Small business and poverty: evidence from post-Soviet cities

ABSTRACT

The relationship between small business and poverty is inadequately substantiated, despite being an important economic and social nexus. We test the direct impact of small business on poverty, and of poverty on small business. Using panel data across 115 post-Soviet cities in Armenia, Azerbaijan, Belarus, Georgia, Moldova, Russia and Ukraine, we examine early (1995–2002) and later (2003–08) transition periods. The findings show small business can reduce urban poverty during transition, and that higher poverty in cities impedes small business. We also find that changes in regional institutional context, knowledge and locational characteristics can facilitate or hamper both small business and poverty.

KEYWORDS:

• small business
• poverty
• city
• transition
• economic development

JEL:

• I30
• R10
• P25

ACKNOWLEDGEMENTS

The authors are grateful to David Audretsch, Denvil Duncan, Ondrej Dvoulety, Johan Eklund, Stepan Zemtsov and the participants at the 2016 Uddevalla Symposium.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. We thank a reviewer for raising this point.

2. Data from individual ONS were made compatible through Initiative No. 09-9031: ‘Driving Urban Economic Growth – Evidence from Transition Economies’, part of the larger 2009–10 project ‘Cities – An Analysis of the Post-Communist Experience’, supported by the Economics Education and Research Consortium in cooperation with Global Development Network.

3. Rosstat described how a firm could get into the business register (author correspondence). For a new firm to get into official statistical reports, it has to be at least one fiscal since registration. This could exclude the turnover of firms that quickly exit (‘one-day firms’), people working at home and some small informal firms. Firms that are registered but do not conduct business activity and do not submit reports for two years can still be taken into account by Rosstat as long as the data are not cancelled by the federal tax service (Barinova et al., 2019). We thank a reviewer for feedback on this issue.

4. Such indicators in transition and developing contexts may be complicated because of issues that could relate to the capacity and structure of data-collection systems, reporting systems, the unrecorded sector, fear and accountability in reporting processes, and ability to track the movements of people (Organisation for Economic Co-operation and Development (OECD), 1997; Dell’Anno, 2016; Eilat & Zinnes, 2002; Svejnar, 2002; Fischer et al., 1996).

5. Data on GRP were taken from annual statistical yearbooks for Russian regions and regional capitals. They are available for all except Russian, Armenian and Azerbaijani cities. We used the share of the population living in a city as a weight to rescale GRP. For example, the GRP of Arkhangelsk in 2006 was US$2315.69 million, with a population of 354,600, while the GRP of the region was approximately US$7181 million, with a population of 1.1 million. We calculate the GRP of Arkhangelsk as 7181 × 0.3223 = US$2315.69 million. This is an approximation, given the strong concentration of residents and weak commuter patterns (except for St Petersburg and Moscow, for which the GRPs are available); most cities see value created within boundaries. We also assume that the proportion of value creation and labour productivity do not change between city core and region where the city is located.

6. While these variables belong to 2008, firm density in Russia was quite stable for one to two years; the stability is likely to be even stronger for leading and lagging regions (Baburin & Zemtsov, 2017, pp. 179–185; Zemtsov & Tsareva, 2018). The continuous presence of stable regional leaders and outsiders indicates the persistence of business and institutional quality in Russian regions. We assume other countries followed a similar pattern.

7. We thank a reviewer for making this point.

 

Additional information

Funding

Maksim Belitski acknowledges financial support for data collection and analysis of the Global Development Network jointly with its regional partners in the Commonwealth of Independent States (CIS) and Central–Eastern Europe, including The Economics Education and Research Consortium (EERC), Kyiv School of Economics (KSE) and CERGE-EI University as part of ‘Inter-Regional Project on ‘Cities’ by the CIS and the CEE Network’ [grant number R09-9031].