metadata: authors: 0: author: Aga,Gemechu A.
1: author: Francis,David C.
2: author: Rodriguez Meza,Jorge Luis
count: World
docna: 0: docna: SMEs, age, and jobs : a review of the
            literature, metrics, and evidence
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,Business Cycles and Stabilization Policies
teratopic: Private Sector Development,Law and Development,Poverty Reduction
abstracts: cdata!: The subject of which firms are the key
            employers—and which of these create or destroy jobs at a
            faster rate—is eminently important for academics and policy
            makers. The relative importance of small versus large firms
            and old versus young firms has in particular been
            extensively debated and studied. Nevertheless, the results
            often hinge on the questions that are asked. Moreover, the
            categorical definitions used to define firm size and age,
            and the nature and coverage of the data used have important
            effects. This paper lays out the relevant definitions and
            metrics that are central to the debate, reviewing the main
            findings to date on the subject (with particular emphasis on
            results in developing economies). The paper adds updated
            results for 117 developing economies using the World Bank’s
            Enterprise Survey Data, finding that (i) small and medium
            enterprises and older establishments are the dominant
            employers in the nonagricultural private sector labor force
            in developing economies, and (ii) net job creation is
            negatively correlated with establishment age and, although
            the effect of size is also negative, its significance is
            sensitive to the definition and methods used.
repnme: repnme: SMEs, age, and jobs : a review of the
            literature, metrics, and evidence
display_title: SMEs, age, and jobs : a review of
            the literature, metrics, and evidence
listing_relative_url: /research/2015/11/25462713/smes-age-jobs-review-literature-metrics-evidence
topicv3: Job Creation and Job Opportunities
            m1326483 1170,Manufacturing Sectors m1326521 1281,Jobs m1503931 2670,Enterprise Surveys m1504072 2762,Jobs Strategies m1504080 2769
publishtoextweb_dt: 2015-11-13T00:00:00Z
historic_topic: Private Sector Development,Law and Development,Poverty Reduction
no_of_pages: 41
author_keys: 0: author_key: 1131266
1: author_key: 1099768
2: author_key: 144470
model_extractions: WPS7493 Policy Research Working Paper 7493 SMEs, Age, and Jobs A Review of the Literature, Metrics, and Evidence _Gemechu Aga David C. Francis Jorge Rodriguez Meza_ Development Economics Global Indicators Group November 2015
Policy Research Working Paper 7493

# **Abstract**

The subject of which firms are the key employers—and which of these create or destroy jobs at a faster rate—is eminently important for academics and policy makers. The relative importance of small versus large firms and old versus young firms has in particular been extensively debated and studied. Nevertheless, the results often hinge on the questions that are asked. Moreover, the categorical definitions used to define firm size and age, and the nature and coverage of the data used have important effects. This paper lays out the relevant definitions and metrics that are central to the debate, reviewing the main findings to date on the subject (with particular emphasis on results in developing economies). The paper adds updated results for 117 developing economies using the World Bank’s Enterprise Survey Data, finding that (i) small and medium enterprises and older establishments are the dominant employers in the nonagricultural private sector labor force in developing economies, and (ii) net job creation is negatively correlated with establishment age and, although the effect of size is also negative, its significance is sensitive to the definition and methods used.

This paper is a product of the Global Indicators Group, Development Economics. 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 dfrancis@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
# **<u>SMEs, Age, and Jobs: a Review of the Literature, Metrics, and Evidence</u>**<sup>**1**</sup>

|Gemechu Aga<sup>a</sup>|gayanaaga@worldbank.org|
|---|---|
|David C. Francis<sup>a,b</sup>|dfrancis@worldbank.org|
|Jorge Rodriguez Meza<sup>a</sup>|jrodriguezmeza@worldbank.org|

a Development Economics, Enterprise Analysis Unit, World Bank Group b Corresponding author JEL codes: E24, L25, L26 Key words: Jobs, employment, SMEs, firm dynamics

> 1 The authors are thankful to Alvaro Gonzalez, Reyes Aterido and members of the World Bank Group’s Jobs CrossCutting Solutions Area for helpful comments.
# **I.** **<u>Background and Motivation</u>**

The subject of job generation and preservation is key in economics, as it is through their jobs that most individuals generate their income. However, despite this central role, there is substantial confusion over which questions are being asked – let alone answered – and which measures are most appropriate. Understanding these questions is essential to framing the surrounding debate; and a good starting point is to ask those questions clearly as well as the metrics required to answer them. Specifically:

- 1) _Where are people employed?_ This refers to where existing jobs are located in an economy, requiring figures that provide a snapshot of the stock of jobs at any period in time.  We refer to this measure as the _share of the stock of employment._

- 2) _What is the rate of job creation or destruction in an economy?_ This concerns the addition or elimination of jobs over time, expressed as a ratio of existing jobs. This is oftentimes as important as understanding where people are employed as it illuminates the employment dynamism of an economy. Where appropriate firm-level data are available, researchers use measures not only of the net employment growth rate, but also of job creation and job destruction. These measures are referred to as the _net employment growth rate, gross job creation rate, and job destruction rate_ , respectively.

- A frequent focal point is the relative contribution of certain types of firms to these job dynamics. We call these measures the _share of net employment growth, share of gross job creation, and share of job contraction_ .

Each of these measures provides complementary views of the jobs landscape: the first question helps identify the key areas for current employment; the second indicates where most jobs are created or destroyed; while the third measures the relative importance of certain types of firms in these changes. The answers offered to these questions by the literature – and the debate surrounding them – have been shown to be quite sensitive to the definitions employed, the metrics used, and data sources available. Nevertheless, several findings have become well-established. We address these in turn, laying out the definitional and measurement issues encountered.

This paper proceeds as follows. Section II discusses the key jobs metrics used in literature to date. Section III provides an extensive literature review, addressing measurement issues as well as data challenges in developing economies. Section IV elaborates results specific for developing economies based on these metrics. Section V concludes.

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# **II.** **<u>Metrics of Job Creation and Firm Size</u>**

The last two decades have witnessed a substantial expansion of firm-level data that has in turn led to deepened analysis of the patterns of employment and job dynamics. These data helped to address important gaps, including the expansion of coverage to include both services and smaller firms, while building longitudinal databases has resulted in more precise measurements at shorter intervals, capturing entry and exit effects.<sup>2</sup> Importantly, these datasets incorporate firm-level characteristics, allowing for the comparison of the relative share of various groups of firms to the levels of employment and job growth (or contraction). As a result, the inclusion of these characteristics has effected a debate about where people are employed as well as where jobs are created or eliminated. Yet, despite this explosion of analysis in developed economies, similar sources are often unavailable in developing economies.

The resulting literature is far from unanimous. And conflicting findings are likely to be the result of underlying data sources – including whether data are panel or cross-sectional – and their coverage. Further, the metrics used also affect findings, stemming from issues such as what point is used as the starting base and how firms are classified. In fact, varying results can be obtained even using seemingly straightforward categories such as a firm’s size or its age. Adding further complications, whether data is collected at the “establishment” or “firm” level has non-trivial implications for the debate. An establishment is commonly understood to mean a singular physical business entity, while a firm is defined as the legal entirety of a business. In other words, a firm can encompass several establishments, a so-called multi-establishment firm. Measures of employment may be affected by the presence of multi-establishment firms as these firms may increase employment by opening new establishments rather than expanding employment in existing establishments.<sup>3</sup> Ayyagari et al (2014) note that the occurrence of multi-establishment firms in the developing world is infrequent and is likely to have little effect in those economies; however, the average number of multi-establishment firms in the World Bank’s Enterprise Surveys (which have been conducted in 215 rounds largely in the developing world) is 15 percent, indicating that this distinction must be taken into account.

