metadata: authors: 0: author: Ayyagari,Meghana
1: author: Demirguc-Kunt,Asli
2: author: Maksimovic,Vojislav
count: India
docna: 0: docna: Size and age of establishments: evidence
            from developing countries
docty: Policy Research Working Paper
subtopic: Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,Employment and Unemployment,Financial Economics,Business Cycles and Stabilization Policies,Finance and Development,Rural Labor Markets
teratopic: Science and Technology Development,Social Protections and Labor,Education,Finance and Financial Sector Development
abstracts: cdata!: Survey data from 120 developing
            countries are used to examine the relation between
            establishment size and age in the formal sector. Existing
            research suggests that manufacturing establishments in
            developing countries do not grow over time, most likely
            because of market imperfections and regulations. To the
            contrary, this paper finds that the average plant in
            developing countries that is more than 40 years old employs
            almost five times as many workers as the average plant that
            is five years old or younger. The analysis finds consistent
            evidence when it looks within a large country, India, based
            on detailed manufacturing census data over 23 years. It also
            finds that differences in financial development across
            Indian states, while substantial, have a minor effect on
            firm growth, consistent with inefficiency of state-owned
            financial systems. These results hold controlling for
            differences in labor regulations across states, capital
            intensity, labor regulations, and firms born before and
            after the major reforms.
geo_reg_key: 517191,517198,517212
repnme: repnme: Size and age of establishments: evidence
            from developing countries
display_title: Size and age of establishments:
            evidence from developing countries
listing_relative_url: /research/2013/12/18623482/size-age-establishments-evidence-developing-countries
topicv3: Labor Markets m1326488 855,Manufacturing Sectors m1326521 1281,Finance and Growth m1327404 1081,Enterprise Surveys m1504072 2762,Census Data m1504073 2763
publishtoextweb_dt: 2013-12-01T00:00:00Z
historic_topic: Science and Technology Development,Social Protections and Labor,Education,Finance and Financial Sector Development
no_of_pages: 77
author_keys: 0: author_key: 606806
1: author_key: 94122
2: author_key: 430603
model_extractions: WPS6718 Policy Research Working Paper 6718 Size and Age of Establishments Evidence from Developing Countries _Meghana Ayyagari Asli Demirguc-Kunt Vojislav Maksimovic_ The World Bank Development Research Group Finance and Private Sector Development Team December 2013
Policy Research Working Paper 6718

# **Abstract**

Survey data from 120 developing countries are used to examine the relation between establishment size and age in the formal sector. Existing research suggests that manufacturing establishments in developing countries do not grow over time, most likely because of market imperfections and regulations. To the contrary, this paper finds that the average plant in developing countries that is more than 40 years old employs almost five times as many workers as the average plant that is five years old or younger. The analysis finds consistent evidence when it looks within a large country, India, based on detailed manufacturing census data over 23 years. It also finds that differences in financial development across Indian states, while substantial, have a minor effect on firm growth, consistent with inefficiency of state-owned financial systems. These results hold controlling for differences in labor regulations across states, capital intensity, labor regulations, and firms born before and after the major reforms.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ademirguckunt@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **_<mark>Size and Age of Establishments: Evidence from Developing Countries</mark>_**

**Meghana Ayyagari Asli Demirguc-Kunt**

**Vojislav Maksimovic**

## **JEL classification:** G00, G3, 01 **Keywords:** Firm life cycle, age, size and growth, financial development, India

## **__________**

*Ayyagari: School of Business, George Washington University, ayyagari@gwu.edu, Ph: 202-994-1292; DemirgüçKunt: World Bank, ademirguckunt@worldbank.org, Ph: 202-473-7479; Maksimovic: Robert H. Smith School of Business at the University of Maryland, vmaksimovic@rhsmith.umd.edu, Ph: 301-405-2125. We would like to thank Miriam Bruhn, Roberto Fattal Jaef, Hiau Looi Kee, Aart Kraay, David McKenzie, and Sergio Schmukler for their valuable comments and suggestions and Xiaoyuan Hu for excellent research assistance. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
# **I.  Introduction**

The determinants of firm size and, more broadly, firm life cycle are still largely a blackbox. We do know that in developed countries such as the U.S. new businesses start small and, if they survive, grow fast as they age (e.g. Evans, 1987; Dunne, Roberts, and Samuelson, 1989; Davis, Haltiwanger, and Schuh, 1996; Caves, 1998; Hsieh and Klenow, 2013). It is an open question as to whether firms in developing countries face a similar size-age profile as in the US given that they face entirely different business environments and operating conditions. An understanding of the firm life cycle in developing countries and its determinants is critical to the policy debate on productivity improvements and achieving convergence between rich and poor countries.

In this paper, we examine the relationship between plant size, age, and growth in the formal sector across 120 developing economies. Specifically we examine if older plants are much larger than young plants and if there are any differences in the size-age relationship across country income groups and industries. To overcome concerns with cross-country analysis, we investigate the firm size-age relation using detailed manufacturing census data from India. We then try to understand how financial institutions are associated with the size-age gap.

Our cross-country sample consists of 69,224 formal firms in 120 countries, surveyed over the period 2006-2012. The strength of our data is that we cover most of the developing countries in the world. The country with the highest GDP/capita in our sample of countries is Slovenia. Our data are comparable across countries since it they sourced from the World Bank Enterprise Surveys (ES) database which samples formally registered firms from over 100 countries to study the business climate constraints to private sector growth and performance. The surveys use 2
standardized survey instruments and a uniform sampling methodology to minimize measurement error and to yield data that are comparable across countries.<sup>1</sup> Our India sample is comprised of repeated cross-section census data for the formal manufacturing sector at roughly five-year intervals - 1983/84; 1989/90; 1994/95; 2000/01; and 2004/05<sup>2</sup> and census data on the informal sector for 1994/1995. India offers an ideal laboratory for testing the role of institutions on firm life cycle given the large persistent differences in institutions, business environment, and income across different regions in India (Ahluwalia, 2002). Being the seventh largest and second most populous country in the world, the size and income of each of its different states and territories can be likened to other developing countries. For instance, in an interesting comparison, the _Economist_ magazine matches India’s most populous state, Uttar Pradesh, to Brazil in terms of population, Qatar in terms of size of the economy, and Kenya in terms of its GDP/capita. Maharashtra, on the other hand is one of India’s richest states and is equivalent to Mexico in terms of population, Singapore in terms of GDP, and Sri Lanka in terms of GDP/capita.<sup>3</sup> Importantly, there is substantial and well researched heterogeneity in financial and labor institutions across these states. At the same time, comprehensive and centrally collected Census data at the firm-level are available to researchers, thereby sidestepping many of the concerns arising from data comparability in cross-country studies. India is also an interesting case study because some recent studies (e.g. Hsieh and

> 1 The mean response rate across our sample of countries is 70% which is superior to most other survey based studies. For instance, Campello, Giambona, Graham, and Harvey (2011) report response rates of 3 to 7% in their survey of how companies use credit lines during a financial crisis. Other studies in corporate finance report response rates between 7%-9% including Graham and Harvey (2001), Brav, Graham, Harvey and Michaelu (2005), and Lins, Servaes, and Tufano (2010).

> 2 Repeated cross-sections helps us look at life-cycle patterns over a longer period of time in addition to providing us more observations at each point in the life-cycle. It also allows us to examine if our results are consistent over different periods of institutional change in India such as financial liberalization and industry de-licensing.

> 3 All the comparisons are based on 2009 values. See “Comparing Indian states and territories with countries,” _The Economist Magazine, June 21, 2011._

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Klenow, 2013) suggest that India has a flat life cycle where 40-year-old plants are no larger than young plants.

