HIAS-E-65

Entrepreneurship in Micro and Small Enterprises―Empirical Findings from Resurveys in Northeastern Areas ofDelhi, India―
Asit Banerji(a) , Jun Goto(b) , Hironori Ishizaki(c) , Takashi Kurosaki(d) , Kaushalesh Lai(a) ,Shampa Paul(a) , Yasuyuki Sawada(e) , and Shunsuke Tsuda(f )(a) Center for Economic and Social Research, India(b) Kobe University(c) Rebright Partners Ltd.
(d) Institute of Economic Research, Hitotsubashi University(e) University of Tokyo(f ) Brown University

March 2018

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo 186-8601, Japan
tel:+81 42 580 8604
http://hias.ad.hit-u.ac.jp/
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Entrepreneurship in Micro and Small EnterprisesʊEmpirical Findings from Resurveys in Northeastern Areas of 'HOKL,QGLDʊMarch 2018
Asit Banerji, Jun Goto, Hironori Ishizaki, Takashi Kurosaki, Kaushalesh Lal,
Shampa Paul, Yasuyuki Sawada, and Shunsuke Tsuda*

Abstract
To deepen our understanding of the urban informal sector and small enterprises in developing
countries, we surveyed micro and small entrepreneurs in northeastern areas of Delhi, India.
The baseline survey was conducted in November–December 2014, covering 506 sample
entrepreneurs in both manufacturing and service sectors. Between June and August 2017, theendline survey was conducted to collect panel information on firm performance. As thedemonetization policy in November 2016, in which high value banknotes were demonetizedovernight, affected small and micro enterprises, specific questions on its impact were added in
the endline survey. Between the two surveys, artefactual field experiments were also applied tothe subsample to collect information on social, risk, and time preferences. In this paper, we
present details of the resurveys implemented under this project and describe the key variables
collected. Among 226 entrepreneurs who participated in the artefactual field experiments,
many showed their tendency for present bias and high discount rates. On average, theparticipant entrepreneurs behaved in an altruistic manner, took substantial risk, and reduced
their risk-taking when they were assigned the role of the leader. Out of 287 entrepreneurs who
participated in the endline survey, 50% were unregistered with the government, implying thatthey were highly informal. During the period between baseline and endline surveys,
innovations to expand the business were not very active, either. The majority of sample
entrepreneurs were affected at least temporarily by the demonetization in 2016.
JEL Classification: O17, O14, L26*

Kurosaki (corresponding author): Institute of Economic Research, Hitotsubashi University, 2-1 Naka,
Kunitachi, Tokyo 186-8603, Japan. E-mail: kurosaki@ier.hit-u.ac.jp. Banerji, Lal, and Paul: Centre of
Economic and Social Research, India. Goto: Kobe University. Ishizaki: Rebright Partners Ltd. Sawada:
University of Tokyo. Tsuda: Brown University. The authors are grateful to Ashok Jain and the Jhilmil
Industrialists Association for their help in data collection. The funding from JSPS Grant-in-Aid for
Scientific Research (26220502) and the priority project under Hitotsubashi Institute for Advanced Studyis highly acknowledged. Sawada’s work for this paper was completed before March 1, 2017.

1

1. Introduction
Considering the critical importance of micro, small, and medium enterprises(MSMEs) in economic growth and poverty reduction in developing countries in general, andthe lack of grass-root evidence on microeconomics of micro firms in India, we conducted asurvey of micro and small enterprises in northeastern areas of Delhi, India (Kurosaki et al.
2015). The questionnaire-based survey was implemented during November–December 2014,
in which 506 entrepreneurs who ran enterprises in the manufacturing or service sector weresurveyed. The sample was drawn from a business directory and they fell in the category of
micro or small enterprises as defined in the MSME Development Act of 2006 (MSMED Act).
In the baseline dataset, out of 506 sample entrepreneurs, 97% businesses were owned
by single individuals, and 46% were not registered with the government. The majority of themwere established during the period between 1994 and 2005, and they hired very few employees(its median was four). To finance the initial investment, most of the entrepreneurs combined
their own savings with informal credit. Formal credit from banks and government agencies
was available only to approximately one-third of our sample firms. Looking at the period fromtheir establishment to the time of the baseline survey, sample firms engaged in innovation
despite difficult environments, especially in the domains of product, process, and marketing. 1

With intense competition from large domestic firms and Chinese products in the Indian market,
firms are compelled to engage in some kind of innovation just to survive in the market. In
addition to the standard list of questions in the baseline survey, we included trust questions in
the General Social Survey (GSS) style. The level of trust in relatives and friends, neighbors,and business buyers/sellers was found to be significantly higher than the level of trust in
government officials, the police, and law officers. Overall, finance and physical infrastructure
were emerging as major bottlenecks for these micro and small enterprises, preventing themfrom growing further. Lack of skills and technology was also constraining these firms.
After the baseline survey, we conducted several resurveys of these micro and small
firms. We first collected parameters characterizing entrepreneurs’ risk, time, and social
preferences through behavioral economics methods. Out of the 506 baseline sample
entrepreneurs, 226 participated in the behavioral economics games. We then conducted an
endline survey between June and August 2017 to collect panel information on firm
performances. Out of the 506 baseline sample entrepreneurs, 287 participated in the endline
survey. As the demonetization of high value banknotes in November 2016 affected small and

1

These are mostly low-cost types of innovation, which can be called “Jugaad” (see for example, Radjou
et al. 2012).

2

micro firms, 2 we added a module on the impact of Demonetisation 3 in the endline survey.
The performance of MSMEs in India is published every year by the concerned
ministry and nation-wide survey results are available from All India Census of MSMEs,
National Sample Survey (NSS), the World Bank Investment Climate Survey, and so on.
However, detailed micro information on innovation, dynamics, and entrepreneurship withbehavioral foundation is lacking. As a result, existing studies on Indian MSMEs are mostly
cross-sectional analyses of enterprises, focusing on the manufacturing sector (Kathuria et al.
2010, 2012; Sato 2008; Iyer et al. 2013; Deshpande and Sharma 2013; Nikaido et al. 2015;
Sasidharan and Raj 2014; Sharma 2014). In sharp contrast, our dataset is a panel of two
periods with detailed information on innovation, dynamics, and entrepreneurs’ preferences,
covering various sectors, including services and different levels of formality. By describing thisdataset, we hope to deepen our understanding of small enterprises in India. We also hope toshed light on the role of the informal sector in the economic development of developing
economies (La Porta and Shleifer 2014).
The remainder of the paper is organized as follows. Section 2 explains institutional
backgrounds and the design of surveys starting with the baseline. Section 3 describes theartefactual field experiments conducted between the baseline and endline surveys anddiscusses key variables in the dataset. Section 4 describes the endline survey and discusses key
variables in the dataset. Section 5 concludes the paper.

2. Data and Backgrounds
2.1 Institutional Backgrounds
The MSME sector in India consists of enterprises engaged in the production of goods
pertaining to any industry specified in the first schedule of the Industry Development andRegulation Act of 1951, and other enterprises engaged in the production of goods andrendering of services, subject to the limiting factor of investment in plant and machinery or
equipment, as noted below. MSMEs are defined by the MSMED Act, based solely on theirinvestment in plant and machinery for manufacturing enterprises, and on equipment forenterprises providing or rendering services. 4 For manufacturing enterprises, a micro firm isthat in which the investment in plant and machinery does not exceed INR 2.5 million, a small
2

See for example, Dreze et al. (2016), Reddy (2017), and Ghosh et al. (2017).
In this paper, the historical event of the Indian demonetization in November 2016 is denoted“Demonetisation” with capitalization, without the definite article, and in British spelling. To refer tosimilar events in general, the term “demonetization” is used without capitalization and in American
spelling.
4
The Gazette of India, Extraordinary, Para II, Section 3, Sub-section (ii), New Delhi, September 30,
2006, Government of India.
3

