Do Management Interventions Last? Evidence from India

This paper examines the persistence of management practices adopted after an extensive consultant-supported intervention that we undertook in a set of multi-plant Indian textile weaving firms from 2008 to 2010 (Bloom et al, 2013). Firms were randomly allocated into treatment and control groups, and the intervention was done at the plant level within each firm. Both treatment and control plants were given recommendations for improving management practices in several areas, and the treatment plants received additional consulting assistance in implementing the recommendations. The intervention led to a substantial adoption of the recommended practices in the treatment plants and a modest one in the control plants, with corresponding improvements in various measures of performance.

2008-09-01

2009-12-01

Management practices, looms per employee, consulting days (in logs), marketing practices

See paper for details

Our original experiment measured the impact of improving management practices in a set of large textile firms near Mumbai. This experiment involved 28 plants across 17 firms in the woven cotton fabric industry. Firms had been in operation for 20 years on average, and all were family-owned and managed. These firms are large, complex organizations, with a median of 2 plants per firm and 4 reporting levels (Table A1 reports baseline summary statistics). We contracted with a leading international management consultancy firm to work with the plants in order to change plant-level management practices rapidly.
The intervention ran from August 2008 until August 2010, with data collection continuing until November 2011. The intervention focused on a set of 38 management practices that are standard in American, European, and Japanese manufacturing firms and which can be grouped into five broad areas: factory operations, quality control, inventory control, human-resources management, and sales and orders management (for details see Appendix Table A2). Each practice was measured as a binary indicator of the adoption (1) or non-adoption (0) of the practice.
The consulting intervention had three phases. The diagnostic phase took one month and was given to all treatment and control experimental plants. It involved evaluating the current management practices of each plant, constructing a performance database, and providing each plant with a detailed analysis of its current management practices along with recommendations for change. The second phase was a four-month implementation phase given only to the treatment experimental plants. In this phase, the consulting firm followed up on the diagnostic report to help introduce as many of the 38 management practices as the plants could be persuaded to adopt. The third phase was a measurement phase, which lasted until November 2011, involving the collection of performance and management data from all treatment and control plants.
See Bloom et al. (2013) for more details

Randomization done by computer

Randomization
occurred at the firm level and was conducted by computer.
We first randomly chose six firms to be the control firms, and one
eligible plant from each of them to be the control plants. The
remaining 11 firms were then the treatment firms. Our initial
funding and capacity constraints of the consulting team meant
that we could start with four plants as a first round, which started
in September 2008. We therefore randomly chose 4 of the 11
treatment firms to be in round 1, randomly selecting one plant
from each firm. In April 2009, we started a second round of
treatment. This comprised selecting a random plant from each
of the remaining seven treatment firms, and, because funding
allowed for it, three more plants selected at random from the
treatment firms with multiple plants.

No

17 firms

28 plants

6 control firms, with 6 control plants
11 treatment firms, with 14 treatment plants

A long-standing question is whether differences in management practices
across firms can explain differences in productivity, especially in developing
countries where these spreads appear particularly large. To investigate this,
we ran a management field experiment on large Indian textile firms. We provided
free consulting on management practices to randomly chosen treatment
plants and compared their performance to a set of control plants. We find that
adopting these management practices raised productivity by 17% in the first
year through improved quality and efficiency and reduced inventory, and
within three years led to the opening of more production plants. Why had
the firms not adopted these profitable practices previously? Our results suggest
that informational barriers were the primary factor explaining this lack of adoption.

Does Management Matter: Evidence from India, QJE 2013

https://people.stanford.edu/nbloom/sites/default/files/dmm.pdf