> 2 Notably, the US Bureau of Labor Statistics (BLS) has developed and leveraged the microdata in the Business employment dynamics (BED) database for well over a decade (see Pivetz et al (2001) for example).

> 3 In estimating firm-level figures for employment, moreover, changing ownership structure, either through mergers, acquisitions, or legal maneuvering, may have a substantial effect Haltiwanger et al (2013) and Pinkston and Speltzer (mimeo).

3
# **1. Share of the Stock of Employment**

To define some basic terms for discussion, we refer to the total employment of each establishment _i_ , by using _E_ . We present the more general case with survey weights ( _wt_ ), which account for establishment _i's_ varied probability of selection relative to the total number of establishments in an economy,<sup>4</sup> for census or administrative data sources, _wt_ would be equal to one for all establishments.<sup>5</sup> We start with the question of where people are employed, using a metric that is defined as the share of employment ( _s_ ) over each analytical category (e.g. firm size, age, exporting status) or “cut” ( _c_ ), given by: Where �<sup>�</sup> �,� is the sum of establishment-level employment in a given analytical cut, given by �<sup>�</sup> �,� � ∑�� �,� ∗���, where ��,� ∗��� is the (weighted) establishment-level estimate of total employment at time _t._ The share of employment by various cuts has garnered particular attention in the literature, yet this measure is partial as it only gives a measure of the current state of employment and not how it is changing over time.

# **2. Net Job Growth Rate, Job Creation Rate, Job Destruction Rate**

To measure the changes in employment, we start with the measure of employment growth at the establishment level. In its most basic form, employment growth between period _t_ and _d_ , can be expressed as _gi,t_ = ��,�����,��,��� Where ��,� is employment in a base period, _b_ . A natural choice for this base is the beginning period of measurement, here employment at a lagged time, that is _b_ = _t-d_ (where _d_ is the time difference between the two periods). Davis et al (1996), however, note that using the initial size as the base can lead to upward bias in the growth estimate for smaller establishments: firms that reduce employment (say due to an adverse shock) are likely to be the quickest to increase employment after emerging from the shock. These same firms will then have a faster growth rate as they approach previous employment levels, showing a “regression to the mean” or “regression fallacy” effect. Using the end-period, _t_ , as the base conversely may have an undesirable downward bias for smaller firms. Davis et al., in response, use a measure to mitigate this bias, where the difference in employment between the two time periods is divided by the average employment over the two periods.

> 4 Other weighting methodologies can include an establishment’s relative contribution to value added or sales. 5 Official and administrative data sources, such as those produced by national statistical offices, are often considered as exhaustive and thus are by definition nationally representative. Where such sources are not available, nationally representative surveys can be employed; as these rely on randomly selected samples, though, they must be considered with their survey weights.

4
<u>(Eit—Eit-a )</u> 5 (Eiet+ Eit-d ) Git AE; = (Eit — Eit—a) AE = AE, * wt; Xi = 5 (Eit + Eit-a ) X = Xj * wt; (2a) ~~x;~~ Ic = > ~~=~~ * Git iec © X, = diec Xi Ic ~~Xs~~ = Yse ~~cg~~ * Ist (2b) ~~AE,~~ ge == ) ~~=~~ SEC X¢ X¢ Jc = (4% * 75%) + (8% * 25%) = (3%) + (2%) = 5% Ic = (3%) + (2%) = 5%
∆�� can be either positive or negative, and it is informative to separate positive-direction job creation rates from negative-direction job destruction (or contraction) rates. This distinction has been widely used and can be calculated separately for cases where ∆�� is positive and negative (using the absolute difference).

We distinguish _dir_ to indicate the direction of ∆��, and so includes (i) establishments with positive employment growth, or (ii) establishments with negative employment growth. These measures are referred to here as the _gross job creation rate_ and _job contraction rate_ , respectively.

# **3. Share of net job growth, share of gross job creation, and share of job contraction**

A statement frequently heard among policy makers is that firms of a certain type contribute X% to job creation (or contraction). This metric simply represents the relative contribution to each rate by each sub-cut s. Using the formulae for the rates for net job growth, this rate is expressed as: Most often, the cut is considered at the economy-level and provides a measure for the contribution of various sub-cuts – small and medium enterprises (SMEs) versus large, young versus old, services versus manufacturing, etc.

# **III. Literature Review of the Evidence on Employment Stock and Growth**

# **1. Where are people employed? Share of Employment Stock**<sup>**8**</sup>

Among the issues that have garnered the most attention in the literature is the role of firm size and whether large firms or SMEs are the largest employers in the private sector. Various theories predict that the answer to this question depends to a large extent on the economy’s stage of economic development. Biggs and Oppenheim (1986) for instance argue that the size composition of firms evolves with the process of structural transformation, noting that – generally – the size distribution of manufacturing firms increases with the level of economic development. Several historical studies of the evolution of specific industries in the US and UK corroborate these findings (Sokoloff 1984; Berg 1992; Lloyd-Jones and Le Roux 1980). However, evidence

> 8 Annex I includes a comprehensive table of this literature, findings, coverage, and methods.

6
shows that elsewhere in most developed and developing economies, SMEs employ the largest proportion of the workforce.

In reality, the findings on where people are employed have showed marked sensitivity to the cutoff and method for defining firm-size categories. Just a handful of studies from the US make this clear. As noted above, Davis et al (1996) show that the base-size definition makes a substantial difference. They utilize manufacturing census data for the US spanning from 1972 to 1988 to illustrate the point. When size is defined using initial employment, large manufacturing establishments (over 250 employees) employ about 57% of the total private sector workforce; this share moves to about 60% when average employment size is used to categorize firms. For a size cutoff of 500 employees, large firms account for about 40% of employment (using the average size this figure is 42%). Neumark et al (2011) expand this work, using the US National Establishment Time Series (NEST) data from 1992 to 2001, covering manufacturing and services sector firms. In fact, contradicting the findings of Davis et al, they report that firms with fewer than 250 workers employ over half of the total private sector workforce, regardless of whether size is determined using initial or average employment.<sup>9</sup> Refuting this, Haltiwanger et al. (2013), employ a dataset<sup>10</sup> that covers all private sector establishments with at least one paid employee, from 1995 to 2005, finding that large firms are in fact the biggest employers of non-farm private sector labor in the US. In particular, they report that firms with 500 or more employees constitute roughly half of the total stock of employment in the US, with large and mature firms representing the single-largest plurality.

