benefits of a registration policy for microenterprise performance in india

Benefits of a registration policy for microenterpriseperformance in India

Smriti Sharma

Accepted: 1 March 2013 / Published online: 26 March 2013

� Springer Science+Business Media New York 2013

Abstract This paper evaluates the effects of

a voluntary registration policy with government

authorities on financial performance of urban mic-

roenterprises in the Indian manufacturing sector.

Using data from the 2006 World Bank survey of

Indian microenterprises and applying the semi-

parametric propensity score matching technique, we

find that being registered leads to significant gains in

sales per employee and value added per employee.

Large gains are also noted for male-owned firms, those

operating with or without paid labor and those

operating outside of the owner’s home.

Keywords Microenterprises � Registration �Firm performance � India

JEL classifications L25 � L26 � 017

1 Introduction

Microenterprises are an important source of livelihood

and also account for a large share of output and

employment in most developing countries. There is a

large literature base that discusses the importance of

microenterprises, their dynamics and the constraints

faced by them (Daniels 1999; Liedholm and Mead

1998; Little et al. 1987; van Praag and Versloot

2007). In India, as in other developing countries, the

most recurrent problems for microenterprises are related

to access to finance and market linkages. In order to

remedy these problems, the Indian government instituted

a voluntary registration policy with substantial benefits

accruing only to the microenterprises that choose to

register. According to the latest available estimates only

a meager 6 % of the 26 million microenterprises are

registered (Ministry of Micro, Small and Medium

Enterprises 2009). A cursory look at the data shows the

registered firms to be larger and more productive. But

these differences could be merely indicating differences

in the underlying ability of registered firm owners and

not the effects of registration per se. This paper is a first

attempt to systematically analyze the effects of registra-

tion with government authorities on the financial

performance of microenterprises engaged in the manu-

facturing sector in urban India.

Being registered with the District Industries Centre

(DIC) of the respective state governments gives the

firm access to benefits such as easier access to

institutional credit, preferential rates of interest,

various types of exemptions and subsidies and mar-

keting assistance and makes them eligible for all other

schemes targeted at the microenterprise sector.

This paper adds to the extant but small litera-

ture on understanding the effects of formality on

S. Sharma (&)

Department of Economics, Delhi School of Economics,

University of Delhi, Delhi 110007, India

e-mail: [email protected]

123

Small Bus Econ (2014) 42:153–164

DOI 10.1007/s11187-013-9475-y

microenterprises in developing countries. Formality is

measured in different ways: payment of taxes, bank

credit utilization, participation in training programs

and business associations. Fajnzylber et al. (2009)

estimated the impact of formality on the performance

of Mexican microenterprises. They found that belong-

ing to business associations increased profits by at

least 10 % while paying taxes increased profits by

22 % or more. McKenzie and Sakho (2010) estimated

the impact of registering for taxes on firm profits

for Bolivian microenterprises by using the distance

between firm and registration office as an instrument

for registration. The effects vary according to the size

of the firm with profits increasing the most for firms

with two to five workers, but lowering profits for

smaller and larger firms. Rand and Torm (2012) found

that becoming officially registered leads to an increase

in firm profits, investments and access to credit for

Vietnamese small and medium enterprises. There is

also an increase in workers with formal contracts,

suggesting greater compliance with labor regulations.

De Vries (2010) found formal retailers to be more

productive and efficient as compared to informal

retailers in Brazil. Fajnzylber et al. (2011) found

that increased levels of official licensing due to a

tax reduction and process simplification system

(SIMPLES) led to higher revenues, profits and

employment among registered Brazilian microenter-

prises. Monteiro and Assuncao (2012) found that

Brazilian retail firms increased investments in long-

run projects, as a result of increased licensing due to

the SIMPLES program. While there is some research

on the effects of formality on firm performance in

Latin American economies, to the best of our knowl-

edge, no such study exists for the case of India that

also has a large informal sector. This study aims to fill

this gap in the current literature.

There is a critical trade-off involved in the decision

to become formal or to register with any type of

government authority. Being formal entails costs in the

form of taxes, compliance with labor and environmen-

tal regulations among others, lengthy processes and

paperwork, and the possibility of paying bribes to

government officials. Djankov et al. (2002) found that

for 30 out of the 85 countries they consider, the

estimated monetary and time cost spent in fulfilling

legal requirements exceeds 50 % of per capita GDP.

On the other hand, formal firms benefit from

access to public goods and services, such as public

infrastructure, police protection, and better enforce-

ment of property rights and contracts. Once a firm is

formal, it can also issue receipts for transactions, which

widens its potential customer base.

