Abstract
This paper investigates the contribution of small firms to employment, job creation, and growth in developing countries. While small firms (<20 employees) have the smallest share of aggregate employment, the small and medium enterprise sector’s (<100 employees) contribution is comparable to that of large firms. Small firms have the largest shares of job creation, and highest sales growth and employment growth, even after controlling for firm age. Large firms, however, have higher productivity growth. Conditional on size, young firms are the fastest growing and large mature firms have the largest employment shares but small young firms have higher job creation rates.
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Notes
This empirical evidence fits in with the dual economy view originally associated with Harris and Todaro (1970), which suggests that the informal economy provides a social safety net until the establishment of formal productive firms that provide jobs and contribute to growth and development.
Note that we do not have micro-enterprises, that is, firms with less than five employees, in our sample.
The mean response rate across our sample of countries is 70 % which is superior to most other survey-based studies. For instance, Campello et al. (2010) report response rates of 3–7 % in their survey of how companies use credit lines during a financial crisis. Other studies in corporate finance report response rates between 7 and 9 % including Graham and Harvey (2001), Brav et al. (2005), Graham et al. (2005), and Lins et al. (2010).
Haltiwanger et al. (2013) also find that there is no systematic relationship between firm size and growth once age is controlled for. Specifically, they argue that the “systematic inverse relationship between firm size and net growth rates in prior analyses is entirely attributable to most new firms being classified in small size classes.” Since surviving new firms grow much faster than older firms in the US, this classification may make it seem that firm size is a determinant of firm growth. By contrast, in our sample of developing countries, we find that small firms are significant contributors to employment growth even after controlling for age.
The ES surveys and their precursor, the World Business Environment Survey have been used to investigate a series of questions in developmental economics including the relationship between property rights and contracting institutions (e.g., Acemoglu and Johnson 2005; Ayyagari et al. 2008a), investment climate and business environment obstacles to growth (e.g., Beck et al. 2005b; Ayyagari et al. 2008b), firm financing patterns (e.g., Beck et al. 2008; Cull and Xu 2005; Ayyagari et al. 2010), and dispute resolution via courts (e.g., Djankov et al. 2003).
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 Chambers of Commerce and business associations or marketing databases where registration is voluntary. In a few cases, the sample frame is created via block enumeration.
We have a few firms in the sample that report having less than five 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.
Most surveys contain three sets of weights—strict, median, and weak weights depending on the eligibility criteria used to construct the sample universe. Under the strict assumption, eligible establishments are those for which it was possible to directly determine eligibility, under the median assumption, eligible establishments are those for which it was possible to directly determine eligibility and those that rejected the screener questionnaire, and under the weak assumption only observed non-eligible units were excluded from universe projections. So under the weak assumption, all establishments for which it was not possible to finalize a contact were assumed eligible. The survey implementation manual recommends the use of median weights for cross-country comparisons.
The Enterprise Surveys ask establishments to report the number of permanent, full-time employees at the end of the fiscal year prior to the year of the survey and three fiscal years ago. So we do not have a measure of job creation and destruction in the year the establishment was born, that is, first started operations in the country.
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.
The SME database in Ayyagari et al. (2007) only covered the formal labor force in manufacturing. The sample of 54 countries in Ayyagari et al. (2007) were mostly rich developed nations and thus differ greatly from the developing country sample in the Enterprise Surveys (ES). Of the 54 counties for which SME250 share of manufacturing labor force is reported in Ayyagari et al. (2007), only 30 countries overlapped with the ES database. When we include the 44 countries for which we have additional data from sources other than the ES, we find the SME250 Manufacturing measure in our data to be significantly correlated with that in Ayyagari et al. (2007).
We only have nine high-income countries in our sample—Croatia, Czech Republic, Estonia, Hungary, Latvia, Poland, Slovak Republic, Slovenia, and The Bahamas.
The job creation shares are computed only on continuing firms and exclude the year of firm birth, and so we are unable to draw conclusions about firm births or the start-ups in our data. However, as shown in Klapper and Love (2011), the rate of firm birth is much lower in developing economies.
