Wages Over the Course of Structural
Transformation: Evidence from India
Rana Hasan and Rhea Molato∗

This paper uses labor force survey data from India for 2000 y 2012 to examine
how wages behave over the course of structural transformation. encontramos que
wage employment between 2000 y 2012 displays the patterns one would
expect for an economy undergoing structural transformation, with employment
shares shifting from agriculture to industry and services, and from rural to urban
areas and larger cities within urban areas. These shifts, as well as a shift to
nonroutine occupations and routine manual occupations outside of agriculture,
are associated with an improvement in average wages. Finalmente, simple Mincerian
wage regressions confirm that jobs in larger firms and big cities are associated
with significantly higher wages—even more so for women. En general, nuestros resultados
are consistent with the notion that policies that encourage the expansion of the
formal sector and employment in larger firms are crucial for development.

Palabras clave: structural change, wage level and structure, wages
JEL codes: E24, J31, L16

I. Introducción

literature on structural

There is a large empirical

transformation that
documents and analyzes the shift of output and employment across sectors. (Ver
Asian Development Bank [ADB] 2013 for a comprehensive survey and analysis
for Asia and the Pacific.) By and large, the shift takes place from lower to higher
productivity sectors and locations over the course of development. This is what
McMillan, Rodrik, and Verduzco-Gallo (2014) find for Asia, Por ejemplo, en
contrast to Africa and Latin America. De término medio, labor productivity in the region
increased 3.87% de 1990 a 2005, of which 3.31% of the growth was registered
by “within” sector improvements in labor productivity and 0.57% by shifts in
employment shares from lower to higher productivity sectors (called “structural
change” by the authors). In the case of India, the labor productivity growth figures

∗Rana Hasan (Autor correspondiente): Director, Economic Research and Regional Cooperation Department, asiático
Development Bank, Manila, Philippines. Correo electrónico: rhasan@adb.org; Rhea Molato: Economics and Statistics Analyst,
Economic Research and Regional Cooperation Department, Asian Development Bank, Manila, Philippines.
Correo electrónico: rmolato@adb.org. The authors thank Erik Jan Eleazar for research assistance and participants at the Asian
Development Review Conference on New Perspectives of Asian Development for very useful comments on a
previous draft of this paper. They would also like to thank the managing editor and journal referees for helpful
suggestions. The usual ADB disclaimer applies.

Asian Development Review, volumen. 36, No. 2, páginas. 131–158
https://doi.org/10.1162/adev_a_00134

© 2019 Asian Development Bank and
Asian Development Bank Institute.
Publicado bajo Creative Commons
Atribución 3.0 Internacional (CC POR 3.0) licencia.

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

.

/

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

132 Asian Development Review

son 4.23% for the overall economy and 3.24% y 0.99% for the within and
structural change components, respectivamente. A diferencia de, labor productivity growth
in Africa and Latin America has been associated with negative contributions from
the structural change component (in addition to being relatively low compared with
Asia).

What is rarer in the literature is an examination of how wages behave over
the course of structural transformation and its various component processes. Un
exception is analysis of the significant wage gaps that exist between agriculture
and other sectors. Several studies, including recent ones such as Alvarez (2017)
and Herrendorf and Schoellman (2017), examine these gaps and attempt
a
determine whether they can be explained by barriers to mobility across sectors
or whether unobserved worker characteristics (p.ej., innate capabilities that enable
some individuals to benefit more from schooling) lead to a systematic sorting of
workers, with the more capable finding employment outside of agriculture.

en este documento, we take a step back and use labor force survey data from India to
examine how wages behave over the course of structural transformation, especially
in terms of its less studied aspects. Específicamente, we first examine how wages and
employment have evolved not only across production sectors, but also in terms of
shifts across occupation groups and from rural to urban areas, distinguishing the
latter in terms of whether urban areas take the form of large cities (population of 1
million or more in the first year of our analysis) or smaller cities and towns. Nosotros entonces
use a standard decomposition that describes how important such shifts have been
in driving average wages in the economy. We also examine employment shifts and
wages across firms with fewer than 10 workers and those with 10 or more workers.
We call the former small firms and treat them as synonymous with operations in
the informal sector; the remaining firms are called large. In keeping with practice in
Indian manufacturing where firms with 10 or more workers (and those using power
in the production process) must be registered under the Factories Act, we treat these
large firms as belonging to the formal sector.1 Finally, we use simple Mincerian
wage regressions to examine the relationship between wages and the more novel
elements of structural transformation we examine—i.e., employment in larger firms
and cities—while controlling for individual characteristics, sector, and occupation.
Our work adds to the standard macro analysis of structural transformation in
two ways. Primero, rather than examine the evolution of productivity over the course of
structural transformation, we consider the evolution of wages. Though intimately
related, arguably it is wages that are more directly linked to individual welfare
than productivity. Segundo, we extend the usual analysis of shifts in the sector
of employment to also consider the role of shifts in occupation and rural–urban
ubicación, and to a more limited extent the role of small firms versus larger firms, en

1Desafortunadamente, and as explained later, data gaps prevent us from understanding the role large firms have

actually played in driving average wages in India from 2000 a 2012.

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

/

.

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Wages Over the Course of Structural Transformation 133

driving average wages. Al hacerlo, we are able to consider the role of occupational
changes and urban agglomerations—factors that the recent literature on growth and
development has been paying much attention to. As Duernecker and Herrendorf
(2017) nota, occupations play an important role in the behavior of key labor
market outcomes such as worker mobility and job polarization. They are also not
affected by a “relabeling” of employment as certain types of work get outsourced
from manufacturing firms to arm’s-length service providers. Similarmente, cities are
often viewed as engines of growth. As Henderson (2014) notas, this is because
the reallocation of employment from agriculture into industry, En particular, takes
place most effectively in cities given agglomeration economies. These additional
dimensions we analyze are closely tied to the idea that structural transformation
involves the capability to produce more diversified and complex products. Este último
requires the emergence of more capable and sophisticated firms, a proxy for which
would be the expansion of employment in firms in the formal sector and/or firms
with employment above some threshold level (p.ej., 10 or more workers), and a shift
in occupations—specifically, a growth in occupations that involve more analytical
trabajar.

We examine the case of India not only because it is interesting in and of
sí mismo, but also because it has broader relevance given that India’s labor force
survey allows one to examine aspects of employment
that are typically not
posible, such as employment in firms and cities of different sizes. Starting from
2000, labor force surveys from India (the employment–unemployment surveys
conducted by the National Sample Survey Office, henceforth NSS-EUS) enable us
to identify whether wage workers are employed in small (informal) or large (formal)
enterprises, and whether urban wage workers are employed in urban centers with a
population of 1 million or more. As far as we are aware, this is not possible with
other labor force surveys in the region.

Our paper mainly focuses on the earnings of wage and salaried workers
because information on earnings of self-employed workers is not readily available
in labor force surveys. Sin embargo, we extend our analysis to self-employed workers
by imputing their earnings based on the earnings of wage and salaried workers with
similar characteristics.

Our main findings are as follows. Primero, we find that employment in India
over the 12 years from 2000 a 2012 displays the patterns one would expect for
an economy undergoing structural transformation. Eso es, the employment shares
of wage workers shift from agriculture to industry and services, and from rural
to urban areas. Based on the previous literature, these shifts are in the direction
of the higher productivity group—for example, see Hasan et al. (2017) on labor
productivity across informal and formal sector firms in Indian apparel.