To the extent that registration involves costs and

reflects initiative undertaken by the more competent

owners to get their enterprises registered, one might

overestimate the effects of registration. Given that

registration with DIC is voluntary, it becomes all the

more important to account for self-selection in esti-

mating the effects of registration. Using 2006 World

Bank survey data on Indian microenterprises, we apply

the semi-parametric propensity score matching tech-

nique to deal with this selectivity problem. This

approach compares microenterprises in registered

and unregistered sectors that have similar estimated

propensities to register, matching each registered

unit with at least one unregistered unit based on the

propensity score in order to estimate the causal effect

of registration on firm performance. We are able to

control for a number of firm owner characteristics

available in the survey that serve as a proxy for the

unobserved owner ability. Our results suggest that

registration leads to a 32 % gain in sales per employee

and 56 % gain in value added per employee according

to the kernel-matching estimator. Further, we break up

the sample along three dimensions: enterprises that

have a female owner versus those that do not;

enterprises operating within the owner’s home and

those operating in another location; enterprises that

hire no labor (i.e. operated by the owner himself)

versus those that hire other labor. With the exception of

female-owned and home-based enterprises, we found

significant gains from registering for each sub-cate-

gory. Robustness checks confirm our findings.

The rest of the paper is organized as follows.

Section 2 talks briefly about the microenterprise sector

in India and the registration policy. Section 3 discusses

the methodology and the data. Section 4 contains the

estimation results. Finally, Section 5 discusses and

concludes.

2 Background

2.1 The Indian microenterprise sector

After India gained independence in 1947, there was an

urgent need to build a stable and sustainable economy.

154 S. Sharma

123

The Industrial Policy Resolution of 1956, for the first

time, emphasized the role of small-scale industries in

the development of the national economy on several

grounds. In India, as in other countries, small-scale

industries are recognized as engines of economic

growth and contributors to industrial, economic,

technological and regional development. They not

only play a decisive role in providing large employ-

ment opportunities at a comparatively lower capital

cost than large industries but also help in the

industrialization of rural and backward areas, thereby

reducing regional imbalances and assuring a more

equitable distribution of income and wealth. Small-

scale enterprises are also complementary to large

industries as ancillary units. The Industrial Policy

Resolution of 1977 re-enforced the effective promo-

tion of small industries in rural areas and small towns.

The focal point of development of small-scale indus-

tries was taken away from the big cities to districts.

The concept of DIC was introduced so that in each

district a single agency could meet the requirements of

the small entrepreneurs.1 Although the primary

responsibility of promotion and development of this

sector lies with the state governments, the Central

government supplements the efforts of the state

governments.

In India, classification of manufacturing enterprises

into micro, small and medium is on the basis of

investment in plant and machinery. The classification

is as follows: enterprises with investment of up to

Rs. 2.5 million (USD 50,000) are classified as micro;

enterprises with investment between Rs. 2.5 million

(USD 50,000) and Rs. 50 million (USD 1 million) are

classified as small; and enterprises with investment

between Rs. 50 million (USD 1 million) and Rs. 100

million (USD 2 million) are classified as medium.2,3

According to the fourth all-India census of the

micro, small and medium enterprises (MSME) carried

out in 2008–2009, the sector contributes 8 % of the

country’s GDP, 45 % of the manufactured output,

40 % of its manufactured exports and 95 % of all

industrial units. It provides employment to about 60

million persons through 26 million enterprises and

produces more than 6,000 products ranging from food

products to rubber and plastic products and basic

metals and chemicals. Only 6 % of the enterprises are

registered. Twenty-nine percent of the enterprises are

in the manufacturing segment and 71 % in the services

segment; 52 % of the enterprises are in rural locations

and women own only 7 % of enterprises. The growth

rate of the MSME sector has been consistently higher

than the growth rate of the overall industrial sector.

For instance, over 2003–2008, the sector recorded an

annual average growth rate of 11.2 %, whereas the

overall industrial growth rate was 9 % (Ministry of

Micro, Small and Medium Enterprises 2011). How-

ever, microenterprises face certain problems with the

most frequent one being access to finance. As a result,

firms are credit constrained and have difficulty in

investing in and upgrading machinery and equipment.

