Working Paper No. 143
Investment Climate and Economic Performance: Some Firm Level Evidence
from India∗∗∗ ∗
by
David Dollar Giuseppe Iarossi Taye Mengistae
May 2002
∗ The findings and interpretations expressed in this paper are those of the authors and do not necessarilyreflect official views of the World Bank, the members of its Board of Executive Directors, or the countriesthey represent.
Stanford University John A. and Cynthia Fry Gunn Building
366 Galvez Street | Stanford, CA | 94305-6015
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1. Introduction
Arguably the most important development in the world economy in the past two
decades is that major developing countries, including the two largest (China and India),
have altered their strategies and begun to integrate more actively with the global
economy. Developing countries that are actively integrating are getting good results, on
average. In the 1990s the top one-third of developing countries, in terms of increased
trade integration, grew at about 5% per capita, while the rich countries grew at about 2%,
and the rest of the developing world had negative growth (Dollar and Kraay, 2001). This
group of countries that is participating more in trade includes India, China, Thailand,
Brazil, Mexico, the Philippines, and Argentina. It is also well known that capital flows to
the developing world are heavily concentrated in twelve countries, and it is very much
the same list of countries.
That the developing countries that are integrating with the global economy are
doing well on average disguises the fact that there is considerable variation in
performance among this group. China has done spectacularly well. Thailand was
growing very rapidly until its financial crisis, and now seems to be rebounding quickly
from that. Mexico is doing well with its growing integration with the U.S. economy.
Brazil, on the other hand, has only been growing at about 2%, and the Philippines, at 3%,
in the second half of the 1990s. India, with per capita growth of 4.4% in the late 1990s,
is about in the middle of the pack, for the large, globalizing economies. Furthermore,
there is large variation in performance across locations within these countries. Chinese
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coastal areas are booming, while much of the interior is not. In both Mexico and India,
there are rapidly growing states and stagnating ones.
The basic question that we address in this paper is to what extent differences in
performance across locations depends on differences in the investment climate – by
which we mean the regulatory environment for firms and the quality of infrastructure,
which itself traces back to a large extent to public regulation? Also, if the investment
climate is important, what are the specific aspects of the investment climate that are the
main bottlenecks? We are going to address these questions using a new, firm-level data-
base from India. The survey covers 1,032 firms from the manufacturing sector, as well as
the software industry. The survey was carried out by the Confederation of Indian
Industries and the World Bank and covers ten Indian states: Maharashtra, Gujarat,
Andhra Pradesh, Karnataka, Tamil Nadu, Punjab, Delhi, Kerala, West Bengal, and Uttar
Pradesh.1
It is useful to begin by discussing in more detail what we mean by the
“investment climate.” The quantity and quality of investment in India or any other
developing country depends on the returns that investors expect and the uncertainties
around those returns. It is useful to think of three broad and interrelated components that
shape these expectations.
1 The selection of states was based on three principles. The first was that the sample should be spread asmuch as possible between high-income states, middle-income states and poorer states. Secondly, thesample should represent what, at the time of the survey, were seen to be reforming states as well as stateswhose policy environment was not thought to be so friendly to business. The third principle was that a stateshould have a sizable number of establishments in at least three of the industries the survey was intended tocover. Based on the World Bank’s classification (World Bank, 1999), the states of Delhi, Maharasthtra,Gujarat, Punjab and West Bengal represent the high-income group of states. Uttar Pradesh is a low-incomestate while Andra Pradesh, Karnataka, Kerala and Tamil Nadu represent middle income states. Per capitaincome averaged Rs 4377 in high-income states in 1996-97 at 1980 prices against Rs 2676 in middle-income states and Rs 1840 in low-income states.
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First, there are macro or country-level issues concerning economic and political
stability and nationwide policy toward foreign trade and investment. Here, India has
clearly improved its policies over the past decade; macro-level economic reforms
contributed to the high growth of the 1990s. Relative macroeconomic and political
stability, trade liberalization plus further commitments within the WTO — these
comprise one crucial set of ingredients to spur investment and productivity growth.
These are national level and hence held constant across Indian states.
But creating a good climate for investment involves two other factors as well: (1)
the policy and regulatory framework for investment and production and (2) basic
infrastructure (power, transport, telecommunications). It is common for developing
countries to start with the macro reforms, which often produce good results compared to
past performance. However, if one does not move ahead on the institutional and
infrastructure agenda, the growth generated by macro reform is likely to peter out. It is
now broadly recognized within India that the country has reached a crucial point where
the challenge is to move forward on the institutional and infrastructure agenda
(Ahluwalia, 2001).
The policies and regulations for firms in competitive industries cover the issues of
entry (starting a business), labor relations (hiring and firing), efficiency of taxation, and
efficiency of regulations concerning the environment, safety, health, and other legitimate
public interests. These issues are regulated in all market economies, so the issue is not
whether to regulate or not. Rather, the issue is whether regulations serve the public
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interest and are implemented efficiently without harassment and corruption.2 While these
things are hard to measure, there is evidence that we will present suggesting that the
efficiency with which production is regulated varies widely across Indian states. Our
hypothesis is that in locations with good investment climates you get more investment,
more efficient investment, and ultimately more job creation and poverty reduction.
