ISSN 2042-2695
CEP Discussion Paper No 1042
January 2011
Does Management Matter? Evidence from India
Nicholas Bloom, Benn Eifert, Aprajit Mahajan,
David McKenzie and John Roberts
Abstract
A long-standing question in social science is to what extent differences in management cause differences in firm
performance. To investigate this we ran a management field experiment on large Indian textile firms. We
provided free consulting on modern management practices to a randomly chosen set of treatment plants and
compared their performance to the control plants. We find that adopting these management practices had three
main effects. First, it raised average productivity by 11% through improved quality and efficiency and reduced
inventory. Second, it increased decentralization of decision making, as better information flow enabled owners
to delegate more decisions to middle managers. Third, it increased the use of computers, necessitated by the data
collection and analysis involved in modern management. Since these practices were profitable this raises the
question of why firms had not adopted these before. Our results suggest that informational barriers were a
primary factor in explaining this lack of adoption. Modern management is a technology that diffuses slowly
between firms, with many Indian firms initially unaware of its existence or impact. Since competition was
limited by constraints on firm entry and growth, badly managed firms were not rapidly driven from the market.
JEL Classifications: L2, M2, O14, O32, O33.
Keywords: management, organization, IT, productivity and India.
This paper was produced as part of the Centre’s Productivity and Innovation Programme. The Centre for
Economic Performance is financed by the Economic and Social Research Council.
Acknowledgements
Financial support was provided by the Alfred Sloan Foundation; the Freeman Spogli Institute, the International
Initiative and the Graduate School of Business at Stanford; the International Growth Centre; IRISS; the
Kauffman Foundation; the Murthy Family; the Knowledge for Change Trust Fund; the National Science
Foundation; the Toulouse Network for Information Technology; and the World Bank. This research would not
have been possible without our partnership with Kay Adams, James Benton and Breck Marshall, the dedicated
work of the consulting team of Asif Abbas, Saurabh Bhatnagar, Shaleen Chavda, Karl Gheewalla, Kusha Goyal,
Shruti Rangarajan, Jitendra Satpute, Shreyan Sarkar, and Ashutosh Tyagi, and the research support of Troy
Smith. We thank our formal discussants Susantu Basu, Ray Fisman, Naushad Forbes, Vojislov Maksimovic,
Ramada Nada, Paul Romer, and Steve Tadelis, as well as seminar audiences at the AEA, Barcelona GSE,
Berkeley, BREAD, Boston University, Chicago, Columbia, Cornell, the EBRD, Harvard Business School,
IESE, Katholieke Universiteit Leuven, Kellogg, the LSE, Maryland, the NBER, NYU, PACDEV, Stanford,
TNIT, Toronto, UBC, UCL, UCLA, UCSC, Wharton, and the World Bank for comments.
Nicholas Bloom is an Associate at the Centre for Economic Performance, London School of
Economics. He is also Assistant Professor, Department of Economics, Stanford University. Benn Eifert is a
Quant (quantitative analyst) at Overland Advisors LLC and a Lecturer at Haas School of Business, UC
Berkeley. Aprajit Mahajan is an Assistant Professor in the Department of Economics, Stanford University.
David McKenzie is a Senior Economist with the Development Research Group, The World Bank. John Roberts
is Professor of Economics at the Graduate School of Business, Stanford University.
Published by
Centre for Economic Performance
London School of Economics and Political Science
Houghton Street
London WC2A 2AE
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in
any form or by any means without the prior permission in writing of the publisher nor be issued to the public or
circulated in any form other than that in which it is published.
Requests for permission to reproduce any article or part of the Working Paper should be sent to the editor at the
above address.
© N. Bloom, B. Eifert, A. Mahajan, D. McKenzie and J. Roberts, submitted 2011
1
I. INTRODUCTION
Economists have long puzzled over why there are such astonishing differences in productivity
across both firms and countries. For example, US plants in homogeneous industries like cement,
block-ice, white pan bread and oak flooring display 100% productivity spreads between the 10
th
and 90
th
percentile (Foster, Haltiwanger and Syverson, 2008).
A natural explanation for these productivity differences lies in variations in management
practices. Indeed, the idea that “managerial technology” affects the productivity of inputs goes
back at least to Walker (1887) and is central to the Lucas (1978) model of firm size. Yet while
management has long been emphasized by the media, business schools and policymakers,
economists have typically been skeptical about its importance.
One reason for skepticism is the belief that competition will drive badly managed firms
out of the market. As a result any residual variations in management practices will reflect firms’
optimal responses to differing market conditions. For example, firms in developing countries
may not adopt quality control systems because wages are so low that repairing defects is cheap.
Hence, their management practices are not “bad”, but the optimal response to low wages.
A second reason for this skepticism is the complexity of management, making it hard to
measure.
1
Recent work, however, has focused on specific management practices which can be
measured, taught in business schools and recommended by consultants. Examples of these
practices include key principles of Toyota’s “lean manufacturing”, such as quality control
procedures, inventory management, and human resource management. A growing literature
measures many such practices and finds large variations across establishments and a strong
association between these practices and higher productivity and profitability.