However, studies from other developed countries are less ambiguous on the role of SMEs as the major source of employment. Lawless (2014), for instance, uses panel data from Ireland (from 1972 to 2010) showing once again that the size cutoff matters. Firms with at least 50 employees account for less than one third of total employment while those with 250 or fewer employees constitute approximately two-thirds of the total workforce. Further, in a comprehensive study of census data from 18 (mostly OECD) countries, Criscuolo et al (2014) find that SMEs, defined as those with 250 or fewer employees, account for over 50% of total employment in all 18 countries except the US. In fact, in six of the countries (Brazil, Japan, Italy, New Zealand, Portugal, and Spain) firms with fewer than 50 employees account for the majority of employment. Using registry data from the UK (1997 to 2008), Hijzen et al (2010) report that firms with 250 or more employees account for about 53% of the total employment, although the contribution falls to approximately 42% when size is measured using average employment. Using manufacturing census data<sup>11</sup> from Norway (1976 to 1986), Klette and Mathiassen (1996) show that firms with 150 or fewer employees (regardless of whether firm size is

> 9 Using a size cutoff of 100 employees, the proportion accounted for by SMEs is 44%

> 10 The US census Bureau’s Longitudinal Business Database (LBD), updated and improved version of the data used by Davis et al (1996).

> 11 Excluding firms where the owner is the only employee.

7
measured using initial or average size) account for about 60% of the total labor force. Similarly, Genda (1998), using census data of firms with 5 or more employees for Japan, reports that firms with fewer than 100 employees account for over 65% of the total, non-agricultural labor force.<sup>12</sup> In fact, Japan appears as somewhat of an outlier among OECD countries in that SMEs play such a large role. This is confirmed by Criscuolo et al (2014), who place the contribution of firms with fewer than 250 employees at about 85% of the total Japanese labor force.

In contrast to this wide body of findings for developed countries, there are few similarly comprehensive studies for developing countries. The results that are available, however, are fairly conclusive that SMEs are the majority source of private sector employment. Ayyagari et al (2014) provide evidence on this issue using crosssectional data for 104 countries based on the World Bank’s Enterprise Surveys.<sup>13</sup> They show that in low income and lower middle-income countries, firms with fewer than 100 employees (excluding those with fewer than 5), account for over 50% of total employment, but employ less than half the total workforce in upper middleincome and high-income countries. With a higher cutoff size of 250 employees, however, SMEs employ well over half of the total workforce. Similarly, Arrow et al (2014), using quarterly survey data from South Africa<sup>14</sup> between 2005 and 2011, report that firms with 250 or fewer employees account for over 50% of total employment.<sup>15</sup> Underscoring these findings is the well-documented and often-discussed dominance of very small (often self-employed and unregistered) enterprises in these economies (Tybout, 2000). Most available data for developing countries, such as the Enterprise Surveys exclude smaller (micro) and informal firms. Therefore, the contribution of small firms to employment share is likely to be underestimated in analysis using these data Rijkers et al (2014) corroborate this using business registry data from Tunisia, covering firms of all sizes over the period 1996-2010.<sup>16</sup> Strikingly, self-employed workers alone account for about 28% of the total non-farm employment, while firms with fewer than 100 employees account for about 62% of total employment in an average year. Similar results reported for Egypt and the West Bank and Gaza find that firms with 5 or fewer employees account for over half of the total workforce in the private sector (World Bank 2015).<sup>17</sup> Studies using manufacturing census data from Ethiopia and India underscore the effects of utilizing datasets that exclude the smallest firms. Bedi and Shiferaw (2013) use the annual Ethiopian manufacturing

> 12 This is even a conservative measure as taking into account firms with fewer than 5 employee would, therefore, raise this contribution.

> 13 The World Bank’s Enterprise Survey (ES) data is used for 64 of the countries; for the rest of the countries, data is from various sources.

> 14 One of the countries not covered by Ayyagari et al (2014)

> 15 The survey excludes firms that are not required to file for VAT (for instance, in 2009, only firms with a turnover of over one million South African Rand, roughly about $130,000, were required to file for VAT), and hence is likely to underestimate the stock of employment accounted for by the SMEs.

> 16 Though excluding agriculture and co-operatives.

> 17 The study also finds that firms with 100 or fewer employees account for over half of the total employment in all 5 countries where data is available - Egypt, Jordan, Tunisia, Turkey and West Bank and Gaza.

8
census for firms with at least 10 employees from 1997 to 2007, reporting that firms employing fewer than 50 account for a scant 15% of total employment. However by excluding services firms as well as manufacturers with fewer than 10 employees this is likely a substantial underestimate: a survey conducted by the country’s Central Statistical Agency (CSA) in 2011 shows that manufacturing firms with fewer than 10 workers<sup>18</sup> employed three times the total generated by all manufacturing firms with 10 or more employees (CSA, 2012, 2013). Hasan and Jandoc (2009) further illustrate the effect of this low-end censoring in India. They combine data from the annual manufacturing census of formally registered firms, conducted by India’s Central Statistical Organization, with a similar dataset for unregistered manufacturing firms. They report that micro enterprises (with 1 to 5 employees) account for about 84% of total employment in manufacturing, while those with 200 or more employees account for only 10%. Excluding single-employee operations does not qualitatively change the picture: firms with 5 to 49 employees account for nearly 60% of the total manufacturing employment (ADB 2009).

Another important dimension in this debate is whether younger or older firms are more important in the share of stock of employment. Here there is greater consensus: older firms appear to be the most important contributors to the stock of employment, in developing and developed countries alike. In their recent comprehensive study for the US, Haltiwanger et al (2013) report that mature firms (10+ years) account for over 70% of the employment stock on average between 1992 and 2005. Further, Criscuolo et al (2014) also show that contributions to employment stock increases with firm age. In particular, mature firms account for over 60% of total employment. What is more, within each size category, older firms account for the largest share of employment stock, indicating that the results are not driven by the positive association between firm age and size. Klette and Mathiassen (1996) report similar results for Norway, showing that the share of the stock of employment steeply increases with firm age; in fact, firms 15 years or older alone account for over 85% of the total manufacturing sector employment.

The predominance of older firms holds in developing economies as well. Rijkers et al (2014) report that for Tunisia, employment shares increase with firm age, noting that firms that are 10 years or older account, on average, for about half of the total employment stock. Similarly, Ayyagari et al (2014) report that older firms, 11+ years of age, are the largest contributors to employment; large-old firms alone, for instance, account for about 35% of the total stock in their study. Eslava and Haltiwanger (2014) provide similar results using manufacturing census data in Colombia, noting that – at least among firms with 10 or more employees – the share of employment stock increases with age.

> 18 This survey excludes handcraft and cottage manufacturing establishments and is restricted to manufacturing establishments with less than 10 employees that utilize power-driven machines to produce goods and services.

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**2. Who creates (and destroys) jobs? Net employment growth, gross job creation, and job contraction**

A strain of thought cites the predominance of (often older) SMEs as an indication of market failure; in efficient and undistorted markets, productive firms remain in business and grow, while unproductive ones leave the market. The persistence of small but old firms as majority employers can be regarded as problematic, and evaluating firm-growth dynamics is thus essential. However defining these metrics is often complicated: in developed economies a standard threshold of 500 or 250 employees is typically used as the SME threshold; in developing economies, the cutoff most commonly used is 100 employees. Complicating this issue are the clear and appreciable effects of whether initial or average firm size is used to categorize firms. Additionally, a growing number of studies focus on the role of firm age in the process of job creation. Although theoretical studies (e.g., Jovanovic 1982) assume that firm growth declines with age, the intuition behind this negative relationship is not often clear. On the one hand, younger firms may begin business with new and highly competitive ideas and strategies (Perez et al 2004), or these new firms are possibly more flexible to adapt to their environment than older ones. Alternatively, what new firms have in flexibility and preparedness, they lack in experience and resources. Younger firms may grow less as mobilizing suitable factors of production and establishing relations with customers and suppliers may take time. Further, models of learning by doing imply older firms benefit from their accumulated stock of experience and grow faster than younger ones (Coad et al, 2014).