3

firm is where the investment in plant and machinery does not exceed INR 50 million, and a
medium enterprise is where the investment in plant and machinery does not exceed INR 100
million. In case of services, a micro enterprise involves investment in equipment that does notexceed INR 1 million; a small enterprise where the investment does not exceed INR 20 million
and a medium enterprise where the investment in equipment does not exceed INR 50 million.
Under the MSMED Act, MSMEs are encouraged to register under MSMED
implementing agencies. Registered firms become eligible for availing various types of MSME
promotion policies, such as indirect tax exemption, ISO support, government credit, andgovernment procurement. Incentive measures lean more toward manufacturing, than toward
services. In addition to these direct benefits, firms registered under the MSMED Act can
expect indirect gains in credit access from private financial institutions. The Fourth All India
Census of MSMEs, which collected information on MSMEs during 2006-07, covered all firms
registered under the MSMED Act on a census basis, and conducted random sampling surveys
of unregistered MSMEs.
If a firm belongs to the manufacturing sector, it is under restrictions imposed by the
Factories Act of 1948. Under the Factories Act, manufacturing firms employing 10 or moreworkers (using power) or 20 or more workers (without using power) are required to register.
Once registered under the Factories Act, firms are subject to labor and environment regulations.
Manufacturing firms registered under the Factories Act are thus more formal than others.
Registered factories comprise the so-called “Organised Manufacturing Sector.” Service firms
in India are also classified similarly, depending on whether the firm is registered under theCompanies Act of 2013, or not. Unlike the case of manufacturing firms, registration is not
required for those firms employing workers above the given threshold. Thus, registration isvoluntary.
Both manufacturing and service firms whose capital investment is below stipulated
limits and factories employing fewer than the threshold number of workers as defined by the
Factories Act of 1948 can register under the MSMED Act. Many micro and small firms that are
not registered under the Factories Act or Companies Act are registered under the MSMED Act.
Furthermore, Goods and Services Tax (GST) was introduced in July 2017, which
unified the myriad indirect taxes prevalent in India. As GST payments were made mostly
through the internet, enterprises were required to register for GST payments online. MSMEs
were also under this net, although micro enterprises and purely self-employed businesses wereallowed to opt for simplified procedures in GST payments, or, they could go without paying
GST. The GST Council, in its 13th meeting, decided to exempt small businesses from GST,
with the threshold limit of annual turnover at INR 2 million (INR 1 million for special category4states). 5

2.2 Design of Surveys
To collect unique information on MSMEs that is not available in government records,
we designed the baseline survey to investigate (1) the characteristics of the enterprise such as
investments made, operational details, financing, output, cost, profit/loss, etc., (2) the historyof the enterprise, (3) the social, educational, and economic backgrounds of the enterprise head
and the family including their migration status, (4) the infrastructural facilities and bottlenecks,
including those relating to training and skills development, (5) the opinions of theentrepreneurs regarding public policies, and (6) the level of general or directed trust elicitedfrom GSS-style trust questions.
The survey was conducted in northeastern areas of Delhi, centered round Shahdara.
Until 2014, most areas where our sample enterprises were located belonged to the Northeast
Delhi District. In 2014, the district was divided into the (new) Northeast Delhi District and the
Shahdara District. Furthermore, after the sampling and the survey, several sample enterprises
were found operating in the East Delhi District, bordering the Northeast and Shahdara Districts.
For this reason, we call the location of our sample entrepreneurs/firms as “northeastern areas ofDelhi.”The survey was conducted by the Centre of Economic and Social Research (CESR).
In the past, CESR has conducted informal sector surveys of waste pickers (Hayami et al. 2006)
and cycle rickshaws (Kurosaki et al. 2007) in the same area. Due to this advantage, CESR wasable to access micro and small entrepreneurs, which otherwise tend to be highly skeptical
toward outsiders.
As there is no official list of unregistered firms, we designed our sampling in
consultation with methodologies adopted by NSS’ unorganised sector surveys. First, we
obtained a copy of the Shahdara Industrial Directory prepared by the Jhilmil Industrialists
Association. With the cooperation of the association’s president, we randomly selected firms
listed in the directory (2013 version) and conducted a questionnaire-based survey of 506 firms
out of approximately 1,000 firms in November–December 2014. The directory contains firms
located in the Jhilmil Industrial Area and surrounding areas. The sampled firms are spread overten locations (clusters), with the Jhilmil Industrial Area being the largest. To obtain the
representative sample, enterprises were surveyed in every industrial location proportionate to
the number of firms in each location. The directory contains both manufacturing and service

5

Source: http://gstcouncil.gov.in/sites/default/files/gst-knowledge/PPT-on-GSTason01052017.pdf.

5sectorfirms,

but

does

not

includeself-employedbusinesses

without

fixed

offices/stores/workshops, or grocers or constructors or restaurants. In the sampling, firms
whose investment levels were over the threshold for small enterprises under the MSMED Act
were excluded.
The median number of employees among the 506 sample firms was 4. About
two-thirds operated in manufacturing and one-third in services, both including various
industries (see Subsections 3.2 and 4.2 for more details). Although the Jhilmil Industrial Area
is characterized by copper and plastic industries, there are other industries as well, resulting ina diverse industrial composition in our sample.
After the baseline survey, we first collected parameters characterizing entrepreneurs’risk, time, and social preferences through behavioral economics methods. The session for thispurpose was named “Business Management Diagnostic Test” (BMDT) and its details are given
in Section 3. The first round of BMDT sessions targeted all the baseline entrepreneurs. It was
conducted between March and September 2015 and 118 out of the 506 entrepreneurs
participated in it.
Targeting those baseline entrepreneurs who did not participate in the first round, thesecond round of BMDT sessions was conducted between March and April 2016. Out of the
388 remaining baseline entrepreneurs, 108 participated in the second BMDT round. In thesecond round, business training was also provided at the end of the session, in which
participants learned about the importance of setting business goals. The training was asimplified version of ILO training manuals and took about 20-30 minutes. Participants in thesecond BMDT round were randomly treated with reminders of the training. The design andimpact of this randomized controlled trial (RCT) will be discussed in a different paper.
<Insert Table 1 >Next, we conducted an endline survey between June and August 2017 to collectpanel information on firm performances, targeting all the baseline entrepreneurs (see Section 4for details). Since Demonetisation affected small and micro firms adversely, as reported byDreze et al. (2016) for example, we added a special section on its impact in the endline survey.
Out of the 506 baseline entrepreneurs, 287 participated in the endline survey. The distributionof our sample across these surveys is summarized in Table 1.

3. Artefactual Field Experiments
3.1 BMDT Sessions
We invited the baseline entrepreneurs to take part in the BMDT sessions, in which

6

participants were asked to respond to 35 behavioral games and two opinion surveys. The
purpose of the behavioral games was to measure social, risk, and time preferences of theentrepreneurs. As our subjects were actual economic agents and the type of experiments wasstandardized and abstract, our experiments belong to the category of artefactual field
experiments according to Levitt and List (2009). In the invitation letter, we told participantsthat they would receive Rs. 1,000 6 as a fee for showing up, an additional bonus depending on
their choices, a diagnostic note of their responses in comparison to other participants, and an
opportunity to make friends with other participants after the session during the tea break. All
participants were given the same set of games, in the same order. We conducted the sessions inpublic spaces, such as schools, and the number of participants ranged from 6 to 39 in eachsession. The whole session took 1.5 to 2 hours.
In each of the 35 behavioral games, Rs. 4,000 was the endowment to the participant,and there were five choices. To incentivize participants to take all the games seriously, we
designed the bonus payment using the strategy method. In the beginning, participants were toldthat at the end of the session, 1 of the 35 games would be chosen for the bonus payment for allparticipants in the session. All participants would then throw a dice and if the result was 1, theywould receive the bonus payment according to their choice, in that game. This implied that the
expected value of the bonus payment was Rs. 667. The maximum bonus was Rs. 10,000,
which would occur if the bonus game chosen was the individual risk game with the multiplierof 2.5, if the participant of dice-number 1 had decided to put all of Rs. 4,000 in the risky bet,
and, if he won the coin toss. This did indeed occur in one case out of 226, in our experiment.
Each of the behavioral games is explained below. The number in brackets “[#]”denotes the order in which the games were placed, from which we asked participants to selectone out of five. To simplify the text, we use the pronoun “him” to denote a participant,(although we had a few female participants), and “her” to denote the counterpart.
(1) Dictator games
The first five games were dictator games, aiming to measure altruism toward others.
We set the following four hypothetical types of counterparts: [1] a randomly selected person inIndia; [2] a randomly selected person from each entrepreneur’s residential area; [3] a randomly
selected person with whom they trade in their business as a buyer or a seller; and [4] arandomly selected person from among the entrepreneur’s relatives and friends. In each game,6

One Indian Rupee was equivalent to 13 to 15 cents of US Dollar, with some fluctuations depending on
exchange-rate fluctuations. The amount of Rs. 1,000 was approximately equivalent to 2 days of
unskilled male wage in Delhi.