We have the following main findings: First, our cross-country analysis shows that in the formal sector in developing countries, older firms are substantially larger than younger firms. While the average mid-aged firm between 10 and 19 years of age is only twice the size of the average plant under the age of 5 years, the average plant that is 40 years and older is 4.65 times the size of the average plant under the age of 5 years in terms of employment.<sup>4</sup> The significantly upward sloping age-size profile is pervasive in the vast majority of countries and in only less than 10% of the countries, the average 40+ year plant is not at least as big as plants younger than 5 years. One possible explanation for our findings is selection effects due to firm exits since we have data only on surviving firms. To improve on our cross-sectional evidence, we look at employment at two points in time – last year and three years back. We find that on average, firms are approximately the same size as two years ago suggesting that the surviving plants we examine are not the fastest growing in the distribution and that our results are unlikely to be driven mostly by plant exits.

Second, when we study the life cycles of Indian plants, we find that the average 40-yearold plant in the formal sector in India is two to four times the size of plants less than five years of age, both when compared contemporaneously and when comparing older and younger firms within the same cohort. Our results hold true when we take sampling weights into account and look at the entire population of firms, and also when we study just the Census sector firms that

> 4 Our results are robust to considering only manufacturing plants and taking into account survey sampling weights. We obtain similar patterns when we look across country income groups. Our results are also robust to dropping firms with government ownership, dropping former Socialist economies where they may a discrepancy in age reported due to large-scale privatization, and also to dropping firms that started operations before countries’ independence. This finding also holds controlling for industry and country fixed effects in a multivariate analysis.

4
are surveyed each year. Thus, while plants in India may not be growing at the same rate as in developed countries such as the US, especially during their early life cycle, there is clear evidence that older plants in the formal manufacturing sector are larger than younger plants.

Third, we find that technological differences across industries shape establishment life cycles in India. We use the identification strategy introduced by Rajan and Zingales (1998) for classifying industries into financially dependent and financially independent depending on the extent to which firms can support their capital expenditures using cash flow from operations, based on the experiences of US firms in the same industries. We find that firms in financially dependent industries are larger at all stages of their life cycle. Thus, there is no evidence that financial dependence affects growth rates of established Indian firms relative to firms in industries that are not financially dependent.<sup>5</sup> Fourth, we find stark differences in firm life cycles in the formal and informal sectors: We find that older firms in the unorganized manufacturing sector in India employ fewer people than firms younger than 5 years old. This is consistent with recent findings in La Porta and Shleifer (2008) who show that informal firms look very different from formal firms in terms of size, productivity, and education level of managers and find little evidence that growth occurs by informal firms eventually becoming large formal establishments.

Fifth, we study the impact of institutional differences on firm life cycles in India. Despite considerable differences in financial depth across Indian states, we find the role of state-level financial development to be marginal in explaining firm life cycles in the broad population of firms and in most of the sub-samples we analyze. These results are robust to a number of checks including looking at just the firms in the right tail of the size distributions, across states with

> 5 Rajan and Zingales (1998) show that financially dependent industries grow _less fast_ than financially independent industries using a sample of developed and developing countries.

5
flexible versus rigid labor market regulation, and using alternate indicators of financial development. We also find no differential impact of financial development on firm life cycles when we look at firms born after India’s financial liberalization in 1991 or at periods after industry de-licensing. We also find only marginal differences in the proportion of manufacturing employment in old and new firms across different levels of financial development. Our results are consistent with the conjecture that the state-owned and controlled financial sector in India is not significantly contributing to firm growth.

Following Beck et al. (2008), we classify industries into small firm dominated and large firm dominated categories using the US industry composition as an instrument. Beck et al. measure an industry’s “technological” composition of small firms relative to large firms as the share of employment in firms with less than 20 employees in the United States (a country with a relatively frictionless financial system) in 1997. We also classify industries into labor intensive and capital intensive industries using the classification in Hasan and Jandoc (2012). Using a difference-in-difference set-up we find that establishments in large-firm dominated industries are larger at each stage of the life cycle, both in the case of capital intensive and labor intensive industries.

These findings contrast with the literature that finds a significant effect of within-country institutional differences on firm performance. For example, Jayantre and Strahan (1996) and Dehejia and Lleras-Muney (2007) show that differences in state-level bank regulation in the United States affects financial development. Similarly, Guiso et al. (2004) examine differences in financial development across regions in Italy, particularly on small firms and entrepreneurship, and find a significant effect.

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However, the literature also shows that in a large cross-section of countries, higher government ownership of banks is associated with slower subsequent financial development and lower income per capita growth and productivity (La Porta et al, 2002; Barth et al. 2001), as well as poorer financial access by small firms (e.g. Beck, Demirguc-Kunt and Maksimovic, 2005). Our findings are also consistent with those of Boyreu-Debray (2003) and Boyreau-Debray and Wei (2005) who show that the depth of the financial sector does not promote growth of provinces in China, another country where the banking system is largely state owned and controlled. Hence, our findings on India provide additional evidence that state-ownership in banking hampers the impact of finance on growth.

The finding that differences in financial depth in a state-owned financial system do not affect the life cycles of firms does not imply that there are no qualitative differences in financial access within those systems or that they do not benefit individual firms. For example, Cole’s (2009) Indian study uses a regression discontinuity design and finds that government ownership of banks adversely affects the development of small enterprises in villages in which these banks have branches. In another study of Chinese banks and enterprises, Ayyagari, Demirguc-Kunt and Makimovic (2010) find that controlling for local financial development, firms that receive bank financing outperform firms that do not. However, Ayyagari et al. do not address the question of whether differences in financial depth at the province level materially affect the aggregate province level growth or the life cycles of the firms comprising the whole of the manufacturing sector.

Our results also contribute to the large literature on firm size and age (Dunne, Roberts, and Samuelson, 1989; Davis, Haltiwanger, and Schuh,1996; Cabral and Matta, 2003, Foster, Haltiwanger, and Syverson, 2012). Given the idiosyncratic distortions in the business 7
environment, it is not clear what type of size-age gap to expect in developing countries. A recent paper by Hsieh and Klenow (2013) suggests that in India and Mexico, in contrast to the case of the U.S., the life cycle of manufacturing plants is relatively flat - that in fact on average the surviving manufacturing plants in India shrink in size between ages 5 and 35 years. The dual economy view in development economics (originally associated with Harris and Todaro, 1970) predicts that informal firms should look very different from formal firms in terms of size, productivity, wages paid, and the industries/markets they operate in. Recent studies such as La Porta and Shleifer (2008) also show that while informal firms account for a large portion of economic activity in developing countries, growth and development come from the creation of highly productive formal firms. Strikingly, Hsieh and Klenow find that plants in both the formal and informal sectors share the same trajectories. Our results using data for the same year 1994/95 as in their paper stand in sharp contrast with their findings on the formal sector. Given the heterogeneity in firm formation we study the formal and informal sectors separately in this paper and do find differing life cycle patterns.

The remainder of the paper is organized as follows. In Section II we describe the data. In Section III we discuss in detail the relationship between establishment size and age in our crosscountry sample. In Section IV we examine plant life cycles in India. Section V concludes.

# **II.  Data**

# **_A. Cross-Country Sample_**

We use the World Bank Enterprise Surveys (ES), which are an ongoing initiative of the World Bank, to benchmark the investment climate in different countries across the world and to analyze firm behavior and performance. The Enterprise Surveys survey from the universe of 8
eligible firms obtained from the country’s statistical office<sup>6</sup> using stratified random sampling with replacement to generate a sample representative of the whole non-agricultural private economy (so fully government-owned firms are excluded from the sampling universe) in the country. The surveys are stratified according to three criteria: _Sector of activity_ (population of industries include manufacturing sectors, construction, services, transport, storage, communications, and computer and related activities), _Firm size_ (the strata include small firms (5-19 employees),<sup>7</sup> medium firms (20-99 employees), and large firms (100 or more employees)), and _Geographical location_ (selected based on centers of economic activity in the country).