The third key aspect of the investment climate is infrastructure, broadly defined.3
When one surveys entrepreneurs about their problems and bottlenecks, they will often
cite infrastructure issues such as power reliability, transport time and cost, and access and
efficiency of finance. It is useful to recognize that the underlying issue here is once again
policies and regulations and how they are implemented. The distinctive feature of the
infrastructure industries and the financial sector is that there are important externalities in
production so that regulation of these industries is more complicated than, say, regulation
of the garment industry. The garment industry is inherently competitive, especially in an
economy open to the world market. For power distribution, surface telephone service,
seaports, and airports, there is, on the other hand, a tendency toward local monopoly. In
the financial sector there is also a trend toward large firms that can diversify risk. There
2 In India, as in many other developing countries, business people complain that enforcement is toodiscretionary, which is a source of corruption and harassment (Ahluwalia, 1999). The cost that businessesincur in consequence seems also seems to vary considerably between Indian states.3 There is a well known and sizeable body of literature on the role of public provision of infrastructure inaggregate growth and productivity. Quite a few papers report that publicly provided infrastructure has beena major source of growth in developed and developing economies alike (e.g., Aschaure, 1989; Brendt andHansson, 1992; Munnel , 1990; Nadirir and Mamuneas, 1994; and Morrison and Shwartz, 1996) whileothers contest this view arguing that the effect of infrastructural investment on aggregagte output is at bestnegligble (e.g., Hulten and Schwab, 1991; Easterly and Rebelo, 1993; Holtz-Eakin, 1994; and Barro andSala-I-Martin, 1995). One of the causes for this ambiguity of findings seems to be dependence of thestudies on the estimationg of aggregate production functions in the context of which the problem of reversecausality on income levels and infrastructure inevitabily arises and is difficult to resolve. One of theadvantages of the use of firm level data is that the provision of infrastuructural services can reasonablyassumed to be exogenous from the perspective of any particular producer. See Reinika and Svenensson(1999) for an earlier study of the impact of public provision of infrastructure on private investment basedon establishment level data.
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are further problems of moral hazard and adverse selection, so that financial markets left
unregulated tend toward boom and bust cycles that are extremely disruptive.
Because of the obvious importance of infrastructure and the financial sector,
many developing countries have tried to keep these services in the public sector. Results,
however, have typically not been good. Public ownership has often led to pricing below
cost so that the provision of these services put huge burdens on the public coffers.4 In
addition, political considerations have come into the allocation of finance and other
subsidized services, and these political allocations are usually not the most efficient ones
in terms of promoting growth and poverty reduction. Experience shows that, with the
right regulatory framework, one can get private investors to efficiently provide power,
telecommunication, and much of transport infrastructure (seaport and airport operations,
for example). Organizing these markets is not easy, however (witness the problems of
highly developed California, which made something of a mess of power deregulation and
in 2001 faced the same kind of power rationing that one often finds in less developed
locations). So, we observe large differences across locations in the quality of
infrastructure services.
Our investigation of the importance of the investment climate for outcomes in
India will proceed as follows: In the next section we look at entrepreneurs’ subjective
4 For example, in India, power generation and distribution was a monopoly of government ownedenterprises under State Electricity Boards (SEBs) up until the early 1990’s. For a long time now, SEBshave followed a deliberate policy of under pricing the supply of electricity to households and farms, onlypart of which they have managed to pass to industry through tariffs well above cost and international ratesThis and the boards’ growing failure to protect transmission and collect bills, has led to serious underinvestment in maintenance and capacity. Recent effort at attracting private investment as a solution includethe opening up of generation and distribution to private capital and the unbundling of SEB’s intoindependent commercial agencies specializing in generation, transmission or distribution only. In moststates these efforts have yet to bear fruit partly because of the absence of a regulatory framework in whichpotential investors have confidence. Meanwhile growing pressure on the existing productive capacity of the
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assessment of the investment climate in different states and their estimate of the costs of
these impediments. We recognize that this information is subjective, but it provides a
benchmark ranking of states in terms of investment climate. In Section 3 we provide our
analytical framework for estimating differences in productivity across firms and for
linking productivity differences to objective measures of bottlenecks. Section 4 describes
our data. In Section 5 we show that the states identified as having better investment
climates in the subjective assessments, in fact have higher labor and total factor
productivity at the firm level. The TFP differences are very close to the estimated cost
differentials that we obtained from the entrepreneurs. Finally, in Section 6 we show that
the productivity differentials can be linked to specific objective bottlenecks such as
reliability of the power supply, the frequency of visits from government officials, time to
clear goods from customs, and other measures. Section 7 concludes.
2. Entrepreneurs’ Perception of Investment Climate
Our data come from the Firm Analysis and Competitiveness Survey (FACS) of
India, which was carried out in March-November 2000, and covered eight of India’s
main export industries, namely, textiles, garments, pharmaceuticals, electronics, electrical
white goods, auto-components, machine tools and software production. The survey
instrument was a written questionnaire addressed to business managers and accountants.
It was designed with the aim of capturing the interaction between investment climate and
business performance through separate but complementary modules on production
technology, finance, business organization, indicators of economic environment, and
sector has meant more and more erratic supply to which industrial users have been responding through owngeneration.