Most existing studies focus on developed countries. In the US, Davis et al (1996) challenge the prevailing view that small firms are the largest contributors to net job creation, arguing that using initial size to categorize firms attributes undue credit to small firms. Using the average measure of size, they show that there is no systematic relationship between firm size and net job creation rates. Small firms indeed do have higher _gross job creation rates_ . However, these firms also display higher _job destruction rates_ . That is, they note that smaller firms demonstrate greater employment churning, observing that “[f]or both new jobs and the typical existing job, job durability increases with employer size” (p. 312). By contrast, the recent US study by Neumark et al (2011) shows that the association between firm size and _net job creation rates_ is in fact negative. In particular, _net job growth rates_ steeply decline with firm size (measured using the average employment level), although at a lower rate when the results are reported at the establishment instead of firm level. Underlying this pattern is the fact that _gross job creation rates_ decline with firm size (when size is measured using the average employment size), albeit non-monotonically, while _job destruction rates_ tend to slightly increase with firm size.<sup>19</sup>

> 19 The authors nevertheless did not report contributions to net job growth by firm size.

10
Again, Haltiwanger et al (2013) add a further twist to the debate. They show that the inverse relationship between firm size and _net job growth_ persists even when the average measure of firm size is used. However, an interesting pattern emerges once start-up firms are singled out and treated separately. Start-up firms account for a large share of net job creation in almost all firm size classes, and particularly so among small firms. To disentangle the effect of start-ups, the authors employ regression analyses, reporting that the negative association between firm size and _net job creation rates_ disappears (or becomes positive) once firm age is included, regardless of how firm size is measured.<sup>20</sup> In terms of job loss due to exit, there is an inverse relationship between size and exit, in line with findings elsewhere that small firms are more likely to exit than large ones.<sup>21</sup> They note, thus, “that the firms that have the most jobs create the most jobs, so workers looking for the places where the most jobs are being created should go where the jobs are: large and mature firms.” (p. 350). Since exit rates decline with firm age, the authors argue that this indicates the “up-or-out” phenomenon where new firms either grow fast or exit the market altogether.

Similar findings have been reported for other developed economies. Criscuolo et al (2014) show that small- and medium-young firms have higher net job growth rates than old-large firms (that is, firms 5 years or older and with 250 or more employees) in the 18 countries studied. Lawless (2014) likewise reports that firm size and _net job growth rates_ are negatively associated. However, as in Haltiwanger et al (2013), start-ups play an important role, and when they are removed from the analysis, _net job growth rates_ increase with firm size, lending support to the view that firm age is perhaps what truly matters. Although smaller firms exhibit high _gross job creation rates_ , they also have the highest _job destruction rates_ , resulting in a substantially negative _net job growth rate_ . The Hijzen et al (2010) study on the UK did not report the association between firm size and _net job creation rates._ However, they show that small firms, with fewer than 20 employees, contribute more to _gross job creation_ than they do to _job destruction_ . Further, jobs created by smaller firms are no less durable than the ones created by larger firms.

Ayyagari et al (2014) argue that SMEs are the main contributors to net job growth in most developing economies. Of the 104 countries in their sample, 85 have positive _net job growth rates_ . Among these, in the median country, firms employing 5 to 99 individuals account for about 75% of the total net jobs created. Moreover, in the 18 countries with negative _net job growth rates_ , small firms in fact registered positive _net job growth rate_ , meaning that job destruction by large firms (with 250+ employees) is so high as to outweigh the jobs created by small

> 20 It is important to note that even without controlling for firm age, the association between firm size and net job creation rates depends on how firm size is defined. Using average measure of firm size, the negative association disappears even without controlling for firm age and the association becomes positive when firm size is controlled for.

> 21 See, for instance, Jovanovic (1982); Pakes and Ericson (1998); Aldrich and Auster (1986); Aga and Francis (2015).

11
firms. Nevertheless, given that the data they use is cross-sectional, firm exit and the associated job destruction is not accounted for, and limited information is provided on jobs created by new entrants.

Rijkers et al (2014) confirm the notion that SMEs account for a larger share of net job growth in Tunisia. In fact, every year from 1996-2010, on average, firms with fewer than 10 employees account for about 55% of total net employment growth and those with fewer than 100 employees account for 73%. Strikingly, for almost all firm size groups, all net jobs are created by firms that are one year-old or younger, indicating again the importance of firm age. They also confirm that, as s in Haltiwanger et al (2013), firm size and _net job creation rates_ are negatively correlated without controlling for firm age. However, when firm age is controlled for, the association becomes positive indicating that net job growth rates are higher among larger firms. This is again an indication that firm age is perhaps more important than size to explain net job growth. Firm exit is negatively associated with size, but the association becomes mute once firm age is accounted for.<sup>22</sup> They report that firms with fewer than 100 employees account for about 49% (based on the average measure of size) to 56% (based on the base size) of _gross job creation_ . With a higher cutoff size of 250 employees, the contribution becomes 56% and 67%, respectively. Contribution to _job destruction_ by these firm categories is generally lower than their contributions to gross job creation. As a result, _net job growth rates_ by firms with 250 or fewer employees is substantial. In fact, based on the base size definition, firms with fewer than 10 employees alone account for almost all the _net job growth_ , although the figure drops to 60% when using the average measure of firm size.

Bedi and Shiferaw (2013) provide some additional evidence pointing to the importance of larger firms to job creation in Ethiopia. Although entry, often by smaller firms, accounts for over 50% of job creation, these jobs tend to be transitory as small firms have a higher likelihood of exit and a limited likelihood of graduating to a larger size category. As a result, over the 10 year period studied, there was a significant re-allocation of jobs from smaller to larger firms. In fact, small firms, between 10 and 50 employees, accounted for just about 28% of the gross job creation, on average. Similarly, Arrow et al (2014) show that larger firms exhibit higher _net job growth rates_ in South Africa. Although the _gross job creation rate_ (at 10%) is roughly similar across various firm size categories, _job destruction rates_ decline monotonically as firm size increases, resulting in higher _net job growth rates_ among larger firms.<sup>23</sup> In terms of job dynamics and firm age, younger firms appear to play a dominant role than the older ones, both for developing and developed economies. Haltiwanger et al (2013) report the negative association between firm age and _net job creation rate_ for the U.S is mainly driven by higher net job creation rates by start-up

> 22 World Bank (2015) provides similar results for Lebanon and Tunisia.

> 23 The authors note that the dataset is likely to underestimate firm birth, potentially underestimating _gross job creation rates_ among smaller firms since, as is widely documented, entrants tend to be smaller firms. Nevertheless, the authors conjecture that this fact doesn’t substantively change the fact that larger firms have higher net job creation rates.