While the ES have been produced since 2002, we restrict our sample to surveys administered during 2006-2012 since these provide sampling weights that address the varying probabilities of selection across different strata and are thus indispensable to making assertions about the whole population.<sup>8</sup> For countries that were surveyed twice during this period, we retain the most recent survey data for each country. Our results are also robust to retaining data for the year with the largest number of firms surveyed.

**Establishment Size** is the number of permanent, full-time employees in the

establishment where permanent, full-time employees are defined as all paid employees that are contracted for a term of one or more fiscal years and/or have a guaranteed renewal of their employment contract and that work 8 or more hours per day. The survey asks establishments to report the employment numbers at the end of the last fiscal year. We compute the average

> 6 The master list of firms is sometimes obtained from other government agencies such as tax or business licensing authorities. In some cases, the sampling universe is generated from lists maintained by the Chamber of Commerce and business associations or marketing databases where registration is voluntary. In a few cases, the sample frame is created via block enumeration.

> 7 We have a few firms in the sample that report having less than 5 employees. All our results are robust to dropping these firms. Our results are also robust to dropping firms that started out as informal enterprises and were formally registered after starting operations.

> 8 Most surveys contain three sets of weights – strict, median, and weak weights depending on the eligibility criteria used to construct the sample universe. The survey implementation manual recommends the use of median weights for cross-country comparisons which is what we use in this paper.

9
establishment size in each age bin. As an alternate measure of average size in each age bin, we use Total Employment/Total # of establishments where Total employment is derived by aggregating the employment reported by each establishment multiplied by its sampling weight.

**Establishment Size Ratio** is the ratio of current establishment size to the size of the establishment when it first started operations.

**Establishment Age** is defined as the number of years since the establishment began operations in the country. We compute age at the end of the last fiscal year to be consistent with the employment question. As before, we construct the following 9 age bins: <5, 5-9, 10-14, 1519, 20-24, 25-29, 30-34, 35-39, and 40+.

We drop missing observations on size and age, drop observations where the date the establishment began operations is before 1800 and obvious data errors (such as one observation where date the establishment began operations is reported to be 2205). We also drop the bottom and top 1% of the employment variable.<sup>9</sup> This leaves us with a final sample of 53,821 firms across 119 countries.<sup>10</sup> Table1 presents the summary statistics and correlations between the main variables. Panel A of Table 1 shows that the mean establishment size in our sample is 70 employees. The mean establishment size ratio is 6.078, suggesting that the average firm in our sample is 6 times its size when it started operations though the ratio extends from a very small number to 1,250. The firms with ratios in excess of 100 are spread out across countries and industries. For instance, the establishment with a size ratio of 1,250 is a Brazilian establishment surveyed in 2009 in the industry sector _Other: Construction, Transportation, etc._ , which is privately held and started operations in 1995 with 1 employee and had 1,250 employees in 2009.

> 9 Our results are robust to winsorizing the employment numbers at 1% rather than dropping the top and bottom 1%. 10 The Enterprise Survey database was downloaded in Dec 2012.

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Panel B shows that Size and Age are positively correlated, suggesting that older firms are larger in our sample. Establishment Size Ratio and Age are also positively correlated, suggesting that firms grow as they age.

Our cross-country data are subject to some caveats. First, the Enterprise Surveys sample only the formal sector in each country and exclude the informal sector. In addition, since the sampling frame is restricted to 5 employees or above, our results do not speak to the micro enterprises.

Second, our data are only on the continuing/surviving firms and hence we have no data on firm exit rates. On a related note, the surveys are stratified only by industry, firm size, and geographical location and so we may not have a completely representative sample of firm ages, though the firms within the strata are randomly sampled.

Third, our analysis is at the establishment level and not at the firm level since the sampling unit in the Enterprise Surveys is the establishment.<sup>11</sup> While this has the advantage that our employment measures are well defined and capture actual jobs at the establishment rather than changes from mergers, acquisitions, and divestitures, we are not able to measure firm size accurately for multi-establishment firms. However we are helped to some extent that the establishments in our sample report whether they are part of a larger firm or whether they are stand-alone. While most of the establishments in our data are stand-alone establishments (86%) and hence can be treated as firms, for robustness, we repeat the analysis on the sub-sample of firms that report that they have a single establishment and find that all our results hold.

> 11 In the Enterprise Surveys, the establishment is defined as a physical location where business is carried out and where industrial operations take place or services are provided. In addition, an establishment must make its own financial decisions, have its own financial statements separate from those of the firm, and have its own management and control over its payroll.

11
Henceforth, we will use the term establishment and firm interchangeably in our discussion of ES data. Fourth, we are unable to identify mergers in our data.

Finally, the ES data are based on survey data rather than individual country censuses.

However, our enquiries with the survey implementation team reveal that mean response rates across our sample of countries is 70%, which suggests that we have a representative sample of firms across economies.

# **_B. India Sample_**

We use data on formal manufacturing plants in India from the Annual Survey of Industries (ASI), which is conducted by the Indian Ministry of Statistics and Program Implementation. The ASI sampling frame consists of all registered factories employing 10 or more workers using power or 20 or more workers without using power. The sampling frame consists of the “Census” sector that is surveyed every year and the “Sample” sector where plants are sampled randomly and unit multipliers are provided to take into account sampling probabilities. The definitions of the Census and Sample sectors have changed over the years as described in the table in Appendix A.<sup>12</sup> In robustness checks, we find all our results to hold when we just analyze the Census sample. We use data from five years: 1983/84; 1989/90; 1994/95; 2000/01, and 2004/05.

The specific ASI variables we use are described below: **Establishment Age** is defined as the year of the census - year of initial production reported by the firms. As in Hsieh and Klenow

> 12 The ASI also contains some establishments outside of manufacturing. Thus while the primary unit of enumeration in the survey is a factory in the case of manufacturing industries, it could also be a workshop (for repair services), an undertaking or a licensee (electricity, gas & water supply undertakings) or an establishment (bidi & cigar industries). According to the Ministry of Statistics, “the owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to census scheme is, however, permitted to furnish a single consolidated return. Such consolidated returns are common  feature mostly in the case of bidi and cigar establishments, electricity and certain public sector undertakings”

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(2013) , we define 9 age bins as follows : <5, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, and 40+. **Establishment Size** is the total number of workers which includes workers employed directly, workers employed through contractors, supervisory and managerial staff, other employees, working proprietors, unpaid family workers, and unpaid working members if the establishment is a cooperative factory. As a measure of growth rates, we construct

**Establishment Size Ratio** which is the ratio of each firm’s establishment size scaled by the average establishment size of all firms in its birth cohort. Thus, we scale the employment of each firm in youngest age bin (<5) in 2004 by the average size of that age bin in 2004, of the firms in the age bin (5-9) in 2004 by the average size in the youngest age bin (<5) in 1999, of the firms in the age bin (10-14) in 2004 by the average size in the youngest age bin (<5) in 1994, of the firms in the age bin (15-19) in 2004 by the average size in the youngest age bin (<5) in 1989, of the firms in the age bin (20-24) in 2004 by the average size in the youngest age bin (<5) in 1983, and so on. Since our sample period extends from 1983/84 to 2004/05 we are able to construct this ratio for only the age bins <5, 5-9, 10-14, 15-19, and 20-24.

