Cover photo: Job-seekers queue at the Allahabad Employment Exchange Office. 2.3 million
people applied for 368 government jobs advertised in the state of Uttar Pradesh.
Source: New York Post
My sincere thanks to:
Lant Pritchett, for being an invaluable thought partner.
Your ideas have had a deep impact on my thinking, beyond the present work.
Michael Walton, for mentorship throughout the research process.
Your feedback pushed me to make my work focused and practical.
Varun Aggarwal, for helping me understand recruiting trends in India.
Your insights grounded my work in some semblance of the real world.
Sahil Shekhar Putting India to Work
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Sahil Shekhar Putting India to Work
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1. EXECUTIVE SUMMARY
Despite two decades of economic growth, more than 70 percent of urban Indian workers don’t
have formal sector jobs. This vast majority is excluded from India’s remarkable progress: their
wages are twenty times lower, they don’t enjoy worker protections, and they can’t access formal
credit. Failing to put the next generation of Indians in formal sector jobs will turn India’s
anticipated demographic dividend into a demographic disaster.
There is no magic potion that can cure India’s ailing formal sector. Regulatory reforms and
investment in infrastructure are necessary, but will take time. The most effective medium-term
strategy to increase formal employment is to increase the skill level of India’s labor force.
Employers are scrambling to find skilled candidates to fill formal sector jobs, paying a large and
increasing premium for the right talent. Students, in response, are rushing to get degrees to improve
their likelihood of getting jobs, fueling a dramatic boom in higher education. A staggering 20,000
colleges have opened in the last decade.
The quality of many of these colleges, however, is notoriously low. Low quality colleges survive
by hiding in the crowd of an expanding, fragmented industry, in which employers and students are
still learning about college reputations. The proliferation of low quality colleges is leading
employers to question the value of degrees as signals of skill. Facing employer skepticism, job
seekers are finding it harder to demonstrate their ability and earn higher wages. The tertiary wage
premium we observe in the labor market belies the even higher premium employers are willing to
pay for quality tertiary graduates.
An intervention that generates reliable information about the quality of graduates can help firms
hire better workers, reward skilled workers with higher wages, and reveal the quality of colleges
to both employers and students. We propose implementing sector specific Job Readiness Tests for
job seekers that (1) assess their employability; (2) are voluntary initially, with a longer term view
to universal coverage; (3) are championed by a group of employers that graduates aspire to work
for; and (4) use independent assessment agencies for implementation.
Job Readiness Tests will be implemented by sector level Job Readiness Councils (JRCs), led by
employers motivated by self-interest in recruiting better talent. Importantly, JRCs must remain
firmly independent from government to ensure industry trust of the tests.
Sahil Shekhar Putting India to Work
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Putting India to Work
Resolving Information Failures to Fill the Skill Gap
March 14, 2016
1. Executive Summary ................................................................................................................... iii
2. Motivation: A Good Job for Every Indian .................................................................................. 1
3. Defining the Problem: Why So Few Good Jobs? ...................................................................... 2
4. Diagnosing the Problem: Why So Few Skills? ........................................................................ 13
5. Designing a Solution: Solving Information Failures in the Labor Market ............................ 25
6. Implementing: An Industry Led, Government Supported Intervention ................................ 35
Appendix 1. Admissions Sorting and Wage Premia ................................................................... 42
Appendix 2. Assumptions for Industry Cost-Benefit Analysis .................................................... 56
Appendix 3. Bibliography ............................................................................................................. 57
Sahil Shekhar Putting India to Work
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2. MOTIVATION: A GOOD JOB FOR EVERY INDIAN
2.1. Growth without “good” jobs
India’s has undergone remarkable economic transformation over the past two decades. Per capita
income grew at a decadal rate of 6.1 percent in the 2000s, almost twice as quickly as any previous
decade,1 and pundits predict India will soon be the fastest growing major economy in the world.2
But, as many Indians know only too well, aggregate economic growth does not necessarily imply
shared prosperity. Excluding farmers, 72 percent of Indians worked in the informal sector in 2011-
12. This means they work in small, unincorporated businesses owned by households. 58 percent
informal workers are self-employed, and 70 percent work in an enterprise with 5 or less workers.
They work without contractual agreements (in many cases for family or social relations) and
outside the purview of government regulations. 75 percent of urban informal workers work in
manufacturing, construction, wholesale and retail trade, and transportation and storage.3
As India’s National Economic Survey put it: these jobs are not, for the most part, “good jobs.”
Wages in the formal sector are, on average, 20 times higher than the informal sector. Formal sector
employees build formal work histories, which gives them access to formal credit. They also enjoy
worker protections enacted by the government regulations.4 Informality also has aggregate
efficiency costs. Estimates suggest formal enterprises (excluding the public sector) are more than
9 times more productive than non-agricultural informal enterprises.5
India’s growth, remarkable as it has been, is simply not producing enough “good jobs” for its
citizens. Of the 10.5 million new manufacturing jobs created between 1989 and 2010, 65 percent
have been in the informal sector. A staggering 98.8 percent of the new manufacturing
establishments created in this period were informal. 6 Of those formal sector jobs that do exist,
1 Kar et. Al., “The Dynamics of Economic Growth”, Effective States and Inclusive Development Research, 2010 2 Growth predictions from Atlas of Economic Complexity, Harvard Center for International Development,
http://atlas.cid.harvard.edu/rankings/growth-predictions/ 3 Statistics from 68th round of the National Sample Survey, 2011-12, as reported in “Informal Sector and Conditions of
Employment in India”, Ministry of Statistics and Program Implementation, 2014. 4 Chapter 10, National Economic Survey 2015-16, Ministry of Finance, 2016 5 2003 estimates; Dougherty et. Al, “What is Holding Back Productivity Growth in India?”, OECD Journal of Economic
Studies, 2009 6 Chapter 10, National Economic Survey 2015-16, Ministry of Finance, 2016
Sahil Shekhar Putting India to Work
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over 60 percent are in the public sector.7 In this report, we explore why the odds are stacked against
companies that create “good jobs” and drive India’s productivity growth. This failure of the formal
sector limits India’s growth potential and excludes millions of Indians from sharing in the
prosperity generated by India’s growth.
2.2. A demographic window
Failure to create good jobs could prove particularly disastrous for India as it undergoes a
demographic boom of unprecedented scale. Over the next ten years, more than 250 million young
Indians will come of working age for the first time8 - a figure comparable in magnitude to the
entire working age population of Europe. By 2025, two-thirds of all Indians will be of working
age, and almost one in five workers around the world will be Indian. 9
As urbanization gathers speed, this demographic boom will be particularly concentrated in India’s
cities. Over the next ten years, more than 100 million people will migrate from rural to urban areas,
and the urban share of India’s population will grow from 32% to 37%. By 2050, almost 400 million
additional Indians will live in cities, accounting for 50% of the total population.10
The window in which this next generation of Indians enter the urban labor force is a critical
opportunity. Failing to put citizens in good jobs could consign them to a life of under-employment
and exclusion, and squander the economic windfall of a one-off demographic dividend. Acting
now could put India on a trajectory to inclusive prosperity.
Caught between a history of few formal sector jobs and a future of many workers, this report
proposes a strategy to create “good jobs” for the next generation of Indians.
3. DEFINING THE PROBLEM: WHY SO FEW GOOD JOBS?
Theories put forward to explain the lack of formal sector jobs fall into three broad categories: lack
of adequate infrastructure to enable private investment, regulatory distortions in factor markets
7 Ministry of Statistics and Program Implementation, accessed at https://data.gov.in/catalog/employment-organised-
sectors-public-and-private 8 United Nations World Population Prospects: The 2015 Revision, United Nations, 2015 9 Ibid. 10 United Nations Urbanization Prospects: The 2014 Revision, United Nations, 2014
Sahil Shekhar Putting India to Work
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(particularly labor), and inadequate supply of human capital to fill the skill-intensive jobs being
created in our economy. 11 We consider each of these theories in turn.
3.1. Infrastructure: not the full story
A popular explanation offered is that low quality infrastructure in urban India has contributed to
the failure of labor intensive industries such as manufacturing. Confronted with poor transport
infrastructure in particular, Indian firms specialize in industries that don’t need to physically
transport goods. IT and IT enabled service companies such as Infosys, Wipro, HCL Technologies
and Tata Consultancy Services have flourished, beaming their inputs and outputs over the internet
and bypassing infrastructural constraints. These service firms hire mid-and-high-skill workers, but
do little to create jobs for India’s abundant low-skill labor. Low skill workers, unable to find jobs
in traditionally labor intensive manufacturing industry, are forced into less productive, small scale
commercial activities in the informal sector to make ends meet.
There is at least some truth in this line of argument. We look at India’s road as an example. Despite
having the second largest network in the world – denser per square kilometer than the US, China
and Mexico – most of India’s roads (59 percent) are small and rural, 12 and less than half are paved.
13 0.6 percent of roads are multilane highways, compared to 3.5 percent in China and 12.9 percent
in Mexico.14 Unsurprisingly, road congestion has been a perennial concern for businesses and
residents in India’s major hubs.
India, however, performs better than countries at a similar level of development on aggregate
infrastructure indices published by the World Economic Forum (WEF) and the World Bank
(Exhibits 1&2). These (and other) measures suggest that, while undoubtedly bad, infrastructure
might not be a binding constraint to private investment in India.
Importantly, infrastructure can only explain some of the facts we observe in the Indian economy.
It can plausibly explain why skill-intensive service industries (particularly IT) have prospered, but
11 Chapter 7, National Economic Survey 2014-15, Ministry of Finance, 2015 12 AT Kearney, “Trends in Indian Infrastructure Development”, 2013, accessed at http://www.i-
cema.in/pdf/trends_india_infra_dev_page.pdf 13 Bureau of Transportation Statistics; RITA, US Department of Transportation, accessed through Statista 14 AT Kearney, Trends in Indian Infrastructure Development, 2013
Sahil Shekhar Putting India to Work
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it cannot explain the peculiar patterns dynamics in firm size and growth. We explore these
unexplained facts further in the next section.
Exhibits 1&2: conditioning for income level, India performs well on World Bank and World Economic Forum
aggregate infrastructure indices
Source: World Economic Forum, World Development Indicators
3.2. Regulatory distortions: second-best solutions deployed
A second line of argument contends that business regulations, many of which are as old as India
itself, create perverse incentives that prevent firms from formalizing or gaining scale. In attempts
to protect small firms from competition, a broad range of policies have been implemented:
subsidized credit for small firms, tax exemptions, preferential government procurement, cheaper
electricity, and small-scale reservations, which prevent larger firms from producing certain
products altogether.15
To illustrate these perverse incentives, we look at the effect of labor regulations in particular. The
Factories Act stipulates rules for work hours and conditions that must be followed in
manufacturing establishments that employ more than 10 people. The Industrial Disputes Act
requires firms with more than 100 employees to obtain government permission before firing a
worker, and firms with more than 50 employees to obtain worker consent before modifying job
descriptions or moving workers between plants.