7an entrepreneur was given the endowment of Rs. 4,000 (four Rs. 1,000 notes) and had tochoose how many notes he would give to the counterpart. If he was selfish and economicallyrational, he would send nothing to the counterpart. The amount given in game [1] showed theextent of pure altruism toward general persons, whereas the amount given in games [2]-[4]showed the extent of directed altruism. In all of these games, the sender was anonymous to therecipient.
Game [5] was a variant of game [2], in which the counterpart was a randomly
selected person from each entrepreneur’s residential area. The selected person would know thatthe money was from the entrepreneur. As the identity of the sender was revealed to theneighbor, (i.e., the recipient), the transfer amount under [5] was likely to be affected by thesender’s concern for his reputation in his neighborhood. By comparing choices in game [2](where the sender was anonymous) and [5] (where the identity of the sender was revealed), we
could elicit the measure of reputation concern as discussed by Ligon and Schechter (2012).
(2) Risk and leadership games
We conducted two forms of risk games: individual and group risk games. First, westarted from the simple individual risk game to measure individual risk attitudes. We asked
participants to divide Rs. 4,000 (four notes of Rs. 1,000) into two boxes: Box I and Box II. The
money put in Box I would change depending on the coin toss result. If the coin toss revealed
heads, the bet in Box I was multiplied by a fixed number. If the coin toss revealed tails, the bet
in Box I was lost. The bet in Box II was kept by the participant, regardless of the result of thecoin toss. Individual risk games consisted of three choices: the multiplication factor of the bet
in Box I was 1.5 (game [6]), 2 (game [7]), and 2.5 (game [8]).
Second, we conducted group risk games called “leadership games,” as proposed by
Ertac and Gurdal (2012). We informed each participant that he was a member of a five-persongroup, which consisted of other participants in the BMDT session room, and other similarbusinessmen. We also informed each participant that he could not know the identities of othergroup members, and no other members could know his identity, either. Then, we asked theparticipant to answer the above three games as a leader of the group, that is, his decision andthe actual pay-off would apply to all the group members. The multiplication factor of the bet in
Box I was varied as before: 1.5 (game [9]), 2 (game [10]), and 2.5 (game [11]). If he wasselfish and economically rational, his choice in group risk games would be exactly the same as
his choice in individual risk games.
Ertac and Gurdal (2012) found that university students in Israel, on average, reduced
the amount of bet in the risky box when they were given the role of the leader. They called this8

phenomenon a “cautious shift.” After the participants finished games [9]-[11], we asked eachparticipant whether he was willing to become the leader to choose for others. 7 The extent of
the cautious shift and the willingness to become the leader measure one aspect of leadership,
namely, risk-taking decision-making as the leader. 8(3) Convex time budget (CTB) games
In this research, we focused on three aspects of time preferences: subjective discountrate, present bias, and intertemporal elasticity of substitution. If an entrepreneur has a highdiscount rate, his investment decision may become short-sighted, and he may miss good
opportunities for long-term growth. Similarly, if an entrepreneur has a present-biased
preference, that is, the tendency to over-value immediate rewards in spite of the fact thatrewards in two different future points are to be valued in a balanced way, his enterprise may
not grow smoothly. Furthermore, a businessman with low intertemporal elasticity of
substitution may smoothen intertemporal flows of rewards far too much, and thereby sacrifice
an opportunity to maximize the current value of future flows of rewards.
In standard time games in which participants choose either immediate (near future)or future (far future) rewards, we obtain information only on discount rates and present bias. In
contrast, CTB, proposed by Andreoni and Sprenger (2012), allows participants to receive
positive amounts in both periods. By this convex design, a set of CTB games allows
researchers to measure the three pieces of information on time preferences, simultaneously.
Table 2 shows the choice sets we used in BMDT, which are based on paper-based CTB
experiments adopted by Sawada and Kuroishi (2015a, 2015b). For instance, in games [12]-[17]in Block 1, participants with high discount rates tend to choose options near A, while thosewith low discount rates are expected to choose options near E. Similarly, participants with low
elasticity of intertemporal substitution are expected to choose options near C, avoiding corner
solutions, whereas those with high elasticity of intertemporal substitution are expected tochoose corner solutions (either A or E). Regarding present bias, we compare across blocks. For
example, between Block 1 and Block 3, game [12] and game [24] have exactly the sameinterest rates and the same interval between two rewards. A time consistent person is expected
to choose the same options in game [12] and [24], a present-biased person typically chooses
option A in game [12] and chooses option E in game [24]. By checking the patterns of choices
7

Ertac and Gurdal (2012) asked this question before making choices in group risk games. We changed
the order as it would be better to ask this question after making choices in group risk games to assurethat participants fully understand the implications of group risk games.
8
Other aspects of leadership are potentially important for entrepreneurs, such as coordinating different
opinions or taking care of followers. Measuring such aspects of leadership is left for further research.

9across 35 games, we can characterize the time preferences of each participant in a qualitative
manner.
<Insert Table 2 >Furthermore, adopting an explicit form of the utility function of participants, asmodeled by Andreoni and Sprenger (2012) and Andreoni et al. (2015), we can estimate threeparameters characterizing their time preferences in a quantitative manner. The individual is
assumed to have the following lifetime total utility function at time t with a quasi-hyperbolic
discounting structure:௞ܷ௧ = ‫ݔ(ݑ‬௧ ) + ߚ σஶ௞ୀଵ ߜ ‫ݔ(ݑ‬௧ା௞ ),(1)
where instantaneous utility function follows the constant relative risk aversion (CRRA) type:‫ݔ(ݑ‬௧ ) = ‫ݔ‬௧ఈ . In this time-separable utility function, ‫ݔ‬௧ generally represents the income orconsumption level at period ‫ݐ‬. In the CTB experiment, it simply corresponds to the monetary
reward given to a participant at period ‫ݐ‬. Parameter ߚ gives us the information about present
biasness, parameter ߜ represents the exponential discount factor, and parameter ߙ gives us

the curvature of the instantaneous utility and thus, the intertemporal elasticity of substitution(1/(1-ߙ)). The discount factor ߜ is a standard exponential one, which is constant across time. The
present bias parameter or quasi-hyperbolic discounting factor, ߚ, shows the extent to which thecurrent income affects the value function. An individual with ߚ < 1 is regarded aspresent-biased; one with ߚ > 1 is regarded as future-biased; one with ߚ = 1 has no present
bias (i.e., entailing only the standard exponential discount factor in any periods including
today).
In the CTB experiment, we asked the participants about the two-period intertemporalchoices of total monetary income of Rs. 4,000 (in prices at period t+k) between period ‫ ݐ‬and‫ ݐ‬+ ݇ with an exogenous interest rate, ‫ݎ‬. Then, we can presume that the participants solve theoptimization problem which maximizes:ఈఈ+ (1 െ 1௧ୀ଴ )ߜ ௞ ‫ݔ‬௧ା௞,ܷ(‫ݔ‬௧ , ‫ݔ‬௧ା௞ ) = ‫ݔ‬௧ఈ + 1௧ୀ଴ ߚߜ ௞ ‫ݔ‬௧ା௞

subject to the following budget constraint:

10

(2)

ܲ‫ݔ‬௧ + ‫ݔ‬௧ା௞ = 4000,(3)

where 1௧ୀ଴ is an indicator function and ܲ ‫ ؠ‬1 + ‫ݎ‬. Note that, from equation (2), we see thatthe present bias parameter does not matter when an individual faces the intertemporal resource
allocation problem between two future periods. In the actual experiment, we asked theparticipants about intertemporal choices ‫ݔ‬௧ and ‫ݔ‬௧ା௞ with various situations by changing thevalues of ‫ݐ‬, ‫ ݐ‬+ ݇, and ܲ.
From (2) and (3), we can obtain the following intertemporal Euler equation:௫ ഀషభ

MRS = ఉభ೟సబ೟ఋೖ௫ ഀషభ = ܲ.

(4)

೟శೖ

By taking logarithm and rearranging, we get the following estimable linear form:

௫

୪୬(ఉ)

୪୬(ఋ)

ଵ

ln ቀ௫ ೟ ቁ = ఈିଵ ‫ݐ‬଴ + ఈିଵ ݇ + ఈିଵ ln(ܲ).
೟శೖ

(5)

Then, we can estimate the linear model of (5), augmented with an error term through theordinary least square (OLS) method and then, solving for three parameters. As an alternative,
we can solve from equations (3) and (4) to obtain the following demand function: ‫ݔ‬௧ =భସ,଴଴଴(௉ఉ భ೟సబ ఋ ೖ )ഀషభభ

ଵା௉(௉ఉ భ೟సబ ఋ ೖ )ഀషభ, which can be estimated by the non-linear least square (NLS) method whenan error term is added.