Segundo, and more importantly, we find that such shifts in employment have
been associated with an improvement in average wages. En particular, we use
the decomposition of changes in aggregate labor productivity into within sector

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

/

.

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

134 Asian Development Review

and structural change (or between sector) components—as in ADB (2013) y
McMillan, Rodrik, and Verduzco-Gallo (2014)—to decompose changes in average
national wages into analogous components. We find that structural change in some
dimensions can account for as much as a quarter to almost a third of the increase in
average wages.

Finalmente, simple Mincerian wage regressions confirm that—when controlling
para
individual demographics, educational attainment, and even industry of
employment and occupational status—a job in a firm with 10 or more workers (y
thus a formal sector firm for all practical purposes) and in a large city is associated
with higher wages. Significantly, the “premium” to being male is lower in larger
firms and cities, suggesting that gender biases diminish along the path of structural
transformación. More generally, we find that returns to education are higher in larger
firms and in urban areas.

En general, our results are consistent with the idea that policies that encourage
expansion of the formal sector and employment in larger firms are crucial for
desarrollo. Whether this expansion needs to occur through the formalization or
expansion of small firms, or whether policy needs to encourage investment by larger
firms in the first place, is not something we can comment on. Our results are also
consistent with the idea that urban agglomerations have a key role in providing
better paying jobs—regardless of the sector of economic activity—especially for
hembras.

This paper is structured as follows. Section II explains how it fits with
the literature on structural transformation and labor market outcomes. Section III
describes the wage decomposition framework and the Mincerian wage equation
used in our analysis. Section IV explains how variables are constructed using India’s
labor force survey data. Section V gives the results, describing employment and
wages in India over the course of its structural transformation. Section VI contains
the conclusions.

II. Literature Review

This paper is motivated by at least two strands of the development literature:
(i) structural transformation and (ii) labor market outcomes. ADB (2013) proporciona
a comprehensive discussion of structural transformation, covering both conceptual
and empirical issues. Structural transformation involves the transformation of the
productive structure of an economy and involves a variety of interrelated processes.
These include (i) changes in the structure of production and employment of an
economy—the starkest manifestation of which involves a reduction in the shares of
output and employment from agriculture and a corresponding increase in output and
employment shares in industry and services; (ii) the production of more diversified
and complex products, cual, Sucesivamente, may be captured by two related processes: (a)
the emergence of more “capable and sophisticated” firms, a crude proxy for which,

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

.

/

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Wages Over the Course of Structural Transformation 135

would be the expansion of employment in firms in the formal sector and/or firms
with employment above some threshold level (p.ej., 10 or more workers); y (b)
a shift in occupations (p.ej., growth in occupations that involve more cognitive or
analytical work); y (iii) the process of urbanization, which involves a growing
share of population, employment, and production in urban areas.

While the various processes listed above involve an increase in productivity,
how these affect workers is less well documented. The widespread concern about
the quality of jobs and the widespread use of terms such as “good jobs” is a
testimony to the idea that even as countries develop and experience structural
transformación, workers may not be benefiting (en
in a commensurate
manner). Notwithstanding the fact that labor productivity and wages are related,
the relationship between the two is far from watertight. Wage growth depends not
only on how much labor productivity grows, but also on how workers’ share in
output evolves. This is partly determined by the nature of technological change,
but other factors also matter, such as the extent of competition in product markets,
workers’ bargaining power, the relative mobility of capital versus labor, e incluso
social norms. Además, general equilibrium considerations also kick in as overall
supply and demand conditions in labor markets influence wages over and above
sectoral labor productivity. For early expositions of this point, see the works of
Luis (1954) and Harris and Todaro (1970).

el menos

This is where the large literature on labor market outcomes comes in.
There are many different types of studies, including those which examine the
relationship between different types of regulations (especially labor regulations) en
outcomes such as wages; studies that examine employment and wage relationships
across sectors and types of firms (including formal and informal firms); y, más
recently, studies that examine the relationship between urban agglomerations and
employment and wages.

Focusing on the last, cities are widely believed to be engines of economic
growth and good jobs. In this context, the urbanization process under way in India
(and Asia more generally) is good news. Sin embargo, the link between urbanization
and economic dynamism is not assured and a number of urban experts have raised
concerns about the nature of urbanization underway in the developing world. Para
ejemplo, Gollin, Jedwab, and Vollrath (2016) bring up the case of two cities,
Shanghai and Lagos. Both are large cities in countries with similar urbanization
tarifas. Sin embargo, it is highly unlikely that their potential to deliver on better economic
outcomes for their residents is the same. Similar concerns are raised by Henderson
(2014). In a nutshell, the concern that the urbanization underway in the region may
not lead to significantly better jobs is driven by the possibility that underinvestment
in infrastructure, weak spatial planning, and poor land-use policies lead the forces
of congestion to overwhelm the standard benefits of agglomeration (es decir., thicker
local labor markets, better input–output linkages, and the potential for knowledge
spillovers).

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

.

/

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

136 Asian Development Review

One strand of the literature on structural transformation that focuses directly
on wages seeks to understand why wages in agriculture—the largest single
employer in most Asian economies—tend to be far lower than wages in other
sectors. Alvarez (2017) and Herrendorf and Schoellman (2017) are two recent
contributions in this line of the literature. Using labor force survey data from around
the developing world, including panel survey data for Brazil by Alvarez (2017) eso
allows him to track entry and exit by workers across sectors, the papers find more
support for the role of unobserved worker characteristics, such as innate capabilities
that enable some individuals to benefit more from schooling, to drive a large part of
the wage gaps. This is contrary, sin embargo, to studies that ascribe an important role
to barriers to reallocation of resources across sectors (ver, Por ejemplo, McMillan,
Rodrik, and Verduzco-Gallo 2014).

III. Framework for Analysis

Much of our analysis relies on reporting average wages for groupings
that are economically meaningful. En particular, in addition to reporting wages
across production sectors, we document average wages across occupational groups;
locations (rural areas, smaller cities and towns, and large cities); and small
(informal) and larger (formal) firms. We also decompose wage growth to understand
how much of it is driven by shifts in employment from lower-wage to higher-wage
groupings. Finalmente, we run standard Mincerian regressions to check whether some of
the more important average wage differentials we work with in our decompositions
remain after controlling for observable characteristics of workers (edad, género, y
educational attainment).

The decomposition of wage growth parallels the work on decomposing
the components of labor productivity growth (ver, Por ejemplo, ADB 2013; y
McMillan, Rodrik, and Verduzco-Gallo 2014) and is computed as follows:

(cid:2)Wt =

(cid:2)

(cid:4)

(cid:3)
(cid:2)Ei
t

W i

t−1

+

(cid:2)

(cid:3)
(cid:2)W i

t )Ei

t−1

(1)

i

i

dónde (cid:2)Wt is the change in average wages; Ei
t is the share of workers in a given
sector, región, or occupation of type i in year t; and W i
is the average wage of
t
workers in that sector, región, or occupation type. The first term captures the growth
of wages caused by the movement of workers from one grouping to another, cual
we call structural change, while the second term captures the growth of wages
caused by rising wages within a group.

As for the Mincerian wage regressions, these simply involve estimating the

following equation for worker i:

ln(wagei) = β1agei + β2agei

2 + β3sexi + β4educationi +

(cid:2)

β jstate j

(cid:2)

+

βkXi + ε

(2)

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

/

.