15 Martin et. Al, “In with the Big, Out with the Small: Removing Small-Scale Reservations in India”, RAND corporation
working paper, February 2014
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These laws can only be enforced on formal sector firms above a threshold size of employment,
leading to distortions in the number and size of formal sector firms. In India, the average 40-year-
old firm hires 40 percent more workers than a 5-year-old firm. In the US, it hires 7 times as many
workers (Exhibit 3). Research also suggests a “kink” in the distribution of manufacturing firm size
at 10 workers – the threshold at which the Factories Act kicks in – suggesting firms are indeed
altering behavior to avoid regulations (Exhibit 4).16
Exhibit 3: Indian firms grow slowly
Source: Hsieh and Klenow (2014)
These distortions in firm dynamics misallocate resources in the economy, channeling them away
from larger, formal, more productive firm to small, informal sector businesses. This can have a
real negative effect on the economy. Studies suggest that states that amended labor laws in pro-
worker direction (and so exacerbated perverse firm incentives) experience lower output,
employment, investment and productivity in formal sector manufacturing.17
Although there’s near consensus among academics that India’s labor laws need revision, reform
efforts are politically fraught. The current government proposed modest amendments to the
Factories Act, Contract Labor Act and Industrial Disputes Act, raising thresholds at which these
acts kicked in. Even this limited reform led to a 150 million worker strike in September 2015.18
Firms, realizing that these laws may be here to stay (at least in the mid-term), have evolved to cope
with regulatory distortions. In some instances, they simply don’t comply,19 though this opens up
avenues for “harassment corruption” by government inspectors.20
16 Amriapu, A. & Gechter, M. “Indian Labor Regulations and the Cost of Corruption: Evidence from the Firm Size
Distribution,” November 2014 17 Besley, T and Burgess, R. “Can Labor Regulation Hinder Economic Performance? Evidence from India”, 2004 18 “Indian worker strike over Modi labour reforms”, BBC news, 2 September 2015 19 Bhattacharjea, A. “Labour Market Regulation and Industrial Performance in India: A Critical Review of the Empirical
Evidence”, Centre for Development Economics, DSE, Working Paper No. 141, June 2006 20 Amriapu, A. & Gechter, M. “Indian Labor Regulations and the Cost of Corruption: Evidence from the Firm Size
Distribution,” November 2014
Sahil Shekhar Putting India to Work
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Exhibit 4: kink in the firm size distribution at 10 employees (threshold for the Factories Act)
Source: Amirapu and Gechter (2014)
In many cases, firms use contract workers through third party agencies in lieu of permanent
workers. The proportion of contractors in the formal manufacturing sector increased from 12
percent in 1999 to 25 percent in 2010.21 Contractors don’t show as permanent employees of the
company, allowing firms to stay small enough on paper to circumvent labor laws and wash their
hands of the need to deal with government inspectors.22 The contract share of employment is higher
in states with rigid labor laws, suggesting that firms are actively using this strategy. 23
Even firms that are large enough to be subject to labor laws increasingly use contract workers to
avoid making permanent hires who might become impossible to fire in the future (due to either
demand shocks or performance issues). Indeed, the contractor share of employment is higher
among plants with more than 100 workers, to whom the Industrial Disputes Act that makes firing
difficult already applies.24
The increasing use of contract workers mitigates the worst effects of regulatory distortions.
Without touching labor laws, firms are effectively limiting their scope by reducing the share of the
labor force they apply to. By reducing the cost of hiring in the formal sector, the use of contractors
has led to an increase in the formal employment in manufacturing, although it remains at one third
of informal levels (Exhibit 5). The re-allocation of resources to more productive, formal sector
21 Chapter 10, National Economic Survey 2015-16, Ministry of Finance, 2016 22 Ibid. 23 Ibid. 24 Ibid.
Sahil Shekhar Putting India to Work
7
firms also has broad economic benefits. Research suggests that increased use of contract labor
boosted manufacturing GDP by 0.5 percent annually between 1998-99 and 2011-12.25
Exhibit 5: formal sector employment has
picked up since 2000
Source: Ghani et. Al (2015)
Though they have taken the edge of labor regulations, contract workers remain a second-best
solution for firms. Recent empirical work suggests that firms that hire more contract workers are
less productive. Contract workers stay with employers for shorter periods of time, reducing a
firm’s incentive to invest in firm-specific human capital. Though contract workers are sometimes
more motivated (eager to earn a permanent position), the net effect of a higher contract share on
firm productivity is negative. 26 The use of contract workers introduces middlemen into the hiring
process, increasing hiring costs. The Indian Cellular Association estimates hiring a contract worker
is 14 percent more expensive than hiring a regular worker.27 Finally, firing permanent employees
remains very difficult, reducing incentive for all the but the most motivated to work.
In sum, while regulatory distortions remain an issue, industry workarounds are diluting the worst
of their effect.
25 Bertrand et. Al. (2015) as cited in Chapter 10, National Economic Survey 2015-16, Ministry of Finance, 2016 26 Soundararajan, V. “Contract Work and Endogenous Firm Productivity in the Indian Manufacturing Sector”, 2015 27 Chapter 10, National Economic Survey 2015-16, Ministry of Finance, 2016
Sahil Shekhar Putting India to Work
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3.3. Human capital: jobs, but no skills
A third line of argument contends that low levels of human capital28 in the labor force are
preventing the creation of more formal sector jobs. Companies simply can’t find the skills they
need to make investments viable (at acceptable cost). Two facts lie at the heart of this argument.
First, despite an abundance of cheap low skill labor, India’s growth has been driven by skill29
intensive industries. Skill intensive industries, measured as those with higher years of schooling
among employees, account for a large and growing share of output and employment (Exhibit 6).30
This proportion has historically been high when compared to countries at similar levels of
development, such as China and Indonesia (Exhibit 7).
We note this is consistent with our discussion in chapter 2. Regulatory distortions that make hiring
expensive may be causing companies to substitute towards higher skill (higher value add) workers.
Poor infrastructure may similarly be causing a shift towards high skill service sectors.
Exhibit 6: Skill intensive sectors dominate
the economy
Source: Amirapu and Subramanian (2015)
Second, India’s labor force has particularly low levels of skill. 29 percent of the labor force is
illiterate.31 More than 40 percent of the labor force has less than primary education – much higher
28 By “human capital”, we mean the skills, knowledge and attributes that make individuals productive in the workforce.
Given human capital is difficult to measure, we metrics such as years of schooling, level of educational attainment and test
scores as proxies. We also use the term “skills” interchangeably with human capital throughout the paper. 29 For expositional convenience, we use the terms “skill” and “human capital” interchangeably for the remainder of this
paper. 30 Amriapu, A. & Subramanian, A. “Manufacturing or Services? An Indian Illustration of a Development Dilemma,” Center for
Global Development Working paper, June 2015. 31 Mehrotra et. Al, “Estimating the Skill Gap on a Realistic Basis for 2022”, IAMR Occasional Paper No. 1, 2013
Sahil Shekhar Putting India to Work
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than what we would expect for a country at India’s level of development (Exhibit 8). In 2010, only
28 percent of India’s labor force had secondary or tertiary education.32
Exhibit 7: Skill intensity has been high
compared to similar countries (1981-1996)
Source: Kochhar et. Al (2006)
Exhibit 8: India’s labor force is particularly
under-educated by global standard
Source: World Development Indicators
These numbers illustrate the low levels of educational attainment and say nothing of what many
pundits cite as the real problem: quality of education. Even India’s best states perform poorly in
international standardized school level assessments (Exhibit 9). At home, Indian employers
complain loudly and frequently about the quality of college graduates. One famous (but by no
means only) example is a 2005 study by NASSCOM (the Indian IT trade association) and
McKinsey, which found that “only 25 per cent of engineering graduates in India have the skills to
be employed in IT jobs without prior training.”33
32 World Development Indicators 33 NASSCOM & McKinsey, “Extending India’s Leadership of the Global IT and BPO Industries”, 2005
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Exhibit 9: Poor performance on standardized
PISA test
Source: Asian Development Bank, Key Indicators for
Asia and the Pacific (2015)
These facts suggest that (a) Indian employers have a high demand for skilled labor, and (b) skill is
in short supply in the Indian labor force. Given an excess demand for skills in the economy, tertiary
graduates have a much higher likelihood of being employed (Exhibit 10). They also enjoy a large
and rising wage premium (Exhibit 11). This premium is increasing more quickly in states where
industry productivity is growing faster (Exhibit 12). The co-movement of the skill premium and
growth suggest that skills may be a binding constraint to private sector growth.
Despite a rising skill premium, employers struggle to find qualified candidates. A McKinsey
survey of 2,832 employers in nine countries found that the average large employer in India had 36
entry level vacancies – more than any other country surveyed. When asked why, 53 percent of
Indian employers cited lack of skills as a common reason for entry-level vacancies – second after
Turkey and well above the group average of 39 percent. 82 percent of Indian employers said they
would be willing to pay more for graduates with the right skills – second only to the United States,
and again well above the group average of 70 percent.34
34 McKinsey & Company, “Education to Employment: Designing a System that Works”, 2012
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11
Exhibit 10: share of employment by Exhibit 11: Rising tertiary skill premium
education level
Source: Education, Skill Development and Labour Source: Azam (2009)
Force, Ministry of Labour & Employment (2014)
Exhibit 12: Skill premium is
correlated with manufacturing
productivity growth across states.
faster growing states exhaust a
limited pool of skilled workers, and
skill premium rise faster
Source: Amirapu and Subramanian (2015)
McKinsey’s survey is not the only one finding loud complaints of low skill job-seekers among
Indian employers. A Manpower Group survey of 41,700 employers in 42 countries found that 58
percent of Indian employers had difficulty filling jobs, seventh highest in the world. By way of
48.7
53.958.6
54.2
43.440.1
44.2
54.6
64
0.34
0.5
0.3
0.37
0.130.15
0.27
0.3
0
0.1
0.2
0.3
0.4
0.5
0.6
1983 1987 1993 1999 2004
Tertiary-Secondary gap Secondary-Middle gap
Middle-Primary gap Primary-Below Primary gap
Sahil Shekhar Putting India to Work
12
comparison, the highest complaints were in Japan, a nation in a very different demographic
situation to the booming Indian working age population.35
Employers’ complaints are borne out in the data. A study by the Asian Development Bank found
that, among Asian countries, India had the second-lowest proportion of tertiary workers in high-
skilled occupations (Exhibit 13).36 Employers are struggling to fill these high-skill positions.
Exhibit 13: education share in high-skilled
occupations. Few tertiary workers in high
skill jobs.
Source: Asian Development Bank (2015) from country
labor force surveys
PRC = People’s Republic of China
3.4. Homing in on skills
As our discussion to this point hopefully illustrates, no single factor can be isolated as the cause
low levels of formal sector employment is a difficult. A range of policy issues that stack the odds
against formal sector, job-creating companies need to be addressed. In subsequent chapters, we
focus our discussion on skills for three reasons:
1. Action is politically supportable and feasible in the short term. Labor regulation reform in
India has been mooted for decades without a shift in its political likelihood. Infrastructure
investments are subject to lengthy time frames for approval and construction, and are already
being aggressively pursued by the government.37 Human capital development efforts, on the
other hand, are riding on a wave of political support. The government has made major
investments in driving primary school enrolment and is now increasingly being lobbied to
35 Manpower Group, “Annual Talent Shortage Survey”, 2015 36 Asian Development Bank, “Key Indicators for Asia and the Pacific”, 2015 37 See, for example, reports of the Prime Minister’s Office directly driving infrastructure project: “PMO steps in to push
stalled infrastructure projects”, The Times of India, Dec 30, 2014
Sahil Shekhar Putting India to Work
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focus on higher education and education quality. Both the previous Congress and the current
BJP government announced a major emphasis on a “Skill India” campaign, aimed at increasing
the level of vocational skills in the labor force. There is a political hunger for action in this
space, and interventions may have a shorter pay back than longer term infrastructure
development projects.
2. Industry “work-arounds” for low skill levels may prove difficult. Industry responded to limited
infrastructure by focusing on infrastructure-light service industries. They responded to
regulatory distortions through use of contract workers. How will industry respond to an
exhausted pool of skilled workers? Despite their best efforts, successive governments have
failed to catalyze the take-off of low-skill-labor intensive manufacturing sectors. Given the
fastest-growing sectors in the economy remain skill intensive, companies will struggle to find
a substitute for human capital.
3. Interventions can serve a dual purpose of growth and inclusion. Helping job-seekers acquire
skills helps business find and hire more productive workers. It also empowers job-seekers to
participate in a growing economy and reap its dividends through higher incomes. In this way,
skills are both a pre-requisite for growth and a guarantee that citizens are included in India’s
rising prosperity.