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managers’ perception of the same environment. One of the questions in this last module
asked respondents to identify states that they thought had a better or worse business
climate than their own state. Which state did a manager think had the best investment
climate? Which had the worst climate? The percentage of respondents who identified a
state’s investment climate as better or worse than that of their current state is shown in
Figure 1, as is the percentage of those who identified the same state as their ‘best-climate’
or their ‘worst-climate’ state.
Maharashtra comes out as the most favored state on any of these indicators of
perception of investment climate, while the state of Uttar Pradesh is shown as the least
favored. The other states are ranked in between these extremes in the order shown in the
figure. Managers were also asked to give their best estimate of the cost advantage5 of
operating in what they regarded as the ‘best-climate’ state and of the cost disadvantage6
of operating in their ‘worst-climate’ state. Yet another index of the relative quality of the
investment climate of a state is therefore the average cost advantage of the state
according to those who thought it had the best investment climate less its cost
disadvantage according to those ranking it as the worst state. We plot this index in
Figure 2 against the difference between the percentage of those who thought the state had
a better investment climate than their own and the percentage of those who thought it had
a worse investment climate. The figure brings out the states of Maharashtra, Gujarat,
Andra Pradesh, Karnataka, and Tamil Nadu, as a cluster of relatively ‘good-climate’
states, that is, as states which a significant proportion of our respondents thought had
5 This is the figure that managers gave to the following survey question. ‘By what percent would your costof production be cut if you were based in [the best] state?’6 This is the figure that managers gave to the following survey question. ‘By what percent would your costof production rise if you were based in [the worst] state?’
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substantial cost advantage over other states. By the same criterion the states of Uttar
Pradesh, West Bengal and Kerala come out as relatively ‘poor-climate’ states. The states
of Punjab and Delhi fall somewhat midway between these two groups and, in this sense,
are ‘medium-climate’ states to our respondents. The perceived cost differential between
poor-climate states and good-climate ones is about 30%, which is quite substantial.
Figure 3 shows that these subjective ratings are broadly consistent with the
investment decisions of respondents. Measured on the vertical axis of the diagram is the
average rate of net fixed capital formation as computed from the FACS data for the years
1998 and 1999 while controlling for initial capital stock, sector of activity, initial capital
intensity and initial debt-to-equity ratio. Although the correspondence between the line
up in Figure 3 and the ratings of Figure 1 somewhat breaks down in the bottom half of
the diagrams, the pattern that the rate of investment is far higher in ‘good-climate’ states
than in the ‘poor-climate’ states is evident.
That regional patterns in the average rate of net business fixed investment tally
fairly well with managers’ rating of states gives us a measure of confidence that the
ratings accurately reflect the perceptions of investment climates by our respondents. But
how realistic are these perceptions? The rest of the paper will address this question in two
steps. First, we will compare the average productivity of firms across states and see
whether or not the pattern conforms to managers’ ratings. Next, we will use our data to
test if objective indicators of the investment climate do adversely affect productivity.
3. Analytical Framework
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The simplest measure of the productivity of a business establishment is value
added per worker. It is determined partly by the capital intensity of production, partly by
the skill of the workforce, and partly by the firm’s overall productivity, that is, by total
factor productivity (TFP). We assume throughout the paper that the details of this
technological relationship are adequately approximated by the specification
(1) ititititit LKAY εαα 21=
where the subscripts i and t refer to establishment and time of observation
respectively,Yis annual value added, K is capital services per year, L is the
corresponding quality-adjusted labor input, A measures total factor productivity, itε is a
unit-mean plant-specific error component that is orthogonal to K , L , and A . Let N be
the number of man-years of labor input in the business. Hall and Jones (1999)7, propose
the quality-adjustment rule
(2) ititit NhL )(expφ=
where h is a vector of human capital variables such as years of schooling and years of
experience of the work force, and (.)φ is the Mincerian earnings function (Mincer, 1974).
Let W be the annual wage bill of the business. Suppressing its stochastic dimension, the
Mincerian earnings equation is that
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(3) )(exp itit hw φ=
where NWw /≡ is the average wage rate in the business. This means that we can use the
average wage rate of a business as a measure of the average skill-level of its workforce.
This specification assumes that the labor market is well integrated across India, and we
will consider later what it means if that assumption is not valid. With that assumption,
we can then rewrite equation (1) as
(4) ititititit wkAy εαα 21=
where NYy /≡ is value added per worker and NKk /≡ is capital per worker.
As it stands equation (4) is an establishment-specific and time-specific production
function. We expect that part of inter-firm differences in total factor productivity reflect
plant-level heterogeneity in technology and firm capability, but that part of it may reflect
regional differences in investment climate as well. We are going to estimate equation (4)
across firms, and try to explain some of the difference in plant-level TFP by investment
climate indicators.
4. Data
Although 1032 establishments were covered by FACS-India, 90 of these were
from the software industry, which we have excluded from the data set used for estimating
productivity equations. Of the remaining 942 establishments, there are 731 for which we
7 See also Bils and Klenow (1998)
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have observations on the full range of variables of production technology and investment
climate used in the present analysis. Production input and output figures were collected
for the years 1997 to 1999 with an average of two figures per establishment for the 731
businesses, which gives us a total of 1451 observations for the estimation of the
productivity equations.