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Wages Over the Course of Structural Transformation 137

where Xi refers to variables of interest and controls such as firm size, rural–urban,
big cities, sector dummies, and occupation dummies. We introduce education in
terms of years of schooling.

IV. Data and Variable Construction

Our main source of data is the NSS-EUS. We use two rounds of the surveys,
the 55th (1999–2000, henceforth 2000) and 68th (2011–2012, henceforth 2012).
Like standard labor force surveys, they provide us information on individual
demographics (gender and age), wages, educational attainment, and sector of
employment and occupation. As they are based on nationally representative surveys
of households, they capture information on workers in both the formal and informal
sectors; they also capture wage and salaried workers as well as self-employed
workers.

Earnings data are collected only from wage and salaried workers. Para esto
reason, we limit much of our analysis to wage workers. Desafortunadamente, información
on wages is missing for 35% of the sample of wage workers in 2000 y
para 29% en 2012. De este modo, average wages have to be computed on the basis of
information provided by 65% of wage workers in 2000 y 71% of wage workers
en 2012. Fortunately, the pattern of missing wage information across groups
appears to be somewhat random, with sufficiently large sample sizes of nonmissing
wage observations across sectors, occupations, and educational categories with
which to compute what should be reliable estimates of average wages.2 For the
wage decomposition analysis, average wages are based on the sample with wage
respuestas, while employment shares are based on the full sample of wage workers.
Significantly, India’s labor force surveys also provide us information on the
size of firms that workers are employed in (es decir., whether the firms have 1–5, 5–9,
10–19, o 20 or more workers) and also whether urban respondents to the survey
reside in a big city or not (es decir., cities with a population of 1 million or more
en 2001). Desafortunadamente, a large number of nonresponses to the question on firm
tamaño, especially in the 2000 labor force survey, limits our ability to draw reliable
conclusions on the role of firm size in our wage decompositions. En particular, mientras
en 2012, 8% of wage workers’ firm size is reported as unknown, es 24% en 2000.3
De este modo, we exclude firm size from our analysis of wage decompositions. Sin embargo, en

2Appendix Table A1 shows the number of sample observations for all wage workers (es decir., those with and
without wage information) and for wage workers with wage data across industry, occupation, and location groups.
The sample sizes are 1,000 or more in almost all cases. Más, the distribution of sample observations is fairly
similar within any group. Por ejemplo, en 2012, 17.3% of all sample wage employees belong to agriculture (10,726
observations out of 61,912) versus 16.5% of all sample wage employees with nonmissing wages (7,193 observaciones
out of 43,691). The equivalent shares in 2000 son 42% y 38.5%, respectivamente.

3It is difficult to ascertain any specific pattern to the nonresponses. The share of unknowns and bad codes
is substantial in both urban and rural areas in 2000; acerca de 22% of urban workers and 26% of rural workers have no
meaningful response to the firm size question. Unknowns and bad codes are also spread out across sectors: 19% de
manufacturing workers; 34% of workers at public utilities; 16% of wholesale and retail trade workers; y 23% de
workers in transport, almacenamiento, and communication services.

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

.

/

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

138 Asian Development Review

our analysis of Mincerian wage regressions, we use the firm size variable as these
regressions are conducted only for 2012.

We ensure that our wage decompositions (equation [1]) are based on defining
big cities consistently across the two rounds of the labor force surveys. We do this
by taking the list of big cities in the 68th round and checking whether each one had
a population of at least 1 million in the 1991 census. This is because big cities in
the 55th round were identified on the basis of the 1991 population census. Cities
with a population of 1 million or more in 1991 were retained as big cities in the
68th round. Implicitly, we assume that all big cities in the 55th round would also be
classified as big in the 68th round.4

We use the data on earnings of wage and salaried workers over the reference
período (7 días) and information on the number of half days worked over the week
to compute daily wage rates. Since it is likely that workers reporting 6 o 7 days of
work a week are actually working 5 o 6 días (p.ej., those employed in the central
government or the corporate sector and getting Saturdays and Sundays off), we top
code days worked per week at 5 días. Fewer than 4% of workers in each year report
working fewer than 4 days a week.

Like many other labor force surveys, India’s does not collect information on
the earnings of the self-employed. To extend the analysis to self-employed workers,
we impute their earnings by predicting wages for them based on the empirical
relationship between the wages of wage workers and individual characteristics
observed in the labor force surveys, and a correction for selection of workers into
self-employment based on Heckman’s two-step procedure in line with a similar
exercise by Das et al. (2015). For the selection equation, we let zi be the probability
that worker i is a wage worker, and zi = 1 si
z∗
i
where δ is the set of identification factors including age, sexo, marital status, y
household size; wi is the coefficient of δ; and ui is the error term. If z∗
≤ 0,
i
then worker i is self-employed. We estimated a Mincerian wage equation for wage
workers as follows:

= wiδ + ui > 0

ln(wage) = β1X + ρσuλ(wiδ)

where X is a vector of worker characteristics that include dummies of age, género,
education, ubicación, marital status, and industry. ρ is the correlation between
unobserved determinants of propensity to be a wage worker and unobserved
determinants of wages, σu is the standard deviation of ui, and λ is the inverse Mills
ratio evaluated at wiδ. Finalmente, the wages of self-employed workers are estimated

4For the wage decompositions, the following cities in the 68th round were reclassified as towns and small
cities in order to make the classification consistent across the 55th and 68th rounds: Agra, Faridabad, Meerut, Nashik,
Patna, and Pimprichinchwad. Sin embargo, for the Mincerian wage regressions, as these only involve data from the 68th
round, the original classification of big cities in the 68th round was retained.

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

/

.

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Wages Over the Course of Structural Transformation 139

as the predicted wages of self-employed workers based on the coefficients of this
regression. In both 2000 y 2012, 49% of workers were self-employed. Outside of
agricultura, the shares are 43% y 39% para 2000 y 2012, respectivamente. Apéndice
Tables A2.1 and A2.2 compare the means of worker characteristics between
self-employed and wage workers in 2000 y 2012, respectivamente, and provide t
statistics on the difference between means of these two groups of workers. El
imputed wages of self-employed workers are on average lower than the actual wages
of wage workers (Appendix Table A2.3).

We adjust wages for temporal and spatial cost-of-living differences using the
national Consumer Price Index and state and urban–rural cost-of-living adjustments
based on official poverty lines, as reported in Saxena (2001) and Government of
India (2013). Wages beyond 3 standard deviations from the mean are considered
outliers and are dropped (less than 1% of the sample).

Since the surveys provide information on levels of education attained, nosotros
convert these into years of education by assuming the following correspondence
between levels of education and years of education: 0 years for those who are
illiterate, 1 year for those with preprimary education, 5 years for those with a
primary education, 8 years for a middle school education, 10 years for a secondary
education, 12 years for a senior secondary education, 14 years for those who
finished a diploma course, 16 years for college graduates, y 19 years for those
who completed postgraduate studies.

We work primarily with economic sectors based on the Groningen Growth
and Development Centre 10-sector Database (Timmer, de Vries, and de Vries
2015), but we also experiment with a simple breakdown between tradable and
nontradable sectors. For the latter classification, we follow Mano and Castillo
(2015), who use the World Input–Output Database to calculate the ratio of exports
to gross value added across countries for each industry and year, and then compute
the average exports-to-gross-value-added ratio during the period 1995–2011. Ellos
classify an industry as tradable if the average exports-to-gross-value-added ratio is
greater than 10%.