For these reasons, we home in on skills and explore issues blocking (and solutions that might
enable) the development of human capital in India.
4. DIAGNOSING THE PROBLEM: WHY SO FEW SKILLS?
We noted in chapter three that skill levels are low in the Indian workforce, leading to a skill
shortage among Indian employers. In this chapter, we go one level deeper and ask: why? Given
demand for skilled labor among employers, why aren’t job-seekers acquiring skills?
4.1. Training sector response
In some cases, they are. Individuals are responding to the growing tertiary skill premium in
particular and seeking to acquire skills. College enrolment increase 2.4 times in the 11 years from
2000-01 to 2011-12. While Arts courses dominate (36 percent of total enrolments), enrolment
Sahil Shekhar Putting India to Work
14
growth has been fastest in engineering (11.8 percent compound annual growth from 1985 to 2010),
followed by education (8.0 percent) and science (6.2 percent) (Exhibit 14).
This has created extraordinary demand for higher education institutions (HEIs). As of 2011-12,
India had 36,113 HEIs – the highest number in the world, and more than 5 times as many as the
US and 7 times as many as China.38 More colleges opened in the six-year period 2005-06 to 2011-
12 than had opened in the thirty years prior (16,444 new colleges vs. 15,238).39
Almost two thirds of these are private, 40 suggesting the private sector has stepped in to plug the
public shortfall in training capacity. Private sector response has been particularly strong in
professional disciplines such as physiotherapy, pharmacy and hotel management (Exhibit 15).
Training supply is responding to skilled labor demand, though there is a long way to go. Gross
tertiary enrolment stood at 24 percent in 2012, up from 10 percent in 2002. It is now higher than
what we would expect from a country at India’s income level.41
Exhibit 14: enrolment growth by course of study
Source: Ministry of Human Resource Development
38 Ernst & Young and FICCI, Private sector participation in Indian higher education, FICCI Higher Education Summit, 2011 39 Ministry of Human Resource Development statistics 40 Ernst & Young and FICCI, Private sector participation in Indian higher education, FICCI Higher Education Summit, 2011 41 World Development Indicators
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Exhibit 15: private sector
participation in
professional higher
education courses
Source: Ernst & Young and
FICCI, Private sector
participation in Indian higher
education, FICCI Higher
Education Summit, 2011
While the quantity of higher education supply has grown impressively, the quality of colleges and
college graduates is notoriously bad. According to India’s National Assessment and Accreditation
Council (NAAC), 89 percent of India’s colleges are rated either average or below average.42
According to recent reports of graduate employability, 34 to 53 percent of graduates are considered
employable.43 The top rated Indian institution in university rankings by Times Higher Education,
US News and QS rankings came in at ranks 251-300, 354 and 147 respectively. Training
companies are churning out more and more graduates, but many of them are simply not
employable.
4.2. Explaining low training quality
We argue that the extremely low quality of higher education institutions can be explained by
information failures in the labor and training markets
Rapid expansion – over 20,000 new colleges have opened in over the past decade, an average of
2,000 each year – has allowed low quality colleges to hide in the crowd. While employers are still
in the process of learning the quality of colleges, graduates of high and low quality college receive
similar wage premiums for their educational investment. Similar labor market outcomes upon
42 As reported in “Indian universities second-grade?”, The Times of India, Feb 21, 2014 43 Pushkar P, “Why India’s universities can’t keep up with the masses”, The Conversation, April 20, 2014
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Sahil Shekhar Putting India to Work
16
graduation slow down student learning about college quality as well, and prevent admission sorting
by ability into high and low quality institutions. Limited sorting in turn further slows down
employer learning about college quality.
This dual information failure – between employers and colleges and students and colleges – leads
to a vicious cycle that prevents, or at least slows, learning and reputation building about the quality
of training institutions, allowing low quality colleges to survive for longer than they otherwise
would.
There are exceptions to this trend. At the very top end of the training market, an elite group of
institutions have built formidable reputations for quality. In a marketplace where information on
quality higher education is scarce, graduates from these elite institutions enjoy super-normal
returns. According to the Financial Times MBA rankings, a graduate from the prestigious Indian
Institute of Management Ahmedabad (IIM-A) makes $167,676 on average three years after
graduation (PPP adjusted). This is the fifth highest salary in the world (after HBS, Stanford,
Wharton and Columbia), and the only non-OECD school in the top 10.44
Excluding this elite, however, the prospect for Indian graduates seeking jobs are relatively bleak.
In 2005, one in three unemployed in India had tertiary degrees – the seventh highest proportion in
the world. For this large group of graduates, having a degree is a necessary but by no means
sufficient condition to securing a well-paying job.
To understand the role of information in educations and labor markets more thoroughly, we test
the extent to which better quality colleges are able to (a) attract better students in the admissions
process, and (b) secure better labor market outcomes of its graduates.
4.3. Can students recognize good colleges?
We study education and employment outcomes for a sample of 1,959 engineering college
graduates, diving deeper into a sector that has experienced the most drastic boom in training supply
in recent years.45
44 Financial Times MBA rankings 2015, accessed at http://rankings.ft.com/businessschoolrankings/global-mba-ranking-
2015 45 Details of our study are included in Appendix 1. We apply the methodology laid out by Macleod et. Al (2015) on a dataset
provided by an Indian assessment agency – the Aspiring Minds Employment Outcomes 2015 database.
Sahil Shekhar Putting India to Work
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If students have information about college quality, we would expect there to be fiercer competition
to get into better colleges. Students with better high school marks would win sought after seats,
leading to a sorting in the admissions process of better students to better colleges.
It’s important to note that a range of factors may prevent perfect sorting by ability. Students may
choose not to go better quality colleges because cost or convenience (distance) considerations.
Colleges may also choose not admit the best students. A prime example of this are India’s policies
of caste-based affirmative action. 49.5 percent of seats in government educational institutions are
reserved for students who belong to Scheduled Castes, Scheduled Tribes or Other Backward
Castes (these groups account for 20 percent, 9 percent and 40 percent of households in India).46
These reservations do not apply to private institutions, however, which comprise 91 percent of
engineering colleges.47
We test admissions sorting among the 2014 graduating cohort in our sample. Measuring the
candidate is relatively straightforward: we use the year 12 scores of students (scaled for their board
of education), which form a basis of the application students would put forward to colleges.
Measuring college quality is more difficult, given its multi-dimensional nature. We argue that,
from a labor market perspective, the best measure of college quality are the wages of its graduates.
In a world of perfect information, 2014 graduates would have had access to the post-graduation
salaries of 2011 graduates (three years senior). Better students would choose to go to colleges
whose graduates have higher wages upon graduation. The average wage of 2011 graduates,
therefore, serves as a measure of college quality.
In Exhibit 16, we compare the quality of each candidate with the quality of the college he/she
attended. If there is no sorting by ability, we would expect to see a horizontal line implying there
is no relationship between candidate and college quality. If there is some sorting, we would expect
to see a positively sloped line. The higher the level of sorting, the steeper we would expect the line
to be. The green line in Exhibit 16 shows the sorting that actually occurs. Admissions sorting is
limited and inconsistent. Better students do go to better colleges, but the relationship is slight. 2014
graduates with year 12 scores in the top ten percentile of our sample went to colleges with an
average 2011 graduate salary of Rs. 341,833. Students with scores 50 percentile ranks lower in our
46 National Sample Survey 61st Round, “Employment and Unemployment Situation Among Social Groups in India 2004-05”,
2006 47 Ernst & Young and FICCI, “Private sector participation in Indian higher education”, 2011
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sample, in the 40 to 50 percentile range, went to colleges with an average salary of Rs. 321,551 –
a difference of just Rs. 20,000, less than 5 percent of the difference in average salary between the
best and worst college.
Exhibit 16: Limited sorting of students
by ability at time of admission
Source: AMEO database (2015)
Exhibit 17: Significant intra-college
ability variation
Source: AMEO database (2015)
In Exhibit 17, we rank colleges by quality on the x-axis. We then look at the quality of the median
candidate enrolled at each college (blue dot), as well as the range of ability from the 25th to the
75th percentile college student enrolled at each college (red line range). As we can see, while
admissions sorting across colleges does occur, intra-college candidate quality variation is in many
cases as large as variation across colleges. There are students in the worst quality college in our
sample with year 12 scores higher than the median student in the best quality college in our sample.
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While we can’t rule out other factors (cost, convenience, quotas), our findings support the
existence of information failures between students and colleges. Students may not know which
colleges are better for their labor market outcomes and may be making enrollment decisions based
on misinformation. Literature also supports the view that new institutions market themselves based
on more visible features, such as campus facilities, resources and the use of technology.48 The US
Department of Education notes that the US News university ranking “weights spending and school
resources as nearly thirty percent of the evaluation, scored six times greater than how students fare
after their education experience.”49
4.3. Can employers recognize good colleges?
Next, we consider whether employers can recognize good colleges and are willing to pay a wage
premium for their graduates. Given admissions sorting in India is substantially incomplete, we
might expect the conditional return to college return to be significantly smaller than it otherwise
would. In a world with better sorting, better colleges would have better graduates not only because
of better standards of learning, but also because of their ability to screen candidates at the time of
admission. To the extent this screening is imperfect, the premium a degree from a better quality
college is diluted.
Returns to college reputation, controlling for the student’s own ability (as measured by scores in
high school, college and on a third party test), are small but statistically significant.50 Attending a
college with a student body scored ten percentage points higher in their year 12 exams (controlling
for a student’s own score) is associated with 3 percent higher wages. By comparison, the same
analysis completed in Colombia concluded that attending a better college was associated with an
8 percent higher wages. That is, the wage premium for college reputation is 2.7 times higher in
Colombia than it is in India.51
This provides further evidence consistent with our argument that information failures are at play
in the training and labor markets. Students and employers are not able to identify the best colleges,
48 Ivy, J. “Higher education institution image: a correspondence analysis approach.” The International Journal of
Educational Management. 15/6, 276-282, 2001 49 U.S. Department of Education, “Better Information for Better College Choice & Institutional Performance”, 2015 50 Details are provided in Appendix 1 51 Macleod et. Al. “The Big Sort: College Reputation and Labor Market Outcomes”, May 2015
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and low quality institutions are able to hide in the crowd and continue to provide sub-standard
education.
4.4. Industry response to information failures52
Employers have access to three imperfect signals of job-seeker quality on a typical fresh graduate’s
CV: her year 12 score, college GPA, and the name of the college she attended.53
Year 12 scores are typically reported along with the board of education the candidate graduated
from. The Indian education system offers students a choice to complete secondary education under
the auspices of one of 54 state or central boards of education.54 The lack of standardized scores
makes it harder for employer to compare across job-seekers and make judgments of relative ability.
College GPAs are also not trusted. As we have discussed, employers don’t know and don’t trust
the reputation of colleges job-seekers have attended and, by extension, the value of the college
GPA as a signal of skill is also marginal.
In the absence of reliable measures of ability, many employers hire on the basis of connections
and social relations. Sociological research suggests managers tend to hire “recruits who are
socially similar to themselves”55 leading to “homosocial reproduction”56 or “status
groups…monopoli[zing] valued economic opportunities.”57 To quantify this discrimination,
researchers submitted otherwise identical CVs with names associated with different religious or
ethnic groups and measured interview call-up rates. An applicant with a Scheduled Caste Hindu
name had a 67 percent chance of getting an interview as compared to an identical applicant with a
high caste Hindu name. A Muslim name had 33 percent odds.58 Network-based hiring is not
52 I thank Varun Aggarwal, co-Founder of Aspiring Minds, an assessment agency, for sharing his insights on recruitment
trends that informed this section of the report. Errors remain mine. 53 Employers may also value of extracurricular activities 54 Council on Boards of School Education in India board list, accessed at
http://www.unishivaji.ac.in/uploads/admin/circ/board_list.pdf 55 Thorath and Attewell, “The Legacy of Social Exclusion: A Correspondence Study of Job Discrimination in India”, Economic
and Political Weekly, Vol. 42, No. 41 (2007) 56 Kanter, Rosabeth (1977): Men and Women of the Corporation, Basic Books, New York. as cited in Thorath and Attewell
(2007) 57 Weber, Max (1968): Economy and Society (edited by Guenther Roth and Claus Wittich), Bedminister Press, New York, as
cited in Thorath and Attewell (2007) 58 Thorath and Attewell, “The Legacy of Social Exclusion: A Correspondence Study of Job Discrimination in India”, Economic
and Political Weekly, Vol. 42, No. 41 (2007)
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meritocratic and a hotbed for discrimination in employment practices. It also does not help
employers find the skill they need to grow.