Haltiwanger et al. (2013) propose basing the size classifications on current average size to mitigate the effects of regression to the mean. In the Appendix, we present job creation data based on size classifications that are an average of the size in the base year and 2 years later and find our results to be qualitatively similar to the results based on base year size classification.
The 18 countries experiencing job losses are Botswana, Burundi, Cote d’Ivoire, El Salvador, Eritrea, Honduras, Lao PDR, Latvia, Former Yugoslav Republic of Macedonia, Mexico, Nepal, Panama, Serbia, Sierra Leone, Tonga, Uzbekistan, Western Samoa, and Yemen Republic. Most of these countries have had civil strife and ethnic conflict, and it is conceivable that, when institutions break down, it is only the small firms that are able to employ people and create jobs.
An alternative measure of firm performance might have been profits but we do not have profit information in our survey. Using sales growth as a measure of performance has a few limitations including sensitivity to economic fluctuations and possible biases due to dramatic fluctuation in sales between two years. In addition, it does not capture investment into assets which might be important for companies with long development times (though we control for industry dummies in all our regressions). See Weinzimmer et al. (1998) and Davidson and Wiklund (2000) for a further discussion of these issues.
The year when the establishment began operations refers to the year in which the establishment actually started producing or providing services. If the establishment was privatized, then the date refers to when the original government-owned establishment began operations.
In three countries, Bahamas, Grenada, and Guyana, the only industries covered were Textiles and Other Manufacturing.
For the remaining surveys (Afghanistan, Ghana, Kenya and Nigeria), we do not have a stratification identifier because block enumeration was used to overcome the lack of a reliable sample frame.
See Sutton (1997) for a review. Early studies such as Birch (1979, 1981, 1987) found an inverse relationship between growth and size and found small firms to be particularly important in job creation. Evans (1987), Dunne et al. (1989), and Dunnes and Hughes (1994) focus on unraveling the roles played by firm age and size as determinants and find that larger firms have lower growth rates but are more likely to survive.
Note, however, that Rauch (2010) shows that, in less developed countries, institutional reforms that disproportionately benefit small businesses may have adverse consequences such as interfering with the impact of trade reform since SMEs tend not to be export oriented and produce low quality output.
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Acknowledgments
We thank Miriam Bruhn, Leora Klapper, David McKenzie, Rita Ramalho, and Roberto Rocha for useful comments and suggestions. We would like to thank Yuzhen He 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.
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Appendix: Job creation: alternate size definition
Appendix: Job creation: alternate size definition
This table presents the contribution to job creation by different size classes. Job creation is the population estimate of the change in the number of permanent, full-time employees over a 2-year period, derived from the World Bank Enterprise Surveys. In cols. 1–3, we report three size classes based on permanent full time employment averaged across the base year and 2 years later: 5–19 employees, 20–99 employees, and 100+ employees. In panel A, we report data for 81countries that had a net positive job creation (across all sizes) over the two periods. In panel B, we report data for 17 countries that had a net job loss (across all sizes) over the two periods. We report summary statistics and median values across income groups and regions at the foot of each panel.