3.2 Characteristics of Entrepreneurs/Firms Participating in BMDTAs already indicated in Table 1, we collected information on risk, time, and social
preferences from 226 entrepreneurs out of the 506-baseline sample. All these sessions were
implemented as designed, following the protocol strictly. Nevertheless, as the participation was
voluntary, we need to examine what kind of micro and small entrepreneurs participated in
BMDT. In other words, we need to characterize the subsample with preference information in
the context of micro and small enterprises in the northeastern areas of Delhi, for which ourbaseline sample was representative, to some extent.
Firm characteristics of BMDT participants are summarized in Table 3, alongside acomparison with non-participants. The average of the years since establishment is 11.8, whichis slightly shorter than non-BMDT samples, but the difference is not statistically significant. To

11capture employment-creation by these micro and small enterprises, we asked respondents inthe baseline survey to indicate the number of people who were working under them. As the
managers themselves were also an important part of the labor force in micro and small
enterprises, we calculated the total labor force as the sum of one (the manager), unpaid family
members (males and females), and employees (males and females). The average number
constituting the labor force, including the owner/manager, is 6.8, significantly lower than the
average among non-BMDT firms. Out of the 226 BMDT firms, 108 were registered, indicatingthat they were more formal than the 118 unregistered firms. The registration rate was lower
than in the non-BMDT sample, indicating that non-cooperation for BMDT was more frequent
among registered firms.
The subsample of 226 firms belonged to ten locations in the northeastern areas ofDelhi. The distribution of enterprises according to location shows that the three largest groups
are from Mandoli & Saboli, Friends Colony, and Jhilmil. In Jhilmil and Friends Colony,
industrial estates form the core of the cluster. These two locations had the largest samples inour baseline, but due to high rates of non-cooperation in BMDT, their shares declined. In
contrast, micro entrepreneurs in Mandoli & Saboli were cooperative for BMDT, resulting inthem ranking as the top among BMDT participants.
Regarding sectors and industries, the number of BMDT firms engaged in
manufacturing is 140 (62%). Enterprises engaged in manufacturing of garments, followed bythose manufacturing electrical wires accounted for the highest share. In comparison with thebaseline sample, those in electrical wires and plastic goods reduced their shares substantially as
they tended to be uninterested in BMDT. The total number of firms engaged in services is 86(38%), for which auto/cycle repair had the highest share. Non-participation was more frequent
among “Other services,” which includes photography, videography, recycling etc. Publishing
services, including printing personal envelopes and business cards, also reduced their share due
to frequent non-participation.
<Insert Table 3 here>
Table 4 shows basic characteristics of 226 BMDT entrepreneurs. The average age
was 39, which was slightly younger than the non-BMDT entrepreneurs with statisticalsignificance at the 5% level. Female samples occupied 8 out of 226, whose share issignificantly higher than non-BMDT businesspersons. The minority of female entrepreneurs
runs its business in tailoring, beauty parlors, etc., and was more cooperative in participating in
the BMDT sessions than males were. Jain and Muslim entrepreneurs were less cooperative,
resulting in their share declining from 19% in the baseline to 11% in the BMDT. The majority

12

of BMDT entrepreneurs only had secondary education. The education distribution issignificantly different between BMDT and non-BMDT entrepreneurs, due to higher
non-participation of those who had low education (less than 10th grade) and the highly
educated (with university degrees). As shown in the last rows of Table 4, BMDT participants
showed higher trust in relatives and friends, neighbors and business buyers/sellers, than in
public sector entities such as municipal corporations, police, etc. Their GSS trust levels were
almost similar to those of non-BMDT participants, with the exception of trust in relatives andfriends. Those indicating higher trust in friends and relatives were less cooperative. This could
be due to lower interest in creating new business networks or obtaining new knowledge from
BMDT among those who already had a strong network of relatives and friends. On the other
hand, all other contrasts shown in Tables 3 and 4 can be interpreted to mean that entrepreneurs
with higher opportunity cost of time were less likely to attend the BMDT sessions.
<Insert Table 4 here>
To examine whether each of the bivariate correlations shown in Tables 3 and 4
remains if we control for other characteristics, we ran multiple regression using OLS. The
BMDT non-participant dummy is the dependent variable. As shown in column (1) of Table 5,
the most important correlates of participation appear to be location and sector (industry). After
controlling for these fixed effects, most of firm and entrepreneur characteristics lost theirstatistical significance. As shown in columns (2)-(9) of Table 5, more parsimonious
specifications show the same result. These results are robust to non-linear specifications suchas probit and logit.
<Insert Table 5 here>

3.3 Summary Statistics of Key Variables
Summary statistics of results regarding choices in dictator games, individual risk
games, group risk games (leadership games), and CTB games are shown in Table 6. In dictator
games, BMDT participants did not behave selfishly, but transferred about 30% to thecounterpart on average when the counterpart was “general.” This is consistent with findings
from other developing countries (Cardenas and Carpenter 2008). When the counterpart wasmore specified, the transfer amount increased, especially for relatives and friends. The change
was very small for neighbors. This indicates that BMDT businesspersons had stronger altruism
toward relatives and friends, followed by those with business concerns, but very little directed
altruism toward neighbors. Combined with the results shown in GSS trust, it appears that13

micro and small entrepreneurs in Delhi trust their neighbors, in the sense that they are notparticularly harmful, but they do not have special altruistic feelings toward their neighbors.
Reflecting such feelings, the transfer to neighbors increased when the identity of the sender
was revealed.
<Insert Table 6 here>
In individual risk games, BMDT participants did not behave in a highly risk-averse
manner. As shown in the row for game [6], about 46% of the endowment was put in the riskybox on average, even when the expected value was less than unity. The amount in the risky bet
increased when the multiplication factor improved, showing that about 52% was put in therisky box in game [8]. Not many participants (12%) behaved in a manner consistent with what
the standard theory of expected utility maximization predicts. This could be due to behaviors
such as loss aversion and non-linear transformation of objective probability into perceived
probability, and/or miscalculation by participants.
In leadership games (group risk games), BMDT participants continued to put morethan 40% of the endowment into the risky box, on average. However, in comparison with theirchoices in individual risk games, the amount in the risky box declined slightly, when theychose for the group as the group leader. This is consistent with the finding of “cautious shift”
observed for students by Ertac and Gurdal (2012). But the extent of cautious shift was smaller
among Indian entrepreneurs than among Israeli students. The amount in the risky bet increased
when the multiplication factor improved, similar to the case of individual risk games and thepercentage of those consistent with the expected utility maximization theory increased slightly
to 13%. As many as 74% of BMDT participants responded that they would be willing to be theleader in this setting, than be the follower. The percentage is in the similar range to the one
found by Ertac and Gurdal (2012). In combination with the results for the cautious shift, Indian
entrepreneurs are similarly oriented toward leadership, but are less flexible when it comes tochanging their risk-taking as the leader than Israeli students are. Whether this could be due tothe difference between students and entrepreneurs or the difference between India and Israel isworth further research.
In CTB games, BMDT participants showed their preference for more immediate
rewards as shown in the average of “qualitative indicator of discount factor” less than 3 (themidpoint). The tendency became stronger when the time interval grew longer, but the change
was small. Comparing choices across blocks with the same interest rate and time intervals, we
can check how many choices show present bias. The average of “qualitative indicator ofpresent bias” is positive (0.248), indicating that BMDT participants were, on average, slightly

14present-biased. However, the standard deviation of this indicator is large, suggesting that there
is high heterogeneity (some are heavily future-biased, some are slightly future-biased, someare almost neutral, some are slightly present-biased, and others are highly present-biased).
Regarding the intertemporal elasticity of substitution (IES), BMDT participants, on average,
chose interior solutions in 44% of the 35 games. This suggests that BMDT participants had afinite value of IES. If IES is infinite (i.e., the intertemporal rewards are aggregated linearly),
participants should choose corner solutions all the time. This did not happen in our sample.
Qualitative inference regarding time preferences derived from CTB games in Table 6
was confirmed quantitatively using the econometric method. Following the parametric model
explained in Subsection 3.1, we estimated the NLS model pooling all participants’ decisions.
The parameter estimates are: ߚ= 0.965 (0.022), ߜ= 0.993 (0.001), and ߙ= 0.639 (0.021)(robust standard errors are shown in the parenthesis using individuals as the cluster). 9 As thenull hypothesis of ߚ=0 is statistically rejected in favor of ߚ<1, our sample entrepreneurs arepresent-biased on average. As the null hypothesis of ߜ=1 is statistically rejected in favor ofߜ<1, our sample entrepreneurs discount future rewards. As the null hypothesis of ߙ=1 isstatistically rejected in favor of ߙ<1, our sample entrepreneurs have finite values of IES.

4. Endline Survey
4.1 Endline Survey with a Module on the Demonetisation Impact
Between June and August 2017, the endline survey was conducted with the target ofresurveying all 506 firms in the baseline. The questionnaire comprised two parts. The first partwas on firm operation and the second part was on the impact of Demonetisation.
In the first part, we asked about (1) changes in firm structure, innovations introduced,
and investments made since the baseline survey, (2) operational details in the preceding month,and (3) network for business information, sales/procurement, and credit. In the second part, we
asked about (1) impact of Demonetisation on sales, employment, purchase of input/raw
material, business hours, and product/services lineups, (2) modes of transactions before, during,
and after Demonetisation, (3) bank accounts used for business and the disposal of demonetizednotes, and (4) income tax payment.
A team of investigators led by the chief investigator, who also conducted the baseline
survey, visited each of the baseline enterprises and asked for the cooperation on the endline
9

Our results for ߚ are in the range that is consistent with the results found in Andreoni et al. (2015),
and Sawada and Kuroishi (2015b). We have lower estimates of ߜ and ߙ than those, but Sawada
and Kuroishi (2015a), which is an only previous research applying CTB to a developing country
context, get closer results to ours. Note that estimate of ߜ at 0.9947 implies an exponential
discount rate of 17% per month.

15

survey. When the owner changed, we interviewed the new owner. When the firm moved within
northeastern areas of Delhi, we traced the firm for resurvey.