12
firms. Taking out these firms from the analysis substantially weakens the association. Nevertheless, if the analysis is restricted to surviving firms (i.e., firms that remained in business during the period studied), there is a clear negative association between _net job creation rates_ and firm age, indicating younger firms create new jobs at a faster rate. In Norway, Klette and Mathiassen (1996) report that gross job creation and destruction rates decline with firm age for manufacturing sector firms. In particular, they show that the _gross job creation rates_ fall off more quickly than _job destruction rates_ as firms mature: consequently, the _net job creation rate_ declines with firm age. Lawless (2014) likewise reports a negative relationship between firm age and _net job growth rates_ . She shows that _gross job creation rates_ decline with firm age while no systematic relation is observed between age and _job destruction rate_ s. Consequently, _net job growth rates_ decline with firm age. Criscuolo et al (2014) furthermore indicate that young firms, 5 or less years old, have higher _net job growth rates_ . Although young and older firms have roughly comparable _job destruction rates_ , younger firms have higher _gross job creation rates_ than their older counterparts; hence the higher _net job growth rates among younger firms_ .

For developing countries, Ayyagari et al (2014) also find that younger firms, 5 years or less, grow faster than older ones. Regarding the contribution to _gross job creation_ , however, the pattern is not clear: younger firms are the largest contributors to gross job creation among small firms (fewer than 20 employees), while older firms are the largest contributors to gross job creation for medium and larger firms.<sup>24</sup> Ma et al (2015) report results using census data for Chinese manufacturing firms over 1998 to 2007, showing that _gross job creation rates_ steeply decline with firm age while _job destruction rates_ increase. Consequently, _net job growth rates_ and firm size are negatively correlated. Eslava and Haltiwanger’s (2014) study on Colombia reports results that are consistent with recent findings for the US (Haltiwanger et al 2013). They show that _net job growth rates_ decline with firm age, noting that the high growth performance by young firms is driven largely by a small group of high growing start-up firms. Nevertheless, they report a pattern where surviving firms grow, but job destruction due to firm exit is not systematically related to firm age, indicating less of the “up-or-out” dynamic reported for US firms. Using manufacturing census data from Ethiopia, Bigsten and Gebreeyesus (2007), report a non-linear relationship between firm age and net job growth rates: job growth declines with firm age for firms 9 years or younger, but increases with age for those over 9 years perhaps indicating that the learning-by-doing benefits are realized only in the later stages of a firm’s life cycle.

# **IV. Updated Results Using Data from the Enterprise Surveys**

In this section we provide estimates for 117 developing economies based on data from the World Bank Group’s Enterprise Surveys. As the bulk of the debate around these measures is centered on the role of size

> 24 Results are not reported on contribution to net job creation and job destruction by firm age

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and age, we focus on these analytical cuts. In doing so, we update measures presented by Ayyagari et al (2014), hereafter “ADM”, expanding on their results by including additional economies published since their contribution as well as by disaggregating net job growth into gross job creation and job contraction separately. We also consider alternate specifications from Haltiwanger et al (2013), denoted by “HJM”. We find evidence confirming several findings from both studies, showing that these results are markedly sensitive to specifications and the definitions used to classify establishments.

The Enterprise Surveys are a standardized and globally comparable database, providing establishmentlevel data representative of the non-agricultural private sector economy. As such, they cover clearly defined economic sectors, in manufacturing and services, following a stratified survey design and thus are interpreted here using survey weights to account for the varied probability of selection of each establishment. As noted in several studies, including ADM, these data are subject to several caveats, including issues of non-response and data coverage. For instance, by design for practical implementation, the surveys do not cover micro establishments (with fewer than 5 employees) nor do they cover the informal (un-registered) economy. Therefore, these data are likely to underestimate the importance of SMEs. The surveys are also answered voluntarily and therefore they may face some degree of non-response. Nevertheless, the surveys provide a widely comparable data source for most developing economies and they constitute the best approximation for employment dynamics in the aggregate. Annex II provides descriptive statistics of the data used for the results provided below.

# 1. <u>The Share of the Employment Stock: Where are the Jobs?</u>

In 70 of the 117 economies, SMEs, with fewer than 100 employees, account for the majority of employment. On average, this share is 57%, with large establishments accounting for 43%. Once the cutoff is changed to 250 employees, SMEs employ the majority of employees in 104 of 117 economies, with a resulting average of 75% of the stock of employment (columns 1-4 of Table 1). These results are robust to the categorization of establishments using the average employment, and these are shown in columns 5-8. That is, no matter how size is defined, SMEs constitute the majority of employment in the developing world. Columns 9-11, moreover, show that establishments that are ten years or younger account for over two-fifths of employment in the developing world; mature establishments that have been operating for 11 years or more constitute over 60% of the stock of employment, consistent with findings reported by ADM. What is more, in line with their findings, the employment share of SMEs is highest among low-income economies, when compared to their counterparts elsewhere.<sup>25</sup>

> 25 Averages by income group not shown.

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**Table 1:  Share of the Employment Stock**

|100 emp|Based o<br>. cutoff|n Initial<br>250 emp|. cutoff|100 em|Based on<br>p. cutoff|Average<br>250 emp|. cutoff||||
|---|---|---|---|---|---|---|---|---|---|---|
|SME|Large|SME|Large|SME|Large|SME|Large|Young<br><=5 yrs.|Mid-age<br>6-10 yrs.|Mature<br>11+ yrs.|
|57.2|42.8|75.4|24.6|60.3|39.7|79.0|21.0|17.9|19.8|62.3|

_Age and initial size defined by the re-call period prior to the survey. Average-period categories defined by average establishment size between t and t-d. For age cuts the share of period average employment is reported._ Table 2 shows these results further divided by establishment size-age categories, where establishments are divided between SMEs and large and by age categories (young, mid-age, and mature).<sup>26</sup> Following the methodology used by ADM and elsewhere, both age and size groupings are defined by the initial period recorded in the survey. Where the period average is used to define size cuts, age remains defined by the initial period. In line with findings from ADM, mature establishments constitute the largest single share of employment stock by all size groups and definitions. As a departure from their findings though, we find that mature SMEs are the largest plurality, though very closely followed by large mature establishments; their findings indicate that these two groupings are likewise the largest source of employment, but in the reverse order.

**Table 2: Share of the Employment Stock – By Establishment Size-Age**

|||Based on|Initial|||Based on|Average||
|---|---|---|---|---|---|---|---|---|
||100 emp|. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|11.5|4.0|13.6|1.8|13.7|4.2|16.2|1.7|
|Mid-age|13.4|6.6|16.1|3.9|13.7|6.1|16.7|3.1|
|Mature|32.4|32.2|45.6|19.0|32.9|29.4|46.1|16.2|

_Age and initial size defined by the re-call period prior to the survey. Average-period categories defined by average establishment size between t and t-d._

# 2. <u>Job Growth Rates: What Establishments Are Creating or Contracting Jobs?</u>

Among developing economies, the net employment growth rate, i.e., the rate at which the stock of employment is growing, is 5.2% per annum. On net, this rate is driven by SMEs, supporting the results from

> 26 It should be noted however that the ES data while stratified by criteria for size, sector, and location, the methodology is not stratified by firm or establishment age and thus does not ensure representativeness on that dimension beyond the random sampling within the specified strata.