Unable to find the skills they need, employers have adopted two broad approaches, which we refer
to as “hire and train” and “screen heavily.”
In the “hire and train” approach, employers stop trusting signals altogether and assume they’re
always hiring the median (or worse) worker. They then invest heavily in training new hires. A
mid-size textile company CEO we interviewed summarized: “If I’m hiring a fresh graduate, I
assume he knows nothing. I don’t care what college he comes from. We have our own training
programs that teach everything the guy needs to know to work in our company.” This company is
not alone – 93 percent of employers train new hires for an average of 31 days – the highest
investment among nine surveyed countries (Exhibit 18).59
Exhibit 18: Training of entry level workers:
employers invest significant amounts in new hire
training
1. Does your company provide training for new hires?
2. On average, how many days of training does a new hire
receive in the first year?
Source: McKinsey Education to Employment report
The “hire and train” approach exacerbates the vicious cycle created by information failures.
Employers place little premium on skills acquired in college or training institutions as they are
investing significantly in re-training their hires. Graduates from better colleges receive smaller
premiums, reducing demand for better quality colleges.
59 Ibid.
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However, not all industries have responded to the paucity of information in this way. Some
companies take the “screen heavily” approach, investing in more rigorous screening methods to
find and recruit the best candidates. This generally requires generating new data on candidate
quality through employer led interviews or tests. Wipro, a leading IT company, has a three round
selection procedure for fresh graduates. First, an online test of programming, quantitative, logical
and verbal skills. Second, a group discussion on a broad range of social and civic issues. Third,
one-on-one technical and HR interviews (strengths and weaknesses, etc.)60 Based on the accounts
of company employees, Tata Motors follows a similar procedure: shortlisting candidates based on
college scores, administering their own written test, group discussion, technical interview followed
by an HR interview.61
The IT industry is a good example of how the “screen heavily” approach changes recruiting
dynamics within a sector. The largest IT companies invest heavily in screening by conducting on-
campus recruitment at select university campuses. This includes, in many cases, purchasing and
delivering standardized employment tests from assessment agencies to supplement traditional
recruiting methods.
Small or mid-size companies don’t hire directly from campus at all. The investment required is
too great. Instead, they recruit 1-2 year experienced hires from the top ten IT companies, piggy-
backing on their screening processes. The top ten companies “keep the best and train the rest”,
retaining the top graduates and churning other screened and now experienced hires to the rest of
the industry. In some cases, the top ten may even be “training the best and keeping the rest,” as
smaller, high value add companies hire the very best graduates once they’ve proven themselves at
the bigger IT firms.
The skill acquisition outcomes that result from the “screen heavily” approach are very different
from the “hire and train” approach. In the “screen heavily” approach, employers generate new data
that effectively helps solve the information failure between employers and colleges/job-seekers.
Armed with this information, firms are willing to pay a premium for skills, and students have an
incentive to acquire skills that allow them to secure better jobs. Bad colleges whose graduates fail
60 “WIPRO interview process selection procedure”, CampusFold.com, 2014, accessed at
http://www.campusfold.com/2014/08/wipro-interview-process-recruitment.html 61 “What is the placement procedure for Tata Motors”, Quora, 2015, accessed at https://www.quora.com/What-is-the-
placement-procedure-for-Tata-Motors
Sahil Shekhar Putting India to Work
23
to pass employer screening efforts will eventually be found out, breaking the vicious cycle created
by a dual information failure.
The IT sector’s approach, while better than nothing, is still sub-optimal. First, it imposes a
screening cost associated with new hires that might reduce the number of people a company is
willing to hire. Second, IT companies that invest in screening and hiring fresh graduates don’t reap
the full benefits of their rewards. As employees are poached by other companies over time, some
of the benefits leak to other employers and, of course, in large part to the employees themselves.
This means firm investment in screening will be lower than the “socially optimal” level.
Nevertheless, many industry commentators consider the IT sector’s approach to skill a success.
Wage premiums have led to graduates rushing to train in technical skills applicable in IT. The
industry’s own analysis of its talent situation concludes that “the talent pool available is expected
to stay in excess of the talent demand for the coming few years”62 – a rare and privileged position
for employers to be in in India’s otherwise skill constrained economy.
We argue that the IT industry’s position is not (just) the result of prescient foresight, better
employer coordination, or luck. Rather, three structural features can help explain which sectors
adopt a “screen heavily” strategy that leads to better talent outcomes.
1. High labor productivity. In human capital intensive industries with high marginal product of
labor, search costs will be relatively small compared to large value gain of attracting better
employees. In these industries, companies would be willing to incur screening costs to find
even slightly better employees, because each employee is worth more to the company. This
helps explain why human capital dependent industries such as IT and professional service firms
invest heavily in candidate sourcing and screening.
2. High variance in job-seeker quality. If there is high variance in graduate quality, employers
would have higher incentive to invest in screening solutions. Let us assume employers are able
implement a screening technology that identifies the top 20 percent of candidates from a
graduating cohort. In industries with high variance in graduate quality, the average screened
candidate will be much better than the average candidate without screening. If the quality of
the graduate cohort has low variance, the average screened candidate will be better, but not
62 NASSCOM, Analysis of Talent Supply and Demand. Employment Requirements and Skill Gaps in the Indian IT-BPM
Industry, 2014
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that much better, than the average unscreened candidate (Exhibit 19). Research suggests that
the variance of employee performance increases with skill level of employees. One study found
that a blue collar worker who is one standard deviation above the mean is 20 percent more
productive than the average worker. In white collar jobs (selling insurance), she is 120 percent
more productive. In a management job, she is 600 percent more productive.63
Exhibit 19: higher job-seeker
quality variance increases the
return on screening
3. Known, low cost screening technology. Technologies for screening certain skills and attributes
are more readily available, reliable and cheaper. Testing technical knowledge, for example, is
easier than testing a candidate’s behavioral attributes or ability to learn. IT companies can
reliably and cheaply test a coder’s programming skill. It’s might be more difficult and
expensive for a bank to test a new employee’s sales skills, or for an auto-manufacturer to test
an electrician’s ability to respond to possible maintenance cases. Evidence suggests that “skill
is relatively much more important for professional/technical employees while behavioral traits
are more important for blue-collar workers.”64
We note that each of these three structural factors predict that skill-intensive industries with higher
value-add per worker (like IT) are more likely to invest in screening. Companies in relatively lower
skill but more labor intensive sectors such as automobile manufacturing or construction are
unlikely to follow this socially preferable path.
63 Staffing.org, Staffing Effectiveness & Retention, 2013 64 Osterman, P. “Skill, Training, and Work Organization,” Industrial Relations, Vol. 34, No. 2, 1995
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5. DESIGNING A SOLUTION: SOLVING INFORMATION FAILURES IN THE LABOR
MARKET
So far, we have argued that low levels of skill are one key reason for low levels of formal sector
employment in India. Low levels of skill arise due to the low quality of education providers, who
are able to survive (and even thrive) due to information failures in the education and employment
markets. Employers find it hard to know which job-seekers (and which colleges) are good.
Students find it hard to know the quality of colleges at the time of admission. These information
failures form a vicious cycle that reduce incentives for individuals to acquire skills and make it
harder for companies to find the skills they need to fill “good jobs.”
While some industries have responded to this problem by investing in screening technologies that
create more information about job-seeker skill levels, not all industries will solve this problem on
their own. Companies in lower skill but labor intensive industries are less likely to invest in
screening of job-seekers, which would in the long run increase skill levels of graduates and create
more formal sector jobs.
A successful solution must help bridge information failures in the labor and education markets,
and particularly in sectors which need help the most. An intervention that creates a credible signal
of job-seeker quality can increase incentives for skill-acquisition and reduce search costs for filling
“good jobs.”
5.1. Learning from current efforts
The Government has implemented various schemes aimed at improving quality in tertiary
education. The National Assessment and Accreditation Council (NAAC), set up in 1994 as an
autonomous institution under the University Grants Commission (UGC), uses self-evaluations and
peer reviews to assess higher education institutions across seven input and process based criteria
and assigns them a letter grade. Similarly, The All India Council for Technical Education (AICTE),
the accrediting agency for engineering and technical higher education institutions, made
accreditation by the National Board of Accreditation (NBA) compulsory for all institutions under
its purview. The NBA is now an autonomous body, independent of AICTE. The Indian Council
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of Agricultural Research also established an Accreditation Board (AB) to perform a similar
function.
The majority of these efforts have focused on assessing college inputs and processes, rather than
student outcomes. Results have been mixed. A self-commissioned impact analysis of NAAC found
that colleges have “started copying the top-bracket institutions….[and some institutions] had spent
much time preparing documents and plans that would impress the peer team.”65 This copying leads
to what Pritchett et. Al. term isomorphic mimicry: “the adoption of the forms of other functional
states and organizations which camouflages a persistent lack of function.”66
Upholding the “objectivity of accreditation” is also emerging as a challenge.67 Assessments with
unfavorable results lead to Universities questioning of the NAAC methodology.68 Most damaging,
however, is the perception that government-led subjective assessments are corrupt. Media reports
of bribery are common. One such report alleges a University spent Rs. 1,289,137 (~US$20,000)
on NAAC members, including gifts of rare shawls, saffron and five-star accommodation, to secure
an “A” rank.69 The AICTE is also perceived as having significant issues with corruption,70 which
in the past have led to suspension of the AICTE Chairman.71
A few outcomes focused quality assurance efforts have also been tried. AICTE requires all colleges
to submit Mandatory Disclosures each year, which includes labor market data (including salaries)
for all degree graduates.72 One study found that 82 colleges out of a sample 500 had not submitted
mandatory disclosures, and many others had obvious mistakes or misreporting. Even when
mandatory disclosures had been submitted, they were difficult to access and compare.73
In vocational education, the government is in the process of implementing an industry-sourced
National Skill Qualification Framework (NSQF) to serve as a curriculum against which vocational
skills can be assessed. Developed by Sector Skill Councils (SSCs) comprised of companies and
other industry bodies, the NSQF outlines National Occupational Standards (NOS) for 1,641 job
65 Stella, A. “Institutional Accreditation in India”, International Higher Education, 2015 66 Pritchett, Woolcock, Andrews, “Capability Traps? The Mechanism of Persistent Implementation Failure”, 2010 67 Ibid. 68 “Shock over grading of 38 deemed universities”, Times of India, November 22 2015 69 “Kashmir university ‘spices’ hands in order to obtain A-ranking”, Daily Mail, May 10 2012 70 “All India Council for Technical Education caught in a cleft”, Business Standard, November 14 2013 71 “AICTE chairman suspended over corruption case”, Indian Express, July 30 2009 72 AICTE, Approval Process Handbook, January 2010 73 Jindal, N. “AICTE, the Regulator which doesn’t share data!”, Careers 360, 6 Jan 2014
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roles across 33 sectors.74 Independent assessment agencies impaneled by SSCs carry out
assessment of candidates against the NSQF upon graduation from government sponsored
vocational education programs.
While the jury is still out on the success of NSQF implementation (with no formal evaluations),
anecdotal evidence of industry recognition and ownership of the certification is mixed. A key issue
raised is that government focus on scaling quickly is compromising the value of SSC certification.