Nation | Size class | ||
|---|---|---|---|
1 | 2 | 3 | |
5–19 | 20–99 | 100+ | |
A. Countries with net job creation | |||
Afghanistan | 64.33 | 78.19 | −42.52 |
Albania | 38.82 | 42.12 | 19.06 |
Angola | 63.65 | 26.27 | 10.07 |
Argentina | 7.65 | 21.74 | 70.61 |
Armenia | 10.08 | 17.07 | 72.85 |
Azerbaijan | 12.92 | 16.55 | 70.52 |
Belarus | 3.70 | 33.62 | 62.67 |
Benin | 86.82 | 29.07 | −15.89 |
Bhutan | 17.45 | 34.43 | 48.12 |
Bolivia | 50.29 | 42.92 | 6.79 |
Bosnia and Herzegovina | 22.88 | 53.08 | 24.05 |
Brazil | 1.83 | 13.66 | 84.51 |
Bulgaria | 8.44 | 51.78 | 39.79 |
Burkina Faso | 73.37 | 155.76 | −129.13 |
Cameroon | 8.57 | 25.70 | 65.73 |
Cape Verde | 27.14 | 51.98 | 20.87 |
Chad | 42.58 | 54.36 | 3.06 |
Chile | 1.21 | 11.91 | 86.88 |
Colombia | 33.44 | 23.68 | 42.89 |
Congo, Dem. Rep. | 3.70 | 12.85 | 83.45 |
Congo, Rep. | 16.12 | 34.92 | 48.96 |
Costa Rica | 23.81 | 33.54 | 42.65 |
Croatia | 10.14 | 31.85 | 58.01 |
Czech Republic | 27.39 | 41.31 | 31.31 |
Ecuador | 12.05 | 31.23 | 56.71 |
Estonia | 16.21 | 22.99 | 60.80 |
Fiji | 15.58 | 13.31 | 71.10 |
Gabon | 61.56 | 14.04 | 24.40 |
Gambia | 37.85 | 35.93 | 26.22 |
Georgia | 5.66 | 8.54 | 85.80 |
Ghana | 25.86 | 16.28 | 57.86 |
Grenada | 41.62 | 148.22 | −89.84 |
Guatemala | 38.85 | 31.06 | 30.09 |
Guinea | 34.49 | 21.40 | 44.11 |
Guinea-Bissau | 71.23 | 7.93 | 20.84 |
Guyana | 4.07 | 32.79 | 63.14 |
Hungary | 26.44 | 64.37 | 9.19 |
Indonesia | 59.01 | −10.86 | 51.85 |
Jamaica | 26.05 | 35.85 | 38.09 |
Kazakhstan | 8.08 | 16.46 | 75.47 |
Kenya | 19.03 | 35.07 | 45.90 |
Kosovo | 39.42 | 10.14 | 50.44 |
Kyrgyz Republic | 189.96 | 278.39 | −368.35 |
Lesotho | 12.14 | 4.55 | 83.31 |
Liberia | 87.94 | 6.92 | 5.15 |
Lithuania | 25.69 | 40.39 | 33.91 |
Madagascar | 10.74 | 29.29 | 59.97 |
Malawi | 5.60 | 31.86 | 62.53 |
Mali | 64.19 | 27.94 | 7.87 |
Mauritania | 96.34 | 74.20 | −70.54 |
Mauritius | 2.07 | 20.81 | 77.12 |
Micronesia, Fed. Sts. | 39.12 | 60.88 | 0.00 |
Moldova | 22.75 | 11.26 | 66.00 |
Mongolia | 15.33 | 50.10 | 34.57 |
Montenegro | 44.81 | 21.99 | 33.20 |
Mozambique | 10.49 | 24.60 | 64.91 |
Namibia | 38.86 | 18.82 | 42.32 |
Nicaragua | 32.45 | 10.00 | 57.55 |
Niger | 72.31 | 43.40 | −15.71 |
Nigeria | 54.61 | 37.27 | 8.12 |
Paraguay | 25.36 | 34.97 | 39.67 |
Peru | 24.31 | 19.46 | 56.23 |
Philippines | 0.14 | −0.56 | 100.42 |
Poland | 23.74 | 51.50 | 24.76 |
Romania | 55.89 | 48.60 | −4.49 |
Russian Federation | 10.84 | 31.63 | 57.53 |
Rwanda | 31.04 | 32.58 | 36.38 |
Senegal | 64.28 | 44.25 | −8.53 |
Slovak Republic | 21.46 | 42.35 | 36.19 |
Slovenia | 12.02 | 29.42 | 58.56 |
South Africa | 20.34 | 48.82 | 30.83 |