4.2 Characteristics of Entrepreneurs/Firms Participating in the Endline Survey
Out of the 506 baseline firms, we were able to resurvey 287. The attrition rate washigher than we expected, with major reasons for attrition being non-cooperation, businessclosure, and moving without contact information. The frequent cases of non-cooperationreflected firms’ rising concerns against disclosing business details to outsiders against the
backdrop of a policy environment comprising formalization drives by the government,
including Demonetisation in November 2016 and the introduction of GST in July 2017.
Firm characteristics of endline participants are summarized in Table 7, with acomparison with non-participants. The average of years since establishment was 11, which issignificantly shorter than that of the attrition samples. The average labor force including theowner/manager was 5.6, significantly lower than the average among attrition samples. Out of
the 287 endline firms, 109 were registered at the baseline. The registration rate was much
lower than that of attrition samples, indicating that non-cooperation for the endline survey was
more frequent among registered firms.
The subsample of 287 firms belonged to ten locations in northeastern areas of Delhi,
with Jhilmil, Mandoli & Saboli, and Friends Colony being the top three locations. Attrition
was more frequent in Jhilmil and Friends Colony, where industrial clusters existed, than in
Mandoli & Saboli, where local micro entrepreneurs were cooperative.
When it came to sectors and industries, the number of BMDT firms engaged in
manufacturing was 172 (60%). The highest share was accounted for by those enterprises
engaged in manufacturing garments. The other two important industries in the baseline, that is,electrical wires and plastic goods, decreased their shares substantially as they did not muchparticipate in the endline survey. The total number of firms engaged in services is 115 (40%),for which auto/cycle repair and “Other services” had the highest shares.
<Insert Table 7 here>
Table 8 shows basic characteristics of 287 BMDT entrepreneurs. The average age
was 39, significantly younger than attrition samples. Female samples occupied only 5 out of
287, and gender difference in endline participation was insignificant. Jain entrepreneurs wereless cooperative for the endline, resulting in their share declining to 3% from 19% in thebaseline. When it came to education, the majority of BMDT entrepreneurs had only secondary
education. Those with tertiary education were more likely to drop out in the endline survey

16

than those with secondary education or lesser. An interesting contrast in the GSS trust was
found. The endline samples had lower level of trust in relatives/friends and neighbors, while
they had a higher level of trust in the municipal corporation, police, and law officers. The latterpattern is consistent with the view that those entrepreneurs who are more cautious against
public sector entities do not tend to be cooperative with external researchers. The former
pattern indicates low interest in connections with outsiders, among those who already had astrong network of relatives/friends and neighbors. Overall, the contrasts shown in Tables 7 and
8 can be interpreted to mean that those entrepreneurs with more cautious attitudes towardoutsiders were less likely to cooperate for the endline survey.
<Insert Table 8 here>
To examine whether each of the bivariate correlations shown in Tables 7 and 8
remains if we control for other characteristics, we ran multiple regression using OLS. The
endline attrition dummy is the dependent variable. As shown in column (1) of Table 9, evenafter we control for location and sector (industry) fixed effects, the positive effect on attrition
probability of Jain dummy, tertiary education, and GSS trust for relatives and friends remained
significant. Registration dummy (positive) and Muslim dummy (negative) also had significant
coefficients in more parsimonious specifications (see columns (2)-(8) of Table 9). These results
are robust to non-linear specifications, such as probit and logit.
<Insert Table 9 here>

4.3 Summary Statistics of Key Variables
Regarding changes in firm structure, almost no change was observed in theownership structure of the firm. The majority remained sole proprietorships. In terms ofregistration, transition was more frequent than we expected. As shown in Table 10, 77% of theendline firms had the same registration status as in the baseline. The rest (23%) changed theirstatus. The change occurred in both directions. Fifty-one firms, which were previously
unregistered, registered themselves by the endline survey. The most important reason forregistration was the preparation for GST, which was particularly strong among entrepreneurs
who bought from large enterprises and wanted to avail themselves of input tax credit. Similartendencies were reported by the Government of India (2008) for all of India. Sixteen firms,
which were previously registered, had unregistered by the time the endline survey took place.
The most important reason for this was failure in renewing registration.

17

<Insert Table 10 here>
Summary statistics of innovation, labor force, and financial indicators are given in
Table 11. Overall, firm growth was minimal or negative. Innovation was adopted only among
15% of the endline sample. The labor force shrank by 1.9 persons, and the average
employment was only 3.7 persons per enterprise. Micro and small enterprises appeared to lose
their potential in job creation. However, the change shown in Table 11 not only reflected therecession faced by these enterprises, but also the structural change in Indian labor markets
toward the use of contract labor (Goldar and Agarwal 2010). Many of our sample enterprises
contracted out a whole operation to the contractor, without knowing how many workers wereinvolved in the operation. This enabled enterprises to avoid the cost of labor management andallowed them to manage their firms flexibly.
<Insert Table 11 here>
Reflecting the reduction of direct employment, the sales declined, as well, on average.
However, profit increased slightly, on average. This also supports the view that the shrinking oflabor force may have enhanced the profitability of small and micro enterprises in Delhi during
the period of the study. The profit sales ratios at 33.4% among all reporting samples and 49.3%
among those with positive profits are similar to, or slightly higher than, those observed at thebaseline. Out of 195 enterprises with the sales/profit information, 90.3% had positive profits.
To examine the impact of Demonetisation on small and micro enterprises, we asked
their owners what the levels of sales, employment, and input purchases were, during and after
Demonetisation with the pre-Demonetisation level being at 100. The “during” Demonetisation
period is defined as the one from November 9, 2016 (the first day after the demonetization of
banknotes) to December 30, 2016 (the last day for depositing old notes in bank accounts). Thisis a subjective assessment whose answer tends to be round numbers such as 50, 75, 90, and100, but still informative of the raw perception of entrepreneurs. The summary statistics ofthese answers are reported in Table 12. During Demonetisation, firm sales declined to half on
average, and recovered only to the level of 70% or so, during the post Demonetisation period.
Employment also declined substantially but the decline was not as large as it was in sales. It
appears that informal sector firms employ workers based on networks (for example, hiring
relatives) so that they are not able to adjust employment flexibly in the face of adverse shocks.
The change in the purchase of raw materials and input followed the change in sales closely.
<Insert Table 12 here>18

Did Demonetisation drive informal entrepreneurs to change their business mode fromcash-based to bank-based? In India, commercial banks offer savings accounts to individuals
and current accounts to enterprises. Both accounts allow the account holder to deposit savings,
remit, draw and deposit checks, etc., but current accounts allow more generous use of checks
and bank transfers. Therefore, among large and medium firms in India, using current accounts
for business is the norm. Table 13 summarizes the information of bank accounts used by oursample entrepreneurs for their businesses before Demonetisation. Only 39% of our sample had
current accounts for their business. Among our sample, 7.6% had no bank accounts for theirbusinesses before Demonetisation. Therefore, they were not able to use bank-basedtransactions at all when Demonetization was announced. The post-Demonetisation status is thesame as shown in Table 13, except for one case, in which a businessman who did not use hissavings account for business started to use it for business after Demonetisation. But thisstickiness may hide changes in intensive margins of bank-based transactions.
<Insert Table 13 here>
Therefore, in Tables 14-17, we summarize the modes of transactions used forreceiving sales revenues, paying wages, and paying for input and raw materials. In terms ofpercentages (Table 14), bank-based transactions occupied about 25% in sales receipts before
Demonetisation and the share marginally increased by 5 percentage points or so during
Demonetisation, mostly reflecting the shortage of cash, and then remained at similar levels
after Demonetisation. The importance of bank transactions in wage payment was lower thansales receipt, both before and after Demonetisation. In paying for raw materials and input, thebank share was higher than the wage payment, but slightly lower than sales receipt.
<Insert Table 14 here>
Using the information in Table 14, we classified the transaction modes into “Cashonly”, “Cash and banks”, and “Banks only”, and then prepared transition matrices. In case ofsales receipt (Table 15), there were 15 firms who moved from “Cash only” to “Cash and banks”
during Demonetisation. Interestingly, 5 out of the 15 movers turned back to “Cash only” after
Demonetisation. The transition table shows that the transition from “Cash only” to bank-basedtransactions was not permanent, and was reversed in some cases. The informal firms’
dependence on cash transactions was indeed strong and sticky.
<Insert Table 15 here>
As shown in Table 16, the mode of wage payment is stickier than the mode of sales

19receipt. Looking at the bottom panel, we see that there were only 3 cases out of 198, whichchanged the mode at all. They all started to pay wages using checks. There is no case of
de-banking (i.e., transition from “Cash and banks” or “Banks only” to “Cash only”) for wage
payments, in contrast to the case of sales receipts. In case of wage payment, workers need tohave bank accounts in order for the entrepreneur to change his mode of payment from cash tocheck (or bank transfer). But many low-income workers employed by these micro firms do not
own any bank accounts.
<Insert Table 16 here>
The transition in modes of transactions in paying for raw materials and input wasmore frequent than wage payment, but stickier than sales receipts. Comparing pre- andpost-Demonetisation, nine out of 206 “Cash only” firms started using bank transactions in
response to the Demonetisation shock (last panel, Table 17).
<Insert Table 17 here>
One reason for the stickiness in transaction modes could be a business network. Mostof our sample firms conduct their business through informal networks. To move from “Cashonly” to bank-based transactions, all members of the network may need to change
simultaneously. This is not an easy task due to high coordination costs. It appears that theDemonetisation shock was not strong enough to achieve this coordination.