We also classify occupations in terms of whether they involve primarily
routine or nonroutine work, and manual or analytical work, based on the
work of Autor, Exacción, and Murnane (2003) and Reijnders and de Vries (2018),
and as described in ADB (2018). Prominent examples of
routine manual
workers include production workers, while routine analytical workers include
clerical workers. Nonroutine manual workers include drivers and personal service
workers. Nonroutine analytical workers include legislators, managers, engineering
professionals, health professionals, teaching professionals, other professionals, y
sales workers.

We use the occupation codes reported in the survey for this purpose. En
2012, 0.4% of wage workers did not have an occupation code, mientras 1% of wage
workers did not have an occupation code in 2000. We drop these observations for
decompositions involving occupations (and for the Mincerian regressions).

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

.

/

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

140 Asian Development Review

We work with the following states and union territories: Andhra Pradesh,
Assam, Bihar, Chandigarh, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh,
Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan,
Tamil Nadu, Uttar Pradesh, and West Bengal.

V. Resultados

A.

Employment and Wages by Groups: A Snapshot

Mesa 1 provides a snapshot of employment shares of wage and salaried
workers, average real wages (correcting for temporal changes in prices as well
as both temporal and spatial variations in prices), and average years of education
across various groups. In terms of employment shares across production sectors,
we see a large reduction from 2000 a 2012 in agricultural wage employment (18
puntos de porcentaje), a large increase in construction (10 puntos de porcentaje), y un
moderate increase in manufacturing (3 puntos de porcentaje).5 As for average wages,
agriculture experiences one of the highest rates of growth at 4.2% annually. (Este
dips to 4% annually when spatial price differentials in addition to temporal changes
in prices are taken into account.) Business services, which are one of the highest
paying sectors on average, experience the lowest growth in wages: 1% annually
when correcting only for temporal price changes and 1.8% annually when spatial
price differentials are also taken into account.

There are few surprises as far as educational attainment is concerned.
Workers in agriculture tend to be the least educated (a lo sumo 3 years of education
on average), while those in business services and public administration, defense,
education, and health services are the best educated.

Turning to locational groupings, the rural share of wage workers declined
to two-thirds by 2012, while the employment share of big cities increased by 3
percentage points between 2000 y 2012. The share of smaller cities and towns
also increased by 3 puntos de porcentaje. No es sorprendente, the most educated are to be
found in bigger cities. Such cities have considerably higher wages on average.

Firms with 10 or more workers (and outside agriculture) pay better and
also have better educated workers. Regarding changes in their prevalence between
2000 y 2012, given the large share of nonresponses on firm size by wage
employees—especially in the 2000 labor force survey, it is difficult to draw
conclusions. But taken at face value, the share of wage employment in large firms
increased slightly between 2000 y 2012.

Turning to the distinction between tradables and nontradables, we see that
the main differences arise from the inclusion of agriculture in the former. Sin

5Appendix Table A3 provides employment shares across the various groups of interest for the self-employed
and all workers (es decir., wage and salaried workers plus the self-employed). For ease of reference, the share of wage
employees is also provided in the table.

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

.

/

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Wages Over the Course of Structural Transformation 141

Mesa 1. Summary Statistics for Wage and Salaried Employees

Employment
Shares (%)

Wages,

Temporal (₹) + Espacial (₹)

Wages,
Temporal

Years of
Schooling

Sectors

2000

2012

2000

2012

2000

2012

2000 2012

Agriculture
Minería
Manufacturing
Utilities
Construction
Trade services
Transport services
Business services
Public administration and

defense, education, health and
social work
Personal services

55
1
11
1
8
6
5
2
10

37
1
14
1
18
7
7
3
9

96
310
275
529
176
225
352
654
561

158
504
355
651
247
318
548
739
764

94
307
285
546
181
233
363
675
572

151
512
390
733
249
349
605
834
817

2
4
7
8
3
7
8
13
12

3
6
7
9
4
8
9
13
13

3

3

146

242

150

266

3

6

Urban–Rural

2000

2012

2000

2012

2000

2012

2000 2012

Rural
Urban—towns and small cities
Urban—big cities

73
19
8

67
22
11

145
355
430

232
490
582

145
361
459

223
548
688

3
7
9

4
9
10

Firm size (without agriculture)

2000

2012

2000

2012

2000

2012

2000 2012

Large firmsa
Small firmsa

42
58

43
57

474
222

588
304

489
228

648
317

9
6

10
6

Tradables (with agriculture)

2000

2012

2000

2012

2000

2012

2000 2012

Tradable
Nontradable

74
26

62
38

159
352

263
436

161
361

275
464

3
8

5
7

Tradables (without agriculture)

2000

2012

2000

2012

2000

2012

2000 2012

Tradable
Nontradable

41
59

39
61

318
352

426
436

328
361

468
464

7
8

8
7

Occupation categories

2000

2012

2000

2012

2000

2012

2000 2012

Routine manual
Nonroutine manual
Routine analytic
Nonroutine analytic
Agriculture

23
8
4
10
55

32
11
4
15
38

219
274
507
581
94

280
369
680
789
158

225
282
525
594
92

295
399
748
864
150

5
5
13
13
2

5
7
13
13
3

aA large firm is defined as a firm with 10 or more workers. En 2012, 8% of wage workers reported that their firm size
was unknown, mientras 24% of wage workers in 2000 did not know the size of their firm.
Notas: Employment shares and average years of schooling are based on the full sample of wage workers, mientras
average wages are based on the sample of wage workers with wage data. Wages are expressed as the average daily
wage in constant 2012 rupees. Temporal uses the Consumer Price Index to adjust for changes in prices over time. Para
Temporal + Espacial, differences in spatial prices are taken into account, in addition to changes in prices over time. A
big city is defined as a city with a population of 1 million or more as per the 1991 census. This sample is limited to
states included in the wage decomposition analysis and Mincerian wage regressions.
Fuente: Authors’ calculations using data from National Sample Survey Office. 2000. “National Sample Survey
1999–2000 (55th round): Schedule 10–Employment and Unemployment Survey.” Government of India, Ministerio
of Statistics and Program Implementation; and National Sample Survey Office. 2012. “National Sample Survey
2011–2012 (68th round): Schedule 1.0–Employment and Unemployment Survey.” Government of India, Ministry of
Statistics and Program Implementation.

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

.

/

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

142 Asian Development Review

agricultura, the two groups are quite similar in terms of average wages and
educational attainment.

Finalmente,

for occupational groupings, we see a large (9 porcentaje
puntos)
increase in routine manual occupations. This group includes the
second-least-educated group on average, ahead of only agriculture. As we shall
see below, this seems to be driven by an exit of wage workers from agriculture to
routine manual work in industry and services.