Chasing a highly improbably target (training ~500 million additional Indians by 2022), politicians
and bureaucrats have incentives to maximize the quantity of graduates, rather than their quality.
Having outsourced assessment to third party assessment agencies, the government has not built
any in-house capability to check or assure quality of graduates, and is facing the tough reality that
industry may not be willing to recognize SSC certificates. In an effort to bolster its uptake, the
government has announced plans to make SSC certification mandatory for application to relevant
government jobs by 2020.75
5.2. Design choices
A successful intervention needs to credibly assess quality of colleges and/or job seekers to provide
employers with a reliable signal of job-seeker quality. To design a credible assessment, we must
answer four fundamental design questions:
1. What should be assessed? Should we assess inputs and processes of institutions or training
courses? Should we assess graduate learning outcomes or employability? Should we report the
salaries graduates are earning post-graduation?
2. Who should be assessed? Should assessment efforts focus on colleges as a unit or on individual
students? Should assessment be mandatory, or should people choose to opt in?
3. What should be the role of the government vs. the private sector? Who should govern the
implementation of the proposed intervention? Who should fund it?
4. Who should do the assessing? Should government agencies such as NAAC lead assessment?
Should assessment be handed over to private sector assessment agencies?
74 National Skill Development Corporation website, accessed at
http://www.nsdcindia.org/sites/default/files/files/Summary_QP-NOS_list_as_on_30th_Dec_2015.pdf 75 “More than a degree, NSQF to become mandatory for Government PSY jobs by 2020”, The Economic Times, April 9 2015
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In answering these design questions, we keep three broad criteria in mind:
Does the answer lead us to an intervention that, if successfully implemented, will address the
information failure in question (technical correctness)?
Does the answer lead us to an intervention that is likely to be implementable in the short to
medium term (administrative feasibility)?
Does the answer lead to an intervention that will garner enough political support (political
supportability)?
We consider each of the four design questions in turn.
5.3. What should be assessed?
Two categories of metrics are available to assess college quality: inputs or outcomes.
A vast literature exists on the potential gap between schooling inputs and learning outcomes.76
Measuring input metrics such as college faculty, resources or processes provide no guarantee of
what we ultimately care about: the quality of college graduates. Measuring inputs may create
perverse incentives for over-investment in areas captured by assessment at the expense of what’s
truly needed to improve the quality of graduates. This is evident in the “isomorphic mimicry”
arising out of the NAAC’s current efforts.
In measuring outcomes, a metric of chief importance to incoming students is their expected salary
after graduation. Departments of Education in several countries, including Chile and the US, report
average salary of graduates by institutions and degree program.77 Post-graduation labor market
information can help students determine the quality of colleges and create a healthy competition
for admission.
While useful for students, this metric would be telling employers what they already know: which
colleges are already recognized as superior. By publishing salaries, we would we amplify this
existing information rather than generate new signals. Salary data is also biased by caste or religion
based discrimination in the hiring process, as discussed in the previous chapter. For these reasons,
reporting wages alone is not the technically optimal solution. Further, while administrative data
76 Pritchett L, “The Rebirth of Education: Schooling Ain’t Learning”, Center for Global Development, October 2013 77 See, for example, collegescorecard.ed.gov
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on salaries is readily available in advanced countries, it is less readily available in India making it
administratively difficult to collect and share this data.
A second outcome metric is the employability of college-leaving graduates. In Colombia, for
example, all higher education graduates are required to sit a standardized test that measures both
generic and domain competencies specific to their degree.78 It is important to note that this does
not necessarily mean testing mastery of the curriculum, which can lead to rote learning on the part
of students. Rather, a combination of hard and soft skills could be tested, aimed at predicting the
productivity of a graduate in a median firm. A great deal of private sector effort is underway to
develop such predictive tests (discussed in the next design question).
Assuming reliable testing is possible, these test scores would provide employers with a new signal
of job-seeker ability, while also improving their learning of college reputation. Changing labor
market outcomes of college graduates would feed back to students making decisions about
admissions, helping them identify better colleges.
Introducing a standardized test for college graduates raises concerns of administrative and political
feasibility. It is not unprecedented, however. The National Board of Examinations (NBE),
established in 1975, administers a standardized test for graduates of Medical Colleges across the
country, following standards laid out by the Medical Council of India.79 We consider these
concerns in greater detail in the next section.
5.4. Who should be assessed?
Efforts to bring transparency to higher education are likely face stiff political opposition. Two
groups are likely to be the most vociferous attackers: low quality colleges who don’t wish to be
revealed, and elite colleges who stand to lose too much from a standardized testing regime.
78 Velosa et. Al, “Returns to Higher Education in Chile and Colombia”, IDB Working Paper Series No. IDB-WP-587, March
2015 79 See http://www.natboard.edu.in/aboutus.php
DESIGN PRINCIPLE 1: Measure employability of graduating students. Testing inputs or
labor outcomes post-graduation is technically sub-optimal.
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Low quality colleges benefit from the opacity of the current education and labor markets, which
allows them to hide in misinformation and confusion. The strength of opposition is compounded
when we consider that it’s widely reported that many low quality colleges are owned by
politicians.80 As one report puts it: “in the over-regulated education sector, politicians with clout,
connections and insider knowledge, can cut through red tape and quickly expand capacity.”81
Elite colleges already enjoy a privileged position and have little to gain from a new assessment of
quality. Indeed, efforts to impose a new assessment could show them in bad light. Given the
visibility and influence of many of these institutions, they are well placed to shut down perceived
threats. They might, for example, be well placed to question the validity of any test that were
imposed upon them.
Given these vested interests, we argue that mandatory assessment of colleges is politically
untenable. We can, however, adopt two tactics that help us mitigate and largely avoid this political
opposition.
First, we propose that assessment be focused on students, not institutions. Adopting this approach
has both political and technical benefits. Politically, assessing students side steps a frontal attack
on colleges. Technically, it gives employers precisely what they want: a signal of the ability of the
job-seeker sitting in front of them, rather than of the quality of the college that she attended.
Second, we propose that the assessment be voluntary rather than mandatory (at least initially).
Voluntary schemes will be perceived as less of a threat by low quality education and providers,
buying an intervention time to scale. Voluntary testing, however, creates a technical problem in
the validity of results. It’s unlikely that the poorest performing colleges or students will opt to sit
for an optional standardized test if they believe that the results will carry negative signals to future
employers. As a consequence, below average students, knowing that the poorest students are not
sitting the exam, will also be reluctant to taking knowing that they will be compared to students
who are better than them. Finally, students from colleges that already have stellar reputations are
unlikely to sit an exam given they have everything to lose and little to gain.
80 “For Political Class, Money Flows via TN Engg Colleges”, The New Indian Express, March 13 2016 81 Pandit, V. “Politicians in education”, Education World Online, March 5 2009
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Only in the scenario when testing reaches a critical mass such that employers demand a test score
from job-seekers, or not providing a test score carries negative information value, will an opt-in
model truly help reveal the worst performing colleges. Initially, it’s more likely that voluntary
testing provides an avenue for some good graduates to signal their ability.
We argue that this technical compromise is necessary to maintain political viability. Launching as
a voluntary scheme with long term ambitions for universal coverage is also a proven tactic among
government programs in India. The NAAC began as a voluntary scheme with a focus on self-
improvement rather than accountability or the meeting of minimum standards. 82 NAAC framed
their accreditation process as “meant for quality institutions…[and] not suitable for the ones that
might be still struggling with some basic problems.”83 As of October 2006, a total of 2,956 colleges
or 13 percent of India’s higher education institutions had been accredited.84 While commendable
from the point of view of self-improvement, this did not constitute a broad signal of college quality.
Accreditation was made mandatory by UGC (in 2013) and various state governments in order to
qualify for state and central funding. In the next year alone, 2,808 colleges and 102 Universities
“volunteered” for accreditation – equal to roughly half the total number of colleges assessed by
NAAC in 18 years of existence.85
Another Indian policy initiative illustrates that voluntary schemes, if executed well, can reach
extraordinary scale. Aadhaar, India’s unique identification scheme, started as and remains a
voluntary scheme. Its voluntary nature helped Unique Identification Authority of India (UIDAI)
avoid political opposition from other Ministries and scale at its own (albeit rapid) pace. At the time
of writing, over 950 million Aadhaar numbers have been issued.86 While much has been written
about the success and challenges of Aadhaar, we simply highlight that its design as a voluntary
scheme did not preclude its rapid scale up to near universal coverage.
Further, voluntary schemes have the benefit of being administrative feasible. The rapid ramp up
of implementation required in mandatory schemes can lead to what Pritchett et. Al. term premature
load bearing “in which wishful thinking about the pace of progress and unrealistic expectations
82 Stella, A. “External Quality Assurance in Indian Higher Education: developments of a decade”, Quality in Higher
Education, Vol. 10, No. 2, July 2004 83 Ibid. 84 Agarwal, P. “Indian Higher Education: Envisioning the Future,” SAGE publishing 2006 85 NAAC Annual Report 2013-14 86 Aadhar online portal, accessed at https://portal.uidai.gov.in/uidwebportal/dashboard.do
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about the level and rate of improvement of capability lead to stresses and demands on systems that
cause capability to weaken (if not collapse).”87 We find evidence of premature load bearing in
Indian higher education quality assurance efforts. In the case of the NAAC, the Chairman of the
UGC acknowledged that, once accreditation was made mandatory, “the present capacity of NAAC
is so very inadequate...”88 to assess all colleges. As discussed in section 5.1, unrealistic targets for
vocational education have also put NSQF under strain. Under pressure to deliver on targets, the
Skill India machinery is scrambling to maintain quality control in its programs and link trainees to
jobs.
We make one final note on eligibility for the eligibility of test-takers. Implementing a standardized
assessment opens up the playing field for students from all universities (not just the elite) to
compete for jobs. It could also create opportunities for non-University students who do not have a
formal education but have informally acquired skills to access formal sector jobs. Equal
opportunity to compete for jobs is a large benefit of a standardized assessment.
5.5. What should be the role of government vs. private sector?
A government led standardized assessment is likely to fail both politically and administratively.
Politically, government interference in University affairs is an age old topic of controversy and
debate in the Indian educational sphere, most recently cited by the Chief Minister of Delhi as
“responsible for the erosion of the education system.”89 If government led tests are seen as a
political imposition, accusations of interference will haunt their legitimacy.
Administratively, implementing a test that predicts graduates’ job performance requires keeping
assessment frameworks up to date with continuously evolving industry knowledge and
requirements. This is a difficult undertaking for bureaucracy. Even the NSQF, which relies heavily
87 Pritchett, Woolcock, Andrews, “Capability Traps? The Mechanism of Persistent Implementation Failure”, 2010 88 “Rethink required to improve higher education: UGC chief”, Business Standard, August 14 2014 89 “Political interference has eroded education system, says Arvind Kejriwal”, The Indian Express, March 27 2015
DESIGN PRINCIPLE 2: Implement voluntary assessment of students. While universal
testing is a desirable end point, mandating testing may lead to premature loadbearing in
implementation and create political opposition.
Sahil Shekhar Putting India to Work
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on industry involvement through Sector Skill Councils (SSCs), has faced challenges in sourcing
standards from industry. While the aggregate coverage figure of 1,641 job roles across 32 sectors
seems impressive, just five sectors have contributed over 40% of this total. 90 Progress has been
highly variable, depending in part on the abilities and motivation of SSC members.
An employer led intervention has better chances of designing and maintaining an industry relevant
curriculum. It also has a better chance of linking education to employment for graduates.
Examining the beginnings of global standardized tests is instructive. The GMAT, now the standard
for evaluating business school applicants, began in 1953 when representatives from nine leading
business schools (Columbia, Harvard, Northwestern, Rutgers, Seton Hall, University of Chicago,
University of Michigan, University of Pennsylvania and Washington University in St. Louis) met
to establish a standardized assessment.91 The SAT was first administered by a teacher at Princeton
and subsequently adopted by Harvard to administer a scholarship program for gifted students.92 In
both cases, a group of aspirational Universities led the launch of the standardized tests.