5. Conclusion
To deepen our understanding of the urban informal sector and small enterprises indeveloping countries, we surveyed micro and small entrepreneurs in northeastern areas ofDelhi, India. The baseline survey was conducted in November–December 2014, covering 506
sample entrepreneurs in both manufacturing and service sectors. In the survey, not only
characteristics of the entrepreneurs and firms, but also trust questions in the GSS style wereaddressed. In the baseline, 46% of our samples were unregistered with the government. In
June–August 2017, the endline survey was conducted to collect panel information on firm
performance. As Demonetisation in November 2016, in which high value banknotes were
demonetized overnight, affected small and micro enterprises substantially, specific questions
on its impact were added in the endline survey. Attrition was frequent and we were able toresurvey 287 out of 506 enterprises in the endline. Between the two surveys, artefactual field
experiments were applied to the subsample to collect information on social, risk, and time
preferences.

20

In this paper, we presented details of the resurveys implemented under this projectand described the key variables collected. Among 226 entrepreneurs who participated in theartefactual field experiments, many showed their tendency for present bias and high discountrates. On average, the participant entrepreneurs behaved in an altruistic manner, took
substantial risk, and reduced their risk-taking when they were assigned the role of the leader.
Out of 287 entrepreneurs who participated in the endline survey, 50% were unregistered withthe government, implying that they were highly informal. During the period between baseline
and endline surveys, innovations to expand the business were not very active, either. The
majority of entrepreneurs severely suffered from Demonetisation.
Based on the description of the dataset provided in this paper, interactions ofgovernment policies, entrepreneurs’ preferences, firms’ endowments, and technology can bequantitatively analyzed. Such analysis will contribute to a better understanding of productivity
enhancement of MSMEs in India and accelerating poverty reduction in urban areas. This is left
for forthcoming research papers.

21

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23

Table 1. Survey timing and data distribution

Baseline sample
of which:
BMDT, 1st round
BMDT, 2nd round w/ goal setting1

Baseline/BMDT survey

Endline survey(2017/6/28-8/23)

Number of Start of the End of theobs.
surveysurveyNumber of% attritionobs.

506 2014/11/4 2014/12/7

287

43.3

118 2015/3/4 2015/9/19
108 2016/3/20 2016/4/24

94
67

20.3
38.0

Notes:
1. The 2nd round of BMDT covered 109 entrepreneurs. However, as two belonged to the same firm, only theoriginal owner is included in the analysis of this paper.

24

Table 2. Intertemporal choice sets in the convex time budget (CTB) experiment
Block
1

2

3

4

Date

Option A

Option B

Option C

Option D

today
5 weeks later
today
5 weeks later
today
5 weeks later
today
5 weeks later
today
5 weeks later
today
5 weeks later
today
9 weeks later
today
9 weeks later
today
9 weeks later
today
9 weeks later
today
9 weeks later
today
9 weeks later
5 weeks later
10 weeks later
5 weeks later
10 weeks later
5 weeks later
10 weeks later
5 weeks later
10 weeks later
5 weeks later
10 weeks later
5 weeks later
10 weeks later9 weeks later
18 weeks later9 weeks later
18 weeks later9 weeks later
18 weeks later9 weeks later
18 weeks later9 weeks later
18 weeks later9 weeks later
18 weeks later

3800
0
3600
0
3400
0
3200
0
2800
0
2200
0
4000
0
3800
0
3400
0
3000
0
2400
0
1800
0
3800
0
3600
0
3400
0
3200
0
2800
0
2200
0
4000
0
3800
0
3400
0
3000
0
2400
0
1800
0

2850
1000
2700
1000
2550
1000
2400
1000
2100
1000
1650
1000
3000
1000
2850
1000
2550
1000
2250
1000
1800
1000
1350
1000
2850
1000
2700
1000
2550
1000
2400
1000
2100
1000
1650
1000
3000
1000
2850
1000
2550
1000
2250
1000
1800
1000
1350
1000

1900
2000
1800
2000
1700
2000
1600
2000
1400
2000
1100
2000
2000
2000
1900
2000
1700
2000
1500
2000
1200
2000
900
2000
1900
2000
1800
2000
1700
2000
1600
2000
1400
2000
1100
2000
2000
2000
1900
2000
1700
2000
1500
2000
1200
2000
900
2000

950
3000
900
3000
850
3000
800
3000
700
3000
550
3000
1000
3000
950
3000
850
3000
750
3000
600
3000
450
3000
950
3000
900
3000
850
3000
800
3000
700
3000
550
3000
1000
3000
950
3000
850
3000
750
3000
600
3000
450
3000

Price
Game #
Ratios (P)
0
1.05
[12]
4000
0
1.11
[13]
4000
0
1.18
[14]
4000
0
1.25
[15]
4000
0
1.43
[16]
4000
0
1.82
[17]
4000
0
1 [18]
4000
0
1.05
[19]
4000
0
1.18
[20]
4000
0
1.33
[21]
4000
0
1.67
[22]
4000
0
2.22
[23]
4000
0
1.05
[24]
4000
0
1.11
[25]
4000
0
1.18
[26]
4000
0
1.25
[27]
4000
0
1.43
[28]
4000
0
1.82
[29]
4000
0
1 [30]
4000
0
1.05
[31]
4000
0
1.18
[32]
4000
0
1.33
[33]
4000
0
1.67
[34]
4000
0
2.22
[35]
4000

Option E

Notes: For each game, the date in the upper row represents t and that in the lower row
represents t+k . The monetary amounts in the upper row represent x t and those in the lower row represent x t+k .

25

Table 3. Firm characteristics and BMDT participation(Bivariate comparison)

Baseline characteristics
Firm age, average in years
Labor force1
Distribution of registration status
Registered
Unregistered
Distribution of business location
Friends Colony
Gokalpur
Jhilmil
Johripur
Karawal Nagar
Mandoli & Saboli
MaujpurNandnagri
Seelampur
Vishwash Nagar
Distribution of industry
Manufacturing sector
Auto parts
Electrical wires
Electronics
Food products
Garments
Metal and steel
Plastic goods
Wood products
Other manufacturing(Manufacturing, sub-total)
Service sector
Auto/cycle repair
Electric/electronics repair & service
Garments stitching/embroidery/tailoring
Publishing service
Metal/steel related service
Other services(Service, sub-total)

Baselinesample(n =506)
12.37
7.87

BMDT Non-BMDT
p -value
samplesample(n =226)(n =280)
11.81
12.82 0.150
6.76
8.77 0.003
0.008

275
231

108
118

167
113

115
16
121
15
35
58
17
36
30
63

46
6
45
3
23
49
10
18
1
25

69
10
76
12
12
9
7
18
29
38

0.000

0.012

12
73
6
23
68
36
62
15
48
(343)

5
27
1
15
33
15
21
4
19
(140)

7
46
5
8
35
21
41
11
29
(203)

36
22
13
39
11
42
(163)

26
14
6
17
6
17
(86)

10
8
7
22
5
25
(77)

Notes: The p -value allows for unequal variance n the case of continuous variable. For distribution, the p -value
is for the chi2 test for the independence.
1. The labor force is defined as the entrepreneur him/her-self, employees, and unpaid family members.

26

Table 4. Entrepreneur characteristics and BMDT participation(Bivariate comparison)
Baselinesample(n =506)
40.23

Baseline characteristics
Average age
Distribution of sex
Male
FemaleReligion
Hindu & others
Jain
MuslimDistribution of birthplace
Migrants
Delhi areas
Distribution of education level
Lower than 10th grade
10th grade or equivalent
12th grade or equivalent technical diploma
Bachelor's degree
Master's degree or more
Average of GSS trust indicators1
GeneralRelatives and friends
Neighbors
Business buyers/sellers
Municipal Corporation
Govt officials for services (water, electricity, etc.)
PoliceLaw officers

BMDT Non-BMDT
p -value
samplesample(n =226)(n =280)
39.34
40.95 0.049
0.023

496
10

218
8

278
2

411
31
64

202
8
16

209
23
48

19
487

9
217

10
270

19
84
196
203
4

5
53
97
71
0

14
31
99
132
4

0.374
0.787
0.796
0.850
0.488
0.320
0.275
0.354

0.354
0.717
0.788
0.836
0.500
0.354
0.305
0.345

0.389
0.843
0.804
0.861
0.479
0.293
0.250
0.361

0.000

0.809

0.000

0.418
0.001
0.668
0.460
0.664
0.193
0.241
0.726

Notes: The p -value allows for unequal variance n the case of continuous variable. For distribution, the p -value is for thechi2 test for the independence.
1. Each of the trust indicator takes the value +1 (most people can be trusted), 0 (some; no opinion), -1 (most people
cannot be trusted).