15
ADM, with a rate roughly equal to the overall average (Table 3). Using categories based on the initial employment, in fact, the net growth rate for large establishments is 0.4%, while the rate among SMEs is 4.8%. The net growth rate for SMEs decreases (as the rate among large establishments increases) when using the period average, confirming the upward (downward) bias of using initial size for SMEs (large establishments) as pointed out by Davis et al (1996). Panel b additionally shows gross job creation rates, indicating again that these rates are highest among SMEs, by all metrics. The gross job creation rates are highest among mature establishments, at a rate of 3.4%, followed by young establishments; however taken together establishments that have been operating for ten years or less (young and mid-age establishments) demonstrate a gross job creation rates slightly above the rate for mature establishments (5% for establishments in operation for 10 years or less compared to 3.4% for mature establishments).

**Table 3: Job Growth Rates**

# (a) Net Employment Growth Rate

||Based o<br>|n Initial<br>|||Based on<br>|Average<br>|<br>||||
|---|---|---|---|---|---|---|---|---|---|---|
|100 em|p. cutoff|250 emp|. cutoff|100 em|p. cutoff|250 emp|. cutoff||||
|||||||||Y|Mid|M|
||||||||||-||
|SME|Large|SME|Large|SME|Large|SME|Large|g<br><=5yrs.|g<br>6-10yrs.|<br>11+yrs.|
|4.8|0.4|5.1|0.1|4.0|1.3|4.7|0.5|2.9|1.1|1.2|
|(b) Gross|Job Crea<br>Based o|tion Rate<br>n Initial|||Based on|Average|||||
|100 em|p. cutoff|250 emp|. cutoff|100 em|p. cutoff|250 emp|. cutoff||||
|SME|Large|SME|Large|SME|Large|SME|Large|Young<br><=5yrs.|Mid-age<br>6-10yrs.|Mature<br>11+yrs.|
|6.5|2.1|7.5|1.1|5.9|2.7|7.3|1.4|3.3|1.7|3.4|

# (c) Job Contraction Rate

|100 em|Based o<br>p. cutoff|n Initial<br>250 em|p. cutoff|100 em|Based on<br> p. cutoff|Average<br>250 emp|. cutoff||||
|---|---|---|---|---|---|---|---|---|---|---|
|SME|Large|SME|Large|SME|Large|SME|Large|Young<br><=5yrs.|Mid-age<br>6-10yrs.|Mature<br>11+yrs.|
|1.7|1.7|2.4|1.0|1.9|1.5|2.6|0.8|0.5|0.6|2.2|

_Age and initial size defined by the re-call period prior to the survey. Average-period categories defined by average establishment size between t and t-d._ Gross job growth rates are more instructive when also compared with job contraction rates<sup>27</sup> as shown in panel c of Table 3. Taken together, a fuller picture emerges: while SMEs show higher gross job creation rates, they also have higher job contraction rates (in all but the SME100, categorization based on initial employment, in which case SMEs and large firms share the same rates of contraction), indicating greater employment churning for these enterprises. Moreover, the differential between SMEs and larger establishments (by all

> 27 We refer to these rates as “contraction”, instead of destruction, as the ES data do not contain information on exiting establishments and thus may effect a survival bias. Contraction rates are thus only calculated for surviving establishments that have reduced their employment numbers.

16
metrics) is smaller in terms of job contraction than in gross job creation; this explains the higher _net job growth rates_ seen among SMEs. Notably, older establishments show higher contraction and gross creation rates, which result in a lower net rate: mature firms reveal higher churning than younger firms. This is a possible indication of a momentum of incumbency whereby older establishments that show employment contraction remain in the market, while young and mid-age may exit the market rather than being capable or willing to weather shocks, a so-called liability of age (for some discussion see Aga and Francis 2015).

Figure 1 further illustrates this point, particularly the volume of job churning (meaning creation and contraction combined) that is substantially higher among SMEs and among mature firms. What is more, the bias between using initial employment values to categorize establishments (as opposed to the average) is also clearly seen: both the gross creation rate and the net growth rate for SMEs are lower using the period average employment to categorize establishments than when using the initial level of employment, while both rates are higher among larger establishments when using the average level of employment for categorization. The opposite trend is shown for contraction rates (shown below the x-axis at zero). In the figure, this creates a convex U-shaped pattern for both gross job creation and job contraction (noting for bars 1-4 and 5-8 of Figure 1).

**Figure 1: Net Job Growth, Gross Job Growth, and Job Contraction**

<!-- Start of picture text --> 8.0 7.5 7.3 Gross Job Creation Rate<br>6.5<br>5.9 Job Contraction Rate<br>6.0<br>Net Job Growth Rate<br>5.1<br>4.0 4.8 4.7 3.3 3.4<br>4.0 2.7<br>2.1<br>1.7<br>2.0 1.4 2.9<br>1.1<br>0.4<br>1.3 0.1 0.5 1.1 1.2<br>0.0<br>0.0 0.0<br>-0.5 -0.6<br>-1.0 -0.8<br>-2.0 -1.5<br>-1.7 -1.7<br>-1.9<br>-2.4 -2.6 -2.2<br>-4.0<br>SME100 (in.) SME100 (avg.) Large100 (in.) Large100 (avg.) SME250 (in.) SME250 (avg.) Large250 (in.) Large250 (avg.) Young Mid-Age Mature<br><!-- End of picture text --> Source: World Bank Enterprise Surveys. Categories defined by base values defined by initial value (in.) or the average value between the two periods (avg.).

17
Table 4 presents the relative scale of these rates by expressing each group’s share of net employment growth, gross job creation, and job contraction. Using categories defined by initial employment, SMEs account for nearly all net employment growth, at a rate well above 90%. Using the more conservative categories based on average employment, SMEs under 100 employees account for more than three quarters of net job creation; 90 percent of net employment creation in these economies is accounted for by establishments with fewer than 250 employees, as shown in panel a. Table 4, Panel b presents the relative share of establishments in gross job creation. Again, SMEs dominate. SMEs with fewer than 100 employees account for 76% of gross job creation; establishments smaller than 250 employees account for roughly 90% of gross job creation. In terms of job contraction (panel c), however, SMEs (those with fewer than 100 employees) and large establishments constitute roughly equal shares. Using the SME250 threshold, however, SMEs account for more than 70% of job contraction. This movement is only exaggerated by using the average establishment size.

Generally then, while SMEs account for the vast majority of job creation, they also constitute the majority of job contraction. The relative share of these establishments to large establishments is such that on net SMEs still contribute positively to the majority of net job growth. Older establishments additionally account for the majority of job contraction and in fact it is this high relative contraction among older establishments that accounts for the low relative share of these establishments to net job creation. This finding is undergirded by several others in the literature in line with vested older establishments such as Jovanovic (1982) That is, older establishments are more likely to remain in the market (thus captured in the ES data) and contract, while younger establishments are predicted to exit the market altogether (which is not captured by the ES data).<sup>28</sup>

**Table 4: Job Growth Shares**

# (a) Net Employment Growth Shares

||Based o|n Initial|||Based on|Average|||||
|---|---|---|---|---|---|---|---|---|---|---|
|100 em|p. cutoff|250 emp|. cutoff|100 em|p. cutoff|250 emp|. cutoff||||
|SME|Large|SME|Large|SME|Large|SME|Large|Young<br><=5yrs.|Mid-age<br>6-10yrs.|Mature<br>11+yrs.|
|92.6|7.4|97.8|2.2|75.9|24.1|90.2|9.8|56.0|21.6|22.4|

# (b) Gross Job Creation Shares

||Based o|n Initial|||Based on|Average|||||
|---|---|---|---|---|---|---|---|---|---|---|
|100 emp|. cutoff|250 emp|. cutoff|100 em|p. cutoff|250 emp|. cutoff||||
|SME|Large|SME|Large|SME|Large|SME|Large|Young<br><=5yrs.|Mid-age<br>6-10yrs.|Mature<br>11+yrs.|
|75.8|24.2|87.2|12.8|68.6|31.4|84.3|15.7|39.5|20.3|40.2|

> 28 Though this finding does in fact hold up in analysis based on ES panels, such as in Aga and Francis (2015).