To deal with outliers, we winsorize the bottom and top 1% of all plant-level variables and drop firms that stated initial year of production to be before 1800 and clear data errors where the year of initial production is given to be after the year of the survey. The data also provide National Industry Classification (NIC) codes that map onto different revisions of the International Standard Industry Classification (ISIC) codes.<sup>13</sup> Using this we construct three digit

> 13 <mark>The census years 1983/84 uses NIC-70 which maps onto ISIC-Revision 2 at the 3-digit level. The 1989/90 and 1994/95 censuses use NIC-87 which maps onto ISIC-Revision 3 at the 3-digit level. The 1999/00 and 2000/01 censuses use NIC-98 which maps onto ISIC-Revision 3 at the 3-digit level. The 2004/05 census uses NIC-04 which maps onto ISIC-Revision 3.1 at the 3-digit level.</mark>

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NIC industry dummies that are consistent across all census-years and restrict the data to only the manufacturing sector.<sup>14</sup> To explore industry heterogeneity, we use three different classifications of industries. First, we follow the procedure in Rajan and Zingales (1998) and estimate an industry’s dependence on external financing using data on US corporations (RZ index). The RZ index is based on the assumption that, since U.S. financial markets are developed, sophisticated, have fewer market imperfections and are relatively open, they should allow US firms to achieve their desired financial structure. Thus assuming that there are technological reasons why some industries depend more on eternal finance than others, the RZ index offers an exogenous way to identify the extent of external dependence of an industry anywhere in the world. The methodology does not require that the US markets are perfect but rather that market imperfections in the US do not distort the ranking of industries in terms of the technological dependence on external financing.

We reconstruct the RZ index for the period 1980-2012 by computing external dependence as (Capital Expenditures-Operating Cash Flow)/Capital Expenditures for each domestic US firm in Compustat. The median value across firms in each industry is a measure of that industry’s dependence on external finance. This index of external dependence is constructed at the SIC 2/3 digit level which is then manually matched to 3-digit ISIC codes that map onto the Indian NIC classification. Appendix C provides the concordance used in the paper. We construct **EFD,** a dummy variable that takes the value 1 if the industry’s dependence on external finance is ≥ the median value of dependence on external finance across industries and 0 if it was < the median across industries.

> 14 We drop recycling from the manufacturing sector since it is not included under manufacturing in the ISIC classification.

14
Second, we create **Small Firm Industry** which is a dummy variable that takes the value 1 if the industry’s share of employment in firms with less than 20 employees is ≥ the median value of the index of small firm domination from Beck, Demirguc-Kunt, Laeven, and Levine (2008) and takes the value 0 if it is < the median index value. Beck et al. (2008) use the methodology in Rajan and Zingales (1998) described above to argue that each industry has a technological firm size that depends on that industry’s particular production process and that these technological differences should persist across countries. They construct each industry’s share of employment in firms with less than 20 employees in the US using data from the 1997 US Census.<sup>15</sup> Finally, we follow Hasan and Jandoc (2012) in constructing **Labor Intensive Industries** which is a dummy variable that takes the value 1 for labor intensive industries and 0 for capital intensive industries.<sup>16</sup> We use data on informal firms from the Unorganized Manufacturing sector for 1994/95 that is collected by the National Sample Survey (NSS) Office of the Ministry of Statistics. The survey uses a stratified two-stage sampling design where the first-stage units are the villages in the rural areas and urban blocks in the urban areas and the second-stage units are the enterprises. The allocation of sample first-stage units between rural and urban areas is in proportion to population as per the 1991 Census with double weightage to the urban sector. As seen in Appendix B, the informal sector in India is very large and dwarfs the formal sector in terms of

> 15 Beck et. al. present alternate indices including using 100 employees as the cut-off and using 1992 census to construct the index. Our results are robust to these alternate classifications.

> 16 Hasan and Jandoc (2012) classify the following industries in India to be capital intensive industries: Machinery, Electrical Machinery, Transport, Metals and Alloys, Rubber/Plastic/Petroleum/Coal and Paper/Paper Products. The labor-intensive industries are: Beverages and Tobacco, Textile Products, Wood/Wood Products, Leather/Leather Products and Non-Metallic Products. The remaining industries are not as clearly distinguishable and include: Food Products, Textiles, Basic Chemicals, Metal Products and Other Manufacturing

15
the number of firms. Establishment Size and Age variables are calculated similar to the formal manufacturing census described above.

# _B.1. Finance and Labor Market Institutions in India_

To take into account institutional differences that may affect firm life cycles, we focus on the level of financial development and the stringency of labor regulations across different states of India. We first describe India’s banking sector and the measures we construct followed by the stringency of labor regulation in India and the associated measures.

India has a rich and mature banking history with the first banks being established by the British East India Company toward the end of the 18<sup>th</sup> century. India’s largest bank today, the State Bank of India, originated in 1806 as the Bank of Calcutta. Following India’s independence, in <mark>1949, the Banking Regulation Act vested the Reserve Bank of India (RBI), India’s Central Bank, with extensive powers for supervision of banking in India. I</mark> n two waves of nationalization in 1969 and 1980, the Indian government nationalized the major private banks, running them as profit-making public sector undertakings that were allowed to compete and operate as commercial banks.<sup>17</sup> This increased the public sector banks’ share of deposits to 92% (Hanson, 2001). The government also experimented with a social banking program during this period to improve access to banking services for the poor, leading to a huge expansion in the number of bank branches (Burgess and Pande, 2005).

In response to a fiscal and balance of payments crisis in 1991, India went through a largescale financial liberalization. Following the recommendations of the Narasimhan Committee Report that suggested reforms in the Indian banking sector, the government allowed for the entry

> 17 In 1969, 14 banks with deposits over Rs 50 crores were nationalized. In 1980, 6 private sector banks were nationalized. A few of the private sector banks were not nationalized because of their small size and regional focus.

16
of new private sector banks (including foreign banks) in 1993. Despite the massive growth of private sector banks, India’s banking system is still largely state dominated. As of 2012, India’s state-owned banks have 73% of the market share of assets and 83% of branches.<sup>18</sup> Even within this government dominated banking sector, there is a large variation across India’s states in level of financial development. Bajpai and Sachs (1999) note that there has been a wide variation in the adoption of economic reforms with states like Maharashtra being very reform oriented and others, especially the poorest BIMARU<sup>19</sup> states (Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh) being laggards. Aghion et al. (2005) also note the reforms in the 1990s to be associated with increasing cross-state inequality in industrial performance.

Our measure of financial development is the ratio of total Commercial Bank Credit outstanding to the Net State Domestic Product (SDP) in each census year and gauges the depth of financial development. The data are sourced from Burgess and Pande (2005) with updates from the Reserve Bank of India (http://dbie.rbi.org.in). According to statistics by the Reserve Bank of India, in March 2006, there were about 217 commercial banks in operation, with 161 of them state owned (includes nationalized banks and regional rural banks), 27 private sector banks and the remaining 29 foreign banks.<sup>20</sup> Despite the entry of private banks, state-controlled banks still account for three-quarters of all loans in India. India’s largest commercial bank, the State Bank of India, is over two hundred years old and is government owned. Banerjee, Cole, and Duflo (2004) state that 83% of banking business in India is through government owned banks and even the private banks are subject to extensive regulations on who they can lend to.

> 18 Speech by Dr Duvvuri Subbarao, Governor of the Reserve Bank of India, at the FICCI–IBA (Federation of Indian Chambers of Commerce & Industry – Indian Banks’ Association) Annual Banking Conference, Mumbai, 13 August 2013. http://www.bis.org/review/r130813b.pdf?frames=0.

> 19 BIMARU resembles the Hindi word “Bimar” meaning sick.

> 20 <u>http://rbidocs.rbi.org.in/rdocs/Publications/PDFs/78903.pdf</u>

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We only have data on financial development across 15 Indian states but these are the major states of India with the highest SDP, accounting for 95% of India’s population and 90% of India’s GDP in 2004/05. Table 2 shows a wide variation in Credit/SDP across the states ranging from 0.073 in 1983 in Assam, one of India’s poorest states to 0.477 in Maharashtra, one of the richest Indian states with a median value of 0.19 (Gujarat) in 1983. These differences persist over our sample period and the values in 2004/05, the last year in our sample, are 0.171 in Assam and 1.148 in Maharashtra. Based on Credit/SDP, we construct a dummy variable, **FD** , which takes the value 1 for a particular state in a particular year if that state is at the median or above the median value of financial development in that year across states and 0 for states that are below the median value of financial development. Across the years, we find six states that are always classified as financially developed – Punjab, Andhra Pradesh, Kerala, Karnataka, Tamil Nadu, and Maharashtra. Only two of the states, Rajasthan and Madhya Pradesh, switch from being classified as financially underdeveloped to developed in 2004/05 while Gujarat and West Bengal are classified as underdeveloped in the last year.