A standardized test for college graduates would do well to similarly start with a community of
aspirational end-users of the information it generates. In our case, implementation could be
anchored with a group of companies that graduates aspire to work for, and who are motivated to
participate for self-interest in better talent sourcing.
As we noted in chapter 4, however, not every industry has the features necessary to create
incentives for firm-level investments in assessment and screening of graduates. Because the
benefits of screening are not captured by any one employer, the economic case will not stack up
for firms in industries with low levels of human capital, low variance in quality of graduates, or
high cost of testing.
The government can catalyze screening by improving the economics of assessment in these
industries. This could be in the form of a per-student-assessed subsidy to assessment agencies,
similar to payment currently being made for vocational education assessments under the Pradhan
90 National Skill Development Corporation website, accessed at
http://www.nsdcindia.org/sites/default/files/files/Summary_QP-NOS_list_as_on_30th_Dec_2015.pdf 91 “The History of the GMAT”, Economist GMAT blog, November 5 2014 92 Leeman, N. The Big Test: The Secret History of the American Meritocracy, 2000
Sahil Shekhar Putting India to Work
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Mantri Kaushal Vikas Yojana (PMKVY) scheme. The subsidy would cover only a part of the
assessment costs, with the remainder being covered by employers and the student.
It’s important to note that this manner of subsidy might create incentives for assessment agencies
to complete as many assessments as possible rather than focus on the quality of each individual
assessment. We discuss mechanisms for quality assurance in the next section.
5.6. Who should do the assessing?
Reliability and industry relevance are key to credible assessment. A large government role in
assessment design and administration is unlikely to succeed. Recognizing this, and responding to
limited in-house capabilities to assess, government has started engaging independent assessors of
employment skills. NSDC, for example, has impaneled 21 assessment agencies to evaluate the
learning of graduates from its vocational training programs.93 Many of these agencies also assess
non-vocational skills and skills specific to white-collar industries, such as engineering. This
expertise could be leveraged in broader assessment across many sectors.
As mentioned in the previous section, subsidy payments to independent assessment agencies
should be made with strong safeguards in place to ensure the quality of assessment remains high.
We argue that the most powerful safeguard is a reputational incentive of certifying agencies. If the
GMAT, for example, started producing unreliable scores, business schools would quickly stop it
as a benchmark test for admissions.
This powerful incentive should not be lost when assessment agencies partner with government.
Certificates issued under the auspices of NSDC, for example, do not feature the name of the
assessment agency who evaluated the candidate. This removes the incentive for quality assurance
93 NSDC website, accessed at http://nscsindia.org/NSCSAssessmentAgency.aspx
DESIGN PRINCIPLE 3: Assessment should be led by a group of companies that graduates
aspire to work for. Government could subsidize a part of assessment costs in order to
catalyze company action in low-skill industries.
Sahil Shekhar Putting India to Work
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by impaneled assessment agencies, who instead are driven to chase a per student assessment fee
and push for quantity rather than quality of evaluation.
Learning from this, industry should retain control of assessment agencies to reduce likelihood of
(perceived) capture of assessment quality in the public sector. Companies in a sector may then
decide to award multi-year contracts to agencies that are able to prove reliability of their tests in
trials. Companies in a sector may choose to award contracts to multiple assessment agencies to
maintain competition that leads to improvement in assessment quality. If this approach were
followed, certificates issued by each assessment agency should carry their name, so employers can
continue to learn about their performance. It is important to note that the use of multiple assessment
agencies within a sector may have technical trade-offs, as it would be harder to compare scores
across assessors. In the long run, however, competition may lead to a single assessment agency
emerging as a market leader with a superior product to service the industry. This decision could
be made by companies at the sector level, depending on the level of maturity of the screening and
assessment technologies already available in the market.
6. IMPLEMENTING: AN INDUSTRY LED, GOVERNMENT SUPPORTED
INTERVENTION
As our discussion so far has made clear, private sector leadership is critical to the success of our
proposed intervention. In this section, we outline a framework for cooperation between
government and employers that can enable implementation.
6.1. Employer led Job Readiness Councils
We recommend that our program be anchored at the sector level. This allows employers the
flexibility to choose a screening technology most suited to their needs. As we noted in the previous
chapter, employers of low-skill workers focus on behavioral attributes that are often measured
DESIGN PRINCIPLE 4: Third party assessment agencies can be engaged to design and
deliver tests, provided a reputational incentive is in place to ensure they maintain the
quality of assessments.
Sahil Shekhar Putting India to Work
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through personality or culture-fit tests. Employers looking for high-skill candidates are likely
emphasize technical skill or soft skills much more.94
Priority sectors could be chosen based on (a) the size of labor force affected, (b) the levels of
human capital of sector employees, and (c) levels of employer coordination. The rationale for
criteria (b) lies in our discussion in section 4.4: the economic case for an employer-led screening
solution is more likely to succeed in industries with higher levels of human capital. Sectors in the
“sweet spot” for our intervention fall just short of the levels of human capital required to
incentivize employers to implement firm-level screening solutions (e.g., IT companies,
professional service companies). Levels of human capital within each sector could be measured
by the share of sector employees who have tertiary education.
Within each sector, “Job Readiness Councils” (JRCs) should be established. 1-2 representatives
from industry associations, such as the Confederation of Indian Industry (CII), will play an
important convening role in JRCs. The authority of JRCs will come from the 3-4 major employers
in the industry, who should all have 1 representative on the council. A representative from a mid-
size company in the industry would also lend the JRC legitimacy among a broader employer base.
Given they are partial funders of the program, a representative from Government would also be
present on the JRC. The representative could be a Director or Joint Secretary level bureaucrat from
the Ministry of Human Resource Development (in industries where job seekers are tertiary
graduates) or the Ministry of Skill Development and Entrepreneurship (where job seekers are
vocational program graduates).
It’s important to note that, while industry associations may play a convening role, employers need
to be the champions and motivators of this proposal. Employers should have full confidence that
the proposed intervention is in their best interests, and intervention design should be re-worked
until it has the full buy-in of the JRC. A motivated group of employers and JRCs should be a pre-
condition for implementing the intervention in any given sector.
94 Osterman, P. “Skill, Training, and Work Organization,” Industrial Relations, Vol. 34, No. 2, 1995
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6.2. Implementation plan
The JRC has three broad functions:
Prepare
1. Raise funds from industry employers to cover implementation and industry share of assessment
costs. This will also serve as a litmus test of industry support.
2. Set up a Standards Unit (SU) tasked with creating industry standards for key job roles. The SU
can comprise of HR representatives from industry employers and education and assessment
experts from industry. The SU should also draw on existing job standards created by member
employers and by government bodies such as Sector Skill Councils (SSCs). Job Readiness
Standards should be sent to industry employers for feedback and ultimately approved by the
JRC.
3. Contract assessment agencies to test against Job Readiness Standards.
Promote and Execute
4. Secure commitment from at least 3-4 employers to at least interview all candidates who place
in the top quartile (threshold can be revised) of Job Readiness Tests, regardless of their
background or other characteristics.
5. Market this commitment aggressively in their recruiting efforts and in colleges. We note that
it will also be in the commercial interest of assessment agencies to source test-takers, as they
are paid on a per-test basis (with reputational incentive to ensure quality).
6. Oversee assessment agency implementation of tests.
Publish
7. Publish anonymized data on test performance, broken down by college/institution of test-sitter,
on a public website.
8. Collect data on performance of Job Ready test-takers in interviews and, over time, on-the-job,
and share with JRC to inform program improvements.
It is important to note that government should play a minimal role in implementation. Government
should not co-brand the initiative, leaving it to the JRC to establish an autonomous brand.
Government should not link its funding to scale or outcome targets that may compromise the
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quality and reliability of tests. Perhaps most importantly, government should also resist the
temptation to merge JRCs with existing Sector Skill Councils (SSCs) or other government skill
agencies. Prima facie, JRCs and SSCs have very similar functions: set industry occupational
standards and test against them. SSCs, however, are focused on graduates of government-
supported short term vocational training programs (though this is changing with a focus on
“Recognition of Prior Learning” in the Ministry). JRCs will focus on all job-seekers, with an initial
focus (as discussed in section 6.1) on sectors with higher levels of human capital. This means that
test-takers initially are likely to be tertiary graduates, who are not the audience of SSCs.
Where conflicts do arise, however, we argue that industry doubts about SSCs arise precisely from
the amount of influence government is able to exert over them. This has led to a premature focus
on scale and universal coverage with insufficient efforts to ensuring quality and connection to
employer needs. Establishing industry led JRCs will duplicate the work of SSCs in some sectors,
but with the important design change that they JRCs are owned and championed by industry. SSC
job standards are approved, for example, by the National Skill Development Agency (NSDA) – a
government body – and not industry employers. SSCs respond to Ministry and NSDA targets to
create occupational standards and certify trainees, not to industry employers.
In cases of conflict, the Ministry of Skill Development and Entrepreneurship (MSDE) may
understandably oppose funding of JRCs, given they have invested considerable effort setting up
their own SSCs. In response, JRCs should:
Remain firm on independence. Government plays a minority role in funding and project
governance precisely to prevent it from exerting undue influence. If insisting on autonomous
operation costs JRCs their government funding, this is an acceptable compromise. Government
funding could be replaced by employer contributions or by the increasing amount of
philanthropic funds being directed to education-to-employment issues by organizations such
as the Rockefeller Foundation.95 These funds could be accessed through CII or a non-profit set
up to oversee the JRC’s activities.
Avoid. A tactical focus on tertiary graduates early on will help JRCs gain scale and establish
credibility before dealing with possible opposition from MSDE.
95 See, for example, Rockefeller Foudnmation’s employment initiative in the US:
https://www.rockefellerfoundation.org/our-work/initiatives/youth-employment/
Sahil Shekhar Putting India to Work
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Collaborate. Cooperation is a preferred outcome to confrontation. The JRC should remain
open to adopting SSC job standards where they exist and are of high quality. In the longer
term, JRCs should look for politically feasible ways to subsume SSC activities and funding
into JRCs (while still maintaining autonomy).
6.3. Monitor and course-correct
Data should be collected at all stages of program to troubleshoot implementation bottlenecks or
program design failures. A preliminary list of metrics to measure the progress, outputs and
outcomes of the intervention are presented in the table below.
Table 6.3.1: Proposed metrics and course-corrections
Metrics Potential causes Course correction
Progress Job readiness
standards not
being created at
expected pace
1. Low industry buy-in. Not
confident in the solution
2. Expertise to write standards is
lacking
1. Consult major employers on
solution. What don’t they like?
2. Engage education experts or
assessment agencies in standard-
writing procedure
Assessment
agencies have not
been contracted
1. Low industry buy-in
2. Economics for assessment are
off
1. Consult major employers
2. Revisit assumptions on
government subsidy, employer
contribution and student
willingness to pay
Unable to raise
money from
employers to fund
initiative
1. Low industry buy-in
1. Consult major employers
Outputs Few candidates
assessed (low test
volume)
1. Candidates not aware of test
2. Candidates don’t think the test
helps their chances of getting a
job
3. Assessment agencies have
limited capacity to implement
1. Increase program marketing
efforts
2. Investigate job-search benefits to
test-takers. If they exist, publicize. If
they don’t, consult employers
3. Re-contract assessment to
more/a different assessment
agency
Average test score 1. Low score + low variance =
test is too hard, inadequate
supply of skills
2. High score + low variance =
test is too easy
3. High variance = test is
performing its task screening
applicants
1. Revisit test methodology/re-
contract assessment agency
1.b. Consider efforts to deliver
training directly
2. Revisit test methodology/ re-
contract assessment agency
Outcomes Collect additional
data from
employers about
1. Candidates with higher test
scores not performing better on
the job (wage, promotion,
retention)
1. Revisit testing methodology
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on-the-job
performance
Impact on higher
education sector
1. Data not being used by
students for admissions
decisions
1. Redouble marketing and
awareness efforts
Continued
implementation
1. Employers stop wanting to give
funds to the program
2. Test volumes decline
Consult employers and/or students
about possible dissatisfaction with
the program
6.4. Costs and Benefits
Selecting three pilot sectors, we estimate the cost to industry per hired test-taker to range from 3
to 10 percent of the average annual salary of workers. Productivity gains are between 1.2 and 2.9
times as large as the cost (with very conservative assumptions). Even if the program fails, the total
expenditure from all employers in the industry is small: ranging from Rs. 128 million to Rs. 319
million, depending on the sector. This represents a small fraction of the total recruiting spending
by companies. Details of our assumptions are included in Appendix 2.