Table 1 gives the definition and notation of variables used in the estimation of
productivity equations. As the dependent variable of each of these equations, annual
value added per worker is denoted as ‘yovern’ in the table and is measured in thousands
of Rupees. By value added we mean total annual sales plus annual change in the stock of
finished goods and work-in-progress less annual consumption of materials and utilities.
The basic right-hand-side variables of the equations are denoted as ‘kovern’ and
‘lnwrate’. The first is defined as the log of the net book value of plant and equipment in
thousands of Rs divided by the number of employees at the end of the year. This is a
measure of capital stock per worker, and not of capital services per worker. We therefore
instrument for it in the production function with the log of consumption of energy per
worker. The instrument is also in thousands of Rs and is denoted ‘eovern’ in Table 1.
The variable ‘lnwrate’ is the log of the annual wage bill--again in thousands of Rupees-
divided by the number of employees at the end of the fiscal year.
All Rupee values are at current prices. Partly as a means of dealing with this and
partly in order to capture time effects in productivity, we include year dummies among
the right-hand-side variables of the estimated productivity equations. The base year is
1999. The assumption that the same technology characterizes all sectors may not be
realistic. We therefore replace it with the weaker assumption that the estimated
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production function is identical across sectors up to the factor A. We do this by including
industry dummies among the regressors in the productivity equations. The base sector is
textiles, with a dummy each for garments, electronics and pharmaceuticals.
Table 1 also includes notation for objective indicators of certain aspects of the
investment climate, namely, the quality of physical infrastructure, labor regulation, other
aspects of industrial regulation, and customs administration. Table 2 gives descriptive
statistics of these indicators along with those of other production function variables. All
indicators of investment climate are measured as logarithms of means of state level
figures. The use of state level means enables us to avoid the simultaneity bias that could
arise if we used establishment level figures. The importance of different aspects of
climate in the Indian context will be discussed later in the paper. The variable ‘lmowpshr’
is the log of the state level mean of the share of own-supply in consumption of
electricity. The less reliable is power supply from the public grid the higher is the share
of own-generated electricity. In this sense, ‘lmowpshr’ is our indicator of the quality of
power supply in a region. The variable ‘lmemail’ is the log of the percentage of
establishments that contact customers via email, and is our indicator of internet
connectivity or, more broadly, of the quality of telecommunications services in a region.
The variable ‘lmfreqvs’ is the log of the state level mean of the number of regulatory
visits government officials made to a plant during the fiscal year 1999. This is our proxy
for the extent of government regulation of business activities. We proxy the extent of
government regulation of labor relations by the variable ‘lmovermn’, which is the log of
the state level mean of reported overmanning at the time of the survey. The variable
‘lmdclear’ is the log of the state level median of the number of days that it took the last
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consignment of imported inputs of an establishment to clear customs. This is our proxy
for the quality of customs administration.
5. Investment Climate and Firm Productivity
Table 3 reports the estimation of the logarithmic transformation of the production
function. The error term itε is assumed to have a random establishment effect that is
assumed to be uncorrelated with any of the regressors. Estimation was carried out by
generlized least squares, instrumenting ‘kovern’ with ‘eovern’. Focusing on the first
column, results of the estimation of equation (4) suggest a factor share of .38 for capital,
which is very much in line with estimates reported in other studies in which a Cobb-
Douglas technology is assumed. With the exception of the dummy for the pharmaceutical
industry none of the year or sector dummies has a statistically significant coefficient.
The second column of Table 3 adds to the regressions indicator variables for the
good climate and poor climate clusters of states (keeping the medium as the reference
group). We recognize that these were subjective classifications, and we are merely
inquiring whether average TFP varies across the groupings. What we find in column (2)
is that TFP is about 26% lower for the average firm in the poor climate states, compared
to the average firm in the good climate states (a difference that is strikingly similar to the
roughly 30% cost differential that entrepreneurs estimated).
Regional gaps in value added per worker can of course be directly read from the
raw data of Table 2. The parameter estimates of the production function can then be used
to see how these gaps decompose into differences in total-factor-productivity, gaps due to
differences in average skill levels, and gaps due to differences in capital per worker.
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Given our assumption, consistent estimates of regional gaps in value added per worker
due to differences in capital intensity are given by the product of the estimated share of
capital 1α and regional gaps in k . Gaps in value added per worker caused by differences
in skill intensity are consistently estimated by our estimate of 2α times regional gaps
in w.
Average value added per worker and its components are given in Table 5 for
good-climate states and poor-climate states as proportions of corresponding figures for
medium climate states. The table corroborates entrepreneurs’ ratings of regional
investment climates as far as the identification of poor-climate states is concerned.
Although value added varies little between the good-climate states and the medium
climate states, labor productivity in poor-climate states is about 45 percent lower than
that in good-climate states. Practically none of this gap is due to regional differences in
capital per worker, of which there are practically none (Table 2). On the other hand, it is
clear that the gap has a lot to do with regional differences both in average skill levels and
in total factor productivity. Average skill levels as measured by the mean wage rate are
28 percent lower in poor-climate states than they are in good-climate states. Using our
estimates of the share of labor in value added, this translates to a 17.5 percentage-point
shortfall in value added per worker of poor-climate states on account of lower skills.