Given the growing interest in urbanization issues in developing countries
(ver, Por ejemplo, Hasan, Jiang, and Kundu 2018), Mesa 2 provides a snapshot
of the employment shares of wage and salaried workers across rural areas, pequeño
cities and towns, and big cities in both 2000 y 2012. No es sorprendente, agricultura
contributes marginally to wage and salaried employment in towns and small cities,
and hardly at all in big cities. Sin embargo, even within rural areas, the share of
agricultural wage employment declined (de 73% a 54%). What is interesting
is how important manufacturing is in India’s big cities—accounting for almost 30%
of wage employment. Además, the role of public administration, defense, y
social services declines during the review period, as does that of personal services.
Curiosamente, there is a sharp contrast in the structure of occupational change
across rural areas and big cities. Rural areas see a large increase in routine manual
trabajar. This seems to be driven by an almost commensurate decline in agricultural
occupations and growth in construction employment. A diferencia de, big cities see a
drop of 6 percentage points in the share of routine manual work and an increase of
8 percentage points in nonroutine analytic work. De este modo, unlike the case of developed
countries, where a decline in routine manual work is attributed to the growing use of
robotics and computers (the so-called fourth industrial revolution), India’s pattern is
consistent with the idea of “overlapping industrial revolutions” (ADB 2018), dónde
some parts of a developing country are going through first or second industrial
revolution processes (thanks to the advent of electricity and the internal combustion
engine), while other parts are experiencing more recent technological revolutions.

B. Wage Decompositions

Table 3a summarizes the decomposition of average wages of wage and
salaried workers into within and structural change (or between) components for
various groupings of interest. When adjusting only for temporal changes in prices
using the Consumer Price Index (first three data columns), the average real wage
growth is between 3.9% y 4.2% per annum.6 It is slightly higher when we adjust

6There is a slight difference in the average wage growth across decompositions due to differences in the
number of observations across decompositions. As noted earlier, a small number of workers do not report their
occupations. Además, 0.3% of wage workers in 2012 do not report the sector of employment. These observations
get dropped in the decompositions involving occupation or sector.

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

/

.

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Wages Over the Course of Structural Transformation 143

Mesa 2. Employment Statistics for Wage and Salaried Employees by City Size

Año: 2000

Sectors

Agriculture
Minería
Manufacturing
Utilities
Construction
Trade services
Transport services
Business services
Publica
Personal services
Total

Occupation
Categories

Employment
Share (%)

Wages,
Temporal (₹)

Wages, Temporal
+ Espacial (₹)

Towns
y
Pequeño
Rural Cities Cities Rural Cities Cities Rural Cities Cities

Towns
y
Pequeño

Towns
y
Pequeño

Grande

Grande

Grande

73
1
7
1
6
2
3
1
6
2
100

9
2
21
2
12
14
10
3
21
5
100

1
0
29
1
8
16
12
6
20
8
100

99
244
192
477
168
175
290
538
528
135
168

127
503
325
671
191
228
406
712
631
140
386

211
908
362
576
259
299
519
770
678
178
434

98
248
195
504
174
181
296
549
542
146
170

131
485
332
658
195
231
410
712
628
137
388

184
964
389
589
275
317
557
816
715
188
461

Employment
Share (%)

Wages,
Temporal (₹)

Wages, Temporal
+ Espacial (₹)

Towns
y
Pequeño
Rural Cities Cities Rural Cities Cities Rural Cities Cities

Towns
y
Pequeño

Towns
y
Pequeño

Grande

Grande

Grande

Routine manual
Nonroutine manual
Routine analytic
Nonroutine analytic
Agriculture
Total

17
5
2
5
72
100

42
17
8
24
9
100

39
23
10
27
1
100

185
263
444
534
96
167

253
296
552
645
138
386

286
292
595
679
181
434

190
270
453
549
96
169

255
297
553
645
142
388

306
317
634
714
193
461

Employment
Share (%)

Wages,
Temporal (₹)

Wages, Temporal
+ Espacial (₹)

Tradable

Tradable
Nontradable
Total

Towns
y
Grande
Pequeño
Rural Cities Cities Rural Cities Cities Rural Cities Cities

Towns
y
Pequeño

Towns
y
Pequeño

Grande

Grande

84
16
100

46
54
100

47
53
100

122
331
168

344
418
386

428
438
434

122
341
170

348
418
387

459
462
461

Continuado.

wages for both temporal and spatial differences in the cost of living (entre 4.2%
y 4.5% per annum). Sin embargo, the qualitative patterns are quite similar. Como
in the case of labor productivity decompositions analyzed by other studies, nosotros
find the within term to be the main driver of growth in economy-wide average
wages. Sin embargo, for all the groups we consider, structural change contributes

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

/

.

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

144 Asian Development Review

Año: 2012

Mesa 2. Continuado.

Employment
Share (%)

Wages,
Temporal (₹)

Wages, Temporal
+ Espacial (₹)

Towns
y
Pequeño
Rural Cities Cities Rural Cities Cities Rural Cities Cities

Towns
y
Pequeño

Towns
y
Pequeño

Grande

Grande

Grande

54
1
9
1
20
3
4
1
6
1
100

5
2
22
3
15
13
11
7
17
4
100

0
0
28
1
9
14
15
10
16
7
100

167
493
283
698
237
273
392
633
719
204
337

212
828
399
846
285
288
568
808
847
205
517

393
1,323
461
817
352
400
880
840
898
271
605

161
447
280
712
229
274
384
646
683
197
326

232
882
451
970
316
318
627
900
943
231
576

465
1,648
553
951
419
474
1,047
1,004
1,055
319
719

Employment
Share (%)

Wages,
Temporal (₹)

Wages, Temporal
+ Espacial (₹)

Towns
y
Grande
Pequeño
Rural Cities Cities Rural Cities Cities Rural Cities Cities

Towns
y
Pequeño

Towns
y
Pequeño

Grande

Grande

Sectors

Agriculture
Minería
Manufacturing
Utilities
Construction
Trade services
Transport services
Business services
Publica
Personal services
Total

Occupation
Categories

Routine manual
Nonroutine manual
Routine analytic
Nonroutine analytic
Agriculture
Total

30
7
2
7
55
100

40
18
7
29
6
100

33
23
8
35
1
100

258
365
642
727
171
338

330
409
754
822
202
517

369
414
792
960
246
606

251
358
615
698
164
327

367
456
838
916
221
576

441
492
942
1,136
284
720

Employment
Share (%)

Wages,
Temporal (₹)

Wages, Temporal
+ Espacial (₹)

Towns
y
Grande
Pequeño
Rural Cities Cities Rural Cities Cities Rural Cities Cities

Towns
y
Pequeño

Towns
y
Pequeño

Grande

Grande

69
31
100

47
53
100

49
51
100

244
420
337

473
548
517

577
632
605

239
404
326

527
611
576

692
745
719

Tradable

Tradable
Nontradable
Total

aPublic refers to public administration and defense, education, salud, and social work.
Notas: Employment shares are based on the full sample of wage workers, while average wages are based on the
sample of wage workers with wage data. Wages are expressed as the average daily wage in constant 2012 rupees.
Temporal uses the Consumer Price Index to adjust for changes in prices over time. For Temporal + Espacial, diferencias
in spatial prices are taken into account, in addition to changes in prices over time. A big city is defined as a city with
a population of 1 million or more as per the 1991 census. This sample is limited to states included in the wage
decomposition analysis and Mincerian wage regressions.
Fuente: Authors’ calculations using data from National Sample Survey Office. 2000. “National Sample Survey
1999–2000 (55th round): Schedule 10–Employment and Unemployment Survey.” Government of India, Ministerio
of Statistics and Program Implementation; and National Sample Survey Office. 2012. “National Sample Survey
2011–2012 (68th round): Schedule 1.0–Employment and Unemployment Survey.” Government of India, Ministry of
Statistics and Program Implementation.

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

.