Other common problems encountered by them are:

inadequate or no power supply, lack of manage-

ment experience, infrastructure services, access to and

knowledge of markets, technical assistance and so on.4

Realizing the magnitude and potential of this sector

and to ease the constraints faced by them, the govern-

ment has undertaken several initiatives to boost the

competitiveness of the sector by way of entrepreneur-

ship development programs, cluster development

schemes, credit guarantee schemes, marketing guid-

ance and special assistance programs for women and

individuals from disadvantaged groups. Certain goods

and services have also been reserved exclusively for

production by this sector. With increasing deregulation,

the list of reserved items has been reduced over time

but some items still continue to be reserved. Very

recently, the government has announced that at least

1 A detailed discussion of the evolution of the small-scale

industries sector in India can be found in Little et al. (1987).2 We use the exchange rate of USD 1 = Rs. 50 for the entire

analysis.3 This classification has been in place since the enactment of the

Micro, Small and Medium Enterprises Development Act

(MSMED), 2006. Prior to this, manufacturing enterprises were

defined as either micro or small on the basis of investment in

plant and machinery. The classification was as follows:

enterprises with investment of up to Rs. 2.5 million were

classified as micro; and enterprises with investment between

Rs. 2.5 million and Rs. 10 million were classified as small. In

addition to this, the Industrial Policy Resolution of 1977

defined a ‘tiny’ sector as one with investment in machinery and

Footnote 3 continued

equipment up to Rs. 0.1 million and situated in towns with a

population of less than 50,000 according to 1971 census, and in

villages.4 Coad and Tamvada (2012) use the 2002–2003 all-India census

of registered small-scale industries to explore determinants of

firm growth and various types of barriers faced by small

enterprises.

Benefits of a registration policy 155

123

20 % of total annual purchases of public sector units

must be from MSMEs. Most of the abovementioned

schemes are targeted only at registered microenterprises.

2.2 Registration policy

Microenterprise registration in India refers to regis-

tering with the DIC of the respective state government.

The registration scheme is voluntary and there are no

fees involved in completing the registration process.

Being registered with the DIC grants the firm an

official small-scale industry status that allows it to

avail of benefits and incentives from central and state

governments. The incentives offered by the central

government generally contain the following: inclusion

under priority sector lending scheme of banks, lower

rates of interest, excise exemption schemes, exemp-

tion under direct tax laws and statutory support such as

reservation of certain goods. State governments offer

their own package of facilities and incentives. These

relate to development of industrial estates, tax subsi-

dies, power tariff subsidies, capital investment subsi-

dies and other support services such as marketing and

export assistance, etc. Apart from being beneficial for

firms, the states also encourage small scale units to

register since it enables them to collect statistics and

to maintain a roll of small enterprises to which the

basket of incentives and support are targeted.

Before applying for registration, an Indian MSME has

to obtain statutory and administrative clearances from

regulatory bodies such as the pollution control board,

municipal authorities for land used, labor boards, and

industry-specific licenses such as drug license underdrug

control order for pharmaceutical firms. Registered

microenterprises are required to comply with labor

regulations. Compliance with tax regulations is an

independent process handled by a different agency.

While it is mandatory for every operating unit—

registered or not—to pay sales tax and to have a tax

identification number, there is no check done by the DIC.

3 Methodology and data

3.1 Methodology

We use the propensity score matching (PSM) meth-

odology introduced by Rosenbaum and Rubin (1983)

and later developed for labor economics (Dehejia and

Wahba 1999; Heckman et al. 1997). PSM analyses

rely on the assumption of ‘‘selection on observables’’,

i.e. conditional on observable characteristics, regis-

tered and unregistered microenterprises do not sys-

tematically differ along unobservable dimensions.

This approach compares microenterprises in regis-

tered and unregistered sectors that have similar

estimated propensities to register, matching each

registered unit with at least one unregistered unit

based on the propensity score in order to estimate the

causal effect of formality on firm performance. This

method avoids functional form assumptions and the

reliance on exclusion restrictions but assumes that

selection is on the basis of observables. While we

cannot take into account some of the unobservables

such as networking skills and risk attitudes of enter-

prise owners that may affect performance, this tech-

nique still represents an improvement over the

standard linear least squares regression.

Let i index the firm. The treatment here is

registration of a firm. If a firm registers itself, it is

called ‘‘treated’’, the treatment indicator Di = 1, and

the outcome of the treatment is the measure of firm

performance w1i. If a firm does not register, it is called

a ‘‘control’’, the treatment indicator Di = 0, and the

outcome is the firm performance w0i. The gain from

treatment for the firm (treatment effect) is therefore

Ai = w1i - w0i. However, at any given point in time,

since a firm can be either registered or unregistered,

the counterfactual is not observed. Since estimating

the individual treatment effect is not possible, studies

estimate average treatment effect on the treated (ATT)

that can be defined as:

ATT ¼ E AijDi ¼ 1ð ÞATT ¼ E w1ijDi ¼ 1ð Þ � E w0ijDi ¼ 1ð Þ

The matching of treated and control units is on the basis

of the predicted probability of participating in the

program—the propensity score—conditional on a vec-

tor of observed characteristics X. Rosenbaum and Rubin

(1983) show that the treatment and control observations

with the same value of the propensity score have the

same distribution of the full vector of regressors X and

it is therefore sufficient to match exactly on the

propensity score to obtain the same probability distri-

bution of X for treated and control individuals.