Large as this is, it is significantly less than the 26 percentage-points shortfall associated
with lower TFP in poor-climate states.
But why should workers be less skilled in poorer climate states in the first place?
Our best explanation of this is there is significant labor mobility within India, especially
for the kind of formal sector workers captured in our survey. The states of Kerala and to
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a lesser extent West Bengal may have done a good job developing human capital, but if
investment opportunities are constrained, then many trained people will leave for where
job and wage growth are higher. Anecdotally, there has been a lot of migration out of
Kerala to other parts of India and the world.
It should also be noted that if labor mobility is not very good among Indian states,
then the assumption that wage differences reflect skill differences would not be valid. In
the extreme case of no labor mobility, higher TFP of firms would translate into higher
wages in good climate states for the same set of skills, than is paid in poor climate ones.
If that were the case, then our estimate of TFP differences would be an under-estimate.
6. Identifying Specific Impediments in the Investment Climate
We have now established that entrepreneurs perceive variations in the investment
climate among Indian states and that firm-level labor productivity and total factor
productivity in fact is higher in “good climate” states than in “poor climate” ones. The
last issue that we take up is whether we can pinpoint some of the specific problems that
make for a poor investment climate and estimate the impact of these bottlenecks on
productivity. The FACS-India survey included a number of objective measures of
aspects of the investment climate. These are reported in table 2 for the good climate,
medium climate, and poor climate states.
Power reliability. One of the important problems that firms face is the
unreliability of the public power grid. Outages and fluctuations are common. These
problems vary across states. The result is that in states with serious power problems,
virtually all firms, including small ones, have their own generators. Producing power on
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such a small scale is inefficient and expensive. We have from the survey information on
the amount of power that firms get from the public grid and its cost, and the amount that
they get from own-generation, and its cost. From this we can calculate the actual average
cost of power that firms pay. It can be seen in table 2 that this average cost is more than
25% higher in poor climate states, compared to good climate ones. This results from the
reliance on own generation and the high cost of own generation in these locations.
Government regulation. India is well known for having a bureaucratic
environment in which there is a lot of regulation of production. Obviously some
regulation is socially important, but excessive regulation can deter investment and
production without serving a public good (and be a source of corruption as well). We get
at this issue by asking about the average number of visits per year by government
regulators (not including tax officials). The number of visits in poor climate states
averages about twice the level as in good climate ones. Also, we found that the number
was about the same for small firms and large ones. One visit per month imposes a large
cost on a small firm in which the owner is the main managerial and technical labor.
Customs administration. Customs administration is a federal responsibility and
thus is not likely to vary in efficiency among states. Nevertheless, it is an important
aspect of the investment climate, since long delays and the unpredictability of deliveries
of inputs associated with poor customs administration could force businesses to maintain
sub-optimal inventory levels. We therefore included questions about how long it took the
last shipment of imports to clear customs and how long was the longest delay in the past
year. On average, the last delay was more than ten days, and the longest delay of the past
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year about three weeks. These figures do not vary to any large extent among states,
which is what one would expect given that customs are under federal control.
Connectivity. We were interested in the role of the internet and asked about the
use of email in conducting business. In some states, the typical small firm is using email
to deal with customers. This usage varies a lot by state, and we think that it is more
likely that this reflects differences in state telecom policies (rather than the fact that all
the smart entrepreneurs are clustered in certain states). Delhi has good connectivity, so
that this is one area in which the medium states (including Delhi) look best. Again, the
poor climate states are at the bottom, with only a third of firms using the internet.
Labor regulations. India has very restrictive regulations concerning labor
redundancy. 8 At the time this survey was conducted, the federal law stipulated that a
plant with more than 100 workers could not make any labor redundant without the
permission of the state government. In practice, this permission is rarely granted in some
states. To get at the potential problem of this regulation, we asked entrepreneurs how
much of their labor force they would make redundant if they were not constrained by
8 This has often been singled out as one of the reasons why India is not doing as well as it should in termsof the growth of its exports (eg., Sachs, Vashney and Bjpai, 1991). The legal basis for the regulation isencoded in the employment security provisions of the Industrial Disputes Act of 1947, the ‘service-rules’provisions of the Industrial Employment Act of 1946 and the provisions of the Contract Labour (Abolitionand Regulation) Act of 1970. The Industrial Disputes Act sets out the rules for settlement of employmenttermination disputes. One of its main provisions requires establishments employing more than 100 peopleto seek the permission of the state government for closure or the retrenchment of workers, whichpermission, critics point out, is rarely granted (Sachs et al, 1999). The Industrial Employment Act providesfor the definition of job content, employee status and area of work by state law or by collective agreement,after which changes would not be made without getting the consent of all workers.8 Zagha (1999) pointsout that this has always made it difficult for businesses ‘to shift workers not only between plants andlocations, but also between different jobs in the same plant.’ One way out of such restrictions would seemto be for businesses to resort to contract workers, which is where the Contract Labour Act comes in. Thislaw gives state governments the right to abolish contract labor in any industry in any part of the state. Instates where recourse to contract labor has been more restricted as a result, keeping employment below thethreshold level of 100 employees or contracting out jobs has been the only way of maintaining flexibility inthe allocation manpower.