India’s labor regulations have been known to be among the most stringent in the world and responsible for the stagnant share of manufacturing outputs in India’s GDP (Dougherty, 2009). Under the Indian Constitution, both central and state governments have joint jurisdiction over labor market regulation. One of the most important set of labor regulations governing Indian industry is the centrally legislated Industrial Disputes Act of 1947 which lays out the arbitration and adjudication procedures in industrial disputes and which has been extensively amended by state governments. A large literature has evolved quantifying labor market regulations across different states of India, the most well-known being Besley and Burgess (2004), that code the legislative state-level amendments to IDA to classify states as pro-worker 18
(score of +1) or pro-employer (-1) or neutral (0) over the period 1958 to 1992. Given the limited time-series variation within states, Hasan, Mitra, and Ramaswamy (2007) build on the BesleyBurgess data set to cumulate the coded scores in net years into a time-invariant index of the general direction in labor regulations, classifying states with anti-employee amendments as those with flexible labor markets and the others as inflexible labor markets. They argue that the scores on cumulative amendments between 1980 and 1997 do not vary much over time within states, with eight of the states showing no amendment activity since 1980. Dougherty (2009) further reports that only 8 amendments (in 3 states) have been recorded since 1990, and only one amendment passed in 2004 appears to be of material importance to labor market outcomes. Gupta et al. (2008) build a composite index based on a simple majority rule across the indicators proposed in Besley and Burgess (2004), Bhattacharjea (2006), and Dougherty (2009), and classify five states to have flexible labor regulations, (Andhra Pradesh, Karnataka, Rajasthan, Tamil Nadu, and Uttar Pradesh), three states to have inflexible labor market regulations (Maharashtra, Orissa, and West Bengal), and the remaining seven to be neutral (Assam, Bihar, Gujarat, Haryana, Kerala, Madhya Pradesh, and Punjab).<sup>21</sup> Following the composite classification in Gupta et al. (2008), we create a **Flexible State** dummy that takes the value 1 for states with flexible labor regulation and 0 for states with rigid or neutral labor regulations as shown in panel B of Table 2. We make one change to the Gupta et al. classification of using the pre-2000 state boundaries in classifying states. So our flexible states classification includes Uttaranchal (which split from Uttar Pradesh in 2000) and our inflexible states classification includes Jharkhand and Chattisgarh that were formerly part of Bihar and Madhya Pradesh, respectively).

> 21 The labor market regulation index is not available for the following states and union territories: Jammu & Kashmir, Chandigarh, Nagaland, Manipur, Tripura, Meghalaya, Daman & Diu, Dadra & Nagar Haveli, Pondicherry, Lakshadweep, and Andaman & Nicobar Islands.

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Table 3 presents summary statistics and correlations for our main variables from the formal manufacturing Census. Table 3 shows that after winsorizing, Establishment Size ranges from 1 to 1,616 employees with a mean of 121 employees. The mean Establishment Size Ratio is 1.38, suggesting that the average firm in our sample is 1.38 times the size of its birth cohort. 62% of the firm-year observations are classified as small firm industries, 23% are classified as labor intensive and 42% are classified as being in industries that are highly dependent on external finance. Sixty-nine % of the establishments are in states with flexible labor regulations and 73% are in financially developed states.

Panel B presents the correlation matrix between these variables. The sample correlations with age dummies show that older firms are larger and growing faster. When we look at the industry classifications we find that none of the correlations between the industry categories are larger than 0.19, suggesting that the three measures – Small Firm Industry, Labor Intensive, and EFD – are capturing three different aspects of industry technology. Overall we find that small firm industries are positively correlated with labor intensive industry and negatively correlated with external finance dependence. The correlations with Establishment Size and Establishment Size Ratio show that firms are larger and grow faster in large firm dominated industries, in capital intensive industries and in industries that are not highly dependent on external finance. The raw correlations also show that firms in financially developed states are larger and grow faster. On the other hand, while firms in states with flexible labor regulations are smaller, they are growing faster.

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# **III.  Establishment Size and Age in Developing Countries**

# **_A. Summary Statistics_**

In this section we present stylized facts on the size of establishments as they age in developing countries. We begin by presenting evidence on the relationship between average plant employment and age in the cross-section in Figure 1. We first compute the mean employment in each age bin in each country. We then compute an index for each country where the index takes the value 1 for establishments less than five years old. For all other age bins, the index is the ratio of the mean employment in that age bin to the mean employment for firms under five years of age. Figure 1 plots the mean and 95% confidence intervals of the index for each age bin.

We find that in developing countries, across all industries, the average size of plants that are less than 20 years old is only twice as large as the average size of plants that are less than 5 years old but after that there is a large increase in the size-age gap. A plant that is 40 years or older on average employs nearly 5 times as many people as a plant that is less than five years old.<sup>22</sup> The 95% confidence intervals show that this number could vary between 4 and 5.3.

The findings in Figure 1 are very robust. The upward-sloping size-age profile is pervasive in our sample of countries. In less than 10% of the countries, a plant that is 40 years or older is not at least as big as a plant that is younger than five years old. In Figure 2, we find similar results when we take sampling weights into account and compute average establishment size as Total Employment/Total # of establishments - the 40+ year old plant employs 5 times as many workers as the typical plant five years or younger. When we look at just manufacturing plants in

> 22 The ratio represents the increase in mean size of the establishments in the 40+ age bin compared to that of establishments in the <5 age bin. It is not the mean annual growth rate of the establishments in the 40+ age bin.

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Figure 3, we again find that the average size of a plant that is 40+ years old is 5.7 times larger than the average size of plants that are less than 5 years old.

In Figure 4, we undertake additional robustness tests. We exclude countries with a Socialist legal tradition because Ayyagari, Demirguc-Kunt, and Maksimovic (2008, 2013a) argue that these countries are fundamentally different from others in their perceptions of property rights protection. One of the other concerns with the former Socialist economies might be that the data on establishment year are noisy because of mass privatizations though the establishments were queried on when the establishment first started operations even if it was under state ownership. However in Figure 4 we find consistent results with our main finding when we drop former Socialist economies. We then explore if our results are being driven by the really old large state firms or firms that were established many years before countries’ independence. We find our results to be robust to dropping firms with any government ownership and dropping firms that were established before the countries’ independence.

Firms in our survey were also asked to report the number of employees at the time the firm began operations (size at birth). So in Figure 5, we normalize the average size of each age cohort by the average size of that cohort at birth. We find that firms that are over 40 years old are 4 times larger than their size at birth. This is consistent with our main finding that older plants in developing countries are larger than their younger counterparts.

In unreported figures, we examine if there are differences in plant size-age gap across different country income groups. Using the sampling weights, we find that in all cases, the average size of 40-year-old plants is much larger than plants under 5 years of age and the sizeage gap ranges from 4.75 times in the case of high-income countries to 5.3 times in the case of 22
low-income countries. Across regions, we find that the size-age gap for firms 40 years and older ranges from 3.32 in East Asia Pacific to 6.03 in Africa.

The evidence thus far provides only suggestive evidence of plant life cycles because it provides a snapshot of the firm size-age relationship for surviving establishments. One possible explanation for the pattern we find may be selection, that is, our results may be being driven by firm exits rather than firm growth. For instance, each year if we had the smallest 5% of firms exiting, we would still find an upward-sloping relationship between establishment size and age. To improve on the cross-sectional evidence, we next look at the data at two snapshots in time from the same surveys. Firms in our survey were asked to report their employment last year and three years ago. In Figure 6, we plot the average employment at the two different snapshots in time. So for instance if we look at the firms in the 15-16 age cohort (bold line), their size at the previous snapshot will be the dashed line of the 13-14 age cohort. The figure shows that the two distributions are comparable so it is not the case that the surviving firms are from the top end of the size distribution. This provides some suggestive evidence that our results are not entirely driven by exit.