However, this methodology requires that the

conditional independence assumption be satisfied,

156 S. Sharma

123

i.e. conditional on the observables X, the outcome is

independent of treatment. This means that selection

into treatment is solely based on the observable

characteristics (included in X) and that there are no

unobservable characteristics that simultaneously affect

treatment and outcome. Another required assumption

is that the probability of treatment for treated and

control group should lie in the same domain, which is

called the common support assumption.

Using the Rosenbaum and Rubin (1983) theorem,

the PSM procedure can be broken down into two

stages. In the first stage, the propensity score Pr

(D = 1|X) is estimated, using a binary discrete choice

model such as probit or logit. In the second stage, units

are matched on the basis of their predicted probabil-

ities of participation. We use two matching algo-

rithms: nearest neighbor (NN) and kernel based

matching. In the case of the NN matching, units in

the treated group are matched to units in the control

group that are closest in terms of the propensity score.

In case of kernel matching, each unit in the treatment

group is matched to a weighted average of all control

group units, with the weight for each unit in the

control group inversely proportional to the difference

between that unit’s estimated propensity score and the

propensity score of the treated unit. Kernel matching

has an advantage of lower variance because more

information is used (Heckman et al. 1998). We apply a

kernel-based method using the Epanechnikov kernel.

The bandwidth for kernel matching is derived using

the Silverman’s Rule of Thumb (1986).

3.2 Data

The data are from the World Bank Investment Climate

Survey of Micro Enterprises conducted in 2006. The

2001 National Sample Survey on the unorganized

manufacturing sector was used as the base frame for

sampling.5 The data were used to (a) select the cities to

be covered in the survey and (b) provide an estimate of

the sample size for each manufacturing sector to be

sampled from that city. In view of the objective of

sampling enterprises in the unorganized sector in

urban areas, only data for urban areas were chosen.

The questionnaire was administered by a private

research organization through face-to-face interviews

with the respondents being the firm managers or

entrepreneurs. The survey canvassed responses on

topics such as firm and owner characteristics, annual

sales, costs of inputs, sex and skill composition of

workforce, access to finance, and respondents’ opin-

ions on whether bribery, licensing, infrastructure,

trade, crime, competition, taxation and informality

constitute barriers to firm growth and performance.

The data consists of 1,549 firms in five large states

spanning all regions of the country (Delhi and Punjab

in north India, West Bengal in eastern India, Maha-

rashtra in western India and Andhra Pradesh in south

India) and covers manufacturing industries such as

auto components, chemicals, food processing, leather,

garments and textiles.

Selection of variables to include in estimating the

propensity score is an important issue. As argued in

Caliendo and Kopeinig (2008), only variables that

simultaneously affect the probability of treatment and

also the outcome variables should be considered. In

addition, included variables should not be affected by

treatment or the anticipation of treatment. In other

words, these variables should be exogenous. More-

over, since the data represents a cross-section of

firms, it is an application of cross-sectional matching

methods.

The final outcome variables are financial perfor-

mance indicators such as sales per employee and value

added per employee. Sales per employee are calcu-

lated as annual sales of the firm divided by paid

number of employees. Value added per employee is

calculated as sales less material purchases and fuel and

electricity costs divided by paid number of employees.

Following McPherson and Liedholm (1996) and

Nugent and Sukiassyan (2008)—to calculate the

propensity score—we account for the educational

attainment of the owner (dummy variables for illiter-

acy, up to 9 years of schooling, high school and some

years of college, and college and higher), years of

experience in the industry, whether the firm owners are

female, whether the owner has received vocational

training, whether the owner operates the enterprise

himself or with paid labor, whether the activity is

carried out at the owner’s residence and age of the

firm. Industry and state-specific factors are accounted

for by including industry and state level dummy

variables, respectively. The state dummy variables

capture differences in tax regimes, labor laws and

other policies across states.

5 The detailed sampling methodology is available in Ferrari and

Dhingra (2009).