The outlay for government is even smaller, barely meriting a line item in any Ministry’s budget.
Social returns on this small investment could be extremely large, however. Industry will benefit
from higher productivity workers and employees will benefit from higher wages. By fixing
information failures, the test will also create incentive for individuals to acquire skill and improve
the quality of higher education institutions. While difficult to quantify, these benefits dwarf the
associated costs.
Perhaps more important than the overall economic case for implementation will be the ability for
industry and government to communicate “program wins” to the public and other stakeholders.
Communicating metrics such as the number of tests administered, proportion of test takers hired,
performance of test takers on the job, and performance of results by college will all help
demonstrate progress and value to program stakeholders.
6.5. Next steps
An industry association (such as the Confederation of Industry Association) is well placed to
convene groups of employers in priority sectors to discuss (a) the need for, and (b) the design of
the proposed solution would be a good first step. In industries with employer buy in, these groups
of employers could form JRCs and continue implementation, as described above.
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APPENDIX 1. ADMISSIONS SORTING AND WAGE PREMIA96
A1.1. Introduction
To understand the extent of information failure in India’s labor and education markets, we tested
college’s reputation for quality affects (a) the students it attracts in the admissions process, and (b)
the labor market outcomes of its graduates. To do this, we follow the methodology outlined by
Macleod et. Al (2015) to measure college quality, admissions sorting and reputational return. We
apply this methodology to a dataset obtained from a leading Indian employment assessment
agency. This dataset is not representative of the labor market in general, so findings are limited to
our sample.
Section A1.2 describes our approach to measuring candidate and college quality. Section A1.3
describes our dataset. Section A1.4 reports our empirical results on admissions sorting and section
A1.5 reports results on income return on college quality. Section A1.6 highlights areas for future
research and concludes.
A1.2. Candidate quality and college quality
Employers hiring fresh graduates typically observe (albeit imperfectly) four measures of candidate
quality on CVs: their year 12 score, college GPA, AMCAT scores (explained below) and the
reputation of the college from which the candidate graduated.
The first three measures are signals of candidate quality that are directly measured and reported in
our database. Year 12 scores are typically reported along with the board of education the candidate
graduated from. The Indian education system offers students a choice to complete secondary
education under the auspices of one of 54 state or central boards of education. 97 Anecdotally,
central boards – the most popular of which is the Central Board of Secondary Education (CBSE)
– have a reputation for being tougher, and therefore scores are expected to be lower. College GPAs
are also measured and reported directly in our data.
96 This appendix reproduces, with some modifications, work completed by the author in ECO2810a Labor Market Analysis,
with Professor Katz. While additional analysis has been conducted, large sections of the text are reproduced verbatim. 97 Council on Boards of School Education in India, http://www.unishivaji.ac.in/uploads/admin/circ/board_list.pdf
Sahil Shekhar Putting India to Work
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In addition to school and college scores, we are also able to measure ability using scores from the
standardized Aspiring Minds Computer Adaptive Test (AMCAT). AMCAT is a third party
employability assessment administered by Aspiring Minds (an assessment agency) to evaluate job
seeker skills. Approximately one million test takers pay Rs 900 (~$15) to sit the test each year.
The AMCAT comprises of three general tests assessing candidates’ logical, quantitative and
English language skills. Each of these is scored on a scale of 0-800, giving a combined total of
2400 points. Candidates may also opt to take domain specific tests in Computer Programming and
in various branches of Engineering (Electrical, Mechanical, etc.). Since different candidates give
different domain-specific tests, scores are reported as the percentile rank of the candidates in their
respective test. We note that candidates with bad scores may choose not to share them with
employers. It is costlier to hide college GPAs or year 12 scores, as their omission may carry
negative informational value about the candidate.
The fourth measure – college reputation – is more difficult to assess, but nevertheless considered
important by employers when hiring fresh graduates. Especially given competitive entrance in
many Indian colleges, reputation is used as a signal of candidate quality. Apart from signaling,
going to a better college may also improve the labor market prospects of a student by improving
their skills or giving them access to alumni networks. College reputation is multi-dimensional,
incorporating quality of student, faculty, financial and other inputs. To measure it, we adopt the
approach outlined by Macleod et. Al (2015), who reason that we would expect to see colleges with
better reputations attract better quality students to enroll and would churn out higher quality
graduates. Following their logic, we offer four methods of measuring college reputation.
Our first two measures of college quality are the mean AMCAT scores of graduates from each
college. We separately report a college’s mean student AMCAT scores in general tests (English,
logical, quantitative) and AMCAT percentile ranks in domain specific tests (computer
programming, engineering, etc.). One might argue, however, that the AMCAT is not widespread
enough for the labor market to use it as the basis for reputation-formation. Given the absence of a
standardized graduation test, the reputation of colleges among employers may depend simply on
the difficulty of college admission.
Our third measure of college reputation follows from this and reports the quality of enrolling
students, measured by their mean year 12 percentile rank (scaled for central or state board
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attendance). Using year 12 percentile ranks measures quality of enrollees rather than the quality
of graduates may be cause for concern if colleges with higher reputation also add more value to
their students. In this case, measuring the quality of enrollees would understate the difference in
quality of graduates from good and bad colleges. To allow for this, following Macleod et. Al
(2015), we make the additional assumption that college value add is positively correlated by with
labor market reputation, and so is preferred by higher ability enrolling students. Therefore, using
year 12 scores gives us a noisy measure of what we – and employers – actually care about: the
quality of college graduates.
Our fourth measure of college reputation adopts the point of view of enrolling students, rather than
employers. We argue that the most important information at the time of enrolment are the salaries
obtained by college graduates. From the point of view of students, we measure college quality at
time t as the average salary of a graduating cohort at time t-3. Students learn from the salaries of
the graduating class at the time of their enrollment.
Using these measures, we test the extent of ability sorting by plotting college quality against
candidate quality. As alluded to in the main report, we note there may be non-information related
reasons for incomplete sorting by ability. We also note that, while admissions sorting undoubtedly
does take place elite Indian institutions, our sample focuses on the more amorphous “middle” of
Indian colleges.
To test wage premiums for college quality, we assume that firms set wages based on expected
ability of workers, given the information available to them: year 12 scores (adjusted for school
board), college GPA, AMCAT scores and college reputation. If this is indeed the case, we would
expect to see positive and significant unconditional returns to year 12 scores, college GPA,
AMCAT scores and college reputation. Conditional returns would be smaller in magnitude, but
still significant.
A1.3. Data
This paper draws on Aspiring Minds’ Employability Outcomes (AMEO) 2015, a dataset released
by Aspiring Minds, an assessment company headquartered in India. AMEO 2015 provides
AMCAT scores for 3,998 engineering undergraduates, together with demographic information,
educational background (high school and college scores and other information) and employment
Sahil Shekhar Putting India to Work
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outcomes (first job salaries). Data was collected via an email survey sent to previous test-takers
who are now in the labor market.
The dataset is not representative of the Indian labor market, and so our results cannot be
generalized beyond our sample. Descriptive statistics are provided in Table 3.1, split by year of
graduation from university. The average age of the candidate in the dataset is 25 years and the
average annual salary is Rs 307,700 (~US$4,600), roughly four times India’s GDP per capita.
Graduates from 1350 colleges are covered. The average test taker scored 1517 out of a total
possible 2400 points in the compulsory module of the AMCAT and had a college GPA of 71.5%.
The average year 12 graduating percentage is 74.5%. It is widely acknowledged that central board
exams are more difficult than state board exams. Anecdotally, employers use rules of thumb such
as subtracting five percentage points from a state board exam score to compare to a central board.
This is corroborated in our data, which shows that despite having lower average year 12 scores,
central board students have higher average AMCAT test scores and higher average salaries.
To more formally compare state and central board scores, we adopt a two-step approach. First, we
convert scores to percentile ranks of students within their board. Second, we scale these percentile
ranks using the AMCAT score as an objective third party assessment. We shift the state board
distribution downwards, multiplying each state percentile rank by the ratio of the average state
board AMCAT to the average central board AMCAT score. Using the ratio of median AMCAT
yields similar results: scaling all state board year 12 scores by a factor of 0.91.
This methodology is crude. Scaling using the AMCAT test, which is taken nearer to the time of
graduation than enrolment, conflates talent of incoming students with value-add of colleges. If
higher value-add central colleges prefer central board students, we would expect their AMCAT
scores to be higher regardless of initial ability. In this case, our scaling factor of 0.91 would be too
small. We consider the scaled percentile to be a lower bookend for the true estimate, and compare
with the unscaled percentile as the upper bookend.
Our analysis excludes respondents who failed to provide a graduation year or who are still in
university (graduation years 2016/17), leaving us with 3,982 candidates. We also exclude graduate
candidates, focusing only on test-takers with a Bachelor of Technology or Bachelor of
Engineering. Given our analysis focuses on college reputation, we only consider the 221 colleges
with five or more graduates represented in the dataset, reducing our sample to 1,959 candidates.
Sahil Shekhar Putting India to Work
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Table A.1.3.1: descriptive statistics of the AMEO 2015 database, by graduation cohort
Graduation year
All
years
Variable N/A 2007 2009 2010 2011 2012 2013 2014 2015 2016 2017
Salary (Rs p.a.)
325,00
0
120,00
0
365,62
5
441,31
8
369,68
4
315,47
8
277,81
0
267,92
5
299,57
4
295,71
4
195,62
5
307,70
0
Age 24 29 29 27 26 25 24 23 23 23 24 25
Year 12 score
(%)
85
87
67
74
73
75
75
75
74
78
69
74
College GPA
(%)
69
65
68
70
69
72
72
72
71
64
68
71
AMCAT
English
695
425
456
475
494
488
509
516
511
574
504
502
AMCAT Logic
655
575
471
472
493
498
512
507
493
503
454
502
AMCAT Quant
680
645
485
508
512
512
524
507
482
587
481
513
AMCAT
domain
percentile rank 0.76 . 0.61 0.53 0.53 0.56 0.47 0.50 0.48 0.66 0.42 0.51
Candidates
1
1
24
292
507
847
1,181
1,036
94
7
8
3,998
Proportion
male 100% 100% 75% 82% 74% 73% 76% 77% 84% 86% 88% 76%
Sahil Shekhar Putting India to Work
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A1.4. Admissions sorting
To understand admissions sorting in Indian colleges, we plot candidate quality (measured by
scaled year 12 percentile rank) against each of our measures of college quality (AMCAT general,
AMCAT domain, year 12 scores, graduate salaries). When measuring college quality by graduate
salary, we introduce a time lag of three years. I.e., the information 2014 graduates are able to see
are the labor market outcomes of graduates three years their senior. In all other cases, we measure
college quality using the 2014 cohort only. Our results are presented in figures 4.1 to 4.4.
In each of figures 4.1 to 4.4, the horizontal line represents the outcome that would be expected if
there was no sorting by ability at admissions and students were allocated randomly to colleges. A
positive slope suggests some sorting. The steeper the slope, the greater the extent of admissions
sorting.