# **_B. Multivariate Analysis_**

In Table 4, in col. 1, we regress Establishment Size on age dummies and find all the age coefficients to be positive and significant, suggesting that older firms are larger than younger firms. For ease of comparison with the youngest firms, we add the constant (which presents the average mean size for firms younger than 5 years old) to each of the age coefficients and divide the sum by the constant. These ratios are reported in bold in square brackets below the coefficient and standard errors. Thus we see that the average 40+ year old firm is 4.783 times the 23
size of the average firm that is younger than five years old. In col. 2, we repeat the analysis in col. 1 using industry dummies and country dummies to control for any time-invariant unobserved heterogeneity across countries and industries. Once again we find that all the age coefficients are positive and significant. Cols. 3 and 4 present results for manufacturing and non-manufacturing sub-samples where we find a similar increase in establishment size with age.

In cols. 1-4, we are comparing the mean size of firms that are 40+ years old with the mean size of firms that are younger than 5 years old. However, young firms could be varying in size across years due to different macroeconomic factors, entry regulations, capital market openness, etc. as well as across countries. In order to see if firms indeed grow as they age over their life cycle, we would want to compare the size of older firms at any given point in time with their size when they were born. The Enterprise Surveys ask firms to report the number of fulltime employees when the firm started operations which allows us to construct Establishment Size Ratio, which is the ratio of each firm’s current employment level to the number of employees when it started operations. Col. 5 presents a regression of Establishment Size Ratio on age dummies and shows that older firms are much larger compared to their size when they first started operations. The constant shows that the average firm that is less than 5 years old is 3.05 times its size when it first started operations whereas the coefficient of the 40+ age bin shows that the average 40+ year old establishment is 11 times its size when it first started operations.

Overall Table 4 shows that firms in developing countries grow as they age and we find no evidence of a declining life cycle in our sample of firms.

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# **IV.  Establishment Size and Age in India**

# **_A. Firm Life Cycle in the Formal Manufacturing Sector_**

To examine the relationship between firm size and age, we pool repeated cross-section data from the Indian census. We begin with some descriptive statistics of our data, following Hsieh and Klenow (2013). For each census year separately, we first compute the mean employment in each age bin reported by that census. We then compute an index for each year of data where the index takes the value 1 for establishments less than five years old. For all other age bins, the index is the ratio of the mean employment in that age bin to the mean employment for firms under five years of age reported in the same census. Figure 7 plots the mean index for each age bin using data for five repeated cross-sections – 1983/84, 1989/90, 1994/95, 2000/01, and 2004/05. The figure shows that the average 40-year-old plant is 3 to 5 times the size of the plants under five years of age. Figure 8 presents data using weighted employment and shows that for the average 40+ year old plant, the ratio varies from 4.8 times in 1983 to 2.2 in 1994. We also see that in recent years, the average 30-year-old plant is not that much larger than plants younger than five years old but after that, older plants are larger than the young plants. To mitigate concerns about sampling issues, we present data only using the census sector in Figure 9 and find that even 20-year-old plants in most years are twice as large as plants younger than five years of age. Figure 9 also shows that over time the size-age gap for the 40+ year old plant has shrunk starting from 4.91 in 1983/84 to 2.62 in 2004/05. In unreported figures we find similar results when we use a weighted average across all industries in each age bin with the weights being the value-added shares of each industry.

The graphs show a much faster growth rate for Indian plants than found by Hsieh and Klenow (2013) who document a flat plant life cycle in Indian manufacturing, including in just 25
the formal sector, using only data form the 1994/95 Census. The differences are particularly stark later in the life cycle where we find older plants to be much larger while they find older plants to be declining in size. We discuss the reasons for the discrepancy below in section IV.E.

In Table 5, we use pooled OLS regressions to explore the age effects in detail. In col. 1, we regress Establishment Size on age dummies and find all the age coefficients to be positive and significant, suggesting that older firms are larger than younger firms. For ease of comparison with the youngest firms, we add the constant (which presents the average mean size for firms younger than 5 years old) to each of the age coefficients and divide the sum by the constant. These ratios are reported in bold in square brackets below the coefficient and standard errors. Thus we see that the average 40+ year old firm is 3.775 times the size of the average firm that is younger than five years old. In col. 2, we repeat the analysis in col. 1 using 3-digit NIC industry dummies and state dummies. While we are not allowing for time-varying changes across states and industries, the dummies help us control for any time-invariant unobserved heterogeneity across states and industries. Once again we find that all the age coefficients are positive and significant. Figure 10 plots the coefficients for the age dummies and the 95% confidence bands and shows that even after controlling for period effects, location, and industry, older firms are larger than firms younger than five years old (reference sample).

In columns 1 and 2, we are comparing the mean size of firms that are 40+ years old in the pooled sample with the mean size of firms that are younger than 5 years old. However, young firms could be varying in size across years due to different macroeconomic factors, entry regulations, capital market openness, etc. Figure 11 plots the mean employment and the 26
confidence intervals for firms younger than five years old in each census year and shows that the average size of the youngest firms almost doubles between 1983 and 1995<sup>23</sup> and then reduces.

In order to obtain true life-cycle effects and to see if firms indeed grow as they age over their life cycle, we would want to compare the size of older firms at any given point in time with their size when they were born. In the absence of panel data we are unable to look at the employment of the exact firm when it was born and hence rely on the mean employment in its birth cohort. Thus we construct Establishment Size Ratio, which is the size of firms in a particular age bin scaled by the mean size of the firms in their birth cohort. Since our sample period extends from 1983 to 2005, the oldest firms in our sample for which we can obtain this comparison are firms that are 20-24 years old in 2005 with their birth cohort being firms that were <5 years old in 1983. Columns 3 and 4 in Table 5 present regressions with the Establishment Size Ratio. Col. 3 shows that firms that are 20-24 years old are 1.419 times the size of average firms in their birth cohort. The constant in col. 3 is again the reference category of firms younger than five years old and since we are scaling each firm’s employment by the mean employment of their birth cohort (firms younger than 5 years old when the firms was born) and using OLS we would expect it to be 1 as it is. Col. 4 shows that after controlling for industry, location, and year dummies, firms that are 20-24 years old are 1.86 times the size when they were born. Col. 4 also shows that there must be heterogeneity across industries (e.g. labor vs. capital intensive), states (e.g. regulation), and firm types (e.g. organization structure) in the size of the firms younger than 5 years old since the constant is no longer 1.

Overall Table 5 shows that manufacturing plants in India grow as they age.

> 23 Hsieh and Klenow (2013) use data from 1995 for their main analysis thus scaling the employment of all firms with that of the youngest firms in 1995, which is also the year when the youngest firms were the largest in our sample period.

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# **_B. Heterogeneity across Industries in Firm Life Cycle_**

In this section we explore if there are technological differences across industries that must be taken into account when considering life-cycle effects. In the regression tables in this section and the following section, we first show the regression results and then show the corresponding margin plots of the interaction coefficients for ease of interpretation. While these margin plots give an idea of the initial starting points of plants in different industries and give an idea of relative size across different age groups, we also present results taking the ratio of the predicted margin of each age group with that of the youngest firms to give an estimate of growth rates.