Swaziland | 13.15 | 18.82 | 68.03 |
Tajikistan | 11.68 | 21.86 | 66.45 |
Tanzania | 21.95 | 27.94 | 50.11 |
The Bahamas | 37.68 | 33.43 | 28.89 |
Timor-Leste | 19.02 | 80.98 | 0.00 |
Togo | 76.89 | 17.37 | 5.74 |
Turkey | 3.62 | 9.43 | 86.95 |
Uganda | 27.82 | 47.35 | 24.83 |
Ukraine | 45.18 | 42.66 | 12.16 |
Uruguay | 28.06 | 36.63 | 35.31 |
Vanuatu | 25.59 | 74.41 | 0.00 |
Venezuela, RB | 30.15 | 26.72 | 43.13 |
Vietnam | 12.67 | 51.93 | 35.40 |
Zambia | 23.76 | 52.67 | 23.57 |
Summary statistics | |||
Minimum | 0.14 | −10.86 | −368.35 |
Mean | 31.66 | 37.54 | 30.79 |
Median | 25.36 | 31.85 | 39.67 |
Maximum | 189.96 | 278.39 | 100.42 |
Median across income groups | |||
Low income | 36.17 | 30.58 | 24.20 |
Lower-middle income | 22.75 | 32.79 | 48.12 |
Upper-middle income | 23.34 | 25.20 | 42.77 |
High income | 22.60 | 37.37 | 33.75 |
Median across regions | |||
AFR | 31.04 | 29.07 | 26.22 |
EAP | 19.02 | 51.93 | 35.40 |
ECA | 18.84 | 32.74 | 45.11 |
LAC | 26.05 | 31.23 | 42.89 |
SAR | 40.89 | 56.31 | 2.80 |
B. Countries with net job loss | |||
Botswana | 936.99 | 293.83 | −1,330.82 |
Burundi | 57.56 | 19.91 | −177.47 |
Cote d’Ivoire | 0.35 | 0.06 | −100.41 |
El Salvador | 250.00 | 209.88 | −559.89 |
Eritrea | −47.81 | −38.78 | −13.40 |
Honduras | 5.47 | 19.04 | −124.51 |
Lao PDR | 1.96 | −3.14 | −98.82 |
Latvia | 9.77 | 16.72 | −126.49 |
Macedonia, FYR | 159.89 | 196.48 | −456.37 |
Mexico | −1.52 | 26.46 | −124.95 |
Nepal | 659.66 | 63.62 | −823.28 |
Panama | 19.29 | 108.68 | −227.97 |
Serbia | 15.10 | 36.41 | −151.51 |
Sierra Leone | 58.84 | 32.31 | −191.15 |
Tonga | 0.28 | −0.31 | −99.97 |
Uzbekistan | −22.17 | −40.12 | −37.71 |
Western Samoa | 6.64 | −1.76 | −104.88 |
Yemen, Rep. | 54.10 | 38.50 | −192.61 |
Summary statistics | |||
Min | −47.81 | −40.12 | −1,330.82 |
Mean | 120.24 | 54.32 | −274.57 |
p50 | 12.44 | 23.19 | −139.00 |
Max | 936.99 | 293.83 | −13.40 |
Median across income groups | |||
Low income | 57.56 | 19.91 | −177.47 |
Lower-middle income | 5.47 | 0.06 | −104.88 |
Upper-middle income | 19.29 | 108.68 | −227.97 |
High income | 9.77 | 16.72 | −126.49 |
Median across regions | |||
AFR | 57.56 | 19.91 | −177.47 |
EAP | 1.96 | −1.76 | −99.97 |
ECA | 12.44 | 26.56 | −139.00 |
LAC | 12.38 | 67.57 | −176.46 |
MNA | 54.10 | 38.50 | −192.61 |
SAR | 659.66 | 63.62 | −823.28 |
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Ayyagari, M., Demirguc-Kunt, A. & Maksimovic, V. Who creates jobs in developing countries?. Small Bus Econ 43, 75–99 (2014). https://doi.org/10.1007/s11187-014-9549-5
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DOI: https://doi.org/10.1007/s11187-014-9549-5