27

Table 5. Correlates of BMDT participation(OLS regression results with non-BMDT dummy as the dependent variable)

Baseline characteristics
Entrepreneur characteristics
Age
Female dummy
Jain dummy
Muslim dummy
Migrant dummy
Education less than 10th grade
Education at 10th grade
Education at the degree level or more
GSS trust for relatives and friends
Firm characteristics
Firm age
Labor force
Registration dummy (ref=unregistered)
Location fixed effects
Industry fixed effects
R2
F -stat for zero slopes except for location& industry fixed effects
Number of observations

(2)
(4)
(5)
(7)
(3)
(6)
(8) Firm's(1) Full
Entrepreneur's Entrepreneur's Entrepreneur's Entrepreneur's Entrepreneur's Entrepreneur'slabor force
specificationage
religion
migration
GSS trustsex
education
0.001(0.004)-0.246(0.191)0.132(0.082)0.158(0.162)-0.157 ***(0.040)0.160 *
(0.075)-0.092(0.092)0.125(0.081)
0.042(0.059)(9) Firm's
registration
status0.001(0.002)-0.257(0.181)
0.143(0.094)0.146(0.174)-0.100(0.063)0.148(0.104)-0.076(0.085)0.106(0.088)0.023(0.062)0.001(0.005)0.000(0.002)-0.087(0.071)
Yes
Yes
0.205

Yes
Yes
0.172

Yes
Yes
0.177

Yes
Yes
0.180

Yes
Yes
0.173

Yes
Yes
0.185

Yes
Yes
0.172

Yes
Yes
0.173

-0.041(0.069)
Yes
Yes
0.172

14.96 ***

0.60

2.01

1.38

2.48

0.90

0.14

0.47

0.31

506

506

506

506

506

506

506

506

506

0.002
(0.003)

Notes: Robust standard errors are reported in parentheses using location as the cluster. * p <0.1, ** p<0.05, *** p<0.01.

28

Table 6. Choices in BMDT
Number in [ ] shows the test number out of 35 BMDT tests
Dictator games
General altruism (transfer, Rs.) [1]
Altruism to relatives & friends [4]Altruism to residence neighbors [2]Altruism to business sellers/buyers [3]Transfer to neighbors when identity revealed [5]Additional transfer to relatives & friends ([4]-[1])Additional transfer to neighbors ([2]-[1])Additional transfer to business ([3]-[1])Additional transfer due to reputation ([5]-[2])Risk games, individual
Rs.in risky bet, factor of 1.5 [6]Rs.in risky bet, factor of 2 [7]Rs.in risky bet, factor of 2.5 [8]Change in response to increased return (Rs.in risky bet, [8]-[6])EU maximizer dummy1
Leadership games (group risk games)
Rs.in risky bet, factor of 1.5 [9]Rs.in risky bet, factor of 2 [10]Rs.in risky bet, factor of 2.5 [11]Change in response to increased return (Rs.in risky bet, [11]-[9])EU maximizer dummy1
Change as a leader (avg of [11]-[8], [10]-[7], [9]-[6])Dummy for willingness to become the leader
CTB (convex time budget) games
Qualitative indicator of discount factor2
Now vs. 5 weeks (avg from [12] to [17])Now vs. 9 weeks (avg from [18] to [23])
5 vs. 10 weeks (avg from [24] to [29])
9 vs. 18 weeks (avg from [30] to [35])Qualitative indicator of present bias3
Frequency of choosing interior solutions (avg from [12] to [35])

n

Mean

Std.Dev.

Minimum Maximum

225
224
223
224
225
223
222
223
222

1240
1580
1305
1496
1502
327
59
247
203

1128
1077
1138
1148
1065
1210
1021
1173
1133

0
0
0
0
0
-4000
-2000
-2000
-4000

4000
4000
4000
4000
4000
4000
4000
4000
3000

225
226
226
225
226

1844
2049
2093
249
0.120

999
972
969
1106

0
0
0
-4000
0

4000
4000
4000
3000
1

226
226
226
226
226
225
226

1695
1881
2053
358
0.128
-121
0.735

1050
993
1094
1236

0
0
0
-4000
0
-3667
0

4000
4000
4000
4000
1
2667
1

224
224
226
226
226
226

2.598
2.451
2.613
2.499
0.248
0.436

1.273
1.108
1.195
1.111
3.941
0.348

1
1
1
1
-9
0

5
5
5
5
12
1

804

Notes: The number of observations (n ) is less than 226 for some games as participants skipped some of the games.
1. When all the choices in [6][7][8] ([9][10][11]) are consistent with the predicted response for an expected-utility-maximizer (witheither risk-loving, risk-neutral, or risk-averse preference), the dummy takes the value of 1.
2. The indicator = 1*(choice=A)+2*(choice=B)+3*(choice=C)+4*(choice=D)+5*(choice=E). Theoretically, min=1 (most impatient),
max=5 (most patient).
3, The indicator = count(indicator2, 0 vs. 5 weeks < indicator2, 5 vs. 10 weeks) + count(indicator2, 0 vs. 9 weeks < indicator2, 9 vs
18 weeks) - count(indicator2, 0 vs. 5 weeks > indicator2, 5 vs. 10 weeks) - count(indicator2, 0 vs. 9 weeks > indicator2, 9 vs. 18
weeks). Theoretically, min=-12 (highly future biased), max=12 (highly present biased).

29

Table 7. Firm characteristics and endline survey participation(Bivariate comparison)

Baseline characteristics
Firm age, average in years
Labor force
Distribution of registration status
Registered
Unregistered
Distribution of business location
Friends Colony
Gokalpur
Jhilmil
Johripur
Karawal Nagar
Mandoli & Saboli
MaujpurNandnagri
Seelampur
Vishwash Nagar
Distribution of industry
Manufacturing sector
Auto parts
Electrical wires
Electronics
Food products
Garments
Metal and steel
Plastic goods
Wood products
Other manufacturing(Manufacturing, sub-total)
Service sector
Auto/cycle repair
Electric/electronics repair & service
Garments stitching/embroidery/tailoring
Publishing service
Metal/steel related service
Other service(Service, sub-total)

Baselinesample(n =506)
12.37
7.87

Endlinesample(n =287)
11.05
5.62

Attritionp -value
sample(n =219)
14.10 0.000
10.82 0.000

275
231

109
178

166
53

115
16
121
15
35
58
17
36
30
63

39
16
48
12
31
43
15
32
18
33

76
0
73
3
4
15
2
4
12
30

0.000

0.000

0.000

12
73
6
23
68
36
62
15
48
(343)

5
24
5
19
47
16
26
9
21
(172)

7
49
1
4
21
20
36
6
27
(171)

36
22
13
39
11
42
(163)

29
16
11
22
7
30
(115)

7
6
2
17
4
12
(48)

Notes: See Table 3.

30

Table 8. Entrepreneur characteristics and endline survey participation(Bivariate comparison)

Baseline characteristics
Average age
Distribution of sex
Male
FemaleReligion
Hindu & others
Jain
MuslimDistribution of birthplace
Migrants
Delhi areas
Distribution of education level
Lower than 10th grade
10th grade or equivalent
12th grade or equivalent technical diploma
Bachelor's degree
Master's degree or more
Average of GSS trust indicators
GeneralRelatives and friends
Neighbors
Business buyers/sellers
Municipal Corporation
Govt officials for services (water, electricity, etc.)
PoliceLaw officers

Baselinesample(n =506)
40.23

Endlinesample(n =287)
38.66

Attritionp -value
sample(n =219)42.29 0.000

496
10

282
5

214
5

411
31
64

230
9
48

181
22
16

19
487

13
274

6
213

19
84
196
203
4

11
64
138
73
1

8
20
58
130
3

0.374
0.787
0.796
0.850
0.488
0.320
0.275
0.354

0.366
0.679
0.763
0.847
0.540
0.338
0.324
0.404

0.384
0.927
0.840
0.854
0.420
0.297
0.210
0.288

0.665

0.000

0.294

0.000

Notes: See Table 4.