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regulation (but still had to pay the statutory redundancy compensation). The average
figures were fairly consistent across the different types of states: firms would lay off 16-
17% of their labor force if there were more labor market flexibility. This finding points
up an important adjustment issue for India. These regulations hamper domestic and
foreign investment and in particular make firms cautious about taking on new workers.
With more flexible labor market policies, there should be more investment and job
creation. However, in the short run there will clearly be net layoffs with a more flexible
policy, and managing that adjustment is an important issue for India. (The Union
government has proposed that the labor law be changed so that plants with fewer than
1000 workers could make labor redundant, but that at the same time the compensation
package be improved to 45 days of pay per year of service compared to the present 15
days of pay. It remains to be seen if these proposals become law.)
To get some sense of the impact of these bottlenecks on investment and
production, we include them in the production function estimation (table 3). In column
(3) we include all of the bottleneck variables without the state dummies. Despite some
correlation among the bottlenecks, four of the five enter the equation significantly, and all
five have the intuitive sign: high productivity is associated with using the internet to
conduct business, low power cost, fewer days to clear goods through customs, fewer
visits from government regulators, and less constraint from the labor regulations (in the
sense of not having more workers than is really desired). (The one bottleneck for which
this relationship is not statistically significant at conventional levels is the frequency of
government visits.)
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We can also get some sense of the extent to which these variables exhaustively
describe the problems in the investment climate by also including in the regression the
dummies for good climate states and poor climate ones (column 4). Without the
bottlenecks included (column 2), the productivity gap between poor states and good ones
is 26 percent. With the bottlenecks included, the gap is not statistically different from
zero. So, the bottlenecks identified here explain the bulk of the productivity gap between
the types of states. We hasten to add that the coefficients on the specific bottlenecks in
column 3 need to be interpreted with caution. If other important bottlenecks or
determinants of productivity have been omitted and are correlated with the included
bottlenecks, then the coefficients on the specific bottlenecks could be biased. With that
caveat in mind, it is nevertheless useful to look at the magnitude of the coefficients to see
if they are economically important. In Table 6 we use these coefficients to examine the
extent to which the productivity difference between poor-climate states and good-climate
ones can be explained by the bottlenecks. We find that the better power situation in the
good climate states accounts for 3 percentage points of productivity difference; frequency
of government visits, another 3 points; and better internet connectivity, 4 points. The
“bite” of the labor regulations, on the other hand, appears to be more severe in the good
climate states. Our interpretation of this is that the good climate states have more
competitive environments, including entry from foreign investors, and that in this
competitive environment the labor regulations are more of a drag on firms than in the less
competitive, poor climate states. As noted, take the point estimates with some
skepticism, because of all of the usual problems with econometric estimates
(measurement error, omitted variables); still, there is a consistent story of entrepreneurs
20
perceiving some states to have better investment climates; firms in those states actually
having higher productivity after controlling for industry, size, and capital and labor
inputs; and the different types of states having measurable differences in objective
indicators such as reliability of power, visits by government officials, and connectivity to
the internet.
The productivity difference that we have looked at so far in this section is total
factor productivity: good climate states get more value added from a given amount of
capital and quality-adjusted labor. However, since we have seen that entrepreneurs
correctly perceive these differences, it is likely that there is more investment in plants in
good climate states. Also, to the extent that there is labor mobility within India, better
quality workers will migrate to the good climate states where there are better
opportunities.
So, it is also useful to introduce the bottleneck variables into a reduced form
productivity equation (table 4). In column 2 we do this without the dummy variables for
states. Now, all five bottlenecks have coefficients with the intuitive sign and statistical
significance at the 1% level. Without the bottleneck variables, the productivity difference
between poor climate states and good climate ones in column 1 is 42 percent. (The fact
that the labor productivity difference is more than 40 points whereas the TFP difference
was 26 points confirms that capital and high-quality labor tend to migrate to the good
policy states.) If we include both dummy variables for type of state and the bottleneck
variables (column 3), the gap is no longer statistically significant. The point estimates
suggest that the better power supply in good-climate states accounts for 3 points of
21
productivity difference; fewer government visits, 3 points; and better connectivity 4
points.
7. Conclusion
It is clear from our data that the investment climate varies significantly across
India’s states. Business managers rate the investment climates of Uttar Pradesh, West
Bengal and Kerala as relatively ‘poor’, those of Maharashtra, Gujarat, Andra Pradesh,
Karnataka and Tamil Nadu as relatively ‘good’, with the states of Punjab and Delhi
somewhere in between. Although the ratings are subjective, they are also clearly driving
the investment decisions of managers. Controlling for initial capital stock, initial capital
intensity, initial debt to equity ratio, and sector of activity, the average rate of annual net
fixed capital formation is four times larger for businesses sampled from the good-climate
states.
The ratings are also broadly realistic, as regional patterns in firm productivity
show. Thus, we find that value added per worker is 45 percent lower in poor-climate
states than in good-climate states. Despite the fact that investment rates are several times
higher in good-states this gap does not seem have anything to do with differences in
capital per worker. On the other hand, approximately one-third of the gap is due to good-
climate states producing or attracting better quality workers. The balance of the gap in
labor productivity reflects lower TFP in poor-climate states.
