIMUM WAGE ON EMPLOYMENT AND EARNINGS IN INDIA 63 Table A.3. Labor Force Participation Rates and the Minimum Wage Before 2005 High minimum wage state −1.372 (6.363) Male High minimum wage state ∗Male After Before After 2005 2005 2005 6.558∗∗ 6.434∗∗ −2.141 (2.734) (7.051) (2.706) 0.166∗ −0.482 (0.413) (0.078) 1.277 −0.240∗∗ (0.108) (1.795) Notes: Weighted to national level with National Sample Survey Organization sample weights. Standard errors, in parentheses, are clustered by state. The notation ∗∗∗ = p < 0.01, ∗∗ = p < 0.05, ∗ = p < 0.10. All regressions include state dummies and time dummies. Source: Authors’ calculations. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . Figure A.1. Kernel Density Estimates of Relative Real Wages by State / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 64 ASIAN DEVELOPMENT REVIEW Figure A.1. Continued. Source: Authors’ calculations. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 4 1 2 8 1 6 4 2 7 7 8 a d e v _ a _ 0 0 0 8 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3
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