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Sticks or Carrots? Comparing Effectiveness of Government Informal Economy Policies in Russia

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Abstract

Which incentives have the strongest impact on the size of the informal economy? Is it about government’s pressure against entrepreneurs operating in this sector, or is it about the benefits of legality? The goal of this paper is to explicitly contrast the role of sticks (court repressiveness) and carrots (financial aid to small- and medium-sized firms) as factors determining the size of the informal economy, using the case of the Russian taxi market. It uses a unique dataset of taxi licensing data from regional transport departments and indicators for taxi market demand and supply to estimate the extent of informal business. When controlling for market demand and supply, it finds a strong and robust positive effect of sanctions on the size of the official market, with higher repressiveness leading to a smaller informal economy. In contrast, the effect of carrots was insignificant. The results suggest that the effectiveness of carrot policies is compromised when entrepreneurs operate informally to avoid dealing with corrupt bureaucrats and have low trust in the government.

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Notes

  1. In this study, corruption is defined as non-compliance with the rules and regulations governing the behavior of public officials.

  2. According to the current Corruption Perception Index Ranking published by Transparency International. The experiments were conducted in USA (13), UK (3), Israel (2), Austria, Canada, Costa Rica, Hong Kong, Italy, New Zealand, Spain and Switzerland (each 1).

  3. Sticks and carrots relate to direct measures targeted at formalization. There are many policies that have an indirect effect on the size of the shadow economy, which are not considered here.

  4. In the case of Russia, despite a drastic reduction of rate and complexity of income taxes to a flat 13% in 2001 the shadow economy has not shrunk (Schneider et al. 2010); however, the tax reform has been connected to a reduction in informal employment (Slonimczyk 2012) and higher tax compliance (Gorodnichenko et al. 2009).

  5. A more general overview of measurements of the informal economy in Russia is given in “Appendix 2.”

  6. There is even a Russian word for informal taxis (bombily) and working as an informal taxi driver (bombit’).

  7. After 2015, Uber and its local competitors such as Yandex.Taxi and Gett began disrupting the Russian taxi market in the bigger Russian cities. As is the case in other countries, these firms were met with resistance from official taxi firms. The incumbents pointed out that Uber drivers often did not have an official taxi license (Buravtseva 2015). As a consequence, Uber began cooperating with the Moscow authorities and implemented policies that required their drivers to upload their taxi licenses during registration on the Uber platform (Shebalina 2016; Uber 2017). In the case of Moscow, the growth of Uber and its competitors led to a drastic reduction of prices (while Uber does not publish ride statistics, Yandex.Taxi grew threefold in 2016, Roem.ru 2017). This resulted in pressure on informal taxis. Especially the younger Muscovites prefer to order cabs on their phones. Here, the combination of Uber-like services and a growing number of licenses is leading to an increasing formalization of the taxi industry. This is indicated by the number of active licenses in Moscow and Moscow region, which increased from 121,000 to 153,000 in the past 18 months (Mer Moskvy 2017). Thus, the developments triggered by Uber are somewhat contrary to what happened in markets with limited license supply, where the number of unlicensed drivers increased. However, Uber is so far (mid-2017) only present in 30 cities in Russia (Taxiuber.ru 2017). The dataset of this paper is not affected by Uber and co., however, as it considers only licenses issued until mid-2015. Also, in the econometric analysis, we control for urbanization (with large urban centers being the only place Uber has been active in Russia) and also conduct a robustness check that excludes the primary markets of the new services (regions with more than 5000 operators).

  8. TAXIreal is bigger than TAXIobs, hence TAXIobs − TAXIreal < 0. If TAXIobs goes up and TAXIreal does not change, TAXIobs − TAXIreal increases (but decreases in absolute terms, i.e., the gap between the observed and the real number of taxis goes down).

  9. Note that, hypothetically, some of these variables (urbanization, economic development, etc.) could also have an impact on legalization, if there were a demand of the population for legality of taxis in Russia. Empirically, however, this demand appears to be very small. In a 2015 survey, the ‘official yellow’ color of the car (which the legal Russian taxis are supposed to bear) was the factor with the smallest importance among all affecting the choice of the taxi service; the most important factors remained the price of the service and the waiting time (VTSIOM 2015).

  10. If we transform the regression in OLS and compute the VIF statistics, for none of the specifications (1)–(6) reported in Table 2 we find evidence of strong multicollinearity (VIF exceeding 10).

  11. As always, the interpretation of the marginal effects we reported should be made with caution and serve merely to demonstrate the extent of the effects.

  12. More specifically, the data are available for the regional capitals.

  13. If we simply control for the number of crimes per capita or satisfaction with one’s security, our results also do not change.

  14. There are several problems with including this variable in the set of covariates. First, the quality of information is extremely poor: suffices to say that Rosstat reports the highest taxi prices (in Rubles) in Adygea and Dagestan—two very poor Russian ethnic republics of the Northern Caucasus. Rosstat is also obviously unable to measure prices of the unofficial taxis (and appears to be imprecisely measuring the official taxi prices as well). Second, the interpretation of including this variable in the set of covariates is not clear, since the price level should also be determined by demand and supply, which we control for anyway.

  15. In particular, we find a significant and positive effect of regional subsidies in case we transform subsidies into logs; in the specification (6) for the median regressions and if we use logs of all covariates. Federal subsidies are significant, if one controls for corruption levels, in specification (6) if we use logs of all covariates, and in specification (2) if one controls for the price level for taxi services, but have a negative effect.

  16. For obvious reasons, data on the illegal market are unavailable; it is prudent to assume that they are mostly dominated by single-cab drivers, who may, however, be involved in various forms of informal networks or enjoy some sort of protection from criminal groups or corrupt local officials.