We trace the TFP gap itself to regional differences in such objective indicators of
investment climate as the reliability of the power supply, the ease of connectivity to the
internet, the frequency of government visits to factories, the efficiency of customs
22
administration, and the rigidity of labor market regulations. Less reliable power supply
and inferior internet connectivity in poor-climate states account for more than a quarter of
the TFP differences. More than a tenth of the differences reflect greater regulatory burden
in the same states. The TFP disadvantage of poor-climate states would have been even
higher if labor market rigidity had not been more of a drag on productivity in good-
climate states.
These point estimates need to be taken with some caution, given the ambiguity of
the meaning of coefficients of proxies and the real possibility of omitted-variable biases.
They nevertheless provide a broad indication of the large gains in productivity and
investment that could be achieved in Indian states by improving regulation of firms and
the regulatory environment for infrastructure provision. Overall, this work shows the
importance of complementing India's national level policies -- a stable macro
environment and growing openness to foreign trade and investment -- with good
institutions and policies at a more local level so that efficient investment is attracted and
jobs created. A natural next step in this research is to link the variations in the investment
climate to differences in employment creation and poverty reduction across Indian states.
23
Appendix A: Sample Design of FACS-India
The seven manufacturing industries covered in the FACS of India account for about 40%
of aggregate manufacturing value added in India and over half of manufactured exports.
The focus on exporting industries arose from the survey objective of linking local
investment climates to the international competitiveness of producers. The selection of
states was based on three principles. The first was that the sample would represent states
at different levels of development, as measured by per capita GSP. The second was that
states whose investment climates were perceived to be better as indicated by, for
example, share in foreign direct investment (FDI) flows should be covered as well as
those whose economic environment was not thought to be as friendly to business. The
third principle was that each survey state would have a significant number of producers in
at least three of the industries on which the survey was intended to focus.
Of the ten states covered by the survey Delhi, Gujarat, Maharashtra, Punjab and West
Bengal are high-income states according to a recent classification by the World Bank
(1999). Uttar Pradesh is a low-income states. Andhra Pradesh, Karnataka, Kerala and
Tamil Nadu are middle-income states. Six of the 10 states attracted practically all of the
FDI flows to India in 1997-98, which we think is a measure of the contrast of their
investment climate with that of the other four, at least as perceived by the business
community at the time.
24
Our sample frame was drawn from the 1998 edition of the Kompass database for India.
This listed 67,000 businesses, from which we excluded establishments employing less
than 20 workers. This and the restriction on lines of activity and states of location led to
an effective sampling frame of 6074. The intended sample size was 1200, of which 48
were allocated to the auto components and machine tools industries for purposive
sampling towards an international benchmarking exercise. The balance was allocated
among the other six industries roughly in proportion to shares in India’s manufacturing
and service exports. Each industry sub-sample was randomly selected according to a rule
in which an establishment’s probability of selection was higher the higher was its
employment size.
25
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26
Sachs, J. D., A. Varshney and N. Bajpai (eds). 1999. India in the Era of EconomicReforms. New Delhi: Oxford University Press.
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27
Figure 1. Investment climate:perception of firms outside the state
UttarPradesh80
60
40
20
0
20
40
60
80Percent of respondents
BetterBest
WorseWorst
Delhi
Kerala
AndhraPradesh
GujaratKarnatakaMaharashtra
PunjabTamilNadu
WestBengal
-20
-10
10
-100 -50 50 100
% ranking better-% ranking worse
Net%
cost
savin
gby
mov
ing
to...