31

0.688
0.000
0.038
0.827
0.016
0.387
0.016
0.008

Table 9. Correlates of attrition in the endline survey(OLS regression results with Endline-attrition dummy as the dependent variable)

Baseline characteristics
Entrepreneur characteristics
Age
Female dummy
Jain dummy
Muslim dummy
Migrant dummy
Education less than 10th grade
Education at 10th grade
Education at the degree level or more
GSS trust for relatives and friends
GSS trust for law officers
Firm characteristics
Firm age
Labor force
Registration dummy (ref=unregistered)
Location fixed effects
Industry fixed effects
R2
F -stat for zero slopes except for location &industry fixed effects
Number of observations(1) Full
specification
0.002(0.003)0.250(0.148)0.221 ***
(0.049)-0.083(0.062)0.063(0.080)0.083(0.100)-0.011(0.033)0.152 **
(0.048)0.093 **
(0.032)-0.038(0.057)

(2)
(3)
(4)
(5)
Entrepreneur's Entrepreneur's Entrepreneur's Entrepreneur'sage
religioneducation
GSS trust(6) Firm age(7) Firm's laborforce(8) Firm's
registration
status0.002(0.002)0.233 ***
(0.056)-0.141 **(0.046)0.066(0.095)-0.023(0.039)0.181 ***
(0.053)0.120 ***
(0.026)-0.045(0.051)

0.003(0.004)0.007 ***
(0.002)0.045(0.030)
Yes
Yes
0.292

0.005
(0.003)

Yes
Yes
0.236

Yes
Yes
0.251

Yes
Yes
0.255

Yes
Yes
0.241

Yes
Yes
0.239

Yes
Yes
0.251

0.108 ***
(0.030)
Yes
Yes
0.239

103.20 ***1.27

27.38 ***

6.97 **

10.95 ***

2.79

13.33 ***

13.12 ***

506

506

506

506

506

506

506

506

0.010 ***
(0.003)

Notes: Robust standard errors are reported in parentheses using location as the cluster. * p <0.1, ** p<0.05, *** p<0.01.

32

Table 10. Transition of registration status
Endline survey, 2017
Registered Unregistered
Total
Baseline survey, 2014
Registered
Unregistered
Total

93
(85.3)
51(28.7)
144(50.2)

16(14.7)
127(71.3)
143(49.8)

109(100.0)178(100.0)287(100.0)Notes: chi-2 stat for the independence is 86.84***. Transition probability is shown in the parenthesis.

33

Table 11. Firm performance in the endline surveynInnovation dummy since the baseline survey
Any innovation
Process innovation
Product innovation
Marketing innovation
Labor force
Number at the endline survey (the owner is included)
Change since the baseline survey
Current profit in the last month1
Profit indicators among all firms
Sales (Rs. million)
Change in sales (Rs. million)2Current profit (Rs. million)
Change in profit (Rs. million)2Profit/Sales Ratio3
Dummy for firms with positive current profit
Profit indicators among firms with positive current profit
Sales (Rs. million)
Increase in sales (Rs. million)2Current profit (Rs. million)
Increase in profit (Rs. million)2Profit/Sales Ratio3

Mean
282
282
282
282

0.149
0.092
0.117
0.067

287
287

3.686
-1.934

197
196
195
194
195
195
176
175
176
175
176

Std.Dev.

Min

Max

0
0
0
0

1
1
1
1

4.007
10.530

1
-51

41
30

0.186
-0.035
0.123
0.082
0.334
0.903

1.090
1.044
1.010
1.139
0.723

0.004
-1.780-0.113-1.942-5.6250

1513.8701413.739
0.965
1

0.204
-0.023
0.138
0.102
0.493

1.152
1.102
1.062
1.197
0.209

0.004
-1.7800.001-1.9420.033

1513.8701413.7390.965

Notes:
1. Current profit is defined as Sales - (intermediate input costs + fuel costs + labor costs + repair expenditure +transport expenditure + license fee + indirect tax + rental fee + contract expenditure + administration cost + otherexpenditure). The sample sizes reported in the same row indicates the sample size of the firms where profits can be
2. The endline survey value minus the baseline survey value.
3. Profit/Sales Ratio is defined as the current profit divided by sales.

34

Table 12. Impact of the demonetisation (subjective assessment)

A. SalesDuring (2016/11/9-2016/12/30)
Post (2016/12/31-endline)
B1. Number of workers
During (2016/11/9-2016/12/30)
Post (2016/12/31-endline)
B2. Working hours per worker
During (2016/11/9-2016/12/30)
Post (2016/12/31-endline)
B3. Total wage payment to workers
During (2016/11/9-2016/12/30)
Post (2016/12/31-endline)
C. Purchase of raw materials and inputs
During (2016/11/9-2016/12/30)
Post (2016/12/31-endline)

Index with the Pre-Demonetisation level as 100
Max
n
Min
Mean1
Std.Dev.1
282
281

51.0
72.2

30.7
29.6

0
0

100
170

204
204

84.5
95.2

29.6
18.6

0
0

166
120

187
199

90.7
95.4

25.9
20.1

0
0

100
100

201
201

83.9
97.6

34.3
68.6

0
0

100
100

279
281

51.1
71.2

30.6
29.5

0
0

100
160

Notes: The number of responses is than 287, as several respondents did not answer the demonetisation part.
Furthermore, when the respondent refused to answer the specific question or the question was not applicableto the respondent, the number of responses declined further. For example, the question "Working hours per
worker" does not apply to the firm if the umber of workers during (or post) demonetisation was zero. The"workers" here is broader than the one adopted in Table 11, including contract workers as well.
1. Simple average (Std.Dev.) over the sample, without weighting.

35

Table 13. Use of bank accounts for the business (Pre-Demonetisation)
Number
21
146
15
93
275

Category
1. No bank account used for the business
2. Saving account in the name of the owner
3. Current account in the name of the business
4. Use both saving account and current accountTotal(Share)(7.6)(53.1)(5.5)(33.8)(100.0)Notes: Due to missing information, the total number of observations is less than 287. Postdemonetisation status is the same as this table, except for 1 case in which the category 1
businessman moved to category 2 (i.e., he started to use his saving account for hisbusiness after the demonetisation). The chi2 stat for independence is 130.8 ***.

36

Table 14. Demonetisation and bank-based transactionsn

Share of bank-based transactions (%)
1
1
Min
Std.Dev.
Mean

A. Share of bank-based transactions in total sales
Pre (-2016/11/8)
281
24.6
276
29.3
During 䠄2016/11/9-2016/12/30)
Post (2016/12/31-endline)281
28.5
B1. Share of bank transfers in total wage payment
Pre (-2016/11/8)
203
2.0
188
2.2
During 䠄2016/11/9-2016/12/30)
Post (2016/12/31-endline)
198
2.3
B2. Share of checks in total wage payment
Pre (-2016/11/8)
203
1.9
188
4.1
During 䠄2016/11/9-2016/12/30)
Post (2016/12/31-endline)
198
3.0
C. Share of bank-based transactions in payment for raw materials and input
271
21.7
Pre 䠄-2016/11/8)
267
25.0
During 䠄2016/11/9-2016/12/30)
Post (2016/12/31-endline)
271
25.2

Max

39.0
41.1
40.7

0.0
0.0
0.0

100.0
100.0
100.0

13.9
14.5
14.5

0.0
0.0
0.0

100.0
100.0
100.0

10.2
18.0
14.5

0.0
0.0
0.0

100.0
100.0
100.0

38.2
40.4
40.1

0.0
0.0
0.0

100.0
100.0
100.0

Notes: The number of responses is less than 287 due to response refusal or question non-applicability (for example, if
the sales were zero during the demonetisation, the share of bank-based transactions cannot be defined). The othercategory of transactions not listed in this table is "Cash".
1. Simple average (Std.Dev.) over the sample, without weighting.

37

Table 15. Demonetisation and transition in transaction modes for sales receipt
During (2016/11/9-2016/12/30)
Cash only Cash & bank Banks only
Pre (-2016/11/8)
Cash only
Cash & bankBanks only

167
15
0
2
44
2
0
1
45
Post (2016/12/31-endline)
Cash only Cash & bank Banks only
During (2016/11/9-2016/12/30)
Cash only
168
10Cash & bank
5
54
2
Banks only0
1
45
Post (2016/12/31-endline)
Cash only Cash & bank Banks only
Pre (-2016/11/8)
Cash only172
110Cash & bank
2
48
2
Banks only0145
Notes: The number of responses is less than 287 due to response refusal
or question non-applicability.

38

Table 16. Demonetisation and transition in transaction modes for wage payment
During (2016/11/9-2016/12/30)
Cash only Cash & bank Banks only
Pre (-2016/11/8)
Cash only
Cash & bankBanks only

171
0
3
0
8
1
0
0
5
Post (2016/12/31-endline)
Cash only Cash & bank Banks only
During (2016/11/9-2016/12/30)
Cash only
17000Cash & bank080Banks only
0
2
7
Post (2016/12/31-endline)
Cash only Cash & bank Banks only
Pre (-2016/11/8)
Cash only
181
1
2
Cash & bank090Banks only005
Notes: The number of responses is less than 287 due to response refusal
or question non-applicability.

39

Table 17. Demonetisation and transition in transaction modes for input/raw material payment
During (2016/11/9-2016/12/30)
Cash only Cash & bank Banks only
Pre (-2016/11/8)
Cash only
Cash & bankBanks only

197
8
0
1
27
5
0
0
41
Post (2016/12/31-endline)
Cash only Cash & bank Banks only
During (2016/11/9-2016/12/30)
Cash only
195
3
0
Cash & bank1
32
2
Banks only
0
3
43
Post (2016/12/31-endline)
Cash only Cash & bank Banks only
Pre (-2016/11/8)
Cash only
197
8
1
Cash & bank0
32
3
Banks only0041
Notes: The number of responses is less than 287 due to response refusal
or question non-applicability.

40