123

Table 1 is an exhaustive list reporting the means of

outcome and conditioning variables for the total

sample of firms, registered firms and the unregistered

firms. Out of the total 1,549 firms, 310 are registered

which amounts to 20 %. The average sale per

employee is Rs. 295581 (USD 5911.62) and the

average value added per employee is Rs. 132805

(USD 2656). A total of 7.5 % of the enterprises are

female-owned, 15 % are owner-operated and 20 %

operate out of the owner’s home. Fifty-two percent of

respondents have completed high school and 23 %

have 9 years of schooling; 12 % of respondents

have undergone some formal vocational training,

and average experience in the sector is *15.5 years.

The average age of the enterprise is 13 years.

A Wilcoxon rank-sum test shows that most vari-

ables differ significantly between registered and

unregistered firms with the exception of vocational

training, years of experience, chemicals, electronics

and electrical industry dummies and Andhra Pradesh

and West Bengal state dummies. On average, regis-

tered firms have higher sales per employee and value

added per employee. Registered firms are older and

owners have higher levels of educational attainment.

Female owned enterprises, home-based enterprises

and those operating with no paid labor are higher in

the unregistered sector. These differences could explain

better performance for the registered enterprises. In the

next section, we test whether any differences in

performance remain after we control for self-selection,

firm, industry and location characteristics.

4 Estimation results

Table 2 reports the results of the first step probit

estimation using the STATA program ‘‘pscore’’ by

Becker and Ichino (2002). Enterprises where the

owners are college educated are significantly more

likely to register, while enterprises with no hired labor

and those carrying out production activity at home are

significantly less likely to register. There are also

industry and location differences in the propensities to

register. The probit regression model yields the

propensity scores used to match the 310 treated units

with control units.

Table 3 reports estimates of ATT calculated accord-

ing to NN and kernel matching using the STATA

program ‘‘psmatch2’’ by Leuven and Sianesi (2003).

Table 3 shows that in the unmatched sample, sales

per employee are Rs. 268597 (USD 5371.94) more in

the registered enterprises compared to unregistered

firms. However, with kernel matching, the ATT equals

Rs. 125740 (USD 2514.8) and on the basis of NN

matching, the ATT is to the tune of Rs. 154364 (USD

3087.28). This translates into a 32.5 % increase in

sales per employee according to kernel matching and

increase of 43 % according to NN matching.

Similarly, the unmatched difference in value added

per employee between registered and unregistered

firms is Rs. 101124 (USD 2022.49) but the ATT equals

Rs. 77132 (USD 1542.64) and Rs. 98201 (USD 1964)

on the basis of kernel matching and NN matching,

respectively. This translates into a 56 % increase in

value added per employee according to kernel match-

ing and an increase of 84 % according to NN

matching.

Table 4 shows that enforcement of the common

support results in only a few units getting dropped,

suggesting no problems in the interpretation of the

results.

A similar analysis is conducted on various subsets

of the data. We split the data along three dimensions:

enterprises that have a female owner versus those that

do not, enterprises operating within the owner’s home

and outside his home, enterprises that hire no labor

(operated by the owner himself) versus those that hire

other labor. For each of these six subsets, we calculate

ATT in terms of sales per employee and value added

per employee using NN and kernel matching as shown

in Table 5.

From Table 5, we see that for female-owned

enterprises, there are no statistically significant gains

from registering with DIC. While the proportion of

female owned firms in the sample is quite small to say

anything conclusively, it could be suggestive of

barriers faced by women in accessing the services

provided to registered firms. For home-based enter-

prises, while there are no significant gains from

registering in terms of sales per employee, there are

marginally significant losses in terms of value added

per employee, when using kernel matching. This can

be explained by the fact that these home-based firms

are operating at a scale too small to benefit in terms of

an increase in sales revenue or that the costs associated

with the scheme are much larger than the benefits

leading to a net loss in value added per employee. For

enterprises that hire other labor and for those with

158 S. Sharma

123

no hired labor (owner operates the enterprise himself),

we observe significant gains, but the gains are much

larger for owner-operated enterprises. Enterprises

with hired labor have to incur costs in order to comply

with labor laws that lead to lesser gains in terms of

value added per employee.

Following Rosenbaum and Rubin (1985), balancing

tests are undertaken using the ‘‘pstest’’ program by

Leuven and Sianesi (2003). A two-sample t test is

undertaken to check whether the matching has elim-

inated differences in each of the covariates between the

matched treated and control units. In Table 6, it can be

seen that prior to the matching, most covariates differ

across the treated and control enterprises. However,

after matching, all covariates (except auto and metals

dummy) are balanced between the treatment and

control groups and no significant differences are found.