Maha
GujaratAP
Karna
TN
DelhiPunjab
KeralaWB
UP
Figure 2. Rankings of investment climate
28
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
Figure 3. Inter-state gaps in mean rate of netfixed investment
UttarPradesh West
Bengal
Kerala
PunjabDelhi
TamilNadu
Karnataka AndhraPradesh
Gujarat Maharashtra
29
Table 1: Notation
Notation Variable
yovern log annual value added per worker in 000 Rs.kovern log of net book value of plant and equipment per worker in 000 Rs.lnwrate log annual wage bill per worker in '000 Rseovern log annual energy bill per worker in '000 Rslnn log of number of employees at the end of the yearGarments dummy=1 for business in the garments industryDrugs dummy=1 for business in the pharmaceuticals industrElectrog dummy=1 for producers of electronics or of electrical white goodsmowpshr state level mean of percentage share of power supply from own generatorsmfreqvis state level mean of annual number of regulatory visits by government officialsmdclear state level mean of number of days it took the last consignment of imports to clear customsmoverman state level mean of reported percentage of overmanningmemail state level proportion of establishments that use the internet to communicate with customers
Table 2: Descriptive Statistics
All Good-climate states Medium-climate states Poor-climate statesVariables Mean Std. Dev Mean Std. Dev Mean Std. Dev Mean Std. Devyovern 5.19 1.19 5.29 1.21 5.12 1.19 4.84 0.99kovern 5.12 1.54 5.26 1.50 4.92 1.70 4.74 1.36eovern 2.60 1.45 2.66 1.52 2.42 1.40 2.61 1.14lnwrate 3.63 0.98 3.68 1.04 3.63 0.87 3.40 0.82lnn 4.17 1.62 4.42 1.64 3.78 1.54 3.56 1.40mowpshr 25.68 9.04 22.55 4.91 30.93 2.63 31.92 18.88moverman 16.94 8.76 18.02 10.38 15.33 0.47 14.47 6.43mfreqvis 10.78 14.35 8.19 2.56 17.03 28.85 12.68 1.68mdclear 8.27 2.47 8.37 1.98 6.71 0.71 10.51 4.15memail 0.45 0.14 0.44 0.14 0.59 0.05 0.31 0.06Drugs 0.28 0.45 0.29 0.45 0.16 0.37 0.44 0.50Electro 0.15 0.36 0.15 0.36 0.22 0.41 0.03 0.17Garments 0.27 0.45 0.24 0.43 0.34 0.47 0.31 0.46
Obs. 1451 931 328 192
30
Table 3: GLS Estimation of Random Effects Speficifcations of Productivity Equations***Dependent variable =log of value added per worker
SpecificationVariable (1) (2) (3) (4)
kovern 0.375 0.381 0.402 0.409(9.27)** (9.45)** (9.89)** (10.00)**
lnwrate 0.628 0.620 0.606 0.603(20.74)** (20.47)** (19.96)** (19.79)**
lnn 0.048 0.039 0.028 0.026(2.79)** (2.22)* (1.61) (1.46)
year98 0.042 0.041 0.040 0.040(1.70) (1.67) (1.64) (1.62)
year97 0.051 0.052 0.050 0.049(1.27) (1.30) (1.25) (1.22)
Drugs 0.225 0.251 0.253 0.237(3.03)** (3.38)** (3.39)** (3.16)**
Electro -0.024 -0.048 -0.077 -0.085(0.27) (0.53) (0.86) (0.96)
Garments -0.082 -0.077 -0.084 -0.088(1.07) (1.01) (1.10) (1.15)
goodic 0.0002 0.214(0.00) (1.67)
pooric2 -0.2599 0.287(2.63)** (1.50)
lmdclear -0.594 -0.883(2.91)** (3.28)**
lmemail 0.299 0.449(2.68)** (2.97)**
lmfreqvs -0.085 -0.176(1.48) (2.14)*
lmovermn -0.416 -0.626(3.03)** (3.36)**
lmowpshr -0.192 -0.148(3.85)** (2.38)*
Constant 0.729 0.792 3.167 4.561(3.68)** (3.92)** (3.85)** (3.88)**
R-squaredbetween 0.59 0.60 0.61 0.61overall 0.56 0.60 0.58 0.58rho 0.74 0.73 0.73 0.73
Observations 1451 1451 1451 1451Number of estbs. 731 731 731 731Absolute value of t-statistics in parentheses* significant at 5% level; ** significant at 1% level
*** Capital services instrumented by energy consumption
31
Table 4: OLSEstimationof RandomEffects Speficifcations of Productivity Equations
Specification
Variable (1) (2) (3)
kovern
lnwrate
lnn 0.099 0.093 0.086
(3.87)** (3.66)** (3.37)**
year98 -0.007 -0.008 -0.009
(0.26) (0.31) (0.33)
year97 -0.110 -0.099 -0.099
(2.56)* (2.30)* (2.31)*
Drugs 0.405 0.368 0.361
(3.68)** (3.37)** (3.27)**
Electro 0.054 0.007 -0.001
(0.41) (0.05) (0.01)
Garments -0.402 -0.434 -0.431
(3.59)** (3.93)** (3.90)**
lmowpshr -0.235 -0.142
(3.22)** (1.56)
lmfreqvs -0.199 -0.213
(2.36)* (1.76)
lmdclear -1.443 -1.562
(4.85)** (3.98)**
lmovermn -1.162 -1.263
(5.84)** (4.66)**
lmemail -0.024 -0.023
(0.15) (0.10)
goodic 0.016 0.209
(0.16) (1.11)
pooric2 -0.396 0.017
(2.71)** (0.06)
Constant 4.829 10.920 11.549
(31.56)** (9.48)** (6.79)**
R-squared
between 0.12 0.16 0.16
overall 0.13 0.16 0.16
rho 0.86 0.85 0.85
obs. 1451 1451 1451
estabs. 731 731 731
Absolute value of t-statistics in parentheses
* significant at 5%level; ** significant at 1%level
32
Table 5: Proportionate Regional Gap in value added per worker vis-à-vis medium climate states
Gap in value added Gap in value added due to differences inRegions capital aveage skill TFP
per worker levelsGood-climate 0.1760 0.0525 0.03493752 0.0002
Poor-climate -0.2746 0.04125 -0.1395881 -0.2599
Table 6: Effects of Indicators of Investment Climate on TFP
Regions Proportinate gap in value added vis-à-vis medium-climate states due tomowpshr memail mdclear mfreqvs movermn
Good-climate 0.0263288 -0.0391536 -0.057014 0.02702457 -0.0291956
Poor-climate -0.0026445 -0.0823962 -0.1158723 0.01089048 0.01040331
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