In column 1 of Table 6 we interact Age Dummies with Small Firm Industry dummy. The main age effects are positive and significant, suggesting that the average older firm is larger and the main effect of Small Firm Industry is negative and significant, suggesting that average firm size in small-firm-dominated industries is smaller than that in large-firm-dominated industries. The interaction coefficients are all negative and significant. To better interpret the interaction effects, we plot the predictive margins of the interaction coefficients in col. 1 along with the 95% confidence intervals in Figure 12. The first panel in Figure 12 shows that the average size of firms in large-firm-dominated industries is higher than that in small-firm-dominated industries across all age groups. Large-firm-dominated industries are also growing faster than small-firmdominated industries. In Panel B of Table 6 we present the ratio of the predictive margins of each age bin to that of the reference group to obtain an estimate of growth rates. Cols. 1 and 2 of Panel B show that the average 40+ year old firm is 5.11 times the size of firms that are younger than five years old in large-firm-dominated industries while that ratio is only 2.74 in small-firmdominated industries.

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In col. 2 of panel A we look at the interaction of labor intensive industry and age. The age effects are again positive and significant while the labor intensive industry dummy is positive and significant, suggesting that on average employment levels are higher in labor intensive industries. The second panel in Figure 12 plots the margin plots of the interaction coefficients and shows that, while firms in capital intensive industries start out smaller than those in labor intensive industries, they grow faster and are larger than firms in labor intensive industries as they age. Cols. 3 and 4 of Panel B show that the average 40+ year old firm is 4.08 times the size of firms that are younger than five years old in capital intensive industries, while that ratio is only 2.38 in labor intensive industries.

The correlation coefficient of the Small Firm Industry dummy and Labor Intensity Dummy is only 6.3% (see panel B of Table 2). However, to investigate if the difference in age effects in small-firm-dominated versus large-firm-dominated industries varies by labor intensity, in col. 3 of panel A of Table 6 we explore the triple interaction of age dummies with Small Firm Industry dummy and Labor Intensity dummy. The interaction of labor intensity and small-firmdominated industry is positive and significant. Figure 13 plots the predictive margins of the triple interaction coefficients and shows that the average size is largest in capital intensive and largefirm-dominated industries while in the small-firm-dominated labor intensive industries growth over the firm’s life cycle is the lowest. The figure also shows that older firms in capital intensive and large-firm-dominated industries are particularly large while the differences between the other industry classifications are not as stark.

In cols. 5-8 of panel B, we take the ratio of the predictive margins in each age group in each industry category with the predictive margin of the youngest firms in that industry category. We find that the average 40+ year old firm is 5.69 times larger than firms younger than 5 years 29
old in large-firm-dominated and capital intensive industries whereas that ratio is 2.71, 3.02, and 2.12 in large firm and labor intensive industries, small-firm-dominated and capital intensive industries, and small-firm-dominated and labor intensive industries, respectively.

To explore the role of external financing in the establishment life cycle and firm growth, we divide the sample into industries that are dependent on external finance and those that are not dependent on external finance using the index of external finance dependence (EFD). In col.4 of Table 6, we interact the age dummies with EFD. The age dummy coefficients are all positive and significant, suggesting that on average older firms are larger. The EFD dummy is positive but not significant whereas the interaction of EFD with age dummies is positive and significant. To better interpret the interaction terms, we plot the predictive margins along with the 95% confidence intervals in Figure 14. The figure shows that while firms in industries that are dependent on external finance start out the same size as firms in industries that are not highly dependent on external finance, they grow faster and are larger as they age. Cols. 9 and 10 of Panel B show that the average 40+ year old firms in industries that are not highly dependent on external finance is 2.93 times the size of firms younger than five years old in those industries whereas that number is 4.89 times in industries that are highly dependent on external finance.

Overall, Table 6 and Figures 12-14 show that the initial starting point of firms and their growth rates are different depending on their industries’ technological size, production technology, and dependence on external finance. Firms grow more over their life cycles in industries predicted to be large-firm-dominated based on US data and in capital-intensive industries and in industries more dependent on external finance.

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# **_C. Financial Development and Firm Life Cycle_**

In this section, we examine variations in firm life cycles across different levels of financial development of the states they are located in and across different industry characteristics. In these regressions, we do not include state fixed effects but instead control for net State Domestic Product (SDP)/capita (in logs). In col.1 of Table 7, we investigate the role of financial development on firm life cycles by interacting age dummies with FD, a dummy variable that takes the value 1 for financially developed states and 0 for financially underdeveloped states. The financial development dummy is by itself positive and significant, suggesting that plants in more financially developed states are larger and so are plants in states with higher SDP/capita. Most of the interaction terms are insignificant. This can also be seen in Figure 15 where we plot the predictive margins of the interaction coefficients and find that financial development does not make a material difference to plant life cycles in India. In col. 1 of Panel B, we take the ratios of the predicted margins in each age bin to that of the youngest firms (<5), and find that plants in developed versus underdeveloped states are growing at very similar rates. In unreported tests we find similar results when we look at just the top 3 and bottom 3 states in terms of financial development. We also do not find evidence supporting the role of financial development for firm life cycles when we use a continuous measure of financial development, Credit/SDP, interacted with age dummies.

In cols. 2-7 we investigate if the financial development of the state is more critical for firm life cycles in some industries versus others. We look at large-firm-dominated and labor intensive industries in col.2, large-firm-dominated capital intensive industries in col. 3, small-firmdominated and labor intensive industries in col.4, small-firm-dominated capital intensive 31
industries in col. 5, industries not dependent on external finance in col. 6, and industries dependent on external finance in col. 7.

The financial development dummy is positive and significant in all specifications except in the case of small-firm-dominated, capital intensive industries. The predictive margins of the interaction coefficients in all 6 columns are shown in Figure 16 where we see that firms in largefirm-dominated labor intensive industries are larger at all ages in financially developed states versus underdeveloped states. In small-firm-dominated labor intensive industries, plants start out larger in the financially developed states but for older plants we find that those in financially underdeveloped states are larger. In all other industry samples, we do not find striking differences between the financially developed and underdeveloped states. In cols. 2-7 of panel B we show the ratio of the predictive margins in each age bin to that of the youngest firms (<5 years old). We find that plants are growing fastest in large-firm-dominated capital intensive industries and slowest in small-firm-dominated labor intensive industries. Plants in industries that are dependent on external finance grow faster compared to plants in industries less dependent on external finance. However when we look at the differences across financial development, surprisingly we find that the older plants in financially underdeveloped states are larger than the youngest firms in those states compared to the size difference between old and young plants in financially developed states.

Together Table 7 and Figures 15-16 show that there are no significant differences in the life cycles of Indian firms across financially developed and underdeveloped states and any key differences in size and growth rates that exist are at the industry level.

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_C.1. Robustness_ In this section we undertake a number of robustness tests to see if there is a differential effect of financial development on firm life cycles. First, in col. 1 of Table 8, we explore whether the effect of financial development is larger for firms in the right tail of the size distribution by looking at firms at or above the 90<sup>th</sup> percentile in terms of size distribution. Most of the interaction terms are insignificant, suggesting only marginal differences in growth rates of these plants in financially developed versus underdeveloped states.

In cols. 2 and 3 we use alternate measures of our financial development indicator – in col. 2 we use Commercial Bank Deposits/SDP as an alternate measure of financial depth and in col. 3 we use Total number of bank branches/1000 persons as a measure of financial outreach. We once again split states each year into financially developed versus underdeveloped depending on the median value of these indicators across states. Cols. 2 and 3 show that the main effect of financial development is negative while the interaction effects with age are mostly insignificant. This is also seen in Figure 18 where we do not find material differences between developed and underdeveloped states using these alternate indicators.