Figure 4.1 shows college reputation measured by AMCAT general scores against year 12
percentile rank. We see admission sorting does indeed take place, but is limited. The local linear
regression line of best fit shows some – but not much – sorting by ability. Figure 4.2 shows
measuring college reputation by AMCAT domain percentile ranks does not change the picture by
much. While these figures show scaled year 12 percentile rank (adjusted for central vs state board
of education), using unscaled year 12 percentile rank does not change the picture.
Figure 4.3 measures college reputation by year 12 percentile rank. As we would expect, measuring
college reputation by year 12 scores mechanically shows stronger sorting. But sorting is still far
from complete, suggesting that factors such as cost-constraints, information failures and
affirmative action are indeed at play.
Finally, figure 4.4 measures college reputation by 2011 graduate salaries. Sorting is incomplete
and inconsistent, suggesting the flow of information of labor market outcomes back to enrolling
students is incomplete.
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We also look at the variation of ability levels within any given college. Figures 4.5 to 4.8 rank
colleges by quality, according to each of our four measures, on the x-axis. On the y-axis, we show
the year 12 percentile score of the median college student (dot) as well as the range from the 25th
to the 75th percentile college student (red line range). As we can see, while admissions sorting
across colleges does occur, intra-college ability variation is just as significant (if not more so) than
variation across colleges.
For suggestive comparison, we include a similar analysis of sorting in Chile (Macleod et. Al
(2005)), who use scores on a standardized entrance test – Icfes – to measure ability, and measure
college reputation as the mean Icfes percentile of college graduates. The authors conclude that
admissions sorting is incomplete in Chile, and a visual comparison of the two charts suggests
similar levels of intra-college ability variation in the two countries.
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A1.5. Earnings
To test our prediction on earnings, we estimate the following regression
𝑤𝑖 = 𝛽0 + 𝛽1𝑦𝑒𝑎𝑟 12 𝑠𝑐𝑜𝑟𝑒𝑖 + 𝛽2𝑐𝑜𝑙𝑙𝑒𝑔𝑒𝐺𝑃𝐴𝑖 + 𝛽3𝐴𝑀𝐶𝐴𝑇𝑔𝑒𝑛𝑒𝑟𝑎𝑙𝑖 + 𝛽4𝐴𝑀𝐶𝐴𝑇𝑑𝑜𝑚𝑎𝑖𝑛𝑖
+ 𝛽5𝑐𝑜𝑙𝑙𝑒𝑔𝑒𝑟𝑒𝑝𝑢𝑡𝑎𝑡𝑖𝑜𝑛𝑖 + 𝛽5𝑔𝑟𝑎𝑑𝑢𝑎𝑡𝑖𝑜𝑛𝑦𝑒𝑎𝑟𝑖 + 𝛽6𝑏𝑜𝑎𝑟𝑑𝑜𝑓𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑖
+ 𝛽7𝑔𝑒𝑛𝑑𝑒𝑟𝑖 + 𝜀𝑖
The dependent variable, 𝑤𝑖, measures log annual salary of workers. We regress log wages against
an individual’s year 12 score (unscaled), college GPA, AMCAT score for general skills (logic,
quantitative and English) and domain specific skills (computer programing, engineering, etc.),
college reputation (measure either by AMCAT scores or year 12 scores) and dummies for
graduation cohort, school board of education and gender.
Estimating 𝛽5 in the equation above gives us the “college reputation premium” in our sample,
describing the average return to going to a better college. Note, we cannot identify this as a causal
effect, but as a descriptive return of going to a better college for students in our sample.
Unconditional return to college quality
Table 5.1 estimates the regression above with each explanatory in turn (together with dummies),
illustrating the unconditional return to reputation (and ability). We see that a one percent point
increase in college reputation measured by year 12 scores is associated with a 0.8% increase in
wages. A 10-point increase in college reputation measured by AMCAT general scores (which
range from 0-2400) is associated with 1.2% higher wages. This is as theory would predict: a
positive unconditional return to reputation. Measuring college reputation by AMCAT domain
scores similarly shows that a one percentile improvement in your domain score rank is associated
with 1.0% increase in wages.
Unconditional return to ability
Unsurprisingly, we note similar positive unconditional returns to ability as measured by year 12
score, college GPA or AMCAT score. One percentage point higher year 12 scores or college GPA
are associated with 1.6% and 1.8% higher wages respectively. Scoring one percentile higher in the
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AMCAT domain test is associated with an 0.4% increase in wages, while scoring 10 points higher
in the AMCAT general tests is associated with a 0.8% increase.
Conditional returns to college reputation and ability
Estimating the full equation above gives us the conditional returns to each explanatory variable.
We expect conditional returns to be smaller than unconditional returns, but still positive. Given
admissions sorting in India is substantially incomplete, we might expect the conditional return to
college return to be significantly smaller. With limited admissions sorting, the signal value of
college reputation is diluted. If, however, the college reputation premium reflects college value-
add apart from its signaling value, we may still expect positive conditional returns. This would be
the case when college value-add (skill improvement, networks) is heterogeneous and positively
correlated with reputation.
Table 5.2 estimates conditional returns. We see that returns to signal of general ability roughly
halve when conditioned on college reputation, reducing from 1.6% to 0.6%-0.8% for year 12 score
(depending on how college reputation is measured) and 1.8% to 0.7-0.8% for college GPA.
Returns to the AMCAT general test reduce from 0.8% to 0.5% per 10 points. Returns to AMCAT
domain scores drop more drastically, from 0.8% to 0.1% once conditioned on college reputation
measured by year 12 scores or AMCAT general scores. Conditioning on college reputation based
on AMCAT domain scores reduces returns even further to 0.08%.
Conditional returns to college reputation are also lower, but still significant, once controlling for
other signals of ability. Regressions 1 and 2 show that the conditional return to reputation as
measured by year 12 scores and AMCAT general scores falls by almost two thirds, from 0.8% to
0.3% and 1.2% to 0.4% respectively. Conditional return to reputation measured by AMCAT
domain, in regression 3, also falls, but only by half – from 0.8% to 0.4%.
This suggests the reputation premium is larger at colleges renowned for their technical skill, rather
than their ability to produce good generalists. This would make sense in the context of hiring
engineers or computer programmers. Regression 4 “throws the kitchen sink” at the data and shows
that, once employers have information on an individual’s test performance, they only pay more for
graduates from colleges with good reputations as measured by technical skill.
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Table 5.1. Regression results, unconditional returns
(1) (2) (3) (4) (5) (6) (7)
VARIABLES lnsalary lnsalary lnsalary lnsalary lnsalary lnsalary lnsalary
Year 12 Percentage
(total 100)
0.0164***
(0.00105)
College GPA
(total 100)
0.0180***
(0.00147)
AM general (total 2400) 0.000830***
(4.55e-05)
AM domain (total 100) 0.00366***
(0.000380)
College quality
(year 12 score)
0.00874***
(0.000716)
College quality
(AMCAT general)
0.00116***
(8.93e-05)
College quality
(AMCAT domain)
0.0102***
(0.000990)
Constant 11.41*** 11.42*** 11.52*** 12.44*** 12.20*** 10.90*** 12.04***
(0.178) (0.191) (0.168) (0.167) (0.167) (0.211) (0.175)
Observations 1,959 1,959 1,959 1,959 1,959 1,959 1,959
R-squared 0.175 0.138 0.207 0.113 0.137 0.146 0.119
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Note: estimates on dummies for graduation cohort, board of education, gender and regression constant are not reported.
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Table 5.2. Regression results, conditional returns
(1) (2) (3) (4)
VARIABLES lnsalary lnsalary lnsalary lnsalary
Year 12 score 0.00599*** 0.00843*** 0.00827*** 0.00712***
(0.00137) (0.00114) (0.00114) (0.00142)
College GPA 0.00768*** 0.00698*** 0.00757*** 0.00742***
(0.00151) (0.00150) (0.00150) (0.00151)
AMCAT general 0.000547*** 0.000460*** 0.000519*** 0.000482***
(5.05e-05) (5.50e-05) (5.11e-05) (5.58e-05)
AMCAT domain 0.00123*** 0.00116*** 0.000785** 0.000901**
(0.000371) (0.000371) (0.000387) (0.000389)
Reputation (measured 0.00288*** 0.00133
by year 12 scores)
(0.000834) (0.000963)
Reputation (measured 0.000417*** 0.000203
by AMCAT general)
(9.56e-05) (0.000125)
Reputation (measured 0.00425*** 0.00263**
by AMCAT domain)
(0.000994) (0.00118)
Observations 1,959 1,959 1,959 1,959
R-squared 0.263 0.265 0.265 0.268
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Note: estimates on dummies for graduation cohort, board of education, gender and regression constant are not reported.
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A1.6. Conclusion and future research
Colleges with better reputations are able to attract better students, but admissions sorting is far
from complete. Measuring reputation using a third party assessment (the AMCAT test) shows very
limited sorting. Factors such as cost, information failures and affirmative action quotas could be
preventing admissions sorting by pure ability.
Nevertheless, graduates from better colleges command a reputation premium in their first jobs.
This is particularly true of colleges with strong technical reputations. It is unclear, however,
whether this premium reflects the value of reputation as a signal of ability, or the value-add of
attending a better college. Data on the earnings growth of graduates would help us distinguish
between signaling and human capital models of college reputation.
While graduates do command a reputation premium, it goes without saying that this premium
might be larger if admissions sorting improved and became a better signal of candidate ability.
Assuming the binding constraint to more sorting are information failures (rather than cost or
quotas), interventions to bridge these failures could trigger a virtuous cycle between greater
admissions sorting improved college reputation premium in the labor market.
We are limited in data to the starting salary of graduates, and do not have access to their subsequent
earnings growth. Efforts to obtain time series data are underway for future research, which could
test a richer set of predictions along the lines of Farber and Gibbons (1996), Altonji and Pierret
(2001) and Macleod et. Al (2015).
Specifically, in future research we hope to test whether college reputation pure signaling, in which
case we expect the unconditional return to college reputation to stay flat with experience, and the
conditional return to fall. We would also expect to see the both the unconditional and conditional
return to unobserved ability to rise with experience.
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APPENDIX 2. ASSUMPTIONS FOR INDUSTRY COST-BENEFIT ANALYSIS
Assumption rationale
Tourism, Hospitality
and Travel
Auto and auto
components
Electronic and IT
hardware
Volume
Estimated annual manpower requirement National Skill Development Corporation estimates, annualised 648,000 390,000 461,000
Share covered by JRTs Voluntary, so intial coverage is low 30% 30% 30%
Total number of assessments 194,400 117,000 138,300
Share of test-takers hired Conservative, given above average candidates volunteer for test 10% 10% 10%
Total number of test-takers hired 19,440 11,700 13,830
Cost
Cost per assessment Multiple of Rs 900 cost in IT 1,000 2,000 3,000
Total assessment cost 194,400,000 234,000,000 414,900,000
Other implementation costs 10% of assessment cost 19,440,000 23,400,000 41,490,000
Total cost 213,840,000 257,400,000 456,390,000
Industry share Higher in industries with higher skill level 60% 60% 60%
Total cost to industry 128,304,000 154,440,000 273,834,000
Cost per test-taker hired 6,600 13,200 19,800
Benefit
Average annual salary
Auto and electronics based on US Bureau of Labor Statistics
hourly wage estimates. Tourism assumed by author 192,000 275,000 240,000
Productivity gain of a better hire
Staffing.org estimate of productivity advantage of a blue collar
worker one standard deviation above the mean 20% 20% 20%
Total productivy gain All test-takers hired are one standard deviation above the mean 746,496,000 643,500,000 663,840,000
Productivity gain captured by industry Assuming 50 percent lost to higher wages 373,248,000 321,750,000 331,920,000
Productivity gain to industry per test-taker hired 19,200 27,500 24,000
Productivity gain : cost ratio 2.9 2.1 1.2
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