Further, in Table 7, the low estimates of the pseudo-R2

and the insignificant likelihood ratio tests suggest no

systematic differences in the distribution of covariates

between the treated and the matched control groups.

The overall balancing tests imply that the matching

procedure has produced samples of microenterprises

that can be regarded as similar, and any difference in

performance between registered and unregistered

enterprises can be inferred as coming from the effect

of registration with the government authorities, condi-

tional on the identifying assumptions holding good.

Table 1 Descriptive statistics

Variable Total mean Registered mean Unregistered mean

Number of firms 1,549 310 1,239

Outcome variables

Sales per employee 295581.8 510424.7 241827.7

Value added per employee 132805.7 213692.3 112567.8

Conditioning variables

Education dummy: no school 0.026 0.003 0.032

Education dummy: 9 years of school 0.234 0.126 0.261

Education dummy: high school and some college 0.522 0.467 0.535

Education dummy: college and above 0.218 0.403 0.171

Years of experience 15.58 16.56 15.34

Vocational training 0.123 0.103 0.127

Female owned 0.075 0.051 0.08

No paid labor 0.150 0.077 0.167

Based at home 0.201 0.158 0.211

Age of the firm 13.22 16.86 12.30

Industry dummy: Auto-metal 0.216 0.3 0.195

Industry dummy: Electrical 0.076 0.058 0.079

Industry dummy: Electronics 0.056 0.07 0.051

Industry dummy: Chemicals 0.044 0.045 0.044

Industry dummy: Food 0.190 0.106 0.21

Industry dummy: Garments 0.207 0.293 0.184

Industry dummy: Textiles 0.134 0.103 0.14

Industry dummy: Leather 0.078 0.022 0.092

State dummy: Delhi 0.261 0.122 0.295

State dummy: Punjab 0.200 0.580 0.104

State dummy: Maharashtra 0.260 0.003 0.324

State dummy: West Bengal 0.151 0.180 0.143

State dummy: Andhra Pradesh 0.128 0.112 0.131

Sales per employee is calculated as annual sales of the firm divided by paid number of employees. Value added per employee is

calculated as sales less material purchases and fuel and electricity costs divided by paid number of employees


123

As a robustness check, we estimate the ATT using

nearest-neighbor matching as suggested by Abadie

and Imbens (2002). Instead of minimizing the differ-

ence in propensity scores between treatment and

control groups, the basic idea is to find a control unit

with the same characteristics for each treated unit.

Therefore, here the matching is on the basis of multi-

dimensional covariates instead of a uni-dimensional

propensity score. We use the STATA program

‘‘nnmatch’’ by Abadie et al. (2004). Table 8 reports

results of ATT estimated using this methodology.

ATT for all firms in terms of sales per employee is

Rs. 137407 (USD 2738.14) and in terms of value

added per employee is Rs. 47284 (USD 945.68).

Results for the various subsets of the data are also

generally similar.