2
This paper provides the first experimental evidence on the importance of management
practices in large firms. The experiment takes large, multi-plant Indian textile firms and
randomly allocates their plants to treatment and control groups. Treatment plants received five
months of extensive management consulting from a large international consulting firm. This
1
Lucas (1978, p. 511) notes that his model “does not say anything about the tasks performed by managers, other
than whatever managers do, some do it better than others”.
2
See for example, Osterman (1994), Huselid and Becker (1996), MacDuffie (1995), Ichniowski, Shaw and
Prennushi (1998), Cappelli and Neumark (2001) and Bloom and Van Reenen (2007). A prominent early example is
Pack (1987), which, like the present study, deals with textile firms in developing countries. In related work, Bertrand
and Schoar (2003) use a manager-firm matched panel and find that manager fixed effects matter for a range of
corporate decisions. Lazear and Oyer (2009) and Bloom and Van Reenen (2010) provide extensive surveys.
2
consulting diagnosed opportunities for improvement in a canonical set of management practices
during the first month, followed by four months of intensive support for the implementation of
these recommendations. The control plants received only the one month of diagnostic consulting.
The treatment intervention led to significant improvements in quality, inventory and
production output. The result was an increase in productivity of 11% and an increase in annual
profitability of about $230,000. Firms also spread these management improvements from their
treatment plants to other plants they owned, providing revealed preference evidence on their
beneficial impact.
Given these results, the natural question is why firms had not previously adopted these
practices. Our evidence suggests that informational constraints were an important factor. Firms
were often not aware of the existence of many modern management practices, like inventory
norms and standard operating procedures, or did not appreciate how these could improve
performance. For example, many firms claimed their quality was as good as other local firms and
so did not need to introduce a quality control process.
We also find two other major impacts of better management practices. First, owners
delegated greater decision making power over hiring, investment and pay to their plant
managers. This happened in large part because the improved collection and dissemination of
information that was part of the change process enabled owners to monitor their plant managers
better. As a result, owners felt more comfortable delegating.
Second, the extensive data collection and processing requirements of modern
management led to a rapid increase in computer use. For example, installing quality control
systems requires firms to record individual quality defects and then analyze these by shift, loom,
and design. So modern management appears to be a skill-biased technical change (SBTC), as
increased computerization raises the demand for educated employees. A large literature has
highlighted SBTC as a key factor increasing income inequality since the 1970s. Our experiment
provides some evidence on the role of modern management in driving SBTC.
3
The major challenge of our experiment is the small cross-sectional sample size. We have
data on only 28 plants across 17 firms. To address concerns over statistical inference in small
samples we implement permutations tests that have exact finite sample size. We also exploit our
large time series of around 100 weeks of data per plant by using estimators that rely on large T
3
See, for example, the survey in Autor, Katz and Kearney (2008).
3
(rather than large N) asymptotics. We believe these approaches are useful for addressing sample
concerns in our paper, and also potentially for other field experiments where the data has a small
cross-section but long time series.
This paper relates to several strands of literature. First, there is the long literature showing
large productivity differences across plants in dozens of countries. From the outset this literature
has attributed much of these spreads to differences in management practices (Mundlak, 1961),
but problems in measurement and identification have made this hard to confirm (Syverson,
2010). This productivity dispersion appears even larger in developing countries (Banerjee and
Duflo, 2005, Hsieh and Klenow, 2009). Despite this, there are still few experiments on
productivity in firms (McKenzie, 2010a) and none involving large multi-plant firms.
Second, our paper builds on the literature on the management practices of firms. There
has been a long debate between the “best-practice” view that some management practices are
universally good so that all firms would benefit from adopting these (Taylor, 1911) and the
“contingency view” that every firm is already adopting optimal practices but these differ firm by
firm (e.g. Woodward, 1958). Much of the empirical literature trying to distinguish between these
views has traditionally been case-study or survey based, making it hard to distinguish between
different explanations and resulting in little consensus in the management literature.
4
This paper
provides experimental evidence that a core set of best practices do exist, at least in one industry.
Third, the paper links to the large theoretical literature on the organization of firms. These
papers generally emphasize optimal decentralization as driven either by minimizing learning and
information processing costs or by optimizing incentives.
5
But the empirical evidence on
decentralization is limited, focusing primarily on de-layering in large publicly traded US firms
(Rajan and Wulf, 2006).
Fourth, the paper contributes to the literature on Information Technology (IT) and
productivity. A growing body of work has examined the relationship between technology and
productivity, emphasizing both the direct productivity impact of IT and also its complementarity
with modern management and organizational practices (e.g. Bresnahan et al. 2002 and Bartel et
al. 2007). But again the evidence has focused on survey data rather than experimental data. Our
experimental evidence suggests one route for computers to affect productivity is by facilitating
4
See, for example, the surveys in Delery and Doty (1996) and Bloom and Van Reenen (2010).
5
See the recent reviews in Garicano and Van Zandt (2010), Mookherjee (2010) and Gibbons and Roberts (2010).