India has been known to have one of the most stringent formal labor laws in the world, which is substantially more protective than the British labor laws along which it was originally modeled, as discussed by Dougherty, Robles, and Krishna (2011). Several studies have shown that stringent labor regulations have been associated with lower levels of manufacturing output, employment, and labor productivity (e.g. Besley and Burgess, 2004; Aghion et al., 2008; Ahsan and Pages, 2009). In cols. 4 and 5 of Table 8, we examine if the effect of financial development on plant life cycle is different in states with flexible labor regulations versus states with stringent labor regulations following the classification in Gupta, Hasan, and Kumar (2008). While the 33
main effect of financial development is positive and significant in flexible states, it is negative and significant in the inflexible states. However, all the interaction coefficients of age with financial development are positive and significant in states with rigid labor regulations. To better interpret the interaction coefficients, we look at the predictive margins with the 95% confidence bands for the interaction coefficients in Figure 18. We find that among the plants in states with rigid labor regulations, those in financially developed states are larger than those in financially underdeveloped states. The differences across financial development are less stark in states with flexible labor regulations.

In cols. 6 and 7 we next investigate if the role of financial development differs across differing periods of industrial policy. Following India’s independence in 1947, the 1951 Industries Development and Regulation Act introduced a system of industrial licensing that came to be known as the “license raj”, which regulated and restricted the entry of new firms and expansion of existing firms. With the Indian economy being stagnant over several decades, a series of liberalization reforms were introduced by the government starting in the 1980s which included de-licensing of industries. A third of the industries were de-licensed during the period 1985-1987 and most of the others in 1991. Aghion et al. (2008) provide information on each three-digit industry that was de-licensed over the period 1980-1997. We extend their data to the end of our sample period in 2005 to identify firms that were born after their industry was delicensed. In col. 6 of Table 8 we look at the interaction of FD and age for firms born after the period of de-licensing. Since the earliest de-licensing year in our sample starts in 1985, we only have the first19 years of firm life cycle and hence use interactions of FD with each year of age. The predictive margins of the interaction coefficients are in Figure 19. We find that while the age coefficients are mostly positive and significant, the FD dummy is not significant and neither are 34
the interaction terms. Figure 19 also shows that there does not seem to be much difference in the early life cycle of firms born after de-licensing in financially developed versus underdeveloped states. While firms in financially developed states seem to be born larger, the difference is not maintained over time.

In col. 7 of Table 8 we examine if there is a differential effect of state-level financial development on plants born after India’s financial liberalization in 1991. Since the last year of data in our sample is 2005, we have 14 years of firms’ life cycles for the firms born after liberalization. Once again most of the interaction terms are not significant. Figure 20 plots the predictive margins of the interaction coefficients along with the 95% confidence intervals. We find that while firms in financially developed states start out larger than firms in financially underdeveloped states in the period before liberalization, these differences do not persist over time. We do not find any material difference in the life cycle of plants born after liberalization in financially developed versus underdeveloped states.

In unreported tests we do not find significant differences in early plant life cycles between states with high versus low levels of financial development even before industry delicensing or liberalization.

# _C.2. Intensive vs. Extensive Margins_

The above section shows that despite great variation within the country, financial institutions have limited explanatory power for plant life cycles in India. In this section we examine if the financial institutions have a differential effect on the _extensive margin_ (Number of Plants) versus _intensive margin_ (Avg Employment/Plant).

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In Figure 21, we plot the proportion of employment in financially developed states in each bin (EM-developed) and the proportion of employment in financially underdeveloped states in each bin (EM-Underdeveloped). We find marginal differences between financially developed versus underdeveloped states. Across both categories of states, the 5-9 aged firms have the highest proportion of employment after which it declines. So older plants have smaller employment shares though the oldest age group of 40+ have higher employment shares than middle-aged plants between 20-39 years of age Next, for each age bin in financially developed states we take the ratio of total number of employees to total number of establishments to give us the intensive margin (IM-developed) and repeat the same for the underdeveloped states to obtain IM-Underdeveloped. While older firms have higher employment/ establishment ratio, we don’t find significant differences across financially developed versus underdeveloped states. All our results hold if we were to take sampling weights into account and examine the intensive and extensive margins in the population.

# **_D. Informal Manufacturing Sector_**

In this section we study the size-age gap in the informal manufacturing sector in India. In a recent paper using World Bank Enterprise survey data for the informal sector, La Porta and Shleifer (2008) show that informal firms look very different from formal firms in terms of size, productivity, and education level of managers and find little evidence that growth occurs by informal firms eventually becoming large formal establishments. They interpret their findings as being consistent with the _dual economy_ view (e.g. Harris and Todaro, 1970) which predicts that in developing countries, in addition to the formal sector, there is an informal sector that employs a large section of the labor force.

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Figure 22 presents the sample statistics for establishment size across different age bins, combining the data for both the rural and urban sectors. We see that older firms in the unorganized manufacturing sector employ fewer people than firms younger than 5 years old. Thus firms in the informal manufacturing sector in India have a very different life cycle than the firms in the formal manufacturing sector. The downward-sloping age-size profile in the informal sector is consistent with the description of the informal sector in La Porta and Shleifer (2008).

# **_E. Reconciling Our Results with Hsieh and Klenow (2013)_**

The analysis above shows that firms in the formal manufacturing sector in India grow as they age in contrast to the findings in Hsieh and Klenow (2013) for the formal manufacturing sector for the same year – 1994/95.<sup>24</sup> In this section, we try to understand if the different results we obtain from those in Hsieh and Klenow (2013) are data driven by taking a closer look at the different types of workers. The Indian Census data provide detailed information on the types of workers in an establishment as shown in Appendix D. In all the figures presented thus far, we use total number of workers (line 13 in ASI data and line 22 in NSS data). In this section, we focus our attention on contract workers in the ASI 1994/95 data set. In Figure 23, we present the size-age relationship using total employment and just contract workers. We find the size-age gap is declining only in the case of contract workers with older firms employing fewer workers than firms under five years of age.

Overall, we find evidence of a flat or declining life cycle in India found by Hsieh and Kloenow only when we look at the informal sector or a particular category of workforce,

> 24 Hseih and Klenow (2013) state <mark>“..we see flat size with respect to age in the formal plants of the Indian ASI alone, just as in the pooled NSS-ASI data”</mark>

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contract labor. However, there is no compelling economic rationale for measuring firm size or labor input in the formal sector in this way, and clear reasons not to do so.

# **IV. Conclusion**

The role of size and age in influencing plant dynamics is crucial to understanding productivity differences across firms and countries. In this paper, we use unique survey data across 120 developing countries to examine the relationship between establishment size and age in the formal sector. We find that the average 40-year-old plant in developing countries is about five times larger than the average plant less than 5 years of age. Our results are robust to a number of checks in smaller samples, including looking at just the manufacturing sector, dropping former Socialist economies, dropping firms with any government ownership and dropping firms that started operations before the countries’ year of independence.

We then undertake an in-depth analysis of plant life cycles in one developing country, India. Based on five repeated cross-sections of census data in India spanning 23 years, this study shows that there are significant age effects in the determination of firm size. While the average 20-year-old plant in India is not that much larger than plants younger than five years, by age 40, the average plant in India in the formal sector is two to four times larger than the average plant five years or younger. We find evidence of a flat life cycle only in the informal sector in India.

We also find that the differences in financial development across Indian states, while substantial, only modestly affect the life cycles of established firms in most industries. The one exception is firms in large-firm-dominated labor intensive industries, which are larger in financially developed states versus underdeveloped states. We also find that plants in financially developed states that have rigid labor market regulations are larger than plants in financially 38
underdeveloped states with rigid labor market regulations. We do not find the institutions to have a big impact on either the intensive (employment/establishment) or extensive margin (number of plants). Our results are consistent with the hypothesis that a majority government-owned banking system, like the one India has, is not an effective way of employing the financial system to support economic growth.

Our findings have important policy implications. We show that on average, across the world, formal new establishments start small and grow as they age. We find no evidence of declining life cycles in the formal sector, suggesting that older firms are important contributors to economies in developing countries in terms of employment. However, we also find that the size-age ratio may not be as large as that in developed countries such as the United States, suggesting that other institutional factors or firm idiosyncratic factors, such as organizational structures, may play a role in establishment life cycles.

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