  17. Note that in the regression tables we measure the subsidies in trillion rubles to obtain coefficients easy to present in a table.

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Acknowledgements

The study has been funded within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and by the Russian Academic Excellence Project “5-100”. We appreciate the very helpful suggestions of three anonymous referees. All mistakes are our own.

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Correspondence to Alexander Libman.

Appendices

Appendix 1: Summary Statistics for Key Variables

Variable

No. obs.

Mean

SD

Min

Max

Bus passenger turnover, bln passenger kilometers

79

1.565

1.489

0.006

6.976

Federal subsidies, trln. RUB (total spending in 3 years)

79

0.330

0.508

0.000

3.423

Herfindahl–Hirschman index, between 0 and 1

76

1.979

2.180

0.175

11.402

Highest number of cars by an operator

76

416.040

523.052

2.000

2904.000

Ln cars per capita (per 1000 people)

79

5.607

0.234

4.376

6.192

Ln income per capita (monthly, thousands of RUB, corrected by regional price levels)

79

3.168

0.199

2.601

3.655

Ln territory, thousands sq. km

79

4.331

1.412

0.336

8.034

Number of licenses

76

6790.500

10,903.800

102.000

65,551.000

Number of operators

76

1817.211

2412.953

65.000

14,097.000

Number of single-car operators

76

1328.934

1749.724

57.000

10,747.000

Population, mln. people

79

1.805

1.805

0.051

12.197

Regional subsidies, trln. RUB (total spending in 3 years)

79

0.150

0.243

0.002

1.816

Repressiveness (corruption), between 0 and 1

76

0.220

0.227

0.000

1.000

Repressiveness (fraud), between 0 and 1

79

0.418

0.165

0.077

0.965

Repressiveness (illicit entrepreneurship), between 0 and 1

44

0.151

0.281

0.000

1.000

Repressiveness (tax evasion), between 0 and 1

67

0.051

0.162

0.000

1.000

Repressiveness, between 0 and 1

79

0.467

0.124

0.259

0.962

Road density, road km per mln. sq. km of the territory

79

0.275

0.384

0.001

2.438

Urbanization, %

79

70.028

12.635

29.200

100.000

Appendix 2: Definition and Measurement of the Informal Economy

The research on the informal economy uses a plurality of different terms for the phenomenon it describes, many of which are only vaguely defined (van Eck and Kazemier 1989 list 45 different terms that are in use). Hart’s (1973) anthropological work is mostly credited with coining the word “informal economy.” A useful disambiguation of the different types of “underground economies” which are often mixed together is offered by Feige (1990). First, it singles out the illegal economy which consists of activities that are prohibited by law. The desired policy goal here is eradication, not formalization. There is also an unreported economy consisting of income that is not declared to the tax authorities with the motive of tax evasion. In contrast, the unrecorded economy comprises household activities or subsistence farming. Finally, the informal economy is production that circumvents formal rules (both avoiding costs and foregoing benefits). It is this part of the underground economy for which institutional causes such as corruption are the most salient (Feige 1990: 992–993). In our study, it is the informal economy we investigate.

While economists have been quite inventive in finding ways to understand the size of the informal economy, the different methods used have not yet led to converging results (Feige and Urban 2008). The existing literature can be differentiated into direct and indirect estimation approaches (a comprehensive overview can be found in Kazemier 2006). One direct approach is to estimate the size of the informal economy by evaluating small samples of actual cases of tax fraud. The Russian Federal State Statistics Services (Rosstat) uses another direct approach, conducting monthly surveys to estimate the share of informal employment in Russia (Rosstat 2016a). However, even if done carefully, measurement through surveys is haunted by distorted self-reporting.

These difficulties led to the development of indirect approaches for measuring the informal economy. We need to highlight that these approaches are not implicitly relevant for our study, because we do not attempt to find out the aggregate size of informality in the economy—thus, we report them only for completeness. One common method is to compare growth rates of formal (observed) GDP with measures that correlate with total GDP (including the informal economy) such as liquidity demand or electricity consumption (Kaufmann and Kaliberda 1996). More recently, indicators and explanatory variables have been used to estimate the size of the informal economy as a latent variable in MIMIC (Multiple Indicator Multiple Causes) models (Schneider et al. 2010). This allows to construct data for an unprecedented number of countries (162 in Schneider et al. 2010). The shortcoming of indirect approaches is that they can only capture relative changes in the size of the informal economy. To generate absolute values, they have to be initialized using estimates derived from direct measures and estimates. Depending on this initial calibration, the results of these models vary drastically (Alexeev and Pyle 2003). This adds to the complexity of the procedure and has led to renewed criticism regarding the transparency of the procedures (Feige 2016) and the possible applications of the resulting datasets (Kirchgässner 2017).

There are relatively few studies that use subnational data for the estimation of informal economies. Some of the exceptions are studies using MIMIC models on Germany (Buehn 2012), India (Chaudhuri et al. 2006) and the USA (Wiseman 2013). In the case of Russia, besides the survey-based Rosstat data on informal employment (Rosstat 2016b), there have been several studies that estimate the size of the informal economy on a regional level. They rely on differences in reported income and expenditures (Nikolayenko et al. 1997), regional data on electricity consumption (Komarova 2003; Kim and Kang 2009; Smith and Thomas 2015; Vorobyev 2015) and MIMIC models (Kireenko et al. 2017). In contrast to these studies, this paper uses a novel dataset derived from taxi licenses and focuses on one specific market. It does not produce a measure for the absolute size of the informal economy nor for changes over time, but only for relative regional differences.

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Kluge, J.N., Libman, A. Sticks or Carrots? Comparing Effectiveness of Government Informal Economy Policies in Russia. Comp Econ Stud 60, 605–637 (2018). https://doi.org/10.1057/s41294-017-0042-4

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