/

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Wages Over the Course of Structural Transformation 145

(a) Wage workers only

Mesa 3. Wage Decomposition Results

Temporal

Temporal + Espacial

Structural

Change Within

(%)

(%)

Economy-
wide
Wage
Growth
(%)

Structural

Change Within

(%)

(%)

Economy-
wide
Wage
Growth
(%)

1.0
1.3
0.5
0.5

1.0

1.2

1.2

3.1
2.9
3.4
3.4

3.0

2.9

2.9

4.0
4.2
3.9
3.9

4.0

4.1

4.1

1.0
1.4
0.5
0.6

1.0

1.2

1.2

3.3
3.2
3.7
3.7

3.3

3.2

3.2

4.4
4.5
4.2
4.2

4.4

4.5

4.5

Configuration

Sector (10 sectors)
Occupations (5 categories)
Urban–Rural
Rural, big cities, towns and

small cities

Urban–Rural × Sector

Sector × Occupation

(2 × 10)

(10 × 5)

Urban–Rural × Sector ×

Occupation (2 × 10 × 5)

(b) Wage workers and self-employed workers

Temporal

Temporal + Espacial

Structural

Change Within

(%)

(%)

Economy-
wide
Wage
Growth
(%)

Structural

Change Within

(%)

(%)

Economy-
wide
Wage
Growth
(%)

0.7
0.7
0.3
0.3

0.7

0.8

0.8

2.3
2.3
2.7
2.7

2.3

2.2

2.2

3.0
3.1
3.0
3.0

3.0

3.0

3.0

0.8
0.8
0.3
0.4

0.8

0.9

0.9

2.5
2.5
2.9
2.9

2.5

2.4

2.4

3.3
3.3
3.3
3.3

3.3

3.3

3.3

Configuration

Sector (10 sectors)
Occupations (5 categories)
Urban–Rural
Rural, big cities, otro

urban areas

Urban–Rural × Sector

Sector × Occupation

(2 × 10)

(10 × 5)

Urban–Rural × Sector ×

Occupation (2 × 10 × 5)

Notas: Temporal uses the Consumer Price Index to adjust for changes in prices over time. For Temporal + Espacial,
differences in spatial prices are taken into account, in addition to changes in prices over time. A big city is defined as
a city with a population of 1 million or more as per the 1991 census. This sample is limited to states included in the
wage decomposition analysis and Mincerian wage regressions. There is a slight difference in the average economy-
wide wage growth across decompositions due to differences in the number of observations across configurations
(0.3% of wage workers in 2012 have no sector data, 0.4% en 2012, y 1% en 2000 have no occupation data). Estos
observations get dropped in the decompositions involving occupation or sector.
Fuente: Authors’ calculations using data from National Sample Survey Office. 2000. “National Sample Survey
1999–2000 (55th round): Schedule 10–Employment and Unemployment Survey.” Government of India, Ministry of
Statistics and Program Implementation; National Sample Survey Office. 2012. “National Sample Survey 2011–2012
(68th round): Schedule 1.0–Employment and Unemployment Survey.” Government of India, Ministry of Statistics
and Program Implementation.

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

/

.

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

146 Asian Development Review

positively to growth in average wages. The contribution of structural change is
around 23%–25% in the sectorwise decomposition (row 1) y alrededor 31% en el
occupationwise decomposition (row 2). The contribution of structural change is
around 12%–13% when we decompose wages in terms of just urban and rural areas,
and 13%–14% when we further distinguish urban areas between those comprising
big cities and other cities and towns (filas 3 y 4). The table also considers what
happens when we consider a more disaggregated grouping based on combining
the sector, occupation, and location groups. Curiosamente, the largest such grouping
(row 7)—involving a total of 100 groups formed over 10 sectors, 5 occupations, y
2 locations—reveals that structural change drives 27%–29% of total wage growth,
which is similar to when just occupation groups are considered.

Table 3b carries out the wage decomposition by including the self-employed
and their predicted wages. Economy-wide average wage growth is now lower (pendiente
to the lower predicted wages of the self-employed). Pero, the decompositions yield
similar results in terms of the relative importance of the structural change and
within group terms. Por ejemplo, the contribution of structural change remains at
23%–24% in the sector-wise decomposition (row 1), and it remains close at
9%–12% when we decompose wages in terms of urban and rural areas and when we
further distinguish urban areas between those comprising big cities and other cities
and towns (filas 3 y 4).

Hasta ahora, our results indicate that India’s economy has undergone structural
transformation in a fairly standard manner. Employment is exiting agriculture,
rural areas, and less remunerative occupations for better-paying production sectors
and occupations, and for urban areas, especially big cities. Although not shown,
employment also appears to be moving from smaller (informal) firms to larger
(formal) firms, subject to the data caveat noted earlier (es decir., that the missing
observations for the firm size variable are randomly distributed in both 2000 y
2012). All of these shifts have helped raise average wages in the economy.

C. Wages across Locations and Firm Type

We now turn to the issue of whether the higher real wages of larger firms
and cities, especially larger cities, holds even when controlling for individual
demographics and educational attainment. Tables 4a–4d present the results of the
Mincerian wage regressions using data only for 2012, the year for which our
information on firm size of workers is fairly complete.7 We drop the agriculture
sector from this analysis since firm size is not a well-defined concept for farms. A
avoid the possibility that the coefficients on wage determinants are largely driven

7We do not adjust for population weights. También, we divide age by 10 and age2 by 100 to improve the

readability of their coefficients.

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

.

/

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Wages Over the Course of Structural Transformation 147

)
6
(

mi
gramo
a
w
gramo
oh
yo

*
*
*
6
3
2
4
0

.

*
*
*
1
8
3
0
0
–

.

)
6
6
1
0
0
(

.

*
*
*
1
5
4
3
0

.

*
*
*
4
2
4
0
0

.

*
*
*
8
8
4
2
0

.

*
*
*
3
9
4
1
0

.

*
*
*
5
4
7
0
0

.

)
1
2
1
0
0
(

.

)
3
7
0
0
0
(

.

)
8
0
0
0
0
(

.

)
5
0
1
0
0
(

.

)
1
2
0
0
0
(

.

*
*
*
4
8
5
2
4

.

)
2
7
0
0
0
(

.

)
0
3
4
0
0
(

.

s
mi
Y

s
mi
Y

s
mi
Y

9
1
5
0
3

,

6
3
1
5
0

.