A second robustness check relates to checking for

outliers. We take into account the possibility of

outliers in sales per employee and value added per

employee variables by trimming the bottom and top

1 percentile of the data and running the analysis on the

trimmed data. We find that the results are qualitatively

similar and statistically significant. In terms of sales

per employee, kernel matching and NN matching yield

ATT to the tune of Rs. 83827.71 (USD 1676.55) and

Table 2 Propensity score estimation using probit model

Variable Coeff. SE z P [ |z|

Some schooling 0.265 0.459 0.58 0.564

High school 0.617 0.454 1.36 0.174

College 0.950 0.457 2.08 0.038

Experience -0.059 0.073 -0.81 0.418

Female owner -0.036 0.187 -0.19 0.846

Owner operated -0.595 0.147 -4.06 0.00

Based at home -0.193 0.117 -1.65 0.099

Voc. training -0.092 0.138 -0.66 0.507

Age of firm 0.080 0.061 1.31 0.19

Auto and metals 0.851 0.240 3.54 0.00

Electricals 0.551 0.285 1.94 0.053

Electronics 0.936 0.291 3.21 0.001

Chemicals 0.577 0.307 1.88 0.06

Food 0.385 0.251 1.53 0.126

Garments 0.657 0.239 2.75 0.006

Textiles 0.623 0.261 2.39 0.017

Delhi -0.656 0.140 -4.68 0.00

Punjab 0.904 0.126 7.18 0.00

Maharashtra -2.229 0.356 -6.25 0.00

Andhra Pradesh -0.178 0.154 -1.16 0.247

Constant -1.846 0.537 -3.44 0.001

Log likelihood -503.38

N 1,549

Wald v2 (20) 544.03

Prob [v2 0.000

Pseudo R2 0.351

Dependent variable is binary variable indicating firm

registration. Omitted category for education dummy is

illiterate, for industry dummy is leather, and for state dummy

is West Bengal

Table 3 ATT estimates using kernel and NN matching

Variable Estimator Sample Treated Controls Difference T-stat

Sales per employee Kernel Unmatched 510424.7 241827.7 268596.9 10.4

Matched (ATT) 511965 386224.2 125740.8 2.76***

Sales per employee NN Unmatched 510424.7 241827.6 268596.9 10.4

Matched (ATT) 511965 357600.8 154364.1 2.81***

Value added per employee Kernel Unmatched 213692.3 112567.8 101124.5 6.46

Matched (ATT) 215068.1 137935.9 77132.1 3.06***

Value added per employee NN Unmatched 213692.3 112567.7 101124.5 6.46

Matched (ATT) 215068.1 116866.4 98201.7 2.54**

ATT average treatment effect on the treated, NN nearest neighbor

Kernel based matching using Epanechnikov kernel and bandwidth derived using Silverman’s Rule of Thumb

***, **, * depicts significance at 1, 5 and 10 %, respectively

Table 4 Number of enterprises in the region of common

support

Support Untreated Treated Total

On-support 1,239 304 1,543

Off-support 0 6 6

Total 1,239 310 1,549

160 S. Sharma

123

Rs. 110268.63 (USD 2205.37), respectively. In terms

of value added per employee, kernel matching and NN

matching yield ATT to the tune of Rs. 33740.06 (USD

674.8) and Rs. 29279.71 (USD 585.59), respectively.

5 Discussion and conclusion

Using the PSM methodology that accounts for self-

selection, firm, owner and location characteristics, we

find remarkable gains for small urban manufacturing

enterprises from registering with the DIC. However,

surprisingly, there are a very low percentage of

registered firms in our sample and also in larger all-

India census conducted by the government. It would

be interesting to further investigate the reason for such

low levels of registration among microenterprises in

India. While a systematic analysis of the possible

channels that could explain this phenomenon is

beyond the scope of the current study, it is instructive

to discuss some possible reasons.

First, a crucial reason for non-registration is that

becoming registered brings one under the ambit of

government policy that means compliance with a host

of regulatory requirements such as labor laws and

environmental regulations. While registration with

DIC and with the tax authority are administered by

different agencies in India and the DIC does not verify

the tax-registration status of the firm, yet from a tax-

evading firm owner’s perspective, being registered

with the DIC exposes him to the risk of getting caught

evading taxes since tax registration and payment is

mandatory.

A second closely related reason for not registering

is the monetary and time cost of registration. For

example, smaller firms in Bangladesh are more likely

to say that the main disadvantages of registering were

paying taxes and dealing with the cost and process of

registering (McKenzie 2010). Similarly, De Mel et al.