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# (c) Job Contraction Shares

||Based o|n Initial|||Based on|Average|||||
|---|---|---|---|---|---|---|---|---|---|---|
|100 em|p. cutoff|250 emp|. cutoff|100 em|p. cutoff|250 emp|. cutoff||||
|SME|Large|SME|Large|SME|Large|SME|Large|Young<br><=5yrs.|Mid-age<br>6-10yrs.|Mature<br>11+yrs.|
|49.9|50.1|70.9|29.1|57.2|42.8|75.1|24.9|14.0|18.3|67.7|

Table 5 shows these growth rates by age-size categories. Again, confirming previous results from ADM young SMEs show the largest rate of net employment growth across all age groups regardless of the size thresholds used to define SMEs and the method used to classify establishments. The table furthermore shows the same trend in the gross job creation rates. Regarding job contraction rates, in Table 5, mature large firms exhibit greater contraction rates than SMEs when the cutoff threshold is 100 employees.

**Table 5: Job Growth Rates by Establishment Size-Age**

<mark>(a) Net Employment Growth Rate</mark>

|||Based o<br>|n Initial<br>|||Based o<br>|n Average<br>||
|---|---|---|---|---|---|---|---|---|
||100 em|p. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|2.6|0.3|2.8|0.1|2.4|0.5|2.7|0.1|
|Mid-age|0.9|0.2|1.0|0.1|0.7|0.4|0.9|0.2|
|Mature|1.2|0.1|1.3|0.1|0.8|0.3|1.0|0.1|

## (b) Gross Job Creation Rate

|||Based o<br>|n Initial<br>|||Based o<br>|n Average<br>||
|---|---|---|---|---|---|---|---|---|
||100 em|p. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|2.9|0.5|3.2|0.2|2.7|0.6|3.1|0.2|
|Mid-age|1.3|0.4|1.5|0.2|1.1|0.6|1.4|0.3|
|Mature|2.2|1.2|2.7|0.7|1.9|1.5|2.6|0.8|

|||Based o<br>|n Initial<br>|||Based o<br>|n Average<br>||
|---|---|---|---|---|---|---|---|---|
||100 em|p. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|0.3|0.2|0.4|0.1|0.3|0.1|0.4|0.1|
|Mid-age|0.4|0.2|0.5|0.1|0.5|0.1|0.6|0.1|
|Mature|1.0|1.3|1.5|0.8|1.1|1.2|1.5|0.7|

Table 6 shows the same rates as shares, broken down by establishment size-age categories. Here again, young SMEs constitute the largest share of both net employment growth and gross job creation. This is the result of a combination of both their relatively high gross job creation share and also a small relative share of job contraction. For larger-mature establishments, by contrast, gross job creation shares are also notably high, consistently higher than mid-age and young, large establishments. However, mature, large establishments also
show the highest contraction shares which explains why their net employment growth share is lower than SMEs. This is visually clear in Figure 2, which shows an appreciable peak for mature establishments; together mature SMEs (29%) and large (39%) make up nearly 68% of job contraction, both a possible indication that their longer tenure in the market may dissuade them from exiting altogether (as opposed to younger SMEs, in line with models set out by for instance Jovanovic 1982).

**Table 6: Job Growth Shares by Establishment Size-Age**

|(a) Net E|mploymen|t Growth Sha<br>|re<br>||||||
|---|---|---|---|---|---|---|---|---|
|||Based o<br>|n Initial<br>|||Based on<br>|Average<br>||
||100 em|p. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|48.6|5.4|51.8|1.6|46.8|9.2|53.2|2.8|
|Mid-age|17.3|3.6|18.0|2.7|13.2|8.5|17.1|4.5|
|Mature|23.3|1.7|23.6|2.3|15.9|6.4|20.0|2.4|
|(b) Gros|s Job Creat|ion Share<br>Based o|n Initial|||Based on|Average||
||100 em|p. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|34.1|5.3|37.3|2.1|32.3|7.2|36.9|2.6|
|Mid-age|15.6|4.7|17.6|2.8|13.6|6.8|17.0|3.4|
|Mature|26.1|14.1|32.3|7.8|22.6|17.6|30.4|9.8|
|(c) Job C|ontraction|Share|||||||
|||Based o|n Initial|||Based on|Average||
||100 em|p. cutoff|250 em|p. cutoff|100 em|p. cutoff|250 em|p. cutoff|
||SME|Large|SME|Large|SME|Large|SME|Large|
|Young|9.0|5.0|11.2|2.8|9.9|4.1|11.7|2.2|
|Mid-age|12.0|6.3|15.5|2.8|14.2|4.2|16.7|1.6|
|Mature|29.0|38.7|44.1|23.7|32.8|34.9|46.4|21.3|

20
# Lut bv pol
out by HJM, as defined in equation (2a) in section 2 above. Using the initial size as the base (columns 1-4), the dummy for large establishments is negative and significant, indicating lower net employment growth compared to small establishment, all else equal. This holds even after age is introduced as a control.<sup>31</sup> These findings closely follow those by ADM, with largely comparable coefficients (note that here employment growth figures are scaled by 100). Likewise once establishment age is used as a control, it is associated with lower net employment growth. These findings also remain significant once the SME size threshold is raised to 250 employees (columns 3-4). However a divergence is also clear: when using the period-average size to categorize establishments (columns 6-7), the coefficient of large versus SMEs is notably smaller (though still significant). In fact, once the size threshold is raised to 250, in fact, there is no statistical difference between SMEs and large establishments. By contrast, the effect of an establishment’s age is negative and persistent across all specifications, indicating a strong effect that older establishments are less dynamics in terms of employment growth.

The second panel of Table 7 presents regression results that are weighted by each establishment’s employment share.<sup>32</sup> While columns 9-12 hold closely to the results in the first panel, when using the average size to classify establishments, once age is controlled for, the difference between SMEs and large establishments becomes insignificant (column 14). This aligns with the conclusions arrived at by HJM, that while establishment size is negatively associated with employment growth, this effect loses significance after controlling for age. The fact that size becomes less important when using the average size – even though all specifications control for age – underscores the importance of how establishment size is defined.