.
d
mi
tu
norte
i
t
norte
oh
C

)
5
(

mi
gramo
a
w
gramo
oh
yo

*
*
*
2
7
2
4
.
0

*
*
*
9
7
3
0
.
0
–

)
0
7
1
0
.
0
(

*
*
*
9
3
3
3
.
0

)
1
2
0
0
.
0
(

*
*
*
5
6
4
0
.
0

)
7
0
1
0
.
0
(

)
8
0
0
0
.
0
(

*
*
*
1
0
2
1
.
0

*
*
*
4
7
0
4
.
4

)
1
7
0
0
.
0
(

)
7
3
4
0
.
0
(

s
mi
Y

s
mi
Y

s
mi
Y

9
1
5
,
0
3

1
2
9
4
.
0

s
mi
i
metro
metro
tu
D
norte
oh
i
t
a
pag
tu
C
C
oh
d
norte
a
r
oh
t
C
mi
S

s
norte
oh
i
s
s
mi
r
gramo
mi
R
mi
gramo
a
W.
norte
a
i
r
mi
C
norte
i
METRO

.
4

mi
yo
b
a
t

)
4
(

mi
gramo
a
w
gramo
oh
yo

*
*
*
8
0
3
4
.
0

*
*
*
1
8
3
0
.
0
–

)
0
7
1
0
.
0
(

*
*
*
6
5
2
3
.
0

)
1
2
0
0
.
0
(

*
*
*
2
7
4
0
.
0

)
7
0
1
0
.
0
(

)
8
0
0
0
.
0
(

*
*
*
7
7
0
2
.
0

)
9
1
1
0
.
0
(

)
3
(

mi
gramo
a
w
gramo
oh
yo

*
*
*
7
8
1
4
.
0

*
*
*
5
7
3
0
.
0
–

)
7
6
1
0
.
0
(

*
*
*
3
9
3
3
.
0

)
1
2
0
0
.
0
(

*
*
*
5
3
4
0
.
0

)
6
0
1
0
.
0
(

*
*
*
5
2
6
2
.
0

)
8
0
0
0
.
0
(

)
3
7
0
0
.
0
(

)
2
(

mi
gramo
a
w
gramo
oh
yo

*
*
*
7
5
2
4
.
0

*
*
*
6
7
3
0
.
0
–

)
1
7
1
0
.
0
(

*
*
*
5
4
2
3
.
0

)
1
2
0
0
.
0
(

*
*
*
9
7
4
0
.
0

)
8
0
1
0
.
0
(

)
8
0
0
0
.
0
(

mi
s
a
B

)
1
(

mi
gramo
a
w
gramo
oh
yo

*
*
*
8
3
1
5
.
0

*
*
*
6
4
4
0
.
0
–

)
1
8
1
0
.
0
(

*
*
*
4
4
6
2
.
0

)
3
2
0
0
.
0
(

*
*
*
5
3
7
0
.
0

)
8
0
1
0
.
0
(

)
6
0
0
0
.
0
(

mi
s
a
B

)
a
(

s
mi
yo
b
a
i
r
a
V

0
1
/
mi
gramo
A

0
0
1
/
2
mi
gramo
A

gramo
norte
i
yo
oh
oh
h
C
s

F
oh

s
r
a
mi
Y

1
=
metro
r
fi
mi
gramo
r
a
l

)
1
=
mi
yo
a

METRO

(

X
mi
S

1
=
y
t
i
C
gramo
i
B

1
=
norte
a
b
r
Ud.

t
norte
a
t
s
norte
oh
C

*
*
*
6
1
7
4
.
4

)
4
3
4
0
.
0
(

*
*
*
8
2
0
3
.
4

)
1
3
4
0
.
0
(

*
*
*
0
2
8
4
.
4

)
7
3
4
0
.
0
(

*
*
*
1
3
3
7
.
3

)
0
7
3
0
.
0
(

s
mi
Y

s
mi
Y

s
mi
Y

9
1
5
,
0
3

4
2
9
4
.
0

s
mi
Y

s
mi
Y

s
mi
Y

9
1
5
,
0
3

0
8
0
5
.
0

s
mi
Y

s
mi
Y

s
mi
Y

9
1
5
,
0
3

3
7
8
4
.
0

oh
norte

oh
norte

s
mi
Y

9
1
5
,
0
3

5
7
1
4
.
0

s
mi
i
metro
metro
tu
d

norte
oh
i
t
a
pag
tu
C
C
oh

s
mi
i
metro
metro
tu
d

r
oh
t
C
mi
S

s
mi
i
metro
metro
tu
d
mi
t
a
t
S

s
norte
oh
i
t
a
v
r
mi
s
b
oh

d
mi
r
a
tu
q
s
–
R

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

/

.

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

148 Asian Development Review

s
mi
i
metro
metro
tu
D
norte
oh
i
t
a
pag
tu
C
C
oh
+
r
oh
t
C
mi
S

s

metro
r
i
F

yo
yo
a
metro
S

)
0
1
(

)
9
(

)
8
(

)
7
(

mi
s
a
B

)
6
(

s
mi
i
metro
metro
tu
D
norte
oh
i
t
a
pag
tu
C
C
oh
+
r
oh
t
C
mi
S

s

metro
r
i
F
mi
gramo
r
a
l

)
5
(

)
4
(

)
3
(

)
2
(

mi
s
a
B

)
1
(

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

.
d
mi
tu
norte
i
t
norte
oh
C

.
4

mi
yo
b
a
t

*
*
*
7
3
9
3
0

.

*
*
*
9
1
9
3
0

.

)
2
9
1
0
0
(

.

*
*
*
4
9
3
0
0
−

.

)
2
9
1
0
0
(

.

*
*
*
3
9
3
0
0
−

.

*
*
*
3
3
9
3
.
0

)
2
9
1
0
.
0
(

*
*
*
3
9
3
0
.
0
−

*
*
*
0
1
9
3
.
0

)
3
9
1
0
.
0
(

*
*
*
2
9
3
0
.
0
−

*
*
*
6
5
8
4
.
0

)
5
0
2
0
.
0
(

*
*
*
9
6
4
0
.
0
−

*
*
*
2
0
3
4
.
0

)
8
9
2
0
.
0
(

*
*
*
9
2
3
0
.
0
−

*
*
*
5
5
2
4
.
0

)
9
9
2
0
.
0
(

*
*
*
5
2
3
0
.
0
−

*
*
*
4
1
3
4
.
0

)
9
9
2
0
.
0
(

*
*
*
9
2
3
0
.
0
−

*
*
*
5
4
2
4
.
0

)
0
0
3
0
.
0
(

*
*
*
2
2
3
0
.
0
−

*
*
*
1
7
1
5
.
0

)
9
1
3
0
.
0
(

*
*
*
9
8
3
0
.
0
−

*
*
*
4
3
2
4
0

.

*
*
*
7
9
2
0
0

.

*
*
*
8
4
6
1
0

.

)
0
1
0
0
0
(

.

)
2
3
1
0
0
(

.

)
4
2
0
0
0
(

.

*
*
*
8
2
3
0
0

.

)
7
6
1
0
0
(

.

*
*
*
6
4
9
0
4

.

)
7
8
0
0
0
(

.

)
1
7
6
0
0
(

.

s
mi
Y

s
mi
Y

s
mi
Y

8
1
5
8
1

,

8
6
2
4
0

.

.
d
mi
tu
norte
i
t
norte
oh
C

*
*
*
8
6
2
4
0

.

)
4
2
0
0
0
(

.

*
*
*
8
9
2
0
0

.

)
2
3
1
0
0
(

.

)
0
1
0
0
0
(

.

*
*
*
0
3
5
0
0

.

*
*
*
1
0
9
0
4

.

)
5
8
0
0
0
(

.