(2011) found that for the case of Sri Lankan microen-

terprises, one of the reasons for low registration among

firms is the lack of adequate paperwork related to the

Table 5 ATT estimates for sub-groups using kernel and NN matching

Variable Treated Controls Sales per employee Value added per employee

ATT T-stat ATT T-stat

Female-owned

NN 15 100 60295.46 0.61 13020.16 0.22

Kernel 15 100 69042.17 0.82 23756.32 0.58

Not female owned

NN 288 1,139 169231.31 2.96*** 59329.78 1.87*

Kernel 288 1,139 130350.76 2.71*** 48376.18 1.86*

No paid labor

NN 24 208 700916.67 2.51** 234283.33 2.01**

Kernel 24 208 758129.40 3.3*** 282622.05 3.27***

Paid labor

NN 280 1,031 93062.82 1.67* 77122.02 1.84*

Kernel 280 1,031 75212.30 1.72* 61415.76 2.36**

Home-based

NN 46 262 79018.12 0.85 -27957.77 -0.39

Kernel 46 262 17156.76 0.25 -67836.24 -1.74*

Not home-based

NN 255 977 165519.75 2.61** 104438.03 2.52**

Kernel 255 977 148983.79 2.84*** 111334.27 3.9***

ATT average treatment effect on the treated, NN nearest neighbor


Bandwidths for kernel matching calculated using Silverman’s Rule of Thumb


123

Table 6 Testing equality of means before and after matching

Variable Sample Mean treated Mean control t test (t) p [ |t|

Illiterate Unmatched 0.003 0.032 -2.86 0.004

Matched 0.003 0.004 -0.09 0.928

Some Schooling Unmatched 0.126 0.262 -5.08 0

Matched 0.128 0.132 -0.15 0.883

Higher Secondary Unmatched 0.468 0.535 -2.13 0.034

Matched 0.477 0.530 -1.3 0.193

College Unmatched 0.403 0.171 9.09 0

Matched 0.391 0.334 1.47 0.142

Experience Unmatched 2.534 2.519 0.32 0.746

Matched 2.564 2.605 -0.71 0.481

Voc. Training Unmatched 0.103 0.128 -1.17 0.244

Matched 0.105 0.121 -0.62 0.536

Female Owner Unmatched 0.052 0.081 -1.74 0.082

Matched 0.053 0.056 -0.2 0.839

Owner operated Unmatched 0.077 0.168 -4.01 0

Matched 0.079 0.081 -0.11 0.913

Based at home Unmatched 0.158 0.211 -2.1 0.036

Matched 0.161 0.160 0.04 0.966

Age of firm Unmatched 2.480 2.052 6.82 0

Matched 2.475 2.559 -1.19 0.236

Auto and metals Unmatched 0.300 0.195 4.02 0

Matched 0.299 0.383 -2.19 0.029

Electricals Unmatched 0.058 0.080 -1.3 0.193

Matched 0.059 0.063 -0.19 0.846

Electronics Unmatched 0.071 0.052 1.33 0.184

Matched 0.059 0.049 0.58 0.564

Chemicals Unmatched 0.045 0.044 0.12 0.904

Matched 0.046 0.036 0.6 0.547

Food Unmatched 0.106 0.211 -4.21 0

Matched 0.109 0.098 0.44 0.659

Garments Unmatched 0.294 0.185 4.25 0

Matched 0.299 0.248 1.41 0.16

Textiles Unmatched 0.103 0.142 -1.79 0.073

Matched 0.105 0.100 0.21 0.833

Leather Unmatched 0.023 0.092 -4.09 0

Matched 0.023 0.023 0.03 0.976

Delhi Unmatched 0.123 0.295 -6.27 0

Matched 0.125 0.120 0.2 0.843

Punjab Unmatched 0.581 0.105 21.27 0

Matched 0.572 0.568 0.11 0.91

Maharashtra Unmatched 0.003 0.324 -12.05 0

Matched 0.003 0.011 -1.09 0.278

West Bengal Unmatched 0.181 0.144 1.63 0.104

Matched 0.184 0.192 -0.25 0.806

Andhra Pradesh Unmatched 0.113 0.132 -0.88 0.379

Matched 0.115 0.110 0.21 0.836

162 S. Sharma

123

leasing of land that they operate on. While there are no

official fees for registration in India, a standard

characteristic of developing countries is bribery and

unofficial payments that add to the pecuniary costs

(De Soto 1989). Before applying for registration, an

Indian MSME has to obtain statutory and administra-

tive clearances from several regulatory bodies. Also, in

India, since being recognized as an MSME is depen-

dent on the level of investment in plant, machinery,

equipment and the nature of output produced, firms are

required to inform the DIC within 30 days in the event

of a change in any of the above. Such requirements

create opportunities for rent-seeking by government

officials and may be perceived as burdensome by

firms, especially more so by firms that lie at the lower

end of the size distribution of small enterprises.

Finally, according to the third all-India census of

small-scale industries in 2002–2003, 53 % of unreg-

istered units cited ‘‘not aware of such a provision’’ as

the main reason for not being registered with the DIC

(Ministry of Small Scale Industries 2003). The second

most common reason cited by 40 % of the units was

‘‘not interested’’. Only 3.87 % cited ‘‘complicated

procedures’’ as a reason for non-registration. Based on

this large scale official data, it appears that informa-

tional asymmetry is a problem. Information problems

can be of the following types: first, firms have no

knowledge of such a scheme; second, the information

regarding the procedures, costs, short- and long-term

benefits may be incorrect or incomplete. Not knowing

the costs and the benefits of formality may lead the

firms to outweigh the costs and undervalue the benefits.

The main contribution of this paper has been to

highlight the notably large financial gains associated

with an important government policy in the Indian

context that has not been previously analyzed. Future

research should aim at understanding, first, reasons for

low registration levels despite significant gains and,

second, the channels through which the registration

scheme benefits the firm in terms of profitability, firm

survival and productivity growth.

Acknowledgments I thank Jeffrey Nugent, Saurabh Singhal,

Deepti Goel, Ashwini Deshpande and two anonymous referees

for detailed comments. All remaining errors are mine.

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