# [Insert table 7 here]

It is also worth noting that part of this divergence is definitional. Columns 1-8 present establishmentlevel results, and thus are indicative of the average experience at this level; they show, in other words, young and small establishments on net are the most dynamic. By weighting observations by their employment share, columns 9-16 provide results in broader strokes, that is, in terms of the aggregate effects on the whole private sector. This distinction is likely to be of particular relevance for informing policy makers, as the former results speak to firm-level interventions (such as training programs), while the latter lend themselves to more broadly administered policies (for example, lowering policy barriers to firm entry).

Tables 8 presents similar results for the sub-set of establishments that report positive employment growth, so-called gross job creation. Here there is no apparent distinction between using either initial

> 31 Table 7 includes categorical age variables; Appendix II (online) shows results are robust to using the continuous measure of age.

> 32 This is the weight as determined by the joint survey and employment weight.

22
establishment size or the two-period average: in all columns, the coefficient for large establishments as well as mid-age and mature dummies are negative and significant, indicating that small and young establishments are expected, _ceteris paribus,_ to have higher job creation. This is likewise consistent whether using the basic estimations in columns 1-8 or using employment weights as shown in columns 9-16. More generally, the divergence between specifications shown in table 7 does not appear to be effected by positive-growth establishments.

# [Insert table 8 here]

By contrast, Table 9 presents estimates of job contraction using establishment-level data for establishments that reported contraction in employment, providing a potential explanation for the divergence of results using either the initial or average establishment size to classify establishments. Only in column 2 is the coefficient for large establishments negative and significant, meaning that these establishments on average contract jobs at a higher rate compared to SMEs. However, interestingly, in columns 5-6, the coefficient for large establishments is _positive_ , reversing its sign, but retaining significance. In other words, by using the twoperiod average to categorize establishments, large establishments actually contract significantly _less_ than SMEs. This finding holds when using employment weights (Columns 13-14), confirming the sensitivity of results to using either initial or average size, through the job contraction channel. Lastly, we observe no effect using categorical age variables, but do see a positive (meaning less contraction) and significant effect when using the continuous measure (Annex III, A5).

# [Insert table 9 here]

These results indicate a corollary of the _regression fallacy_ effects discussed by Davis et al.; namely that smaller contracting establishments are limited in terms of their relative contraction (a smaller establishment can only shed so many employees), while larger establishments can experience much larger adverse shocks. Thus, using the initial size, smaller establishments are more likely to exhibit lower contraction rates, which is borne out by the results; using the average yields greater contraction. These differing results likewise underscore the importance of considering exit effects by using longitudinal data as opposed to only cross section, as those establishments that are small and exit (that is those that will shed all jobs) are not included. By not including these exit effects, therefore, the effect of larger firm size and age is likely to be over-estimated. Nonetheless, this differential helps explain the loss of significant effect for small and medium establishments when estimating net effects using the average size.

23
# **V. Discussion and Conclusion**

A comprehensive comparison of the existing evidence on relative contributions to employment stock and job creation is complicated by the diversity of data used in various studies. Nevertheless, a pattern emerges from reviewing the literature. In terms of the static snapshot of where people are employed, SMEs are the largest employers, employing in most cases over half of the total labor force in the non-agricultural and nonfinancial private sector. What is more, this patterns holds both for developing and developed countries, although the US is somewhat of an exception in that large firms appear to be the largest employers.

The evidence on the dynamic aspect of which types of firms create jobs at a faster rate is a bit scattered, particularly for developing countries and as it relates to firm size. Studies on developed economies increasingly show that firm size is less of an important variable, but rather employment creation is driven by a small set of young and fast-growing firms pointing to the importance of firm age rather than size. For developing countries, however, there are generally few studies that use suitable dataset to satisfactorily answer this question. The limited studies that are available provide mixed results regarding size and job creation, and what is more, these are sensitive to the definitions and methods that are used. In terms of firm age, by contrast, there appears to be a consensus in that older firms are the largest employers, but the younger firms are the ones that create jobs at a faster rate. Taken together, these findings indicate that policy aimed at fostering job creation should carefully consider how firm size is defined.

We also illustrate how sensitive the results can be, focusing on developing economies and using the World Bank’s Enterprise Surveys data. Three salient points emerge. The first is one of interpretation based on different specifications. As noted above, the use of OLS results such as those employed by ADM provides a best approximation of establishment-level activity and thus provides a picture of each establishment’s adding or shedding of jobs. Our replication of their topline results then clearly confirms that young establishments and SMEs are the most dynamic in terms of adding jobs. On net, these are also the fastest growing firms when considering only establishments that added to their workforce. By contrast, employment-weighted measures provide a relative measure of establishments based on their underlying share of the stock of employment and thus illuminate the relative dynamism of sectors of the economy. Here, in line with findings from HJM, we find that establishment age, not size, predominates. As we have shown, this distinction matters as results are sensitive to the use of either specification. This finding has certain policy-making relevance as well. The latter method indicates which establishments are themselves the most dynamic and so may be of interest for interventions targeted toward fast-growing recipients; the former, by contrast, paints such policies in broader brushstrokes and may be especially relevant for sector-level actions.

Secondly, despite these distinctions for findings regarding _net_ employment growth, we find consistent and robust results for the role of establishment size and age in terms of gross job creation. That is, where 24
establishments are growing, they tend to be smaller and younger. From these results, we also cannot say anything about the quality of these jobs: if new jobs added are higher-quality than the ones that are shed, welfare gains are likely to be positive. By contrast, we find that results for establishments with job contraction are particularly sensitive to researchers’ choice and specifications, indicating that careful consideration should be paid to the effects of job contraction as well. We show that the use of initial or average establishment size has particular implications for results obtained for job-contracting establishments. This not only underlines the need to consider in particular those establishments for analysis, but also highlights a future need for research into establishment exit and robust panels, both of which can act as complements to periodic survey databases such as the ES.

A last point of discussion, then, is perhaps the most pertinent: the revealed sensitivity of job dynamics estimates to the means and thresholds used to define SMEs should give researchers and policy makers occasion to consider the use of those definitions. Clear implications have been shown following the analysis of Davis et al. of regression-to-the-mean/fallacy effects, indicating the appropriateness of period-average definitions. Their initial study as well as many subsequent ones relies on longitudinal data – thus with each firm being classified between _t_ and _t-1_ – and it is not clear what these effects will be using a _t-d_ recall variable (in addition to potential measurement error introduced by using recall questions). Moreover, in actuality surveyed establishments do not report their average employment but rather at specific moments in time. Nor do practitioners know an establishment’s future size when implementing policy interventions that are themselves designed to foster growth. Discussions of the relevance of policy interventions will need to be based on directly applicable definitions. What is more, it is precisely the business cycle shocks that result in reversion effects – in reality the quick addition or shedding of jobs – that policy makers may be interested in addressing.

Does this then undermine a policy focus on SMEs? We find evidence that establishment age matters and the size of an establishment also plays an important role, a role that is sensitive to the definition used by researchers. Moreover, we find that this sensitivity comes from the consideration of job contraction, and that this picture is incomplete without the consideration of exit effects. While this is a limitation, it highlights the need for robust and complete panels, using further data collection efforts and techniques – for instance the construction of “synthetic” panels to account for non-response. Several previous studies highlight that the likelihood of exit is higher among SMEs; rather than removing the impetus for policies targeting those establishments, this finding may indicate a need for interventions that enable smaller firms to grow as they would in the face of, for example, lower business environment obstacles. This is another area of potential research.

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