)
3
7
6
0
0
(

.

s
mi
Y

s
mi
Y

s
mi
Y

*
*
*
4
1
2
4
.
0

*
*
*
9
9
2
0
.
0

*
*
*
5
9
7
1
.
0

)
0
1
0
0
.
0
(

)
1
3
1
0
.
0
(

)
4
2
0
0
.
0
(

)
2
6
1
0
.
0
(

*
*
*
0
4
2
4
.
0

)
4
2
0
0
.
0
(

*
*
*
2
0
3
0
.
0

)
2
3
1
0
.
0
(

)
0
1
0
0
.
0
(

*
*
*
5
4
1
3
.
0

)
6
2
0
0
.
0
(

*
*
*
3
7
5
0
.
0

)
0
3
1
0
.
0
(

)
8
0
0
0
.
0
(

*
*
*
1
8
0
1
.
4

)
1
7
6
0
.
0
(

*
*
*
5
2
1
1
.
4

)
3
7
6
0
.
0
(

*
*
*
9
1
3
8
.
3

)
7
2
4
0
.
0
(

s
mi
Y

s
mi
Y

s
mi
Y

s
mi
Y

s
mi
Y

s
mi
Y

oh
norte

oh
norte

s
mi
Y

*
*
*
8
9
4
2
.
0

*
*
*
4
2
5
0
.
0

*
*
*
6
9
9
0
.
0

)
3
1
0
0
.
0
(

)
8
6
1
0
.
0
(

)
7
3
0
0
.
0
(

*
*
*
2
8
0
1
.
0

)
2
7
1
0
.
0
(

*
*
*
2
7
8
3
.
4

)
0
2
1
0
.
0
(

)
4
7
6
0
.
0
(

s
mi
Y

s
mi
Y

s
mi
Y

*
*
*
0
0
5
2
.
0

)
7
3
0
0
.
0
(

*
*
*
4
2
5
0
.
0

)
8
6
1
0
.
0
(

)
3
1
0
0
.
0
(

*
*
*
9
6
2
1
.
0

*
*
*
6
7
8
3
.
4

)
6
1
1
0
.
0
(

)
5
7
6
0
.
0
(

s
mi
Y

s
mi
Y

s
mi
Y

*
*
*
7
9
3
2
.
0

*
*
*
7
3
5
0
.
0

*
*
*
6
1
4
1
.
0

)
3
1
0
0
.
0
(

)
8
6
1
0
.
0
(

)
7
3
0
0
.
0
(

)
7
6
1
0
.
0
(

*
*
*
4
7
3
2
.
0

)
8
3
0
0
.
0
(

*
*
*
1
4
5
0
.
0

)
8
6
1
0
.
0
(

)
3
1
0
0
.
0
(

*
*
*
9
7
7
1
.
0

*
*
*
1
1
8
0
.
0

)
2
7
1
0
.
0
(

)
0
4
0
0
.
0
(

)
9
0
0
0
.
0
(

*
*
*
1
1
5
4
.
4

)
2
7
6
0
.
0
(

*
*
*
6
8
6
4
.
4

)
4
7
6
0
.
0
(

*
*
*
6
4
7
7
.
3

)
2
4
6
0
.
0
(

s
mi
Y

s
mi
Y

s
mi
Y

s
mi
Y

s
mi
Y

s
mi
Y

oh
norte

oh
norte

s
mi
Y

8
1
5
8
1

,

8
3
2
4
0

.

8
1
5
,
8
1

4
6
2
4
.
0

8
1
5
,
8
1

6
2
2
4
.
0

8
1
5
,
8
1

0
4
3
3
.
0

1
0
0
,
2
1

6
2
4
5
.
0

1
0
0
,
2
1

3
1
4
5
.
0

1
0
0
,
2
1

5
9
3
5
.
0

1
0
0
,
2
1

7
6
3
5
.
0

1
0
0
,
2
1

0
9
6
4
.
0

gramo
norte
i
yo
oh
oh
h
C
s

F
oh
s
r
a
mi
Y

)
1
=
mi
yo
a

METRO

(
X
mi
S

1
=
y
t
i
C
gramo
i
B

1
=
norte
a
b
r
Ud.

t
norte
a
t
s
norte
oh
C

s
mi
i
metro
metro
tu
d
norte
oh
i
t
a
pag
tu
C
C
oh

s
mi
i
metro
metro
tu
d
r
oh
t
C
mi
S

s
mi
i
metro
metro
tu
d
mi
t
a
t
S

s
norte
oh
i
t
a
v
r
mi
s
b
oh

d
mi
r
a
tu
q
s
–
R

s
mi
yo
b
a
i
r
a
V

0
1
/
mi
gramo
A

0
0
1
/
2
mi
gramo
A

mi
z
i
s

metro
r
i
F
)
b
(

yo

D
oh
w
norte
oh
a
d
mi
d

F
r
oh
metro
h

t
t

pag

:
/
/

d
i
r
mi
C
t
.

metro

i
t
.

/

mi
d
tu
a
d
mi
v
/
a
r
t
i
C
mi
–
pag
d

yo

F
/

/

/

/

3
6
2
1
3
1
1
6
4
4
1
9
4
a
d
mi
v
_
a
_
0
0
1
3
4
pag
d

.

/

F

b
y
gramo
tu
mi
s
t

t

oh
norte
0
8
S
mi
pag
mi
metro
b
mi
r
2
0
2
3

Wages Over the Course of Structural Transformation 149

s
mi
i
metro
metro
tu
D
norte
oh
i
t
a
pag
tu
C
C
oh
+
r
oh
t
C
mi
S

yo
a
r
tu
R

)
0
1
(

)
9
(

)
8
(

)
7
(

mi
s
a
B

)
6
(

s
mi
i
metro
metro
tu
D
norte
oh
i
t
a
pag
tu
C
C
oh
+
r
oh
t
C
mi
S

norte
a
b
r
Ud.

)
5
(

)
4
(

)
3
(

)
2
(

mi
s
a
B

)
1
(

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

mi
gramo
a
w
gramo
oh
yo

*
*
*
1
4
5
4
0

.

)
9
2
0
0
0
(

.

*
*
*
0
1
3
0
0

.

)
2
5
1
0
0
(

.

)
1
1
0
0
0
(

.

–

*
*
*
4
8
6
1
0

.

*
*
*
5
5
4
3
4

.

)
5
0
1
0
0
(

.

)
8
0
6
0
0
(

.

s
mi
Y

s
mi
Y

s
mi
Y

9
3
6
4
1

,

5
0
8
4
0

.

.
d
mi
tu
norte
i
t
norte
oh
C

*
*
*
1
4
5
4
0

.

)
9
2
0
0
0
(

.

*
*
*
0
1
3
0
0

.

)
2
5
1
0
0
(

.

)
1
1
0
0
0
(

.

*
*
*
4
8
6
1
0

.

*
*
*
5
5
4
3
4

.

)
5
0
1
0
0
(

.

)
8
0
6
0
0
(

.

s
mi
Y

s
mi
Y

s
mi
Y

*
*
*
6
3
1
3
0

.

*
*
*
6
3
1
3
0

.

*
*
*
7
6
1
3
.
0

*
*
*
1
6
2
0
0
–

.

*
*
*
1
6
2
0
0
–

.

)
0
3
2
0
0
(

.

)
0
3
2
0
0
(

.

)
2
3
2
0
.
0
(

*
*
*
1
6
2
0
.
0
−

*
*
*
8
2
5
4
.
0

)
9
2
0
0
.
0
(

*
*
*
6
2
3
0
.
0

)
3
5
1
0
.
0
(

)
1
1
0
0
.
0
(

−

*
*
*
7
6
1
3
.
0

)
2
3
2
0
.
0
(

*
*
*
1
6
2
0
.
0
−

*
*
*
8
2
5
4
.
0

)
9
2
0
0
.
0
(

*
*
*
6
2
3
0
.
0

)
3
5
1
0
.
0
(

)
1
1
0
0
.
0
(

*
*
*
2
4
3
4
.
0

)
7
4
2
0
.
0
(

*
*
*
1
5
3
0
.
0
−

*
*
*
1
7
1
3
.
0

)
1
3
0
0
.
0
(